PLANNING FOR DECISIONAL INCAPACITY:

RESISTANCE TO COGNITIVE BIAS IN OLDER ADULTS

by

RICHARD JOSEPH MARTIN

Submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy

The Frances Payne Bolton School of Nursing

CASE WESTERN RESERVE UNIVERSITY

August 2019

CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES

We hereby approve the dissertation of Richard Joseph Martin candidate for the degree of Doctor of Philosophy*.

Committee Chair Ronald L. Hickman Jr.

Committee Member Jaclene A. Zauszniewski

Committee Member Christopher J. Burant

Committee Member Mariana P. Carrera

Date of Defense April 26, 2019

*We also certify that written approval has been obtained for any proprietary material contained therein.

Dedication

I dedicate this work to my wife, Siobhan Maloney Martin ND, RN. Since that first

Mama Santa’s pizza box, for nearly three dozen years, this journey has always been

“Maloney Martin”. Thank you, Siobhan; I promise that I will now go clean my office.

Table of Contents Dedication ...... 3 List of Figures ...... 7 List of Tables ...... 8 Acknowledgements ...... 9 Abstract ...... 10 CHAPTER I ...... 11 Introduction ...... 11 Advance Care Planning ...... 11 Statement of the Problem ...... 13 Theoretical Foundation ...... 28 The Motivational Model of Aging and Decision-Making Competence ...... 28 Theoretical Constructs...... 33 Stable Individual Characteristics of the Developing Person ...... 33 Motivational Orientation: ...... 38 Decision-Making Skill ...... 39 Decision-Making Competence ...... 40 Immediate Context ...... 41 Research Model: Application of the MMADMC to Advance Care Planning ...... 42 Research Questions ...... 44 CHAPTER II ...... 45 Review of the Literature ...... 45 The Motivation Model of Aging and Decision-Making Competence ...... 45 The Developing Person ...... 52 Demographic Attribute (Age) ...... 52 Demographic Attribute (Sex) ...... 55 Demographic Attribute (Race) ...... 56 Psychological Factors (Perceived Control) ...... 56 Psychological Factors (Dispositional Optimism and Dispositional Pessimism) ...... 61 Psychological Factors (Purpose in Life) ...... 64 Health (Subjective Health Stress) ...... 69 Wealth (Subjective Financial Strain) ...... 73 Motivational Orientation ...... 77 Deliberative Motivation (Cognitive Effort) ...... 77 Decision-Making Skill ...... 86 Deliberative Cognitive Skill (Working Memory) ...... 86 Decision-Making Competence ...... 98 Resistance to Cognitive Bias (Resistance to Risky-Choice Framing Effect) ...... 98 Decision Outputs and Outcomes ...... 110 Advance Care Planning (Discuss, Designate, Document) ...... 110 Summary of Empirical Support for Theoretical Premises of MMADMC ...... 118 CHAPTER III ...... 120 Methods ...... 121 Design ...... 121 Sample, Selection Criterion, Setting, Data Collection ...... 122 Threats to Internal and External Validity of the Study ...... 125 Power and Sensitivity Analysis ...... 128 Study Variables and Measurements ...... 130 Demographic Attribute (Age) ...... 132 Demographic Attribute (Sex) ...... 133 Demographic Attribute (Race) ...... 133 Psychological Factors (Perceived Control) ...... 133 Psychological Factors (Dispositional Optimism & Dispositional Pessimism) ...... 134 Psychological Factors (Purpose in Life) ...... 138 Health (Subjective Health Stress) ...... 139 Wealth (Subjective Financial Strain) ...... 141 Deliberative Motivation (Cognitive Effort) ...... 143 Deliberative Cognitive Skill (Working Memory) ...... 144 Resistance to Cognitive Bias (Resistance to the Framing Effect) ...... 149 Advance Care Planning (Discuss, Designate, Document) ...... 152 Data Access, Cleaning and Management ...... 154 Data Analysis Plan...... 155 Data Screening ...... 155 Assumptions of Multiple Linear Regression ...... 157 Multiple Linear Regression Procedure and Assessment ...... 159 Binary Logistic Regression Procedure and Assessment ...... 160 Human Subjects Protections ...... 161 CHAPTER IV ...... 163 Results ...... 163 Sample Characteristics ...... 164 Psychometric Analysis of Study Instruments ...... 165 Description of Study Variables ...... 166 Findings Related to Research Questions ...... 171 Summary of Findings ...... 182 Chapter V ...... 183 Discussion ...... 183 Discussion of the Research Questions ...... 184 Discussion of the Overall Study Model ...... 192 Limitations ...... 194 Study Implications ...... 198 Conclusion ...... 202 References ...... 204

List of Figures

Figure 1. Theoretical Framework ...... 29

Figure 2. Theoretical Substruction ...... 34

Figure 3. Research Model with Substruction ...... 43

Figure 4. Sample Derivation ...... 125

Figure 5. Description of Study measures ...... 154

List of Tables

Table 1 ...... 164

Table 2 ...... 166

Table 3 ...... 168

Table 4 ...... 169

Table 5 ...... 173

Table 6 ...... 175

Table 7 ...... 177

Table 8 ...... 179

Table 9 ...... 181

Acknowledgements

With genuine gratitude, I would like to acknowledge my dissertation committee: Drs.

Hickman, Zauszniewski, Burant, and Carrera… Ron, Dr.Z, Chris and Mariana. I was

privileged to have the counsel of esteemed scholars who also proved to be wise,

empathic, and faithful friends. I am deeply thankful for the generous support of the

Robert Wood Johnson Foundation Future of Nursing Scholars program, and the personal

encouragement of Dr. Susan Hassmiller. I am forever grateful for the opportunity to learn with and from my PhD student colleagues, notably: Dr. Grant Pignatiello, Dr. Nate

Schreiner, Christina Henrich, Julia O’Brien and, most especially, Dr. Marym Alaamri. I

wish to acknowledge the steadfast encouragement of my children. Sarah, Liam and

Alison: thank you for supporting my curiosity and decision to return to academic life, for both encouraging and challenging my thinking, and for tolerating my constant nerdiness.

Lastly, I thank God for this opportunity to return to a life of inquiry in the discipline that I

revere. I acknowledge that am a truly blessed man. Planning for Decisional Incapacity: Resistance to Cognitive Bias in Older Adults

Abstract

by

RICHARD JOSEPH MARTIN

Over 70% of adults will, at some time in their lives, lack decisional capacity and need another person to make health-related decisions on their behalf (Silviera, Kim & Langa,

2010). As advancing age increases the increasing risk of incapacity due to a sudden neurological event (e.g., stroke) or a progressive dementia (e.g., Alzheimer’s Disease), one would further presume that older adults would be even more motivated toward discussion of preferences for future healthcare and medical treatment, designation of a person to serve as a future surrogate health care decision-maker, and documentation of preferences for future healthcare and medical treatment). But, one-quarter of all adults will not have completed any of these advance care planning behaviors prior to death

(Bischoff, Sudore, Miou, Boscardin & Smith, 2013). This study applied the Motivational

Model of Aging and Decision-Making Competence (Strough, Parker & Bruine de Bruin,

2015) to examine the potential influence of stable attributes of older adults, deliberative motivation, deliberative cognition, and cognitive bias in advance care planning for future decisional incapacity among a sample of 266 older adult participants of the 2012 cohort of the Health and Retirement Study. Overall, using the selected measures, the model did not predict advance care planning behavior in this sample. Analyses of the individual components of the process did indicate a number of meaningful relationships that warrant future research.

CHAPTER I

Introduction

Advance Care Planning Advance care planning is “a process that supports adults at any age or stage of health in understanding their personal values, life goals, and preferences regarding future medical treatment. (Sudore et al., 2017, p.821). This definition of advance care planning, the outcome of recent consensus panels of scholars and professional, aptly states the reflective process involved in consideration of, and planning for, future care.

But, while the definition above is positive and benign, the context of advance care planning is more ominous. Fundamentally, advance care planning is the process of preparing for potential decisional incapacity and the profound vulnerability of entrusting care decisions to another person. Personal values, life goals, and care preferences are identified in anticipation of future decisional incapacity that will make it impossible to autonomously assert personal values and preferences, and thein necessary for another person to make these decisions. From this perspective, advance care planning can be defined as the present engagement of consciousness and assertion of autonomy to prepare for the potential future loss of autonomy and even the loss of consciousness itself.

Advance care planning is preparation for profound vulnerability. Incapacity in decision- making could affect older adults experienced abruptly, as in a sudden trauma or stroke, or in a chronic or slowly progressive decline, such as dementia. The advance care planning process is intended to inform, authorize, and influence the behavior of a potential surrogate decision maker (sometimes referred to as the agent or proxy) who are often spouses, adult children, fictive kin, neighbors or paid legal representative (e.g., attorney or formal caregiver).

The goal of advance care planning is consistency between and individual’s values, goals and preferences and the care that is received during a serious and chronic illness

(Sudore et al., 2017, 2018). Accordingly, much of the focus of previous research on advance care planning has been medical decisions, with particular emphasis on the use of life sustaining technology. But, advance care planning is also relevant to elucidate values and preferences that would enable a proxy decision maker to make less critical, but no less significant, care decisions such as long term care and housing options. Accordingly, the advance care planning process is considered an essential health promotion activity for all adults to ensure that every individual receives care that aligns with her or his values and preferences (Fried, Bullock, Ione & O’Leary, 2009; Sudore & Fried, 2010).

Within the context of the discipline of nursing, it is conceptually useful to consider advance care planning as an anticipatory health (or disease) self-management activity (Newsome et al., 2018). In recognition of a potential future health threat (e.g., decisional incapacity due to a sudden illness or a progressive dementia) a self- management process ensues to evaluate the threat, consider options, and activate behavior toward mitigating the effects of that threat. The self-management process to address the risk of future decisional incapacity is advance care planning . Advance care planning is presumed to emanate from rational consideration of the potential risks and consequences of future health conditions and rational consideration, deliberation, of the vulnerability and life consequences that could emerge from the absence of decisional capacity. The decisional outputs of advance care planning are predicated upon a deliberative, rational decisional process and the dynamic integration of diverse self- regulatory mechanisms, including motivation, cognition, emotional regulation, and the application of knowledge, life experience, and self-efficacy (Payne, Prentice-Dunn &

Allen, 2009; Allen, Phillips, Pekmezi, Crowther & Prentice-Dunn, 2009).

Statement of the Problem Over 70% of adults will, at some time in their lives, lack decisional capacity and need another person to make health-related decisions on their behalf (Silviera, Kim &

Langa, 2010). In light of the risks of sudden incapacity from injury or critical illness facing adults of any age, it would seem apparent that all rational adults would initiate advance care planning. As advancing age increases the increasing risk of incapacity due to a sudden neurological event (e.g., stroke) or a progressive dementia (e.g., Alzheimer’s

Disease , one would further presume that older adults would be even more motivated toward advance care planning. But, in fact, age only accounts for 3% of the likelihood of advance care planning (Yadav et al., 2017) and fully one-quarter of all adults over age

50 will not have completed any advance care planning prior to death (Bischoff, Sudore,

Miou, Boscardin & Smith, 2013). Of those older adults that have initiated advance care planning, only half will have done so adequately to ensure that a surrogate decision maker could definitively affirm the incapacitated person’s values and care preferences

(Bischoff et al., 2013). Ionue, Ihara and Terillion (2017) recently reported that, in a sample of 1153 persons, 73% had completed formal advance care planning (a durable power of attorney or a living will) but 28% had not discussed those documents with anyone since.

This neglect of effective advance care planning among older adults is irrational.

In light of the discernible and increasing risk of potential incapacity, neglect is indicative of an anomaly in expected rational behavior on the part of a mature, autonomous older adult. But, rational consideration of the risk of incapacity requires the willingness to consider an emotionally distressing view of future. Advance care planning requires contemplation of profound vulnerability and an existential threat to personal control.

Based upon an emerging body of knowledge from decision science, the threat itself may overwhelm the deliberative, rational process needed to plan for that threat.

The purpose of this study was to examine the advance care planning process in older adults, as informed by the Motivational Model of Aging and Decision-Making

Competence (MMADMC; Strough, Parker & Bruine de Bruin, 2015) that views decisional process through the lens of normative behavioral decision-making. The focus of a normative approach to behavioral decision-making is the examination of anomalies in expected rational behavior. The normative approach posits that the evidence of consistent anomalies in rational thinking (cognitive biases) may indicate potential for delayed or impaired decision-making outputs and subsequent life outcomes, including health (Bruine de Bruin, Parker & Fischhoff, 2007; Kahneman, 2011). This approach is consistent with the psychology of normative behavioral decision-making (decision science focused on rationality, deliberation and logic) and the emerging field of behavioral economics, the interdisciplinary synthesis of economics (the social science of rational allocation of resources such as effort, time, and money) and psychology (the behavioral science of cognition, emotion and motivation).

The study of the advance care planning process and the specific dynamics of deliberation and rational decision-making, is peculiarly relevant for study from a normative behavioral decision-making framework. Normative behavioral decision- making theory and constructs may provide a previously unexamined approach to the prediction of advance care planning behaviors. Therein, improved predictive ability could support more specific targeting of populations at risk for neglect of this essential self-management behavior, and could facilitate customized tailoring of interventions to increase successful implementation of advance care planning.

Advance care planning is both a decisional and behavioral process. Advance care planning is directed toward the risk of incapacity to make decisions, yet, ironically, advance care planning is itself a decisional process. As such, advance care planning includes the motivational and cognitive elements of all decisional processes and is therein amenable to analysis through decision science, including normative behavioral decision- aking and behavioral economics. While described as “planning,” it is important to be clear on the conceptual space that advance care planning occupies from a decision science perspective. While not discreet choices, advance care planning behaviors also do not involve extended executive functions, such as that which might be involved in retirement planning or significant health behavior or lifestyle change. Advance care planning is focused on a limited number of decisions in a circumscribed life domain, but one for which there is potentially substantial emotional and developmental valence. The few decisions to be made are decisions of profound effect, focused on a future characterized by the comprehensive and profound vulnerability of decisional incapacity.

Advance care planning is a process that culminates in a number of potential decisional outputs. Decisional outputs of advance care planning include behaviors, such as discussing beliefs, values and preferences with another person. Decisional outputs of advance care planning also include legal documents that include, but are not limited to, appointment of another person as a future surrogate decision-maker (the durable Power of

Attorney for Health Care) and a written statement of values and preferences in the event of need for life-sustaining medical care (the Living Will). While often treated as a unitary construct, it is useful to differentiate those decisional outputs as distinct in both process and effect. The findings of studies of the prevalence of advance care planning

(Bishchoff et al., 2013; Yadav et al., 2017) indicate a lack of consistency in the implementation of these three decisional outputs. These studies indicate that up to a third of adults that have completed some form of advance care planning have only legal documents or discussions, but not both. This research study separately examined the decisional processes leading to each of these three outputs.

Greater clarity as to the process of advance care planning is relevant for accurate analysis of research to increase participation in advance care planning (uptake) and for tailoring of interventions to increase advance care planning behavior in selected populations. But, a lack of clarity surrounding the purpose or quality of these three advance care planning decision outputs is clinically relevant. Surrogate medical decisions are often made in the context of clinical urgency, limited or ambiguous information, substantial social and financial impact to families, and potentially significant legal liability and financial incentives for medical providers. Inadequate planning and absence of advance care planning can delay treatment decisions as medical providers ensure that they are receiving direction from a legally valid and adequately informed surrogate. In the absence of clarity regarding the patient’s values and preferences for life-sustaining care, it is possible that the most assertive family member or the judgment of a medical provider is likely to override the patient’s preferences and diminish his or her autonomy. Lack of clarity may surface in the context of significant care transitions following a clinical event, such as EMS transport or discharge from an extended care facility, with potential for outright violation of patient values and preferences.

Inconsistent or unexamined application of advance care planning behaviors as a unitary concept also has policy implications.

A greater understanding of the process of individual advance care planning outputs could also have value from the perspective of public policy. The federal Patient

Self-Determination Act of 1990 requires that healthcare providers and facilities receiving

Medicare and Medicaid must assess and document whether any documented advance care planning directive (durable Power of Attorney for Health Care, Living Will) exists.

Providers and facilities must then provide written information and an opportunity for patients to complete these documents. But, the federal law provides that the law be implemented at a state policy level. State by state variability includes variation in the definition and assessment of incapacity, breadth of treatments that can be delegated to a surrogate, and ethical qualifications for acting as a surrogate (Miller, 2017). Irrational or biased advance care planning could yield inconsistencies such as multiple documents with divergent directives, or communication with future surrogate that contradicts a written living will. A lack of legal clarity and need for the involvement of ethics consultation, legal counsel, or the probate court could further delay treatment decision and appropriate compassionate, and economically-efficient care.

As consistent with the definition of advance care planning presented in the introduction of this proposal, advance care planning is a process of personal clarification and interpersonal communication of beliefs and values to an appointed potential surrogate and future healthcare providers. Beliefs are assertions of objectively verifiable facts and probabilistic likelihoods (Bruine de Bruine, Parker & Fischhoff, 2007).

Dialogue about such beliefs (usual course, prognostic expectations, treatment outcomes, post-acute functional impairment, long-term care or palliative services, pain management capabilities, etc.) allows the opportunity to address inaccurate information or misunderstanding. Beliefs are commonly described in probabilities and prognostics.

Beliefs about healthcare options indicate understanding of accessibility, availability, and affordability of health services. In contrast to beliefs, values are deeply held, inherently subjective, axiological foundations to the acceptability of a choice, including choice of health services. Values indicate the subjective mechanisms of utility and choice among available options (Bruine de Bruin, Parker & Fischhoff, 2007).

Beliefs and values are core constructs in the discipline of behavioral decision- making. Historically, behavioral decision-making emerged from the intersection between

“normative” approaches to behavioral decision theory, which defined decision quality relative to rationality of process, and “descriptive” approaches to behavioral decision theory, which attempts to explain deviation from normative standards and cognitive biases. Normative behavioral decision-making theory focuses on the quality of the decision process, rather than the outcome itself. Accuracy and consistency in the assessment of beliefs and values, and the integration of those beliefs and values, are the normative standards for a “good” decision (Bruine de Bruin, Parker & Fischhoff, 2007,

2012; Strough, Parker & Bruine de Bruin, 2015).

Discussions are vital to the ability to serve in proxy for another’s decisions, but even detailed discussions with a family member or loved one must be accompanied by adequate legal authority (a durable Power of Attorney) to act upon that shared information. The durable Power of Attorney for Health Care (dPOA-HC), while often embedded in a document that may include a release of protected medical information and even an abbreviated Living Will statement of preference, is the legally recognized mechanism for appointment of a surrogate decision maker or decisional proxy. The dPOA-HC grants to the surrogate decision maker (formally identified as the Agent,

Proxy, or Attorney-in-Fact) the ability to make health care decisions. There are a number of parameter within which the dPOA-HC can be used. First, the dPOA-HC only springs into authority upon the incapacity of the grantor. The dPOA-HC does not have authority to independently make decisions or be the sole recipient of medical information for a patient with capacity for self-determination. The dPOA-HC has a springing authority only at the moment of incapacity. While this may be easy to discern upon a state of unconsciousness or a distinctively incapacitating event, it is far more nebulous in the emergence of a progressive dementia or even in the aphasic communication disabilities of a stroke. Furthermore, as an instrument of self-determined appointment, the dPOA-HC is intentionally created to avoid the delays in time and statutory complications of probate adjudication of incompetency and appointment of legal guardian. Therein, the dPOA-HC inherently requires a clinical assessment of incapacity and clinical judgment that surrogate decision-making is now valid. Even a primary care provider only has intermittent access to the cognitive capabilities of the patient and the interpersonal dynamic with the proxy; an emergency provider has no such information. Therein, in community settings, primary care clinics and hospital wards, some degree of ambiguity is often present as to the validity of the need for a surrogate decision-maker that may not be as difficult to discern in the emergency or intensive care environments. Second, the dPOA-HC, is no longer valid if and when capacity is regained, but the dPOA-HC is valid for valid for any future incident of incapacity. Lastly, the decision-making authority granted by the dPOA-HC is bound within a specific ethical model known as substituted judgment. In this ethical model, the decisional surrogate (Agent, Proxy, Attorney-in-

Fact) should, whenever possible, attempt to make decisions based on the stated values of the grantor for whom decisions are being made. While empirical research is limited, researchers find a disconcerting lack of uniformity between appointment as dPOA-HC and conversations of beliefs, values and preferences to confidently serve in the legally appointed role. As previously stated, some studies report that older adults have had discussions of plans for future medical care but did not legally appoint any person to carry out those preferences (Bishhoff et al., 2013; Yadav et al., 2017). Furthermore, a recent advance care planning intervention with self-selected dyads found that many family members appointed to serve as future decisional surrogates did not feel qualified to communicate the values of a spouse or parent (Bravo, Trottier, Arcand, Boire-Lavigne,

Blanchette, Dubois & Bellemare, 2016; Bravo, Sene, Arcand & Hérault, 2018).

The Living Will is a written statement, not an appointed agent. The Living Will is a usually a statement of medical treatment preferences, generally focused on resuscitation and “life-sustaining” treatment in the presence of expected permanent unconsciousness or cognitive impairment. The Living Will, while often used as a guide to the surrogate’s decision-making, may often supersede the authority of that surrogate.

Potential conflict between providers and appointed surrogates, and potential violations of patient preferences, could result as ethical and legal professionals are consulted to determine whether the authority of the Living Will must take priority over the expressed wishes of the surrogate. Documentation of specific care preferences, rather than discussion of more general beliefs and values, may be seen as advantageous for patient autonomy and provider limitation of liability, but this approach has inherent difficulty.

The actual clinical decision context for surrogate decision-making may arrive far in the future from these discussions. Technological change and available treatment options may be markedly different from the time of advance care planning and the implementation of advance care planning. The clinical context, including the impact of multi-morbidity, other considerations of quality of life, and prognostic likelihood of success, time of recovery, future functional potential, etc. may all be very different from the context at the time of drafting the living will document. In addition, the Living Will was created in a medico-legal context that may have changed considerably by the time of implementation.

Likewise, a number of scholars have long debated the morality of quasi-contractual assertions of autonomy outside of the context of the quality of life at the time of implementation. Some ethicists and legal scholars argue that it is morally questionable to allow the values of a middle-aged person forecasting future quality of life to determine the life or death of that same person, if otherwise objectively happy, but cognitively impaired (Wolf et al., 1991).

The benefits of successful and comprehensive advance care planning accrue to the person, family and loved ones, and the greater society. Advance care planning is an opportunity for the exploration of values of the future patient and, therein, an affirmation of the autonomy of that patient. While not uniformly supported empirically, it is presumed that effective advance care planning increases the likelihood that patient preferences and self-determination will be respected (Newsome et al., 2018). Advance care planning is relevant at any age, due to the potential for an incapacitating injury or illness, but advance care planning is particularly relevant as we age. In addition to the risk of sudden loss of cognitive or communication capacity (e.g., stroke), the potential for progressive cognitive decline and dementia increases with age (Fishman, 2015). For some older adults, the window of opportunity to consider and implement advance care planning with full cognitive capacity may be limited. Therein, it is clinically imperative that mediating factors, which delay implementation of advance care planning are identified. Using that knowledge, interventional research and clinical decision support can be targeted toward those persons whose delay or neglect in advance care planning could result in ultimate denial of autonomy and self-determination.

Designated proxies, often family members, benefit from the successful advance care planning process when acting as decisional surrogates through greater clarity of patient values and preferences to guide decisions made during the stressful and urgent context of critical care decision-making or discharge planning. Prior research has identified the inherent stresses of surrogate decision-making and the impact of ambiguity or absence of prior communication of patient preferences and necessary authority to direct future medical care on the experience of surrogates (Sudore & Fried, 2010).

Absent the adequate provision of direction or clarification of beliefs and values within effective advance care planning, a surrogate decision maker (even one that has been formally appointed and legally empowered as the “agent” or “Attorney in Fact” through a durable Power of Attorney for health care) may be unethically influenced by considerations other than the patient’s values and assertions of self-determination. A myriad of factors, including personal moral convictions, sense of duty, influence of medical providers, and financial considerations could prompt the surrogate to make decisions inconsistent with the choices that would have been made by the patient with full decisional capacity. Accordingly, current research supports the potential positive influence of advance care planning on reduced decisional conflict and decisional regret

(Sudore & Fried, 2010. While, often, the impact of advance care planning is focused on the preparation for a critical care episode and end-of-life use of services, it is equally important to consider the role of advance care planning in furthering discussions of preferred long term care options, caregiving and housing arrangements, palliative care preferences, and planning for the well-being of surviving spouses or dependents.

The presence or absence of consistent and coordinated advance care planning outputs (discussion, dPOA-HC, Living Will) has impact beyond the emergency and critical care environments. In particular, it is important to note the indirect financial impact of this medical decision-making. While separate Powers of Attorney are required for agency to manage a grantor’s financial affairs, in many cases, it is the medical decisions that have financial impact far in excess of usual expenses and lifelong norms of spending (Zafar & Abernathy, 2013). The economic impact of these surrogate healthcare decisions (both decisions of medical care and of long term care options) could be made in the context of substantial moral hazard. For beneficiaries of Medicare, the national public insurance for disabled, blind and those adults age 65 and older, the cost of medical care, is often not directly relevant to the decision to obtain that care. More than three- quarters of that cost for Medicare beneficiaries is the responsibility of taxpayers. Yet, given the magnitude of the expense for life-sustaining intensive care, it is possible that, if not covered by a private supplemental insurance, even the remaining 20-25% of cost

(deductibles and co-payments) could prompt impoverishment, public assistance through

Medicaid, and potential need for bankruptcy. The direct family financial impact is sometimes even greater, relative to the cost of extended and long-term care. Custodial long-term care services, nursing care to support a sustained functional impairment or decline, are not paid by Medicare. Instead, these costs are paid privately. Since few older adults have long term care insurance, most older families pay for custodial nursing care with retirement savings, until personal funds are exhausted to the level of eligibility for public assistance from Medicaid.

It is reasonable to presume that, in aggregate, many individuals’ successful implementation of advance care planning should accrue societal benefits of reduced use, and therein the societal cost, of undesired or futile, and expensive health care services.

((Dixon, Matosevic & Knapp, 2015). Bishchoff et al. (2013) found that, among older adults who had done advance care planning, 92% elected to limit the use of technologically-oriented health care when faced with poor prognosis and potential of medical futility. Despite this apparent support for reduction in medical technology and critical care services, there is only mixed empirical support for any macro-economic impact of advance care planning in the reduction of health care spending and third party payment ((Dixon, Matosevic & Knapp, 2015). Using large population-based datasets,

Silviera et al. (2014) demonstrated that, while lifetime rates of advance care planning activity have increased markedly over the past 10 years (from 47% in 2000 to 72% in

2010), the increase has not had strong effect on the use of acute and critical care services, nor in the reduction of deaths in the hospital. A number of explanations have been proposed for this discrepancy. Certainly, in the immediacy of a life-threatening medical event, it is plausible that prior planning is ignored or viewed as not yet relevant. It can be theoretically argued that the ambiguous nature of emerging clinical information and clinical judgment over time limits the ability to forego or terminate life-sustaining medical treatment without ambivalence. It is also possible that the “medical momentum” of a protocol-driven health care system takes effect. As stated earlier, providers have diverse interests and incentives: financial, professional, and reduction of liability.

Likewise, surrogates are often shielded from many of those same incentives in the moral hazard of third-party payment. In part, the muted effect of advance care planning may be due to ineffective, partial, implementation of advance care planning and inconsistency in the written “advance directive” document products of the advance care planning process. The previously noted work of Bishchoff and colleagues (2013), found that while more than three quarters (76%) of participants had done some advance care planning during their lifetime, less than half of those participants (35%) had both discussed their values and preferences with someone and lawfully appointed that person as a durable power of attorney. In other words, at some point (or points) in time, advance care planning actions were taken, but not in a manner that reflected both the patient’s values and preferences, and the authority to use that information to make decisions.

Numerous interventional studies have been directed toward increasing the rates of participation in advance care planning, the uptake effects of advance care planning related information and counseling, and the effectiveness of advance care planning on health service outcomes. advance care planning interventions have focused on increased awareness of the need for advance care planning, education about the advance care planning process, familial and intergenerational cooperation and education/simulation of critical care at the end of life. In general, these interventions have demonstrated mixed results and small effect sizes (Lovell & Yates, 2014; Weathers et al., 2016).

Where provided, the foundational conceptual models implicit in advance care planning interventions have focused on awareness and education and socio-cultural influences, including religious beliefs. To date, only limited empirical research and interventional studies have examined advance care planning from the perspective of normative behavioral decision-making or behavioral economics (Halpern, 2012). A greater understanding of the underlying decisional process of advance care planning could offer a number of opportunities for decision support interventions. These opportunities can be found in examination of the decisional antecedent, the process mechanisms, and the influence of decisional context. Elucidation of the decisional process itself, in particular the mechanisms of deliberation and potential for decisional bias and impaired decision-making competence, could provide valuable insight as to potential of advance care planning self-management behavior. Therein, interventions to increase advance care planning can be tailored to address the specific decisional mechanisms, rather than broad efforts in awareness or education.

As stated previously, advance care planning is peculiarly amenable to analysis from the perspective of normative behavioral decision-making or behavioral economics.

Within the broader interdisciplinary body of decision science, normative theory and behavioral economics places peculiar emphasis on rationality and deliberative thought.

The discipline of behavioral economics developed from the discovery and examination of cognitive bias, anomalies from the rationality that was assumed in classical economic models. advance care planning is a decisional process that is likewise consistent with that assumption of rationality. A valid advance care planning process requires consideration of objective information and communication of beliefs, values, and intent; it is presumed to require deliberative thought. But, in light of an emotionally disturbing projected outcome (the profound vulnerability of decisional incapacities), theorists of behavioral decision-making predict that, for some, deliberative process will be impaired and substituted with intuition and heuristic shortcuts.

The “normative” approach to behavioral decision-making, common in behavioral economics, is focused on understanding the process and mediating factors that facilitate or impede rational thought. This school of decision science is focused on decisional process (such as resistance to cognitive bias), rather than attempting to infer process on the basis of decisional outcomes that can be strongly affected by decision context or random variation. From the normative behavioral decision science perspective, the quality of decision-making is evaluated by the decision process, not the decision outcome; a good decision is one that adheres to a rational process, not necessarily one that yields a preferred outcome (Bruine de Bruin, Parker & Fischhoff, 2007, 2012).

Neuropsychological assessment is often focused on a medico-legal determination of decisional capacity or incapacity. Scholars of behavioral decision-making and behavioral economics assert that, even in the absence of neurological damage or loss, individuals process information, consider or ignore decisions, and act or avoid action based on predictable neuro-cognitive patterns. This perspective is informed by a dual- process model of cognition and decision-making ((Kahneman, 2011; Reyna & Brainerd,

2011). In dual-process theory, human cognition can be characterized as divided between “fast”, intuitive, thinking that uses heuristic shortcuts and “slow”, deliberative thought that requires cognitive effort and emotional regulation. (Kahneman, 2011). Behavioral economic approaches, based in normative behavioral decision science, focus on the process of deliberative “slow” decision-making. These approaches are most applicable for those decisions and planning processes where objective information is of value and deliberative thought toward rationality is desirable or necessary (e.g., health, finances).

These approaches are particularly relevant when the objective outcomes of a decision are substantial, even if the deliberative process is emotionally or cognitively taxing.

Conversely, when decisions are directed toward subjective priorities, coping and maintenance of well-being, some scholars argue that deliberation and rationality is not necessarily preferable in decision-making, particularly for older adults. Fuzzy Trace

Theory (Reyna, 2004), for example, asserts that many decisions of later life, particularly those focused on the preservation of well-being and adaptation to loss, can be better served by judgments based on intuition.

Theoretical Foundation: The Motivational Model of Aging and Decision-Making Competence This study was an exploratory examination of factors and mechanisms that may be associated with advance care planning, using a novel interdisciplinary theoretical framework as a structural process for advance care planning. The theoretical framework that served as the basis for this study is the Motivational Model of Aging and Decision-

Making Competence (MMADMC) proposed by Strough, Parker, & Bruine de Bruin

(2015). The MMADMC is a process model of decision-making that is gleaned from the fusion of two grand theoretical approaches: dual-process theory (foundational to many theories of behavioral decision-making and behavioral economics), and life-span developmental psychology theories of adult motivation and cognition. This hybrid process model (Figure 1) proposes that stable elements of an aging person lead to a decisional outcome (or the absence thereof), based upon the sequential influences of motivational orientation, decision-making skills (deliberative cognition, affect and emotional regulation, knowledge and life experience), and decision-making competence

(defined as resistance to cognitive bias). This theoretical model provides an explanatory framework for this examination of the process of deliberation in advance care planning for the threat of decisional incapacity.

Figure 1. Theoretical Framework

The MMADMC is distinctive from other decision theory. First, unlike many dual-process models that emphasize “hot vs. cold” cognition and theorize that current affect and immediate, domain-specific emotions are the antecedent drivers of dual- process, this model begins with stable characteristics of the adult: demographic attributes, subjective stability of personal health and finances, and stable psychological factors. The model is based upon individual attributes that are either inherently stable or are acquired characteristics that gain substantial stability with age past mid-life. This approach is consistent with efforts to target specific groups that might be peculiarly vulnerable to cognitive bias and subsequent delays in decisions and planning, such as advance care planning. This approach could also support interventions to increase advance care planning by providing indicators for tailoring interventions to individual differences in model components. While not essential to the application of the model in this study, it is important to note the inherent dynamism of the model in its published form. Strough and colleagues present the process model as recursive; the outputs and outcomes of prior decisional process have the potential to influence the developing person. Therein, the developing person is not only presumed to be aging chronologically over time, but also growing and developing throughout life, a principle central to the discipline of life-span developmental psychology. The MMADMC assumes that past decisional process, decisional outcomes (and time) will materially shape some attributes of the developing person and, therein, impact the future decisional process.

This theoretical framework is likewise distinctive among current decision theory in the emphasis placed on the role of motivational orientation. Motivational orientation is the conceptualized as an individual’s stable, patterned motivational tendency toward decision-making and cognition, including the individual’s propensity to exert cognitive effort in deliberation, i.e., to think hard and think deeply. In the model, motivational orientation directly influences decision-making skills such as cognition and partially mediates the relationship between those stable individual characteristics of an aging person and the person’s decision-making skills.

Decision-making skills (deliberation, affect, experience) are those elements of the theoretical process that provide the capacity and breadth for decision-making. Decision- making skills of deliberation include cognitive functions such as fluid and crystallized intelligence, executive functioning, working and episodic memory. This study focused on the decision-making skill of deliberation. As per the MMADMC, decision-making skills include affect and experience, in addition to deliberation. Decision-making skills of affect include emotional regulation of both integral affect (emotional response emanating from the decision itself) and incidental affect (the influence of emotional response from other life events or concerns), coping skills in both primary coping

(problem-solving) and secondary coping (managing loss). Lastly, decision-making skills include general or domain-specific life experience that shape the use of heuristics and influence the potential for cognitive bias.

As per the theory’s moniker, this theoretical model incorporates the construct of decision-making competence (Bruine de Bruin, Parker & Fischhoff, 2007). Decision making competence (DMC) is derived from the normative approach to behavioral decision-making in which decisional quality is assessed by accuracy and consistency in the decisional process, rather than by the decisional outcomes. DMC is conceptualized as the ability to resist cognitive bias and anomalies in rationality. In this theoretical framework, DMC is based upon four processes proposed by Bruine de Bruin, Parker &

Fischhoff (2007) as integral to decision-making competence: belief assessment (the management of probabilities, likelihoods, and risks relative to actions and outcomes), value assessment (the coherence and stability of the value relationship between actions and outcomes), integration (of beliefs and values), and metacognition (the capacity to accurately assess one’s own decisional ability). For example, DMC would include the degree to which risk judgments are consistent with probability theory and resistant to the base rate fallacy. DMC would include the degree to which and individual continues in a failing investment of time or money (Sunk Cost Fallacy). Relative to this study, DMC would include consistency in choice and resistance to changing an assessed value (value assessment), solely based on the positive or negative framing of the question. This

Framing Effect (Tversky & Kahnemann, 1981), like the Base Rate or Sunk Cost fallacies, is conceptualized as an illustration of a unique decisional ability that, while related to underlying cognitive functioning, is distinctive to the decisional process. DMC, as indicated by the accuracy and consistency demonstrated in resisting these cognitive biases, is conceptualized as the mediator of decisional outputs (decisions, choices, judgments, plans) and subsequent consequential outcomes (e.g., behaviors, life decision outcomes and feelings of happiness or regret relative to health, financial decisions, career decisions, etc.).

Lastly, in the MMADMC, Strough and her colleagues assert that decisions are contextual. The immediate context includes consideration of the specific decision domain itself (e.g., health, finances, relationships). Disposition, attitudes, belief and values regarding the decision doain are hypothesized to influence the entire decisional process. Likewise, the process is moderated by the contextual influence of the presence or absence of other persons in the decisional process. The influence of the presence of others could include shared decision-making process, but it could also include shared impact of the decision outcomes.

Theoretical Constructs

Stable Individual Characteristics of the Developing Person As noted in description of the theoretical framework, the antecedents of the deliberative decisional process leading to advance care planning are stable characteristics of the aging adult. These individual characteristics are hypothesized in the model to have a direct influence on an individual’s motivational orientation and decision-making skills.

The MMADMC describes these characteristics as broadly organized into three categories: demographic attributes, “health and wealth”, and stable psychological factors

(Strough et al., 2015; p.242). The MMADMC did not include an exhaustive list of potential individual characteristics, but instead a general description that these characteristics are stable and developed over sustained life experience. In this study, specific characteristics were selected based on theoretical and empirical support for their potential influence in the deliberative process (motivation and cognition) and the ultimate decisional output of advance care planning.

Figure 2. Theoretical Substruction

Demographic Attributes (Age, Sex, Race) Age is the dominant antecedent concept in the theoretical model and in this study.

Considerable empirical research, to be reviewed in Chapter 2, has examined the relationships between chronological age and motivation, cognition, and DMC (including resistance to Framing Effect). As importantly, age is a significant risk factor for events leading to decisional incapacity and, therein, the personal relevance of advance care planning. Lastly, the theoretical debate regarding the interaction of motivation and cognition, mentioned earlier, hinges on differing interpretations of the influence of older adult development and later life cognitive decline; both dynamics are fundamentally related to aging. That said, it is always important to remember that chronological age is inherently a proxy for some other characteristic: life experience, wisdom, functional aging, biological aging. Considerable age-related effects that are significant when older adults are compared to younger populations wash out within intra-group examination of individual differences.

Gender is specifically identified as an element of the developing person and a potential antecedent to the decision process outlined in the theoretical model. Due to limitations inherent in the use of secondary data, only binary sex (male, female) was examined in this study. Sex has been identified as a significant predictor of differences in decision-making in models that include consideration of both motivation and cognition.

Race is not specifically identified in the published description of the MMADMC.

While certainly a stable characteristic, race is itself a complex social construct with both subjective and objective meaning. Race has potentially significant and likewise complex relationship to other characteristics of the developing adult. Race was included as a demographic attribute in this model because of consistent evidence of racial disparities in both advance care planning uptake and health care service outcomes, including use and cost of intensive care, hospital days, end of life location (Sanders, Robinson & Block,

2016). To date, no study of this racial disparity has examined it from the perspective of decision-making and cognitive bias.

Psychological Factors (Perceived Control) Perceived control is not specifically identified as a characteristic of the developing person in the published MMADMC. But perceived control, whether conceptualized as a generalized, global expression (as in this study) or in the domain-specific form of self- efficacy (Bandura, 1999), has been consistently identified as key consideration in motivation, cognition, and planning actions. Generalized Perceived Control, in contrast to domain specific and modifiable self-efficacy, is herein considered to be antecedent of the deliberative process leading, directly influential to motivational orientation, and therein, theoretically, indirectly influential in advance care planning.

Psychological Factors (Dispositional Optimism, Dispositional Pessimism) Dispositional optimism and/or pessimism is a patterned view of the future. In this study model, dispositional optimism and dispositional pessimism are included as psychological factors influential to motivational orientation, and antecedent to the deliberative process. Dispositional optimism and pessimism are not explicitly included in the MMADMC but are consistent with the model in two ways. First, they represent an orientation toward the that could influence motivation toward deliberative cognition and, ultimately, influence advance care planning. Secondly, a foundational construct to the

MMADMC is the positivity effect (positivity bias) of older adults (Mather, 2016), to be described in detail in Chapter 2; optimism and pessimism are dispositions that could be associated with that effect. Dispositional optimism has been conceptualized and measured as both a single dimension (negatively-worded “pessimistic” items are inversely coded in a single measure of dispositional optimism) and as two distinct dimensions. In this study, dispositional optimism and dispositional pessimism were examined as separate dimensions. This decision is based on a lack of clear empirical evidence of these dynamics relative to motivation in older adults. This is a descriptive study of a novel application of the MMADMC to a future-oriented decision (preparing for the threat of future decisional incapacity). This gap in current knowledge and lack of clear theoretical guidance encourages a broader, more descriptive, approach to examination of the concept. Psychological Factors (Purpose in Life) Purpose in Life (PiL) is a stable orientation toward intentionality and planning for the future. Ryff (1995) conceptualized PiL as an element of eudaimonic well-being. In contrast to hedonic well-being, an orientation toward happiness that is grounded in the immediate presence of pleasure and absence of pain, and therein proximate to an affective state, eudaimonic well-being is a patterned orientation toward future goals, planning for the future, sense of purpose, and meaning in life (Ryff, 1995, 2013). PiL was not explicitly included in exemplars for the developing person construct within the

MMADMC, but PiL is conceptually associated with the construct of “temporal horizons” in the earliest version of the theoretical model (Strough, Karnes, Schlosnagle, 2011).

Drawing from Socio-emotional Selectivity Theory (SEST; Carstensen and Mikels, 2005),

Strough and colleagues include the individual’s temporal horizon as an attribute of the developing person. According to SEST, the dynamic of “time running out,” (common in, but not exclusive to, aging) prompts a change in motivation and cognition toward a

“positivity” bias influenced by emotionally significant relationships, from deliberative processing of information to heuristic processing, and from pursuit of future goals and plans to a pursuit of present-oriented hedonic well-being. The construct of eudaimonic well-being is considered to be an antecedent of the deliberative process leading to the advance care planning decisional outputs.

Health (Subjective Health Stress) In the theoretical model upon which this study is based, the “health and wealth” of the developing person is as among the resources available to facilitate decision-making and having direct influence on motivational orientation and decision-making skills.

While not explicit in the published version of the MMADMC, it is herein assumed that health is a subjective sense of stability in health status, rather than an objective measure of health or morbidity. For this research, health stability is further operationalized as the degree to which the respondent is upset due to a personal health problem that is current and ongoing for at least one year. This is a measure of subjective health stress that may or may not be well associated with objective measures of health or chronic illness.

Wealth (Subjective Financial Strain) Within the MMADMC, Strough et al. (2015) propose that the “wealth” of the developing person directly influences both motivational orientation and decision-making skills. While not explicit in the published version of the MMADMC, it is herein assumed that wealth is a subjective sense of financial stability, rather than the absolute objective financial resources or some general measure of socioeconomic status. For this research, financial strain is further operationalized as the degree to which the respondent is upset due to a financial strain that is current and ongoing for at least one year.

Motivational Orientation: Deliberative Motivation (Cognitive Effort) From the theoretical lens used in this study, the decision-making process is influenced not only by the attributes of the developing person, but also by that person’s motivational orientation toward deliberative decision-making. Deliberative motivation is herein defined as an individual’s propensity to exert cognitive effort toward a challenge of hard or deep thought. The renewed emphasis on the role of motivation in dual-process models of decision-making has come from two sources. First, the role of motivationally- driven neurological activation has been highlighted by work using fMRI and EEG

(Mather, 2016; Wilhelms & Reyna, 2014). In particular, neuro-economic studies indicate that previously identified brain pathways involving both emotional processing

(principally limbic focused, often involving the amygdala) and those involving concentration and rational thought (executive functions of the pre-frontal cortex) are influenced not only by immediate affect or domain-specific motivation about the outcome, decision, or choice, but also by general motivational orientation (patterned tendency) of the individual toward exerting cognitive effort. This pattern has been identified as a mechanism of activation or inhibition in decision making and planning.

Secondly, the role of “motivated cognition” has been identified as a key construct of interest in life-span development and aging research. Motivated cognition (Mather &

Carstensen, 2005) is the developmentally dynamic interaction between motivation toward cognitive effort and cognitive skill. Deliberative motivation, as measured by the self- reported tendency toward exerting cognitive effort, has been identified as a significant predictor of cognitive performance (Westbrook & Braver, 2015; Hess et al., 2018)

Decision-Making Skill: Deliberative Cognitive Skill (Working Memory) The MMADMC proposes that decision-making skills include deliberation

(cognitive functions including fluid intelligence, episodic and working memory and executive functioning), affect (emotional regulatory functioning of affect and use of coping skills), and experience (crystallized intelligence and education, general and domain-specific life experiences. This research study focused solely on the decision- making skill of deliberation.

Decision-making requires numerous cognitive skills. The integration of separate cognitive capabilities (memory, fluid intelligence, verbal processing, mathematical reasoning, crystallized intelligence, etc.), with objectively sound meta-cognition, is required for processing decisions and making choices and judgments. While human cognition, particularly cognition in aging, is highly integrated, specific cognitive functions are highlighted in deliberative processing. In particular, deliberation requires working memory, a specific cognitive skill among the executive functioning abilities centered in the pre-frontal cortex. Working memory is not the exclusive function of a focal brain region, but rather an integrating cognitive system that incorporates attention and inhibition of irrelevant stimuli (including emotions) with executive functioning of the pre-frontal cortex. Working memory is a cognitive function that is peculiarly relevant to the decision-making skill of deliberation. Working memory involves the ability to maintain sustained attention toward a cognitive task, therein activating both short term memory and executive functioning simultaneously. Working memory is the ability to hold and manipulate information simultaneously. Working memory demonstrates age- related decline, even in the absence of any diagnosed cognitive impairment (Mild

Cognitive Impairment, dementia). Working memory has been proposed as a potential cognitive skill mediator of age-related differences in Decision-Making Competence and the potential for distortions in beliefs or values by cognitive bias (Del Missier, Mäntylä,

Hansson, Bruine de Bruin, Parker & Nilsson, 2013).

Decision-Making Competence: Resistance to Cognitive Bias (The Framing Effect) The last element of the theorized deliberative decisional mechanism is Decision-

Making Competence (DMC). Decision-Making Competence is a construct first explored in adolescent populations by Andrew Parker of the RAND Corporation (Parker &

Fischhoff, 2005). DMC in aging has been most extensively examined by the team of

Wandi Bruine de Bruin, Parker, and Baruch Fischhoff; they approach DMC from the perspective of a normative model of behavioral decision making (Bruine de Bruin,

Parker & Fishchhoff, 2007). The Normative model of presumes the goal of rational decision making, based on subjective assessment of expected utility, accurate use of probabilities and likelihood, and consistency with logical axioms and process (Finucane et al., 2005). For example, better performance on decision making tasks of belief assessment (e.g, following probability theory and resisting a Base Rate Fallacy) or value assessment (e.g., focusing solely on the future likelihood of success rather than past investment and therein resisting the Sunk Cost Effect) indicate a generalized tendency to override the use heuristics with deliberative cognition (Bruine de Bruin et. al, 2007;

Strough et al., 2015). Therein, while the individual cognitive biases identified as elements of DMC may emphasize different cognitive schema (time, investment, probability, etc.), they are all conceptualized as components of a consistent individual difference construct: the ability to resist cognitive bias. Specific to this study, one of the identified elements of DMC is resistance to Framing Effect, the tendency to change the perceived value of a choice on the basis of whether it is positively or negatively framed.

In this study, the Framing Effect is peculiarly valuable beyond the aforementioned role as an example of deliberative inhibition of heuristically-driven cognitive bias. As further described in Chapter 2, the Framing Effect is a well-studied cognitive bias that has been used to explore the dynamic of loss aversion as understood in Prospect Theory

(Kahneman and Tversky, 1974; Kahneman, 2011) and the previously noted age-related positivity effect (Mather, 2016).

Immediate Context: Decision Domain (Threat of Future Decisional Incapacity) The theoretical model includes the construct of decisional context. Within the model, it is presumed that the immediate context of a decision (along with the socio- cultural and historical context of the decision) has a broad influence on the entire process model. Immediate context is conceptualized as the decision domain and operationalized as the subjective perception of decline in memory and/or decline in decision-making skill. As described in the selected theoretical model, the specific domain, or topic, of the decision is theorized to affect the functioning of the entire decisional process model.

Common adult decision domains can include concerns about health, finances, relationships, work and retirement, etc. Relative to advance care planning, the decision domain is the subjective risk and perceived threat of future decisional incapacity.

Hypothetically, the entire deliberative process, as conceptualized in the theoretical model

(deliberative motivation, deliberative cognitive skill, resistance to cognitive bias), would be affected (moderated) by the relevance of the decision domain. While it can be argued that advancing age alone should serve to increase the contextual focus and therein moderate the process model, it is herein assumed that perceived decline in physical health or cognitive function, both potential sources of immanent future decisional incapacity, will indicate greater relevance of the decision domain. The subjective awareness of a decline in a decline in memory is a likely indicator of a greater sense of contextual relevance, even urgency, for advance care planning.

This research focused exclusively on the deliberative process modeling in the

MMADMC. No analysis of contextual influence is planned at this phase of theory testing.

Research Model: Application of the MMADMC to Advance Care Planning

This study was an exploratory examination of factors associated with the decisional process of advance care planning, as informed by the MMADMC. Figure 3. Research Model with Substruction

Research Questions

The study systematically addressed the following research questions:

1. What is the influence of age, sex, race, perceived control, dispositional optimism,

dispositional pessimism, purpose in life, subjective health stress and subjective

financial strain on the propensity for cognitive effort?

2. What is the influence of age, sex, race, perceived control, dispositional optimism,

dispositional pessimism, purpose in life, subjective health stress, subjective

financial strain and propensity for cognitive effort on working memory?

3. What is the influence of working memory on resistance to the framing effect?

4. What is the influence of resistance to the framing effect on advance care planning

(discuss future care, designate a future surrogate, document future care

preferences)?

5. What is the influence of resistance to framing effect on advance care planning

(discuss future care, designate a future surrogate, document future care

preferences) when controlling for the influence of age, sex, race, perceived

control, dispositional optimism, dispositional pessimism, purpose in life,

subjective health stress, subjective financial strain, propensity for cognitive effort,

and working memory?

CHAPTER II

Review of the Literature

The purpose of this chapter is to review the relevant research literature to establish the theoretical and empirical foundations for the study and identify gaps in the current body of knowledge that establish the study’s rationale. This review is organized in an order that is consistent with the theoretical framework, the Motivational Model of

Aging and Decision-Making Competence (MMADMC; Strough, Parker, & Bruine de

Bruin, 2015). Following a review of the theory’s historical influences, key constructs and chief premises, I will provide a review of the literature for each theoretical construct and corresponding research concept(s), presented in order of the theoretical process, from antecedent characteristics, through the decision-making process, to ultimate decision outputs.

The Motivation Model of Aging and Decision-Making Competence The MMADMC is a hybrid theoretical model of decision-making that posits a process for the interaction of motivation, cognitive skills, and the potential influence of cognitive bias, on decision outputs. The theory was originally developed, in the context of life-span developmental psychology, to explain judgment and decision-making biases

(Strough, Karns & Schosnagle, 2011). Further refinement (Strough, Parker & Bruine de

Bruin, 2015), included formal incorporation of the construct of decision-making competence (Bruine de Bruin, Parker & Fischhoff, 2007, 2010, 2012). The MMADMC is a synthesis of two theoretical approaches: dual process theories of cognition and decision-making (Kahneman, 2011; Peters, Hess, Auman & Vastijall, 2007) and theories of developmental changes in the relationship between motivation and cognition that have emerged in response to the age-based positivity effect (Carstensen & Mikels, 2005; Hess,

2015).

The MMADMC is an explicitly dual process model of decision-making. Dual process is the theoretical premise that thought arises in two distinct patterns or cognitive systems: intuitive/experiential vs. deliberative/informational. Daniel Kahneman (2011) describes these neurocognitive process as System 1 and System 2. Specifically, System 1 is the term used to describe the pattern of most mundane decision-making and judgment.

System 1 decisional processing is intuitive, based on experiential dynamics and habitual patterns. System 1 is an expression of the influence of emotion and is particularly likely to dominate in the context of emotional regulation. This “fast” system efficiently processes information based on patterned heuristic shortcuts rather than deliberative thought processing. Accordingly, while efficient, this type of thinking can also produce systematic deviations from normative standards of rationality, generally described as cognitive bias. Conversely, System 2 functions in more cognitively taxing, deliberative thought. System 2 requires motivation to exert cognitive effort and cognitive skills (such as working memory) to inhibit those same affective influences. A theoretical premise of dual process is that evidence of cognitive bias in processing and ultimate irrationality in decision-making is an indicator of the functioning of the quickly affective System 1, when there was a need for the slowly deliberative effort of System 2.

While acknowledging the dual-process character of the MMADMC, Strough et al.

(2011; 2015) qualify that characterization in light of the explicit inclusion of elements derived from life-span developmental paradigm. This approach adds a level of complexity to the basic dual process. Strough and her colleagues (2015) propose that the skills engaged in the decision-making process include not only a single dimension of deliberative cognitive skills, but three dimensions that include life experience and emotional regulation and coping skills essential in the management of affect. Unlike deliberative skills that generally decline in aging, emotional regulation and crystallized intelligence (formal education, life experience) can remain intact or even improve as the individuals increases in age. In the MMADMC, it is the integration of these three dimensions that determine the use and efficacy of heuristics and therein results in vulnerability or resistance to cognitive bias (decision-making competence) (Strough et al., 2011). In this regard, the developmental paradigm that is foundational to the

MMADMC is consistent with more recent modifications to dual process theory, such as

Fuzzy Trace Theory (Reyna, 2004; 2011). In Fuzzy Trace Theory, intuitive heuristic processing is influenced by development and life experience. Repeated use of heuristics that yield positive decision outcomes will be repeated. Cognitive bias is not necessarily the absence of deliberation and use of heuristics. Rather, according to Fuzzy Trace

Theory, it is the use of ineffective heuristics that determines bias (Reyna, 2012).

The dual process dynamic within the MMADMC as primarily represented in the construct of adult decision-making competence (Finucane, Slovic, Hibbard, Peters, Mertz

& MacGregor 2002; Bruine de Bruin, Parker, Fischhoff, 2007; Finucane & Guillion,

2010). In the historical development of decision science, a distinction was often made between “decisional” cognitive models and neuropsychological mechanisms of judgment and decision-making (emphasis on discrete choices, evaluation and selection of preferred options) and the “executive” psychological dynamics of problem-solving (emphasis on assessment, mental representation of process, planning). This distinction in research focus and method was blurred principally due to the influence of the study of cognitive aging. Life-span developmental psychologists attempted to identify constructs and develop theoretical models that could merge research on the cognitive changes and losses of aging with that of the body of knowledge around later life emotional regulation, motivation, experience, and wisdom. Increasingly, the construct of decision-making competence (Finacune et al., 2002; Bruine de Bruin et al., 2007, Finucane & Guillion,

2010) emerged from the context of research focused on aging and everyday problem- solving.

DMC is based upon four processes integral to rational decision-making: belief assessment (the management of probabilities, likelihoods, and risks relative to actions and outcomes), value assessment (the coherence and stability of the value relationship between actions and outcomes), integration (of beliefs and values), and metacognition

(the capacity to accurately assess one’s own decisional ability) (Bruine de Bruin et al.,

2007). In DMC, decisional quality is assessed not by the outcome of the decision (and, therein, vulnerable to the effects of multiple unknown mediators and random chance), but rather by the accuracy and consistency of the decisional process itself.

Consistent with dual process theory, DMC is conceptualized as the ability to exert the cognitive effort required to effectively deliberate and resist decisional biases

(cognitive biases) and anomalies in rationality. A specific cognitive bias, the framing effect, first identified by Tversky and Kahneman (1981) is the focus of decision-making competence in this study. Within the first theoretical formulation of the MMADMC

(Strough et al, 2011), multiple explanations for the framing effect were considered; by publication of the model a few years later (Strough et al., 2015), the framing effect was conceptualized from the perspective of decision-making competence.

The MMADMC is a motivational model. The MMADMC is described by Strough et. al (2015) as a theory of judgment and decision-making process that is peculiarly focused on the influence of intrinsic motivation on the cognitive processing dynamics of adult development and aging. From the original model, emphasis was given to the

“central role to motivation” (Strough et al, 2011; p.5) on the effect of cognitive bias on decision-making. The influence of motivation and motivationally-sensitive factors is broadly demonstrated in multiple elements of the model, but MMADMC isolates motivational orientation as a mediating factor in the process model. Motivational orientation is a stable pattern of intrinsic motivation, such as tendencies toward positivity or negativity, emotional regulation, or loss aversion. The stated context for the mediating influence of motivational orientation is described relative to a motivational theory called

Socio-emotional Selectivity Theory (SST; Carstensen & Mikels, 2005; Mather &

Carstesen, 2005; Reed & Mikels, 2014). In brief, SST asserts that normal aging creates a perceived compression of future time perspective; time is less perceived as “time lived” and increasingly perceived as “time left” (Carstensen, 2006). According to SST, this compression of time creates a change in motivation that interacts with the cognitive processes described in dual process theory. Specifically, with a perceived sense of reduced time, motivations are increasingly directed toward emotional regulation and hedonic well-being, particularly in the context of close social relationships. Carstensen and her colleagues consider SST as explanatory of an empirically recognized preference for positivity and inhibition of negative or threatening stimuli or information in older adults (Mather & Carstensen, 2005; Carstensen & Mikels, 2005; Reed & Carstensen,

2012; Reed & Carstensen, 2015). Accordingly, cognitive effort is not directed toward deliberative tasks such as information-seeking and planning to address a future threat.

Strough et. al. (2015) considers SST to be a theoretical basis for the dynamics of motivational orientation within the MMADMC process.

The MMADMC is a process model. Stable attributes of the developing person influence both decision-making skills and motivational orientation, which also influences decision-making skill. This interaction will, in turn, predict decision-making competence

(resistance to cognitive bias) which mediates the rationality of the decision output. While untested in the cross-sectional design of this study, the MMADMC is recursive: the decisional outputs (choices, actions) and outcomes (life consequences) provide feedback to the progressive development of the aging individual and, therein, effect the next decisional process.

Lastly, the MMADMC includes sensitivity to the decisional context (both immediate context and broader socio-cultural/historical context). For example, the decision-making process for decisions about money may differ from decisions regarding health or medical treatment. Of particular importance to this study, the immediate context of advance care planning is more than simply the decision domain of health promotion or self-management; it is the consideration of the profound vulnerability inherent in a state of future incapacity.

The MMADMC is proposed as an explanatory model for the motivational and cognitive processes of bias, irrational decisions, and poor decisional outcomes. Therein, it could also serve as a guide for targeting intervention to improve decision-making. The authors cite the potential for understanding normatively irrational behavior, and guide potential interventions, ranging from financial decisions to health-promotion behavior. In another publication, Strough, Bruine de Bruin & Peters (2015) propose that a greater understanding of the process and relationships between antecedent characteristics of the developing person, motivational orientation, and cognition could guide interventions to strengthen DMC, reduce cognitive bias, and yield better decision outcomes. They propose that perceived control (specifically, domain-specific self-efficacy for health or finances and/or self-efficacy in making decisions) could increase the potential for motivated cognition and better DMC. Likewise, they propose that the use of cognitive strategies such as mental models could enhance the relationship between motivation and cognition in older adults. No empirical evidence of the testing of strategies guided by the

MMADMC was presented and none has been identified in the preparation of this study.

Accordingly, there is no published study of advance care planning that has utilized the

MMADMC theoretical framework. Two published studies (Bruine de Bruin et al., 2007;

Parker, Bruine de Bruin & Fischhoff, 2015) have demonstrated the relationship between selected DMC/cognitive bias (including framing effect) and decisional outcomes, including some health behaviors (sunscreen, condom use). Only a few health-related decision outcomes assessed were particularly relevant to an older adult population (e.g., developing Type 2 diabetes). None of the identified decisional outcomes are apparently relevant to the dynamics of advance care planning.

The following review of the research literature will proceed through the theoretical process, from antecedent factors of the developing person to the consequence of advance care planning. In examining the state of relevant knowledge in each concept of the theoretical process, effort was made to then examine what is known of that concept with those previously reviewed.

The Developing Person

Demographic Attribute (Age) Chronological age is included as a covariate demographic measure in the vast majority of empirical studies cited in this chapter. In most cases, age is a descriptor of the sample or a control variable to better examine direct effects of a variable of interest.

Rarely is age so relevant a concept as it was in this study. Chronological age is a theoretically significant concept in two ways. First, age is among the few individual attributes of the developing person construct that are explicitly included in the published model (Strough et al., 2015). As was supported in this review of the literature, it is likely that age exerts a pervasive influence throughout the entire model. The theoretically anticipated dynamics and the many of the key constructs of the process model

(motivational orientation, cognitive ability, cognitive bias, potential risk for incapacity) have expected age-based effects and differences. Considerable empirical research supports meaningful relationships between chronological age and motivation, cognition, and DMC (including resistance to Framing Effect); these are explored throughout this chapter. Therein, age is far more than a covariate demographic variable in this study.

Life-span developmental theory asserts that chronological age functions as a proxy variable; in light of the heterogeneity of adult development, the valid measurement of age can be further specified relative to biological, functional, and psycho-social development. In the MMADMC, the variable of chronological age represents multiple dimensions of the construct of the developing person. Developmentally, chronological age serves as a proxy for both common life experiences (e.g., physical and social challenge and loss) as well as cumulative life experience. While examined at a cross- sectional time point in this study, it is important to recall that the process described in the

MMADMC process - is recursive; decisional process leads to decision outcomes that, over time, impact the developing person and subsequent future decision process.

Therein, the variable of chronological age serves as an indicator of that recursive process within the individual. As previously discussed, from the perspective of SEST, chronological age is not just an accounting of years lived; it is a psychological indicator of presumed years left and a motivational influence on future priorities and plans

(Carstensen, 2006).

Age is not always best measured as an objective, chronological phenomenon.

Subjective perception of age, including the correlate of subjective life expectancy, is a useful construct for research in health-related decision-making and planning (Kotter-

Gruhen, Kornadt & Stephan, 2016). Bergland, Nicolaisan & Thurson (2013) demonstrated that the subjective sense of age is strongly related to perceived health status and personal mastery, an element of perceived control (and a variable examined in this study); for adults from age 60-69, mastery solely explained variance between chronological age and how old a participant felt. In a recent study of subjective age among cognitively intact participants, subjective age was identified as a significant predictor of future cognitive impairment, at both the magnitude of mild cognitive impairment and later-diagnosed dementia (Stephan, Sutin, Luchetti & Terrachiano,

2017). In her longitudinal analysis of 12 years of mortality data and subjective assessments, Perozek (2008) found that subjective assessments of life expectancy were capable of predicting actuarial modifications that were made to the Social Security tables used for the federal benefits programs of Social Security, Medicare and Medicaid.

Conversely, Elder (2013) argued to the contrary, that subjective life expectancy is only marginally useful in predicting actual life expectancy. But, of relevance to the SST- based dynamic foundational to the theoretical framework of this study, Elder found that the inability to use subjective life expectancy as a predictor of actual mortality, as asserted by Perozek (2008), was based on a strong consistent pattern of error or bias: younger adults underestimate the likelihood of reaching later ages and older adults overestimate the likelihood of living to even later years (Elder, 2013). This pattern could have implications for research in decision-making and planning, as well as implications for decision support in both financial and health planning, including advance care planning.

Advancing age is a significant risk factor for events leading to decisional incapacity and, therein, the personal relevance of advance care planning. While risk of incapacity from injury or sudden illness exists at all ages, the risk of most health crises that could bring about incapacity, either in a sudden event (e.g., stroke) or in degenerative decline (e.g., Alzheimer’s disease) increase with chronological age. Older adults would rationally have greater motivation to ensure that they have discussed their wishes and preferences, documented an appointed proxy, and provided written directives as the age- based risk of incapacity increases with each additional year of life. Yet, based on review of the research literature on advance care planning behavior and advance care planning uptake, the influence of chronological age on advance care planning is more ambiguous than might be anticipated. In general, the research literature does not consistently support that increased age is associated with an increase likelihood of advance care planning.

One study that examined advance care planning in a representative community-based sample from the HRS identified a significant increase in advance care planning with older age, but no correlation coefficient/effect was reported (Bishchoff, Sudore, Miou,

Boscardin & Smith 2013). Likewise, Lovell & Yates (2014) found a significant effect of age on advance care planning in only two of 27 studies examined in a systematic review.

A closer look at the research literature indicates that if it less than clear as to how strongly age is a factor in the likelihood of advance care planning. In at least one systematic review (Yadov, 2017), age was identified as a consistent factor related to increase completion of advance directives; but, upon detailed analysis, while still statistically significant (relative to a large sample size), age explained less than 3% of the overall variance in advance care planning behavior. This finding is supported by examination of longitudinal trends in advance care planning uptake over the past 10 years

(Silviera, 2014) who demonstrated that, while length of time between advance care planning and death has significantly increased statistically, the actual increase during that decade was only approximately 3 months for both appointment of a lawful proxy and completion of a living will.

Within this study model, age is hypothesized as directly influential on both motivation (cognitive effort) and cognition (working memory).

Demographic Attribute (Sex) Gender is specifically identified as an element of the developing person and a potential antecedent to the decision process outlined in the theoretical model. Due to limitations inherent in the use of secondary data, only binary sex (male, female) was examined in this study. Sex has been identified as a significant predictor of differences in decision- making in models that include consideration of both motivation and cognition. In addition, sex has been identified as significantly associated with both motivation and cognition in older adults (Maldanato et al., 2017).

Demographic Attribute (Race) Race is itself a complex social construct, but evidence that race could function as a meaningful antecedent to the decision process emerges from the consistent racial differences that have been identified in both advance care planning outputs (advanced directives) and outcomes (use and cost of intensive care, hospital days, end of life location).

Psychological Factors (Perceived Control) Though not explicitly identified as an example of the developing person construct within the MMADMC, the attribute of perceived personal control is included in this study model. Perceived personal control, as characterized in the form of control beliefs

(Lachman & Weaver, 1998; Prenda & Lachman, 2001; Lachman & Prenda 2004) is an adult attribute that, while developmentally sensitive to iterative change, achieves relative stability by midlife (Lachman, 2006; Neupert, Almeida & Charles, 2007; Lachman,

Neupert & Agrigoroaei, 2011). While important distinctions are made among related concepts of perceived control (locus of control, control beliefs, mastery, self-efficacy, primary and secondary control), the evidence from research in decision-making, adult development, and self-management all support the important role that a perceived control plays in decisional and behavioral processes.

A few conceptual distinctions are worth of attention in this review. The control concept that was examined in this study is that of sense of control, also known as control beliefs (Lachman & Weaver, 1998; Lachman, 2006). Sense of control is derived from the construct of locus of control (Rotter, 1989), in which beliefs about personal agency and fate establish an orientation toward life: external or internal locus of control. Sense of control is conceptualized as stable by middle-aged adulthood (Lachman, 2006) .

Predictable patterns of change in sense of control with advancing age has been empirically demonstrated (Lachman, 2006; Neupert et al., 2007; Lachman et. al, 2011).

Furthermore, sense of control is not synonymous with self-efficacy. Self-efficacy, the personal confidence in the ability to achieve a particular task or outcome, is derived from

Social Cognitive Theory (Bandura 1999). Self-efficacy has been shown to have a pervasive role in mediating or moderating the processes of intention or goal to action

(Lachman et al., 2011). But, as per Social Cognitive Theory, self-efficacy is domain specific; self-efficacy is related to the specific task or challenge. Conversely, perceived control within the MMADMC would be considered as a general attribute of the aging adult; the control beliefs (mastery, constraint) of sense of control are more consistent with that conceptualization (Lachman et al. 2011). As per Lachman and Prenda (2004), sense of control has an iterative, recursive process, relative to well-being and life satisfaction.

Sense of control acts as a mediator of both positive motivation and goal pursuit as well as health and financial stress.

Sense of control is comprised of two dimensions that exist in tension: beliefs about mastery and beliefs about constraints. Mastery (Pearlin & Schooler, 1978) is sustained agency over time; the degree of confidence in pursuing goals and meeting challenges is either made stronger or weaker by life experience. Constrains (Lachman,

2004) are the perceived external barriers to desired goals. It is important to distinguish these two dimensions in order to understand sense of control in the context of aging. Empirical research has supported the conclusion that overall sense of control declines in aging. In fact, the relationship between sense of control and age throughout adult development is an “inverted U”; for most persons, peak level of mastery with least influence of constraint is in middle age. With advancing age, particularly through the seventies and into the eighties, sense of control declines. But, upon multivariate analysis, it is apparent that the change in sense of control is related solely to the influence of change in constraints (relative the physical and social losses of aging) and not due to change in mastery. Mastery, a generalized sense of greater of lesser confidence of personal agency, remains stable throughout life (Lachman, 2006; Neupert et al., 2007;

Lachman, 2009).

Most of the empirical research cited in this discussion used a 10 item instrument

(responses to statements of control belief) to measure a sense of control. The instrument was developed by Lachman and Weaver (1998) for the Midlife in the US (MIDUS) study

(n=3032; age 18-75). The sense of control instrument combined 5 items of a well- recognized measure of generalized (not domain-specific self-efficacy) self-mastery

(Pearlin and Schooler, 1978) with 5 additional items that measure usual (“often”, “most”) constraints to personal control. Using the MIDUS national survey, the authors demonstrated that the sense of control instrument is internally consistent, with a

Cronbach alpha =.85 (Prenda & Lachman, 2001). Psychometric analysis of data from the

2012 HRS cohort further affirms the internal consistency at the subscale level (Cronbach alpha equal to .91 for the mastery subscale and an alpha of .87 for the constraints subscale). Neupert (2007) affirmed the bi-dimensionality of the scale using factor analysis and demonstrated that, while distinct, the two subscales were significantly correlated at r=.44. Lachman and Prenda (2004) demonstrated predictive validity of sense of control with well-being, function, and self-rated health. Gerstorf (2010), using the composite score for sense of control, asserted predictive validity for the instrument in that, after controlling for other hypothesized covariates, sense of control predicted less decline in health and greater increases in social support.

Substantial empirical research is supportive of the belief that sense of control has profound influence on health. Lachman and Prenda (2004) reported significant associations between both level of mastery and chronic health conditions (-.16), as well as constraints and chronic health conditions (.3). Importantly, the results of longitudinal analysis imply that the relationship between sense of control and health is more complex than simple linear associations. Gerstof, Rocke & Lachman (2010) examined the relationship between control beliefs and self-reported health, chronic conditions, functional status, and social supports in 6210 adults at two time points, 9 years apart.

They found a statistically significant and meaningful effect (.37) between greater initial sense of control and reduced decline nine years later. As importantly for future research and potential intervention, the relationships between sense of control, health, and social support were multi-directional. It appeared that sense of control was both an initial precursor to better health and, later, an outcome of better health.

Sense of control has also been demonstrated as relevant to cognition, including executive functions and planning. From a 10-year longitudinal analysis of 3272 older adults, Lee (2016) reported a significant inverse relationship between mastery and the presence of subjective memory complaints (r=-.24) and a positive association between control beliefs of constraints and subjective memory complaints (r=.29). Furthermore, longitudinal examination found that changed in sense of control over the decade predicted the future development of subjective memory complaints. In a study of perceived control and cognition over time (two time points, a decade apart), Hahn and

Lachman (2015) reported a result that could be of particular relevance to this examination of advance care planning using the MMADMC theoretical framework. With data from only 103 subjects, Hahn and Lachman found that baseline sense of control was associated with baseline working memory functioning (r=.29); furthermore, declines in sense of control were decline in working memory were also significantly related (r=.19). Prenda and Lachman (2001) studied 2971 adults from age 25-75 to examine propensity toward making plans. Using a five question instrument developed for the MIDUS longitudinal study (Cronbach alpha=.67), they discovered that, while intentionality toward planning declined with later age, planning more greatly contributed to life satisfaction for older adults than younger cohorts. Even more relevant to this study, they found that the significant relationships between future planning and life satisfaction in older adults was fully mediated by sense of control. They speculated that, while older adult motivation toward planning may decline, the effort of planning yields increase in sense of control

(either through increases in mastery or, more likely, by reducing constraints). This finding may have significance for examination of a motivational model of advance care planning behaviors.

To date, only one recent study has examined the topic of control relative to advance care planning. (Chui, 2016). The study examined decision control preference for end-of-life decisions, rather than perceived control as defined this study. Psychological Factors (Dispositional Optimism and Dispositional Pessimism) Strough et al. (201l, 2015) explicitly identify Socio-emotional selectivity theory

(SST; Carstensen, 2006) as a primary influence in the development of a motivational model of adult decision-making and cognitive bias. As described in this chapter, SST is a theory founded upon an empirical observation of age-related positivity bias (to be described in greater detail in this chapter). Yet, despite this theoretical foundation in disposition toward positive information (or inattention to negative information), Strough and her colleagues do not make mention of dispositional optimism or pessimism as exemplary of the developing person. In this review, I will argue that dispositional optimism (and pessimism, as a distinct construct) should be included in the study model to best examine the MMADMC process relative to advance care planning.

Optimism and pessimism are cognitive constructions of future outcomes. While not any necessary preferentially oriented toward the future, optimists have a dispositional orientation toward expectation of a good outcome (Carver & Scheier, 2014). Depending upon interpretation of an ongoing debate regarding the dimensionality of the construct(s), pessimists are persons that either are not optimistic (unidimensional) or are persons who, independent of their level of optimism, have a dispositional orientation toward expectation of a bad outcome. To be clear, the vast majority of studies reviewed solely assess level of optimism and treat pessimism as the inverse of a single dimension.

Despite theoretical similarity, review of the empirical evidence for relationship between age-related positivity and dispositional optimism/pessimism is equivocal. In a sample of 280 older adults (mean age of 67), Isaacowitz (2005) did not find a significant association between aging and optimism. This finding was interpreted as supportive of the construct as highly stable throughout adult life. . Conversely, in a recent study of 9790 participants of the HRS (over age 50) in which the LOT-R was used to assess dispositional optimism, an age effect was identified. Specifically, the effect was an inverted “U”: optimism increased from age 50 until roughly age 68, then shifted direction. The relationship between age and optimism among individual age 68 and younger was significant and positive, but among individuals older than 68, the association between age and optimism was significant and negative (Chopik, Kim &

Smith, 2015).

According to Carver and Scheier, optimism/pessimism is based upon cognitive interpretation of the relationships between goals, values and confidence

(expectancy/doubt). Moreover, nearly twenty years ago, they wrote of optimism as related to motivation (Carver and Sheier, 2001). In fact, in a more recent review, the authors explicitly describe optimism (they use a unidimensional definition) as a motivational construct in that increased optimism, expectation of a positive future outcome, motivates action, both in the context of a positive goal pursuit as well as in the context of adversity, obstacle, or threat (Carver & Sheier, 2014). This conceptualization of dispositional optimism/pessimism is wholly consistent with the motivation emphasis within the MMADMC and the immediate context of the threat of decisional incapacity necessitating advance care planning.

Whereas optimism and pessimism have been discussed as elements of human disposition and character since antiquity, the scientific study of optimism and pessimism emerged in the mid 1980’s. Both the initial emergence and a secondary period of interest in the mid 1990’s were related to advances in measurement. The Life Orientation Test

(LOT) and revised version (LOT-R), developed by Charles Carver and Michael Scheier provide a psychometrically strong tool to explore the parameters of optimism and pessimism in a manner consistent with the long history of psychological research on personality (Scheier & Carver,1985; Scheier, Carver & Bridges,1994). This study used a

6 item version of the LOT-R with adequate internal consistency for both optimism and pessimism; Cronbach alpha of .8 and .77, respectively.

In three and a half decades of research, optimism/pessimism has proven to be a robust and sensitive indicator of human behavior and cognition, including decision- making. Substantial empirical evidence affirms the advantages of optimism for physical health and mortality, mental health and psychological well-being, and ability to manage life transitions (Carver & Scheier, 2014). In fact, recent findings support that optimists have a reduced risk of stroke (OR=.89) and heart failure (OR=.74), even after controlling for demographics, chronic illnesses, selected biomarkers, affect, depression, and personality type (Kim, Park & Petersen, 2011; Kim, Smith & Kubzansky, 2014). The hypothetical mechanism of this advantage was identified nearly two decades ago and is still the most supported explanation of the effect of optimism on positive life outcome: coping response. Lisa Aspinwall and her colleagues demonstrated that the relationship between optimism and better health outcomes is mediated by coping response (more active, less avoidant). Therein, it is actually in coping with life’s obstacles that the optimist’s expectation of good outcome mediates actual good outcomes (Aspinwall,

Richter & Hoffman, 2001). As described earlier in this chapter, perceived control was previously found to be significantly related to coping response; it is therein not surprising that Fergusen and Goodwin (2010), in a study of the effect of optimism on well-being in older adults, reported a strong association between optimism and control (r=.45) and that perceived control fully mediated the relationship between optimism and subjective

(hedonic) well-being.

Of peculiar relevance to this study of process leading to advance care planning,

Aspinwall et al. (2001) identified a consistent pattern of coping responses within optimists that have cognitive and, therein, decisional implications from the perspective of dual process theory. Individual that are more optimistic respond to threat more actively

(seeking information, searching for alternatives, disengaging from a futile option and attempting a new option). This result remains significant even after controlling for intelligence, education, and socio-economic indicators (Aspinwall & Brunhart, 1996;

Aspinwall et al., 2001). This finding is consistent with Scheier et al. (1994) who reported that individuals with higher levels of optimism used more active coping, including planning to address a threat. In a study of the relationship between dispositional optimism and planning for future care needs, Sorensen, Hirsch and Lyness (2014) reported that individuals with higher levels of optimism also had a greater awareness of the risk of future need for care. Hypothetically, individuals higher in optimism would be more attentive to the need for advance care planning.

Psychological Factors (Purpose in Life) advance care planning is most likely done with an implicit hope of wasted time and effort. Like many human efforts taken to ameliorate risk of a future consequence, advance care planning is done to prepare for future incapacity that would preferably never occur. The tension between a view of the future as holding expansive promise and a view that recognizes the potential for incapacity and vulnerability is the rationale for including purpose in life as an element of the developing person to be examined in this study. Based on repeated references to the motivational dynamics of SST (Carstensen, 2005), the influence of time remaining in life and future orientation, while not explicitly included in the MMADMC, is a pervasive implicit element of the model. Purpose in life is a stable adult orientation toward intentions and plans for the future.

The purpose in life construct emerges from the sub-disciplinary school of positive psychology, but the roots of purpose in life are ancient. Ryff (1989) first proposed the concept of purpose in life as an element of the larger construct of eudaimonic well-being, a reference to the distinction between hedonic and eudaimonic versions of happiness, consistent with the higher and lower “spirits” of man as described in Aristotle’s

Nichomachean Ethics. Other elements of eudaimonic well-being include self-acceptance, positive relations with others, personal growth, autonomy, and environmental mastery

(Ryff, 1989; Ryff & Keyes, 1995; Ryff & Singer, 2008; Ryff, 2013) Eudaimonic well- being is contrasted with hedonic (subjective) well-being that is focused on positive or negative affect, life satisfaction, or the absence of stress or burden. Influenced by the importance of meaning in life gleaned from the work of Victor Frankl (1963), Ryff proposed eudaimonic well-being as a broader view of human experience in which an individual could experience a positive psychological state, irrespective of the presence or absence of those hedonic factors that contribute to the affective feeling of pleasure and absence of pain. While not making explicit reference to dual-process theory, Ryan and

Deci (2001) clarify the distinction between types of well-being in relation to emotion and affect that is relevant to this study. They summarize that emotion and affect predict subjective/hedonic well-being, but that, in opposite direction, it is eudaimonic well-being that predicts emotions and affect. Palgi (2013) tested the hypothesis that chronic stress would induce a decline in both subjective (hedonic) well-being and psychological (eudaimonic well-being) and demonstrated that, while significant likely due to large sample size (n-7,268), the effect of chronic stressors on hedonic well-being was markedly greater than the effect of stress on eudaimonic well-being. Interpretation of these findings is challenged by design and methods. Clarity regarding directionality of the effect is limited by the study’s cross-sectional design; it is equally feasible that individuals with higher eudaimonic well-being are less likely to report chronic stressors as upsetting.

Ryff (1989) conceptualized PiL within the context of eudaimonic well-being.

Purpose in life is an element of eudaimonic well-being that describes an orientation toward intentions, goals, and planning for the future. The specific characteristics of PiL include the presence of goals, aims and objectives, a sense of directedness toward the future, and a sense of meaning toward both the past and the present. PiL is a subjective measure of the degree to which an individual is believes that life has meaning and is experiencing the past, present and future relative to their own perceived potential.

Purpose in life is (PiL) is herein included as an attribute of the developing person construct for three reasons. First, PiL is conceptually associated with the concept of

“temporal horizons”, an explicit example of the developing person construct in the earliest version of the MMADMC theory (Strough, Karnes & Schlosnagle, 2011).

Second, PiL is consistent with the future-oriented influence of SST (Carstensen, 2005) on the MMADMC. Last, as described in the introduction to this section, advance care planning represents an expression of autonomy directed toward the future, albeit a profoundly threatening future. Purpose in Life is measured as a seven-item subscale of the Scale of

Psychological Well-Being (Ryff and Keyes, 1995), a multi-dimensional assessment of well-being that has been used in more than 350 published studies (Ryff, 2014). Internal consistency for the PiL subscale, established with the 2012 HRS cohort, is minimally adequate (Cronbach alpha = .77). Higher scores in the purpose in life subscale indicate greater goals in life, sense of meaning to present and past, aims and objectives for the future, and beliefs regarding purpose and meaning (Ryff and Keyes, 1995).

Ryff asserts that eudaimonic well-being should be presumed stable in later life

(Ryff & Singer, 1989; Ryff, 2014). Empirical evidence is equivocal, but generally supportive of that assertion. In a meta-analysis of age effect on PiL, Pinquart (2002) reported that increasing age is significantly related to decline in PiL, but with only minimal effect (r=.12). In fact, chronological age only explained 1.4% of the variance in

PiL. More detailed analysis indicated a potential non-linear effect: while only a small amount of decline in PiL was noted in mid-life (prior to age 60), a greater decline in PiL was seen from age 60-70 then at later ages. Whereas most studies have examined the relationship between age and PiL cross-sectionally, Springer, Pudrovska & Hauser (2011) used a longitudinal analysis to examine maturity and PiL. They concluded that it is unlikely that age/maturity has any meaningful effect on PiL in an adult population.

While Springer and her colleagues found a significant age effects in longitudinal analysis

(likely due to a sample size of 8769), the effects were very small.

PiL demonstrates remarkable relationships with physical health and cognition in older adults. Using the HRS for longitudinal analysis, Eric Kim and his colleagues have demonstrated that greater PiL significantly reduces the risk of sleep disturbance (OR=.84) and risk of stroke (OR=.78), after controlling for age, sex, race, SES, anxiety, depression, and a collection of relevant bio-markers (Kim, Hershner & Strecher, 2015; Kim, Sun,

Park & Peterson, 2013). It is not wholly clear as to whether the effect of PiL on health is direct or indirect; in a study of health promotion activity among 7168 older adults, after controlling for age, race, marital status, education, wealth, insurance coverage and number of chronic conditions, it was found that increases in PiL significantly increased the likelihood of cholesterol checks (OR=1.18), mammogram (OR=1.27), Pap smear

(1.16) and prostate examination (1.31). Whether directly or indirectly, PiL has a marked impact on health. Likewise, PiL has significant association with cognitive functioning and neurological health. In a 7-year longitudinal study of 900 community-dwelling older adults, higher PiL was associated with a markedly lower risk of developing mild cognitive impairment (hazard ratio of .71) and dementia (hazard ratio of .48), after controlling for age, depression and co-morbid chronic illness. After 7 years, older adults with the highest levels of PiL were 2.4 more likely to be free of significant cognitive impairment. In a later, post mortem study of 246 decedents, Boyle and colleagues demonstrated that PiL significantly moderated the relationship between biomarker evidence of brain pathology associated with Alzheimer’s disease (atrophy, amyloid plaques, neurofibrillary tangles) and neuropsychological assessment of cognition, after controlling for depression and co-morbid chronic illness (Boyle, Yu, Wilson, Gamble,

Buchman & Bennett, 2012).

PiL has both direct and indirect association with other concepts in the study model. Ferguson and Goodwin (2010) conducted a series of multivariate analyses to explore the relationships between PiL, perceived control, and dispositional optimism, with a sample of 225 older adults (mean age = 73) in Australia. Two of the three measures for these variables are, in fact, the same measures form the HRS used in this study. Not surprisingly, they found that PiL was significantly correlated with perceived control, and PiL was also significantly related to dispositional optimism. Interestingly, the direct positive relationship between dispositional optimism and PiL was partially mediated by perceived control.

Health (Subjective Health Stress) The MMADMC identifies health as a stable attribute of the developing person.

Strough et al. (2015) do not provide additional detail regarding the conceptualization of health beyond the broader description of the construct of the developing person as stable attributes. Consistent with the construct of the developing person, health is presumed to refer to the general pattern of ongoing health or illness in the life of the individual, rather than a current acute illness. The general state of health is most commonly inferred from objective assessment of morbidity (e.g., list of conditions or diagnoses, physical assessment and biomarkers) or from a simple single-item assessment of self-reported health status. In an effort to capture the element of health stability or instability from among the measures of the 2012 HRS dataset, this study conceptualizes general health stability or instability as the degree of subjective stress from ongoing health conditions

(specifically, those conditions that have lasted for a year or more). This approach places emphasis on the subjective nature of health, not only in the self-report, but in the identification of stress related to health. The use a subjective measure of health status is consistent with the MMADMC theoretical emphasis on intrinsic motivational orientation.

This conceptualization of health stress (or the absence thereof) as an indicator of health stability is also consistent with the MMADMC foundations in both dual process theory and life-span development; a subjective assessment of health stress recognizes the influence of emotional regulation and coping as elements of health stability in the developing older adult.

There is a substantial theoretical and empirical literature focused on the relationship between health and ongoing stress in the context of aging (Almeida, Piazza,

Stawski & Klein, 2011). Many studies have examined the effects of ongoing stress on health. Less work has been done looking at the potential causes, mechanism or effects of stress that is derived from or influenced by chronic health conditions. Even less attention has been directed toward the influence of health-related stress on decision-making or planning, including advance care planning. For this review, following a brief overview of the basic dynamics of stress, I will focus on those studies most relevant to the processes described within the MMADMC.

One of the earliest foundations for the contemporary understanding of the role of stress from a subjective perspective is the Transactional Theory of Stress and Coping

(Lazarus & Folkman, 1984). Lazarus and his colleagues placed emphasis on the subjective appraisal of a stressor as most meaningful to the stress response. Dependent upon the appraisal, the stressor could be interpreted as a threat to be addressed and warranting additional appraisals of coping requirements and capabilities. The distinction between chronic stress and an acutely stressing life event, has also been long recognized. Lazarus and Folkman acknowledged the iterative, developmental nature of the stress appraisal process; the frequency/chronicity, intensity, duration, and ultimate success or failure in management of a chronic stressor contributes to its future appraisal and coping response. Consistent with that distinction, Leonard Pearlin and his colleagues used the term “strain” to emphasize the life-course, iterative, developmental nature of chronic stress emanating from health challenges, finances, or role transitions. They asserted that the subjective experience of stress, and the coping response to stress, is dependent upon not only the immediate stressor (health, financial, role) but also the baseline level of strain (Pearlin, Menaghan, Leiberman & Mullan, 1981; Pearlin,

Schieman, Fazio & Meersman, 2005; Pearlin, 2010).

The stress-coping process described by Pearlin and his colleagues is consistent with the iterative, developmental nature of decision-making, as described in the

MMADMC: both the experience of stress and the experience of successful coping with the stress affect the future iterations of the stress process. Pearlin (2010) proposed that repeated iterations of successful coping (“agency”) become a patterned and habitual response (“mastery”). This type of coping response is active problem solving, often requiring effortful, deliberative thought, such as is described by the MMADMC.

The relationship between sustained stress and decision-making continues to be actively studied, using both psychological and biological methods. Studies of adults reporting chronic stress indicate declines in a number of cognitive functions including episodic memory and processing speed. Of particular relevance to this study is a significant decline in working memory and inhibitory control among older adults exposed to higher levels of chronic stress (Almeida et al., 2011).

In a recent review of the biology of chronic stress, Porcelli and Delgado (2017) speculate on a shift toward habitual brain functioning and away from more complex frontal lobe-dominant brain function, among persons reported to be under sustained chronic stress. Animal studies support the opportunity to experimentally study how chronic stress affects both behavior and brain. Dias-Ferreira, Sousa, Melo, Morgado,

Mesquita, Cerqueira, Costa and Sousa (2009) reported that rats experimentally exposed to chronic stress shift cognitive functioning from a goal direction to habit-based behaviors, in a manner consistent with dual process theory. The rats demonstrating this shift were found to have corresponding structural changes in the brain, including atrophy of the medial pre-frontal cortex. The authors inferred that chronic stress not only challenges cognitive functioning, but actually changes the underlying decisional anatomy of the brain.

Using a biological paradigm, McEwen developed the theory of chronic stress- induced allostatic load. In brief, allostatic load theory maintains that the recurrent and sustained activation of the body’s primary stress related biological responses (the sympathetic-adrenal-medullary axis and the hypothalamic-pituitary-adrenal axis) cause

“wear and tear” on the brain, as well as other end organs. Long term effects include decline in cognitive ability due to cortisol-induced damage to the brain (notably, the hypothalamus that is central to memory and the frontal cortex that is the central to executive functioning and working memory ((Juster, McEwen & Lupien, 2010; Almeida et al., 2011). The allostatic load theory is particularly useful in recognizing the bi- directional nature of a chronic health strain. In the case of stress induced by a chronic health condition, the dynamics of the chronic illness are contributing to the strain and the strain may, in fact, be worsening the chronic health condition directly or indirectly through wear and tear on the homeostatic capabilities of the body, including the brain. Rationally, it would be presumed that sustained subjective experience of stress regarding a health concern would induce a threat appraisal for which advance care planning would be a logical primary coping response. In fact, one study used HRS data from indicated that for every additional chronic health condition, there is a 13% increased likelihood (OR=1.13) of appointing a proxy with a durable power of attorney

(Gerst, 2008). But, theoretically, the previously reviewed findings posit a tension, relative to the decision-making dynamics of advance care planning. But, as per the findings reviewed, that same chronic stress, in combination with the direct or indirect effects of the chronic health condition, could inhibit the deliberative decisional process and potentially causing damage to the very brain structures that support it.

Wealth (Subjective Financial Strain) Like health, the concept of wealth is also explicitly identified as an example of the attributes of the developing person construct in the MMADMC. As with health, the theoretical framework provides no additional operational definition of wealth in any published versions of the model, other than characterization as a stable attribute of the developing person (Strough et al. 2015; Strough et al., 2011). Consistent with the construct of the developing person, wealth is presumed to refer to the stable pattern of economic sufficiency or financial security.

The relative nature of sufficiency and security poses a considerable challenge to the use of an objective economic measure or algorithm. Accordingly, as with the operationalization of health, this study examined wealth from a purely subjective perspective, as well. Likewise, stability of wealth is herein examined as the subjective appraisal of stress emanating from personal finances that has continued for at least one year. This type of stress is commonly referred to as financial strain (Pearlin, 2005; Kahn

& Pearlin, 2006).

Caution is to be used with regard to the use of the term strain in the stress process research literature. Since earliest studies of financial strain (Pearlin et al., 1981), the term was used interchangeably to describe both the subjective experience of financial insufficiency as well as the objective indicators of that perceived (or presumed) insufficiency (eg., difficulty in paying bills on time). Kahn and Pearlin (2006), in a landmark study of the life-course impact of financial strain, acknowledge that strain is related but independent of income and that, unlike objective wealth, strain is a phenomenon that accrues over an extended period of time. Nonetheless, Kahn and

Pearlin used a self-report of objective financial difficulty to assess strain. A number of researchers have simultaneously used the term in each of those forms; in one portion of the questionnaire, participants are asked to respond to a question of objective “strain”

(difficulty in paying bills on time) and in another portion of the questionnaire, a question regarding the presence and intensity of “ongoing financial strain” (HRS, 2012). In a recent study, Asebedo and Wilmarth (2017) used responses to these two questions to examine some of the relationships between the subjective and objective experience of financial strain. The researchers found that, while objective financial strain significantly predicts depressive symptoms, the effect is fully mediated by the subjective assessment of strain. In fact, they found a significant increase in depressive symptoms with greater levels of “upset” with ongoing financial strain. Arber, Fenn and Meadows (2014), in an examination of the relationship between income, financial well-being, and self-rated health among 5654 income-diverse middle-aged and older adults, concluded that actual income is unrelated to subjective assessment of income adequacy. Furthermore, in a result similar to Asebedo and Wilmarth’s (2017) findings regarding depression, they found that, for the older adults, the significant relationship between income and health was fully mediated by subjective financial well-being. (Of note, Arber et al. (2014) is a

British study; all participants had access to the same public health insurance, therein eliminating a confound common to age comparisons in the United States).

For reasons outlined in the discussion of health stress, a wholly subjective operationalization of financial strain (“ongoing financial strain”) was used for this study.

Accordingly, objective financial sufficiency is solely implied on the basis of subjective financial strain; it is intentionally likely that respondents will have completely different personal assessments of the relative value of wealth and the relationship between perceived strain and actual sufficiency or security. In an effort to capture the element of health stability or instability from among the measures of the 2012 HRS dataset, this study conceptualized general wealth stability or instability as the degree of subjective stress from ongoing financial strain (specifically, a financial strain that has lasted for a year or more).

Financial strain is a distinctive form of stress, relative to the MMADMC theoretical framework and the other concepts examined in this study. In a manner similar to the distinction between chronic and acute illness, Kahn and Pearlin (2006) place emphasis on the sustained and ongoing nature of financial strain, as distinct from an immediate financial crisis. They state that “persistence of hardship matters more than episodic occurrence or timing” (p.17). Accordingly, when considered as a sustained source of subjective stress, all of the previous discussion of the biological and cognitive effects of stress are hypothetically relevant for the experience of financial strain.

Similar to the discussion of the literature regarding health stress, there is a relative dearth of empirical research on the direct effects of financial strain on cognition and decision making. At present, the foremost body of research in this area is centered on the behavioral economic theory of scarcity. Distinct from the classic economic construct of scarcity, the theory of scarcity proposed by Sendil Mullainathan, Eldar Shafir, and Anuj

Shah describes a mechanism that describes how subjective financial strain (or a lack of a non-monetary resource, such as time) leads to specific cognitive changes in working memory (loss of bandwidth, tunneling of attention) that result in seemingly irrational decisions and choices (Shah, Mullainathan and Shafir, 2012; Mullainathan and Shafir,

2013; Shah, Shafir & Mullainathan, 2015). Scarcity is the subjective sense of having more needs that resources to meet those needs. Even more simply, it is “having less than you feel you need” (Mullainathan and Shafir, 2013; p.4). The experience of scarcity has intrinsic, essential effects on cognitive functioning. The experience of scarcity tunnels attention, working memory, and executive functions toward the object of scarcity, the unmet need. This “tunneling” creates a cognitive load (reduced “bandwidth”) that predictably inhibits cognitive resources from other needs and goals. In particular, unmet present needs reduces the potential for attention to future needs. Scarcity has been demonstrated to cause hyper-focused attention, increased cognitive load, and limited future time perspective (Shah, Mullainathan and Shafir, 2012; Mullainathan and Shafir,

2013). Laboratory experimental studies and inferences from field research indicate that long-term planning is reduced in the presence of scarcity (Mani, Mullainathan, Shafir, and Zhao (2013); Liao, 2015). Scarcity has therein been proposed as a key mechanism in decisional inertia, and neglect or irrationality in planning. The similarity between the mediational process described in scarcity and that which is described in the MMADMC process (in particular, the significance of working memory on rationality and cognitive bias) is intriguing.

While the relationship between (ongoing) financial strain and advance care planning is not yet examined in the published research literature, a recent review examined a number of qualitative and quantitative studies of personal financial issues relative to advance care planning. Concern regarding becoming a financial burden to family emerged as a theme of both qualitative and large survey studies (Ford, Cummings

& Cassell, 2017). For example, in a California state-wide survey of 1669 persons asked about factors of greatest significance relative to end of life, the most frequent response

(67%) was a concern of becoming a financial burden. Concern for the financial hardship placed on family is comparable to the 66% of respondents that prioritized being free of pain. (California Healthcare Foundation, 2012). Based on themes emerging from a review of the literature on personal finance and advance care planning, the authors proposed a theoretical model in which health related financial concern functions as a mediator of a planning for incapacity (Ford et al., 2017).

Motivational Orientation

Deliberative Motivation (Cognitive Effort) As implied by the name of the theoretical model, the Motivational Model of Aging and

Decision-Making Competence (MMADMC) is distinctive in the explicit emphasis upon the importance of motivation to the decisional process. As the research literature regarding motivation is vast, it is pertinent to further clarify and circumscribe the meaning of motivation within the model before endeavoring to review the literature on motivation and relevant correlates to the study model.

Strough et al (2015) describe motivation as motivational orientation. This is an important distinction. In common usage, and in a standard model of decision making and planning, it would be presumed that the construct of motivation refers to motivation toward the decision domain (e.g., the threat of decisional incapacity) or the available decision outputs (e.g., advance care planning). While acknowledging the importance of the decision domain as a pervasive contextual influence, the MMADMC models motivation as a general pattern (orientation) toward the decisional process itself. The

MMADC is a dual-process theoretical model. From within the paradigm of dual process,

Strough (2011) argues that motivation is required not only toward the decision domain or outcome, but toward the mental effort to successfully inhibit emotion, avoid cognitive bias, and do the deliberative work of normative, rational, decision-making.

Exemplars of motivational orientation include generalized negativity vs. positivity, loss avoidance vs. gain promotion, information-seeking vs. hedonic/affective preservation. From the descriptions of the MMADMC, motivational orientation is predominately conceptualized as a developmentally-sensitive tendency to either maximize positive emotion and hedonic well-being or maximize information and deliberative process. It is a pattern or habit of motivation directed toward the intention and will to think hard. The theoretical construct of motivational orientation is not motivated intentionality toward the outcome, but rather a motivational orientation toward the decisional process. From the perspective of the decisional process as described in the MMADMC, motivational orientation is the intentionality (or lack thereof) to do the work of integrating deliberation with affect and experience, in an effort to resist cognitive bias and make a rational decision. It is intention to assert cognitive effort. Accordingly, the theoretical construct of motivational orientation is best represented in the study concept of deliberative motivation, i.e., motivation toward exerting the cognitive effort required for hard, deep and challenging thought

Deliberative motivation, operationalized for this study as the propensity for cognitive effort, is a tendency toward intention to think hard and think deeply, to deliberate. This patterned inclination toward or away from exerting cognitive effort was first developed and measured within the bi-dimensional construct of Need for Cognition

(Cacioppo & Petty, 1982; Cacioppo, Petty & Kao, 1984). The need for cognition, described as both cognitive motivation and motivated cognition, is not actually a need or a drive, but rather an intrinsic motivation, defined as a “stable individual difference in people’s tendency to engage in and enjoy effortful cognitive activity” (Cacciopo, Petty,

Feinstein, & Jarvis, 1996, p198). The construct of need for cognition is bi-dimensional: while correlated, the tendency to engage (cognitive effort) is not the same as the tendency to enjoy (cognitive enjoyment). Accordingly, the Need for Cognition Scale (Cacioppo et al., 1984) has separately validated subscales for each dimension. For this study of the

MMADMC dual process on advance care planning, only the cognitive effort dimension

(and subscale) is relevant.

Empirical research examining cognitive motivation (motivated cognition) through use of the Need for Cognition measure of cognitive effort is substantial (Cacciopo, 1996;

Phillips, Fletcher, Marks & Hine, 2016). This is, in part, due to the incorporation of the need for cognition concept and measurement instrument into the Rational Experiential

Inventory (REI; Epstein, 1996), the most commonly used self-report survey measure of dual process (Phillips et al., 2016). The REI is a psychometrically sound measure of analytical/rational or intuitive/experiential thinking style. The REI consists of an instrument to measure faith in intuition and the cognitive effort subscale of the need for cognition measure (used as a measure of deliberative motivation in this study). Therein, many studies that have used the REI may be of value in understanding the concept of cognitive effort as a measurable conceptualization of motivational orientation within the

MMADMC process.

The concept of deliberative motivation, as conceptualized from the theoretical construct of motivational orientation, is a patterned usual tendency toward the intention or willingness to seek information, deliberate, think hard. It is, therein, a description of subjectively perceived motivational intent, but not necessarily evidence of objective motivated effort. This objective indicator is, confusingly, often also referred to as cognitive effort. In review of the research literature, it is essential to ensure clarity regarding the use of the term, cognitive effort. In validation studies of the short version of the Need for Cognition scale, Cacioppo et al. (1996) reported that individuals scoring higher in motivation toward cognitive effort also reported less difficulty in sustained problem solving and mental challenge. Using a neuro-economic paradigm of cognitive effort and a novel method, Westbrook and his colleagues elucidate the difference between cognitive effort as a subjective indicator of motivational intention and cognitive effort as an objective, deliberative cognitive expenditure (Westbrook, Kester & Braver,

2013; Westbrook and Braver, 2015). Westbrook (2013) logically asserts that an individual who has an abundance of subjectively stated motivation to deliberate may never actually demonstrate that subjectively stated motivation through behavioral objectively assessed cognitive effort. Westbrook et al. (2013) used an experimental effort discounting protocol to measure the degree to which participants were willing to increase cognitive effort in exchange for additional reward. They then compared those behavioral choices (objective indicators of objective cognitive effort) with self-rated motivational orientation (the cognitive effort subscale of the need for cognition measure). They demonstrated that participants were generally miserly in extending additional cognitive effort and that this tendency toward cognitive resource preservation increased with age.

Effort extended in the behavioral protocol and cognitive effort scores on the need for cognition instrument were significantly related, but at small effect size. Ennis, Hess &

Smith (2013) also examined the associations between objective indicators of cognitive effort and contemporaneous subjective assessment of cognitive effort. Using a memory task of increasing difficulty, Ennis et al. (2013) measured systolic blood pressure change, previously validated as a physiological indicator of cognitive effort in Hess & Ennis

(2011), and real-time reporting of effort and of motivation to “do well” in younger and older adults. They then compared with scores from the cognitive effort subscale of the need for cognition assessment. Consistent with Westbrook et al. (2013), they reported significant relationships between self-reported intrinsic motivation toward cognitive effort and both subjective and objective indicators of actual cognitive effort expended.

Likewise, they identified a significant age effect: older adults that expressed greater effort in the task, and those that curtailed the procedure at lower levels of cognitive effort, described themselves as having a lower level of intrinsic motivation toward cognitive effort. While acknowledging that the cross-sectional design does not allow for inferences of causality, Ennis et al. (2013) hypothesize in a direction consistent with Selective

Engagement Theory (Hess, 2014); they infer that the cognitive effort changed the motivation, rather than the explanation consistent with the construct of need for cognition that would presume that the motivational orientation predicted the actual performance.

Numerous studies have applied the cognitive effort concept as an indicator of motivational orientation to examine the relationships between motivation and cognitive functioning in older adults. More detailed examination of the cognitive dynamics are discussed in this review. In general, there is consistent evidence supporting that the subjective assessment of cognitive effort corresponds to objective indications of both cognitive effort and cognitive performance in attention and working memory. For example, Bruine de Bruin, McNair, Taylor, Summers & Strough (2014) demonstrated that cognitive effort (within need for cognition) fully mediated mathematical numeracy problems in older adults. Difficulty in basic math and probability performance was explained not by lack of mathematical skill or cognitive ability, but rather by motivational orientation toward cognitive effort in those functions. In an EEG-based study of aging working memory, Enge, Fleischhauer, Brocke & Strobel (2008) found a significant relationship between subjective assessment of motivation toward cognitive effort and objective neurophysiological measures of cognitive workload. Specifically, they found that individuals high in motivation for cognitive effort (Need for Cognition) demonstrated larger amplitude activation in the P3b region of the frontal cortex, a top- down indicator of deliberative cognitive effort. The overall pattern of EEG evoked potentials indicated greater attentional and processing effort for lower probability stimuli, at medium to large effect size, among individual high in need for cognitive/cognitive effort.

There is no clear consensus as to whether need for cognition (including the dimension of motivation toward cognitive effort) changes with age after middle age.

Bruine de Bruin et al. (2014) report a significant inverse correlation between age and need for cognition, of small to medium effect (r=-.21, p<.001). Conversely, other researchers have found relative age stability. In fact, after analysis of a broad study of

5004 adults, Soubelet & Salthouse (2016) recently reported greater stability over age in subjective need for cognition than in any measure of objective cognitive performance.

Need for cognition did not significantly change, even amidst substantial evidence of decline in numerous measures of fluid intelligence (spatial, memory, processing speed, reasoning). Need for cognition, including the subscale of cognitive effort, was significantly associated with overall cognitive ability at all ages. Relative to the impact of age, they did find a significant effect between age, need for cognition and both usual level of cognitive activity and usual cognitive demands. Obviously, the directionality of these interactions is of interest, but could not be assessed within the cross-sectional study design. With few exceptions, researchers reported combined scores for need for cognition, with no attempt to analyze separate subskills. Therefore, a muting of the effects of or on cognitive effort be related to the unidimensional measurement of a bi- dimensional construct.

As per Strough et al. (2011, 2015) the construct of motivational orientation within the MMADMC is conceptually linked to a broader body of theory about aging and motivation. One major stream of life-span developmental theory is focused on loss and compensatory control. As described in the previous discussion of sense of control, decline in control is related to increasing constraints, not loss of mastery (Neupert et al.,

2007). Lachman et al. (2011) propose that mastery is maintained due to changes in motivation toward goals and selective modification of aspirations and plans. This hypothesis is consistent with principles from multiple theoretical models including The

Life-span Theory of Control (Heckhausen & Schulz, 1995), Selective Optimization and

Compensation (SOC; Baltes & Baltes, 1990) Socio-emotional Selectivity Theory

(Carstensen, 2005), The Motivational Theory of Life-Span Development (Heckhausen,

Wrosch, Schulz, 2010) and Selective Engagement Theory (Hess, 2014). Briefly, each theoretical approach has strong common ground and a few important distinctions.

Heckhausen & Schulz assert that, upon age-based constraints, individuals will shift from problem-solving “primary” control strategies (change the environment) to emotional coping “secondary” control strategies (change self). Baltes & Baltes (1990) describe normal aging as a process of dealing with successive losses, notably cognitive and psychomotor losses (SOC; selective optimization with compensation). Accordingly, well-being and engagement is maintained by (explicitly or implicitly) selectively optimizing life goals and activities toward those areas that are most preserved, and compensating for losses by placing more focused attention on fewer areas of concern. As previously described, Socio-emotional Selectivity Theory (SST; Carstensen, 2005; Reed

& Carstensen, 2012) asserts that, in light of perceived reduction in time remaining, motivation shifts away from information and deliberation and toward significant emotional relationships. In Selective Engagement Theory, Hess argues that subtle age- based losses of cognitive capability (notably, working memory) prompt motivational changes and selective use of cognitive resources toward tasks of personal relevance. In each theoretical model there are common dynamics. First, within the context of age- related loss, there are common patterns of change in motivation, goal direction, and deliberative, problem-solving effort that accompany normal aging. Second, these theories presume that the motivational and cognitive elements of adult decision-making are integrated and interdependently influential in the context of aging. This is supportive of a dual-process paradigm to explain the potential effects of cognitive bias on decisions and behaviors (advance care planning) to address the age-related risk of decisional incapacity. These premises are is likewise evident in the MMADMC. The rationale for this study was, in part, that an understanding of the processes of motivational orientation and, therein, cognitive effort toward a rational goal (or threat) has potential implications for advance care planning.

The MMADMC is also founded upon theoretical premises that are interpretations of an empirical finding that is at the interface of motivation and cognition in aging: the positivity effect (Mather & Carstensen, 2005). There is consistent evidence from studies of cognitive psychology of an age-related tendency toward positive information and images and/or away from negative information and images. This effect has been consistently demonstrated at multiple levels of cognitive processing, from basic sensation and perception, to attention and inhibition, to long term memory storage and retrieval. In meta-analysis of 100 studies (n=7129) results indicated that the age-related positivity effect for attention and memory is reliable; a statistically significant, small to medium positivity effect (d=.26) was reported. Older adults demonstrate a bias toward positive information over negative; the exact opposite effect was seen in young adults. As importantly, the positivity effect is even greater in studies that do not constrain cognitive processing (through fixed choices or constraints of time); when afforded the opportunity for more open-ended, self-motivated executive functioning, the positivity bias increased to a medium effect size (d=.48). This empirical finding of age-related positivity is identified as a key element in most contemporary theoretical models of aging motivation and cognition that are relevant to the MMADMC, including the aforementioned SST

(Reed & Carstensen, 2012; Reed & Carstensen, 2015), selective engagement (Hess,

XXX) and emerging models based on fMRI and EEG studies of neural communication between the amygdala (a limbic brain structure central to emotion) and the executive functions of the medial pre-frontal cortex (Mather, 2016; Sakaki, Nga & Mather, 2013).

Decision-Making Skill

Deliberative Cognitive Skill (Working Memory) The decisional process described in the MMADMC posits that the attributes of the developing person will influence both the motivational orientation and the decision- making skills of that person. It is additionally hypothesized that the motivational orientation will influence the expression of those decision-making skills, as well.

Decision-making skills, therein, play a significant theoretical role as mediator of both the stable attributes of the adult and the patterned motivational orientation of that adult.

In the initial formulation of the MMADMC, Strough et al. provide rationale for a

“three dimensional” representation of decision-making skills as consistent with the mechanism of dual process working in the context of adult development (Strough, et al.,

2011). In this representation, each of the three dimensions of decision-making skills

(deliberation, affect, experience) interact with the others; successful integration produces efficient resistance to cognitive bias (decision-making competence) and yields an effective decision output. In this model, the necessary skills of decision-making skills are more than cognition alone. Certainly, decision-making skills include cognitive ability necessary for deliberative thought: fluid intelligence, episodic memory, working memory and executive functioning. But, in addition, influenced by dual process theory, the

MMADMC posits that decision-making skills include the balanced integration of affect

(integral and incidental) and coping skills. The third dimension of decision-making skills to be integrated are those most influenced by life-span development: decisional skills emanating from life experience. These include crystallized intelligence (e.g., that which could have been gained through formal education) and both general and domain specific life experiences. Ultimately, the ability to make decisional outputs (choices, plans) that yield positive outcomes (health, finances, relationships) depends upon integration of those three dimensions. For example, when applied to the decisional processes of advance care planning, the MMADMC presumes that decision-making skills could include not only the ability to deliberate about preferences, options and contextual considerations. The MMADMC would further posit that the decision-making of advance care planning requires the emotional regulatory ability to control the affective response to a profoundly threatening consideration (the vulnerability of incapacity) , and the coping skills to identify informational and social resources (family, health care and legal professionals). Lastly, decision-making for advance care planning would hypothetically include access to life experiences (personal or vicarious) regarding incapacity in crisis or dementia, medical treatment options, and legal requirements. In this study, only one dimension of decision-making skill was examined. This study focused on deliberation. In part, this focus is pragmatic. Indicators of each of the other two dimensions of decision-making skills (affect, life experience) are available through examination of variables within the HRS, but incorporation of those variables would reasonably require longitudinal analysis and broadly expand the scope of analysis.

Testing of those additional dimensions was deferred for future examination. This study focused on the dimension of deliberation because advance care planning requires examination of beliefs and values, processing information regarding complexities of legal authority and medical directives, and consideration of vulnerability and trusted relationships. While certainly influenced by affect and life experiences, an important element of the work of advance care planning is deliberative.

Within the many individual cognitive skills that are useful to deliberative decision-making (crystallized intelligence, episodic memory), the cognitive skill of working memory was the indicator of the deliberative decision-making skill in this study.

Working memory is a cognitive skill that is essential to the higher “executive” cognitive functions that are traditionally described as an individual’s fluid intelligence: pattern recognition, abstraction, problem-solving (Horn & Cattell, 1967). Working memory also plays a prominent role in dual process theories of decision-making. Working memory is the cognitive process in effect when an individual is attempting to focus, concentrate and consider information, particularly in the context of an intruding thought or feeling.

Working memory is a process, an interaction between attention and executive control.

Therein, working memory is a construct, not a focal cognitive system or neuroanatomical organ. While heavily dependent upon a few specific regions of the pre-frontal cortex, working memory is an integrated set of cognitive functions, each with their distinct neuroanatomical foci and pathways, that involve diffusely distributed anatomical regions that are, from a neuro-anatomical perspective, localized at considerable distance from each other. Together, these brain processes produce a unified skill: the ability to manage short-term storage and retrieval of information for future access and manipulation.

D’Espisito & Postle (2015) describe working memory as a system-level function that is distributed functionally (attention, memory, executive control), anatomically (centered in the pre-frontal cortex, but projecting broadly to the hippocampus for long term memory retrieval and the limbic structures, such as the amygdala, relative to emotional inhibition), and neuro-chemically (rare among cognitive functions, working memory involves widespread involvement of the neurotransmitter dopamine and the dopaminergic receptors that are integral to the brain’s reward systems). They describe the foremost neurological attributes of working memory as persistent neural activity, top-down control, and dopaminergic modulation. Together, these attributes yield a system that allows an individual to hold information for the short term, shift attention and concentration to another task, and have the executive control to re-integrate with the initial, now potentially updated, information (Kirova, Bayes & Lagalwar, 2015).

Working memory is most easily observed in tasks of divided attention, and inhibition of distraction, including emotional distraction. The deliberative functions of advance care planning that are to be examined in this study involves the ability to focus on an information-dense, complex, and emotionally challenging thought (decisional incapacity). It is herein presumed that the deliberative functions of advance care planning require thinking hard about a complex (persistent neural activity) and emotionally challenging issue (top-down inhibitory control of emotion) in the context of potential motivational orientation toward motivationally driven cogntive bias (dopamine as a primary neurotransmitter of the motivation).

Working memory use can be hard. Since working memory is, by definition, a system under working under divided attention, it is a useful indicator of cognitive capacity under load (D’Espisito & Postle, 2015). As the central control mechanisms of working memory are attentional and executive, not actually memory, working memory capacity is highly sensitive to overload. Working memory can be challenged either in the sheer amount of information to be temporarily held, the number of mental manipulations and consequent concentration required, or the amount of irrelevant distraction. Not surprisingly, significant age effects are observed in working memory, independent of diagnosed neurological degenerative disease. Hahn and Lachman (2015) report significant (small to medium) age effect on both baseline working memory and decline in working memory over a 10-year period. Furthermore, working memory decline is often a sensitive early indicator of general cognitive decline and as well as an early indicator of diminishing capacity due to neurological disease such as Mild Cognitive Impairment

(MCI) or progressive dementia, such as Alzheimer’s Disease (McCabe, Roedinger,

McDaniel, Balota & Hambrick, 2010). Accordingly, tests of working memory are often used in mental status examination (Kirova et al., 2015). While decline in working memory capacity itself can produce corresponding functional difficulty in instrumental activities, McCabe et al. (2010) assert that the most important influence of working memory on function is the role of working memory as the most common cognitive mediator of other “higher’ executive functions such as consideration of alternative options, problem-solving and planning.

There are numerous cognitive tests of working memory; many of the most common involve some form of digit span manipulation. For this study, I used a simple and long-standing assessment of working memory, often used in mental status testing of older adults: serial subtraction (by 7). Serial subtraction was first developed in the early

1940s for use with children and with psychiatric patients and has been used extensively with geriatric patients in clinical practice (Shum, McFarland & Bain,1990; Meyer, Xu,

Thornby, Chowdhury & Quach, 2002; McCardle, Fisher & Kadlec 2007, McCardle &

Robert, 2015). Serial subtraction requires the subtraction of a consistent number from the remainder of the previous subtraction. While versions have been used that required subtraction by 3 or by 13; the most common form of the test is to subtract 7 from 100, and then continue to subtract 7 from each difference, for a specific number of iterations.

In this study, as per the 2012 HRS, five iterations are tested.

The primary mechanism of the serial 7 task that serves as test of working memory is a cognitive skill that is developed early in elementary school: “borrowing” a number

(the inverse function of “carrying” a number. Using a qualitative approach to identify the mental processes of serial 7, Kase (2007) noted that the indicator of both subjective cognitive challenge and effort occurred during those iterations that involved calculation requiring borrowing from the digit that holds a ten-fold value in the integer. Therein, those calculations that involve borrowing (e.g., 93-7=86) are more likely to produce error than a simple subtraction without borrowing (e.g., 79-7=72). This distinction is supportive of the argument that working memory is being assessed. The more complex calculation that requires borrowing demands holding the original number in short term storage, borrowing 10 from the preceding digit, retrieving the former partial difference and finishing the calculation. At initial structure, subtracting from 100, the first four of five (80%) of calculations of the serial 7 examination require borrowing (a perfect score would yield the following sequence: 93-86-79-72-65). But, any individual error results in a reset of the difference and potential change in the level need for borrowing. As described elsewhere in the discussion of methods for this study, the serial 7 test is administered without correction during the assessment; data collection only includes the statement “and 7 from that?” (HRS 2012 codebook).

While a simple test of mental status, the serial 7 has demonstrated convergent validity with more extensive and complex tests of working memory in older adult population (Schum et al., 1990; Mikels, Larken, Reuter-Lorenz & Carstensen, 2005).

Both construct and predictive validity of serial 7 was supported by Meyer et al. (2002) by demonstrating that longitudinal serial 7 results could predict the development of mild cognitive impairment (MCI) and differentiate persistent MCI from Alzheimer’s-type dementia. More recently, the serial 7 task demonstrated adequate convergent validity

(r=.53) with a more extensive set of working memory measures (Hahn & Lachman,

2015). In a very recent effort to establish age-norms and standards for serial subtraction tests with more detailed neuropsychological measures, serial 7 examination was asserted as a clear indicator of working memory. In recognition of the “borrowing” and “re- grouping” mental activity inherent in serial subtraction, the authors described the neuropsychological processes as encoding and focus, the attentional and short term memory activities of working memory (Bristow, Jih, Slabich & Gunn, 2017). Using the HRS, McCardle et al. (2007) examined serial 7 performance among the roughly twenty thousand participants over age 50 (mean age of 65). They found that, in five iterations, nearly 70% of the population showed no error, but that among those who failed on at least one iteration, the error rate was normally distributed. This is consistent with more result results from a similarly large and normally distributed sample that demonstrated 2.6 errors on average (SD=1.7) in five iterations (Levy, Uber, Dillard, Weir

& Fagelin, 2014).

The primary concern regarding the validity of the serial seven is the potential confound of numeracy. Numeracy, comparable to verbal literacy, is a construct that describes a combination of crystallized intelligence (learned skill, life experience) and motivation that inhibits facility in the use of numbers and the activity of calculation

(Reyna & Brainerd, 2007; Peters & Levin, 2008; Reyna, Nelson, Han & Diekmann,

2009; Peters, Hart & Fraenkel, 2011; Levy et al. 2014). Numeracy has been implicated as a potential confounding factor in performance of the serial 7 task. Kazmarck (2000) conducted a comprehensive examination of the validity of the individual measures used in common mental status batteries (including the serial 7 test) by comparison against more detailed and extensive neuropsychological testing. He found that basic skill in calculation significantly affects performance on the serial 7 test. This may account for the differential performance on the serial 7 test among lower educated individual and those historically prone toward disparity in both formal education and economic opportunity. Sloan & Wang (2005) used multiple cohort waves of the HRS to examine serial 7 performance in older adults (over age 70). Their analysis led them to the conclusion that, in addition to an expected effect for older age, lower education predicted poorer performance in the serial 7 test. Furthermore, even after controlling for age and education, older African-American participants performed worse on the test. This finding was supported by the work of Reyna and her colleagues in examining numeracy in older adults (Reyna & Brainerd, 2007; Reyna, Nelson, Han & Dieckmann, 2009); they, too, found significant correlations between numeracy (including evidence in response to serial subtraction) and education, non-Caucasian race, and indicators of poverty.

Working memory (as a cognitive function indicative of decision-making skill) has been demonstrated to have relationship with multiple variables examined in this study.

Korten, Sliwinski, Comij & Smythe (2014), using an age diverse sample of 324 individuals, found a significant direct effect of the current severity of chronic life stress on working memory (small to medium effect). A very recent review of the neurotoxic effects of chronic stress described the differential dose-response to stress-related glucocorticoids (such as cortisol) in the brain structures involved in working memory function. The same stress hormones that stimulate pre-frontal cortical function (including efficient retrieval processing with the hippocampal long term memory center and effective inhibition of emotional stimulation from the amygdala) declines precipitously in higher dosages, particularly among older persons (Lupien et al., 2018).

Working memory is associated with perceived control in the context of aging.

Hahn & Lachman (2015), in an examination of change over 10 years, studied the working memory of 103 middle-age adults. They provided empirical evidence of a significant association (r=.33; medium -large effect) in the relationship between decline in working memory over 10 years and perceived control (as measured in this study). Results of the repeated measure design supports the authors conclusion that levels of perceived control predict future decline in working memory. (The MMADMC likewise posits this directionality, but adds motivational orientation as a partially mediating factor. This study used the same measures as Hahn & Lachman (2015) and will afford the opportunity for examination of both direct and indirect effects.)

There is limited published research that has examined the relationship between optimism and working memory in an older adult population. As previously noted in this review, Aspinwall et al. (1996; 2001) found that individuals who demonstrated a higher degree of optimism demonstrated increased attention to information, including both positive and negative information regarding health risk. Consistent with the theoretical premises of the MMADMC, they speculated that the mechanism is motivational, but included consideration of necessary cognitive resources as relevant. More recently,

Gawronski, Kim, Langa & Kubzansky (2016) used HRS mental status data (including serial 7 measure) of 4624 adults over age 65 to longitudinally demonstrate that optimists

(as determined by higher score on the LOT-R) had significantly lower likelihood of developing a diagnosed cognitive impairment over 4 years; no specific analysis of serial subtraction/working memory was provided.

While the mechanism is not understood, there is empirical support for the influence of Purpose in Life (PiL) on working memory. Lewis (2016) used an age- diverse sample of adults to examine the relationship between PiL and a number of measures of adult cognition. After controlling for age, education and self-rated health, they demonstrated that PiL significantly predicts cognitive ability in general and, specifically, in working memory/executive function (a backward counting task similar to serial subtraction). The effects are relatively small (r=.16 in bivariate analysis; B=.04 in linear regression); accordingly, it is possible that significant findings are related to the large sample size (n=3489). But, the effect of PiL on cognition is not without precedent.

In 2010, Boyle et. al., in a 7 year longitudinal study of 900 cognitively intact, community-dwelling older adults provided evidence of a significant and meaningful reduction in the risk of mild cognitive impairment (OR=.71). Mild cognitive impairment

(MCI) can serve as a useful exemplar of the dynamics that drive a motivational model of advance care planning; MCI is, for many older adults a precursor to decisional incapacity of dementia. As mentioned previously in this review, decline in working memory is among the most sensitive early indicators of mild cognitive impairment. Relative to the potential confounding effect of numeracy inherent in the measurement of working memory by serial subtraction, Pertl, Benkin, Zamarian, Martini, Bodner, Karner &

Delazer (2014) showed that numeracy for mathematical or probabilistic health information (e.g., numeric descriptions of risk) is significantly impaired in MCI.

The concept of cognitive effort, measured as a subscale of Need for Cognition

(Cacioppo & Petty, 1982; Cacioppo, Petty, Feinstein & Jarvis, 1996), not surprisingly, has significant relationship with cognition in general, and working memory, specifically.

According to Westbrook et al. (2013), from the perspective of neuro-economics, cognitive effort means objective expenditure of cognitive resources, the actual brain activity of deliberation, or the “opportunity cost of working memory allocation”

(Westbrook, 2013, pg. 400).

In a young adult population, Fleishauer, Enge, Brocke, Ulrich, Strobel & Strobel

(2010) found significant relationship between cognitive effort and multiple indicators of attention, executive functioning and fluid intelligence. Based upon the work of Salthouse & Pink (2008) in presenting the convergent and discriminant validity of fluid intelligence and working memory, Fleishauer et al. determined that subjective cognitive effort was significantly correlated with objective cognitive skill of working memory (effect sizes are small to medium). Similar results have been corroborated with older adults. A sample of

1174 older adults randomly sampled from the HRS showed evidence that cognitive effort and measures of working memory are well correlated (r=.5) but, upon confirmatory factor analysis, demonstrate distinctly separate factors (Maldonato, Sperandeo, Costa, Cioffi &

Cozzolino (2017).

As mentioned previously in this review, Westbrook et al. (2013) reported significant relationship between cognitive effort (as measured by the assessment of need for cognition; Cacioppo & Petty, 1994) and an experimental protocol that used a test of working memory (n-back). Not surprisingly, older participants demonstrated poorer overall performance in working memory. But, intrinsic motivation toward cognition that was foremost predictor of subjective assessment of difficulty and number of increasingly difficult trials to exhaustion. Ennis et al. (2013) similarly used a test of verbal working memory with increasing difficulty in their examination of the effects of both current and assessed intrinsic motivation (again, using the need for cognition measure), with expected poorer performance in working memory among older adults. But, in addition, they reported a significant three-way interaction of age, level of difficulty and need for cognition (cognitive effort); only older adult participants demonstrated the relationship between intrinsic motivation and performance in the increasing challenge to working memory. More recently, Maldanato et al. (2017) presented a well-fitting structural equation model which implies causality of cognitive ability on cognitive effort. While the relationship between these concepts is empirically supported, the directionality

(primarily studied in cross-sectional design) is in dispute. Peters et al. (2007) speculated, on the basis of dual-process theory, that motivation (cognitive effort) drives selective use of cognitive resources, principally working memory. This line of thought has evolved into the current work around Selective Engagement Theory (Hess, 2014; Hess et al.,

2018) which posits that motivation toward cognitive effort and working memory have a recursive relationship: implicit awareness of limitations in working memory prompts changes in motivation and selective use of working memory, which recursively provide motivational feedback for future motivation toward cognitive effort and subsequent future use of working memory. In the Selective Engagement model, the two elements of process (motivational orientation/cognitive effort and decision-making skill/working memory) are bi-directional. This is not consistent with the MMADMC in which posits that motivational orientation toward cognitive effort should uni-directionally predict working memory performance (Strough et al., 2015). While the debate is active, a recent study (Hess et al., 2018) adds support for the Selective Engagement Model: path analysis indicated that the significant direct effect of “cognitive cost” (use of working memory) on a number of cognitively demanding everyday tasks was partially mediated by the indirect effect of cognitive effort (motivation).

Decision-Making Competence

Resistance to Cognitive Bias (Resistance to Risky-Choice Framing Effect) In the MMADMC, Strough et al. (2015) hypothesize that the net result of the motivational and cognitive process of deliberation is the ability (or inability) to resist cognitive bias. The final mediating construct in decision-making process, as theoretically modeling in the MMADMC, is decision-making competence. Decision-making competence (DMC) is broadly defined as the ability to resist cognitive bias and produce decisional output (choices, behaviors) that are consistent with normative logical rules of rational decision-making (Bruine de Bruin, Parker & Fischhoff, 2007, 2014). DMC is a construct first explored in adolescent populations (Parker & Fischhoff, 199, 2005) and initially applied to older adult populations by Finucane, Slovic, Hibbard, Peters, Mertz and MacGregor (2002). The contemporary literature in DMC is now focused on DMC in later life (Finucane & Gullion, 2010) and most exhaustively examined by the team of

Wandi Bruine de Bruin, Andrew Parker, Baruch Fischhoff (2007, 2012, 2014). All of these authors describe DMC with a common principle from the perspective of normative behavioral decision making: the quality of decision-making is to be judged on the basis of the integrity of its process, not the outcomes of the decisions themselves. Finucane et. al.

(2002) placed initial emphasis on decisional consistency and comprehension of potential outcome. Parker, Fischhoff and Bruine de Bruin further developed the normative approach to DMC as the ability to perform four cognitive functions: belief assessment, value assessment, integration, and meta-cognition (Bruine de Bruin et al, 2007, 2014).

Belief assessment requires the ability to assess basic probability in real-life context.

Effective decision making requires a clarity regarding the choices and actions that will yield most desirable outcomes and avoid undesirable outcomes. Value assessment is the ability to maintain adherence to norms of coherence and consistency with logic. For example, consideration of past investment, rather than solely future risk or benefit, is an illogical value assessment; it is also a common cognitive bias known as the sunk cost fallacy. Integration is the ability to combine probabilistic beliefs and value consistency together, particularly when no mathematical indicators are provided. As life is most often a “word problem”, DMC requires the ability to recognize and apply implicit formal decision rules. Lastly, meta-cognition is necessary to evaluate one’s own decision- making limitations and potential for bias. Overconfidence in decision-making or inability to accept gaps between confidence in knowledge and actual knowledge is an illustration of a failure in meta-cognition.

DMC is not only conceptualized as a set of four decision-making skills and a collection of logical anomalies to test for cognitive bias. DMC is asserted to be a generalizable latent construct for which those anomalies are reflective. Efforts toward the psychometric measurement of DMC are not focused solely on the individual cognitive biases, nor the four elements of normative decision-making, but rather on the development of a measure of a common construct by which human decision-making can be assessed. While the individual cognitive biases identified as elements of DMC may emphasize different cognitive schema (time, investment, probability, etc.), they are all conceptualized as components of a consistent individual difference construct that are indicative of an individuals’ capability to deliberatively focus and make rational decisions.

One of the most exhaustively studied illustrations of DMC is resistance to the framing effect (Parker & Fischhoff, 2006; Bruin de Bruin et al., 2007, 2014; Strough et al. 2015), the conceptualization of the construct of DMC for this study. There are numerous types of decisional frames that present the same phenomenon is a positive and negative valence. The specific “risky choice” framing task used as the empirical indicator of DMC in this study is, in fact, the most extensively applied (34%) of all cognitive bias/heuristic tasks identified in a systematic review of the medical decision- making literature (Blumenthal-Barby & Krieger, 2015). In a risky choice framing task, one option is of fixed likelihood, a “sure thing”; the other is a risk that could yield better or worse outcomes. The risky choice framing task that was included in the 2012 HRS and was used in this study was first introduced by Amos Tversky and Daniel Kahneman, the foremost research team foundational to the development of the discipline of behavioral economics (Tversky & Kahneman (1981, 1986). This framing task is actually two versions of the same elicited choice, but asked once in a “positive” frame and once in a “negative” frame. The two questions should, based on normative decisional logic, yield the same choice (wholly dependent upon the individual risk preference of the respondent). The framing is two decisional activities: an assessment of preferred level of risky choice versus sure thing, and then a test of the consistency of that choice when the choice is asked a second time, but framed in an opposite valence. In this study, the framing effect was not seen in the choice itself (higher or lower risk in each frame), but in the consistency of the choice in the positive and negative frame. A framing effect is when, after stating a preferred option, that preference is reversed, solely on the basis of the positive or negative valence of the framing.

For this study, a frequently used scenario, often referred to as the “Asian epidemic”

(Tversky & Kahneman, 1981), is provided for which the respondent will make a choice:

“Imagine that the United States is preparing for the outbreak of an epidemic expected to kill 600 people. Two alternative programs to combat the disease have been proposed.

Scientists estimate that the outcome of each program is as follows….. which program would you favor: Program A or Program B?”. The question is asked twice, at different points in the assessment. The only difference in the two elicitations is the choice of positive or negative framing demonstrated in the words chosen to describe the impact of a hypothetical epidemic, relative to the hypothetical alternative treatment programs: “if

Program A is adopted, 300 people will be saved; if Program B is adopted, there is a 50-

50 chance that either 600 people will be saved or none will be saved”. The participant’s choice is recorded and, later in the assessment, the scenario stem is repeated but, this time (randomly alternated for half of the sample), the respondent is told: “If Program A is adopted 300 people will die. If Program B is adopted, there is a 50-50 chance that either none will die or 600 people will die.”

Logically, in the Asian epidemic scenario, the same program should be adopted in each choice, based solely on the participant’s tolerance for risk and not on the basis of the positive (“live”) or negative (“die”) frame of the scenario. A framing effect is a preference reversal based upon on the framing of the question in the opposite valence.

Logically, framing effects violate the principle of rationality called descriptive invariance; equivalent consequences should not have prompted a change in preference

(Kuhberger & Tanner, 2009). From the perspective of DMC, the resistance to a framing effect represents an indication of constancy in value assessment and ability to integrate

(Bruine de Bruin et al., 2007) by maintaining the same choice. Cognitive bias, as represented by the framing effect, is an indication of a failure in DMC that, according to the process described in the MMADMC, would reduce the likelihood of optimal decision output and, ultimately, outcomes (Strough et al., 2015).

As simple and seemingly inconsequential as the distinction in the Asian epidemic scenario may appear, the framing effect has been demonstrated to be a robust measure of cognitive bias. The Tversky and Kahneman’s (1981) “Asian disease problem” have been demonstrated to have a particularly strong effect. Kuhlberger (1998) conducted a meta- analysis on over 130 published studies and found that, among all types of framing effect presentations, this approach produced the greatest and most consistent effect (in aggregate, a medium effect of Cohen’s d=.57). This is consistent with comparable reported effect sizes from individual studies using the Asian disease protocol that reported medium level effect sizes of r=.29 (Frisch, 1993) and r=.31 (Stanovich & West,

1998).

In some research directly comparing older and younger individuals, this effect appears to be even more pronounced among older adults. Throughout the decades, considerable research, including a number of systematic reviews and meta-analytic studies, have been conducted of age differences in risky-choice framing. Caution is required in interpretation of some of this research. Some of these studies use the term

“framing effect” as solely a description of the influence of the positive or negative valence of the frame on the preference of risk vs. no risk choice. These studies and meta- analyses (Mata, Josef, Samanez-Larkin & Hertwig, 2011; Best & Charness, 2015) are therein useful as indicators of risky choice decision-making and of a potential positivity bias, but, in the absence of a preference reversal, these studies (and the effect sizes or age differences reported) are not indicators of the cognitive bias demonstrated in the framing effect as conceptualized in DMC. In a more recent examination of cognitive strategies in framing with sample of 110 younger and 111 older adults, Cooper, Blanco & Maddox

(2017) reported a significant age by framing effect of a medium magnitude (partial eta- squared = .16). Strough et al. (2015) present data analyzed from unpublished findings of the Bruine de Bruin et al. (2007) data collected for psychometric analysis of an instrument to measure DMC. They present an age-spectrum analysis of decline in resistance to framing that demonstrates relative stability to roughly age 60, then marked decline with advancing chronological age.

Within the MMADMC, resistance to a framing effect is conceptualized as an indicator of DMC. In other words, an illogical preference reversal is indicative of reduced resistance to cognitive bias. Inconsistency in preferred risk based on framing (a failure in value assessment) and an inability to recognize the implicit equal probabilities inherent in the two questions with the risk preference (integration) is, according to the construct of DMC within the MMADMC, evidence of cognitive bias and impaired DMC.

This conclusion is theoretically consistent, irrespective of the underlying causal mechanism of the framing effect. But, a brief discussion of the causal hypotheses of the framing effect is informative, particularly in light of potential relevance to advance care planning.

The foremost explanation of the framing effect is Prospect Theory (Kahneman &

Tversky, 1979; Kahneman, 2011). Prospect theory posits that the framing questions require each respondent to do two separate cognitive tasks. First, an individual edits and codes the information provided; probabilities must be weighed and assigned a heuristic.

Then, each individual will establish their own relative value function (gains and losses) to correspond with the weight. The resulting patterns for most individuals are the foundations of prospect theory: most of us heuristically overvalue potential losses as compared to similar probability of gain. The non-linearity of the response in a framing effect is evidence of an implicit, dual-process deviation from rational logic. Prospect theory explains this as an individual having a separate value function for losses as compared to gains. “Losses loom larger than gains” (Kahneman, 2011, p.282). The preference reversal in a twice-tested framing effect demonstrates a disproportionately greater motivation toward loss aversion. Relative to this examination of advance care planning, Kahneman (2011) argues that the heuristic asymmetric response, disproportionately giving greater value (response) to the avoidance of loss is consistent with threat perception necessary to survival.

There are alternative explanations for the effect. In Fuzzy Trace theory (FTT),

Valerie Reyna and her colleagues also consider a framing effect from within a dual process paradigm, but not as an indicator of irrationality (Reyna & Brainerd, 1991;

Reyna, 2004; Reyna & Brainerd, 2011; Reyna, 2012). In FTT, a framing effect is the result of a consistent expression of value as emanating from an emotionally-integrated heuristic intuition called a gist (as contrasted with a verbatim or probabilistic). In FTT, there is singular processing, not the two elements of prospect theory. The Asian epidemic scenario is, according to FTT, held in gist reasoning and expressed as simply: saving is good, death is bad. Normative rules of decision-making imply that life is a word problem in which choices are modified to logic and probability. In FTT, all expressions of mathematical probability are instead transferred (fuzzy trace) to categorical, nominally-measured statements of meaning, held as a heuristic of intuitive gist (“none or some”, “less or more”). Kuhberger and Tanner (2010), in recognition of the fuzzy gist value of null information (“none vs. all” as the most basic gist), performed a series of experiments to simply add the additional statement of zero complement to the Asian epidemic scenario and could successfully induce or eliminate previous framing effects.

Mather (2006), in a review of the neuroscience of decision-making in aging, considered whether the apparent loss aversion demonstrated in a framing effect is actually the consequence of a forced choice. Based on considerations of changes to the aging brain, she speculated that a preference reversal in the Asian epidemic scenario was indicative of motivation to avoid a decision, possibly relative to the positivity bias previously described. Huang, Su & Chang (2015) extended that protocol with a young adult sample (more likely to express a bias toward negativity) and demonstrated that the framing effect is moderated by adding a “no choice” option.

Mather’s consideration of a relationship between positivity bias and framing effect in an aging brain has gained some empirical support from neuroscience examination of the framing effect in older individuals. De Martin and colleagues demonstrated neuroanatomical support of the dual process nature of framing effect in a manner consistent with prior fMRI studies of positivity bias. The demonstrated pronounced activation of the amygdala (emotional processing) in the presence of the framing effect. In the absence of the framing effect, activation of the amygdala was not significant, but the pre-frontal cortex (deliberation) was active (DeMartino, Kumaran,

Seymour & Dolan, 2006). Likewise, Rehmert & Kisley (2013) used a framing manipulation and EEG measurement by EEG to demonstrate that, while younger adults could be induced to alter attention from negative bias to positivity on the basis of framing, older adults showed a strong bias toward the positive frame and resistance to attention to negative stimuli on the basis of the frame. Additional insight regarding the risky choice framing effect in older adults emerges from studies that attempt to understand the process or manipulate the effect.

Kim, Goldstein, Hasher & Zachs (2005) prompted 186 older and 186 younger participants to provide verbal justifications for their choices in the Asian epidemic scenario; there was a significant decline in framing effects in both age groups.

Woodhead, Lynch & Edelstein (2011) used a “read aloud” mixed-method to attempt to discern the presence and type of decisional strategies used by younger and older individuals in experiencing a framing effect in a medical decision. Their findings strongly support a dual process interpretation of the effect in older adults. They found that older adults were significantly less likely to use a deliberative, data-oriented strategy, but as likely as young persons to use an experientially oriented strategy. In fact, they found that older adults were less likely to use any strategy at all. When strategy was considered in the analysis, no age effect existed in framing effect.

Again, relative to the MMADMC, resistance to a framing effect is consistent with the construct of DMC, irrespective of the directionality of the framing effect (the relationship between risk aversion or risk taking relative to the positive or negative framing). Any preference reversal solely based on a re-framing is indicative of cognitive bias, i.e., reduced resistance to cognitive bias. The directionality of that reversal, while not necessary in the testing of the theoretical process, may provide additional insight relative to the positivity effect and theoretical explanations thereof.

Risky choice framing has been widely examined relative to aging cognition, including working memory. Bruin de Bruin et. al. (2010; 2012) demonstrated that, among older adults, the direct effect of age on resistance to framing is fully mediated by fluid intelligence, strongly influenced by working memory capacity and function

(Salthouse & Pink, 2008). Likewise, Boyle, Yu, Wilson, Gamble, Buchman & Bennett

(2012) examined the longitudinal relationship between measures of decision making competence, including framing effects, and cognitive assessments made during the prior over a 5-6 years. They found that the rate of cognitive decline of all older adults

(irrespective of whether decline was diagnosed as mild cognitive impairment or dementia) predicted DMC. A series of studies by Del Messier, Mantyla & Bruin de

Bruin (2012, 2013), are particularly important to understanding the role of working memory and DMC, including the resistance to framing effect. In an initial study of 213 undergraduate students, executive control (working memory and fluid intelligence) positively predicted resistance to framing effect. The effect was further elucidated as related to the working memory functions of inhibition and updating. But while significant, working memory functions explained just 8% of total variance in resistance to the framing effect. In a second study of 568 older adults (2013), Del Missier et al. demonstrated that the effect of age on the framing effect is fully mediated by working memory. Even after controlling for age-related declines in working memory, working memory predicted resistance to the framing effect. In fact, of a battery of measures that included assessments of semantic memory, episodic memory and long term memory, only working memory predicted resistance to the framing effect. “Resistance to framing is positively and selectively related to individual differences in working memory” (Del

Missier et al., 2013, p.15).

The motivational orientation of cognitive effort (need for cognition) has also been examined relative to the resistance to the framing effect, often as measured by the Asian disease scenario. In general, a motivational willingness to expend cognitive effort is only weakly positively related to resistance to the framing effect. Among a number of studies with an undergraduate student sample, individuals that are low in motivation toward cognitive effort tend to demonstrate the cognitive bias of a framing effect more often

(Smith & Levin, 1996; Simon, Fagely & Halloran 2004; Bjorkland & Backstrom, 2008;

West & Stanovich, 2012). A number of studies showed no relationship between motivational orientation as measured by the cognitive effort (need for cognition) and risky choice framing effect (Levin, Gaeth, Schreiber & Lauriola, 2002; LeBoef & Shafir,

2003; Mahoney, Buboltz, Levin, Doverspike & Svyantek, 2011). In part the explanation for the limited effect in relationship may be indicated by the work of Peters & Levin

(2008) who reported that, among a sample of undergraduates, the main effect of cognitive effort/need for cognition on risky choice framing (Asian epidemic scenario) was moderated by the participant level of numeracy (ability in managing basic math and probability). This may be relevant to older adults: Bruine de Bruine et al. (2015) reported that, in a sample of older adults, they found that the need for cognition (including cognitive effort) fully mediated the effect of age on numeracy. Any direct role of motivational orientation in the form of cognitive effort on the risky choice framing effect is unclear.

No research literature was found to examine the relationship between DMC as resistance to a risky-choice framing effect and any other study variables. Decision Outputs and Outcomes

Advance Care Planning (Discuss, Designate, Document) This review will provide a general overview of the literature on uptake

(participation) in advance care planning among community dwelling older adults. This review will focus solely on those middle age and older adults that have both legal authority and cognitive capacity to make advance care planning decisions for themselves.

While the literature on cognitive capacity to make life planning decisions and the many empirical studies and interventional trials of encouraging advance care planning uptake among those persons with dementia or living are tangentially relevant, this review will focus solely on factors related to those individuals who have the capability of autonomous advance care planning, but may or not have chosen to do so.

A recent descriptive overview of 80 previous systemic reviews of the research literature in advance care planning is a very useful indicator of the state of the advance care planning uptake literature (Jimenez, Tan,Virk, Low, Car & Ho, 2018). 9 topical themes emerged from the analysis, including contextual themes (end of life, attitudes), implementation issues (surrogate decision-making, decision aids, communication, specific populations) and outcomes of advance care planning (effectiveness in end of life treatment preference, cost of care). The sheer size of the review brought to clarity the foremost conclusions of relevance to this study. The literature on advance care planning uptake has often been focused relatively narrowly on the implicit importance of stated end-of-life preferences and written advance directives adequate to guide emergent and critical care decision-making. Whether explicitly stated from the perspective as “end-of- life” or from the perspective of the provider or health economist, the emphasis has been on the output: the directive documents (durable powers of attorney, living will). This emphasis is certainly reasonable, as those considerations are crucial to health care consumers and their loved ones, healthcare providers, and stewards of the medical costs for older adults. The descriptive research literature in advance care planning uptake has often focused on broad, socio-demographic factors that are linked to advance care planning uptake or inertia. These factors indicated who did or did not participate in advance care planning, but absent adequate measures of intention or cognitive process, it would not actually be possible to state whether a decision was made or not, much less the dynamics of any decision-making.

Considerable advances have been made in completion of advance directives that indicate advance care planning. Silviera, Wiitala & Pieta (2014) used 2000-2010 data to determine advance care planning uptake trends. They indicated that the rate of advance care planning uptake has steadily increased over the first decade of the century that now a majority of older adults have some form of written advance directive by, on average, slightly less than 2 years from time of death. Khosla, Curl & Washington (2016) used data from the 2002-2010 HRS to demonstrate that overall uptake of advance care planning increased by 6-12% annually. While all of this is positive from the perspective of the value of uptake and availability of documents, marked inconsistencies remain in completion of separate advance care planning outputs (discussion, powers of attorney, living will) that, upon deeper examination of comparable studies raises some continued concern about the decisional processes that underlie these outputs. For example, Ionue,

Ihara & Terillion (2017) recently reported that, in a sample of 1153 persons , 73% had completed either a durable power of attorney or a living will, but 28% have not discussed those documents with anyone since. This is consistent with the analysis of Bishchoff et al. (2013) of 4394 HRS participants; they found that, while 76% had engaged in some advance care planning by death (as evidenced by a statement of discussion, a durable power of attorney or a living will), less than half of the sample had both a legal document and an informed surrogate. These inconsistencies in implementation raise concern about the actual “planning” in advance care planning.

A new emerging consensus (Sudore et al., 2018; Newsome, 2018; Sudore et al.,

2017) is emerging that is echo a few important contributions to the conceptual literature that have focused on advance care planning as a process, and have called for research that will examine that process. In a series of published consensus statements that emanated from a Delphi panel approach, Sudore et al. (2017, 2018) attempt to define advance care planning and state the desired outcome of advance care planning. The emphasis of both the definition and many of the high priority outcomes is on process. advance care planning is defined as an individual process of clarity regarding values, choices, preferences. It is further defined as a process of communication with both a trusted person who can act in proxy decision-making and a health care provider(s). Interestingly, despite the substantial emphasis on advance directive documentation in the research literature, and the completion of written directives as research outcomes in advance care planning uptake intervention research, the consensus group stated “documentation” as third in importance and “advance directive documentation” as tenth (Sudore et al., 2018).

While many of the priorities are lacking in clarity and have potential redundancy, the work of the consensus group indicates a shift from the emphasis on medico-legal documentation, end-of-life and cost of care considerations that are predominate in the previously mentioned review (Jiminez et al., 2018) The emerging emphasis is focused on surrogate designation (which, of course, could involve the legal authority in a durable power of attorney document), surrogate preparation and communication. Roughly 7 years prior to the consensus group, Sudore and Fried (2010) argued that the primary value of advance care planning is in this type of communication between an individual and the person(s) likely to make decisions for them in a state of incapacity. Sudore &

Fried commended an emphasis on preparing a future surrogate to be prepared for decision-making in the moment, just-in-time, and based on well-communicated values and discussion of reasonable parameters for individual judgment, rather than explicit stated preferences. Again, the focus on advance care planning is on the coordination of communication (discussions) and documents (directives). As stated, while aggregate advance care planning uptake appears to be increasing, this coordination continues to be lacking, indicative of flawed process. A recent randomized clinical trial of an advance care planning uptake educational and self-efficacy intervention for 235 dyads (individuals and their chosen future surrogates) is illustrative of this misfit between emphasis on completion of documents and on clarity of process. After a focused dyadic intervention that led to a significant increase in documentation and significant increase in the future surrogate’s confidence in ability to serve, surrogate post-testing indicated that the intervention had no effect on the predictive ability of the predictive ability of the future surrogate regarding the values and preferences of the individual granting authority (Bravo et al., 2016). One could argue that the escalation of confidence in the absence of knowledge and skill could inhibit ethical substituted judgment by the future surrogate.

In summary, a substantial minority of roughly one-quarter of older adults that do no advance care planning at any time. Furthermore, upon closer examination, there is a much more substantial number of older adults that are completing some advance directives in a manner that calls into question the integrity and usefulness of the process as a means of expression of individual autonomy and confidence in care preferences for future decisional incapacity not only at end of life but for the rest of life in the context of progressive dementia or neurological damage (e.g., stroke).

A dearth of research literature focused on advance care planning from the perspective of decisional or behavioral process. Fried, Bulloch, Ione & O’Leary (2009) proposed that advance care planning should be considered in the context of a behavior change model, such as the Trans-theoretical (TTM; Prochaska & Velicer, 1997). Using the stages of change approach in the TTM, a conceptual process model could both guide process and also identify characteristics of individuals in that process (pre-contemplative, contemplative, preparation, action, maintenance) in an effort to better tailor decision support intervention. Sudore et al. (2014) used a similar behavior change model, based on a fusion of Social Cognitive theory (Bandura, 1999) and TTM. They reported a significant change in intentionality toward advance care planning (pre-contemplation to contemplation) but no significant change in actual behavior.

Advance care planning is a decision process (examination of risks, of values and preferences, and trusted relationships), as evidenced by a set of behaviors (discussion with a potential future surrogate decision maker, legal appointment of another person as a future surrogate/proxy decision-maker through durable powers of attorney, and written statements of preference for future medical care in the form of a living will). In the context of this study model, advance care planning is the decisional and behavioral output of the MMADMC. advance care planning in an older adult population would appear to have relevance to the motivational and cognitive elements described in MMADMC.

Failure in the successful completion of those three indicators of advance care planning should, theoretically, be explained by some element or combination of elements of the

MMADMC process.

As advance care planning is meaningful to alleviating risks to the autonomy of older adults, burden to their loved ones, and inefficient use of healthcare resources, it would seem likely that understanding the decisional process of advance care planning would be a focus of decision science and behavioral economic research. Yet, despite a substantial literature in the uptake and prevalence of advance care planning, the impact of advance care planning (particularly on end-of-life care), and an emerging body of interventional studies and clinical trials to enhance uptake of advance care planning, there is very little in the research literature regarding the underlying decisional and behavioral process of advance care planning. At present, while dispositional factors, motivation, cognition and even bias (e.g., being overly optimistic) are mentioned in discussions of advance care planning, few empirical studies have actually incorporated those concepts into either a theoretical process of advance care planning nor as a complement to empirical study of other factors.

Likewise, while the literature in DMC makes consistent mention of medical decision-making as a relevant decisional output and outcomes among older adults, no specific mention of advance care planning has been found in the published empirical studies or theory relating to MMADMC, DMC or related approaches to the role of cognitive bias in decision making. No specific study of advance care planning and resistance to framing effect or other cognitive bias as conceptualized by DMC. A few authors have proposed direct applications of principals from behavioral decision-making and behavioral economics.

Halpern (2012) most explicitly proposed the use of constructs from these disciplines in efforts to increase completion of advance directives. He develops a theoretical model that relates the advance care planning process with a set of disparate cognitive biases

(affective forecasting error, optimism bias, present bias, focusing effects, default options). While not directly related to advance care planning in the context of this study,

Winter & Parker (2007) applied Prospect Theory to examine decisions regarding life sustaining treatment. One study (Sudore, Schillinger, Knight & Fried, 2010) did not explicitly acknowledge cognitive bias, but incorporated the decisional element of uncertainty in an examination of treatment preferences among older adults.

A number of interventional studies provided adequate detail to identify evidence of decisional priming and negative or positive framing (“medical”, “treatments”,

“burden”, “death and dying”) in vignettes and questions of participants (Sudore et al.,

2013; McMahon, Knight, Fried & Sudore, 2013).

The potential for this kind of decisional priming and framing existed in most studies as they most often provided question stems, vignettes or scenarios. Therein, a recent meta- synthesis of qualitative studies (Ke, Huang, Hu, O’Connor & Lee, 2017) was useful in identifying themes that may have emerged more from the participants than from the researchers. While primary themes included death and dying and end-of-life considerations, distinct themes not often pursued in quantitative research emerged from the qualitative literature. Specifically, there was an emphasis on burden to family in general (including whom to choose as future surrogate decision-maker relative to the life burden, and financial burden, specifically (a theme relevant to one of the study concepts in this research). In addition, a theme of personal autonomy and perceived control was identified.

As per the MMADMC, no direct effect of working memory (deliberative decision-making skill) on advance care planning is hypothesized. A review of the advance care planning literature yields numerous references to cognition, but all in the context of incapacity or dementia, not specifically relevant to this study. Cognitively, while described as “planning”, it is important to be clear on the conceptual space that advance care planning occupies from a decision science perspective. While not discreet choices, advance care planning behaviors also do not involve extended executive functions, such as that which might be involved in retirement planning or significant health behavior or lifestyle change. advance care planning is focused on a limited number of decisions in a circumscribed life domain, but one for which there is potentially substantial emotional and developmental valence. The few decisions to be made are decisions of profound effect, focused on a future characterized by the comprehensive and profound vulnerability of decisional incapacity. Therefore, advance care planning is hypothesized as a decision-making and action under a dual process dynamic.

Accordingly, it is anticipated that the deliberative process of considering choices and integrating information regarding directives is costly to working memory.

As per the MMADMC, no direct effect is anticipated between cognitive effort and advance care planning. No published studies have examined motivation toward cognitive effort directly relative to advance care planning. Summary of Empirical Support for Theoretical Premises of MMADMC

As gleaned from the preceding review of the research literature, the degree of support for the theoretical premises of the MMADMC is mixed. The following summary is organized by theoretical statements derived from the process model.

The Developing Person (demographic attributes, psychological factors, health and wealth) influences Motivational Orientation (deliberative motivation). There is a strong relationship between chronological age and motivation, supported both empirically and in a number of alternative theoretical constructions. There is empirical support for the role of either sex as directly related to cognitive motivation

(cognitive effort). Conversely, the relationship between race and cognitive motivation is ambiguous. The psychological factors chosen for this study (perceived control, dispositional optimism and pessimism, purpose in life) all have strong inter-relationship and significant relationships have been demonstrated for each, relative to cognitive motivation (need for cognition). Likewise, consistent associations have been found between motivational cognition and stability in both health and personal finances, though directionality of the relationship can be reasonably argued with equivocal empirical support for each argument. The concern for causal directionality is less significant for both the demographic attributes and the psychological factors, as both are considered non-modifiable.

Motivation Orientation (deliberative motivation) influences Decision-Making Skill (deliberative cognitive skill). There is strong support for the relationship between cognitive motivation (need for cognition, cognitive effort) and cognitive skill (performance). In particular, cognitive motivation has significant association with the deliberative cognitive skill of working memory. But caution is warranted, relative to application of these empirical finding to the MMADMC. There is ambiguous directionality in many of these findings. In the

MMADMC, Strough et al. (2015) indicate a sequential process, a predictive path model.

Most of the empirical support for these relationships is solely correlational. It is certainly possible, and sometimes asserted in the preceding literature, that the causal directionality of the relationships is the exact opposite of the MMADMC: cognitive motivation may not always predict cognitive skill, but rather skill (including declining skills in older adults) influence motivation. This directional ambiguity was not directly addressed in this cross-sectional study.

Decision-Making Skill (deliberative cognitive skill) influences Decision-Making Competence (resistance to cognitive bias). As reviewed, there is a strong and consistent association between cognitive skill

(most notably, that of working memory) and numerous types of cognitive bias. This relationship has been affirmed with older adults who are experience mild cognitive impairment and those older adults completely free of symptoms of cognitive decline. In particular, the cognitive bias of the framing effect is strongly related to cogntive skill

(including working memory).

Decision-Making Competence (resistance to cognitive bias) influences Advance Care Planning. As described, the potential influence of cognitive bias on advance care planning is compelling, but wholly theoretical. At present, no experimental study or decision support intervention in advance care planning uptake has been built upon the dynamics of decision-making competence, resistance to cognitive bias, or the principles of normative behavioral decision-making and behavioral economics.

CHAPTER III

Methods

The purpose of this research was to examine the decisional process of advance care planning, using the theoretical framework of the Motivational Model of Aging and

Decision-Making Competence (MMADMC; Strough, Parker & Bruine de Bruin, 2015) and data derived from the 2012 cohort of the Health and Retirement Study (HRS). This chapter will review the study design, setting, sample, recruitment process, data collection procedures, instruments, data management and analysis, and protection of human subjects in order to address the following research questions:

1. What is the influence of age, sex, race, perceived control, dispositional optimism,

dispositional pessimism, purpose in life, subjective health stress and subjective

financial strain on the propensity for cognitive effort?

2. What is the influence of age, sex, race, perceived control, dispositional optimism,

dispositional pessimism, purpose in life, subjective health stress, subjective

financial strain and propensity for cognitive effort on working memory?

3. What is the influence of working memory on resistance to the framing effect?

4. (a) What is the influence of resistance to the framing effect on advance care

planning (discuss future care, designate a future surrogate, document future care

preferences)?

5. What is the influence of resistance to framing effect on advance care planning

(discuss future care, designate a future surrogate, document future care

preferences) when controlling for the influence of age, sex, race, perceived control, dispositional optimism, dispositional pessimism, purpose in life,

subjective health stress, subjective financial strain, propensity for cognitive effort,

and working memory?

Methods Design This study used a cross-sectional, descriptive, correlational design. Accordingly, all data examined in this study were collected at one time-point; no longitudinal analysis was included in this study. A cross-sectional design is an effective approach for preliminary examination of a novel construct or mechanism (Creswell & Creswell, 2017).

Prior to longitudinal examination necessary for testing the predictive (or implied causal) relationship between hypothesized variables, a cross-sectional analysis will provide preliminary understanding of the relationships among the variables selected to represent the theoretical concepts, and the overall model validity and parsimony.

Cross sectional design has significant limitations relative to theory validation. In any study using a cross-sectional design, there is inherent temporal ambiguity, as all concepts are measured at one time, as in snapshot. Absent longitudinal examination, it is not possible to directly test the predictive or causal nature of the relationships among study variables within a cross-sectional design (Creswell & Creswell, 2017). Results of a study with cross-sectional design can only indicate association, interpretation requires caution in inferences of predictive capability or implied causality.

The temporal ambiguity inherent in a cross-sectional design is a particularly important consideration in the design of this study. Temporal order and temporal process are assumed in a process model such as the MMADMC. But, using a cross-sectional design, it is not possible to directly examine the role of time in the process. Instead, the temporal ordering of the proposed paths is based upon the MMADMC theoretical framework and empirical support from outside of this analysis.

Sample, Selection Criterion, Setting, Data Collection This study selected participants from the 2012 cohort of the Health and

Retirement Study (HRS). The HRS is a longitudinal survey of more than 37,000 individuals over age 50. The HRS 2012 sample includes over 23,000 U.S. households and, through national recruitment, geographical stratification, and over-sampling of population segments is nationally representative of all U.S. households of older adults.

20,554 adults participated in face-to-face or telephone interviews (each representing half of the sample) (Sonnega et al., 2014).

Inclusion criteria for the HRS are all adults over age 50 in the contiguous United

State who reside in households (community-dwelling). Exclusion criterion for participation in the HRS core assessment includes institutionalized persons (prisons, jails, nursing homes, long-term or dependent care facilities).

The sample for this study was a sub-sample of the total 20,554 participants of the

2012 HRS wave. The subsample (n=1604) was derived from those participants who were randomly selected for a specific 2012 experimental module assessment and had also been included in assessments of all other relevant measures (Psychosocial and Lifestyle, Wills and Insurance). Experimental modules are administered to randomly-selected sub- samples of HRS self-respondents, after the main interview is completed, with a strict limit of two to three minutes in average length. Each respondent is randomly assigned to one and only one module. Respondents can refuse to participate in any module before assignment, and can refuse to answer questions in the module to which they are randomly assigned, but interviewers cannot offer nor can respondents choose to respond to a module other than the one selected at random. In order to preserve the random selection of participants in all modules, the experimental module was determined to be appropriate for both telephone and face-to-face modes and designed for the entire HRS sample.

The specific sub-sample for this study was randomly selected for a set of experimental module questions to assess loss aversion in risky choice framing (Gottlieb and Mitchell, 2015). These questions included a modified version of the Asian epidemic risky choice frame (Tversky and Kahneman, 1981). Gottlieb and Mitchell (2015) reported a response rate of 89% and, after removal of nursing home residents, a final sample size of 1589.

Unlike Gottlieb and Mitchell, who examined only that single variable of the experimental module, this study examined relationships with a number of HRS variables that were obtained only in alternating waves of the HRS (therein, collected only every 4 years). HRS participants are surveyed every 2 years, but not all measures are collected at every timepoint. Specific measures used for this study varied in the data collection protocols. Most restrictively, the questions used to measure resistance to framing effect

(resistance to cognitive bias) were part of an “experimental module” collected from a random sample of only approximately 10% of the 2012 cohort (n=1604). In addition, eight of the predictor variables included in the study model were measured with questions and instruments from the Leave Behind Questionnaire (LBQ); the LBQ is only collected from a random sample of half of participants, every other data collection period.

Accordingly, the study sample is restricted to the least common multiple of all study measures. Specifically, questions regarding advance care planning behaviors were obtained only in those 2012 participants who were randomly assigned to Wills and

Insurance questions, therein reducing the sample to 761. Finally, all of the scales measuring the stable attributes of the developing person, as well as the assessment of deliberative motivation, were obtained from only those participants that were randomly assigned to provide the HRS Psychosocial and Lifestyle Questionnaire (also known as the

“Leave Behind Questionnaire”). This markedly reduced the available sample.

While HRS data is collected by a national staff coordinated at the University of

Michigan, the data used for this study was additionally cleaned for non-systematic missing data (other than missing completely at random) by project staff of the RAND

Corporation. No variables in this study required use of any imputation method to address missing data. Ultimately, after listwise deletion for minimal non-response to scaled items in order to ensure uniform sample size across variables, 266 subjects were included in this study. The sample derivation process is outline in Figure 4.

To ensure adequate statistical power, an updated sensitivity analysis was conducted.

Using the available sample (n=266), sensitivity analysis determined the magnitude of necessary effect to be considered as statistically significant, relative to parameters set to avoid both Type 1 and Type 2 errors. Alpha was set to .01 and power to .9. Using those conservative parameters, the effect size required to reject the null hypothesis and identify of significant relationships in either RQ1 or RQ2 (F2=.11, for the RQ2 regression with 11 predictors) is slightly less than what is normatively considered as a medium effect

(Cohen, 1992) . A logistic regression with those same parameters requires an odds ratio of 1.55 (a small effect; Chen, Cohen & Chen, 2010) to achieve statistical significance. Based on the sample size of 266, a statistically significant difference between those groups of people would be detected at an odds ratio of 2.07. Again, this is considered a small to medium effect (Chen, Cohen & Chen, 2010).

Figure 4. Sample Derivation

Threats to Internal and External Validity of the Study Internal validity is the confident inference that a change in and independent predictor variable is responsible for a variation measured in the dependent outcome variable (Creswell & Creswell, 2017; Polit & Beck, 2012). The influence of one of these threats increases the potential for either a false positive (Type 1) or false negative (Type

2) error. Accordingly, efforts to reduce to control for the influence of these threats begins at the design phase of the study (Creswell & Creswell, 2017; Grove, Burns & Gray,

2012). Some threats to internal validity are not relevant to this study because they solely affect research that includes an element of time (longitudinal, repeated measure). These would include a threat of maturation (participants developed or changed in the course of the study) or mortality (participants terminated the research by attrition or by literal death) or regression toward the mean (the effect of repeated measures minimizing outliers with repeated measure). These were not relevant threats to this cross-sectional study.

Potentially relevant threats to internal validity include selection bias, historical effects, threats to internal validity from testing and instrumentation. Selection bias occurs when a non-random assignment of participants that introduces error in comparison of groups. This threat was relevant to this study design, but was intentionally limited by the sampling methodology of the Health and Retirement Study (Heeringa & Connor, 1995) and the application of random assignment to the experimental module groups.

The foremost concern with regard to subjects recruited in the HRS is not a concern of internal validity, but potentially of external validity (generalizability).

Subjects in this national longitudinal study are volunteers willing to undergo substantial testing. Participants received $100 in compensation for 3-4 hours of interview and questionnaire completion. Participants in this type of convenience sample could likely be different from non-volunteers. This raises a threat to external validity and a challenge to the representativeness of the sample. But, as subjects from within that volunteer population are randomly assigned to experimental modules and otherwise received all measures, any selection bias threat to internal validity is minimal. Certainly, historical effects could impact the collection of psychological measures, as in this study. Any historical or macro social event occurring during the 2012 data collection, particularly an event that impacted some participants in a non-random manner could introduce a threat to internal validity. As the data for the HRS sample is collected over a period of months, historical effects are certainly possible. There were, during 2012, a number of national efforts to encourage advance care planning uptake; it is possible that this cohort could be impacted disproportionately, but no obvious threat is present. Testing threats emanate from participants’ prior exposure to the testing materials. This is certainly a threat to a multi-decade study such as the HRS. Most of the measures (the sole exception is the measure of resistance to cognitive bias, the framing effect) are repeated every two years.

Some participants will have seen them for the first time while others will have completed them a dozen times. Instrumentation threats to internal validity are distinct from validity of the measure itself (the internal consistency of the measure). Instrumentation threats relate to the veracity of data collection. In this case, while HRS hires and trains professional data collectors, the data collection protocol is slightly altered in alternating waves (ie, every two years), between telephone and in-person interviews. Many of the measures in this study (health stress, financial strain, perceived control, dispositional optimism/pessimism, purpose in life, cognitive effort) are collected as a “leave-behind questionnaire”. Therein, far less control over administration and data collection procedure is possible. HRS-provided reports on sampling and participation rates supports this concern. Whereas response rate to the baseline interview ranged from 88% (for those 65-74) to 91% (for those age 75-84), the overall response rate to the Leave-Behind

Questionnaire was 77% (Sonnega et al., 2014).

Lastly, as previously noted, any cross-sectional study has the threat to internal validity from temporal ambiguity (Walraven, Oake, Jennings & Forster, 2010). In cross- sectional design, the temporal order of events (in this case, the paths of the process model) are tested solely based on theory, prior empirical evidence and logic. The order cannot be tested directly without longitudinal (over time) analysis. the basis of the theoretical framework (MMADMC; Strough, Parker & Bruine de Bruin, 2015). As described, the developmental nature of this theory (in fact, an iterative, recursive developmental model) when tested in cross-sectional design will increase the threat to internal validity in interpretation due to temporal ambiguity.

Power and Sensitivity Analysis Statistical power is the likelihood of identifying a statistically significant outcome

(relationship, difference) when, in fact, that outcome truly exists (within specified parameters for error). Power is necessary to avoid a type II error, in which an incorrect conclusion is drawn that there is no statistical significance (a false “null” hypothesis).

This type of error is commonly contrasted with a type I error, in which a statistically significant outcome was claimed (a “null” hypothesis rejected) when, in fact, the outcome was the result of random error. Power is a function of sample size (the greater the number of participants, the more likely that any effect will be identified) and the effect size (the degree to which the phenomenon, association, difference or change can be detected in a population), relative to the acceptable level of each type of error. Therein, a power analysis is derived from the relationship between sample size, effect size, alpha

(the predetermined acceptable levels or risk for type I error), and beta (the predetermined acceptable level of risk for a type II error).

Commonly, power analysis is conducted to determine the necessary sample size to ensure minimal error in conclusions, relative to anticipated effect size. Effect sizes are reported in the literature for many relationships between variables in this study.

Depending on the studies, a range of effects are reported (most common is Pearson r correlation); upon translation to a common heuristic (Cohen, 1995), most effects among variables range from small to medium. Power analysis would usually entail using that effect size data (with established parameters for willingness to commit Type 1 or Type 2 error) and determine sample size to the recruited for the study. This process was somewhat different in this study. As previously described, the sample size of this study was limited to the participants of the 2012 cohort that were asked all of the questions chosen to measure the theoretical process. Here, sample size is predetermined, relative to the available sample (n=266) from the 2012 HRS.

As the sample size for this study is known, it is appropriate to conduct sensitivity analysis to determine what minimum size of effect would be required to ensure accurate detection of significant findings at acceptable pre-determined levels of error (Type

I/alpha or Type II/beta). The study addressed RQ1 and RQ2 through linear multiple regression. Accordingly, sensitivity analysis was conducted for multiple regression. The larger regression model (RQ2) included 11 predictor variables. Sensitivity was conducted with a one-tailed test, as the MMADMC stipulates directionality of relationships The sensitivity analysis was conducted for a sample of 266 participants and 11 predictors (regressing working memory on all of the variables of demographics, psychological factors, health and wealth, and deliberative motivation. Using conservative parameters for Type I error (alpha=.01; there is a 99% likelihood that a significant relationship is, in fact, real) and Type II error (beta=.1; Power =.9; there is a

90% likelihood that a significant relationship will be detected). Inversely stated: the sensitivity analysis was conducted with the expectation that there is less than or equal to a

1 in 100 chance of a Type 1 error (an inaccurate assertion of a significant relationship) and less than a 1 in 10 chance of a Type II error (lack of detection of a truly significant relationship). While the logic is similar, sensitivity analysis is distinct for RQ3 and RQ4.

Since the outcomes of both DMC (presence or absence of framing effect) and advance care planning (presence or absence of each advance care planning behavior) are dichotomous, I conducted separate binary logistic regression to address these research questions .

Study Variables and Measurements The MMADMC is a novel and emerging theoretical model, first published in

2015. While adequately measurable to allow for direct testing of the theoretical processes, the MMADMC is relatively abstract. Published description of the MMADMC does not provide explicit guidance for empirical indicators of theoretical concepts. The variables selected for this study emerged from application of the theoretical model.

Selected examples of variables consistent with the each of the theoretical constructs are provided by the authors in the published description of the theory (Strough, Parker &

Bruine de Bruin, 2015), as well as in a preliminary model (Strough, Karns &

Schlosnagle, 2011) and foundational work in the construct of decision-making competence (Bruine de Bruin, Parker & Fischhoff, 2007). Where specific relevant conceptual exemplars were provided, they have been incorporated into the study model.

Still, the variables and empirical indicators chosen to represent theoretical constructs are peculiar to this study. As a secondary analysis, the choices for measurement of variables

(e.g., selection of instruments and/or items to measure latent constructs) and administration of data collection (e.g., instructions, order of administration, use of cues) were outside of the control of this study.

The following is a review of the variable-level empirical indicators of each study concept. Unlike the discretion available in choice of study concepts to represent the theoretical construct from among the available concepts in the HRS dataset, the measures chosen (any psychometric or administrative modifications to the original measures) are outside of the control of this investigation.

Mathematically, reliability of scores is an alpha coefficient, ranging from 0-1.

The score is an indicator of the proportion of the observed variance the observed variance that is due to random error. The most commonly reported estimate of reliability is

Cronbach’s coefficient alpha, an indicator of the internal consistency of the items within a measure (therein, the degree to which those items all measure the same latent construct). For most measures used in this study, at least one attribute of the measures

(internal consistency, as measured by Cronbach’s coefficient alpha) has been provided for the aggregate HRS population. As this study sample is randomly selected from that larger HRS population, indicators of internal consistency are expected to be comparable.

Where available, the following review of measures includes that aggregate HRS alpha, as well as those reported from research conducted with comparable older adult samples.

The review below will also address concerns of validity. Most important to methods and measurement, the construct validity is the degree to which the scores recorded are accurate indictors of the study concepts (and, ultimately, the hypothetical constructs proposed in the theory being tested) (Perron & Gillespie, 2015). In general, where available, examinations of the relationship between these measures with similar measures (convergent validity) or dissimilar measure (discriminant validity) in comparable samples are reported as an indicator of the construct validity of the measure used.

Demographic Attribute (Age) Chronological age is often included as a covariate demographic variable in nursing research. Yet, it is relatively uncommon to more deeply examine the measurement of age and the meaning of chronological age. Life-span developmental theory asserts that chronological age functions as a proxy variable; in light of the heterogeneity of adult development, the valid measurement of age can be further specified relative to biological, functional, and psycho-social development, as well as subjective age. This principle is particularly relevant to the present study. Age is an independent predictor variable of the research model and an essential construct in the MMADMC. Far beyond a covariate control variable, age is a theoretical construct that is expected to play a meaningful role in the theoretical process. Age has been previously associated with multiple study variables, with a range of correlations that indicate a range of effect sizes from small to medium-large (Cohen, 1992).

In the MMADMC, the variable of chronological age represents multiple dimensions of the construct of the developing person. Developmentally, chronological age serves as a proxy for both common life experiences (e.g., physical and social challenge and loss) as well as cumulative life experience. While examined at a cross- sectional time point in this study, it is important to recall that the theoretical model is recursive; decisional process leads to decision outcomes that, over time, impact the developing person and subsequent future decision process. Therein, the variable of chronological age serves as an indicator of that recursive process over time. (Strough et.al, 2011; Strough et al., 2015). In this study, as in the entire HRS, age is measured as chronological age by year, as of last birthday.

Demographic Attribute (Sex) Interviewer determined sex (not self-identified gender) is available in the HRS . Self- identification of sex or gender was not requested of participants in the 2012 HRS cohort.

Sex (female or male) was reported by the interviewer in initial face to face interview. No additional questions are asked, which would indicate a change in gender since prior assessment.

Demographic Attribute (Race) Race was measured as the self-reported identification by White, Black/African-American or “other”.

Psychological Factors (Perceived Control) Subjectively perceived personal control is included in this study model as a stable attribute of the developing person. In this study, using variables as measured within the

2012 cohort of the HRS, perceived personal control was measured using Sense of

Control, a 10 item instrument developed by Lachman and Weaver (1998) for the Midlife in the US (MIDUS) study (n=3032; age 18-75). The sense of control instrument combined 5 items of a well-recognized measure of generalized (not domain-specific self- efficacy) self-mastery (Pearlin and Schooler, 1978) with 5 additional items that measure usual (“often”, “most”) constraints to personal control (Lachman & Weaver, 1998).

Using the MIDUS national survey, the authors demonstrated that the sense of control instrument is internally consistent, with a Cronbach alpha =.85 (Prenda & Lachman, 2001). Psychometric analysis of data from the 2012 HRS cohort further affirms the internal consistency at the subscale level (Cronbach alpha equal to .91 for the mastery subscale and an alpha of .87 for the constraints subscale). Neupert (2007) affirmed the bi-dimensionality of the scale using factor analysis and demonstrated that, while distinct, the two subscales were significantly correlated at r=.44. Lachman and Prenda (2004) demonstrated predictive validity of sense of control with well-being, function, and self- rated health. Gerstorf (2010), using the composite score for sense of control, asserted predictive validity for the instrument in that, after controlling for other hypothesized covariates, sense of control predicted less decline in health and greater increases in social support. As explicated in Chapter two, prior research has examined the relationship between perceived control and other study variables. These studies demonstrate a range of effect sizes, from medium to large effect.

In the 2012 HRS, which provided data for this study, the sense of control instrument was administered as a portion of the “leave behind” Psychosocial and

Lifestyle Questionnaire. Responses were scored on a 5 point Likert scale ranging from

“strongly agree” to “strongly disagree”, constraint responses are reverse coded, and the two sub-scales and averaged to provide a combined score for perceived control in which higher scores indicate greater perceived control.

Psychological Factors (Dispositional Optimism & Dispositional Pessimism) In this study, dispositional optimism and dispositional pessimism were included as attributes of the developing person that are hypothesized to contribute to the advance care planning process. Dispositional optimism and pessimism are global and generalized orientations toward expected life outcomes (Schier and Carver, 1984). Rather than expectancies derived from information pertaining to a specific potential outcome, dispositional optimism and pessimism represent a generalized orientation through which specific information and likelihood is filtered. Dispositional optimism and dispositional pessimism are widely accepted to be stable attributes, similar to personality (Schier &

Carver, 1994).

In this study, the Life Orientation Test-Revised (Sheier & Carver, 1994) was used to measure dispositional optimism and dispositional pessimism. The 2012 cohort of the

HRS was administered the Life Orientation Test-Revised (LOT-R), a six-item version of the original 1985 Life Orientation Test (Scheier & Carver, 1985), via a “leave behind” questionnaire of psychosocial measures. The six-item LOT-R includes 3 statements with

“positive” wording, indicating expectation of a good future (optimism). The other 3 statements are worded as “negative”, indicating an expectation of a negative future

(operationalized as either the inverse of optimism or as pessimism). Response to each statement is measured on a 6 point Likert scale, ranging from “strongly disagree” to

“strongly agree” (“strongly”, “somewhat”, or no qualifier).

The LOT-R is strongly correlated (.95) to the longer original instrument (Scheier

& Carver, 1994). Like the original Life Orientation Test, this revised and shortened measure demonstrated strong psychometric properties. Psychometric analysis provided with publication of the LOT-R supports the assumption of adequate internal consistency

(Cronbach alpha = .78). The authors provided additional evidence of discriminant validity relative to other relevant intrapsychic measure including neuroticism, anxiety, mastery and self-esteem (Scheier & Carver, 1994). Consistent with the inclusion as a stable psychological attribute of the developing person, dispositional optimism and pessimism are empirically supported as stable over time. The authors reported test-retest reliability of .79 at 28 months (Scheier & Carver,

1994). More recent reporting of test-retest reliability (Chopik, Kim & Smith, 2015) with

9800 subjects of the HRS demonstrated test-retest reliability of .61 at 4 years.

Throughout the first decades of use, behavioral scientists debated the dimensionality of the LOT-R. As published, the instrument was conceptualized as a single dimension; dispositional optimism and pessimism were considered to be inverse poles of dispositional life orientation and were generally reported as degree of dispositional optimism (Scheier & Carver, 1994). Practically, the three negatively worded items were not scored as a separate scale, but were instead reverse coded to generate a single measure of dispositional optimism. Using factor analytic method in diverse populations, a number of scholars (Burke, Joyner, Czech & Wilson, 2000;

Herzberg, Glaesmer & Hoyer, 2008; Kim, 2014; Segerstrom, Evans, & Eisenlohr-Moul,

2011) asserted that the LOT-R measures two distinct dimensions: dispositional optimism and dispositional pessimism. In a detailed psychometric analysis from a large (over

46,000) and age-diverse (18-105) sample, Herzberg and colleagues (2008) used

Confirmatory Factor Analysis to demonstrate a two-factor model of the LOT-R with comparable evidence of reliability and validity. In the most recent version of the HRS

Documentation Report for the Psychosocial and Lifestyle Questionnaire (Smith et al,

2017), instructions for scoring the LOT-R are provided to allow the investigator to choose whether to treat the LOT-R as a 6 item unidimensional measure of dispositional optimism in life orientation (by using reverse coding) or as a bi-dimensional instrument that measures dispositional optimism (with 3 items) and dispositional pessimism (with 3 items).

In this study, the LOT-R was used to measure dispositional optimism and dispositional pessimism as two distinct dimensions. The basis for that decision is both empirical and theoretical. Based on consideration of factor analysis previously discussed, among older adults, it appears that dispositional optimism and dispositional pessimism are two distinct phenomena. Isacowitz (2005) reported no significant correlation between optimism and pessimism in a sample of 280 older adults. In fact, a recent study indicated that the correlation between optimism and pessimism declines with age (Chopik, Kim &

Smith, 2015). In the 2012 cohort of the HRS, bi-dimensional measurement demonstrated strong internal consistency for both dispositional optimism (Cronbach alpha=.80) and dispositional optimism (Cronbach alpha=.77). Theoretically, consideration of the distinct dimensions of optimism and pessimism is consistent with the conceptual underpinning of the MMADMC. As stated previously, among the tenets of Socio-emotional Selectivity

Theory (a conceptual foundation for MMADMC and, in particular, the inclusion of motivation as an element of decision-making process) is the assertion of an age-related

“positivity bias” (Mather, 2016). An emerging body of evidence supports that, as we age, negative information is selectively ignored. Hypothetically, neglect of advance care planning could be influenced by this positivity effect. Conversely, an alternative hypothesis to the influence of disposition on motivation would propose that a pessimistic disposition leading to nihilism could also be argued as influential in advance care planning. For these reasons, in this study, dispositional optimism and dispositional pessimism were independently measured. Psychological Factors (Purpose in Life) Purpose in Life (a dimension of eudaimonic well-being) is defined as an orientation toward purpose and meaning in life and toward future goals and plans (Ryff, 1989,

1995,). In this study, eudaimonic well-being was operationalized by the Purpose in Life subscale of the Scale of Psychological Well-Being (Ryff, 1995), a multi-dimensional assessment of well-being from the perspective of positive psychology, meaning, and purpose (rather than hedonic affect) that has been used in more than 350 published studies (Ryff, 2014). Higher scores in the purpose in life subscale indicate greater goals in life, sense of meaning to present and past, aims and objectives for the future, and beliefs regarding purpose and meaning (Ryff, 1995).

The psychometric qualities of the Scale of Psychological Well-Being have been exhaustively studied, with particular focus on the dimensionality of the scale.

Researchers have presented factor analytic evidence to support Purpose in Life as a distinct dimension while other scholars have emphasized the overall degree to which subscales are inter-correlated and share factor loadings. This debate is not relevant to the focus of this study. Irrespective of the degree of variance that Purpose in Life shares with other hypothesized dimensions of the total instrument (e.g., personal growth, self- acceptance), there is adequate empirical support for the internal consistency of the

Purpose in Life subscale as a measure of psychological factors of the developing person that would be theoretically relevant to advance care planning.

The instrument used in the 2012 HRS Psychosocial questionnaire has been demonstrated to have adequate reliability. Using the same 7-item version included in the

2012 HRS, the authors demonstrated internal consistency reliability in the range of alpha=.82.- .90 (Ryff, 1997). Specifically, using data from the entire 2012 cohort of the

HRS from which this study population is randomly sampled, Cronbach’s alpha of the

Purpose in Life subscale demonstrated adequate internal stability (alpha=.77). Construct validity for the Purpose in Life subscale is further supported by demonstrated convergence with a number of measures of life satisfaction. Support for discriminant validity for a distinctive PiL subscale is found when the PiL is compared with hedonic measures of subjective well-being, such as the PANAS; in exploratory factor analysis; the PiL loads on a factor other than positive or negative affect (Linley, Maltby, Osborne,

Wood & Hurling, 2009).

The Purpose in Life subscale is a 7-item measure, assessing degree of agreement or disagreement, using 6 point Likert response scheme (“strongly”, “somewhat”

“slightly”). Operationally, the 7-item subscale requires reverse coding of 4 items and is then averaged. Higher scores on the PiL subscale indicate a stronger orientation toward anticipating and planning for the future.

Health (Subjective Health Stress) Health is among the attributes of the developing person proposed within the

MMADMC. Consistent with this theoretical framework, health is defined as a sustained and stable attribute, a pattern or general tendency, that impacts the motivational and cognitive processes leading to decision-making competence and ultimate decisional outputs and outcomes. It is a challenge to measure health as a stable attribute, particularly within a cross-sectional study design where stability cannot be inferred longitudinally. In the study of the deliberative process leading to advance care planning, health was be measured as the presence and degree of health-related stress. This measurement approach was selected from among a number of choices for the measurement of health from the HRS survey, based on two criteria: subjective assessment (rather than objective health history or examination) and cross-sectional assessment of stability (a sustained period of stress from a health problem lasting at least one year). Accordingly, in this study, health was intentionally measured by a self- reported and subjective indication of severity of stress. In addition, for purposes of this study, the temporal stability of health stress (“ongoing ” stress) is likewise measured within this single item measure. In the HRS, participants are asked to identify whether they have experienced a particular type of “current and ongoing” stressor that has lasted for at least 12 months, including the single item assessment of the presence or absence of any “ongoing health problem (in yourself)”. If identified, participants then indicate the degree to which the stress is “upsetting”, on a three point Likert scale (“not upsetting”,

“somewhat upsetting”, “very upsetting”). Scores range from 0 (stress not present) to 3

(for present and very upsetting). As higher scores would indicate a greater degree of health-related stress, scores was reverse-coded for this analysis in order to indicate greater stability in health.

Using this single item measure from the 2006 HRS cohort, Brown (2018) demonstrated that stress related to ongoing health problems was the most common among a list of eight stressors (including financial, residential, relationship and caregiving), affecting over 60% of white respondents and nearly 68% of African-

American elders. Wealth (Subjective Financial Strain) Wealth is included among the attributes of the developing person that are proposed within the MMADMC. In the theoretical framework, wealth is defined as a sustained and stable attribute, a pattern or general tendency of the adult. Consistent with the previous discussion of health, wealth is not operationalized within published description of the theory. In this study, wealth was operationalized as the presence and degree of sustained financial strain. Financial strain is a sustained subjective experience of stress emanating from a perceived lack of adequacy in material resources (Kahn &

Pearlin, 2006).

Financial strain, the ongoing, sustained experience of financial stress, is herein operationalized from a subjective perspective, measured as type of perceived ongoing stress(similar to health stress). In prior studies of the HRS population, financial strain has also been measured from an objective perspective, as a measure of self-reported indicators of objective financial inadequacy (housing instability, food insecurity, unpaid bills, debt collection, etc.). In the study, the attribute of financial stability (like health) was measured as the degree of ongoing stress (lasting at least a year), rather than the presence or absence of any presumed objective financial indicator or consequence of instability. This subjective operationalization of financial strain was chosen for a number of reasons. First, the subjective experience of personal finances is consistent with the subjective nature of other stable attributes of the developing person within this study model (dispositional optimism and pessimism, perceived control, eudaimonic well- being/purpose in life). Second, consistent with stress process models and the dual- process theory, it is herein presumed that the subjective experience of sustained financial stress (strain) related to the perception of financial inadequacy is more relevant to cognitive motivation, cognition, and the deliberative process than objective indicators of adequacy.

In the HRS, financial strain is a single item measure from the eight-item stress instrument described earlier in this chapter. In this study, wealth was intentionally measured by this self-reported and subjective indication of severity of financial strain.

In the HRS, participants are asked to identify whether they have experienced a particular type of “current and ongoing” stressor that has lasted for at least 12 months, including the single item assessment of the presence or absence of any “ongoing financial strain”. If identified, participants then indicate the degree to which the financial strain is

“upsetting”, on a three point Likert scale (“not upsetting”, “somewhat upsetting”, “very upsetting”). Scores range from 0 for no stressor to 3 for present and very upsetting. As higher scores would indicate a greater degree of strain, scores was reverse-coded for this analysis in order to indicate greater stability in wealth.

A recent analysis of the effects of financial strain on the health, morale, and social functioning in the HRS population (Witherspoon, 2017) used this single-item indicator of financial strain. Consistent with the conceptual rationale presented earlier, results from this study of 811 subjects support that the single-item financial stress measure is strongly correlated with objective difficulty in paying bills (r=.75), therein demonstrating construct validity by convergence with a source of financial stress. Using this single-item measurement of financial strain, Witherspoon (2017) supports the predictive validity of the measure by demonstrating that financial strain significantly predicts a decline in subjective well-being and life satisfaction (b=.41; OR=.66). Deliberative Motivation (Cognitive Effort) As implied by the name of the theory, the MMADMC is distinctive in its emphasis upon the importance of a motivational orientation to the decisional process and output. Strough, Parker and Bruine de Bruin (2015) describe motivational orientation as a developmentally-sensitive tendency either to maximize positive emotion and hedonic well-being or to maximize information and deliberative process. As described in earlier chapters, the theoretical construct of motivational orientation is not motivated intentionality toward the outcome, but rather a motivational orientation toward the decisional process. The decisional process examined within this study is deliberative resistance to bias. Accordingly, the theoretical construct of motivational orientation is best represented in the study concept of cognitive effort. Propensity for cognitive effort is an intentionality to focus, think hard, deliberate and plan. The variable to be measured is not the cognitive capacity or ability to focus, think hard, deliberate and plan; it is the motivation to do so. In this study, cognitive effort was measured with the cognitive effort subscale of the Need for Cognition Scale (Cacioppo, 1982, 1984). The Need for

Cognition, derived from the concept of cognitive motivation (Cohen, 1955; 1957) is not actually a need, but rather an intrinsic motivation, defined as a “stable individual difference in people’s tendency to engage in and enjoy effortful cognitive activity”

(Cacciopo, Petty, Feinstein, & Jarvis, 1996, p198). The Need for Cognition scale is comprised of separate subscales for cognitive enjoyment and cognitive effort. Only the cognitive effort subscale was used in this study.

Throughout the thirty years between the initial publication and psychometric analysis of the Need for Cognition Scale, there has been a number of developments in the instrument size and re-examination of its psychometric properties. After more than a decade of use in over 100 empirical studies, the authors provided an exhaustive review, providing substantial support of for both convergent validity and discriminant validity with a range of established measures (Cacioppo et al., 1996). The instrument was initially published (Cacioppo & Petty, 1982) with 34 items, then reduced by the authors to 18 items. Since that time, abridged versions of 15 items, 9 items, 8 items and 6 items have all demonstrated sound psychometric properties including internal consistency with coefficient alpha’s ranging from .77 to .89. The psychometric properties of the 6 item instrument used for the 2012 cohort of the HRS and, for this study, has been examined within the CogUSA project (Cognition and Aging in the USA 2007-2009; McArdle,

Rogers & Willis, 2015), yielding two dimensions (cognitive enjoyment and cognitive effort) of three items each. Using data from the 2012 cohort of the HRS, the three-item cognitive effort subscale used in this study demonstrates adequate internal consistency with a Cronbach alpha of .77. (Smith, Ryan, Fisher, Sonnega & Weir, 2016). Using the same six item scaling, Coelho, Hanel and Wolf (2018) recently reported internal consistency of alpha = .87. The instrument is scored as 5 point Likert responses (“not at all like me” to “very much like me”) to three statements indicating a lack of cognitive effort, then reverse coded and averaged. Higher scores on the 3-item cognitive effort subscale indicate greater motivation toward cognitive effort.

Deliberative Cognitive Skill (Working Memory) As described in Chapter 1, the Motivational Model of Aging and Decision

Making Competence (MMADMC) presumes that the relationships between both the stable attributes of a developing person and that person’s motivational orientation and the decision-making competence (resistance to bias) are mediated by decision-making skills of cognition, such as working memory. Working memory is actually a set of integrated cognitive skills, including attention, short term memory and executive functioning.

Working memory is the cognitive ability to hold information in short term memory while putting attentional focus on a mental task. It is generally considered evidence of effortful concentration and deliberative cognitive activity (Del Missier et al., 2014).

In this study, working memory was assessed by a well-established mental status test called serial subtraction (or, commonly, “serial sevens”). The test of serial subtraction was first introduced as a timed mental status test 70 years prior to its use in this study (Heyman, 1942). Throughout the decades, administration has been modified for use with diverse populations, most notably as a subskill of the Mini-Mental State

Examination for dementia screening (Folstein, Robins & Helzer, 1983). As such, serial subtraction has been demonstrated as a valid measure for assessment of mild cognitive impairment (MCI) in older adults.

There is long-standing support for the valid use of the serial subtraction as a measure of working memory. Shum (1990) examined the validity of serial subtraction and found that serial seven assessment shared a common factor loading with two more challenging measures of attention and executive function (serial subtraction of 13, Stroop

Color-Word inhibition test) that are consistent with the definition of working memory used in this study. Conversely, subsequent research has raised concern regarding the validity of the test as a measure of cognitive skill such as working memory, due to the influence of other cognitive functions, as well as educational attainment and crystallized intelligence. First, serial subtraction was originally designed as a timed test (Heyman, 1942); subsequent work has demonstrated that a significant portion of both individual differences and age-related declines in cognition are due to declines in cognitive processing speed. When provided greater time for response, evidence of cognitive decline with age is minimized.

While a simple test of mental status, the serial 7 has demonstrated convergent validity with more extensive and complex tests of working memory in older adult population (Mikels, Larken, Reuter-Lorenz & Carstensen, 2005; Schum et al., 1990).

Both construct and predictive validity of serial 7 was supported by Meyer and colleagues

(2002) by demonstrating that longitudinal serial 7 results could predict the development of mild cognitive impairment (MCI) and differentiate persistent MCI from Alzheimer’s- type dementia. More recently, the serial 7 task demonstrated adequate convergent validity (r=.53) with a more extensive set of working memory measures (Hahn &

Lachman, 2015). In a very recent effort to establish age-norms and standards for serial subtraction tests with more detailed neuropsychological measures, serial 7 examination was asserted as a clear indicator of working memory. In recognition of the “borrowing” and “re-grouping” mental activity inherent in serial subtraction, the neuropsychological processes were described as encoding and focus, the attention and short term memory activities described as working memory (Bristow, Jih, Slabich & Gunn, 2017).

Using the HRS, McCardle and colleagues (2007) examined serial 7 performance among the roughly twenty thousand participants over age 50 (mean age of 65). They found that, in five iterations, nearly 70% of the population showed no error, but that among those who failed on at least one iteration, the error rate was normally distributed.

This is consistent with more result results from a similarly large and normally distributed sample that demonstrated 2.6 errors on average (SD=1.7) in five iterations (Levy, Uber,

Dillard, Weir & Fagelin, 2014).

Most relevant to this study, the validity of serial subtraction as a measure of working memory has been questioned relative to the fundamental dependence upon knowledge and confidence in the learned skill of subtraction. Karzmark (2000) asserted concerns regarding validity of the test as a measure of cognitive ability when arithmetic skills were controlled. Likewise, Reyna and Brainerd (2007) and Bruine de Bruin,

McNair, Taylor, Summers and Strough (2014) argue that numeracy (the amalgam of cognitive skill, acquired knowledge, and self-efficacy related to use of numbers) is strongly related to decision-making competence and the potential for cognitive bias in decision-making. The primary concern regarding the validity of the serial seven is the potential confound of numeracy. Numeracy, comparable to verbal literacy, is a construct that describes a combination of crystallized intelligence (learned skill, life experience) and motivation that inhibits facility in the use of numbers and the activity of calculation

(Levy et al. 2014; Peters, Hart & Fraenkel, 2011; Peters & Levin, 2008; Reyna &

Brainerd, 2007; Reyna, Nelson, Han & Diekmann, 2009;). Numeracy has been implicated as a potential confounding factor in performance of the serial 7 task.

Kazmarck (2000) conducted a comprehensive examination of the validity of the individual measures used in common mental status batteries (including the serial 7 test) by comparison against more detailed and extensive neuropsychological testing. He found that basic skill in calculation significantly affects performance on the serial 7 test. This may account for the differential performance on the serial 7 test among lower educated individual and those historically prone toward disparity in both formal education and economic opportunity. Sloan and Wang (2005) used multiple cohort waves of the HRS to examine serial 7 performance in older adults (over age 70). Their analysis led them to the conclusion that, in addition to an expected effect for older age, lower education predicted poorer performance in the serial 7 test. Furthermore, even after controlling for age and education, older African-American participants performed worse on the test. This finding was supported by the work of Reyna and her colleagues in examining numeracy in older adults (Reyna & Brainerd, 2007; Reyna, Nelson, Han & Dieckmann, 2009); they, too, found significant correlations between numeracy (including evidence in response to serial subtraction) and education, non-Caucasian race, and indicators of poverty.

The serial subtraction test used in this study required a respondent to subtract the number seven from 100, and then continue to subtract seven from each new remainder. In this study, as per the 2012 HRS cognitive assessment, each participant is prompted to do five iterations of serial seven subtraction. In the HRS protocol, there is no stated time parameter and participants are cued upon each iteration to again subtract 7 from the prior answer. Accordingly, it is not necessary to perfectly answer 5 iterations of subtraction from 100 (93, 86, 79, 72, 65); a correct subtraction of 7 from a previously incorrect response is scored as a correct response. Aggregate score is the simple sum of all correct responses, with no weighting for specific order of accurate or inaccurate response. In addition, as the serial subtraction test was conducted by telephone for roughly half of the sample (in-person examination is conducted every other 2 year wave), respondents were specifically asked whether they had used an aid to calculations (pencil and paper, calculator) following the assessment. Since, in this study, serial subtraction is a measure of working memory and deliberative cognition, only respondents for whom the administrator was confident that no aid was used were included.

Resistance to Cognitive Bias (Resistance to the Framing Effect) One of the most extensively studied illustrations of decision-making competence (DMC) is resistance to the framing effect (Bruin de Bruin et al., 2007, 2014; Parker & Fischhoff,

2006; Strough et al. 2015). The specific “risky choice” framing task used as the empirical indicator of DMC in this study is, in fact, the most extensively applied (34%) of all cognitive bias/heuristic tasks identified in a systematic review of the medical decision-making literature (Blumenthal-Barby & Krieger, 2015). In a risky choice framing task, one option is of fixed likelihood, a “sure thing”; the other is a risk that could yield better or worse outcomes. The risky choice framing task that was included in the 2012 HRS and used in this study was first introduced by Amos Tversky and Daniel

Kahneman, the Nobel laureate jointly awarded for research foundational to the development of the discipline of behavioral economics (Tversky & Kahneman,1981,

1986). This particular framing task is actually two versions of the same elicited choice, but asked once in a “positive” frame and once in a “negative” frame. The two questions should, based on normative decisional logic, yield the same choice (wholly dependent upon the individual risk preference of the respondent). The framing is two decisional activities: an assessment of preferred level of risky choice versus sure thing, and then a test of the consistency of that choice when the choice is asked again, but framed in an opposite valence. In this study, the framing effect was not seen in the choice itself

(higher or lower risk in each frame), but in the consistency of the choice in the positive and negative frame. A framing effect is when, after stating a preferred option, that preference is reversed, solely on the basis of the positive or negative valence of the framing.

For this study, a frequently used scenario, often referred to as the “Asian epidemic” (Tversky & Kahneman, 1981), was provided for which the respondent will make a choice: “Imagine that the United States is preparing for the outbreak of an epidemic expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Scientists estimate that the outcome of each program is as follows….. which program would you favor: Program A or Program B?”. The question is asked twice, at different points in the assessment. The only difference in the two elicitations is the choice of positive or negative framing demonstrated in the words chosen to describe the impact of a hypothetical epidemic, relative to the hypothetical alternative treatment programs: “if Program A is adopted, 300 people will be saved; if

Program B is adopted, there is a 50-50 chance that either 600 people will be saved or none will be saved”. The participant’s choice is recorded and, later in the assessment, the scenario stem is repeated but, this time (randomly alternated for half of the sample), the respondent is told: “If Program A is adopted 300 people will die. If Program B is adopted, there is a 50-50 chance that either none will die or 600 people will die.”

Logically, in the Asian epidemic scenario, the same program should be adopted in each choice, based solely on the participant’s tolerance for risk and not on the basis of the positive (“live”) or negative (“die”) frame of the scenario. A framing effect is a preference reversal based upon on the framing of the question in the opposite valence.

Logically, framing effects violate the principle of rationality called descriptive invariance; equivalent consequences should not have prompted a change in preference (Kuhberger & Tanner, 2009). From the perspective of DMC, the resistance to a framing effect represents an indication of constancy in value assessment and ability to integrate

(Bruine de Bruin et al., 2007) by maintaining the same choice. Cognitive bias, as represented by the framing effect, is an indication of a failure in DMC that, according to the process described in the MMADMC, would reduce the likelihood of optimal decision output and, ultimately, outcomes (Strough et al., 2015).

Kuhlberger (1998) conducted a meta-analysis on over 130 published studies and found that, among all types of framing effect presentations, the Tversky and Kahneman’s

(1981) “Asian disease problem” approach produced the greatest and most consistent effect (in aggregate, a medium effect of Cohen’s d=.57). This is consistent with comparable reported effect sizes from individual studies using the Asian disease protocol that reported medium level effect sizes of r=.29 (Frisch, 1993) and r=.31 (Stanovich &

West, 1998).

The version of the Asian disease problem used in the HRS has been modified in a manner that may be relevant to this study. In the original work (Tversky & Kahneman,

1981) and most recent research, the ratio of live to die is 1/3 chance (save or die, 200 of

600). In this presentation for the HRS 2012 experimental module, the ratio is 50-50 chance (300 of 600). Whereas this difference may seem only in the relative greater complexity of fractions, it may mean more. Many years before development of the DMC construct, Bruine de Bruin led a series of experiments on the how individuals perceived the probability of 50%, when in nominal form (Fifty-Fifty) and found that, for a significant number of participants, it was used as a substitute for “I don’t know,” an expression of epistemic uncertainty. (Bruine de Bruin, Fischhoff, Milstein & Halpern- Felsher,2000), Bruine de Bruin, Fishbeck, Stiber & Fischhoff (2002). While the framing effect question asked in this study is, ultimately, a dichotomous forced choice, the 50/50 effect may create a distracting heuristic.

Advance Care Planning (Discuss, Designate, Document) Advance care planning (advance care planning) was the dependent outcome variable in this study. As described earlier, while often studied as a unitary construct, advance care planning is comprised of (at least) three distinct behaviors: discussing future care preferences with a loved one, appointing someone as a future surrogate decision-maker via documentation of a healthcare Power of Attorney, and designating specific care preferences via a written Living Will. In this study, these three behaviors were specifically examined to better understand their relation to each other (RQ1) as well as their potential individual roles as decisional outputs of the theoretical process in the

Motivational Model of Aging and Decision-making Competence.

Procedurally, participants are each asked whether they have initiated each of the behaviors with the following introduction:

Now we would like to ask you some questions about healthcare decisions that might need to be made for you in the future, if you are unable to make them yourself. People sometimes make plans about the types of care or medical treatment they would want or not want, if they were to become seriously ill.

advance care planning is measured by the dichotomous (yes/no) response to whether they have ever initiated each of the three behaviors. Therein is presented a challenge to the cross-sectional design discussed earlier in this chapter. The dependent variable is measured based on whether the participant has ever initiated the advance care planning behaviors. In the cross-sectional analysis of just a single data-point (the 2012

HRS interview and questionnaire), it is impossible to discern when the advance care planning behaviors were initiated relative to the presence or degree of any other variable.

In general, as per the theoretical model, this should not increase the likelihood for inference of spurious relationships and predictive paths. As described in previous chapters, most of the dynamic elements of the Motivational Model of Aging and

Decision-making Competence are very stable; most variables (sense of control, purpose in life, motivational orientation, stress or strain) change slowly and over long periods of time. Theoretically, it can be presumed that these relatively stable characteristics would be present at any time point when advance care planning behaviors were initiated. Still, the inherent temporal ambiguity of the cross-sectional approach requires hesitation in potential inference and confidence in validation of the theoretical process. In fact, some variables are likely to have changed over time since advance care planning behaviors were initiated and may, in fact, have changed in ways that are relevant to the validity of the theoretical model. For example, impairment in cognitive skill/working memory

(including the emergence of Mild Cognitive Impairment or dementia) cannot be inferred as predictive of advance care planning behavior without additional longitudinal analysis.

Figure 5. Description of Study measures

Data Access, Cleaning and Management As a publicly funded project of the National Institute on Aging and the Social

Security Administration, HRS data is maintained by staff housed at the Institute for

Social Research of the University of Michigan and provided for download to any registered user via the HRS website. All permissions have been granted for access to the publicly accessible HRS data. Data from the 2012 Cohort that is required for this analysis has been de-identified, cleaned for missing data, and provided in multiple formats (along with specific codebooks and guidance for data management), including

SPSS, used for the analysis in this study. To avoid data loss, all de-identified data was stored electronically on a locked computer with two passwords. All data is de-identified prior to download. Data Analysis Plan The planned data analysis to address the four research questions through two multiple linear regressions (RQ1 and RQ2) and four binary (dichotomous) logistic regressions

(RQ3 and RQ4).

Data Screening Irrespective of the statistical techniques used to answer research questions, some basic functions are essential preliminary tasks for any quantitative analysis. First, a frequency distribution was run to identify data entry or download errors. While data entry or coding errors are not anticipated in accessing HRS data, it is, at present, unclear what level of “pre-cleaning” will have taken place with this specific HRS dataset. In many cases, HRS data has been cleaned and provided with reports of frequency and distribution by RAND Corporation, whereas some HRS data is only available in “raw” form for which these basic steps are even more essential. In either case, it was not expected that there were any data collection, entry or coding errors in the publically accessible download of 2012 data. Still, frequency distributions are essential to begin to get a gestalt of the data and ensure clean data for analysis.

Frequencies will also identify patterns of missing data. Even with exhaustive effort in data collection, missing data is an inevitability in research involving human subjects, including research in nursing, behavioral science and social science (Penny &

Atkinson, 2012; Musil, Warner, Yobas & Jones, 2002). Accordingly, it is imperative to have a plan for the management of missing data. Missing data is a threat for a number of reasons. First, missing data is a threat to the necessary statistical power previously described. Moreover, the rationale and inference of missing data is equally important.

Data can be missing in a manner that is completely random and poses no threat to the validity of the research conclusions; this is referred to as MCAR (missing completely at random). Conversely, if data is missing based upon MAR (missing at random) or MNAR

(missing not at random), it is possible that there could be a methodological threat to the validity of the study. Accordingly, the data will first be examined for total proportion of missing data as well as these patterns. Commonly, missing data is managed through deletion (pairwise deletion in which as many model parameters are preserved, but complicate analysis with multiple variable sample sizes; list-wise deletion that fully eliminates a participant based on any missing data) or through imputation (such as substituting a mean score for the missing value). Furthermore, while RAND can provide dataset with recommended imputations for missing data, no imputed data was required or used for this study. While substantial missing data is not anticipated, the large sample size and adequacy of expected power would support list-wise deletion, if necessary.

Next, descriptive statistics should be run to assess for normality. Normality is not essential for either type of regression analysis. Of course, dichotomous outcomes

(RQ3&4) cannot be normally distributed. Normality is not technically a primary assumption of linear regression (RQ1 and RQ2), but a highly skewed or kurtotic distribution could indirectly affect the normality of residuals assumed in multiple regression. Normality is tested in two ways. First, graphical models (histograms and probability plots) of skew (horizontal symmetry of the distribution) and kurtosis

(peakedness) provide a visual representation. Second, descriptive statistics of skew and kurtosis should be examined for range. While perfect normality would equal zero, a range for both skew (-3 to 3) and for kurtosis (usually -8 to 8), but in the case of a sample size as large as in this study, a range of -20 to 20). While data transformations are available, substantially skewed or kurtotic distributions are likely to present multiple challenges to analysis, including indirect effect on linearity (Field, 2014; Warner, 2014).

Assumptions of Multiple Linear Regression Multivariate linear regression, used to answer RQ1and RQ2, must be consistent with the assumptions inherent in the general linear model. Two of those assumptions are preliminarily tested through examination of those simple frequencies. First, all variable must have adequate variance within the distribution; in general, no single response for any variable should be in excess of 90% of all the data for that variable. Secondly, frequencies (and corresponding scatterplots) are the initial means of identifying outliers that may indicate errors in collection or data entry or maybe influential cases that could distort the necessary linearity for successful analysis. The previously mentioned scatterplots also provide an opportunity to visually examine for outliers. Outliers are not inherently problematic and should be removed only without caution and rationale.

Conversely, absence of outliers influential to linear analysis is a primary assumption of regression. It is important to first assess whether the outlier data points are influential to the linear multivariate relationship, e.g., to the slope of a regression line. Cook’s distance is a statistical indicator of the magnitude of the outlier. Cook’s distance greater than 1 is an indicator of an influential outlier. For an influential case, deletion may be reasonable, but comparative analysis with and without the influential case is required.

Linearity is a primary assumption in linear regression. It is assumed that there exists linear relationship between any independent predictor and any dependent outcome variables. While, technically, the assumption for the analysis of RQ1 and RQ2 is multivariate linearity, it is practically examined through separate examination of linearity after controlling for other covariates. Using scatter plots and partial regression plots, it is possible to test linear relationship of one variable to another by running a line of fit through the scattered data and comparing with a true line, a quadratic fit line, and a cubic fit line running alongside. Deviations of less than 2% from the lines are generally considered acceptable. Deviations beyond that can sometimes be managed with transformation of variable (quadratic or cubic) and, while they will facilitate the analysis, parsimony may be lost.

It is next essential that the variance of the error is constant across all of the independent predictor variables, ie., that the data is homoscedastic. In this case, the plots to be used to assess for heteroscedasticity compare studentized deleted residuals against predicted values. A heteroscedastic scatterplot of residual will demonstrate a fanning, indicating a lack of constancy. This problem can be fixed by transformation of the dependent variable scores. Error (residual, unexplained variance) is of particular importance in multivariate analysis. Heteroscedasticity maybe be indicative of non- normal distributions of the residuals.

Multicollinearity may also be a concern. Multicollinearity occurs when there is a strong correlation between two or more independent variables. Conceptually, multicollinearity is when two presumed independent predictors may be measuring the same phenomenon. The first method for assessment of multicollinearity is “eyeball”; a correlation matrix of the IVs was examined to identify excessive levels of inter- correlation. Variables correlated at r = .9 or above are considered to be redundant. In addition to examination of the correlation matrix, the collinearity diagnostics table should be evaluated for three major components: the variance inflation factor (VIF), the tolerance, and the condition index. The VIF, an indicator of whether a predictor has a strong linear relationship with the other predictors, suggests multicollinearity if greater than 10. Tolerance measures the effect of one independent variable on all other independent variables; tolerance below 0.1 is an indicator of multicollinearity. The condition index measures the degree of dependence of one variable on the other variable; a condition index of 30 or higher suggests multicollinearity (Field, 2014). Most methods for responding to multicollinearity problems are conceptual, rather than mathematical. A redundant variable can be removed. Redundant variables can be combined and subjected to factor analytic methods such as principal components analysis to ensure clarity of the latent construct.

Multiple Linear Regression Procedure and Assessment Prior to regression, bivariate correlations are examined to assess the relationships between variables. For RQ1 and RQ2, Pearson r correlations (ranging from -1 to 1) indicate degree of bivariate association.

Procedurally, each multiple linear regression to address RQ1 and RQ2 is conducted separately and in order of the theoretical model process. For RQ1, all of the exogenous variables of demographic attributes (age, sex, race), psychological factors

(perceived control, optimism, pessimism, purpose in life), health (health stress) and wealth (financial strain) are entered into the regression simultaneously and cognitive motivation is regressed on these independent predictors. For RQ2, cognitive motivation is added to the simultaneous regression and cognitive skill (working memory) is regressed on all of the variables. The regressions are interpreted by a set of specific summary statistics including the standardized and unstandardized beta weights for each IV on the DV, R (the strength of the relationship between the variables), and R-squared (the amount of variance explained by the regression), and whether, in light of the previously established parameters for error (p<.001), the IVs are significantly predicting the DV of each regression.

Binary Logistic Regression Procedure and Assessment Binary logistic regression (i.e., regression in which the dependent outcome variable is dichotomous) have less restrictive assumptions than multiple linear regression, but comparable attention must be paid to data screening. For instance, by definition, there can be no normal distribution of a dichotomous variable. Likewise, while there cannot be a linear relationship between an independent predictor and a dichotomous outcome, logistic regression requires that there be linearity of the log of the data. Still, it is essential that all scores on the outcome variable are independent of each other, mutually exclusive, and exhaustive (either yes or no; no maybe).

Binary logistic regression is distinct from simple logistic regression, particularly in the underlying mathematics of probabilities. Interpretation therein requires a different set of “descriptive” statistics to interpret the model fit and significance, based upon differences between predicted and observed values, and the magnitude of the relationship in the form of an odds ratio. Statistical significance of the model fit is provided by using a number of tests that are based on a Chi-square statistic (a significant Wald ratio, a non- significant Hosmer & Lemeshow test) to discern the difference between groups

(responses). But, as with all chi-square tests, this test is highly sensitive to large sample size as is relevant in this study. The more stringent alpha of .01 is used to limit the potential for statistically significant relationships at very small effect size. Additional assessment of the model-fit (effect size of the model) can be gleaned from a change in the

2-Log Likelihood, the Cox and Snell R-square and the Nagelkerke R-square, indicating the amount of variance explained by the variable in the regression (Field, 2013).

Additional assessment of difference between observed and predicted can be discerned through examination of the Classification Table (indicating that the binary DV options are correctly classified by the IV). Ultimately, if significant, odds-ratios were used to provide a relative indicator of likelihood. For RQ3, resistance to cognitive bias, dependent upon working memory skill. For RQ4, the odds-ratio indicated the likelihood of each advance care planning behavior, dependent upon the presence or absence of cognitive bias. The odds ratio and fit measures, including the Nagelkerke pseudo R- square, used to examine RQ5 indicated the likelihood of advance care planning behaviors and well as the constituent contribution of each predictor variable.

Human Subjects Protections Upon approval of this proposal from the School of Nursing, approval was obtained from the Case Western Reserve Institutional Review Board. The protocol was approved as exempt from IRB oversight. The rationale for that assertion wass based upon guidelines for exempt status as per 45 CFR 46.101(b). Specifically, the entire HRS is a currently approved study, funded by the National Institute on Aging, is under the continued review, approval and supervision of IRBs at the University of Michigan. For this study, only publicly accessible, de-identified data, with no protected information or means of access to subjects was obtained. The data used for this study contained no individual identifiers or links to individual identifiers. As per a Memorandum provided by the HRS, the HRS publicly available dataset provided through the HRS website will

“have been sufficiently purged of secondary identifying information that they pose no significant threat to respondent anonymity”. Moreover, the Memorandum states “We assert that in most cases the HRS public use files qualify as anonymized datasets and that secondary data analysis using these files may qualify for exempt IRB status, under 45

CFR 46.101(b).” (HRS, University of Michigan, Use Agreement Correspondence).

The preceding methods and analytic procedures are intended to individually examine the conceptual relationships and process hypothesized in the MMADMC. This series of individual analyses of relationships is a reasonable preliminary step in evaluation of the theoretical model. Dependent upon results, a number of additional analysis should follow. As this study is significantly limited in ability to assess the theoretical model due to the inherent temporal ambiguity of a cross-sectional design, longitudinal analysis could examine the participant behavior prior to this 2012 assessment (2010 and earlier) and following this assessment (2014, 2016). In addition, dependent upon the outcomes of individual relationships between theoretically derived concepts, a comprehensive test of the model using structural equation modeling would comprehensively assess model fit and examine the direct and indirect paths. CHAPTER IV Results

This chapter describes the sample characteristics and presents the results of the statistical analyses performed to address the research questions.

The study systematically examined the following research questions:

1. What is the influence of age, sex, race, perceived control, dispositional optimism,

dispositional pessimism, purpose in life, subjective health stress and subjective

financial strain on the propensity for cognitive effort?

2. What is the influence of age, sex, race, perceived control, dispositional optimism,

dispositional pessimism, purpose in life, subjective health stress, subjective

financial strain and propensity for cognitive effort on working memory?

3. What is the influence of working memory on resistance to the framing effect?

4. What is the influence of resistance to the framing effect on advance care planning

(discuss future care, designate a future surrogate, document future care

preferences)?

5. What is the influence of resistance to framing effect on advance care planning

(discuss future care, designate a future surrogate, document future care

preferences) when controlling for the influence of age, sex, race, perceived

control, dispositional optimism, dispositional pessimism, purpose in life,

subjective health stress, subjective financial strain, propensity for cognitive effort,

and working memory?

Sample Characteristics

This study was a secondary analysis of data from the 2012 cohort of the longitudinal Health and Retirement Study (HRS; National Institute on Aging; University of Michigan Institute for Social Research). This study used a cross-sectional design of the 2012 HRS data collection only.

Table 1 Sample Demographic Characteristics (N = 266)

Age in years (Mean ± SD) 75.2 ± 6.90

Formal Education in years (Mean ± SD) 12.30 ± 3.38

Sex : N (%) Female 145 (54.5) Male 121 (45.5)

Race/Ethnicity: N (%) White/Caucasian 224 (84.2) All Other 42 (15.8)

Marital Status: N (%) Married 154 (57.9) Widowed 78 (29.3) Divorced, Separated 24 (9.1) Never Married 10 (3.8)

Table 1 displays demographic characteristics of the sample. The sample is of relatively advanced age, averaging 75 years old, with a range from 65 to 99 years old.

The average participant has received formal education in excess of high school.

Consistent with older age, women outnumber men in the sample. The sample is predominately comprised of Caucasians; persons of color make up just less than 16% of the sample. Of note, both sex and race are reported in binary categories. Sex is measured in the HRS as binary male or female, as determined by the interviewer (best guess approach), rather than self-report. Race is self-reported as Caucasian (white), African-

American (black), Hispanic, and other, at time of initial interview to the HRS longitudinal study, and then not collected again. For this study, race was limited to two categories only: Caucasian and All Other.

Psychometric Analysis of Study Instruments The psychometric properties of the scaled instruments employed in this study have been previously examined for the entire HRS 2012, provided in HRS codebooks, and referenced in the preceding chapter. Table 2 displays these published internal consistency reliability coefficients generated from all 2012 HRS respondents to the

Psychosocial and Lifestyle “Leave Behind” Questionnaire (N=7,245) and from this study sample of 266 participants. All scaled instruments in this subsample of 266 participants demonstrated an acceptable level of internal consistency reliability, generally accepted as a Cronbach’s α coefficient ≥ .70 (Tabachnick & Fidell 2007).

Table 2 Psychometric properties of study instruments (N = 266)

Instrument Total Items Cronbach’s α Cronbach’s

(N=266) α

(N=7,245)

Dispositional Optimism 3 .81 .80

Dispositional Pessimism 3 .82 .77

Perceived Control: Absence of Constraint 5 .89 .87

Perceived Control: Mastery 5 .91 .91

Purpose in Life 7 .72 .77

Cognitive Effort 3 .78 .79

Description of Study Variables As noted previously, the sample is of advanced age (averaging 75 years old), slightly more women than men, and predominately Caucasian. Stress scores for both health-related stress and financial strain range from no stressor at all (a score of 1) to

“very upsetting” (a score of 4). Despite an average age of 75, these community dwelling participants reported a relatively low average degree of health-related stress (“not upsetting” to “somewhat upsetting”). Less than half of the sample reported any degree of upset regarding a chronic health stressor. On average, participants report no or only “not upsetting” financial strain; only; less than 14% of all participants reported any degree of upset from chronic financial strain at all.

On average, participants successfully completed serial subtraction 7 in 3-4 of 5 iterations. This is notable in that, consistent with prior research, a simple mental status test showed adequate variability and absence of a ceiling effect. Furthermore, the average number of errors approximates the number of arithmetic “borrowing” functions in the five iterations (Kase, 2007). As will be described in greater detail to follow, roughly three-quarter of participants resisted the framing effect.

In this study, self-reports of advance care planning behaviors were recorded as a dichotomous variable. Nearly two thirds (63%) of participants discussed their preferences for future care and medical treatment with another person. Roughly half of participants (51%) have taken some action to appoint a future surrogate for health and medical treatment decisions (ie, a durable Power of Attorney for Health). Exactly half of the sample (50%) reported that they had documented their personal preferences for future care and medical treatment (ie, a Living Will). These findings are consistent with prior research that was discussed previously.

A description of the study variables is presented in Table 3.

Table 3 Descriptive Statistics of Study Variables (N= 266) Variables Mean (SD) Min- Possible Skew Kurtosis Max Score Age 75.2 (6.90) 65-99 - .76 .47

Health Stress 2.25 (.98) 1-4 4 .21 -1.01

Financial Strain 1.45 (.77) 1-4 4 1.54 1.29

Optimism 13.56 (3.54) 3-18 18 .87 .62

Pessimism 7.76 (4.04) 3-18 18 .51 -.81

Perceived Control: 23.68 (6.42) 5-30 30 -.85 -.29 Absence of Constraint

Perceived Control: 23.64 (5.68) 5-30 30 -1.06 .67 Mastery

Purpose in Life 30.91 (6.44) 9-42 42 -.30 -.12

Cognitive Effort 12.64 (3.13) 6-18 18 .11 -.92

Working Memory 3.59 (1.65) 0-5 5 -.97 -.33

Resistance to Framing .77 (.44) 0-1 - -1.06 -.83

As described in the data analytic plan, bivariate correlations were examined to explore relationships and to address concerns of redundancy. Bivariate correlations are presented in Table 4.

Table 4

RQ1 and RQ2: Correlations Between Variables

Age Sex Race Health Fin Opt Pess Cntrl- Cntrl- PiL Cog Stress Strain C M Effort

Age - Sex† -.03 - Race† -.11 .04 - Health Stress .07 -.07 -.01 - Financial Strain -.10 -.05 -.01 .20** - Optimism -.11 .01 -.04 -.19** -.11* - Pessimism -.01 -.06 -.05 .15* .13* -.21** - Control-Constraint -.12 -.03 .04 -.31** -.11 .13 -.44** - Control-Mastery -.18* .01 .00 -.32** -.09 .25** -.27** .49** - Purpose in Life -.22** -.04 .03 -.25** -.06 .24** -.38** .50** .43** - Cognitive Effort -.10 -.08 -.01 -.14 .03 .01 -.23** .23** .14 .31** - Working Memory -.18* -.11 -.01 -.07 -.06 -.01 -.25** .18** .09 .20** .17**

Note. All participants, N = 266. *p < .01 level. **p <.001 level. †Point biserial correlations (Sex is measured as observed male or female. Race is measured as self-identified white or non-white.)

The bivariate correlations are notable for results that were consistent with expectations of prior research, as well as notable absence of expected results. For example, low level correlations were present between many of the psychological variables. In particular, variables measuring orientation toward the future (purpose in life, dispositional optimism, dispositional pessimism) demonstrated moderate levels of association. Notably, even stronger correlations exist between these future-oriented psychological factors and sense of control (particularly, the absence of constraints on personal control). In fact, purpose in life is as strongly related to each of the measures of control as the separate dimensions of control (absence of constraint, presence of mastery) are to each other. As expected, dispositional optimism and pessimism demonstrate a significant correlation of relatively small effect; they function as distinct dimensions.

Deliberative motivation (measured as cognitive effort) was significantly associated with both future-oriented variables (purpose in life and pessimism (inverse)) as well as sense of control emanating from lack of constraint. The bivariate correlations are also notable for what was expected from prior research, but not present in this sample and these measures. Notably, neither race nor binary gender (dichotomously measured; point bi- serial correlations) were associated with any variable. As will be discussed, this raises concerns regarding measurement insensitivity and lack of variance in these dichotomous variables. Furthermore, while age was statistically significantly associated with a number of variable, no correlations were large enough to be meaningful. Again, the lack of variance in a sample that ranged from age 65 (rather than 50) may explain why these finding are distinct from other studies that used HRS data. While correlations with subjective health stress are weakly consistent with prior research, this study found no meaningful associations between subjective financial strain and any other measures.

Relative to RQ2, there was a statistically significant correlation between the propensity for cognitive effort (the measure of deliberative motivation) and working memory (the measure of deliberative cognitive skill) but that correlation is so weak as to be questionably meaningful.

Findings Related to Research Questions Research Question 1. What is the influence of age, sex, race, perceived control, dispositional optimism, dispositional pessimism, purpose in life, subjective health stress and subjective financial strain on the motivational tendency toward cognitive effort?

Research question one was addressed by analyzing data with multiple linear regression. Multiple regression provides the means for evaluation of the first set of hypothesized relationships from the MMADMC: that stable characteristics of the developing person predict motivation toward cognitive effort (MMADMC; Strough et al.

2015).

Prior to analyzing the linear regression, the following statistical assumptions were evaluated through the methodologies discussed in Chapter 3: variability, absence of influential outlier cases, linearity, homogeneity of error variance, normality of residual error, and absence of multicollinearity. One statistical assumption was violated: there was a meaningful violation of linearity in the relationship between subjective health stress and cognitive effort (nearly 4% variation from a true line when evaluated against both a quadratic and cubic polynomial). Both quadratic and cubic polynomial transformations were applied and assessed for improvement in the model fit. Following transformation, no significant improvement in r2 was achieved between linear model (.02) and the models using either quadratic polynomials (.03) or cubic polynomials (.03). A

1% increase in explained variance (in a variable that is contributing little explained variance) was not worth the loss of parsimony in the use of the transformed variable. To further examine the relevance of the violation, all subsequent regressions were conducted both with and without the non-linear variable. Upon comparison, no model changed by greater than 2% and the significance of no variable was altered by the presence or absence of the subjective health stress variable. Accordingly, due to the explicit inclusion of health as an element of the MMADMC, the variable was included in the regression analysis.

A multiple linear regression was computed to determine whether the combination of age, sex, race, health stress, financial strain, optimism, pessimism, perceived control

(both freedom from constraint and mastery), and purpose in life predicted motivation toward cognitive effort. A significant model was found ((F(10, 255)=4.24; p<.001)), but

2 with only an R adj =.11. While the 10 predictor variable model was statistically significant, only two of the individual predictor variables were significantly contributing to explained variance. The standardized regression coefficient for purpose in life (β =

.25; p<.001) indicated a significant but small contribution of purpose in life to the explained variance. In addition, the standardized regression weight for pessimism (β = -

.15; p=.02) indicated that the absence of pessimism (a negative regression coefficient) is influencing cognitive effort, but with minimal effect. Table 5 displays the regression coefficients in the model.

Table 5 Multiple Regression predicting Cognitive Effort for Research Question 1

Variables Standardized Unstandardized SE

Beta Beta [95% CI]

Age -.05 -.02 [-.08, .03] .03

Sex (Female) -.08 -.53 [-1.25, .20] .37

Race .04 -.31 [-1.30, .68] .50

Health Stress -.09 -.30 [-.70, .10] .20

Financial Strain .06 .25 [-.23, .74] .24

Optimism -.10 -.09 [-.20, .02] .06

Pessimism -.15 -.12 [-.22, -.02] .05

Control-C .04 .02 [-.06, .09] .04

Control-M -.04 -.02 [-.10, .06] .04

Purpose in Life .25** .02 [.05, .19] .03

Note. All participants; N=266. *p < .01, **p < .001. 2 R adj=.11, F(10, 255)=4.24, p<.001. Research Question 2. What is the influence of age, sex, race, perceived control, dispositional optimism, dispositional pessimism, purpose in life, subjective health stress, subjective financial strain and propensity for cognitive effort on working memory? RQ2 is likewise answered by analyzing data with linear multiple regression. This second regression provides evaluation of the next step in the hypothesized process of the

MMADMC (MMADMC; Strough et al. 2015): the influence of stable characteristics of the developing person and that person’s motivational tendency toward cognitive effort, on that person’s working memory function.

Prior to analyzing the linear regression, the following statistical assumptions were evaluated through the methodologies discussed in Chapter 3: variability, absence of influential outlier cases, linearity, homogeneity of error variance, normality of residual error, and absence of multicollinearity. No assumptions were violated; there was, therefore, no need for transformations or additional examination at the variable level.

A multiple linear regression was computed to determine whether the combination of age, sex, race, health stress, financial strain, optimism, pessimism, perceived control

(both freedom from constraint and mastery), purpose in life, and cognitive effort predicted motivation working memory. While the 11 predictor variable model was statistically significant, only three of the individual predictor variables were significantly contributing to explained variance: inverse pessimism (β = -.23; p=.001) and older age (β

= -.19; p=.001) are influencing working memory to small effect. Table 5 displays the regression coefficients in the model.

Table 6 Multiple Linear Regression predicting Working Memory for Research Question 2

Variables Standardized Unstandardized SE

Beta Beta [95% CI]

Age -.19** -.04 [-.07, -.02] .02

Sex (Female) -.12 -.39 [-.78, -.01] .20

Race .03 .15 [-.37, .68] .27

Health Stress -.01 -.02 [-.24, .19] .11

Financial Strain -.06 -.12 [-.38, .13] .13

Optimism -.10 -.05 [-.10, .01] .03

Pessimism -.23** -.09 [-.15, .04] .03

Control-Constraint .04 .01 [-.03, .05] .02

Control-Mastery -.04 -.01 [-.05, .03] .02

Purpose in Life .06 .02 [-.02, .05] .02

Cognitive Effort .07 .04 [-.03,1.00] .03

Note. All participants; N=266. *p < .01, **p < .001 2 R adj=.10, F(11, 254) = 3.544, p<.001

Research Question 3. What is the influence of working memory on resistance to the framing effect? Within the MMADMC, resistance to the framing effect is conceptualized as resistance to cognitive bias and, therein, evidence of decision-making competence (Bruine de Bruin et al. 2007, 2014). Therefore, the variable is measured as a binary “yes or no” evaluation and is analyzed as a dichotomous variable. Accordingly,

Research Question 3 is answered by analyzing data with binary logistic regression.

The presence or absence of decision-making competence (Bruine de Bruin et al,

2007) or resistance to cognitive bias is herein measured by a single item assessment of the risky choice framing effect. Examination of the frequency of the effect indicates that roughly three quarters of participants (73%) successfully resisted the effect; slightly greater than a quarter of all participants (27%) demonstrated cognitive bias as indicated by the framing effect. Of note, resistance to the framing effect had no significant bivariate correlation to any of the predictor or outcome variables in this study.

Prior to analyzing the logistic regression, statistical assumptions were evaluated.

While not as numerous as the statistical assumptions of linear regression, logistic regression does require that the following conditions are not violated: observations are independent of each other, data is absent the effects of influential outliers, and no evidence of multicollinearity exists among any predictor variables. While logistic regression does assume linearity of independent variables and log odds, unlike linear multiple regression, the assumptions of logistic regression do not require testing for linearity or homoscedastic error. No necessary assumptions were violated in this analysis. A simple bivariate logistic regression was performed to assess whether working memory skill predicted resistance to the framing effect. As displayed in Table 6, the regression model was not significant: χ2 (1) = .001; p=.976. Nagelkerke R2 <.000; and

OR=.99. There is no indication that working memory skill predicts resistance to framing effect as measured in this study and with this sample.

Table 7 Logistic Regression of Working Memory on Resistance to Framing Effect for Research Question 3

Variables Odds Ratio

[95.0% CI]

Working Memory .997 [.85, 1.18]

Note. All participants; N=266.

Research Question 4. What is the influence of resistance to the framing effect on advance care planning behaviors (discuss future care preferences, designate a future surrogate, document future care preferences)?

The three decisional outputs that indicate advance care planning behavior (discuss future care, designate a future surrogate, document future care preferences) were all dichotomous categorical variables. Therefore, three bivariate logistic regressions were conducted to assess the degree to which resistance to the framing effect influences each of the three advance care planning behaviors. The first bivariate logistic regression was performed to assess whether resistance to the framing effect predicted discussion of future care and medical treatment preferences. The regression model was not significant: χ2 (1) = .111; p=.739.

Nagelkerke R2=.001. There is no evidence that resistance to the framing effect, as measured in this study, predicts discussion of future care and medical treatment preferences in this sample of older adults. A second bivariate logistic regression was performed to assess whether resistance to the framing effect predicted the likelihood of designating a surrogate for future health care and medical treatment decisions. The regression model was also not significant: χ2(1) = 1.252; p=.263. Nagelkerke R2=.006.

The third and final bivariate logistic regression was performed to assess whether resistance to the framing effect predicted documentation of future care preferences (e.g., a Living Will). This regression model was also not significant: χ2= .481(1), p=.488,

Nagelkerke r2=.002. No logistic regression of any advance care planning behavior on the framing effect was significant. Table 8 displays the odds ratios with 95% confidence intervals for the three bivariate regressions.

Table 8 Logistic Regression of Resistance to Framing Effect on advance care planning Behaviors (Discuss Future Care Preferences, Designate Future Surrogate, Document Future Preferences) for Research Question 4

Variables Discuss Designate Document

OR [95.0% CI] OR [95.0% CI] OR [95.0% CI]

Resist Framing .91 [.52, 1.60] 1.36 [.79, 2.4] 1.212 [.70, 2.09]

Note. All participants; N=266.

Research Question 5: What is the influence of resistance to framing effect on advance care planning (discuss future care preferences, designate a future surrogate, document future care preferences) when controlling for the influence of age, sex, race, perceived control, dispositional optimism, dispositional pessimism, purpose in life, subjective health stress, subjective financial strain, propensity for cognitive effort, and working memory?

The three decisional outputs that indicate advance care planning behavior (discuss future care, designate a future surrogate, document future care preferences) were all dichotomous categorical variables. Therefore, in order to assess the degree to which resistance to the framing effect influences each of the three advance care planning behaviors, while controlling for all of the other independent variables in the model, three multiple logistic regressions were conducted.

The first multiple logistic regression was conducted to examine whether the model could predict discussion of future care preferences. This logistic regression did not significantly predict discussion behaviors: χ2 (13)= 21.295; p=.067, Nagelkerke R2=.105. The second multiple logistic regression significantly predicted designation of a future surrogate for health decisions: χ2= 28.78 (13); p=.007. The Nagelkerke r2=.14; while statistically significant, the thirteen variable model explains only approximately14% of the variance. In this significant model, age (p=.001) and absence of pessimism (p=.016) were significant individual regression weights in the model; both had small effect on the prediction of participants’ designation of a future surrogate for health decisions. A third logistic regression was conducted to examine whether the model of all variables could predict documentation of preferences for future care. This model was significant at an acceptable level of confidence: χ2= 25.13 (13); p=.022. Based upon the variance explained by the Nagelkerke R2=.12, this thirteen-predictor model, while statistically significant, explains only 12% of the variance. Only one individual predictor variable was significantly contributing to the explained variance: (the absence of) pessimism (β=.13). The effect size of this predictor (OR=.88) is small. Odds ratios with 95% confidence intervals for each model are reported in Table 9.

Table 9 Logistic Regression of Resistance to Framing Effect on advance care planning Behaviors (Discuss Future Care Preferences, Designate Future Surrogate, Document Future Preferences) for Research Question 4

Variables Discuss Designate Document

OR [95.0% CI] OR [95.0% CI] OR [95.0% CI]

Age 99 [.95, 1.3] **1.08 [1.03, 1.12] 1.03 [.99, 1.08]

Sex 1.31 [.77, .24] 1.17 [.69, 1.97] 1.16 [.69, 1.94]

Race .69 [.71, 2.93] 1.10 [.54, 2.23] 1.46 [.72, 2.98]

Health Stress 1.60 [1.18, .17] 1.13 [.84, 1.51] .93 [.70, 1.24]

Financial Strain .99 [.70, 1.41] 1.04 [.74, 1.46] 1.02 [.73, 1.44]

Optimism 1.11 [1.03, 1.21] 1.02 [.95,1.10] 1.02 [.94, 1.10]

Pessimism .99 [.92, 1.07] .91 [.84, .98] **.88 [.81, .95]

Control-C 1.00 [.95, 1.06] 1.01 [.95, 1.06] .98 [.93, 1.03]

Control-M 1.02 [.97, 1.08] 1.00 [.95, 1.06] 1.02 [.96, 1.07]

Purpose in Life 1.01 [.98, 1.08] .98 [.94, 1.04] .98 [.93, 1.03]

Cognitive Effort 1.02 [.93, 1.11] .10 [.91, 1.09] 1.02 [.93, 1.11]

Working Memory 1.12 [.95, 1.32] 1.19 [1.0, 1.41] 1.16 [.98, 1.37]

Resist Framing .80 [.43, 1.46] 1.65 [.91, 2.97] 1.35 [.76, 2.43]

Note. All participants N=266. *p < .01, **p < .001.

. Summary of Findings The purpose of this analysis was to examine the application of the MMADMC (Strough et al., 2015) in the prediction of three related but distinct behaviors of advance care planning: discussion of care preferences with another person, designation of another person to serve as a future surrogate for healthcare and medical treatment decisions, and documentation of preferences for future care and medical treatments. Overall, the findings from this study indicate that for these outcomes, with these measures, and in this sample, the model provides limited utility. More specifically, there was no evidence that cognitive bias as measured in this study influences advance care planning behavior.

When all variables were examined, significant models explained a small to moderate amount of variance and a number of predictors demonstrated small effects that warrant further research. In particular, the absence of pessimism was identified as a consistently significant covariate.

Chapter V Discussion

The following chapter will provide a discussion of the study results and potential implications for science, practice, and policy. Results and potential implications are considered in the context of the study’s limitations.

The analytic plan of this study was intended to sequentially examine the theoretical process of the Motivational Model of Aging and Decision-Making

Competence (MMADMC) on advance care planning (defined as actions taken in preparation for the potential of future incapacity to make health care and medical treatment decisions; operationalized by discussion of future care preference, designation of a potential future surrogate decision-maker, and documentation of future care and medical treatment preferences). Research questions (RQ) were ordered to examine each sequential step in the process. In review, this theoretical sequence is comprised of: the influence of stable attributes of the developing person on both motivational orientation toward deliberative thought, herein operationalized as cognitive effort (RQ1), and on deliberative cognitive ability, herein operationalized as working memory (RQ2), the influence of cognitive ability on the resistance to cognitive bias, herein operationalized as resistance to the framing effect (RQ3), and the influence of resistance to cognitive bias on the decisional output of three advance care planning behaviors, both alone (RQ4) and when controlling for all other independent variables (RQ5) . Discussion of the Research Questions Research Question One. RQ1 examined the influence of age, sex, race, perceived control, dispositional optimism, dispositional pessimism, purpose in life, subjective health stress and subjective financial strain on the propensity for cognitive effort. This 10 predictor model did significantly predict the propensity for cognitive effort, to a small degree of effect (11% of the variance).

The regression model yielded potentially useful findings. Two dispositional attributes, purpose in life and (the absence of) pessimism, each demonstrated significant influence of medium effect on cognitive effort (the propensity toward motivated deliberative cognition). Conceptually, the two measures share a common theme of future time orientation and “temporal horizons”, a construct from the earliest version of the

MMADMC, the Motivational Model of Judgment and Decision-Making (Strough,

Karnes & Schlosnagle, 2011). Purpose in life (PiL), a dimension of the positive psychology construct of psychological or eudaimonic well-being (Ryff, 1989) is an orientation toward goals, intention, and planning for the future. The items of the PiL measure (Ryff & Keyes, 1995) solicit responses that indicate a participant’s tendency toward proactive expectation of a meaningful future. Dispositional pessimism (as distinct from solely the inverse of optimism) is likewise a stable orientation toward the future.

The three-item scale for dispositional pessimism (Scheier, Carver & Bridges, 1994), measures the anticipation of bad future events. Conversely, the variable that was statistically significant in this model (and throughout this study) was the inverse or absence of pessimism: participants did not expect a negative future. To be clear, rarely did dispositional optimism contribute significantly to any of the predictive models in this study: participants did not necessarily anticipate a bright future, they simply did not anticipate a bleak one.

These two variables (PiL and pessimism) share a common theme of orientation toward the future, one more active and the other more passive. The commonality is supported by previous research with a comparable sample of older adults which demonstrated a significant correlation between PiL and optimism (Ferguson and

Goodwin, 2010). In the current study, the combination of these two measures of orientation toward the future significantly predicted a small to medium degree of the variance in a participant’s motivational orientation toward extending cognitive effort.

This significant outcome is consistent with Carver and Scheier’s (2001) description of dispositional orientation toward the future (optimism/pessimism) as a motivational construct. Optimistic or pessimistic dispositions affect both motivation toward goal pursuit and motivation toward vigilance in the context of adversity, obstacle or threat (Carver and Scheier, 2001; 2014). Scheier et al. (1994) reported that individuals with higher levels of optimism used more active coping, including planning to address a threat. Aspinwall et al. (2001) identified a consistent pattern of cognitive coping responses among individuals that are more optimistic. Irrespective of intelligence, education, or socio-economic status, optimists respond to threat with more cognitive effort (seeking information, searching for alternatives).

Research Question Two. RQ2 explored the influence of age, sex, race, perceived control, dispositional optimism, dispositional pessimism, purpose in life, subjective health stress, subjective financial strain, and the propensity for cognitive effort on working memory, a measure of deliberative cognitive skill. Using these attributes (chosen from among HRS measures to best represent the published MMADMC), this 11 predictor variable model did significantly predict the working memory skill, to a small to medium degree of effect (10% of the variance).

As with RQ1, detailed analysis of the performance of the regression model yielded potentially useful findings. Three attributes each demonstrated significant influence of at least a small effect on working memory: age, gender, and (the absence of) pessimism. Age is a previously identified predictor of performance in most cognitive tests, including tasks of working memory. The effect of age was significant, but small. It has also been previously identified that women, particularly older women, perform less well on numeracy-based cognitive tasks. As described earlier, the basis for this finding might be more related to self-efficacy or experience than cognitive ability or skill.

Notably, again, the inverse of dispositional pessimism (future expectation that was not bleak) was a significant influence on working memory function, at a small to medium effect.

The role of dispositional optimism/pessimism on deliberative cognitive ability has been previously identified. Gawronski, Kim, Langa & Kubzansky (2016) used the HRS mental status data (including the serial 7 measure used in this study) of 4624 adults over age 65 to longitudinally demonstrate that optimists (as determined by higher score on the

LOT-R) had significantly lower likelihood of developing a diagnosed cognitive impairment over 4 years; in that study, no specific analysis of serial subtraction/working memory was provided.

It is worth noting that cognitive effort (the measure of deliberative motivation) did not significantly contribute to the significant multiple regression model. As described earlier, there is considerable support from the extant research literature for a relationship between motivation and cognition (Enge et al., 2008; Bruine de Bruin et al., 2014). As per the MMADMC, deliberative motivation (propensity toward cognitive effort) should predict deliberative cognitive skill (working memory); other theoretical models, such as

Selective Engagement Theory (Hess, 2015) assert the alternative. In this study, it was not influential at all.

Research Question Three. RQ3 evaluated the influence of deliberative cognitive skill (working memory) on resistance to cognitive bias (resistance to the the framing effect). Simple binary logistic regression indicated that working memory performance did not significantly influence resistance to framing effect, as measured in this study. Accordingly, from the perspective of the theoretical model, there is no evidence of resistance to cognitive bias and the hypothesized mediation of the deliberative process on decisional outputs of advance care planning. In short, the absence of a predictive relationship between working memory and resistance to framing is an abrupt stop in the application of the conceptual model, as theorized.

It is important to recall that a metric for the framing effect in this study is consistent with the construct of decision-making competence (Bruine de Bruin et al.,

2007). Herein, the framing effect is solely measured as a reversal of choice, an inconsistency in value assessment (Bruine de Bruin et al., 2007; Strough et al., 2015).

This is in contrast to the classic approach to assessment of the framing effect in the context of Prospect Theory (Tversky and Kahneman, 1981), in which the directionality of the framing effect (moving toward greater risky choice in the context of loss) is indicative of loss aversion. The metric used in this study, unique to the construct of decision- making competence that is foundational to the MMADMC, is also insensitive to potential age-related positivity effects (Mather, 2005). Accordingly, half of the sample were presented the negative frame (“300 will die”) first, then were presented the positive frame

(“300 will be saved”); the other half of the sample were presented with the questions in the opposite order. Presuming adequate power, post hoc analysis of these results could indicate whether a different conceptual approach to the framing effect would yield different results.

The frequency of framing effect (27% of the sample), as measured herein, was adequate for logistic regression, but it should be noted that it was roughly 50% less than expected, based upon other published studies reviewed previously (Tversky &

Kahneman, 1981; Mather, 2006). Rationale for that difference could have been based in how the framing questions were asked in the HRS experimental module. In the original

“Asian epidemic scenario” (Tversky & Kahneman, 1981), the no-risk to risky choice ratios were stated in absolute number as well as both ratio and percentile and in the different risk ratio:

If Program A is adopted, 200 people will be saved. (72%). If Program B is

adopted, there is 1/3 probability that 600 people will be saved, and 2/3 probability

that no people will be saved. (28%)… If Program C is adopted 400 people will

die. (22%). If Program D is adopted there is 1/3 probability that nobody will die,

and 2/3 probability that 600 people will die. (78%). (Tversky & Kahneman,

1986, p.505).

In the development of the Adult Decision-Making Competence Inventory (Bruine de Bruin et al, 2007), the scenario is modified to animals rather than people, the ratio for risky choice was 25% to 75%., and a 6 point Likert-type strength of response range added to variability and analysis as continuous data:

Imagine that recent evidence has shown that a pesticide is threatening the lives of

1,200 endangered animals. Two response options have been suggested: If Option

A is used, 600 animals will be saved for sure. If Option B is used, there is a 75%

chance that 800 animals will be saved and a 25% chance that no animals will be

saved…. Imagine that recent evidence has shown that a pesticide is threatening

the lives of 1,200 endangered animals. Two response options have been

suggested: If Option A is used, 600 animals will be lost for sure. If Option B is

used, there is a 75% chance that 400 animals will be lost and a 25% chance that

1,200 animals will be lost. (Bruine de Bruin et al., 2007).

The framing items in the HRS were presented with potentially simpler math (“50-

50”) and without additional mathematical terminology:

Imagine that the United States is preparing for the outbreak of an epidemic

expected to kill 600 people. Two alternative programs to combat the disease have

been proposed. Scientists estimate that the outcome of each program is as follows:

If Program A is adopted, 300 people will be saved. If Program B is adopted, there

is a 50-50 chance that either 600 people will be saved or none will be saved….. If

Program A is adopted, 300 people will be die. If Program B is adopted, there is a

50-50 chance that either none will die or 600 will die (Health and Retirement

Study, Codebook, 2012).

It is possible that the presentation of the question affected the outcomes of the logistic regression for two reasons. First, the relative simplicity of the math in the HRS question may have required less deliberative effort and, therein, less influence of cognitive skill in working memory. In addition, it is possible that the simple “50-50” choice may have inadvertently prompted a heuristic of epistemic uncertainty. Epistemic uncertainty is when a response of “fifty-fifty” or a choice of 50% likely is actually a statement of no opinion or no idea. It is speculated that the response is either a heuristic shortcut to expedite an answer or a more socially desirable admission of ignorance

(Fischhoff & Bruine de Bruin, 1999). In either case, the numeric presentation may have distracted from the value of the framing effect as a measure of consistency in value assessment and, therein, decision-making competence. Lastly, the measurement of resistance to the framing effect as an indicator of consistency in value assessment and decision-making competence in the work of Bruine de Bruin and her colleagues was not a single measure of the risky choice framing effect; resistance to framing effect in the

Adult Decision-Making Competence Inventory (Bruine de Bruin et al., 2007) included a number of attribute framing questions (e.g, “80% lean vs. 20% fat”), as well. The modified single item measure may have limited the ability to detect the framing effect adequately for use with the MMADMC.

Research Question Four. RQ4 was an inquiry to determine the influence of resistance to cognitive bias (resistance to the framing effect) on each of the three advance care planning behaviors (discussion of future care and medical treatment preferences, designation of a future surrogate for health care and medical treatment decisions, and documentation of future care and medical treatment preferences).

In summary, the theoretical role of resistance to cognitive bias ( as measured by the framing effect, in the context of decision-making competence) was not supported in this sample. As a binomial regression, resistance to the framing effect did not predict any of the three advance care planning behaviors. In this sample, with these measures, there is no evidence that advance care planning behaviors are influenced by the presence of absence of a framing effect.

Research Question Five. RQ5 was an inquiry to determine the influence of resistance to cognitive bias (resistance to the framing effect) on each of the three advance care planning behaviors (discussion of future care and medical treatment preferences, designation of a future surrogate for health care and medical treatment decisions, and documentation of future care and medical treatment preferences), while controlling for all other independent variables of the model. Research question five was addressed as a multiple logistic regression that examined the influence of resistance to the framing effect, while controlling for all of the other covariate predictor variables of the model

(age, sex, race, perceived control, dispositional optimism, dispositional pessimism, purpose in life, subjective health stress, subjective financial strain, propensity for cognitive effort, and working memory).

The 13 variable multiple logistic regressions did not significantly predict whether a participant would discuss future care preferences with another person. The model did significantly predict participants’ designation of a future surrogate for health decisions through a durable Power of Attorney (Nr2=.14) and documentation of care preferences through a Living Will (also, Nr2=.12).

Age and (absence of) pessimism were significant individual regression weights in the prediction of designation of a future surrogate. Pessimism was also a significant individual predictor in the documentation of future care preferences. Pessimism predicted both designation of a future surrogate (β=.10; OR=.91) and documentation of a future care preferences (β=.10; OR=.91). While the effect size is small, it is relevant to consider the breadth of the confidence interval for each; the inverse effect of pessimism on these advance care planning behaviors could be as substantial as OR=.84-.81. For ease of interpretation, this effect can be translated as the effect of the absence of pessimism: OR=1.19-1.23. The absence of pessimism, a dispositional orientation toward the future, appears to have a small but consistent effect on both decisional processes

(motivation, cognition) and formal advance care planning decisional outcomes

(designation of a future surrogate, documentation of future care preferences).

Resistance to the framing effect was not a significant predictor variable in any multiple logistic regression model. In this sample, with these measures, there is no evidence that advance care planning behaviors are influenced by the presence of absence of a framing effect.

Discussion of the Overall Study Model This study was designed to examine three distinct, but related, advance care planning behaviors through the theoretical framework of the MMADMC (MMADMC;

Strough, Parker, Bruine de Bruin, 2015). As described previously, the MMADMC

(Strough, Parker & Bruin de Bruin, 2015) is a theoretical synthesis of an earlier model focused on the developmentally sensitive relationships between motivational and decision-making skills (Strough, Karnes & Schlosnagle, 2011) and the addition of the construct of decision-making competence (Bruine de Bruin, Parker & Fischhoff, 2007).

Therein, the MMADMC is a synthesis of theory and empirical evidence that have origin in two distinct areas of study: adult developmental psychology and normative behavioral decision-making. Adult developmental psychology is emphasized in the initial paths of the process.

These initial paths describe the decisional influence of stable attributes of an older adult on motivation toward deliberative cognition (cognitive effort), and on deliberative decisional cognition itself (working memory). These initial processes of the MMADMC, were first outlined in the earlier Motivational Model of Judgment and Decision-Making

(Strough et al. 2011). The earlier Strough et al. (2011) model emphasized the importance of motivation on the skills required for unbiased decision-making. That model posited a process in which an adult’s personal and social resources combine with subjective time horizons (a construct essential to Carstensen et al.’s (2005) Socio-emotional Selectivity

Theory) to influence motivation. Motivation then influences decision-making skills

(deliberative, experiential, affective) to produce biased or unbiased decisions.

The initial elements of that model (those based in theory of adult developmental psychology) were supported by the data in this study. Statistically significant models explained a small to medium amount of the variance in both cognitive effort (deliberative motivation) and working memory (deliberative cognition). Furthermore, the influence of a few variables selected to represent the theoretical constructs of orientation toward time and the future (the absence of pessimism, purpose in life) resonated throughout the process and may indicate useful direction for model development and future research.

The latter paths of the MMADMC theoretical process posit a relationship between decision-making skill, decision-making competence, and decisional output (choices, intentions, plans). Consistent with theory and empirical research of normative behavioral decision-making, decision-making competence is the theoretical assumption that an anomaly in rational thinking (a cognitive bias) is a predictor of irrational decision- making; resistance to cognitive bias is essential to deliberative, rational decision outputs

(Bruin de Bruin et al., 2007). In this study, the anomaly representing cognitive bias was the presence of a framing effect. Resistance to that effect was hypothesized as an influence on the rational decision to prepare for future decisional capacity by engaging in any or all three advance care planning behaviors.

In general, when applied with the use of these selected measures and this sample, the MMADMC failed to provide a cohesive model to predict advance care planning behavior. There was no statistically significant evidence of the influence of cognitive bias on any advance care planning behavior. But, while application of the MMADMC as a unitary model does not provide a clear process to understand advance care planning behavior (in this sample, with the measures selected), analyses of the individual components of the MMADMC process indicate a number of meaningful constructs and processes that warrant future research.

Limitations There are a number of limitations, threats to the internal validity, that are broadly applicable to the study as a whole, as well as each research question to be examined.

These threats could both explain the absence of support for theoretical expectations, as well as temper the implications of significant findings. These limitations emanate from design, sampling, and measurement.

Any discussion of the implications of this study must be considered in light of the limitations of a cross-sectional design. This study is examining a process involving developmental constructs in order to better understand a set of actions that occurred at a specific point in time. The cross-sectional design does not allow for specific measurement of time and temporal order. As a process model, the MMADMC proposes that elements of the model lead to the decisional outputs (advance care planning behaviors). But, in the absence of longitudinal data, it is not possible to validate the timing of these processes, their predictive relationships or implied causality. Cross- sectional data is inherently temporally ambiguous. For instance, it is possible that participant’s advance care planning behaviors markedly pre-dated working memory assessment and resistance to the framing effect. In this study, we only know whether the advance care planning behaviors were completed as of 2012, not that they were completed in that time period. In general, follow up research using longitudinal HRS data should provide the opportunity to clarify temporal ordering and predictive relationships. But the challenge may be conceptual, as well as analytic; some issues of temporal ambiguity may be inherent in the MMADMC as published. Within the

MMADMC process model, stable dispositional factors are combined with patterned but potentially modifiable behavioral tendencies (motivation, heuristics) as well as dynamic factors of aging and later life (health, cognitive ability). The degree of stability and rate of change in each of the elements of the theoretical process can vary greatly and may not support the predictive assertions of the model.

The study was limited by a number of measurement issues. As a preliminary study in the application of the MMADMC, not all elements of the model were included in this examination. Intentionally, this study chose to measure only the deliberative process (motivation toward cognitive effort, working memory as an indicator of deliberative cognitive ability). The MMADMC also includes other decision-making skills (experiential, affective) not included in this study. It is possible that those decision- making skills, alone or in synergy with each other, could have successfully predicted resistance to cognitive bias. The significant but small predictive ability of the models for

RQ1 and RQ2 may be an indicator of the need for other contextual factors in the models.

Certainly, those factors could include variables examined in advance care planning research described in the previous literature review. In particular, the consistent theme of future orientation identified in this study could be supportive of both qualitative and quantitative research that identifies cultural and spiritual beliefs and values as relevant to the decision-making involved in advance care planning. To be clear, some of those contextual considerations are present in the full MMADMC, but were not included in this study model. Notably, this study did not include examination of the theoretically proposed contextual effects of the decision domain (including the personal meaning of advance care planning for the threat of future decisional incapacity) and the presence of others (both the objective involvement of spouse and family, as well as the subjective awareness of impact on others including future caregiver burden and financial hardship).

Lastly, one of the limitations of secondary data analysis is the necessary selection of available measures and the inherent inability to prospectively determine the level of specificity in measurement of key constructs. In this study, single item measures (lacking the useful redundancy and potential for triangulation in a multi-item measure) and dichotomous binary responses (lacking a continuous level of measurement more capable of detecting variance) were used for multiple study variables. Most importantly, a single item assessment of binary presence or absence of a framing effect was used to determine resistance to cognitive bias (decision-making competence), the theoretical mediator of decision-making. The measurement of the framing effect has been discussed in detail earlier in this chapter. In summary, a dichotomous single item measure of the risky choice framing effect may have limited both the ability to detect the influence of working memory on resistance to the framing effect, as well as the influence of the framing effect on any of the advance care planning behaviors. Health decisions and health planning behaviors are contextual; as per the MMADMC, the decision process is moderated by the context of the decision domain (eg., the emergence of symptoms of cognitive decline or other degenerative illness) and from the context of the presence of others, such as family.

This preliminary study chose to exclude any measurement of that context. It is also possible that the lack of statistical significance and meaningful effect of the cognitive bias mechanisms in the model is directly related to the type of dependent outcome variable. Many of the decisional outputs of behavioral economics are discrete choices.

Health behaviors, including advance care planning, are often complex elements of planning, rather than a discrete choice. It is possible that even the most sensitive and robust measures of cognitive bias may be inadequate to predict a complex, socio- emotional planning behavior like advance care planning. In fact, it is possible that the decisional process of a planned health behavior may also involve more complex and dynamic cognitive and behavioral elements not even included in the full MMADMC , such as behavioral activation (Magidson, Roberts, Collado-Rodriguez & Lejeuz, 2014), self-efficacy (Bandura et al., 1999), and resourcefulness (Zauszniewski, 2016).

Another potential limitation that could have broadly affect the results is the homogeneity of specific attributes of the sample. The HRS sample is large and diverse and intentionally recruited to represent the entire older American population. But, the volunteer subjects of an HRS longitudinal cohort may be distinctive for systematic reasons that increase the likelihood of voluntary participation in a longitudinal study. These people have the time, resources, capacity and interest to participate in a substantive longitudinal study. As described earlier, despite an average age of 75 years old, this mostly Caucasian, married, educated sample had very low levels of health stress and no financial strain. The lack of age diversity (a floor of age 65) in this sample may have been particularly limiting to application of the MMADMC model that is based on principles gleaned from research in developmental psychology that included midlife and young-old subjects. For example, despite prior published evidence of a significant relationship of large effect between the framing effect and age (Bruine de Bruin et al.,

2007; Strough et al., 2011), no relationship was found in this study.

Study Implications Science. This study, while limited in its findings, represents a novel line of inquiry in health science, decision science, and behavioral science: the systematic examination of a comprehensive decision-making model that incorporates constructs and mechanisms from normative approaches to behavioral decision-making. To date, behavioral decision-making models have often remained within the context of psychology laboratories with research samples comprised of undergraduate students.

Application of emerging theoretical models in behavioral economics have been primarily focused in behavioral finance, marketing, and mass communication. Within health care, isolated applications of selected rational anomalies are sometimes used to explain complex clinical or organizational phenomena, absent a foundational theoretical structure to guide research and interpret findings. This study was an attempt to construct research to examine an important health care delivery and health policy question through the explicit application of a comprehensive conceptual framework and process model that is itself a synthesis of adult developmental psychology and behavioral decision-making. The limited findings of this study are nonetheless deserving of additional research. As this study was limited by the cross-sectional design, longitudinal analysis of the HRS data preceding and the 3 subsequent data collections of this 2012 cohort could provide insight into predictive patterns and causal inferences. In particular, examination of the natural history of dispositional, stable attributes was useful. In addition, examination of more detailed health and cognitive assessments will support analysis of decision domain and other contextual factors. Lastly, the addition of other examples of cognitive biases from within the HRS may provide additional support for, or refutation of, the role of decision-making competence in advance care planning.

Practice. As described previously, the roughly one-quarter of adults that have done no advance care planning present a potential risk: a risk to their own autonomy, a risk of burden to loved ones, and a risk of untimely and inefficient use of health care resources. This study attempted to use a novel interdisciplinary theoretical framework to contribute some additional understanding regarding the motivated cognition and decisional process of those who do and do not plan for future decisional incapacity.

One conceptual but clinically relevant element of this study was the inherent and necessary consideration of disposition. The MMADMC is both developmental, implying change over time, and based upon stable attributes of adults. Whether or not the principle factors that affect advance care planning (or any planned health decision or behavior) are fixed traits or modifiable states is of applied importance, beyond solely the ordering of variables in a theoretical path model. Clinically, a health decision or planned action that is influenced by non-modifiable traits present a challenge to target vulnerable populations and to tailor interventions to be sensitive to the parameters that derive from those traits. For instance, decisional biases that are strongly influenced by non-modifiable traits may present ethical challenges to balance autonomy and liberty against potential vulnerability to irrational decision-making. Potentially, assessment of cognitive biases could be used for the decisionally-challenged, just as neuropsychological assessment is used for the neurologically impaired. Conversely, if the key factors affecting health planning decisions such as advance care planning are modifiable states, then a wholly different interventional approach is appropriate. If these dispositions or orientations are amenable to re-training, then the decisional processes that are influence by these attributes could also be improved.

For example, in this study, an orientation toward the future characterized by the absence of pessimism, appears to exert a small but consistent influence on the decisional process and some advance care planning behaviors. If, unlike the conceptual basis of dispositional pessimism (Sheier & Carver, 1985), orientation toward the future could be modified (Seligman 2006; Aspinwall et al. 2006; Kahana, Kahana & Lee, 2014;

Ouwehand, de Ridder & Bensing, 2007), then, rather than tailoring interventions around dispositional factors, interventions could include techniques to directly modify that future orientation. The conceptual and empirical examination of the MMADMC required by this study generated recognition of the importance of distinctions between trait and state, not only for measurement, but for clinical application and intervention.

Policy. Advance care planning (planning for the possibility of future decisional incapacity) is a matter not only of individuals, families, and health care providers. The outputs of these decisions are either informed or ignorant surrogates. The outputs are either legal documents that can bring efficiency to the use of scarce and expensive healthcare resources or the absence of those documents and either potential delays in treatment or possible unwanted care. Accordingly, advance care planning has been seen as a policy priority by medical administrators, hospital attorneys and risk officers, and palliative care agencies and advocates.

While this study did not provide evidence of the any relationship between cognitive bias and advance care planning behaviors, this study provided a novel exploration of a relatively new but comprehensive theory based on behavioral economic dynamics, in an effort to understand the characteristics of persons who do or do not participate in health planning behaviors such as advance care planning for future decisional incapacity. The potential role of cognitive bias in decision-making could necessitate a major shift in our current approach to clinical biomedical ethics. It has generally been presumed that, in the absence of cognitive incapacity due to neurological deficits (such as dementia), the individual’s informed choice is an expression of autonomy and self-determination. Decision-making models based on normative behavioral decision-making and behavioral economics introduce an alternative lens: even in the absence of cognitive impairment, some persons will demonstrate cognitive bias that inhibits thoughtful, deliberative, and truly autonomous decision-making. If models

(such as the MMADMC used in this study) prove to be valuable in identifying those for whom cognitive bias is likely, decision support will be a policy priority.

At present, our ethical guidelines, built into policies of law, medicine, nursing, social work, and healthcare administration are founded upon the importance and integrity of self-determined choice. Specifically, with cognitively intact older adults, our society has attempted to move toward respect for autonomy of the older adult, rather than ether medical paternalism or decisional authority seized by adult children. If, in fact, cognitive bias is inhibiting true self-determined choice, a different approach to ethics may be required than our current emphasis on autonomy and self-determination as respect for persons.

Conclusion

This study was an exploration of the MMADMC (Strough et al., 2015) and application of the model to better understand advance care planning for future decisional incapacity among a sample of 266 older adults. Overall, the MMADMC failed to provide a cohesive model to predict advance care planning behavior when applied with the use of these selected measures and this sample, Specifically, this study found no influence of cognitive bias within the model and on advance care planning behavior But, while application of the MMADMC as a unitary model did not provide a clear process to understand advance care planning behavior (in this sample, with the measures selected), analyses of the individual components of the MMADMC process indicate a number of meaningful constructs and processes that warrant future research. The elements of the model based upon motivational theory derived from adult developmental psychology were supported. In particular, an open orientation toward the future, as represented by lower levels of dispositional pessimism, higher levels of both dispositional optimism, and purpose in life may play a meaningful role in the decisional processes leading to advance care planning. In addition, the analysis of bivariate correlations indicated that personal sense of control, particularly the absence of constraints, is related to these future-oriented psychological factors. Further research is warranted to longitudinally determine the directionality of these correlations and determine whether absence of constraints contributes to the individual’s orientation toward the future, or whether the dispositional orientation toward the future (optimism, pessimism, purpose in life) influences an individual’s sense of control and level of constraint.

The findings of this study support the potential utility of the concept of “the absence of pessimism”. Examination of the inverse of pessimism, as distinct from optimism, is warranted. This examination should include concept analysis to grasp the qualitative meaning of inverse pessimism or “the absence of pessimism”. This study measured pessimism as a stable trait-like disposition. From this perspective, a greater understanding of the dynamics of pessimism in the processes of future health decisions such as advance care planning could be clinically meaningful as a factor to target specific populations at greater risk of decision conflict or neglect. It could also be useful to tailor interventions with regard to this future-oriented disposition. But further examination of the presumed dispositional aspects of the construct would be both conceptually useful and potentially clinically relevant. Longitudinal examination of any change in pessimism over time would affirm or challenge the assumption of pessimism as a trait, and warrant consideration of its potential as a modifiable attribute that could be amenable to psychosocial and decision-support intervention.

References

Abbott, R. A., Ploubidis, G. B., Huppert, F. A., Kuh, D., Wadsworth, M. E., & Croudace,

T. J. (2006). Psychometric evaluation and predictive validity of Ryff's

psychological well-being items in a UK birth cohort sample of women. Health

and quality of life outcomes, 4(1), 76.

Abbott, R. A., Ploubidis, G. B., Huppert, F. A., Kuh, D., & Croudace, T. J. (2010). An

evaluation of the precision of measurement of Ryff’s Psychological Well-Being

Scales in a population sample. Social indicators research, 97(3), 357-373.

Aggarwal, N. T., Wilson, R. S., Beck, T. L., Rajan, K. B., De Leon, C. F. M., Evans, D.

A., & Everson-Rose, S. A. (2014). Perceived stress and change in cognitive

function among adults aged 65 and older. Psychosomatic medicine, 76(1), 80.

Allen, R. S., Phillips, L. L., Pekmezi, D., Crowther, M. R., & Prentice-Dunn, S. (2008).

Living well with living wills: Application of Protection Motivation Theory to

living wills among older Caucasian and African American adults. Clinical

gerontologist, 32(1), 44-59.

Amer, T., Campbell, K. L., & Hasher, L. (2016). Cognitive control as a double-edged

sword. Trends in cognitive sciences, 20(12), 905-915.

Arber, S., Fenn, K., & Meadows, R. (2014). Subjective financial well-being, income and

health inequalities in mid and later life in Britain. Social Science &

Medicine, 100, 12-20.

Asebedo, S. D., & Wilmarth, M. J. (2017). Does How We Feel About Financial Strain

Matter for Mental Health?. Journal of Financial Therapy, 8(1), 5. Aspinwall, L., & Brunhart, S. M. (1996). Distinguishing optimism from denial:

Optimistic beliefs predict attention to health threats. Personality and Social

Psychology Bulletin, 22(10), 993-1003.

Aspinwall, L. G., Richter, L., & Hoffman, R. R. (2001). Understanding how optimism

works: An examination of optimists’ adaptive moderation of belief and behavior.

Optimism and pessimism: Implications for theory, research, and practice, 217-

238.

Baltes, P. B., & Baltes, M. M. (1990). Psychological perspectives on successful aging:

The model of selective optimization with compensation. Successful aging:

Perspectives from the behavioral sciences, 1(1), 1-34.

Bandura, A., Freeman, W. H., & Lightsey, R. (1999). Self-efficacy: The exercise of

control.

Baron, R. M., & Kenny, D. A. The Moderator-Mediator Variable Distinction in Social

Psychological Research: Conceptual, Strategic, and Statistical Considerations.

Beenackers, M. A., Oude Groeniger, J., van Lenthe, F. J., & Kamphuis, C. (2017). The

role of financial strain and self-control in explaining health behaviours: the

GLOBE study. European journal of public health.

Bergland, A., Nicolaisen, M., & Thorsen, K. (2014). Predictors of subjective age in

people aged 40–79 years: A five-year follow-up study. The impact of mastery,

mental and physical health. Aging & Mental Health, 18(5), 653-661.

Best, R., & Charness, N. (2015). Age differences in the effect of framing on risky choice:

A meta-analysis. Psychology and aging, 30(3), 688. Billings, J. A., & Bernacki, R. (2014). Strategic targeting of advance care planning

interventions: the Goldilocks phenomenon. JAMA internal medicine, 174(4), 620-

624.

Bischoff, K. E., Sudore, R., Miao, Y., Boscardin, W. J., & Smith, A. K. (2013). Advance

care planning and the quality of end‐of‐life care in older adults. Journal of the

American Geriatrics Society, 61(2), 209-214.

Björklund, F., & Bäckström, M. (2008). Individual differences in processing styles:

validity of the Rational–Experiential Inventory. Scandinavian journal of

psychology, 49(5), 439-446.

Blumenthal-Barby, J. S., & Burroughs, H. (2012). Seeking better health care outcomes:

the ethics of using the “nudge”. The American Journal of Bioethics, 12(2), 1-10.

Blumenthal-Barby, J. S., & Krieger, H. (2015). Cognitive biases and heuristics in medical

decision making: a critical review using a systematic search strategy. Medical

Decision Making, 35(4), 539-557.

Boyle, P. A., Buchman, A. S., Barnes, L. L., & Bennett, D. A. (2010). Effect of a purpose

in life on risk of incident Alzheimer disease and mild cognitive impairment in

community-dwelling older persons. Archives of general psychiatry, 67(3), 304-

310.

Boyle, P. A., Yu, L., Wilson, R. S., Gamble, K., Buchman, A. S., & Bennett, D. A.

(2012). Poor decision making is a consequence of cognitive decline among older

persons without Alzheimer’s disease or mild cognitive impairment. PloS

one, 7(8), e43647. Braver, T. S., Krug, M. K., Chiew, K. S., Kool, W., Westbrook, J. A., Clement, N. J., ...

& Cools, R. (2014). Mechanisms of motivation–cognition interaction: challenges

and opportunities. Cognitive, Affective, & Behavioral Neuroscience, 14(2), 443-

472.

Bravo, G., Trottier, L., Arcand, M., Boire-Lavigne, A. M., Blanchette, D., Dubois, M. F.

& Bellemare, S. (2016). Promoting advance care planning among community-

based older adults: a randomized controlled trial. Patient education and

counseling, 99(11), 1785-1795.

Bravo, G., Sene, M., Arcand, M., & Hérault, É. (2018). Effects of advance care planning

on confidence in surrogates’ ability to make healthcare decisions consistent with

older adults’ wishes: Findings from a randomized controlled trial. Patient

education and counseling, 101(7), 1256-1261.

Bristow, T., Jih, C. S., Slabich, A., & Gunn, J. (2016). Standardization and adult norms

for the sequential subtracting tasks of serial 3’s and 7’s. Applied

Neuropsychology: Adult, 23(5), 372-378.

Brown, L. L., Mitchell, U. A., & Ailshire, J. (2018). Disentangling the Stress Process:

Race/Ethnic Differences in the Exposure and Appraisal of Chronic Stressors

among Older Adults. The Journals of Gerontology: Series B.

Bruine de Bruin, W., Parker, A. M., & Fischhoff, B. (2007). Individual differences in

adult decision-making competence. Journal of personality and social

psychology, 92(5), 938. Bruine de Bruin, W., Parker, A. M., & Fischhoff, B. (2012). Explaining adult age

differences in decision‐making competence. Journal of Behavioral Decision

Making, 25(4), 352-360.

Bruine de Bruin, W. Parker, A.M, & Fischhoff, B. (2014). Individual differences in

decision-making competence across the lifespan. Neuroeconomics, judgment, and

decision making, 219-236.

Bruine de Bruin, W., McNair, S. J., Taylor, A. L., Summers, B., & Strough, J. (2015).

“Thinking about Numbers Is Not My Idea of Fun” Need for Cognition Mediates

Age Differences in Numeracy Performance. Medical Decision Making, 35(1), 22-

26.

Bruine de Bruin, W., Fischhoff, B., Millstein, S. G., & Halpern-Felsher, B. L. (2000).

Verbal and numerical expressions of probability:" It's a fifty-fifty

chance". Organizational behavior and human decision processes, 81(1), 115-131.

Bruine de Bruin, W., Fischbeck, P. S., Stiber, N. A., & Fischhoff, B. (2002). What

number is “fifty‐fifty”?: Redistributing excessive 50% responses in elicited

probabilities. Risk Analysis: An International Journal, 22(4), 713-723.

Buckley, R. F., Maruff, P., Ames, D., Bourgeat, P., Martins, R. N., Masters, C. L., ... &

Villemagne, V. L. (2016). Subjective memory decline predicts greater rates of

clinical progression in preclinical Alzheimer's disease. Alzheimer's &

Dementia, 12(7), 796-804.

Burke, K. L., Joyner, A. B., Czech, D. R., & Wilson, M. J. (2000). An investigation of

concurrent validity between two optimism/pessimism questionnaires: The life orientation test-revised and the optimism/pessimism scale. Current

Psychology, 19(2), 129-136.

Burmester, B., Leathem, J., & Merrick, P. (2016). Subjective cognitive complaints and

objective cognitive function in aging: A systematic review and meta-analysis of

recent cross-sectional findings. Neuropsychology review, 26(4), 376-393.

Burns, R. A., & Machin, M. A. (2009). Investigating the structural validity of Ryff’s

psychological well-being scales across two samples. Social indicators

research, 93(2),

Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of personality and

social psychology, 42(1), 116.

Cacioppo, J. T., Petty, R. E., & Feng Kao, C. (1984). The efficient assessment of need for

cognition. Journal of personality assessment, 48(3), 306-307.

Cacioppo, J. T., Petty, R. E., Feinstein, J. A., & Jarvis, W. B. G. (1996). Dispositional

differences in cognitive motivation: The life and times of individuals varying in

need for cognition. Psychological bulletin, 119(2), 197.

California Healthcare Foundation. Final Chapter: Californians' Attitudes and Experiences

with Death and Dying. 2012

Carr, D. (2011). Racial differences in end-of-life planning: Why don't Blacks and Latinos

prepare for the inevitable?. OMEGA-Journal of Death and Dying, 63(1), 1-20.

Carr, D. (2012). Racial and ethnic differences in advance care planning: Identifying

subgroup patterns and obstacles. Journal of Aging and Health, 24(6), 923-947.

Carr, D., & Luth, E. (2016). End-of-Life Planning and Health Care. In Handbook of

Aging and the Social Sciences (Eighth Edition) (pp. 375-394). Carr, D., & Luth, E. A. (2017). Advance care planning: contemporary issues and future

directions. Innovation in Aging, 1(1), igx012.

Clark, M. A., Person, S. D., Gosline, A., Gawande, A. A., & Block, S. D. (2018). Racial

and Ethnic Differences in Advance Care Planning: Results of a Statewide

Population-Based Survey. Journal of palliative medicine.

Carstensen, L. L., & Mikels, J. A. (2005). At the intersection of emotion and cognition:

Aging and the positivity effect. Current directions in psychological

science, 14(3), 117-121.

Carstensen, L. L. (2006). The influence of a sense of time on human

development. Science, 312(5782), 1913-1915.

Cazan A. M. (2016). The factor structure of the short need for cognition scale. Bulletin of

the Transilvania University of Brasov. Series VII: Social Sciences. Law, 9(1).

Centers for Medicare & Medicaid Services. (2016). Frequently asked questions about

billing the physician fee schedule for advance care planning services.

Chang, E. C. (2001). Optimism & pessimism: Implications for theory, research, and

practice. American Psychological Association.

Chang, L., & McBride-Chang, C. (1996). The factor structure of the Life Orientation

Test. Educational and Psychological Measurement, 56(2), 325-329.

Chen, H., Cohen, P., & Chen, S. (2010). How big is a big odds ratio? Interpreting the

magnitudes of odds ratios in epidemiological studies. Communications in

Statistics—Simulation and Computation®, 39(4), 860-864. Cheong, K., Fisher, P., Goh, J., Ng, L., Koh, H. M., & Yap, P. (2015). Advance care

planning in people with early cognitive impairment. BMJ supportive & palliative

care, 5(1), 63-69.

Chiu, C., Feuz, M. A., McMahan, R. D., Miao, Y., & Sudore, R. L. (2016). “Doctor,

make my decisions”: decision control preferences, advance care planning, and

satisfaction with communication among diverse older adults. Journal of pain and

symptom management, 51(1), 33-40.

Choi, S., Kim, M., & McDonough, I. M. (2018). Do older adults with Alzheimer's

disease engage in estate planning and advance care planning preparation?. Aging

& mental health, 1-8.

Chopik, W. J., Kim, E. S., & Smith, J. (2015). Changes in optimism are associated with

changes in health over time among older adults. Social psychological and

personality science, 6(7), 814-822.

Cohen, J. (1992). A power primer. Psychological bulletin, 112(1), 155.

Coleman, E. A. (2003). Falling through the cracks: challenges and opportunities for

improving transitional care for persons with continuous complex care

needs. Journal of the American Geriatrics Society, 51(4), 549-555.

Cooper, J. A., Blanco, N. J., & Maddox, W. T. (2017). Framing matters: Effects of

framing on older adults’ exploratory decision-making. Psychology and

aging, 32(1), 60.

Coppola, K. M., Ditto, P. H., Danks, J. H., & Smucker, W. D. (2001). Accuracy of

primary care and hospital-based physicians' predictions of elderly outpatients' treatment preferences with and without advance directives. Archives of Internal

Medicine, 161(3), 431-440.

Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and

mixed methods approaches. Sage publications.

Culhane, S. E., Morera, O. F., & Hosch, H. M. (2004). The factor structure of the Need

for Cognition Short Form in a Hispanic sample. The Journal of

Psychology, 138(1), 77-90.

Cutler, S. J. (2015). Worries about getting Alzheimer’s: who’s concerned?. American

Journal of Alzheimer's Disease & Other Dementias®, 30(6), 591-598.

De Martino, B., Kumaran, D., Seymour, B., & Dolan, R. J. (2006). Frames, biases, and

rational decision-making in the human brain. Science, 313(5787), 684-687.

Del Missier, F., Mäntylä, T., & De Bruin, W. B. (2012). Decision‐making competence,

executive functioning, and general cognitive abilities. Journal of Behavioral

Decision Making, 25(4), 331-351.

Del Missier, F., Mäntylä, T., Hansson, P., Bruine de Bruin, W., Parker, A. M., & Nilsson,

L. G. (2013). The multifold relationship between memory and decision making:

An individual-differences study. Journal of Experimental Psychology: Learning,

Memory, and Cognition, 39(5), 1344.

Delaney, R., Strough, J., Parker, A. M., & de Bruin, W. B. (2015). Variations in decision-

making profiles by age and gender: A cluster-analytic approach. Personality and

individual differences, 85, 19-24.

Depping, M. K., & Freund, A. M. (2011). Normal aging and decision making: The role of

motivation. Human Development, 54(6), 349-367. D'esposito, M., & Postle, B. R. (2015). The cognitive neuroscience of working

memory. Annual review of psychology, 66.

Detering, K. M., Hancock, A. D., Reade, M. C., & Silvester, W. (2010). The impact of

advance care planning on end of life care in elderly patients: randomised

controlled trial. Bmj, 340, c1345.

Dewberry, C., Juanchich, M., & Narendran, S. (2013). Decision-making competence in

everyday life: The roles of general cognitive styles, decision-making styles and

personality. Personality and Individual Differences, 55(7), 783-788.

Ditto, P. H., Danks, J. H., Smucker, W. D., Bookwala, J., Coppola, K. M., Dresser, R., ...

& Zyzanski, S. (2001). Advance directives as acts of communication: a

randomized controlled trial. Archives of Internal Medicine, 161(3), 421-430.

Ditto, P. H., Hawkins, N. A., & Pizarro, D. A. (2005). Imagining the end of life: On the

psychology of advance medical decision making. Motivation and Emotion, 29(4),

475-496.

Dixon, J., Matosevic, T., & Knapp, M. (2015). The economic evidence for advance care

planning: systematic review of evidence. Palliative medicine, 29(10), 869-884.

Dixon, J., Karagiannidou, M., & Knapp, M. (2018). The effectiveness of advance care

planning in improving end-of-life outcomes for people with dementia and their

Carers: a systematic review and critical discussion. Journal of pain and symptom

management, 55(1), 132-150.

Durbin, K. A., Barber, S. J., Brown, M., & Mather, M. (2018). Optimism for the Future

in Younger and Older Adults. The Journals of Gerontology: Series B. Elder, T. E. (2013). The predictive validity of subjective mortality expectations: Evidence

from the health and retirement study. Demography, 50(2), 569-589.

Enge, S., Fleischhauer, M., Brocke, B., & Strobel, A. (2008). Neurophysiological

measures of involuntary and voluntary attention allocation and dispositional

differences in need for cognition. Personality and Social Psychology

Bulletin, 34(6), 862-874.

Ennis, G. E., Hess, T. M., & Smith, B. T. (2013). The impact of age and motivation on

cognitive effort: Implications for cognitive engagement in older adulthood.

Psychology and Aging, 28(2), 495.

Epstein, S., Pacini, R., Denes-Raj, V., & Heier, H. (1996). Individual differences in

intuitive–experiential and analytical–rational thinking styles. Journal of

personality and social psychology, 71(2), 390.

Fagerlin, A., Ditto, P. H., Danks, J. H., & Houts, R. M. (2001). Projection in surrogate

decisions about life-sustaining medical treatments. Health Psychology, 20(3), 166.

Fagley, N. S., & Miller, P. M. (1990). The effect of framing on choice: Interactions with

risk-taking propensity, cognitive style, and sex. Personality and social psychology

bulletin, 16(3), 496-510.

Ferguson, S. J., & Goodwin, A. D. (2010). Optimism and well-being in older adults: The

mediating role of social support and perceived control. The International Journal

of Aging and Human Development, 71(1), 43-68.

Finucane, M. L., & Lees, N. B. (2005, November). Decision-making competence of older

adults: Models and methods. In Workshop on decision making needs of older

adults, the National Academies. Finucane, M. L., Mertz, C. K., Slovic, P., & Schmidt, E. S. (2005). Task complexity and

older adults' decision-making competence. Psychology and aging, 20(1), 71.

Finucane, M. L. (2008). Emotion, affect, and risk communication with older adults:

challenges and opportunities. Journal of risk research, 11(8), 983-997.

Finucane, M. L., & Gullion, C. M. (2010). Developing a tool for measuring the decision-

making competence of older adults. Psychology and aging, 25(2), 271.

Finucane, M. L., Slovic, P., Hibbard, J. H., Peters, E., Mertz, C. K., &

MacGregor, D. G. (2002). Aging and decision‐making competence: An analysis

of comprehension and consistency skills in older versus younger adults

considering health‐plan options. Journal of Behavioral Decision Making, 15(2),

141-164.

Fischhoff, B., & Bruine De Bruin, W. (1999). Fifty–fifty= 50%?. Journal of Behavioral

Decision Making, 12(2), 149-163.

Fishman, E. (2015). Lifetime risk of dementia in the United States.

Fleischhauer, M., Enge, S., Brocke, B., Ullrich, J., Strobel, A., & Strobel, A. (2010).

Same or different? Clarifying the relationship of need for cognition to personality

and intelligence. Personality and Social Psychology Bulletin, 36(1), 82-96.

Ford, T. R., Cummings, R. C., & Cassel, J. (2017, July). Concerns about cost of future

medical care as a factor in advance care planning: Review and agenda for future

research. In Cancer Forum (Vol. 41, No. 2, p. 42). The Cancer Council Australia.

Forsterlee, R., & Ho, R. (1999). An examination of the short form of the Need for

Cognition Scale applied in an Australian sample. Educational and Psychological

Measurement, 59(3), 471-480. Frankl, V. E., Lasch, I., & Allport, G. W. (1963). Man's search for meaning: An

introduction to logotherapy (pp. 104-105). New York: Pocket Books.

Fried, T. R., Bullock, K., Iannone, L., & O'leary, J. R. (2009). Understanding advance

care planning as a process of health behavior change. Journal of the American

Geriatrics Society, 57(9), 1547-1555.

Fried, T. R., Redding, C. A., Robbins, M. L., Paiva, A., O'leary, J. R., & Iannone, L.

(2010). Stages of change for the component behaviors of advance care

planning. Journal of the American Geriatrics Society, 58(12), 2329-2336.

Fried, T. R., Zenoni, M., Iannone, L., O'leary, J., & Fenton, B. T. (2017). Engagement in

Advance Care Planning and Surrogates’ Knowledge of Patients’ Treatment

Goals. Journal of the American Geriatrics Society, 65(8), 1712-1718.

Fried, T., Zenoni, M., & Iannone, L. (2017). A dyadic perspective on engagement in

advance care planning. Journal of the American Geriatrics Society, 65(1), 172-

178.

Frisch, D. (1993). Reasons for framing effects. Organizational behavior and human

decision processes, 54(3), 399-429.

Furnham, A., & Thorne, J. D. (2013). Need for cognition. Journal of Individual

Differences.

Gallagher, M. W., Lopez, S. J., & Preacher, K. J. (2009). The hierarchical structure of

well‐being. Journal of personality, 77(4), 1025-1050.

Gawronski, K. A., Kim, E. S., Langa, K. M., & Kubzansky, L. D. (2016). Dispositional

optimism and incidence of cognitive impairment in older adults. Psychosomatic

medicine, 78(7), 819. Genewick, J. E., Lipski, D. M., Schupack, K. M., & Buffington, A. L. (2018).

Characteristics of Patients With Existing Advance Directives: Evaluating

Motivations Around Advance Care Planning. American Journal of Hospice and

Palliative Medicine®, 35(4), 664-668.

Gerst, K., & Burr, J. A. (2008). Planning for end-of-life care: Black-white differences in

the completion of advance directives. Research on Aging, 30(4), 428-449.

Gerstorf, D., Röcke, C., & Lachman, M. E. (2010). Antecedent–consequent relations of

perceived control to health and social support: Longitudinal evidence for

between-domain associations across adulthood. Journals of Gerontology Series B:

Psychological Sciences and Social Sciences, 66(1), 61-71.

Gottlieb, D., & Mitchell, O. S. (2015). Narrow framing and long-term care insurance

(No. w21048). National Bureau of Economic Research.

Grove, S. K., Burns, N., & Gray, J. (2014). Understanding nursing research: Building an

evidence-based practice. Elsevier Health Sciences.

Hahn, E. A., & Lachman, M. E. (2015). Everyday experiences of memory problems and

control: The adaptive role of selective optimization with compensation in the

context of memory decline. Aging, Neuropsychology, and Cognition, 22(1), 25-

41.

Halpern, S. D. (2012, August). Shaping end-of-life care: behavioral economics and

advance directives. In Seminars in respiratory and critical care medicine (Vol.

33, No. 04, pp. 393-400). Thieme Medical Publishers. Halpern, S. D., Loewenstein, G., Volpp, K. G., Cooney, E., Vranas, K., Quill, C. M., ... &

Arnold, R. (2013). Default options in advance directives influence how patients

set goals for end-of-life care. Health Affairs, 32(2), 408-417.

Health and Retirement Study, Core Measures and Psychosocial Questionnaire, public use

dataset. Produced and distributed by the University of Michigan with funding

from the National Institute on Aging (grant number NIA U01AG009740). Ann

Arbor, MI, (2012).

Hevey, D., Thomas, K., Pertl, M., Maher, L., Craig, A., & Ni Chuinneagain, S. (2012).

Method effects and the Need for Cognition Scale. International Journal of

Educational and Psychological Assessment, 12(1), 20-33.

Heckhausen, J., & Schulz, R. (1995). A life-span theory of control. Psychological

review, 102(2), 284.

Heckhausen, J. (1997). Developmental regulation across adulthood: Primary and

secondary control of age-related challenges. Developmental psychology, 33(1),

176.

Heckhausen, J., Wrosch, C., & Schulz, R. (2010). A motivational theory of life-span

development. Psychological review, 117(1), 32.

Herzberg, P. Y., Glaesmer, H., & Hoyer, J. (2006). Separating optimism and pessimism:

a robust psychometric analysis of the revised Life Orientation Test (LOT-

R). Psychological assessment, 18(4), 433.

Hess, T. M. (2014). Selective engagement of cognitive resources: Motivational influences

on older adults’ cognitive functioning. Perspectives on Psychological

Science, 9(4), 388-407. Hess, T. M., Growney, C. M., O'Brien, E. L., Neupert, S. D., & Sherwood, A. (2018).

The role of cognitive costs, attitudes about aging, and intrinsic motivation in

predicting engagement in everyday activities. Psychology and aging, 33(6), 953.

Hess, T. M., Strough, J., & Löckenhoff, C. (Eds.). (2015). Aging and decision making:

Empirical and applied perspectives. Academic Press.

Hess, T. M., & Ennis, G. E. (2011). Age differences in the effort and costs associated

with cognitive activity. Journals of Gerontology Series B: Psychological Sciences

and Social Sciences, 67(4), 447-455.

Hess, T. M. (2015). A prospect theory-based evaluation of dual-process influences on

aging and decision making: support for a contextual perspective. In Aging and

Decision Making (pp. 189-212).

Higgins, P. A., & Straub, A. J. (2006). Understanding the error of our ways: Mapping the

concepts of validity and reliability. Nursing outlook, 54(1), 23-29.

Horn, J. L., & Cattell, R. B. (1967). Age differences in fluid and crystallized intelligence.

Acta psychologica, 26, 107-129.

Houben, C. H., Spruit, M. A., Groenen, M. T., Wouters, E. F., & Janssen, D. J. (2014).

Efficacy of advance care planning: a systematic review and meta-

analysis. Journal of the American Medical Directors Association, 15(7), 477-489.

Huang, H. H., Su, H. J., & Chang, C. J. (2015). The moderating effect of a no-choice

option on risky framing effect. Asia Pacific Management Review, 20(1), 18-23.

Inoue, M., Ihara, E., & Terrillion, A. (2017). Making Your Wishes Known: Who

Completes an Advance Directive and Shares It With Their Health Care Team or

Loved Ones?. Journal of Applied Gerontology, 0733464817748778. Isaacowitz, D. M. (2005). Correlates of well-being in adulthood and old age: A tale of

two optimisms. Journal of Research in Personality, 39(2), 224-244.

James, B. D., Boyle, P. A., Yu, L., Han, S. D., & Bennett, D. A. (2015). Cognitive

decline is associated with risk aversion and temporal discounting in older adults

without dementia. PloS one, 10(4), e0121900.

Jessen, F., Wiese, B., Bachmann, C., Eifflaender-Gorfer, S., Haller, F., Kölsch, H., ... &

Wollny, A. (2010). Prediction of dementia by subjective memory impairment:

effects of severity and temporal association with cognitive impairment. Archives

of general psychiatry, 67(4), 414-422.

Jessen, F., Wolfsgruber, S., Wiese, B., Bickel, H., Mösch, E., Kaduszkiewicz, H., ... &

Weyerer, S. (2014). AD dementia risk in late MCI, in early MCI, and in

subjective memory impairment. Alzheimer's & Dementia, 10(1), 76-83.

Jimenez, G., Tan, W. S., Virk, A. K., Low, C. K., Car, J., & Ho, A. H. Y. (2018). State of

advance care planning research: A descriptive overview of systematic

reviews. Palliative & supportive care, 1-11.

Kafka, G. J., & Kozma, A. (2002). The construct validity of Ryff's scales of

psychological well-being (SPWB) and their relationship to measures of subjective

well-being. Social Indicators Research, 57(2), 171-190.

Kahana, E., Kahana, B., & Lee, J. E. (2014). Proactive approaches to successful aging:

One clear path through the forest. Gerontology, 60(5), 466-474.

Kahn, J. R., & Pearlin, L. I. (2006). Financial strain over the life course and health among

older adults. Journal of health and social behavior, 47(1), 17-31. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under

Risk. Econometrica, 47(2), 263-292.

Kahneman, D. (2011). Thinking, fast and slow (Vol. 1). New York: Farrar, Straus and

Giroux.

Kane, M. J., Brown, L. H., McVay, J. C., Silvia, P. J., Myin-Germeys, I., & Kwapil, T. R.

(2007). For whom the mind wanders, and when: An experience-sampling study of

working memory and executive control in daily life. Psychological science, 18(7),

614-621.

Karzmark, P. (2000). Validity of the serial seven procedure. International journal of

geriatric psychiatry, 15(8), 677-679.

Kase, S., Ritter, F., & Schoelles, M. J. (2009, January). Serial subtraction errors revealed.

In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 31,

No. 31).

Ke, L. S., Huang, X., Hu, W. Y., O’Connor, M., & Lee, S. (2017). Experiences and

perspectives of older people regarding advance care planning: A meta-synthesis

of qualitative studies. Palliative medicine, 31(5), 394-405.

Kelley, A. S., Langa, K. M., Smith, A. K., Cagle, J., Ornstein, K., Silveira, M. J., ... &

Ritchie, C. S. (2014). Leveraging the health and retirement study to advance

palliative care research. Journal of palliative medicine, 17(5), 506-511.

Khosla, N., Curl, A. L., & Washington, K. T. (2016). Trends in engagement in advance

care planning behaviors and the role of socioeconomic status. American Journal

of Hospice and Palliative Medicine®, 33(7), 651-657. Kim, S., Goldstein, D., Hasher, L., & Zacks, R. T. (2005). Framing effects in younger

and older adults. The Journals of Gerontology Series B: Psychological Sciences

and Social Sciences, 60(4), P215-P218.

Kim, E. S., Park, N., & Peterson, C. (2011). Dispositional optimism protects older adults

from stroke: the Health and Retirement Study. Stroke, 42(10), 2855-2859.

Kim, E. S., Sun, J. K., Park, N., & Peterson, C. (2013). Purpose in life and reduced

incidence of stroke in older adults:'The Health and Retirement Study'. Journal of

psychosomatic research, 74(5), 427-432.

Kim, E. S., Smith, J., & Kubzansky, L. D. (2014). Prospective study of the association

between dispositional optimism and incident heart failure. Circulation: Heart

Failure, 7(3), 394-400.

Kim, E. S., Hershner, S. D., & Strecher, V. J. (2015). Purpose in life and incidence of

sleep disturbances. Journal of behavioral medicine, 38(3), 590-597.

Kim, E. S., Strecher, V. J., & Ryff, C. D. (2014). Purpose in life and use of preventive

health care services. Proceedings of the National Academy of Sciences, 111(46),

16331-16336.

Kirova, A. M., Bays, R. B., & Lagalwar, S. (2015). Working memory and executive

function decline across normal aging, mild cognitive impairment, and

Alzheimer’s disease. BioMed research international, 2015.

Kline, R. B. (2005). Principles and practice of structural equation modeling. Guilford

publications. Korten, N. C., Sliwinski, M. J., Comijs, H. C., & Smyth, J. M. (2014). Mediators of the

relationship between life events and memory functioning in a community sample

of adults. Applied cognitive psychology, 28(5), 626-633.

Koss, C. S. (2017). Beyond the individual: the interdependence of advance directive

completion by older married adults. Journal of the American Geriatrics

Society, 65(7), 1615-1620.

Kotter-Grühn, D., Kornadt, A. E., & Stephan, Y. (2016). Looking beyond chronological

age: Current knowledge and future directions in the study of subjective

age. Gerontology, 62(1), 86-93.

Krause, M. R., Serlin, R. C., Ward, S. E., Rony, Y. Z., Ezenwa, M. O., & Naab, F.

(2010). Testing mediation in nursing research: Beyond baron and kenny. Nursing

Research, 59(4), 288-294.

Kühberger, A. (1998). The influence of framing on risky decisions: A meta-analysis.

Organizational behavior and human decision processes, 75(1), 23-55.

Kühberger, A., & Tanner, C. (2010). Risky choice framing: Task versions and a

comparison of prospect theory and fuzzy‐trace theory. Journal of behavioral

decision making, 23(3), 314-329.

Kuo, F. Y., Hsu, C. W., & Day, R. F. (2009). An exploratory study of cognitive effort

involved in decision under Framing—an application of the eye-tracking

technology. Decision Support Systems, 48(1), 81-91.

Lachman, M. E. (2006). Perceived control over aging-related declines: Adaptive beliefs

and behaviors. Current Directions in Psychological Science, 15(6), 282-286. Lachman, M. E., & Firth, K. M. (2004). The adaptive value of feeling in control during

midlife. How healthy are we, 320-349.

Lachman, M. E., Neupert, S. D., & Agrigoroaei, S. (2011). The relevance of control

beliefs for health and aging. In Handbook of the Psychology of Aging (Seventh

Edition) (pp. 175-190).

Lachman, M. E., & Weaver, S. L. (1998). Sociodemographic variations in the sense of

control by domain: Findings from the MacArthur studies of midlife. Psychology

and aging, 13(4), 553.

Lang, F. R., & Carstensen, L. L. (2002). Time counts: Future time perspective, goals, and

social relationships. Psychology and aging, 17(1), 125.

LeBoeuf, R. A., & Shafir, E. (2003). Deep thoughts and shallow frames: On the

susceptibility to framing effects. Journal of Behavioral Decision Making, 16(2),

77-92.

Lee, P. L. (2016). Control beliefs level and change as predictors of subjective memory

complaints. Aging & mental health, 20(3), 329-335.

Levin, I. P., Gaeth, G. J., Schreiber, J., & Lauriola, M. (2002). A new look at framing

effects: Distribution of effect sizes, individual differences, and independence of

types of effects. Organizational behavior and human decision processes, 88(1),

411-429.

Levin, I. P., McElroy, T., Gaeth, G. J., Hedgcock, W., Denburg, N. L., & Tranel, D.

(2015). Studying Decision Processes through Behavioral and Neuroscience

Analyses of Framing Effects. WILHELMS, EA And REYNA, VF, 131-156. Levy, H., Ubel, P. A., Dillard, A. J., Weir, D. R., & Fagerlin, A. (2014). Health

numeracy: the importance of domain in assessing numeracy. Medical Decision

Making, 34(1), 107-115.

Lewis, N. A., Turiano, N. A., Payne, B. R., & Hill, P. L. (2017). Purpose in life and

cognitive functioning in adulthood. Aging, Neuropsychology, and

Cognition, 24(6), 662-671.

Liao, H. (2015) The Effect of Self-Control on Tunnel Vision Evoked by Scarcity: An

Intertwined Relationship, Department of Psychology Claremont McKenna

College.

Lingler, J. H., Hirschman, K. B., Garand, L., Dew, M. A., Becker, J. T., Schulz, R., &

DeKosky, S. T. (2008). Frequency and correlates of advance planning among

cognitively impaired older adults. The American Journal of Geriatric

Psychiatry, 16(8), 643-649.

Linley, P. A., Maltby, J., Wood, A. M., Osborne, G., & Hurling, R. (2009). Measuring

happiness: The higher order factor structure of subjective and psychological well-

being measures. Personality and individual differences, 47(8), 878-884.

Lins de Holanda Coelho, G., HP Hanel, P., & J. Wolf, L. (2018). The Very Efficient

Assessment of Need for Cognition: Developing a Six-Item Version. Assessment,

1073191118793208.

Lovell, A., & Yates, P. (2014). Advance care planning in palliative care: a systematic

literature review of the contextual factors influencing its uptake 2008–

2012. Palliative medicine, 28(8), 1026-1035. Lupien, S. J., Juster, R. P., Raymond, C., & Marin, M. F. (2018). The Effects of Chronic

Stress on the Human Brain: From Neurotoxicity, to Vulnerability, to

Opportunity. Frontiers in neuroendocrinology.

MacKenzie, M. A., Smith-Howell, E., Bomba, P. A., & Meghani, S. H. (2018).

Respecting Choices and related models of advance care planning: a systematic

review of published evidence. American Journal of Hospice and Palliative

Medicine®, 35(6), 897-907.

Magidson, J. F., Roberts, B. W., Collado-Rodriguez, A., & Lejuez, C. W. (2014).Theory-

driven intervention for changing personality: Expectancy value theory, behavioral

activation, and conscientiousness. Developmental psychology, 50(5), 1442.

Mahoney, K. T., Buboltz, W., Levin, I. P., Doverspike, D., & Svyantek, D. J. (2011).

Individual differences in a within-subjects risky-choice framing

study. Personality and Individual Differences, 51(3), 248-257.

Maldonato, N. M., Sperandeo, R., Costa, V., Cioffi, V., & Cozzolino, P. (2017). Does

Brain Sweat Pay Off?: The Association between the Need for Cognition and

Cognitive Function among the American Elderly. J Psychol Psychother, 7(326),

2161-0487.

Mani, A., Mullainathan, S., Shafir, E., & Zhao, J. (2013). Poverty impedes cognitive

function. science, 341(6149), 976-980.

Manning, R. T. (1982). The serial sevens test. Archives of internal medicine, 142(6),

1192-1192.

Mather, M. (2006). A review of decision-making processes: Weighing the risks and

benefits of aging. When I’m, 64(145), 145-173. Mather, M., & Carstensen, L. L. (2005). Aging and motivated cognition: The positivity

effect in attention and memory. Trends in cognitive sciences, 9(10), 496-502.

Mather, M. (2016). The Affective Neuroscience of Aging. Annual review of psychology,

67, 213.

Maxwell, S. E., Kelley, K., & Rausch, J. R. (2008). Sample size planning for statistical

power and accuracy in parameter estimation. Annual review of psychology, 59.

McArdle, J. J., Fisher, G. G., & Kadlec, K. M. (2007). Latent variable analyses of age

trends of cognition in the Health and Retirement Study, 1992-2004. Psychology

and aging, 22(3), 525.

McArdle, John, Rodgers, Willard, and Willis, Robert. Cognition and Aging in the USA

(CogUSA) 2007-2009. Ann Arbor, MI: Inter-university Consortium for Political

and Social Research [distributor], 2015-04-16.

https://doi.org/10.3886/ICPSR36053.v1

McCabe, D. P., Roediger III, H. L., McDaniel, M. A., Balota, D. A., & Hambrick, D. Z.

(2010). The relationship between working memory capacity and executive

functioning: evidence for a common executive attention

construct. Neuropsychology, 24(2), 222.

McMahan, R. D., Knight, S. J., Fried, T. R., & Sudore, R. L. (2013). Advance care

planning beyond advance directives: perspectives from patients and

surrogates. Journal of pain and symptom management, 46(3), 355-365.

Meyer, J. S., Xu, G., Thornby, J., Chowdhury, M., & Quach, M. (2002). Longitudinal

analysis of abnormal domains comprising mild cognitive impairment (MCI)

during aging. Journal of the neurological sciences, 201(1-2), 19-25. Mikels, J. A., Larkin, G. R., Reuter-Lorenz, P. A., & Carstensen, L. L. (2005). Divergent

trajectories in the aging mind: Changes in working memory for affective versus

visual information with age. Psychology and aging, 20(4), 542.

Miller, B. (2017). Nurses in the Know: The History and Future of Advance Directives.

OJIN: The Online Journal of Issues in Nursing, 22(3).

Molinuevo, J. L., Rabin, L. A., Amariglio, R., Buckley, R., Dubois, B., Ellis, K. A., ... &

Reisberg, B. (2017). Implementation of subjective cognitive decline criteria in

research studies. Alzheimer's & Dementia, 13(3), 296-311.

Montibeller, G., & Von Winterfeldt, D. (2015). Cognitive and motivational biases in

decision and risk analysis. Risk Analysis, 35(7), 1230-1251.

Mooney, C. J., Elliot, A. J., Douthit, K. Z., Marquis, A., & Seplaki, C. L. (2016).

Perceived control mediates effects of socioeconomic status and chronic stress on

physical frailty: findings from the health and retirement study. The Journals of

Gerontology: Series B.

Moorman, S. M., & Carr, D. (2008). Spouses' effectiveness as end-of-life health care

surrogates: accuracy, uncertainty, and errors of overtreatment or

undertreatment. The Gerontologist, 48(6), 811-819.

Morton, J. M. (2017). Reasoning under Scarcity. Australasian Journal of

Philosophy, 95(3), 543-559.

Mullainathan, S., & Shafir, E. (2013). Scarcity: Why having too little means so much.

Macmillan. Musil, C. M., Jones, S. L., & Warner, C. D. (1998). Structural equation modeling and its

relationship to multiple regression and factor analysis. Research in Nursing &

Health, 21(3), 271-281.

Neupert, S. D., Almeida, D. M., & Charles, S. T. (2007). Age differences in reactivity to

daily stressors: The role of personal control. The Journals of Gerontology Series

B: Psychological Sciences and Social Sciences, 62(4), P216-P225.

Nicholls, N., & Zimper, A. (2015). Subjective life expectancy. The Encyclopedia of

Adulthood and Aging, 1-5.

Nunnally Jr, J. C. (1970). Introduction to psychological measurement.

Ouwehand, C., de Ridder, D. T., & Bensing, J. M. (2007). A review of successful aging

models: Proposing proactive coping as an important additional strategy. Clinical

psychology review, 27(8), 873-884.

Palgi, Y. (2013). Ongoing cumulative chronic stressors as predictors of well-being in the

second half of life. Journal of Happiness Studies, 14(4), 1127-1144.

Parker, A. M., Bruine de Bruin, W., & Fischhoff, B. (2015). Negative decision outcomes

are more common among people with lower decision-making competence: an

item-level analysis of the Decision Outcome Inventory (DOI). Frontiers in

psychology, 6, 363.

Parker, A. M., & Fischhoff, B. (1999). Individual differences in decision-making

competence. In annual meeting of the American Economic Association, New

York, NY. Parker, A. M., & Fischhoff, B. (2005). Decision‐making competence: External validation

through an individual‐differences approach. Journal of Behavioral Decision

Making, 18(1), 1-27.

Payne, K. L., Prentice-Dunn, S., & Allen, R. S. (2009). A comparison of two

interventions to increase completion of advance directives. Clinical

Gerontologist, 33(1), 49-61.

Pearlin, L. I., & Schooler, C. (1978). The structure of coping. Journal of health and

social behavior, 2-21.

Pearlin, L. I., Menaghan, E. G., Lieberman, M. A., & Mullan, J. T. (1981). The stress

process. Journal of Health and Social behavior, 337-356.

Pearlin, L. I., Schieman, S., Fazio, E. M., & Meersman, S. C. (2005). Stress, health, and

the life course: Some conceptual perspectives. Journal of Health and Social

Behavior, 46(2), 205-219.

Pearlin, L. I. (2009). The life course and the stress process: Some conceptual

comparisons. Journals of Gerontology Series B: Psychological Sciences and

Social Sciences, 65(2), 207-215

Gillespie, D. F., & Perron, B. E. (2015). Key concepts in measurement. Pocket Guides to

Social Work R.

Perrotin, A., La Joie, R., de La Sayette, V., Barré, L., Mézenge, F., Mutlu, J., ... &

Chételat, G. (2017). Subjective cognitive decline in cognitively normal elders

from the community or from a memory clinic: differential affective and

imaging correlates. Alzheimer's & Dementia, 13(5), 550-560. Pertl, M. T., Benke, T., Zamarian, L., Martini, C., Bodner, T., Karner, E., & Delazer, M.

(2014). Do patients with mild cognitive impairment understand numerical health

information?. Journal of Alzheimer's Disease, 40(3), 531-540.

Peters, E., Hess, T. M., Auman, C., & Västfjäll, D. (2007). Adult age differences in dual

information processes and their influence on judgments and decisions: A

review. Perspectives on Psychological Science, 2, 1-23.

Peters, E., & Levin, I. P. (2008). Dissecting the risky-choice framing effect: Numeracy as

an individual-difference factor in weighting risky and riskless options.

Peters, E., Hart, P. S., & Fraenkel, L. (2011). Informing patients: the influence of

numeracy, framing, and format of side effect information on risk

perceptions. Medical Decision Making, 31(3), 432-436.

Phillips, W. J., Fletcher, J. M., Marks, A. D., & Hine, D. W. (2016). Thinking styles and

decision making: A meta-analysis. Psychological Bulletin, 142(3), 260.

Pinquart, M. (2002). Creating and maintaining purpose in life in old age: A meta-

analysis. Ageing international, 27(2), 90-114.

Plassman, B. L., Langa, K. M., Fisher, G. G., Heeringa, S. G., Weir, D. R., Ofstedal, M.

B., ... & Steffens, D. C. (2008). Prevalence of cognitive impairment without

dementia in the United States. Annals of internal medicine, 148(6), 427-434.

Polit, D. F., & Beck, C. T. (2012). Nursing research: Principles and methods. Lippincott

Williams & Wilkins.

Prenda, K. M., & Lachman, M. E. (2001). Planning for the future: a life management

strategy for increasing control and life satisfaction in adulthood. Psychology and

aging, 16(2), 206. Queen, T. L., & Hess, T. M. (2017). Linkages between resources, motivation, and

engagement in everyday activities.

Rabin, L. A., Smart, C. M., & Amariglio, R. E. (2017). Subjective cognitive decline in

preclinical Alzheimer's disease. Annual review of clinical psychology, 13, 369-

396.

Reed, A. E., & Carstensen, L. L. (2012). The theory behind the age-related positivity

effect. Frontiers in psychology, 3, 339.

Reed, A. E., & Carstensen, L. L. (2015). Age-related positivity effect and its implications

for social and health gerontology. Encyclopedia of Geropsychology, 1-9.

Reed, A. E., Chan, L., & Mikels, J. A. (2014). Meta-analysis of the age-related positivity

effect: age differences in preferences for positive over negative

information. Psychology and aging, 29(1), 1.

Rehmert, A. E., & Kisley, M. A. (2013). Can older adults resist the positivity effect in

neural responding? The impact of verbal framing on event-related brain potentials

elicited by emotional images. Emotion, 13(5), 949.

Reid, L. M., & MacLullich, A. M. (2006). Subjective memory complaints and cognitive

impairment in older people. Dementia and geriatric cognitive disorders, 22(5-6),

471-485.

Reisberg, B., Shulman, M. B., Torossian, C., Leng, L., & Zhu, W. (2010). Outcome over

seven years of healthy adults with and without subjective cognitive

impairment. Alzheimer's & Dementia, 6(1), 11-24. Reyna, V. F., & Brainerd, C. J. (1991). Fuzzy‐trace theory and framing effects in choice:

Gist extraction, truncation, and conversion. Journal of Behavioral Decision

Making, 4(4), 249-262.

Reyna, V. F. (2004). How people make decisions that involve risk: A dual-processes

approach. Current directions in psychological science, 13(2), 60-66.

Reyna, V. F., & Brainerd, C. J. (2007). The importance of mathematics in health and

human judgment: Numeracy, risk communication, and medical decision

making. Learning and Individual Differences, 17(2), 147-159.

Reyna, V. F., Nelson, W. L., Han, P. K., & Dieckmann, N. F. (2009). How numeracy

influences risk comprehension and medical decision making. Psychological

bulletin, 135(6), 943.

Reyna, V. F., & Brainerd, C. J. (2011). Dual processes in decision making and

developmental neuroscience: A fuzzy-trace model. Developmental Review, 31(2-

3), 180-206.

Reyna, V. F. (2012). A new intuitionism: Meaning, memory, and development in Fuzzy-

Trace Theory. Judgment and Decision making.

Rotter, J. B. (1990). Internal versus external control of reinforcement: A case history of a

variable. American psychologist, 45(4), 489.

Roux, C., Goldsmith, K., Blair, S., & Kim, J. K. (2014). When Those Who Have the

Least Spend the Most: Understanding the Relationship Between Resource

Scarcity, Socioeconomic Status and Materialism. ACR North American Advances. Ryan, R. M., & Deci, E. L. (2001). On happiness and human potentials: A review of

research on hedonic and eudaimonic well-being. Annual review of

psychology, 52(1), 141-166.

Ryff, C. D. (1989). Happiness is everything, or is it? Explorations on the meaning of

psychological well-being. Journal of personality and social psychology, 57(6),

1069.

Ryff, C. D. (1995). Psychological well-being in adult life. Current directions in

psychological science, 4(4), 99-104.

Ryff, C. D., & Keyes, C. L. M. (1995). The structure of psychological well-being

revisited. Journal of personality and social psychology, 69(4), 719.

Ryff, C. D., & Singer, B. H. (2008). Know thyself and become what you are: A

eudaimonic approach to psychological well-being. Journal of happiness

studies, 9(1), 13-39.

Ryff, C. D. (2014). Psychological well-being revisited: Advances in the science and

practice of eudaimonia. Psychotherapy and psychosomatics, 83(1), 10-28.

Sadowski, C. J., & Gulgoz, S. (1992). Internal consistency and test-retest reliability of the

Need for Cognition Scale. Perceptual and Motor Skills, 74(2), 610-610.

Sakaki, M., Nga, L., & Mather, M. (2013). Amygdala Functional Connectivity with

Medial Prefrontal Cortex at Rest Predicts the Positivity Effect in Older Adultsʼ

Memory. Journal of Cognitive Neuroscience, 25(8), 1206-1224.

Salthouse, T. A., & Pink, J. E. (2008). Why is working memory related to fluid

intelligence?. Psychonomic bulletin & review, 15(2), 364-371. Samanez-Larkin, G. R., Gibbs, S. E., Khanna, K., Nielsen, L., Carstensen, L. L., &

Knutson, B. (2007). Anticipation of monetary gain but not loss in healthy older

adults. Nature neuroscience, 10(6), 787.

Samanez-Larkin, G. R., Robertson, E. R., Mikels, J. A., Carstensen, L. L., & Gotlib, I. H.

(2014). Selective attention to emotion in the aging brain.

Sanders, J. J., Robinson, M. T., & Block, S. D. (2016). Factors impacting advance care

planning among African Americans: results of a systematic integrated

review. Journal of palliative medicine, 19(2), 202-227.

Sanders, J. J., Johnson, K. S., Cannady, K., Paladino, J., Ford, D. W., Block, S. D., &

Sterba, K. R. (2018). From Barriers to Assets: Rethinking factors impacting

advance care planning for African Americans. Palliative & supportive care, 1-8.

Schiebener, J., Wegmann, E., Gathmann, B., Laier, C., Pawlikowski, M., & Brand, M.

(2014). Among three different executive functions, general executive control

ability is a key predictor of decision making under objective risk. Frontiers in

psychology, 5, 1386.

Scheier, M. E., & Carver, C. S. (1987). Dispositional optimism and physical well‐being:

The influence of generalized outcome expectancies on health. Journal of

personality, 55(2), 169-210.

Scheier, M. F., & Carver, C. S. (1985). Optimism, coping, and health: assessment and

implications of generalized outcome expectancies. Health psychology, 4(3), 219.

Scheier, M. F., Carver, C. S., & Bridges, M. W. (1994). Distinguishing optimism from

neuroticism (and trait anxiety, self-mastery, and self-esteem): a reevaluation of the Life Orientation Test. Journal of personality and social psychology, 67(6),

1063.

Scott, S. B., Graham-Engeland, J. E., Engeland, C. G., Smyth, J. M., Almeida, D. M.,

Katz, M. J., ... & Sliwinski, M. J. (2015). The effects of stress on cognitive aging,

physiology and emotion (ESCAPE) project. BMC psychiatry, 15(1), 146.

Segerstrom, S. C., Evans, D. R., & Eisenlohr-Moul, T. A. (2011). Optimism and

pessimism dimensions in the Life Orientation Test-Revised: Method and

meaning. Journal of Research in Personality, 45(1), 126-129.

Seligman, M. E. (2006). Learned optimism: How to change your mind and your life.

Vintage.

Shah, A. K., Mullainathan, S., & Shafir, E. (2012). Some consequences of having too

little. Science, 338(6107), 682-685.

Shah, A. K., Shafir, E., & Mullainathan, S. (2015). Scarcity frames value. Psychological

Science, 26(4), 402-412.

Shalowitz, D. I., Garrett-Mayer, E., & Wendler, D. (2006). The accuracy of surrogate

decision makers: a systematic review. Archives of internal medicine, 166(5), 493-

497.

Shepperd, J. A., Emanuel, A. S., Dodd, V. J., & Logan, H. L. (2016). The reliability of

psychological instruments in community samples: A cautionary note. Journal of

health psychology, 21(9), 2033-2041.

Schmutte, P. S., & Ryff, C. D. (1997). Personality and well-being: reexamining methods

and meanings. Journal of Personality and Social Psychology, 73(3), 549. Shum, D. H., McFarland, K. A., & Bain, J. D. (1990). Construct validity of eight tests of

attention: Comparison of normal and closed head injured samples. The Clinical

Neuropsychologist, 4(2), 151-162.

Silveira, M. J., Kim, S. Y., & Langa, K. M. (2010). Advance directives and outcomes of

surrogate decision making before death. New England Journal of

Medicine, 362(13), 1211-1218.

Silveira, M. J., Wiitala, W., & Piette, J. (2014). Advance directive completion by elderly

Americans: a decade of change. Journal of the American Geriatrics

Society, 62(4), 706-710.

Simon, A. F., Fagley, N. S., & Halleran, J. G. (2004). Decision framing: Moderating

effects of individual differences and cognitive processing. Journal of Behavioral

Decision Making, 17(2), 77-93.

Slyer, J. T., Archibald, E., Moyo, F., & Truglio-Londrigan, M. (2018). Advance care

planning and anticipatory decision making in patients with Alzheimer

disease. The Nurse Practitioner, 43(6), 23-31.

Sloan, F. A., & Wang, J. (2005). Disparities among older adults in measures of cognitive

function by race or ethnicity. The Journals of Gerontology Series B:

Psychological Sciences and Social Sciences, 60(5), P242-P250.

Smith, S. M., & Levin, I. P. (1996). Need for cognition and choice framing

effects. Journal of Behavioral Decision Making, 9(4), 283-290.

Smith, J., Fisher, G., Ryan, L., Clarke, P., House, J., & Weir, D. (2013). Psychosocial and

lifestyle questionnaire. Survey Research Center, Institute for Social Research. Sonnega, A., Faul, J. D., Ofstedal, M. B., Langa, K. M., Phillips, J. W., & Weir, D. R.

(2014). Cohort profile: The health and retirement study (HRS). International

journal of epidemiology, 43(2), 576-585.

Sörensen, S., Hirsch, J. K., & Lyness, J. M. (2014). Optimism and planning for future

care needs among older adults. GeroPsych.

Soubelet, A., & Salthouse, T. A. (2017). Does need for cognition have the same meaning

at different ages?. Assessment, 24(8), 987-998.

Springer, K. W., & Hauser, R. M. (2006). An assessment of the construct validity of

Ryff’s scales of psychological well-being: Method, mode, and measurement

effects. Social science research, 35(4), 1080-1102.

Springer, K. W., Pudrovska, T., & Hauser, R. M. (2011). Does psychological well-being

change with age? Longitudinal tests of age variations and further exploration of

the multidimensionality of Ryff’s model of psychological well-being. Social

Science Research, 40(1), 392-398.

Stanovich, K. E., & West, R. F. (2008). On the relative independence of thinking biases

and cognitive ability. Journal of personality and social psychology, 94(4), 672.

E. Stanovich, K., & West, R. F. (1998). Individual differences in framing and conjunction

effects. Thinking & Reasoning, 4(4), 289-317.

Stephan, Y., Sutin, A. R., Luchetti, M., & Terracciano, A. (2016). Feeling older and the

development of cognitive impairment and dementia. Journals of Gerontology

Series B: Psychological Sciences and Social Sciences, 72(6), 966-973. Strough, J., Karns, T. E., & Schlosnagle, L. (2011). Decision‐making heuristics and

biases across the life span. Annals of the New York Academy of Sciences, 1235(1),

57-74.

Strough, J., Bruin, W. B. D., & Peters, E. (2015). New perspectives for motivating better

decisions in older adults. Frontiers in Psychology, 6, 783.

Strough, J., Parker, A. M., & de Bruin, W. B. (2015). Understanding life-span

developmental changes in decision-making competence. In Aging and Decision

Making (pp. 235-257).

Sudore, R. L., Schillinger, D., Knight, S. J., & Fried, T. R. (2010). Uncertainty about

advance care planning treatment preferences among diverse older adults. Journal

of health communication, 15(S2), 159-171.

Sudore, R. L., & Fried, T. R. (2010). Redefining the “planning” in advance care planning:

preparing for end-of-life decision making. Annals of internal medicine, 153(4),

256-261.

Sudore, R. L., Stewart, A. L., Knight, S. J., McMahan, R. D., Feuz, M., Miao, Y., &

Barnes, D. E. (2013). Development and validation of a questionnaire to detect

behavior change in multiple advance care planning behaviors. PLoS One, 8(9),

e72465.

Sudore, R. L., Knight, S. J., McMahan, R. D., Feuz, M., Farrell, D., Miao, Y., & Barnes,

D. E. (2014). A novel website to prepare diverse older adults for decision making

and advance care planning: a pilot study. Journal of pain and symptom

management, 47(4), 674-686. Sudore, R. L., Lum, H. D., You, J. J., Hanson, L. C., Meier, D. E., Pantilat, S. Z., ... &

Kutner, J. S. (2017). Defining advance care planning for adults: a consensus

definition from a multidisciplinary Delphi panel. Journal of pain and symptom

management, 53(5), 821-832.

Sudore R. L., Heyland, D. K., Lum, H. D., Rietjens, J. A., Korfage, I. J., Ritchie, C. S., ...

& Howard, M. (2018). Outcomes that define successful advance care planning: a

Delphi panel consensus. Journal of pain and symptom management, 55(2), 245-

255.

Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics. Allyn &

Bacon/Pearson Education.

Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of

choice. science, 211(4481), 453-458.

Tversky, A., & Kahneman, D. (1986). Rational choice and the framing of

decisions. Journal of business, S251-S278.

Tversky, A., & Kahneman, D. (1986). The framing of decisions and the evaluation of

prospects. In Studies in Logic and the Foundations of Mathematics (Vol. 114, pp.

503-520). Elsevier. van Harten, A. C., Mielke, M. M., Swenson-Dravis, D. M., Hagen, C. E., Edwards, K. K.,

Roberts, R. O., ... & Petersen, R. C. (2018). Subjective cognitive decline and risk

of MCI: the Mayo Clinic Study of Aging. Neurology, 91(4), e300-e312.

Van’t Riet, J., Cox, A. D., Cox, D., Zimet, G. D., De Bruijn, G. J., Van den Putte, B., ...

& Ruiter, R. A. (2016). Does perceived risk influence the effects of message framing? Revisiting the link between prospect theory and message

framing. Health psychology review, 10(4), 447-459.

Van Walraven, C., Oake, N., Jennings, A., & Forster, A. J. (2010). The association

between continuity of care and outcomes: a systematic and critical review.

Journal of evaluation in clinical practice, 16(5), 947-956.

Wang, Y., Highhouse, S., Lake, C. J., Petersen, N. L., & Rada, T. B. (2017). Meta‐

analytic investigations of the relation between intuition and analysis. Journal of

Behavioral Decision Making, 30(1), 15-25.

Warner, R. M. (2014). Applied statistics 2e+ SPSS version 22.0. Sage Publications.

Weathers, E., O’Caoimh, R., Cornally, N., Fitzgerald, C., Kearns, T., Coffey, A., ... &

Molloy, D. W. (2016). Advance care planning: A systematic review of

randomised controlled trials conducted with older adults. Maturitas, 91, 101-109.

Weathers, E., O’Caoimh, R., Cornally, N., Fitzgerald, C., Kearns, T., Coffey, A., ... &

Molloy, D. W. (2016). Advance care planning: A systematic review of

randomised controlled trials conducted with older adults. Maturitas, 91, 101-109.

West, R. F., Meserve, R. J., & Stanovich, K. E. (2012). Cognitive sophistication does not

attenuate the bias blind spot. Journal of personality and social psychology,

103(3), 506.

Westbrook, A., & Braver, T. S. (2015). Cognitive effort: A neuroeconomic

approach. Cognitive, Affective, & Behavioral Neuroscience, 15(2), 395-415.

Wicks, M. N., Alejandro, J., Bertrand, D., Boyd, C. J., Coleman, C. L., Haozous, E., ... &

Meek, P. M. (2018). Achieving advance care planning in diverse, underserved

populations. Nursing outlook, 66(3), 311-315. Wilhelms, E. A., & Reyna, V. F. (Eds.). (2014). Neuroeconomics, judgment, and decision

making. Psychology Press.

Winter, L., & Parker, B. (2007). Current health and preferences for life-prolonging

treatments: An application of prospect theory to end-of-life decision

making. Social science & medicine, 65(8), 1695-1707.

Witteman, C., van den Bercken, J., Claes, L., & Godoy, A. (2009). Assessing rational and

intuitive thinking styles. European Journal of Psychological Assessment, 25(1),

39-47.

Wolf, S. M., Boyle, P., Callahan, D., Fins, J. J., Jennings, B., Nelson, J. L., Barondess,

J.A., Brock, D.W., Dresser, R., Emanuel, L. & Johnson, S. (1991). Sources of

concern about the patient self-determination act. New England Journal of

Medicine, 325(23), 1666-1671.

Woodhead, E. L., Lynch, E. B., & Edelstein, B. A. (2011). Decisional strategy

determines whether frame infuences treatment preferences for medical

decisions. Psychology and Aging, 26(2), 285.

Wrosch, C., Heckhausen, J., & Lachman, M. E. (2000). Primary and secondary control

strategies for mnaging health and financial stress across adulthood. Psychology

and Aging, 15(3), 387.

Yadav, K. N., Gabler, N. B., Cooney, E., Kent, S., Kim, J., Herbst, N., ... & Courtright,

K. R. (2017). Approximately one in three US adults completes any type of

advance directive for end-of-life care. Health Affairs, 36(7), 1244-1251.

Zafar, S. Y., Peppercorn, J. M., Schrag, D., Taylor, D. H., Goetzinger, A. M., Zhong, X.,

& Abernethy, A. P. (2013). The financial toxicity of cancer treatment: a pilot study assessing out-of-pocket expenses and the insured cancer patient's

experience. The Oncologist, 18(4), 381-390.

Zafar, S. Y., & Abernethy, A. P. (2013). Financial toxicity, part I: a new name for a

growing problem. Oncology (Williston Park, NY), 27(2), 80.

Zauszniewski, J. A. (2016). Resourcefulness. Western Journal of Nursing Research,

38(12), 1551–1553. https://doi.org/10.1177/0193945916665079

Zhao, X., Huang, C., Li, X., Zhao, X., & Peng, J. (2015). Dispositional optimism, self‐

framing and medical decision‐making. International Journal of

Psychology, 50(2), 121-127.