Positive and Negative Affect in Cognitive Behavioral Therapy for Depression

Home , Abnormal psychology

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Arts. In

The Graduate School of The Ohio State University

Megan L. Whelen, B.S.

Graduate Program in Psychology

The Ohio State University

2020

Thesis Committee:

Dr. Daniel R. Strunk, Advisor

Dr. Jennifer S. Cheavens

Dr. Laura Wagner

Megan L. Whelen

2020

ABSTRACT

Patients with major depressive disorder (MDD) tend to present with low positive affect (PA) and high negative affect (NA). Some have proposed that current psychotherapeutic approaches, such as cognitive behavioral therapy (CBT), treat NA while neglecting PA, and that targeting PA would provide additional improvement of depressive symptoms. However, few treatment studies have tracked NA and PA longitudinally to evaluate their role in CBT. In this study, I will test the relative importance of PA and NA for symptom improvement. In addition to affect, cognitive change is also thought to be important for symptom change in CBT, so I will also examine the relative importance of affect and cognitive change for symptom change. Further, cognitive change is thought to be an important driver, not only of symptom change during CBT, but also of affect change. That relationship may be reciprocal; mood reactivity studies suggest a predictive relationship between negative mood and distorted cognitions. The relation between affect and cognitive change has received little empirical attention. Accordingly, I will test the effects of cognitive change on PA and NA and the effects of PA and NA on cognitive change. Findings from this study will help to elucidate the role of positive and negative affect as well as cognitive change in CBT for depression.

Vita

2016……………………………………………………...B.S. Psychology, University of Houston

2018 to Present………...... Graduate Student, Department of

Psychology, The Ohio State University

Publications

Fitzpatrick, O. M., Whelen, M. L., Falkenström, F., & Strunk, D. R. (2020). Who benefits the

most from cognitive change in cognitive therapy of depression? A study of interpersonal

factors. Journal of Consulting and Clinical Psychology, 88, 128-136. doi:

10.1037/ccp0000463

Robinson-Whelen, S., Taylor, H.B., Feltz, M., & Whelen, M. (2016). Loneliness among people

with spinal cord injury: Exploring the psychometric properties of the 3item loneliness

scale. Archives of Physical Medicine and Rehabilitation, 97, 1728-1734. doi:

10.1016/j.apmr.2016.04.008

Fields of Study

Major Field: Psychology

TABLE OF CONTENTS

Abstract ...... i Vita ...... ii List of Tables ...... iv List of Figures ...... v 1. Introduction ...... 1 1.1 Positive and Negative Affect ...... 2 1.2 The Efficacy of CBT ...... 6 1.3 CBT Procedures ...... 7 1.4 Cognitive Change ...... 8 1.5 Potential Benefits of Negative Affect in Psychotherapy ...... 11 1.6 Cognitive Change and Affect ...... 14 1.7 Methodological Considerations ...... 16 1.7.1 Intervals Between Assessments ...... 16 1.7.2 Disaggregating Within and Between-Person Variance...... 18 1.7.3 Lagged Dependent Variables ...... 18 1.8 This Study ...... 20 2. Methods...... 22 2.1 Participants ...... 22 2.2 Therapists ...... 23 2.3 Measures ...... 23 2.3.1 Affect ...... 22 2.3.2 Depressive Symptoms ...... 23 2.3.3 Cognitive Change...... 23 2.4 Analytic Strategy ...... 24 3. Results ...... 28 4. Discussion ...... 32 References ...... 39 Appendix A: Tables and Figures ...... 51 Figure 1 ...... 51 Figure 2 ...... 52 Figure 3 ...... 53 Figure 4 ...... 54 Figure 5 ...... 55 Figure 6 ...... 56 Figure 7 ...... 57 Table 1 ...... 58 Table 2 ...... 59 Appendix B: Model Details ...... 60

iii

List of Tables Table 1. Within-Person Correlations Among Variables ...... 58

Table 2. Between-Person Correlations Among Variables ...... 59

List of Figures Figure 1. Representation of the RDSEM Model...... 51 Figure 2. Depressive Symptoms at each Session...... 52 Figure 3. Cognitive Change at each Session...... 53 Figure 4. Positive Affect at Pre- and Post-Session ...... 54 Figure 5. Positive Affect Difference Scores from Pre- to Post-Session with CIs...... 55 Figure 6. Negative Affect at Pre- and Post-Session ...... 56 Figure 7. Negative Affect Difference Scores from Pre- to Post-Session with CIs ...... 57

Introduction

Cognitive behavioral therapy (CBT) for depression is an efficacious treatment that utilizes cognitive and behaviorally-oriented change strategies. Cognitive change strategies are a central part of the treatment, and these strategies, as their name implies, are thought to elicit cognitive changes, which in turn improve patients’ moods (Beck, Rush, Shaw, & Emery, 1979;

Lorenzo-Luaces, German, & DeRubeis, 2015). In general, it appears that cognitive change predicts symptom change, but its relation to affect change is unknown.

Some have recently taken an interest in positive affect (PA)-focused treatments as a way to improve outcomes (e.g., Craske et al., 2019; Hofmann et al., 2015; Dunn, Widnall, et al.,

2019). However, little is known about the effect current psychotherapeutic approaches have on

PA relative to negative affect (NA), or whether efforts to target PA more directly would lead to additional symptom change. Other treatment developers have suggested that there are sometimes benefits to increasing a patient’s NA in-session (e.g., Hayes et al., 2007). Mood reactivity studies suggest that there is a relationship between affect and cognitive change. These studies use an artificial negative mood induction paradigm. When events in a patient’s life affect their mood, cognitive change and affect may be even more strongly related, as the content of their thoughts may be directly tied to the negatively perceived events.

There are a number of methodological challenges in psychotherapy process research

(Pfeifer & Strunk, 2015). The frequency of assessments should be carefully chosen to correspond with the hypothesized time course over which the effects of interest are hypothesized to be taking place. Further, it is ideal to choose a modeling approach that allows the researcher to control for prior values of the dependent variable (Hamaker, Kuiper, & Grasman, 2015; Allison, 1

Williams, & Moral-Benito, 2017) and to disaggregate within- and between-person variance.

Disaggregating within- and between-person variance ensures that the observed relation between variables is not confounded by any stable between-person characteristics. This increases the likelihood that the effects observed are causal.

In this study, I investigate key relations among cognitive change, positive and negative affect, and depressive symptoms in a sample of patients participating in CBT for depression.

First, I will discuss affect in psychotherapy; then I will review the procedures of CBT, the literature on cognitive change, and existing literature examining both affect and cognitive change. Following that will be a consideration of methodological challenges such as choosing an appropriate time interval in discrete time modeling and controlling for prior values of the dependent variable without biasing model estimates.

Positive and Negative Affect

Across a large number of studies, two factors have consistently emerged in the measurement of mood: PA and NA (Watson & Tellegen, 1985). Patients with major depressive disorder (MDD) often present with low PA and high NA. There is an association between blunted neural response to rewards and low PA (Keedwell, Andrew, Williams, Brammer, &

Phillips, 2005; Shankman et al., 2013). This may be because reward responding and PA both rely on a neural network responsible for the production of dopamine (Weinberg, Liu, Hajcak, &

Shankman, 2015). Additionally, depressive symptoms are often related to greater NA and more decreased PA in response to stressors. In one study of depressed outpatients, higher depression scores predicted greater NA and negative cognitions the day after experiencing interpersonal

stress (Gunthert, Cohen, Butler, & Beck, 2007). Additionally, NA responses to negative events had a longer duration in MDD participants relative to healthy controls in an experience sampling study (Peeters, Nicolson, Berkhof, Delespaul, & deVries, 2003). The duration was even longer in participants with a family history of MDD or among those who had been in a depressive episode for a longer period. Further, higher levels of depression predicted muted PA reactions to high coping efficacy ratings among college students (Gunthert, Cohen, & Armeli, 2002). Stronger NA and PA responses to interpersonal stressors in a daily diary study were also shown to predict the development of depressive symptoms among college students (O’Neill, Cohen, Tolpin, &

Gunthert, 2004). Taken together, these findings suggest that depression is associated with having a greater affective response to stressors.

Affect has been shown to improve over the course of treatment. A meta-analysis aggregating ten psychotherapy studies of depressed patients examined change in PA and NA from pre- to post-treatment (Boumparis, Karyotaki, Kleiboer, Hofmann, & Cuijpers, 2016). In each study, patients were randomly assigned to a treatment condition or a non-active control group. PA improves over the course of psychotherapy relative to control groups (g = .41). NA improves as well (g = .32). While this estimate is numerically smaller than the one for PA, the estimates are not entirely comparable. In calculating change in NA, the authors excluded one of the ten studies because it reported a much larger change in NA than the other studies. Given the small number of psychotherapy studies that measure PA and NA, further research is needed.

Deficient reward responding runs in families and does not depend on the presence of current depressive symptoms. Weinberg et al. (2015) measured PA and neural response to

rewards in sibling pairs with differing levels of depressive symptoms. They hypothesized that individuals with depressive symptoms and low PA will have siblings with blunted reward responding. They found that the sibling with lower depressive symptoms displayed more attenuated response to rewards if the more depressed sibling was lower in PA; this association remained after controlling for PA and depressive symptoms of the less symptomatic sibling. The authors suggest that blunted neural response to rewards is familial. Therefore, it represents a vulnerability factor for depression and a promising treatment target. Treatment developers have recently come to a similar conclusion. Several researchers have proposed new treatments that are designed to have a greater emphasis on increasing PA. Craske and colleagues (2019) tested a brief PA psychotherapy treatment against a novel NA treatment in a sample with clinically elevated depression or anxiety. The PA treatment was successful, if not PA-specific; results suggested that the PA treatment was more effective at improving NA, PA, and depressive symptoms than the NA treatment. Similarly, Hofmann et al. (2015) ran two small uncontrolled studies testing Loving-Kindness Meditation as a treatment to enhance PA. Patients with both dysthymia and persistent depressive disorder exhibited improvements in PA, NA, and depressive symptoms (symptoms changed by d = 3.33 in one study and d = 1.90 in the other). Dunn,

Widnall, et al. (2019) likewise ran an uncontrolled study of an Augmented Depression Therapy treatment with a focus on PA and found it to be effective at improving patients’ NA and PA.

There has been an increased emphasis on PA-focused treatments because researchers have hypothesized that current depression treatments, such as CBT and antidepressant medication (ADM), are more effective at improving NA than PA. It is worth considering how

best to empirically address this hypothesis. One option is to evaluate the amount of change in affect that patients experience in treatment relative to a control group, as in the meta-analysis by

Boumparis and colleagues (2016). The effect sizes they obtained for NA and PA appear similar, so perhaps psychotherapy targets NA and PA to a similar extent. Another approach is the one taken by Dunn, German, and colleagues (2019). They assessed how much affect was disturbed in two depressed samples before treatment by comparing their scores on the Positive and Negative

Affect Scale (Watson, Clark, & Tellegen, 1988) to normative data. PA was more disturbed in depressed participants than NA. After treatment with antidepressant medication, CBT, or a combination of both, PA improved to a lesser extent than NA, and was therefore also more disturbed than NA at post-treatment. This suggests that depression treatments are less effective at increasing PA than at decreasing NA. The authors conclude this is a deficiency in current treatments, and more should be done to target PA.

However, the relative impact of PA compared to NA on symptom change is still unknown. One possibility is that targeting one type of affect in treatment would be more helpful for facilitating symptom change than targeting the other. Another possibility is that targeting either type of affect would yield equivalent results. Dunn, German and colleagues (2019) found that changes in PA and NA from pre to post-treatment were each associated with changes in depressive symptoms over this same period. However, whether affect predicts subsequent symptoms or symptoms predict subsequent affect, or both, is still unknown. To return to the meta-analysis mentioned above, Boumparis and colleagues (2016) also assessed the association between affect change and symptom change; however, one of the ten studies included had

markedly larger associations between affect and symptom change than the others. They obtained differing results based on whether this study was included in the meta-analysis, and so they failed to come to a conclusion about the relation of affect and symptoms. Another issue is that affect and symptoms likely change on a relatively brief time scale. Session-to-session analyses may be able to more effectively capture the potential influence of affect on symptom change within a patient over the course of treatment. It is clear that more investigation is needed to determine whether each affect variable predicts depressive symptoms and the strength of these relationships. The present study aims to answer these questions using a sample of patients who completed a course of CBT. I have decided to focus on CBT because it is one of the most studied treatments for depression and also a treatment that has been suggested to target NA more than

PA. Prior to considering the role of affect in CBT, I first review CBT procedures and efficacy and current research on mechanisms of treatment.

The Efficacy of CBT

CBT is an effective treatment for MDD. Clinical trials have demonstrated its efficacy by comparing it to placebo or to an active treatment, most commonly ADM. The Treatment of

Depression Collaborative Research Program was an early clinical trial that compared four treatments for depression: Interpersonal Psychotherapy, CBT, ADM, and placebo (Elkin et al.,

1989). All active treatment conditions were superior to placebo and equivalent to each other at post-treatment. However, Elkin and colleagues (1995) analyzed the data of the more severe depressed patients and found that ADM and interpersonal psychotherapy were more effective for these patients than CBT. Because of this, the APA recommended a pharmacotherapeutic

approach for severe patients (Karasu, Gelenberg, Merriam, & Wang, 2000). However, DeRubeis,

Gelfand, Tang, and Simons (1999) pooled the data for severe patients from four studies and found that across clinical trials, CBT and ADM did not differ in their efficacy. More recent meta- analytic evidence supports the conclusion that CBT and ADM are equivalent at post-treatment

(Cuijpers, Berking, et al., 2013). CBT retains some of its effects after treatment discontinuation as well. Hollon et al. (2005) found that patients in the previously mentioned trial had lower rates of relapse in the CBT condition relative to discontinued ADM and comparable rates of relapse to continued ADM. Therefore, CBT is superior to placebo conditions and at least as effective as other active treatments such as ADM.

CBT Procedures

This paper is concerned with changes in affect, cognition, and depressive symptoms that take place over the course of a session of CBT, or from one session to the next. The relation between these variables may be of an exceptionally brief duration and may only be apparent while examining them in the course of the same session. Alternatively, the effects of a therapy session on a patient may be sustained to the start of the next session. To understand how patients may experience such changes in or following a session of CBT, it is important to understand what commonly occurs in CBT sessions. CBT for depression was pioneered by Beck (1967) who found the prevailing psychodynamic accounts of depression to be unsatisfactory (Hollon &

Beck, 2013). Underlying Beck’s approach is a cognitive theory of depression which posits that depressed patients have negatively biased automatic thoughts and negative beliefs, which contribute to and serve to maintain their depressive symptoms. These negative thoughts include

thoughts about themselves, the future, and the world. Cognitive therapists aid patients in evaluating their negative thoughts. The procedures they use to accomplish this are outlined in

Beck and colleagues (1979) and more recently characterized by DeRubeis, Webb, Tang, and

Beck (2009). First, the therapist teaches the patient to pay attention to the content of his thoughts, particularly his beliefs and reactions to events. Then the therapist guides him in examining the evidence for those thoughts, which often results in the patient arriving at a different view. The patient’s feelings about the situation change as a result. The patient is taught to use this skill outside of therapy as well. Over time, the patient and therapist identify an underlying schema around which the patient’s negative thoughts are organized; for example, the conviction that the patient is a failure unless they do everything perfectly. Evidence for and against these core schemas can be weighed just as they can with the patient’s automatic thoughts. As the patient becomes aware of the core schema and continues to question its validity, it becomes weaker, and a more adaptive schema may replace it.

CBT also includes a focus on changing the patient’s behaviors. For example, a therapist may suggest that the patient engage in self-monitoring, in which the patient keeps an hourly record of their activities and moods. This can serve as a record of activities that improve the patient’s mood and those that worsen it, and can also challenge beliefs the patient holds, for example, that no activity will bring him a sense of enjoyment, or that he is too busy to spend time on enjoyable activities. The patient and therapist can also collaboratively schedule activities for the patient to engage in to increase the patient’s sense of pleasure and accomplishment.

Through this process, the patient is thought to come to experience improvement in his depressive symptoms (DeRubeis et al., 2009).

Cognitive Change

A number of researchers have attempted to discern whether these procedures lead to cognitive change, and whether cognitive change leads to symptom change. Ilardi and Craighead

(1994) noted that a substantial amount of symptom change occurs in the first four sessions of therapy; they argued that this was before the therapist had started using cognitive change procedures, and therefore, these procedures have little to do with a patient’s symptom improvement. To further examine this issue, Tang and DeRubeis (1999) examined a pooled data set and observed large changes in many patients’ depressive symptoms over only a one-session interval. Patients who experienced sudden symptom gains had better outcomes at post-treatment and at follow-up. They found that pregain sessions were characterized by substantial cognitive change, suggesting that the cognitive changes patients underwent partially explain their symptom changes. However, the authors found that therapists’ application of CBT techniques was not greater in pregain sessions than control sessions (the session prior to the pregain session).

Perhaps this is because specific CBT techniques did not contribute to cognitive change any more than other therapist behaviors. Alternatively, since only 16 patients were included in this analysis, it is possible that the researchers did not have enough power to detect the effect of interest.

In contrast, some findings from other studies indicate that CBT procedures do predict cognitive change. Schmidt, Pfeifer, and Strunk (2019) found that therapist adherence to cognitive

procedures predicted cognitive change and that cognitive change predicted symptom change in a sample receiving CBT for depression. This would suggest that the strategies that therapists utilize in CBT facilitate symptom improvement. Also in support of this view, Stone and Strunk

(2019) identified 62 patients from the same sample who had two consecutive sessions with markedly different amounts of cognitive change. They found that therapists’ adherence to cognitive procedures predicted whether the session was a high cognitive change or a low cognitive change session. Therefore, there is some evidence consistent with the view that cognitive change procedures may produce cognitive change, but methodological challenges have made it difficult to answer these questions definitively (Lorenzo-Luaces et al., 2015; Pfeifer &

Strunk, 2015).

It is still not known if cognitive change procedures produce more symptom change or more cognitive change than other therapeutic procedures (Lorenzo-Luaces et al., 2015). A narrative review by Garratt, Ingram, Rand, & Sawalani (2007) concluded that cognitive changes within a patient are associated with therapeutic improvement in CBT for depression, but found mixed support for the hypothesis that cognitive change is specific to CBT. Cognitive change may be important in other treatments for depression that do not employ cognitive change procedures.

Garratt and colleagues (2007) reviewed 13 studies that compared the amount of cognitive change produced by medication and by CBT. Most of the studies compared the amount of change in cognition from pre- to post-treatment following a successful course of CBT or ADM, using a variety of cognitive measures. Findings were inconsistent, with five studies finding a significant difference in favor of CBT and eight studies finding no difference. The cognitive measure

chosen did not appear to impact results, but sample size did: studies finding a difference averaged 35 participants while those finding no difference averaged 22. It is currently unclear whether cognitive changes vary depending on treatment type; additional high-quality studies will be needed to answer this question definitively. If cognitive change is important for symptom change, it is important to clarify which procedures lead to cognitive change, as this is one possible route to achieving good outcomes.

Further complicating the matter, cognitive change brought about by cognitive change procedures may have different properties than cognitive change brought about by different means, such as antidepressant medication (Lorenzo-Luaces et al., 2015). Patients who have discontinued CBT have a lower risk of relapse than those who have discontinued antidepressant medication (Cuijpers, Hollon, et al., 2013). Therefore, it is possible that cognitive change brought about by cognitive change procedures brings about symptom change that is greater or more enduring.

While this paper is not concerned with the extent to which cognitive change procedures produce cognitive change, or the extent to which cognitive change produces symptom change, it is important to understand the central role that cognitive change has played in CBT research thus far.

Potential Benefits of Negative Affect in Psychotherapy

Some researchers have hypothesized that affect change plays a central role in improvement over the course of CBT. Samoilov and Goldfried (2000) assert that in-session emotional arousal is important for therapeutic change. These authors suggest that the emotions in

which patients experience their negative thoughts should be activated while the patient and therapist are discussing these thoughts so that therapy can have its full impact. The patient’s affective state may appear rather negative; counterintuitively, this is thought to portend success in treatment. Hayes and Strauss (1998) found that more destabilization in a mid-treatment session was associated with more positive outcomes at the end of treatment. They defined destabilization as “turbulence in cognitive, affective, behavioral, and somatic functioning” which may include signs of distress from the client. Destabilization was also associated with affective intensity in the session. This is consistent with the idea that intensity of in-session affect is helpful for therapeutic outcomes.

Exposure-based Cognitive Therapy (EBCT) is a treatment designed to take full advantage of this (Hayes et al., 2007). Sessions 1-8 of EBCT are called the stress management phase. This is designed to encourage the patient to increase their use of beneficial habits. Some of these habits include learning how to improve their sleep, regulating their emotions, and increasing their social activity and physical activity. Sessions 9-18 are called the exposure-activation phase and they involve techniques designed to expose the patient to the emotions associated with their depression. Patients read over things they wrote in the early sessions about their depression, and they describe for their therapist their thoughts and feelings during their most recent depressive episode, focusing especially on their hopelessness and their view of themselves. Their

“depressive network” is activated so it can be processed fully. Therapists then help the patient generate alternative thoughts and remind them of information that is inconsistent with their negative beliefs. Sessions 19 through posttreatment are the consolidation and positive growth

phase, in which patients set goals, develop a balanced sense of self that includes positive and negative qualities, clarify their sense of purpose, and learn about relapse prevention.

Hayes et al. (2005) and Hayes et al. (2007) examined the trajectory of patients’ symptom improvement in EBCT and patients seemed to respond as expected to the treatment design.

Patients displayed a rapid early response to treatment followed by a mid-treatment increase in symptoms that aligned with the exposure-activation phase. Hayes and colleagues called this increase a depressive spike. This brief worsening of symptoms predicted better post-treatment outcome in EBCT. This is thought to be the case because patients’ apparent distress is indicative of emotional processing. Affectively charged moments in therapy show the client is overcoming avoidance of their emotions and activating the maladaptive coping strategies that are maintaining their disorder (Grosse Holtforth et al., 2019). Therefore, it stands to reason that therapists should bring these moments about to facilitate their client’s symptomatic improvement. Yasinski et al.

(2019) assessed some of these variables in a sample of patients with treatment-resistant depression receiving CBT. They found that when psychological flexibility (the ability to shift perspective, balance competing needs, and adapt to changing situations) was low, as coded by raters, more emotional processing was related to better outcomes in the follow-up period. This provides some support for the view that emotional processing may be a beneficial part of therapy, at least for some patients.

In a recent randomized controlled trial, CBT was compared to Exposure-Based Cognitive

Therapy-adapted (EBCT-R). The trial revealed no difference (as defined by change in depressive symptoms) at post-treatment or 12-month follow-up (Grosse Holtforth et al., 2019). This was

contrary to the authors’ hypothesis. They expected EBCT-R to show superiority to CBT at follow-up; in theory, emotional processing techniques would produce more sustained symptom change. However, it appears that for acute and long-term outcomes, emotional processing techniques are just as effective as CBT techniques. Since use of techniques that are designed to increase NA in patients yields equivalent symptom improvement to CBT techniques, it is still unclear what role affect plays in outcomes. The effect of in-session affect change, and the nature of its relationship with depressive symptoms, remains to be investigated. The intuitive hypothesis would be that, if affect did predict outcomes, improved affect would aid symptom improvement.

Conversely, research relevant to the depressive spike suggests that the opposite may be the case.

Patients who leave treatment in a worse mood may see the most improvement, as they are fully activating their depressive network.

Cognitive Change and Affect

Though there is a paucity of research measuring the relationship between affect and cognitive change in psychotherapy, there is some research on the relation between affect and depressive styles of thinking. The Dysfunctional Attitude Scale (DAS; Weissman & Beck, 1978;

Weissman, 1979) is a measure of depressive cognition. The items consist of negative statements that typify depressive cognitive distortions and statements indicating healthy thinking. According to Beck (1967), patients who respond to stress or negative mood with more distorted thinking are more vulnerable to depression. In accordance with this, the Temple Wisconsin project found that participants with higher DAS scores had a higher lifetime prevalence of depression and greater severity of depressive episodes (Alloy et al., 2000). Other studies have found that a greater

increase in DAS scores following a mood induction (i.e., greater cognitive reactivity) predicts relapse among formerly depressed patients (Segal et al., 2006; Segal, Gemar, & Williams, 1999).

If an increase in NA leads to more dysfunctional thoughts, this has implications for psychotherapy. If patients experience affect change in-session, they may have fewer dysfunctional thoughts or be more able to counteract dysfunctional thinking. Beevers (2005) elaborates on a similar idea in a paper proposing a dual process model of depressive cognition.

Dual process theories posit that people switch between associative and reflective processing.

Beevers suggests that when depressed patients use the associative mode of processing, which relies on quick and frequently rehearsed associations, their associations are often negatively biased. When they engage in the more effortful reflective mode of processing, they are able to correct this thinking. It is possible that patients can learn in therapy to use reflective processing to counteract their biased associative processing. In accordance with this, formerly depressed patients who complete the DAS after a negative mood induction endorse fewer negative thoughts if they were previously treated with CBT than if they had previously been treated with medication (Segal et al., 1999, 2006). This may indicate that the relation between cognitions and affect is weaker after patients have gone through CBT, so perhaps a small effect should be expected when assessing this relationship.

Theories of psychotherapy also have some bearing on the question of how affect and cognitive change are related. EBCT does not emphasize many of the cognitive change procedures, such as identifying and challenging negative thoughts, which are core to CBT. Still, there is some acknowledgement that changing the patient’s cognitions is important for symptom

improvement. EBCT is less focused on a patient’s negative automatic thoughts and more focused on challenging a patient’s schemas and beliefs about themselves. This is only thought to bring about meaningful change when patients have fully engaged with their negative emotions.

Cognitive change procedures are posited to be most effective during affectively charged moments during a therapy session (Hayes et al., 2007); therefore, NA would predict greater cognitive change. Conversely, a prediction consistent with Beck’s theory would be that cognitive change would predict less NA: when a therapist helps a patient alter their thinking during a session, this is expected to help the patient feel less negatively.

Another study was conducted that may have some bearing on the relation between affect and cognitive change. Sudden gains were investigated in a sample of 50 patients receiving CBT for depression. Clients expressed more hope in the sessions preceding sudden gains (Abel,

Hayes, Henley, & Kuyken, 2016). This may indicate that PA facilitated symptom change

(though the authors did not assess other components of PA, so the effect may be specific to hope). Increased PA may have co-occurred with large cognitive changes, as well, if, as Tang and

DeRubeis (1999) found, large cognitive changes also took place in pre-gain sessions. These studies may be describing closely related findings if PA and cognitive changes occur together in pre-gain sessions.

Methodological Considerations

When considering whether a relationship between variables in a psychotherapy study is likely to be causal, a number of methodological considerations are important. Some often- overlooked issues are discussed below.

Intervals Between Assessments. It is ideal for measurement of the predictor variable to precede measurement of the dependent variable, first because, on a basic level, causes take time to exert their effects (Gollob and Reichardt, 1987). Second, temporal precedence of the predictor variable rules out the possibility of a reverse causal relation explaining the significance between the variables. For example, if symptoms and affect are measured at the same time point, it is not clear whether symptoms are causing affect or if affect is causing symptoms. However, it is difficult to determine by how much the predictor variable should precede the dependent variable.

The strength of the relation between two variables may be determined, in part, by the lag a researcher chooses. Some researchers have called this the lag problem (e.g., Ryan, Kuiper, &

Hamaker, 2018). If a study fails to find a significant relation between two variables, it may be because those variables are unrelated, but it may also be the case that the relation would have been significant had the researchers measured those variables with a shorter or longer interval between them.

Since this is the case, what is the correct interval to use when trying to asses a potential causal relation between two or more variables? Some studies define the optimal time interval as the one that produces the strongest relationship between variables (Dormann & Griffin, 2015).

There are other innovative methods for solving the lag problem, such as continuous time modeling, in which the effects obtained are independent of the time interval between variables

(Ryan, Kuiper, & Hamaker, 2018). Another recommendation is to run models with lags that are the most plausible (Ebner-Priemer & Trull, 2009). For example, mood might be expected to fluctuate within a relatively short time window, so mood and any variables that affect it should

be collected multiple times a day so these quickly changing relationships can be assessed (Ebner-

Priemer & Trull, 2009). Other symptoms, such as sleep dysregulation, may take longer to show meaningful change. Gollob and Reichardt (1987) recommend investigating a number of intervals: “Because different time lags have different effects, one must study many different lags to understand causal effects fully” (p.82). In the present study, affect was measured at the beginning and end of each session while cognitive change was assessed at the end of each session. The relation between affect and cognitive change may be of an exceptionally brief duration (as suggested by studies of cognitive reactivity) and may only be apparent while examining both variables in the course of the same session. What the patient works on during a session may produce change in affect, and cognitive change (assessed immediately after the session) may contribute to this change in affect. Alternatively, the effects of cognitive change may last long enough that cognitive change at the end of a session has some influence on a patient’s affect at the start of the next session. The cognitive changes that the patient experienced over the course of their session may be sustained through the course of the week and predict improved affect days later. The ideal time lags for these predictions is unclear, so different lags will be investigated and their implications discussed.

Disaggregating within and between-person variance. Repeated assessment allows researchers to disaggregate within- and between-person effects (Wang & Maxwell, 2015). When repeated measures are collected, any association between variables may be driven by the between-person or the within-person variation in the measures. Between-person associations might be due to a variety of stable personal characteristics, but within-person associations are

not. While some more recent studies have disaggregated within- and between-person effects, a large portion of the psychotherapy literature consists of studies that have not done this. This leaves open the possibility that their results are confounded by stable between-person variables that influence both the predictor and dependent variable (Wang & Maxwell, 2015; Curran &

Bauer, 2011; Allison, 2014).

Lagged Dependent Variables. Some phenomena are state-dependent, in which the value of the dependent variable depends in part on the value of the dependent variable at the previous time point. There is assumed to be a causal relationship between the two. State dependence is usually modeled by including a lagged dependent variable. Some phenomena are instead related by unobserved heterogeneity. In this case, the outcome at time t is related to the outcome at time t+1 because of a third variable. Unobserved heterogeneity is usually modeled by inclusion of a random intercept. When a model includes both a lagged dependent variable and a random intercept, they are necessarily correlated with each other (Falkenström, Finkel, Sandell, Rubel, &

Holmqvist, 2017). The combination of the random intercept and the person-time error term of a model is known as the combined error term; since the lagged dependent variable is correlated with this combined error term, this violates the assumption of endogeneity, or the assumption that the predictors in a model are uncorrelated with the error term (Falkenström et al., 2017).

This often leads to upward biased estimates for the autoregressive term and downward biased estimates for other predictors in the model (Allison, 2015). Some studies instead use repeated measures models to disaggregate within-subject variance by person-mean-centering their observations and by specifying the covariance structure of the residuals. This is an alternative to

specifying random effects in the model. Unfortunately, this still does not circumvent the endogeneity bias because of the removal of between-person factors that influence both the model’s error term and the person-mean-centered lagged dependent variable (for details, see

Falkenström et al., 2017).

In the present study, data were collected at every therapy session to ascertain the estimated relationship between variables on a short time scale. Additionally, as I detail in the analytic strategy section, I utilized a modeling approach that disaggregates within- and between- person variance and that allows for inclusion of a lagged dependent variable without endogeneity bias.

This Study

This study is an examination of the relations between PA, NA, depressive symptoms, and cognitive change, using data from each session in a sample of patients receiving CBT for depression. I will investigate whether positive and negative affect predict improvement in depressive symptoms. Consistent with current thinking on the importance of affect, I predict that

PA and NA will predict BDI. Additionally, I will investigate whether cognitive change predicts affect change, which would be consistent with the cognitive model of depression. I expect that cognitive change will predict affect in the same session but not next-session affect, as changes in affect are often fleeting. I will also conduct an exploratory test of whether affect change predicts cognitive change.

A previous study showed that cognitive change predicts depressive symptoms in this sample (Fitzpatrick, Whelen, Falkenström, & Strunk, 2020). In that study, a session-to-session

modeling approach similar to the one in this paper was used to show that cognitive change predicts BDI in a sample of 125 patients receiving CBT for depression. Given these findings, I will test whether affect or cognitive change predict depressive symptoms when included in the same model. A result that only an affect variable in this model predicts depressive symptoms would be consistent with arguments that therapists should increase their focus on affect in session. If only cognitive change predicts, it would be consistent with arguments that therapists should use strategies that are intended to increase cognitive change in patients.

Methods Participants

Inclusion criteria were as follows: a) meeting criteria for MDD in the DSM-IV (APA,

2000); b) at least 18 years old; c) willing to provide informed consent. Exclusion criteria were: a) lifetime history of bipolar I or II; b) history of psychosis; c) primary diagnosis other than MDD if judged to necessitate another type of initial treatment; d) severe risk of suicide or self-harm precluding outpatient care; e) substance dependence in the past six months; f) indication of secondary gain from seeking treatment. Participants were assessed using the Structured Clinical

Interview for the Diagnostic and Statistical Manual of Mental Disorders-IV (First, Spitzer,

Gibbon, & Williams, 2002).

Participants were recruited from Columbus, Ohio and the surrounding area. The study was advertised using fliers placed in community centers as well as online postings. Interested participants completed an initial phone screen for a preliminary assessment of study eligibility.

Screeners assessed: whether the participant was likely to meet MDD criteria; self-reported history of manic episodes; current substance dependence; whether the participant was on ADM; if so, whether they were taking a stable dose; and willingness to participate in 16 weeks of individual psychotherapy. Of the potential participants who completed a phone screen, 193 were scheduled for an intake assessment. Of the 149 participants who attended their scheduled intake appointment, 126 of them met eligibility criteria for the study. Those excluded on the basis of the intake assessment were most commonly excluded for failure to meet full criteria for MDD, history of manic episodes, and having a primary diagnosis other than MDD.

Therapists

Therapists were five graduate students. They received about 60 hours of clinical training in CT prior to the study. Over the course of the study, therapists received weekly individual and group supervision with a licensed clinical psychologist. They followed the treatment manual for

CBT for depression (Beck et al., 1979). Clients were assigned to therapists pseudo-randomly; each client’s odds of being assigned to a therapist were weighted according to the number of available slots in therapists’ caseloads, with the constraint that a patient’s intake assessor could not serve as that patient’s therapist.

During the first four weeks of treatment, sessions were offered twice weekly. In weeks five to 12, patients and therapists collaboratively decided whether to schedule once weekly or twice-weekly sessions. After week 12, sessions were provided weekly. The maximum number of sessions attended was 29 (n = 1) and the average was 15.93 (SD = 5.14, range 1-29).

Females comprised 60% of the sample. Additionally, 83% of the sample was Caucasian,

8% was Asian, 7% was African American, and 2% was Hispanic. At intake, participants had an average BDI-II score of 32.8 (SD = 8.81, range = 11-56). A comorbid axis-I disorder was present in 71% of participants. Social anxiety disorder was the most common comorbid diagnosis, with

43% of participants meeting criteria for that disorder.

Measures

Affect. PA was assessed with a three-item self-report measure developed for this study in which participants were asked to rate on a seven-point scale (ranging from 0-6) the extent to which they were feeling interested, excited, and hopeful. NA was also assessed with three items

that participants rated from 0-6. Participants rated the extent to which they were feeling frustrated, discouraged, and upset. They completed this measure before and after each session.

Depressive symptoms. Depressive symptoms were assessed at the beginning of each session with the Beck Depression Inventory-II (BDI-II; Beck, Steer, & Brown, 1996). The BDI-

II is a 21-item self-report measure. Participants rate each item from 0 to 3, with higher ratings indicating a greater degree of depressive symptomatology. The BDI-II asks about the past two weeks, but for this study, the measure was modified to ask about the past week. The BDI-II is a widely used and valid measure among adults with depression (Titov et al., 2011).

Cognitive change. Cognitive change was measured at the end of each session with the

Immediate Cognitive Change Scale. This is a self-report measure developed for the purpose of this study (see Schmidt et al., 2019). The scale contains five items assessing cognitive change, and participants rate how much each item describes their experiences during the preceding therapy session, from 0 (not at all) to 6 (completely).

Analytic Strategy

Before running primary analyses, I assessed change across sessions in all study variables.

I ran a series of HLM models using PROC MIXED in SAS 9.3. The variable of interest was entered as the DV and the session variable was entered as the IV; all models contained a random intercept and random slope. An unstructured covariance structure was the best-fitting covariance structure for all models.

I used the Dynamic Structural Equation Modeling (DSEM) approach in Mplus for the primary analyses (Asparouhov, Hamaker, & Muthen, 2018). DSEM is a multilevel modeling

approach to time series data that uses Bayesian estimation. It is the only multilevel approach that accounts for person-specific effects while avoiding endogeneity bias (Zyphur et al., 2019). I used the two-level DSEM model, in which the data is decomposed into a within-person component and a between-person component. Consider yit, a vector of variables for individual i at time t. Its decomposition consists of a within-person vector with means ui and a within-person vector with deviation from those means yitw (Hamaker, Asparouhov, Brose, Schmiedek, & Muthen, 2018).

Yitw is modeled at the within-person level. The between-level model consists of the random effects from the within-person level model and the vector of random means ui, which are all represented as latent variables.

All variables in this study are characterized by a significant linear trend throughout the course of treatment (see the results section for details). However, the two-level DSEM model assumes stationarity of the data (McNeish & Hamaker, 2019). Therefore, all variables were detrended by regressing each on the session variable. According to a simulation study, this method of detrending in DSEM does not estimate variance terms correctly (Asparouhov, 2018).

This can be resolved by using a special case of the two-level DSEM model called residual

DSEM (RDSEM; Asparouhov & Muthen, 2018; McNeish & Hamaker, 2019). RDSEM models the lagged relationship between any two variables using the within-level residuals rather than the observed variables. The within-level model now consists of a structural component and a residual component. The structural component only involves concurrent relationships between variables (Asparouhov et al., 2018). In the residual component of the model, the residuals of the

structural equations become the dependent variables that the time-lagged relationships between the variables are modeled on.

The autoregressive effect of a variable refers to the residuals of each variable at time t-1 predicting the residuals of the same variable at time t. The autoregressive effects of BDI and cognitive change were included in every model where these variables appeared. The autoregressive effects of the affect variables were included in every model except for affect change models, where instead, pre-session affect variables predicted their post-session counterparts. Whenever pre-session PA, pre-session NA, post-session PA, post-session NA, cognitive change, and BDI appeared in a model, each variable was regressed on the session variable (to detrend that variable). For all models, I started by making every effect in the within level of the model random. If the model did not converge, I removed random effects starting with the random effect with the lowest variance. Random effects were removed one by one in order of lowest to highest variance until the model converged. For details of which models were tested, see Appendix A. In all models, all random variables (random means and random effects from the within level) were correlated on the between level. All variables were decomposed into their within and between components, with their random means included in the between level, with the exception of the session variable, which was grand-mean centered and included only on the within level.

As an example, Figure 1 depicts the model used to test the prediction of BDI from in- session change in PA and cognitive change (Model 7; see description below). As shown in the decomposition panel, within and between components are modeled as latent variables. The

within level is shown on the top right of the figure. The structural component of the within level contains five random effects: session predicting cognitive change (χC), session predicting BDI

(χB), session predicting post-session PA (χP), session predicting pre-session PA (χPR), and pre- session PA predicting post-session PA (φPP). The residual component contains four random effects: the autoregressive effect of BDI (φBB), the autoregressive effect of cognitive change

(φCC), the lagged effect of cognitive change on BDI (φBC), and the lagged effect of post-session

PA on BDI (φBP). The between level contains the correlation between all random variables.

I planned to run each model for at least 3,000 iterations and until Proportional Scale

Reduction (PSR) was sufficiently low, with a thinning of 10 (for a total of 30,000 iterations;

Gelman et al., 2014; Hamaker et al., 2018). The number of iterations required did not exceed this minimum of 3,000. To check convergence, I examined PSR values and trace plots. All models converged with a PSR at or below 1.01. I report within-person standardized DSEM model estimates, which are calculated by standardizing parameter estimates per person and then averaging those estimates.

Results First, I assessed the internal consistency of the three items each representing positive or negative affect. Though the range of sessions was 1-29, relatively few participants completed the maximum number of sessions. The average number of sessions completed was 15.93; therefore,

Cronbach’s alpha was calculated at sessions 1 - 16. Alpha ranged from .69 - .93 for post-session

NA and .90 - .95 for post-session PA. Alpha ranged from .86 - .95 for pre-session PA and .81 -

.93 for pre-session NA.

I then calculated intraclass correlation coefficients (ICCs) to estimate the proportion of variation in affect that was between vs. within-patient. These ICCs showed that post-session PA was composed of 73% between-patient variation, while post-session NA contained 47% between-patient variation. Pre-session positive and negative affect contained 69% and 39% between-patient variation, respectively.

As discussed above, I observed significant linear trends for all variables. Pre-session PA increased by 0.14 points per session (SE = 0.02), t(121) = 5.91, p < .001, with an effect size of d

= 1.07. Pre-session NA decreased by 0.24 points per session (SE = 02), t(121) = -11.99, p < .001, with an effect size of d = -2.18. These effect sizes are significantly different, z = 5.24, p<.001.

Significant change was also observed at post-session. On average, post-session PA increased significantly over time, at a rate of 0.06 points per session (SE = 0.02), t(121) = 2.82, p = .006.

Post-session NA decreased significantly over time, at a rate of 0.12 points at each session (SE =

0.02), t(121) = -7.46, p < .001. BDI decreased significantly over time, at a rate of 0.92 points per session (SE = 0.06), t(121) = 14.37, p < .001; the decrease in BDI scores over time is represented

in Figure 2. Cognitive change increased over time, at a rate of 0.30 points per session (SE =

0.03), t(121) = 10.25, p < .001; see Figure 3.

Figures 4-7 depict change in affect from sessions one to 16, as well as change in affect from pre- to post-session. From inspecting these figures, it appears that the larger differences in pre- and post-session affect took place in the first five sessions. Pre-session NA and PA also appear to improve over time. Figure 4 shows that patients had an unusually high post-session PA score in the first session and decreased in the next four sessions from this high point, after which post-session PA appears to increase throughout the rest of the sessions. Similarly, Figure 5 shows that the difference between pre- and post-session PA appears greatest in the early sessions. From

Figure 6 it appears that pre-session NA steadily decreased over time. The most pronounced differences between pre- and post-session NA appear to take place in sessions 1-3 (see Figure 7).

Tables 1 and 2 show the correlations between study variables. Within-person correlations are shown in Table 1 and between-person correlations are shown in Table 2. There was a moderate to large within-person correlation between the pre-session affect variables and depressive symptoms. Table 1 shows that the within-person correlation between pre-session NA and BDI was -0.57, and the within-person correlation between pre-session PA and depressive symptoms was -0.44. There was a small to moderate within-person correlation between the post- session affect variables and depressive symptoms. The within-person correlation between post- session NA and BDI was .36, and the within-person correlation between post-session PA and depressive symptoms was -0.26.

Pre-session Affect

Model 1 Results. This model tested whether patient affect at the start of the session predicted depressive symptoms. I tested pre-session PA and pre-session NA as predictors of session-to-session change in BDI. I also controlled for the autoregressive effect of both affect variables. Pre-session PA did not predict change in BDI (-0.00, 95% CI = [-0.05, 0.05]). Pre- session NA did not predict change in BDI either (0.04, 95% CI = [-0.02, 0.09]).

Model 2 and 3 Results. I tested, in a reciprocal model, whether pre-session PA predicts cognitive change at the end of the session and whether cognitive change at the end of the session predicts PA at the start of the next session. I controlled for the autoregressive effects of cognitive change and pre-session PA. Pre-session PA predicts cognitive change (0.22, 95% CI = [0.17,

0.26]) and cognitive change predicts PA (0.06, 95% CI = [0.01, 0.11]). I ran a similar model with

NA and found that pre-session NA predicts cognitive change (-0.13, 95% CI = [-0.17, -0.08]) and cognitive change predicts NA (-0.09, 95% CI = [-0.14, -0.04]).

Change in Affect

Model 4 Results. This model tested whether change in PA and NA from pre- to post- session predicted change in BDI from the beginning of that session to the beginning of the next session. In-session change in affect is represented in the model by regressing the post-session affect variable on the corresponding pre-session affect variable. Change in BDI is represented by controlling for the autoregressive effect of BDI. Change in PA from pre- to post-session

predicted lower BDI scores at the next session (-0.05, 95% CI = [-0.09, -0.00]). Change in NA also predicted change in BDI (0.06, 95% CI = [0.01, 0.11]).

Model 5 and 6 Results. Next, I tested whether cognitive change predicts in-session change in affect. Change in affect is represented by regressing the post-session affect variable on the corresponding pre-session affect variable. I controlled for the autoregressive effect of cognitive change. The first model examined whether cognitive change predicts change in PA at the end of the same session for which cognitive change was reported. Cognitive change predicts change in PA (standardized estimate = 0.36, 95% CI = [0.30, 0.42]). The second model examined whether cognitive change predicts change in NA at the end of the same session.

Cognitive change also predicts change in NA (-0.32, 95% CI = [-0.42, -0.26]).

Model 7 and 8 Results. Given the previous finding that cognitive change predicts depressive symptoms in this sample (Fitzpatrick et al., 2020), I ran a model with both cognitive change and in-session change in PA predicting depressive symptoms. I controlled for the autoregressive effects of BDI and cognitive change. Cognitive change does not predict BDI (-

0.05, 95% CI = [-0.10, -0.005]) and change in PA does not predict BDI (-0.04, 95% CI = [-0.09,

0.01]). I then ran an identical model with NA and found that cognitive change predicts BDI (-

0.06, 95% CI = [-0.11, -0.01]) and change in NA predicts BDI (0.07, 95% CI = [0.02, 0.12]).

Discussion In this study, I found significant changes in both NA (d = -2.18) and PA (d = 1.07) over the course of CBT for depression, with significantly greater change observed for pre-session NA than pre-session PA. Four other findings are especially worthy of note. First, PA and NA change within sessions both predicted changes in depressive symptoms to the next session. These effects were similar in magnitude, suggesting variability in PA and NA is similarly related to depressive symptom change in CBT—even in the absence of any efforts to provide an enhanced focus on

PA. Second, cognitive change was associated with changes in NA and changes in PA within sessions (i.e., over a 50-60 minute period). Although these relations were not predictive, they are consistent with the possibility that cognitive change may produce short-term changes in both NA and PA. Third, in models that tested the temporal relation of cognitive change and affect, I found evidence for a reciprocal relation. Specifically, pre-session NA and PA predicted cognitive change at the end of the same session. In addition, cognitive change predicted NA and PA at the beginning of the next session. Though the time lags involved in these relations differed due to the assessment schedule (a point which I discuss below), these findings suggest that cognitive change may drive NA as well as PA—and that changes in affect may further facilitate cognitive change. Finally, in a model testing NA and cognitive change as predictors of depressive symptom change, both NA and cognitive change predicted depressive symptoms. In a model with PA and cognitive change examined as predictors of depressive symptoms, neither predictor remained significant (though effect sizes were comparable to those observed in the NA model).

These results suggest that both NA and PA change substantially in CBT, with NA changing to a greater extent, and that changes in each are related to in-session cognitive changes.

What type of affect should therapists be targeting? It has been argued that therapists should increase their focus on PA to improve outcomes and that current treatments such as CBT are targeting NA to a greater extent than PA (e.g., Vinograd & Craske, 2020). In this study, I aimed to test the assumptions that 1) change in PA predicts outcomes and 2) CBT is neglecting

PA relative to NA. To address the first assumption, I tested the effect of change in NA and PA on symptom change. Results show that change in affect over the course of a session predicts symptom improvement, but pre-session affect does not. This suggests that the affect change that takes place over the course of a therapy session is especially meaningful. The effects observed appeared roughly comparable, so change in NA and PA may be equally important for symptom change. Future research can also examine the relative importance of PA and NA for outcomes other than symptom change. Change in different types of affect may have differential effects on outcomes such as quality of life, functional outcomes, life satisfaction, purpose in life, comorbid disorders, or likelihood of relapse.

There are a variety of methods available to address the assumption that current treatments neglect PA. Researchers have taken different approaches. Dunn, German, and colleagues (2019) conclude that PA should receive more attention from therapists because depressed participants vary from normal controls on PA more than NA at intake. Additionally, at post-treatment, patient

NA is closer to the general population average (z = −0.08, SD = 0.99) than PA (z = −0.77, SD =

1.97). Boumparis and colleagues (2016) utilized an alternative approach for assessing change in

affect. They drew on ten studies that randomized participants to psychotherapy or to a non-active control group and found significant change in PA and NA relative to controls. They did not present a statistical comparison of the effect sizes, so it is not clear if one variable differed from controls more than the other or if one variable exhibited a greater amount of change from pre- to post-treatment than the other. The present study aimed to present a different test of this question by examining the rate of change of pre-session NA and PA. Both pre-session NA and PA are characterized by a significant linear slope, though participants have a greater rate of change in

NA than PA over the course of treatment. These results suggest that researchers are correct to argue that CBT disproportionately targets NA over PA. Treatment approaches that are able to increase within-patient change in PA may be able to yield improved outcomes.

If therapists were to decide to target PA on the basis of these results, it is not clear what procedures they would use. Some have proposed treatment packages that are intended to improve

PA to a greater extent than current psychotherapies (e.g., Craske et al., 2019; Hofmann et al.,

2015). These can be tested in RCTs against existing treatments to determine if they successfully produce superior PA increases or superior symptom reduction (e.g., Dunn, Widnall, et al., 2019).

Even more beneficial would be observational coding studies or component studies that attempt to isolate specific therapeutic techniques and their influence on PA.

Results of the reciprocal model show that pre-session PA and NA predict cognitive change at the end of a session. The emotional processing perspective on psychotherapy would suggest that patients are more likely to experience changes in domains such as cognition, behaviors, and somatic functioning after experiencing greater NA in-session. On the contrary,

results show that higher PA and lower NA predict greater cognitive change. However, it could also be argued that only specific therapeutic techniques designed to capitalize on NA would produce positive outcomes from experiencing NA in-session. The results of this model suggest that patients who are in a better mood are more open to seeing things from a different perspective. Another possibility is that patients’ cognitive change increased throughout the week, causing an increase in their pre-session affect, as well as higher cognitive change scores at the end of the session. Even though the cognitive change measure asks about the patient’s cognitive change in session, it is possible that cognitive changes that took place throughout the week could influence their responses. This concept is consistent with the findings of Schmidt and colleagues

(2019). Using the same sample as the present study, they found that the relation of post-session cognitive change and next-session symptom change was mediated by cognitive change that took place between sessions. Therefore, it is possible that patients practice the skills they worked on in session during the week, and this has some impact on their pre-session affect.

I also tested whether affect change and cognitive change uniquely contribute to symptom change when included in the same model. Results suggest that cognitive change and NA change both predict next-session symptom change. It appears that cognitive change is important for symptom change irrespective of its influence on NA, and vice-versa. This strengthens the argument that cognitive change is uniquely important for improvement in symptoms. It also suggests that NA change is uniquely important and could be a promising target for treatment. In contrast, cognitive change and PA change do not predict next-session BDI change when included in the same model. PA change significantly predicts BDI in model 4, so perhaps some of this

predictive work is explained by the relationship between PA and cognitive change, such that when cognitive change is included as a covariate, the effect of PA on BDI is no longer meaningful. The conclusion that PA is less important than NA for BDI change when cognitive change has taken place is not warranted, however. These models do not present a direct test of the difference in the predictive power of these two variables.

The results of models 5 and 6 show that there is a concurrent relation between affect change over the course of a session and cognitive change at the end of the session. It is reflective of the cognitive reactivity literature that cognitive change and NA are related on a short time scale. Since depressed patients endorse more dysfunctional thinking after a negative mood induction, it is consistent with expectations that decreased NA predicts greater cognitive change.

This study also contains a novel test of the short-term relation between PA and cognitive change, which is of similar magnitude to the relation of NA and cognitive change. It is worth noting that the relation between cognitive change and affect depends to some extent on the time interval between measurements. The results from the reciprocal models show that there is a numerically smaller relation when the lag is extended: cognitive change predicts next-session NA and PA with within-person standardized effect sizes of -0.13 and 0.06, respectively. It is consistent with

Beck’s theory that cognitive change is helpful for reducing NA, though he did not suggest the time scale over which these changes would take place. Cognitive change also appears to have relatively long-term effects (of at least several days) on PA. As discussed above, I found that depressed participants in CBT have a greater rate of change in NA than PA. However, cognitive

change, one of the primary hypothesized mechanisms of CBT, appears to have an effect on both.

Future research can clarify which processes of change in CBT predict NA and which predict PA.

A few limitations are worthy of note. One limitation of the present study is that uncertainty remains about the optimal intervals to use when assessing the relation between study variables. Timing of variable measurement is highly consequential; it can be difficult to compare results across studies when the studies have chosen different lags to examine. While the present study uses discrete time modeling, continuous time modeling has been proposed as one way to address the lag problem. These models allow researchers to examine differences in cross-lagged effects at different intervals. From this, one can estimate the time interval at which the relation between two variables is expected to be strongest (Ryan et al., 2018). This approach may be useful in situations where it is not clear which time intervals would be the most informative to examine. A second limitation is that, while this study found that affect predicts symptom change, it did not test which therapist behaviors contribute to change in NA or PA. It is therefore difficult to make clinical recommendations based on these findings. This will be a consequential issue to examine in future research. A third limitation of the present study is that it used measures of PA and NA that have not been validated in other samples. Each measure used only three words to represent affect. It is possible that the results presented here would change if other words had been selected for those measures. Other studies should be conducted, either to validate the measures used here, or to test the hypotheses in this study with other measures of affect.

In summary, this study found evidence that CBT targets NA to a greater extent than PA.

This study also found predictive relationships between affect and cognitive change and between

affect and symptom change in CBT for depression. Given that PA is relatively neglected in CBT, and that in-session change in PA and NA predicts next-session symptom change, an important next step is for researchers to determine how various therapeutic procedures influence affect, with a focus on PA. Additionally, there may be important moderators of this relationship. For example, perhaps chronically anhedonic depressed patients would see especially large improvement in depressive symptoms following improvements in PA. Our findings also align with the possibility that cognitive change produces change in NA, which is consistent with the theoretical model of CBT. PA is also related to cognitive change, a perhaps unexpected result given that procedures that are commonly thought to produce cognitive change are sometimes thought to primarily influence NA. The results of this study will hopefully lead to a greater understanding of processes within the patient that predict positive outcomes in psychotherapy.

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Appendix A: Tables and Figures

Figure 1. Representation of the RDSEM Model.

Note. The left panel contains the decomposition of observed variables. The top right contains the within level, with the structural component (which models concurrent relationships) on the left and the residual component (which models the time-lagged relationships between residuals of the structural component) on the right. The bottom right contains the between level model, which contains random effects from the within level as well as the random means from the decomposition. Observed variables are represented with boxes. Latent variables are represented with circles. Paths marked with symbols represent random effects.

CC = Cognitive Change; BDI = Beck Depression Inventory-II; POST-PA = post-session positive affect; PRE-PA = pre-session positive affect. (w) indicates within-person component. µ indicates between-person component. e indicates residuals. ϛ indicates measurement errors in the residual component. t indicates time or session number.

Figure 2. Depressive Symptoms at Each Session.

Note. Figure 2 represents the mean of BDI scores at the beginning of each session. N ranges from 89 to 124. The number of patients at each session varies because some patients dropped out over the course of treatment. Primary analyses were run on sessions 1-29, but the figure represents only sessions 1-16 as there are relatively fewer observations at later sessions. BDI = Beck Depression Inventory-II.

Figure 3. Cognitive Change at Each Session.

Note. Figure 3 represents the mean of cognitive change scores at the beginning of each session. N ranges from 87 to 122.

Figure 4. Positive Affect at Pre- and Post-Session.

Note. Figure 4 contains the means of positive affect scores before and after each session. N ranges from 88 to 121 for post-session PA and 89 to 122 for pre-session PA. PA = positive affect.

Figure 5. Positive Affect Difference Scores from Pre- to Post-Session with Confidence Intervals.

Note. Figure 5 represents the difference between the means of pre- and post-session positive affect scores at each session.

Figure 6. Negative Affect at Pre- and Post-Session.

Note. Figure 6 contains the means of negative affect scores before and after each session. N ranges from 88 to 121 for post-session NA and 89 to 122 for pre-session NA.

Figure 7. Negative Affect Difference Scores from Pre- to Post-Session with Confidence Intervals.

Note. Figure 7 represents the difference between the means of pre- and post-session negative affect scores at each session.

Table 1. Within-person Correlations among Variables.

1 2 3 4 5

1. Post-session PA ------

2. Post-session NA -0.44 ------

3. Depressive Symptoms -0.26 0.36 ------

4. Pre-session PA 0.57 -0.30 -0.44 -- --

5. Pre-session NA -0.32 0.54 -0.57 -0.47 --

6. Cognitive Change 0.44 -0.43 -0.35 0.33 -0.30

Note. PA = Positive Affect. NA = Negative Affect.

Table 2. Between-person Correlations among Variables.

1 2 3 4 5

1. Post-session PA ------

2. Post-session NA -0.31 ------

3. Depressive Symptoms -0.46 0.61 ------

4. Pre-session PA 0.95 -0.24 -0.47 -- --

5. Pre-session NA -0.21 0.91 0.67 -0.23 --

6. Cognitive Change 0.70 -0.41 -0.30 0.67 -0.31

Note. PA = Positive Affect. NA = Negative Affect.

Appendix B: Model Details Model 1. Model 1 tested whether pre-session NA and pre-session PA predict change in session-to-session change in BDI. The model was first run with five random effects in the residual component of the within level: the autoregressive effects of BDI, pre-session PA, and pre-session NA, the lagged effect of pre-session PA on BDI, and the lagged effect of pre-session

NA on BDI. The structural component contained three random effects, namely, the effect of session on BDI, pre-session PA, and pre-session NA. This model did not converge. Next, the model was run again with the random effect with the lowest variance removed, and again with the next-lowest removed, until it converged. In order, the random effects removed were: the random trend of pre-session NA; the random trend of pre-session PA; the random autoregressive effect of pre-session NA; the random effect of pre-session NA predicting BDI; the random autoregressive effect of pre-session PA; and the random autoregressive effect of BDI.

Therefore, in the final model, the residual component contained one random effect: the lagged effect of pre-session PA on BDI. There were four fixed effects in the residual component: the autoregressive effects of BDI, pre-session PA, and pre-session NA, and the lagged effect of pre-session NA on BDI. The structural component contained one random effect: the random trend of BDI. The trends of pre-session PA and pre-session NA were fixed.

Model 2. Model 2 tested whether pre-session PA predicts cognitive change at the end of the session and whether cognitive change at the end of the session predicts PA at the start of the next session. The model was initially run with all three possible random effects included in the residual component: the autoregressive effects of pre-session PA and cognitive change and the lagged effect of cognitive change on pre-session PA. The structural component contained three 60

random effects: the effect of pre-session PA on cognitive change and the effect of session on pre- session PA and cognitive change. All random variables were correlated on the between level.

This model did not converge, so the model was run again with the random effect of cognitive change predicting pre-session PA removed. Subsequently, the random trend of pre-session PA was removed, then the autoregressive effect of pre-session PA, then the random trend of cognitive change.

In the final model, the residual component contains one random effect: the autoregressive effect of cognitive change. The autoregressive effect of pre-session PA and the lagged effect of cognitive change on pre-session PA were fixed in the residual component. The structural component contains one random effect: the effect of pre-session PA on cognitive change. The trends of pre-session PA and cognitive change were fixed.

Model 3. Model 3 was structured identically to Model 2 but with NA instead of PA. This model tested whether pre-session NA predicts cognitive change at the end of the session and whether cognitive change at the end of the session predicts NA at the start of the next session.

The model was run with all three possible random effects included in the residual component: the autoregressive effects of pre-session NA and cognitive change and the lagged effect of cognitive change on pre-session NA. The structural component contained three random effects: the effect of pre-session NA on cognitive change and the effect of session on pre-session NA on cognitive change. All random variables were correlated on the between level.

Model 4. Model 4 tested the effect of in-session change in PA and NA on BDI. The model was initially run with all three possible random effects in the residual component: the

autoregressive effect of BDI, the effect of post-session PA on BDI, and the effect of post-session

NA on BDI. The structural component contained seven random effects: the effect of session on

BDI, post-session PA, post-session NA, pre-session PA, and pre-session NA; and the effect of pre-session PA on post-session PA and the effect of pre-session NA on post-session NA. This model did not converge. This model was run several more times with the following random effects removed: the random effect of pre-session PA predicting post-session PA; the random trend of post-session NA; the random trend of post-session PA; the random trend of pre-session

NA; the random effect of pre-session NA predicting post-session NA; the random trend of pre- session PA; and the random autoregressive effect of BDI.

The final model contained two random effects in the residual component: the random lagged effect of post-session PA on BDI and the random lagged effect of post-session NA on

BDI. The autoregressive effect of BDI was fixed. The only random effect included in the structural component was the random trend of BDI. The other six effects in the structural component were fixed: the trends of post-session PA, post-session NA, pre-session PA, and pre- session NA; the effect of pre-session NA on post-session NA; and the effect of pre-session PA on post-session PA.

Model 5. Model 5 tested the effect of cognitive change at the end of a session on change in PA over the course of the session. The residual component contained the random autoregressive effect of cognitive change. The structural component contained five random effects: the effect of pre-session PA on post-session PA; the effect of cognitive change on post-

session PA; the random trend of post-session PA; the random trend of cognitive change; and the random trend of pre-session PA. This model converged.

Model 6. Model 6 was identical to Model 5 but using NA instead of PA. This model converged with all random effects included.

Model 7. Model 7 tested whether cognitive change and PA change predict BDI when included in the same model. The residual component contained four random effects: the autoregressive effects of BDI and cognitive change, the lagged effect of cognitive change on

BDI, and the lagged effect of post-session PA on BDI. The structural component contained five random effects: the effect of session on cognitive change, BDI, post-session PA, and pre-session

PA, and the effect of pre-session PA on post-session PA. This model did not converge. The model was run several times with the following random effects removed: the random trend of post-session PA; the random effect of pre-session PA predicting post-session PA; the random effect of cognitive change predicting BDI; the random trend of pre-session PA; the random autoregressive effect of BDI; and the random autoregressive effect of cognitive change.

The final model contained one random effect in the residual component: the random lagged effect of post-session PA predicting the next observation of BDI. The autoregressive effects of BDI and cognitive change, and the effect of cognitive change on BDI, were fixed. The structural component contained two random effects: the random trends of cognitive change and

BDI. The trends of post-session PA and pre-session PA were fixed. The effect of pre-session PA on post-session PA was also fixed.

Model 8. Model 8 was set up in the same way as Model 7, but with NA instead of PA.

The residual component contained four random effects: the autoregressive effects of BDI and cognitive change, the lagged effect of post-session NA on BDI, and the lagged effect of cognitive change on BDI. The structural component contained five random effects: the effect of session on cognitive change, BDI, post-session NA, and pre-session NA, and the effect of pre- session NA on post-session NA. These random variables and the four random means were all correlated at the between level. This model did not converge. The following random effects were removed: the random trend of post-session NA; the random trend of pre-session NA; the random effect of pre-session NA predicting post-session NA; the lagged random effect of cognitive change predicting BDI; the random autoregressive effect of cognitive change; the random trend of cognitive change; and the random autoregressive effect of BDI.

The final model contained one random effect in the residual component: the lagged effect of post-session NA on BDI. The residual component contained three fixed effects: the autoregressive effect of BDI, the autoregressive effect of cognitive change, and the lagged effect of cognitive change on BDI. The only fixed effect in the structural component was the random trend of BDI. There were four fixed effects in the structural component: the trend of cognitive change, post-session NA, and pre-session NA; and the effect of pre-session NA on post-session

NA.