Multiple health behaviors, subjective health, quality of life and sleep quality: Theory-based investigations and implications for health promotion and disease prevention
by
Shu Ling Tan
A thesis submitted in partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Psychology
Approved Dissertation Committee: Prof. Dr. Sonia Lippke (Jacobs University Bremen) Prof. Dr. Christian Stamov Roßnagel (Jacobs University Bremen) Prof. Dr. Yan Ping Duan (Hong Kong Baptist University) Prof. Dr. Benjamin Schüz (University of Bremen)
Date of Defense: 30th November 2017 Department of Psychology and Methods
Statutory Declaration
Family Name, Given/First Name Tan, Shu Ling
Matriculation number 20331478
Type of submitted thesis PhD Thesis
English: Declaration of Authorship
I hereby declare that the thesis submitted was created and written solely by myself without any external support. Any sources, direct or indirect, are marked as such. I am aware of the fact that the contents of the thesis in digital form may be revised with regard to usage of unauthorized aid as well as whether the whole or parts of it may be identified as plagiarism. I do agree my work to be entered into a database for it to be compared with existing sources, where it will remain in order to enable further comparisons with future theses. This does not grant any rights of reproduction and usage, however.
The Thesis has been written independently and has not been submitted at any other university for the conferral of a PhD degree; neither has the thesis been previously published in full.
German: Erklärung der Autorenschaft (Urheberschaft)
Ich erkläre hiermit, dass die vorliegende Arbeit ohne fremde Hilfe ausschließlich von mir erstellt und geschrieben worden ist. Jedwede verwendeten Quellen, direkter oder indirekter Art, sind als solche kenntlich gemacht worden. Mir ist die Tatsache bewusst, dass der Inhalt der Thesis in digitaler Form geprüft werden kann im Hinblick darauf, ob es sich ganz oder in Teilen um ein Plagiat handelt. Ich bin damit einverstanden, dass meine Arbeit in einer Datenbank eingegeben werden kann, um mit bereits bestehenden Quellen verglichen zu werden und dort auch verbleibt, um mit zukünftigen Arbeiten verglichen werden zu können. Dies berechtigt jedoch nicht zur Verwendung oder Vervielfältigung.
Diese Arbeit wurde in der vorliegenden Form weder einer anderen Prüfungsbehörde vorgelegt noch wurde das Gesamtdokument bisher veröffentlicht.
...... Date, Signature
Table of Contents
Acknowledgement ...... 1
List of Abbreviations...... 3
List of Tables ...... 4
List of Figures ...... 5
Abstract ...... 6
1. Chapter 1: Introduction ...... 8
1.1. Physical Activity and Healthy Diet ...... 12 1.2. Sleep ...... 14 1.3. Multiple Health Behaviors and Age-Group Differences ...... 16 1.4. Theoretical Frameworks ...... 19 1.5. Objectives, Research Questions and Hypotheses ...... 25 2. Chapter 2: Associations of physical activity, with subjective health...... 40
3. Chapter 3: Associations of sleep and healthy diet, with subjective health and
quality of life of older adults...... 71
4. Chapter 4: Associations of sleep, physical activity and healthy diet, with subjective
health, quality of life and sleep quality, across age group differences ...... 104
5. Chapter 5: Discussion ...... 146
5.1. Summary of the Main Findings ...... 148 5.2. General Discussion ...... 151 5.3. Connotations for Theoretical Frameworks ...... 163 5.4. Practical Implications ...... 168 5.5. Strengths, Limitations and Suggestions for Future Research ...... 171 5.6. Conclusions ...... 174 Appendix ...... 189
Curriculum Vitae: Shu Ling Tan ...... 193
Acknowledgements
This is the most emotional part of this thesis, as I truly believe ‘thank you’ is not enough to express my gratitude and appreciation for having great inspiring people whom I came across during this doctorate journey.
First of all, thank you Prof. Dr. Sonia Lippke for giving me the opportunity by opening the door of Ph.D. where I stepped in and transformed, alongside with your fruitful support and guidance. Special thanks go to Prof. Dr. Christian Stamov Roßnagel for your inspiring advice, as well as Prof. Dr. Yan Ping Duan and Prof. Dr. Benjamin Schüz for always being understanding and supportive. Thank you all for revising not only this thesis, but also all your inspiring research works and teaching.
Thank you very much to the German Federal Ministry of Education and Research
(Bundesministerium für Bildung und Forschung, BMBF) for funding the IROHLA research project, and the Wilhelm-Stiftung für Rehabilitationsforschung for funding the RENATA research project, which made the production of this thesis possible.
Thank you to all my colleagues in the department and supportive professors. Thank you to the co-authors, Dr. Juliane Paech, Dr. Dominique Reinwand, Dr. Julian Wienert, and Dr. Hein de Vries, for their support in writing and revising the manuscripts. Thank you, Ruth Koops van’t Jagt from the University of Groningen for the fruitful cooperation in the IROHLA project.
Thank you, Dr. Vera Storm, for her support as a colleague and a co-author, most importantly as a good friend. Same goes to Dr. Amanda Whittal for all the proofreading, and thank you for always knowing how to calm me down, seeking solutions for and with me, when
I have gone through difficulties and downtimes, and most importantly, to teach me to be mindful at work and of living.
Much appreciation to my best friends Mabel Ng Xin Yu and Zuzana Leicisinova, who are always by my side across distance, I am very thankful for the encouragements by reminding
1 Acknowledgements me my best possible selves and the excitement in achieving the success, as well as all the laughter and tears we shared. Not to forget all my friends who have given their best wishes and supports. Thank you.
From the bottom of my heart, I am very grateful to my partner, Andreas Volker, who is always there to support me in many ways. Thank you for having great patience in embracing my downs and flaws, by reminding me the bright side from the darkness, and never fail to encourage and motivate me to step out from my comfort zone to reach my goals. Not forgetting his family (Ann, Karl, Stefanie, Sebastian, Jonne, Daniel, Paula, and Friederike), who have given me another family far from home in this country, with love, warmth, supports and caring.
Last but not least, exclusive thankfulness across a distance of thousands of miles, to my parents, Tan Kang Cheng and Lim Maw Mou, for giving me their endless love, being my inspiration to become someone better, and believing in me. Thank you, to my dearest sisters
(Fong Leng, Yi Ling, and Shi Peang), brother-in-laws (Jeffrey and Lian Chuan), and adorable nieces (Xuan, Tong, Tian, and Liang), who always know how to put a smile on my face, lending me your ears when I needed to be listened to, and backing me up in any circumstances.
------Thank you------
2 List of Abbreviations
BMI: body mass index
CCAM: Compensatory Carry-Over Action Model
CVDs: Cardiovascular diseases
IPAQ: International Physical Activity Questionnaire
LMM: Linear Mixed Model
NCDs: non-communicable diseases
PSQI: The Pittsburgh Sleep Quality Index
SEM: structural equation modeling
WHO: World Health Organization
WHOQOL-BREF: World Health Organization Quality of Life Instruments (shorter version)
3 List of Tables
Table 1. Overview of the Empirical Studies of this Thesis ...... 29
Table 2. Means, Standard Deviations and Inter-relations (Pearson’s, r) of the Major Study Variables
Across Time (Chapter 2) ...... 54
Table 3. Summary of the Main Variables in this Study (Chapter 2) ...... 56
Table 4. Model of Subjective Health and Physical Activity (Chapter 2) ...... 57
Table 5. Model of Predictors of Subjective Health (Chapter 2) ...... 59
Table 6. Means, Standard Deviation and Correlations of the Major Study Variables (Chapter 3)...... 85
Table 7. Descriptive Statistics on Sociodemographic Variables (Chapter 4) ...... 119
Table 8. Means, Standard Deviations, and Correlations of the Major Study Variables (Chapter 4) .. 120
Table 9. Analysis of Age-Group Differences on Main Study Variables (Chapter 4) ...... 123
Table 10. Summary of the Main Findings and Conclusions ...... 149
4 List of Figures
Figure 1. The Compensatory Carry-Over Action Model (CCAM) by Lippke (2014), where CC =
Compensatory Cognitions ...... 21
Figure 2. Overview of main study variables in this thesis, based on the Compensatory Carry-Over
Action Model (CCAM) by Lippke (2014) ...... 22
Figure 3. Chapter 2 – Focus on health behavior of physical activity and higher-level goal of subjective
health (highlighted in green) ...... 41
Figure 4. Flow of participants in this study (Chapter 2) through each measurement points ...... 48
Figure 5. Chapter 3 – Focus on healthy diet and sleep, and their associations with quality of life and
subjective health (highlighted in green) ...... 72
Figure 6. Path diagram for proposed model predicting older adults’ subjective health and quality of
life with standardized coefficients, with ***p < .001, ** p < .01, and * p < .05 (Chapter 3). 87
Figure 7. Chapter 4 – Focus on physical activity, healthy diet, and sleep, and their associations with
sleep quality, subjective health and quality of life ...... 105
Figure 8. Conceptual path analysis model with standardized regression coefficients showing the
associations among restful sleep, physical activity (PA), fruit and vegetable intake (FVI),
sleep quality, quality of life and subjective health (QOL&SH), with ***p < .001, ** p < .01,
and * p < .05 (Chapter 4) ...... 122
Figure 9. Conceptual path analysis model with standardized regression coefficients showing the
associations among restful sleep, physical activity (PA), fruit and vegetable intake (FVI),
sleep quality, quality of life and subjective health (QOL&SH) across age groups, begin with
young adults, middle-aged adults and older adults, with *** p < .001, ** p < .01, and * p
< .05 (Chapter 4) ...... 125
5 Abstract
Health includes physical health and beyond, including subjective health, quality of life and sleep quality. Many lifestyle factors can contribute to the deterioration of health and the health rehabilitation. The current rise in health problems are related to lifestyle behaviors such as sedentary habits, unhealthy diets, and sleep deprivation or deficiency. Thus, different lifestyle- related health behaviors need to be considered together in health promotion and rehabilitation.
Moreover, the rise in non-communicable diseases (NCDS) and high-level of stress can result in being less productive at work, shorter lifespans, and a more significant burden on the healthcare system. It is therefore essential to identify (1) how health behaviors interrelate, and
(2) whether health behaviors relate to psychological mechanisms that may relate to stress management that increase well-being, and subjective health and quality of life (Lippke, 2014).
Therefore, the primary goal of this thesis is to identify the interrelations among multiple health behaviors, and their associations with subjective health, quality of life, and sleep quality.
It is known that physical activity and a healthy diet are behaviors that contribute to good health and well-being, yet little is known about sleep as another lifestyle-related health behavior.
While sleep problems are often linked to an increased risk of lifestyle-related diseases, sufficient good-quality sleep is related to many positive health outcomes. Thus, the second goal of this study aims to include sleep as health behavior, by investigating the interrelations among sleep, physical activity and healthy diets, including a low-fat diet and fruit and vegetable intake. As health and engagement in health behaviors vary with age and subjective perspectives, the third goal of this thesis is to investigate age-group differences on multiple health behaviors, and their associations with subjective health, quality of life and sleep quality.
This thesis utilized theory-based and empirical investigations based on quantitative questionnaire data. Results suggest that individuals who were physically active were more likely to perceive their health as better, over a period of up to eight years (N = 640). Some initial insights were provided regarding how sleep interrelates with physical activity and healthy diets among
6 Abstract older adults (N = 126). The results further suggest that these multiple health behaviors are associated with increased subjective health, quality of life, and sleep quality, and show significant age group differences (N = 790). Distinct age groups may have different approaches for leading a healthy lifestyle, and the results found middle-aged adults had less intention or action to engage in health behaviors, which were associated with poor subjective health, quality of life and sleep quality.
The findings based on the Compensatory Carry-Over Action Model (CCAM) reflect (1) that carry-over mechanisms of one health behavior may facilitate another health behavior; (2) the understanding of self-regulation of multiple health behaviors in achieving higher-level goals could enhance health and well-being. In particular, medical rehabilitation typically focuses on maximizing the functioning daily performance of behaviors, such as being physically active and consuming healthy diet. The addition of sleep as a health behavior is one way the findings may be informative for practical implications of health promotion. Creating awareness of the role of these health behaviors and adapting lifestyle management with multiple health behaviors, including sleep, could be two possibilities. Regarding policy and priorities, sleep health should be included in interventions, health screening and health consultations. An approach with age- differentiated health behavior regarding stress- and health management may be more effective in helping specific age groups with the long-term adoption of health behaviors more appropriately. Future studies should identify the causal pathways of sleep on health and well- being and take social cognitive and socio-demographic factors into consideration, as these variables may interrelate with multiple health behavior change. Such future investigations could contribute to expanding the development of theory- and evidence-based implications in this highly relevant field.
7 Chapter 1
Chapter 1:
Introduction
8 Chapter 1
1. Introduction
“Health is a state of complete physical, mental and social well-being and not merely the absence of disease” (World Health Organization, WHO, 1946, p. 100). This definition indicates that health is more than just physical health. Thus, this inspires the examination of physical health and beyond, including subjective health, quality of life and sleep quality.
Subjective health, or self-perceived health status, assesses physical and mental health, and is an important outcome measure and a standard criterion of successful development (Hunt &
McEwen, 1980). Quality of life refers to general well-being, and involves individual values and a wide range of life domains in daily living, which is an outcome beyond morbidity and biological functioning (Felce & Perry, 1995; Karimi & Brazier, 2016). Good subjective health and good quality of life contribute to positive functioning and well-being, which are the valued outcomes and end targets of health related interventions (Bowling, 2017). Therefore, individuals, especially rehabilitation patients and older adults with chronic diseases, perceive good subjective health and good quality of life as higher-level goals. Yet, this remains as challenge for most individuals, due to health complaints that increase with age, high-levels of stress, and unhealthy lifestyles.
The current increasing chronic health problems, or non-communicable diseases
(NCDs), have become a primary health concern. NCDs, including cardiovascular diseases
(CVDs), cancer, chronic respiratory disease, and diabetes, are driven by influences that include unhealthy lifestyles and population aging. The prevalence of NCDs often results in destructive health outcomes for individuals and communities, including premature mortality, disease burden, increasing burden on economic development and the well-being of majority of the older population, especially individuals aged 50 years and over (WHO, 2017). Poor
9 Chapter 1 health threatens to overwhelm health systems, implies less productive work and academic performance, and a shorter lifespan (Arena et al., 2015; WHO, 2017).
Therefore, it is critical to focus on reducing the risk factors associated with NCDs. Most
NCDs could be prevented or treated by making changes to lifestyle-related health behaviors
(Mann, Ridder, & Fujita, 2013; WHO, 2017). For instance, physical inactivity and an unhealthy diet (WHO, 2017), as well as sleep problems, are all clinically related to health problems
(Romero-Corral, Caples, Lopez-Jimenez, & Somers, 2010; Dong, Zhang, & Qin, 2013; Itani, Jike,
Watanabe & Kaneita, 2017; Tobaldini et al., 2017). In fact, sleep is gradually attracting attention as a lifestyle-related health behavior. Sleep health, like physical activity and a healthy diet, is associated with a reduction of stress and increase well-being, as well as enhance subjective health, quality of life, and sleep quality (Perry, Patil, & Presley-Cantrell,
2013; Hagger, 2014; Grandner, 2014). Sleep deprivation or deficiency, however, has not yet been recognized by the World Health Organization as a risk factor for NCDs or been included in health screening, consultation or intervention. Rehabilitation also often overlooks the importance of sleep in clinical assessment and treatment (Gudberg & Johansen-Berg, 2015).
Many individuals have an unhealthy lifestyle with high-level of stress, increasing sedentary habits, consumption of too much high-fat food, and sleep deprivation or deficiency.
The co-occurrence of these health-risk behaviors is highly related with increased chronic disease (Spring, Moller, & Coons, 2012), as well as poor quality of life (Dey, Gmel, Studer, &
Mohler-Kuo, 2014). With this in mind, scientific studies on lifestyle-related health behaviors for all levels of health promotion and medical rehabilitation are needed (Arena et al., 2015;
WHO, 2017). Engagement in single or multiple health behaviors with carry-over mechanisms, may relate to the motivation or self-regulation that link to well-being and higher-level goals
(Lippke, 2014). For this reason, the primary goal of this thesis is to identify the interrelations
10 Chapter 1 among multiple health behaviors, and their associations with subjective health, quality of life, and sleep quality.
Sleep seems to play a role in a healthy lifestyle, in particular when it clusters with other more common health behaviors like physical activity and a healthy diet (Bayán-Bravo et al.,
2017; Filip et al., 2017). To expand upon previous literature, which examined the health behaviors separately, the second goal of this thesis is to include sleep as a health behavior, by investigating the interrelations between sleep and other health behaviors, namely, physical activity and healthy diet.
Finally, recent facts show that premature deaths from NCDs occur between the age of
30 and 69 years (WHO, 2017). This may indicate that almost all age groups are vulnerable to the risk factors which contribute to NCDs. The globalization of unhealthy lifestyles and an aging population for which life expectancy is growing are significant issues in developed countries (WHO, 2017). Within these broad issues, it is essential to understand individual differences, which vary with age and subjective perspectives, as the third goal of this thesis.
The risk factors of NCDs could be minimized through the adoption of multiple health behavior changes, it has been suggested that interventions targeting multiple health behaviors have a greater impact on health and well-being than those targeting single health behavior (Prochaska, Spring, & Nigg, 2008; Arena et al., 2015; Geller, Lippke, & Nigg, 2017).
However, increasing stress in daily life makes the process of changing or maintaining multiple health behaviors a challenge or even a burden to many individuals, and affects their well- being and higher-level goals of good subjective health and good quality of life (Clark et al.,
2011; McKenzie & Harris, 2013). To conceptualize this complex matter by combining the psychological and behavioral mechanisms of physical and mental health, the theoretical framework of the Compensatory Carry-Over Action Model (CCAM), originating from Lippke
11 Chapter 1
(2014), has been chosen. CCAM is a novel approach for understanding the multiple health behavior change process with the underlying psychological and behavioral mechanisms that promote a healthy lifestyle and prevent NCDs. CCAM contributes to an understanding of carry-over mechanisms with the interrelations of the multiple health behaviors, and their relations with stress-management that can increase well-being, and higher-level goals, which will be explained further later.
This chapter begins with an overview of the existing scientific research on lifestyle- related health behaviors and their associations with subjective health, quality of life and sleep quality. The literature review demonstrates the need for studying multiple health behaviors, in particular among different age groups. Evidence-based theoretical frameworks are described to evaluate the psychological and behavioral mechanisms of physical and mental health. Finally, the objectives, research questions and hypotheses that are mentioned in the chapters of this thesis will be outlined.
1.1. Physical Activity and Healthy Diet
Lifestyle-related health risk behaviors can be related to energy imbalance. For instance, insufficient energy expenditure due to physical inactivity with increased sedentary behavior, or excessive energy intake from high-fat diet and low fruit and vegetable intake.
These lifestyle-related unhealthy behaviors have all been identified as factors which not only lead to increased health risks from NCDs, but also link to poor quality of life and subjective health (Hill, Wyatt, & Peters, 2012; Lachat et al., 2013; WHO, 2017).
A substantial amount of research has examined single health behavior and the outcomes. For example, systematic reviews and meta-analyses have provided evidence and conclusions about numerous physical and psychological outcomes from physical activity, including quality of life outcomes (Conn, Hafdahl & Brown, 2009), and health outcomes
12 Chapter 1
(Warburton & Bredin, 2017). Besides, a high-level of physical activity has been identified as consistently linked to a high-level of subjective health, predominantly in older adults aged 50 years and above (Notthoff, Reisch, & Gerstorf, 2017). Similarly, fruit and vegetable intake have been found to decreased the risk of all-cause mortality (Schwingshackl et al., 2017).
Physical inactivity or sedentary behavior is highly related to lower subjective well- being among healthy adults (Panza, Taylor, Thompson, White, & Pescatello, 2017); and consumption of an unhealthy diet contributes to poor subjective health with more health complaints (El Ansari, Suominen, & Berg-Beckhoff, 2015). Although engaging in a single health-risk behavior yields negative outcomes, the impact of multiple health-risk behaviors is even more worrying (Geller et al., 2017). Although past studies show evidence of the interrelations among these long-established multiple health behaviors like physical activity and consuming a healthy diet (Lippke & Ziegelmann, 2006; Lachat et al., 2013; Reinwand et al., 2016; Duan, Wienert, Hu, Si, & Lippke, 2017), the research into these health behaviors still requires expansion.
A study conducted by Lippke, Nigg and Maddock (2012), presented results which show that there are consistently strong relationships between physical activity and healthy diet, and the results also support the notion that people who are in an advanced phase for one behavior are more likely to be in an advanced phase for another behavior. Thus, past studies suggest that interventions should be targeted towards different behavioral patterns rather than single behaviors (Lippke et al., 2012). This is mainly because success in one behavior can be used to accelerate changes in other behaviors, mainly when the two behaviors are either health-promoting or health-risky, which could be due to carry-over (or transfer) mechanisms, including self-regulatory strategies (Lippke et al., 2012; Spring et al., 2012; Lippke, 2014). A previous study shows that adoption of physical activity may facilitate engagement in another
13 Chapter 1 health behavior such as fruit and vegetable intake, both of which are energy-related behaviors with the goal of preventing obesity and chronic illness by achieving energy balance
(Fleig, Lippke, Pomp, & Schwarzer, 2011; Kreausukon, Gellert, Lippke, & Schwarzer, 2012;
Fleig, Küper, Lippke, Schwarzer, & Wiedemann, 2015).
1.2. Sleep
As mentioned, lifestyle-related health risk factors, like physical inactivity and an unhealthy diet, are largely preventable by the adoption or improvement of a healthy lifestyle or multiple health behavior changes (Geller et al., 2017). This could also apply to sleep, which is also a lifestyle-related behavior. Sleep has attracted attention in health-related studies due to sleep problems, such as insufficient sleep and poor sleep quality, being closely associated with higher risks of NCDs, for example, CVDs, hypertension, and diabetes (Romero-Corral et al., 2010; Kita et al., 2012; Guo et al., 2013; Dong et al., 2013; Itani et al., 2017; Tobaldini et al., 2017). Health problems like NCDs, could be linked to sleep problems, which have been established as associated with the imbalance of energy intake and energy expenditure
(Markwald et al., 2013; Calvin et al., 2013; Patterson et al., 2014; Murphy, Holmes, & Brooks,
2017). A recent study discovered habitual short sleep duration (i.e. less than six hours a day) is connected to increased food energy intake (Murphy et al., 2017). Moreover, people who reported insufficient sleep were more likely to be overweight in a previous study (Knutson,
Spiegel, Penev & Van Cauter, 2007), and sleep problems are usually concurrent with other diseases (Perry et al., 2013); for example, sleep apnea is related to CVDs and obesity (Romero-
Corral et al., 2010; Dong et al., 2013).
Among the lifestyle behavior literature, an extensive study in the United Kingdom shows individuals with either diabetes or CVDs, or both, related to having a progressively more unhealthy lifestyle with a lower-level of physical activity and poorer sleep, compared to
14 Chapter 1 a control group of healthy individuals (Cassidy, Chau, Catt, Bauman & Trenell, 2016). A systematic review of 153 studies shows that sleep deficiency was significantly associated with an increased mortality rate, and similar findings were observed in NCDs (Itani et al., 2017).
Another systematic review shows that sufficient sleep was associated with better emotional regulation and better quality of life (Chaput & Dutil, 2016).
Sleep problems are not only related to stressful and unhealthy lifestyles, but also associated with poor subjective health and quality of life (Ohida et al., 2001; Grandner, 2014).
Focusing on the associations between health behaviors and well-being, a systematic review reveals that sleep, including restful sleep, sleep quantity and sleep quality, is one of the critical health behaviors for good health, together with physical activity and consuming a healthy diet
(Boehm & Kubzansky, 2012). Past research suggests that individuals who were overweight and obese, but adopted a healthy lifestyle with health behaviors, experienced improved sleep quantity and quality, and other health benefits (Shade, Berger, Dizona, Pozehl & Pullen, 2016).
Health-related empirical investigations targeting these multiple health behaviors are essential (Kwan, Faulkner, Arbour-Nicitopoulos & Cairney, 2013).
While sleep has not been considered in health screening or consultation, this shows the lack of acknowledgment by health practitioners or organizations of the importance of sleep, together with a lack of awareness among the general public of many health problems caused by sleep problems (Perry et al., 2013; Filip et al., 2017). Healthy sleep, with the absence of sleep disturbances, or abnormal sleep duration (too short or too long), is an aspect of health behavior which has positive health outcomes in reducing weight gain, easing the symptoms of CVDs, and lowering level of stress with less psychological disturbance (Perry et al., 2013; Grandner, 2014). In fact, sleep is an essential component of health with important positive functions. For exampe, sleep may contribute to preventing the occurrence of NCDs,
15 Chapter 1 maintaining metabolic and emotional regulation and performance, and contributing to quality of life and stress management, which is as critical as physical activity and nutritional diet (Perry et al., 2013; Hagger, 2014; Filip et al., 2017). These positive functions correspond to the definition of health behavior as “behavior patterns, actions and habits that relate to health maintenance, to health restoration and health improvement” (Gochman, 1997, p. 3).
Sufficient sleep and good-quality sleep are often associated with a healthier diet and more frequent physical activity (Khan, Chu, Kirk, & Veugelers, 2015). Existing studies show that they are all crucial in achieving optimal functioning of physical and mental health
(Flueckiger, Lieb, Meyer, Witthauer & Mata, 2016; Filip et al., 2017). Thus, previous studies have brought sleep into health-related research, and strongly suggest studying sleep further to raise the awareness of sleep as an important health behavior (Perry et al., 2013; Irish et al.,
2013; Doku, Koivusilta & Rimpelä, 2013). Thus, this thesis considers sleep as part of a healthy lifestyle, and investigates the interrelations between sleep and physical activity and healthy diet, and their associations with stress management to potentially increase well-being and higher-level goals.
1.3. Multiple Health Behaviors and Age-Group Differences
While engagement in a single health behavior yield positive outcomes, there is a demand to examine multiple health behaviors, which facilitate each other with carry-over mechanisms that relate to self-regulatory factors (Spring et al., 2012; Lippke, 2014; Geller et al., 2017). This is especially the case for NCDs, which could be prevented or delayed by engaging in multiple health behaviors, such as physical activity and healthy diet, particularly in older age. Nonetheless, NCDs also affect people in life, because of lifestyle changes over time (WHO, 2017). A systematic review reveals that physical activity improved sleep, but the
16 Chapter 1 results varied with age; the findings were exceptionally robust among middle-aged and older adults, unlike younger adults (Dolezal, Neufeld, Boland, Martin & Cooper, 2017).
To achieve optimal health and well-being, it is essential to start engaging in healthy behavior at younger ages and continue throughout the lifespan, because many individuals will experience significant declines in capacity and cognitive functions at much younger ages
(Michael, Newton & Kirkwood, 2013). In fact, health behavior change differences by age and health status (Zanjani, Schaie, & Willis, 2006). Unfortunately, younger adults may not see the impact of their unhealthy behaviors until the future, while older adults may immediately observe life-threatening impacts (Hlavaty, 2011). With people living longer globally and the pace of population aging being much faster than in the past, all countries must primarily tackle challenges to ensure that their health and social systems are prepared to adapt to this demographic shift (WHO, 2017).
Despite the advantages of healthy behaviors shown in previous studies, there are still many young people who go through a steep decline in physical activity (Cao, Lippke & Liu,
2011). A study reported young adults engaged at least two health-risk behaviors, which include physical inactivity and inadequate sleep duration (Continente, Pérez, Espelt, Ariza &
López, 2017). A past study examined young adults in the United States, and the findings showed that obesity and high glucose or lipid levels, associated with earlier onset of chronic diseases, like diabetes and CVDs, and increased mortality risk before age 55 (Saydah, Bullard,
Imperatore, Geiss & Gregg, 2013).
To prevent health risks, it is essential to recognize and deal with the causes, including variations in barriers to engaging in health behaviors, and differences in individuals’ subjective perceptions of their health status and quality of life (WHO, 2011). Along these lines, a systematic review revealed that most middle-aged adults identified lack of time as one of
17 Chapter 1 the barriers to engaging in health behaviors (Kelly et al., 2017). A recent study produced similar results, but for young adults (Ashton, Hutchesson, Rollo, Morgan & Collins, 2017). One of the barriers to health behaviors for older adults was poor health (Moschny, Platen,
Klaaßen-Mielke, Trampisch & Hinrichs, 2011). In contrast, health benefits including stress relief, improved well-being and healthy aging, and integration of behaviors into lifestyle were the facilitators for middle-aged adults to engage in health behaviors (Kelly et al., 2017). The outcomes were quite distinct from young adults, as they engaged in health behaviors to improve body image, fitness, energy and health (Ashton et al., 2017).
For this reason, it is evident that there are variances in barriers and facilitators to engaging in health behaviors, and how differently these age groups perceived their health status and quality of life (Menec, Chipperfield & Perry, 1999). A past cross-sectional study identified physical activity as being negatively associated with multi-morbidity among older and young adults, and subjective health was positively related to the achievement of the minimum recommended levels of physical activity among middle-aged and older adults, but the opposite occurred among young adults (Cimarras-Otal et al., 2014).
Although previous studies have investigated health behaviors at a specific period of time and separately examined particular age groups, evidence of the multiple health behaviors and their associations with subjective health and quality of life in young, mid-life and old age altogether is limited. Therefore, this leads to one purpose of this study: to explore this further by examining lifestyle differences among young adults, middle-aged adults and older adults.
18 Chapter 1
1.4. Theoretical Frameworks
Three empirical investigations were carried out to answer the research questions based on several evidence-based theoretical backgrounds, to understand the complex health behaviors and the underlying psychological and behavioral mechanisms.
Foundational Principles. This includes psychological, biological, social and environmental dynamics, which affect the lifespan and well-being of individuals, especially those with disabilities and chronic disease. This theoretical framework was initiated by Wright
(1972), and has been reviewed recently (Dunn, Ehde & Wegener, 2016).
Among the principles, this current thesis embedded two relevant principles. There are
(1) personal or psychological assets (i.e. positive affect) and, (2) self-perception of bodily states (i.e. subjective health), or distress (i.e. negative affect), which accompany most of the rehabilitation patients and are worth examining further (Wright, 1972; Dunn et al., 2016).
These principles are particularly relevant to this thesis, primarily in Chapter 2 and 4, in which former and current rehabilitation patients were recruited.
Everyone has positive personal assets or resources, and those who reported regular positive affect were more likely to engage in health behaviors. Therefore, positive affect could be an important asset in motivating or facilitating the initiation and/or maintenance of good health, during or after rehabilitation (Wright, 1972; Nierenberg et al., 2016). Besides, the experiences of physical states are often subjective and usually different from the actual sensations. The subjective insights shape how individuals think, feel and behave differently.
Thus, changing environmental situations or personal attitudes and expectations could be beneficial for adjusting individual’s perceptions (Dunn et al., 2016).
Subjective Well-Being (SWB). To understand the psychological factors, subjective well-being (SWB, Diener, 1984) has been defined as the combination of affective components
19 Chapter 1 of more positive affect with less negative affect, and cognitive component of greater satisfaction with different domains of life, including satisfaction with health (Kashdan, Biswas-
Diener, & King, 2008). Satisfaction with health or subjective health indicates self-perceived health beyond physical health, including mental health and well-being (Gataūlinas &
Banceviča, 2014). Subjective health is not only a consistent predictor of morbidity and mortality, but also a standard criterion in health surveys, clinical settings and rehabilitation programs (Hunt & McEwen, 1980).
SWB can be beneficial to health and longevity, especially when applied to health behaviors and the physical health of the immune and cardiovascular systems (Diener,
Pressman, Hunter, & Delgadillo-Chase, 2017). This theory has been applied in the longitudinal study in Chapter 2, to observe SWB in natural circumstances in interrelations with other aspects that influence health, like health behavior. Thus, the association between subjective health and physical activity are examined in a longitudinal study.
Compensatory Carry-Over Action Model (CCAM). Originated by Lippke (2014), CCAM is a novel approach for the multiple health behavior change process, for understanding the underlying psychological and behavioral mechanisms to promote a healthy lifestyle and prevent NCDs, especially diabetes and obesity. Thus, this model has been chosen because this thesis focuses on the understanding of carry-over mechanisms and self-regulation of multiple health behaviors in relating to subjective health and quality of life.
In general, CCAM, illustrated in Figure 1, combines different social-cognitive factors such as intention, self-efficacy, and planning, for single health behaviors, which in turn facilitate multiple health behaviors. Moreover, the CCAM emphasizes multiple health behavior change, including compensatory cognitions and carry-over mechanisms.
Particularly, the carry-over mechanisms of multiple health behaviors may carry over
20 Chapter 1 resources from one health behavior to another, with the resources, including experiences, skills, and cognitions, such as motivation and self-regulation. In addition, different health behaviors are assumed to relate to life goals, known in the model as higher-level goals, and stress-management that increase well-being.
Figure 1. The Compensatory Carry-Over Action Model (CCAM) by Lippke (2014), where CC =
Compensatory Cognitions.
The first part of the model, which includes social-cognitive factors and compensatory cognitions, and model-testing as a whole have not been considered in this thesis. This is because the goals of the thesis are mainly focus on the second part of the model, which portrays (1) one single health behavior (e.g. physical activity) interrelate with another single health behavior (e.g. healthy diet/sleep) via carry-over mechanisms, and (2) multiple health behaviors associate with stress management that increase well-being (e.g. good sleep quality), and higher-level goals (e.g. good subjective health or quality of life). The conceptual model is illustrated in Figure 2.
21 Chapter 1
Figure 2. Overview of main study variables in this thesis, based on the Compensatory Carry-
Over Action Model (CCAM) by Lippke (2014).
Among the carry-over mechanisms within multiple health behaviors, self-regulation describes various processes by which individuals pursue and attain goals that motivate action, maintain human behaviors, and facilitate future changes (Mann et al., 2013). For instance, when individuals engage in unhealthy behavior, it is essential they decide to improve their current condition and determine the goals (e.g. self-perceived good health and quality of life), and execute appropriate behaviors (e.g. being physically active).
Goals are mental representation of desired outcomes and individuals may adopt a goal as small as exercising at least half an hour a day or as large as pursuing optimal health and well-being (Mann et al., 2013). This thesis focuses on the latter, which are the life goals, or known as higher-level goals. There are many reasons that individuals may not adopt specific health goals, which could be due to restrictions from illnesses, and lack of awareness
22 Chapter 1 of the health conditions or health consequences of their behavior (Mann et al., 2013). Among different types of goals like career goals or specific health goals, higher-level goals consist of individual values that integrate self-identity with a sense of direction and psychological purpose. Thus, compared to specific health goals, higher-level goals are more constant, clear, and durable to motivate individuals to engage and maintain in different health behaviors, and less complex, which may interfere with other goals (Mann et al., 2013; Lippke, 2014).
Subjective health and quality of life, for instance, both capture more constant subjective perception and evaluation of individuals’ general health and well-being, in general and clinical settings (Meiselman, 2016; Bowling, 2017; Diener et al., 2017).
In current lifestyles, efforts to adopt and maintain health behaviors can be stressful and challenging, and add to existing stress. Daily hassles, chronic stressors, work stress, life transitions such as retirement could all lead to psychological distress. High-levels of stress not only showed more sedentary behaviors, less physically active and healthy diet, but also yielded poor subjective health and lower level of quality of life (Clark et al., 2011).
Consequently, managing emotional distress seems potentially to be part of the healthy trends due to physical and mental health affect each other (Clark et al., 2011; McKenzie & Harris,
2013). Particularly, healthy behaviors have been identified as having the capability to act as a buffer against the adverse outcomes of stress and lead to the achievement of higher-level goals (Lippke, 2014). Thus, despite this thesis focuses on the examinations of the associations between health behaviors and stress-management that may potentially increases well-being, such as sleep quality. Some individuals engage in health behaviors (e.g. physical activity) to cope with stress and thus quite often being included in stress-management which was highly associated with good sleep quality (Hui & Grandner, 2015).
23 Chapter 1
Well-being is potentially both a motivator to strive towards the desired outcomes in life, and an outcome target in life and intervention (Ryff, 1989; MacLeod, 2013). Besides, quality of life encompasses various domains of life, including health, which could be studied in relation to an individual’s goals and motivation with self-regulation and management of one’s life priorities (Efklides & Moraitou, 2013). Thus, good quality of life not only relate to managing stress and well-being, but also could be the higher-level goal. In this thesis, quality of life has been adopted as both a higher-level goal and stress-management technique that could increase well-being, in separate studies (Chapter 3 and 4). Together with subjective health as one of the leading indicators of quality of life, all these are closely linked to lifestyle choices as health factors (Bowling, 2017). Although almost 7 out of 10 European Union residents self-reported that they were in good health, more than half of them reported that their quality of life was not optimal (Eurostats, 2015). Therefore, healthy and lengthy lives are not just an essential personal aim, but also an indication of societal well-being and success
(Eurostats, 2015). The association between subjective health and quality of life may operate differently as a function of age, however, there is insufficient evidence to imply that it does
(Ryff, 1989; MacLeod, 2013). For this reason, in Chapter 4 a cross-sectional study investigates age group differences.
With CCAM as the theoretical backdrop, studies in Chapter 3 and 4 investigated whether being physically active, consuming a healthy diet (including low-fat diet and sufficient fruit and vegetable intake) and getting sufficient, restful, good quality sleep, are associated with better stress management, which can potentially increase well-being, and higher-level goals of perceiving themselves as healthier and having a good quality of life.
24 Chapter 1
1.5. Objectives, Research Questions, and Hypotheses
The primary goal of this thesis to identify the interrelations among different health behaviors in a healthy lifestyle, and their associations with subjective health, quality of life, and sleep quality. In accordance with this, the following research questions were outlined:
1. How does a single lifestyle-related health behavior of physical activity associate
with subjective health across time? (Chapter 2)
2. What are the predictors of subjective health? (Chapter 2)
3. How do multiple lifestyle-related health behaviors of sleep and the consumption of
a low-fat diet associate with quality of life and subjective health? (Chapter 3)
4. How do multiple lifestyle-related health behaviors of sleep, physical activity and a
healthy diet of fruit and vegetables associate with subjective health, quality of life
and sleep quality? (Chapter 4)
In including sleep as part of the healthy lifestyle, by investigating the interrelations between sleep and health behaviors of physical activity and/or healthy diet, various measurements of sleep have been used. The research questions are outlined below:
5. How do subjective measures of sleep (i.e., sleep duration, sleep quality and daytime
functioning) interrelate with a low-fat diet? (Chapter 3)
6. How does restful sleep interrelate with physical activity, and a healthy diet of fruit
and vegetables? (Chapter 4)
In considering socio-demographic information, the third goal of this thesis is to investigate the age-group differences in health behaviors and their associations with subjective health, quality of life and sleep quality. Therefore, the relevant research questions are as follows:
25 Chapter 1
7. Are there age group differences for multiple health behaviors of restful sleep,
physical activity, and a healthy diet of fruit and vegetables, and their associations
with subjective health, quality of life and sleep quality? (Chapter 4)
All research questions were based on the above review of the existing literature and
CCAM theoretical framework. To answer the research questions, the objectives and hypotheses of this thesis are outlined as below:
Study 1 (Chapter 2): – A longitudinal study: association of physical activity with subjective health and positive affect.
Objectives:
To examine the associations of subjective health, positive affect, negative affect and physical activity of orthopedic patients in an outpatient rehabilitation center, who were admitted due to musculoskeletal disease, with follow-ups of up to eight years. With the intention of extending the scope of a theoretical formulation of subjective well-being, the interrelations of subjective health, positive affect of feeling calm and peaceful, and negative affect of feeling low and downhearted were tested as a preliminary analysis. To contribute to understandings the interrelation of subjective health, positive affect and negative affect, as well as health behavior of physical activity, it was hypothesized that among previous rehabilitation patients:
Hypotheses:
1. Means of subjective health and positive affect would increase, while means of
negative affect would decrease over time.
2. Being physically active would report greater subjective health and a stronger growth
in subjective health over time.
26 Chapter 1
3. Positive affect, negative affect, and being physical activity are predictors of subjective
health.
Study 2 (Chapter 3) – A cross-sectional study: associations of sleep and healthy diet, with subjective health and quality of life of older adults.
Objectives:
To examine the interrelations of multiple health behaviors (in this case, consuming a low-fat diet and subjective measurements of sleep—duration, sleep quality and daytime functioning), and their associations with subjective health and quality of life, among older adults aged 50 and above. This study hypothesized that:
Hypotheses:
1. Sleep duration, sleep quality and consuming a low-fat diet are interrelated with the
sleep measurement of daytime functioning.
2. Daytime functioning is interrelated with sleep duration, sleep quality, and consuming
a low-fat diet, and is associated with subjective health and quality of life in older
adults.
Study 3 (Chapter 4) – A cross-sectional study: associations of sleep, physical activity and healthy diet, with subjective health, quality of life and sleep quality, across different age groups.
Objectives:
To identify the interrelations of restful sleep, physical activity and a healthy diet of fruit and vegetables, and their associations with quality of life, subjective health and sleep quality. The participants were recruited from Germany and the Netherlands. Regardless of the country differences, this study hypothesized that,:
Hypotheses:
27 Chapter 1
1. Restful sleep, physical activity, and a healthy diet of fruit and vegetables are inter-
related.
2. Restful sleep, physical activity, and a healthy diet of fruit and vegetables are associated
with increased overall quality of life and subjective health, via sleep quality.
3. There are age groups differences in sleep (i.e., restful sleep and sleep quality), health
behaviors (i.e., physical activity and a healthy diet of fruit and vegetables) and quality
of life and subjective health, among young adults, middle-aged adults, and older
adults.
4. Health behaviors (i.e., restful sleep, physical activity, and fruit and vegetable intake)
are associated with sleep quality, quality of life and subjective health across age
groups.
To investigate single and multiple health behaviors, and their associations with stress management that may potentially increases well-being and higher-level goals, this thesis includes three studies with various designs, samples and settings, which are outlined in Table
1. The following chapters describe and present three studies (in Chapters 2 to 4). All findings are summarized in Chapter 5, together with a general discussion, practical and theoretical implications, suggestions for future research, and conclusions.
28 Chapter 1
Table 1
Overview of the Empirical Studies of this Thesis
Chapter Health Study Design Setting Baseline Follow-ups Primary Behavior (Years) Outcomes ½ 3 8 2 Physical Longitudinal Orthopedic Ö Ö Ö Ö Subjective activity rehabilitation health 3 Sleep, Cross- Senior day Ö Subjective low-fat sectional cares, sport health, diet clubs Quality of (older adults) life 4 Sleep, Cross- General Ö Sleep physical sectional populations quality, activity, from internet Subjective FVI platforms & health, Cardiac Quality of rehabilitation life Note. FVI= fruit and vegetable intake
29 Chapter 1
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39 Chapter 2
Chapter 2:
Associations of physical activity,
with subjective health
This chapter has been accepted and will be published as Tan, S.L., Duan, Y.P. & Lippke, S. (2018, In press). A longitudinal study of orthopedic rehabilitation patients: Physical activity, subjective health and positive affect. Clinical Applications of Positive Psychology: An International Perspective.
40 Chapter 2
2. Chapter 2 (Study 1)
This chapter focuses on the association between physical activity and subjective health over the course of eight years.
Figure 3. Chapter 2 – Focus on health behavior of physical activity and higher-level goal of subjective health (highlighted in green).
41 Chapter 2
Abstract
Objective: Since subjective well-being among orthopedic rehabilitation patients is not always optimal, it is essential to examine the interrelations between subjective health, positive affect, negative affect and long term physical activity in clinical settings.
Methods: An eight-year longitudinal design was implemented, in which subjective health, positive affect, negative affect and physical activity were assessed in a paper-pencil questionnaire at baseline (N = 640), and follow-ups at six months (n = 494), three years (n =
330), and eight years (n = 224). Drop-out indicated participants who did not complete all measurement points reported lower level of subjective health with lower means of positive affect and younger age. Linear Mixed Model with the maximum likelihood method was applied to examine the main variables.
Results: After the rehabilitation through which exercise therapy was introduced, participants who were physically active with higher means of positive affect and lower means of negative affect were more likely to report better subjective health at baseline and all follow-ups.
Although subjective health declined after a peak at the eight-year follow-up, it remained higher compared to the baseline.
Conclusions: Changeable subjective perceptions of one’s health and physical activity are related to extensive positive outcomes in the longer term. The outcomes could be informative for clinical applications, in enhancing well-being and achieving sustainable rehabilitation care.
Keywords: subjective well-being; foundational principles; longitudinal study; clinical samples; positive affect; negative affect; subjective health; physical activity
42 Chapter 2
Introduction
Every individual – including those with a disability or chronic diseases – has positive assets, like positive affect and positive self-perceived health, that could be essential resources during or after a rehabilitation experience in clinical settings according to the Foundational
Principles (Wright, 1972; Nierenberg et al., 2016). The experience of positive state is important for healthy functioning, overcoming physical challenges, and having a better quality of life across one’s lifespan (Scheibe, English, Tsai, & Carstensen, 2013). Moreover, the positive affect of calm and peace was strongly related to individuals’ physical health (Scheibe et al., 2013).
In addition, positive self-perceived health or subjective health is not only an important outcome measure, but also an influential predictor for different health behaviors that are significant in clinical settings and rehabilitation programs (Hunt & McEwen, 1980; Doiron,
Fiebig, Johar, & Suziedelyte, 2014; Liu et al., 2016). For instance, if patients are advised to adopt physical activity, they might feel constrained by their health status, but they might also perceive the benefits when initiating and maintaining a new behavior in terms of their perceived health. The experiences of bodily states in general are often subjective insights into the occurrences that shape how individuals think, feel and behave. Therefore, the changing personal attitudes and expectations could beneficially adjust an individual’s perceptions
(Dunn et al., 2016).
Seligman (2008) outlined the importance of measuring individuals’ subjective health to identify correlates of other health-related constructs, such as physical activity and positive affect. For example, the health benefits of subjective well-being (Diener & Chan, 2011) and positive affect (Fredrickson, 2000; Salovey, Rothman, Detweiler, & Steward, 2000; Schmidt,
Ziemer, Piontkowski, & Raque-Bogdan, 2013) have been well reviewed.
43 Chapter 2
However, many rehabilitation patients with orthopedic conditions usually do not meet the recommended level of physical activity (Peiris, Taylor, & Shields, 2013), and their subjective well-being is not always optimal. Therefore, it is important to examine the possible physical and psychological factors that influence the functioning of individuals, especially in clinical settings.
Theoretical Framework – Subjective Well-Being
To improve the positive functioning of people, rather than only physical health, the presence of mental health and well-being which buffer against the development of negative affective states (Nierenberg et al., 2016). Subjective well-being is commonly defined by (1) affective components of more positive affect and less negative affect, as well as (2) the cognitive component of satisfaction with different domains of life (Diener, 2006; Kashdan,
Biswas-Diener, & King, 2008; Diener & Chan, 2011), such as satisfaction with health. Based on the bottom-up approach of the needs conceptual model of subjective well-being (Gataūlinas
& Banceviča, 2014), satisfaction with health forms life satisfaction as a whole through its role in the satisfaction of needs, since subjective health indicates self-perceived health beyond physical health, including mental health and well-being.
A previous study found significant effects of subjective health on positive affect and negative affect, suggesting that subjective perception of health is a critical indicator for well- being (Cho, Martin, Margrett, MacDonald, & Poon, 2011). Salovey and colleagues (2000) also suggested that positive affect may promote healthy perceptions, beliefs and physical well- being, where the effects include the prevention of illness and the motivation of health behaviors, with greater self-efficacy. Additionally, the positive affect of feeling calm and peace was strongly related to individuals’ physical health (Scheibe et al., 2013).
44 Chapter 2
To understand positive affect, it is essential to identify and comprehend the perspective of negative affect. Kökönyei and colleagues (2015) discovered that subjective health complaints were mediated by perceived stress and negative affect; thus, the study highlighted that negative affect is a significant process in health and they could be potential targets for health prevention. Individuals with more negative affect tend to have more health problems, while those with more positive affect tend to have better health practices, such as physical activity and healthy diet (Diener & Chan, 2011; Salovey et al., 2000).
Physical Activity
In addition to the components of subjective well-being, a substantial amount of research has shown that physical activity has sustainable health benefits (Rodrigues, Gomes,
Tanhoffer, & Leite, 2014; McKinney et al., 2016). In addition, physical activity improves mood and well-being, with one previous study having discovered that people who were physically inactive were significantly more likely to experience negative affect than those who were physically active (Lu et al., 2012). A systematic review concluded that physical activity is associated with more positive affect and higher satisfaction with life, although little is known about the associations between physical activity and well-being changes across the lifespan, as well as the underlying mechanisms of this relation (Kanning & Hansen, 2016).
Individual’s affective states, behaviors, and cognitions change across life stages, and it is likely that these changes will influence the processes connecting physical activity and well-being (Hyde, Maher, & Elavsky, 2013). Nonetheless, a gap still exists since everyone experiences emotions differently; thus, individual differences such as subjective perceptions and daily lifestyle need to be further explored (Robbins, Judge, & Campbell, 2010).
Past research has typically considered only one or another part of this interrelation, and only a few studies have tested long-term outcomes. Therefore, the current study
45 Chapter 2 investigated the relations among subjective health, positive affect and negative affect together, as well as the associations with physical activity, over the course of eight years.
Hypotheses
To contribute to understanding the interrelation of components of subjective well- being with the health behavior of physical activity, it was hypothesized that among previous rehabilitation patients:
(1) Means of subjective health and positive affect would increase, while means of
negative affect would decrease over time.
(2) Being physically active would report greater subjective health and a stronger growth
in subjective health over time.
(3) Positive affect, negative affect and being physically active are predictors of subjective
health.
Method
Design
An eight-year longitudinal design was implemented in Germany, with the data collection at baseline taking place between March 2002 and March 2003. This study presents data from a larger interdisciplinary trial, with different aspects related to physical activity, self-regulation and social-cognitive factors. Some data have been published separately (Paech
& Lippke, 2017; Ziegelmann, Lippke, & Schwarzer, 2006; Lippke, Ziegelmann, & Schwarzer,
2004), but subjective health and emotion-related questions have not been addressed before, and have not been included in previous analyses.
Participants and Procedures
The study was carried out in an outpatient orthopedic rehabilitation center, where patients were admitted due to musculoskeletal diseases: back pain, spinal illnesses, surgeries,
46 Chapter 2 joint conditions, and injuries. One of the main purposes of the rehabilitation program was to enhance their physical functioning and well-being. All participants were advised to participate in a three-week exercise therapy program on a daily basis during rehabilitation, which was guided and monitored by sports therapists and physicians. After rehabilitation, engagement in physical activity was voluntary but recommended.
Patients who met the inclusion criteria below were approached to participate in the study: (i) being capable of exercising on one’s own; (ii) no cognitive impairments and being able to fill out a questionnaire without any additional help; and (iii) German language proficiency. As the study involved telephone follow-ups, only patients with access to a telephone were recruited. All the information and materials included in the study contained code numbers rather than names, to ensure anonymity and encourage frank responses.
As shown in Figure 4, a total of 641 participants participated in the study during their rehabilitation stay. Of these, one participant aged 15 had to be excluded from the sample as an outlier to avoid the violation of assumptions and confounding effects. Informed consent obtained and the ethical principles of the American Psychological Association (APA, 1992) were met. After obtaining informed consent, all participants were given paper-and-pencil self-report questionnaires prior to rehabilitation as a baseline measurement (T1, n=640), including demographic variables such as age, body mass index (BMI), marital status and education level. After three weeks of rehabilitation, patients were discharged and scheduled to fill out a follow-up questionnaire at six months (T2, n=494), at three years (T3, n=330) and eight years (T4, n=224). Individual differences were considered, in order to gain reliable outcomes with self-reported measurements (Paulhus & Vazire, 2007). To avoid subject response bias, clear language to frame the questions was adopted and a Likert-scale was used to provide sufficient options to respondents. The order of the question was randomized to
47 Chapter 2 avoid question order bias.
Figure 4. Flow of participants in this study (Chapter 2) through each measurement points.
The participants were aged between 18 to 80 years (Mage=46.31; SD=11.74), 61.2%
(N=389) were female, and the average BMI was 26.05kg/m². Among the participants, 54% were working full time, and 70% were married and/or living with a partner.
Measures
Since this study was carried out in Germany, German versions of the questionnaire were used. All items given below as examples are the measurements from English versions.
Demographic variables. Socio-demographic characteristics included: gender, year of birth, marital status, education level and BMI.
48 Chapter 2
Subjective health, positive affect and negative affect. The twelve-item Short-Form
Health Survey (SF-12) is a measurement used to evaluate health-related quality of life, predominantly for well-being in physical and emotional dimensions of life, which is specifically useful for large studies (Ware, Kosinski, & Keller, 1996; Farivar, Cunningham, & Hays, 2007).
In this study, the item for general health was used to access participants’ subjective health by asking ‘In general, how would you rate your health?’, ranging from ‘Excellent’ to ‘Poor'. To access positive affect and negative affect, two items were used – ‘How much of the time during the past 4 weeks, have you felt calm and peaceful?’ to access participants’ positive affect, and ‘…have you felt low and downhearted?’ to access participants’ negative affect – with scale scores ranging from 1 (All of the time) to 6 (None of the time). Past studies have used these two perceived mental health items to access depressive symptoms for screening purposes, targeting treatment and prevention (De Smedt et al., 2013; Vilagut et al., 2013).
Furthermore, these two items have been used separately. For example, the item of feeling low and downhearted was used separately to examine inflammatory arthritis patients and their health-related quality of life and negative affect (Harrison et al., 2009). The item of feeling calm and peaceful was used to investigate positive affect on age-group differences
(Scheibe et al., 2013).
Physical activity. The stages algorithm developed by Lippke and colleagues (2010) was used to access participants’ physical activity. Participants were asked ‘Did you engage in physical activity at least three times per week, for at least 30 minutes or more, in such a way you were moderately exhausted?’, whereby they could answer either ‘Yes’ or ‘No’, the same as the following question: ‘Do you intend to start (new or old activities) soon?’ Participants who implied being active in the past were categorized as ‘Active’, while those who implied that they had not been active and/or intended to perform physical activity were categorized
49 Chapter 2 as ‘Inactive’. The reliability and validity of this measure was found to be high (Lippke, Fleig,
Pomp, & Schwarzer, 2010) and it has been used in other studies (Duan, Lippke, Wagner, &
Brehm, 2011; Jackson, Lippke, & Gray, 2011).
Data Analyses
All data analyses were performed with SPSS 24 software. Follow-ups were performed at intervals of six months, three years and eight years. Drop-out analyses were performed with Chi-square (c2) tests to explore categorical variables, and t-tests were carried out to explore continuous variables. Multicollinearity was observed among variables, whereby the analyses confirmed that multicollinearity did not affect any of the significant effects reported below. A correlation analysis was run to determine the interrelations among the main variables of this study, namely subjective health, positive affect and negative affect, age and
BMI. Furthermore, the values of Pearson’s r were also used to measure the effect size of the relationships among variables with the purpose of avoiding incorrect inferences, biased results and less precise estimates (Field, 2009).
Even though drop-out is a common phenomenon in longitudinal studies within health- related studies (Bhamra, Tinker, Mein, Ashcroft, & Askham, 2008), missing values should be treated appropriately. Multiple Value Analysis (MVA) was used to describe the missing data and provide information in comparison to complete data. At the baseline measurement (T1, n=640), missing values were between 0.6% and 5.9% for all the main variables, including demographic variables such as age, BMI, marital status, employment status and education level. At six-month follow-up (T2, n=494), missing values of the main variables were between
23.4% and 24.7%. The missing values for the follow-up measurements at three years (T3, n=330) and at eight years (T4, n=224) were between 48.4% and 51.9% and between 65.2% and 67.3%, respectively.
50 Chapter 2
For the analyses of longitudinal data, the Generalized Linear Models (GLM) approach has been criticized for violating the assumption of independence of observations (Shek & Ma,
2011; West, Welch, & Galecki, 2014). Therefore, Linear Mixed Models (LMM) with the maximum likelihood method was used as it was recommended as an effective and flexible way of dealing with missing values, without excluding any missing measurement (Shieh,
2003). This is mainly due to LMM being well-adapted for an unbalanced sample size across different points in time, which might be due to drop-out or missing values (Seltman, 2016).
LMM was used here as it is commonly used to examine changes in human behaviors over time. Multiple measurements per individual generally lead to correlated errors, which could cause violation of assumptions. LMM is flexible in examining individual changes and choosing the best-fitting model, which leads to increased accuracy of outcomes (Shek & Ma, 2011).
The unstructured covariance structure model was adapted in this study as it often offers the best fit and is most commonly found in longitudinal data, given that is the most parsimonious (Shek & Ma, 2011). The application of the individual growth curve (IGC) model was used to develop different types of polynomial growth curve models to examine individual growth and use non-linear growth curves to describe between- and within-subject changes over time (Field, 2009; Shek & Ma, 2011). Since analyses were performed at the individual level and groups of different measurement points were randomized, intra-class correlation coefficient (ICC) was used to adjust the standard error estimates before calculating the confidence interval. ICCs were identified for variance of the random effects, which were also used as effect size measures (Shek & Ma, 2011; Rosnow, Rosenthal, & Rubin, 2000; Field,
2009). The LMM methodology was applied to identify which variables could be the predictors of subjective health.
51 Chapter 2
Results
Dropout Analysis
Significant results were discovered for age differences, whereby those who dropped out tended to be younger than those who completed all measurement points, particularly at the three-year follow-up (T3: t(325) = -2.124, p = .034) and eight-year follow-up (T4: t(220) =
-2.887, p = .004). There was no significant difference in BMI, with all p values being larger than
.05. Participants who dropped out at six month (T2, t(483) = -2.926, p = .004) and three year
(T3, t(320) = -2.428, p = .015) follow-ups reported a significantly lower level of subjective health compared to those who had completed all measurement points.
Similarly, people who dropped out at six month, three year, and eight year follow-ups appeared to show less positive affect of feeling calm and peaceful, with T2: t(463) = -2.284, p
= .023; T3: t(308) = -2.737, p = .006; and T4: t(208) = -2.223, p = .027. A Chi-square test was performed to examine the relation between drop-out and categorical variables of gender and physical activity stages, whereby no significant difference was found, with all p values being larger than .05.
Preliminary Analysis
To inspect the degree of interdependence between the variables, the main continuous variables were used in a correlation analysis (see Table 2). All main variables were significantly interrelated across all measurement points (T1-T4), with p < .01, except the baseline of positive affect, and negative affect, with subjective health at T4 at eight-year follow-up. As shown in Table 2, subjective health significantly positively interrelated with positive affect across time, but negatively interrelated with negative affect. Matched with the assumption of this study, positive affect was significantly negatively interrelated with negative affect.
52 Chapter 2
In Table 2, the age of the participants presented positive correlations with BMI, r =
.26, p < .01, and positive affect of all measurement points. By contrast, age was negatively correlated with subjective health and negative affect, and was especially significant for T1 and
T2. In addition, BMI showed negative correlations with subjective health from T1 to T3.
Physical activity was included as a dichotomous variable, and T1 was significantly negative correlated with age and BMI. From T1 to T4, physical activity was positively correlated with subjective health. Physical activity was positively associated with physical activity at a previous point in time; for instance, physical activity at baseline (T1) was positively correlated with being physically active after six months (T2).
53
Table 2 Means, Standard Deviations and Inter-relations (Pearson’s, r) of the Major Study Variables Across Time (Chapter 2).
Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
1. Age - 2. BMI .26** - 3. Sub. Health, T1 -.08* -.12** - 4. Sub. Health, T2 -.13* -.12** .47** - 5. Sub. Health, T3 -.08 -.18** .39** .65** - 6. Sub. Health, T4 -.07 -.12 .41** .58** .59** - 7. Calm & Peace, T1 .15** .03 .34** .17** .20** .12 - 8. Calm & Peace, T2 .14** -.03 .25** .43** .34** .32** .44** - 9. Calm & Peace, T3 .17** -.05 .29** .40** .45** .31** .39** .58** - 10. Calm & Peace, T4 .22** .12 .19** .23** .21** .34** .36** .50** .47** - Ch apter 2
54 11. Low & Down, T1 -.14** -.07 -.36** -.22** -.18** -.12 -.58** -.39** -.26** -.23** -
12. Low & Down, T2 -.11* .07 -.32** -.44** -.35** -.32** -.37** -.66** -.45** -.35** .43** -
13. Low & Down, T3 -.08 .12* -.32** -.42** -.46** -.38** -.26** -.47** -.61** -.40** .34** .51** - 14. Low & Down, T4 -.10 <.01 -.30** -.37** -.37** -.49** -.35** -.39** -.39** -.60** .36** .41** .59** - 15. P.A., T1 -.18** -.10* .13** .07 <-.01 .11 .02 -.04 -.04 .04 -.02 <.01 .01 -.09 - 16. P.A., T2 -.08 -.06 .08 .20** .16** .01 <-.01 .07 .03 -.13 -.05 -.12** -.09 -.05 .15** - 17. P. A., T3 -.06 -.09 .07 .13* .19** .18* .11 .20** .20** .03 -.08 -.20** -.16** -.06 .07 .29** - 18. P. A., T4 -.04 -.05 -.03 -.04 -.04 .14* -.06 .01 .02 .01 .04 .04 -.03 -.07 .04 .12* .16** -
M 46.31 26.05 2.51 2.78 2.87 2.79 3.65 3.95 4.13 4.07 2.56 2.26 2.15 2.11 0.45 0.69 0.61 0.91 SD 11.74 4.73 0.79 0.83 0.86 0.87 1.29 1.23 1.24 1.21 1.29 1.22 1.14 1.17 0.50 0.46 0.49 0.29 Note. BMI = Body Mass Index; Sub. Health = Subjective Health; Calm & Peace = Positive Affect; Low & Down = Negative Affect; P.A. = Physical Activity. T1 = Baseline; T2 = At 6-month follow-up; T3 = After 3 years; T4 = After 8 years; Physical Activity, dummy variable, with 0 = Inactive, 1 = Active ** p < .01. * p < .05, two-tailed significance levels of correlations.
Chapter 2
Main Analysis
LMM was carried out to identify possible outcomes of each subject at different measurement points, and multiple related outcomes at one point in time. Therefore, ‘Time’
– which indicates all four different time points – is the primary interest of this study and assembled as a fixed effect. Fsubjective_health(3, 13828.02) = 226.34, p < .001 indicates significant time differences on subjective health, as well as on positive affect and negative affect, with
Fpositive_affect(3, 13806.77) = 166.22, p < .001 and Fnegative_affect(3, 13790.50) = 148.78, p < .001, respectively. The results in Table 3 reveal that subjective health and positive affect increased at T2 and T3, but slightly decreased at T4. Subjective health slightly but significantly increased from T1 to T4, and positive affect showed similar significant trends. By contrast, negative affect decreased at T2, T3 and T4 compared with the baseline.
Growth models were run and first linear terms were observed and followed by quadratic terms, whereby both showed significant results for all of the dependent variables, although including cubic terms did not yield a better model fit for any of the three dependent variables. Thus, in further analyses, the effect of different time measurements was understood to be linear and quadratic, as shown in Tables 3 and 4, as well as the pairwise comparisons, 95% confidence intervals and the intra-class correlation coefficient (ICC). Based on the values of the ICC, the random intercepts of the participants accounted for 56% of the total variance for subjective health, about 49% of the total variance for positive affect and
50% of the total variance for negative affect.
55
Table 3 Summary of the Main Variables in this Study (Chapter 2).
T1 T2 T3 T4 Growth Curve
M n M n M n M n Pairwise Intercept Linear Quadratic
(SE) [95% CI] (SE) [95% CI] (SE) [95% CI] (SE) [95% CI] Comparison b (SE) b (SE) b (SE)
Sub. 2.52 624 2.75 490 2.83 330 2.80 223 T1 < T2 & T3 & T4** 2.16 0.42 -0.07
Health (0.03) [2.46, 2.57] (0.03) [2.70, 2.81] (0.03) [2.77, 2.88] (0.03) [2.74, 2.85] T3 > T4* (0.04)** (0.02)** (<0.01)**
Calm & 3.63 605 3.92 489 4.08 328 3.96 223 T1 < T2 & T3 & T4** 3.10 0.62 -0.10 Ch
2
Peace (0.04) [3.56, 3.71] (0.04) [3.85, 3.99] (0.04) [4.01, 4.16] (0.04) [3.89, 4.04] T2 < T4* (0.11)** (0.09)** (0.02)** apter 56
Low & 2.57 602 2.29 490 2.20 328 2.19 209 T1 > T2 & T3 & T4** 2.96 -0.46 0.07
2
Down (0.04) [2.49, 2.64] (0.04) [2.22, 2.36] (0.04) [2.12, 2.27] (0.04) [2.12, 2.27] (0.10)** (0.08)** (0.02)**
Note. Sub. Health = Subjective Health; Calm & Peace = Positive Affect; Low & Down = Negative Affect.
CI = Confidence Interval. T1 = Baseline; T2 = At 6-month follow-up; T3 = At 3-year follow-up; T4 = At 8-year follow-up.
** p < .01. * p < .05.
Chapter 2
Table 4 Model of Subjective Health and Physical Activity (Chapter 2). Subjective Health
Physical T1 T2 T3 T4 Estimate Pairwise
Activity M SE M SE M SE M SE (SE) Comparison
Inactive 2.43 0.03 2.62 0.03 2.71 0.03 2.63 0.03 -0.19 T1 < T2 & T3 &
(0.01)** T4**
Active 2.62 0.03 2.81 0.03 2.90 0.03 2.82 0.03 - T1 < T2 < T3**
Intercept T1 T2 T3 T3 > T1 & T2 &
Estimate 2.82 -0.20 0.01 0.08 T4**
(SE) (0.03)** (0.02)** (0.01) (0.02)** Active>Inactive**
Growth Intercept, Linear, Quadratic, ICC
Curve b (SE) b (SE) b (SE)
2.15 0.47 -0.08 0.55
(0.08)** (0.06)** (0.01)**
Note. T1 = Baseline; T2 = At 6-month follow-up; T3 = At 3-year follow-up; T4 = At 8-year follow-up; ICC=Intra-class Correlation Coefficient (effect size). Intercept value represents the value for T4 as baseline based on the setting at SPSS chose last category.
** p < .01. * p < .05.
As mentioned earlier, the study participants were categorized as being ‘Active’ and
‘Inactive’. When all measurement time points were included into the model, with physical activity at baseline, significant differences in subjective health were found within both groups over time (Ftime(3, 13790.57) = 153.01, p < .001, and Fphysical_activity(1, 14126.36) = 211.83, p <
57 Chapter 2
.001, respectively). Means and standard errors for the four time points are reported in Table
4, as well as standardized beta coefficient values, ICC and the growth models.
Similar linear and quadratic terms as in the combined model could be identified. The significant linear trend for subjective health across time indicates that the rate of linear growth increased over time. The significant quadratic trend for subjective health across time reveals that the rate of quadratic growth decreased at some point in time, which aligned to the means reported earlier. In addition, participants who were physically inactive showed significantly lower levels of subjective health compared to those who were physically active, with b = -0.14, SE = 0.01, t = -9.87, p < .001, 95% CI [-0.16, -0.11].
As shown in Table 5, both positive affect and negative affect significantly predicted subjective health (F(1, 14253.92) = 533.73, p < .001, and F(1, 14272.49) = 410.51, p < .001). In
Table 5, the analysis showed higher means of positive affect, whereby subjective health was higher with b = 0.13, SE = 0.01, t =23.10, p < .001, 95% CI [0.12, 0.14], while for those who showed higher means of negative affect, subjective health was lower with b = -0.12, SE = 0.01, t = -20.26, p < .001, 95% CI [-0.13, -0.11].
Physical activity also displayed as a significant predictor for subjective health, with F(1,
14122.67) = 145.60, p < .001. There was a curvilinear relationship between physical activity and subjective health in the sense that being physically inactive predicted a lower level of subjective health, with b = -0.15, SE = 0.01, t = -12.07, p < .001, 95% CI [-0.17, -0.12], as the comparison when ‘Active’ and ‘Inactive’ groups were compared.
Moreover, socio-demographic characteristics entered as covariates (age and BMI of the participants) significantly predicted subjective health, with Fage (1, 895.53) = 34.51, p <
.001, and FBMI (1, 864.08) = 14.20, p < .001, respectively. Younger age and lower BMI also
58 Chapter 2 predicted better subjective health, b = -0.01, t = -5.88, p < .001, and b = -0.02, t =-20.26, p <
.001, separately.
Table 5 Model of Predictors of Subjective Health (Chapter 2).
Subjective Health, T4
Fixed Effect b SE df t p 95% CI Parameter Intercept (T4) 3.43 0.13 888.92 26.96 < 0.01 [3.18, 3.68]
T1 -0.13 0.02 13802.62 -9.10 < 0.01 [-0.16. -0.10]
T2 <0.01 0.01 13713.79 0.19 0.85 [-0.02, 0.03]
T3 0.06 0.01 13732.86 4.27 < 0.01 [0.03, 0.09]
T4 ------
Age -0.01 <0.01 895.53 -5.88 <0.01 [-0.014, -0.007]
BMI -0.02 <0.01 864.08 -3.77 <0.01 [-0.025, -0.008]
Calm & Peace 0.13 <0.01 14253.92 23.10 <0.01 [0.12, 0.14]
Low & Down -0.12 <0.01 14272.49 -20.10 <0.01 [-0.13, -0.11]
Inactive (in -0.15 0.01 14122.67 -12.07 <0.01 [-0.17, -0.12] comparison to Active) Estimates of Covariance Parameter
Residual 0.29
Intercept 0.27
ICC 0.48
Note. BMI = Body Mass Index; Calm & Peace = Positive Affect; Low & Down = Negative Affect; CI =
Confidence Interval; ICC=Intra-class Correlation Coefficient (effect size).
T1 = Baseline; T2 = At 6-month follow-up; T3 = At 3-year follow-up; T4 = At 8-year follow-up.
Intercept value represents the value for T4 based on the SPSS default settings.
59 Chapter 2
Discussions
Many orthopedic rehabilitation patients with physical limitations quite often report low levels of well-being (Ayers, Franklin, & Ring, 2013) and do not meet the recommended level of physical activity during and after the rehabilitation (Peiris et al., 2013). Thus, it would be beneficial to understand the positive assets that accompany most individuals, especially rehabilitation patients, due to the importance of healthy functioning and quality of life across one’s lifespan (Wright, 1972; Nierenberg et al., 2016; Dunn et al., 2016). For this reason, the findings of this longitudinal study showed that subjective health was significantly positively associated with positive affect, but negatively associated with negative affect, across all the measurement points from baseline to up to eight years of follow-ups. This finding strengthened the theoretical formation of subjective well-being, which we identified through the associations explaining the relationships of the paradigms of affective component (i.e. positive affect and negative affect) and the cognitive component of subjective health
((Diener, 2006; Kashdan et al., 2008; Diener & Chan, 2011; Gataūlinas & Banceviča, 2014).
This finding is consistent with the existing literature, Cho and colleagues (2011) discovered significant associations among subjective health, positive affect and negative affect, which play the key roles in achieving sustainable optimal health and well-being (Hefferon & Mutrie,
2012). Preliminary analysis was conducted to provide an overview of the correlational natures, and it confirmed the interrelations among these main variables at all measurement points that are essential for further investigation.
As hypothesized, participants self-reported greater subjective health and positive affect compared to baseline, and showed increased linear trends during follow-up at six months and three years. Although there were signs of slightly reduced subjective health and positive affect at the eight-year follow-up, it always remained higher than at the baseline.
60 Chapter 2
Similar trends in the opposite direction were displayed for negative affect. These findings consolidate previous research claiming that participants experienced more positive affect over time with the increasing age (Scheibe et al., 2013), and experienced less negative affect, since positive affect tends to last longer and negative affect fades quicker at a later age
(Carstensen, Mayr, Pasupathi, & Nesselroade, 2000). As Fredrickson (2000) suggested, the experience of positive affect broadens an individual’s typical way of thinking and builds their personal resources for coping and regulating, which has an undoing effect on problems initiated by negative affect, like depression, being overweight and health problems.
In these current findings, given that the changes of the elements of subjective health and affective states in subjective well-being happened after the participants were discharged from rehabilitation, the findings could indicate that participants’ well-being increased after the rehabilitation, which is aligned with previous studies (Kanimozhi & Karupaaiah, 2014). A past study suggested that subjective well-being is associated with the perceptions of the new circumstances of life after rehabilitation, which could influence the accountability towards rehabilitation treatments (Fanciullacci, Straudi, Basaglia, & Chisari, 2017). A similar study suggested a cognitive component –in this case, subjective health –whereby the changes in health status could predict subjective well-being (Realo, Johannson, & Schmidt, 2017).
During rehabilitation, daily exercise therapy was introduced to enhance patients’ physical functioning and encourage patients to be more physically active, which may improve their physical and mental health. This is mainly due to most orthopedic rehabilitation patients not only reporting low-levels of physical activity, but also self-perceived poor health, thus affecting their motivation to engage in physical activity (Peiris et al., 2013). Consequently, it is imperative to examine subjective health between being physically active and inactive in the long term. This current finding reveals that individuals who are physically active are positively
61 Chapter 2 associated with better subjective health in the long run, compared to individuals who are physically inactive. Compared to baseline, subjective health increased at follow-ups after the rehabilitation where exercise therapy was introduced; thus, the findings may indicate that physical activity can increase the subjective health and well-being of the orthopedic rehabilitation patients. This is aligned with a past study concluding that physical activity is strongly associated with self-perceived health status (Pino et al., 2013). Although the reverse direction could be possible with individuals who self-perceived better health are more likely to exercise more, no significant results have been identified. Kull (2002) previously discovered that physically-active individuals experience not only better subjective health, but also better mental health compared with physically-inactive individuals. A systematic review explored a lifespan perspective of physical activity and the components of subjective well-being, emphasizing the importance of their associations and the need to translate these findings into people’s lives (Hyde, Maher, & Elavsky, 2013).
In addition, subjective health at eight-year follow-up was predicted by subjective health at previous measurement points (at baseline and follow-ups at three years), with more positive affect and less negative affect, as well as being physically active. While being aware that subjective well-being is strongly influenced by individuals’ health status and emotional states (Steptoe, Deaton, & Stone, 2014), time spent being physically active predicted improved subjective health emotional experiences, which may assist individuals in improving their mental well-being (Dodge & Lambert, 2009; Lu et al., 2012; Hogan, Catalino, Mata, &
Fredrickson, 2014). Moreover, increasing age predicted lower subjective health in this present study, which is in line with an earlier study in Germany, reporting that subjective health declines with age (Gunzelmann, Hinz, & Brähler, 2006). One plausible explanation might be
62 Chapter 2 the orthopedic rehabilitation, whereby patients are more prone to illnesses and chronic disease with advancing age. Thus, it is essential to consider aging factors in rehabilitation.
There are also limitations that should be acknowledged. These include (1) high drop- out rates and an unequal sample size at different measurement points, and (2) the fact that compared to those who dropped out during the follow-ups, participants who completed all time measurements showed higher means of subjective health and positive affect, which may indicate that they were more interested and motivated in improving health and physical conditions after orthopedic rehabilitation. Moreover, (3) the interrelationships of the main study variables are insufficient to determine causal effect, although the findings of the current study have contributed to the understanding of the associations of the main study variables.
Based on our findings, future research should be directed towards uncovering other possible factors like employment status, coping mechanisms, and characteristics of medical rehabilitation, by considering other health behaviors like restorative sleep and healthy eating, which are also critical for maintaining physical well-being and quality of life (Schmidt et al.,
2013). Additionally, it is also worth exploring age-group differences, since everyone experiences emotions differently with age, as suggested by Robbins and colleagues (2010).
In sum, the findings are potentially informative for practical behavioral interventions for rehabilitation, and constructive suggestions throughout the human lifespan targeted toward changeable elements. The understanding of body-mind relationships is important in clinical settings. Any positive development or constructive changes across time could be beneficial in alleviating difficulties during or after rehabilitation, as well as improving rehabilitation patients’ quality of life and well-being. The findings shed light on acknowledging the notion that health behaviors like physical activity, and the experience of positive affect may influence individuals’ perceptions of their health. This study not only provides empirical
63 Chapter 2 evidence for the subjective well-being theory, but also adds new knowledge of the notion that orthopedic patients can obtain long-lasting subjective well-being if they maintain an active lifestyle and perceive more positive affect. These seem to be key aspects in buffering against negative affective states in their daily living.
64 Chapter 2
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70 Chapter 3
Chapter 3:
Associations of sleep and healthy
diet, with subjective health and
quality of life of older adults
This chapter is published as Tan, S.L., Whittal, A. & Lippke, S. (2018). Associations among sleep, diet, quality of life and subjective health. Health Behavior and Policy Review, 5(2), 46-
58. doi:10.14485/HBPR.5.2.5. Due to copyright reasons, this full article could be accessed via http://www.ingentaconnect.com/contentone/psp/hbpr/2018/00000005/00000002/art000
05
71 Chapter 3
3. Chapter 3 (Study 2)
This chapter focuses on the association of healthy diet of consuming a low-fat diet and sleep, and their associations with subjective health and quality of life.
Figure 5. Chapter 3 – Focus on healthy diet and sleep, and their associations with quality of life and subjective health (highlighted in green).
72 Chapter 3
Abstract
Objective: To improve understanding of the health behavior of older adults by examining the interrelationships between consuming a low-fat diet and subjective measures of sleep
(including sleep duration, sleep quality and daytime functioning), and their associations with subjective health and quality of life, based on the assumptions of the Compensatory Carry-
Over Action Model.
Methods: Older adults (N=126) aged 50 years and above completed a self-report paper-and- pencil questionnaire about health behaviors of low-fat diet and sleep, quality of life, and subjective health.
Results: An exploratory path analysis yielded significant results and revealed sleep duration, sleep quality and consuming a low-fat diet were related to daytime functioning, which was positively interrelated with quality of life and subjective health. Quality of life was positively interrelated with subjective health. The positive relationship between daytime functioning and subjective health may be connected to increased quality of life. Although a low-fat diet was not directly related to subjective health, it was positively related to increased quality of life, which was positively associated with subjective health.
Conclusions: Together with a healthy diet, sleep seems to play a role for older adults in maintaining a functioning healthy lifestyle, improved quality of life, and a positive perception of health.
Keywords: older adults; sleep quality; daytime functioning; low-fat diet; subjective health; quality of life
73 Chapter 4
Chapter 4:
Associations of sleep, physical
activity and healthy diet, with subjective health, quality of life and
sleep quality, across age group
differences
This chapter has been resubmitted and peer-reviewed. It is currently under review as Tan, S.L., Storm,
V., Reinwand, D.A., Wienert, J., de Vries, H., & Lippke, S. (under review). Understanding the positive associations of sleep, physical activity, fruit and vegetable intake, as predictors of quality of life and subjective health across age groups: A theory based, cross-sectional web-based study. Frontiers in
Psychology.
104 Chapter 4
4. Chapter 4 (Study 3)
This chapter focuses on the association of physical activity, healthy diet, and sleep, and their associations with sleep quality, subjective health and quality of life.
Figure 7. Chapter 4 – Focus on physical activity, healthy diet, and sleep, and their associations with sleep quality, subjective health and quality of life.
105 Chapter 4
Abstract
Objective: Due to the increase in unhealthy lifestyles and associated health risks, the promotion of healthy lifestyles to improve the prevention of non-communicable diseases is imperative. Thus, research aiming to identify strategies to modify health behaviors has been encouraged. Little is known about addressing multiple health behaviors across age groups
(i.e., young, middle-aged, and older adults) and the underlying mechanisms. The theoretical framework of this study is Compensatory Carry-Over Action Model which postulates that different health behaviors (i.e. physical activity and fruit and vegetable intake) are interrelated, and they are driven by underlying mechanisms (more details in the main text).
Additionally, restful sleep as one of the main indicators of good sleep quality has been suggested as a mechanism that relates to other health behaviors and well-being, and should therefore also be investigated within this study. The present study aims to identify the interrelations of restful sleep, physical activity, fruit and vegetable intake, and their associations with sleep quality as well as overall quality of life and subjective health in different age groups.
Methods: A web-based cross-sectional study was conducted in Germany and the
Netherlands. 790 participants aged 20 to 85 years filled in the web-based baseline questionnaire about their restful sleep, physical activity, fruit and vegetable intake, sleep quality, quality of life and subjective health. Descriptive analysis, multivariate analysis of covariance, path analysis, and multi-group analysis were conducted.
Results: Restful sleep, physical activity, and fruit and vegetable intake were associated with increased sleep quality, which in turn was associated with increased overall quality of life and subjective health. The path analysis model fitted the data well, and there were age-group differences regarding multiple health behaviors and sleep quality, quality of life and
106 Chapter 4 subjective health. Compared to young and older adults, middle-aged adults showed poorest sleep quality and overall quality of life and subjective health, which were associated with less intention or action in engaging multiple health behaviors.
Conclusions: A better understanding of age-group differences in clustering of health behaviors may set the stage for designing effective customized age-specific interventions to improve health and well-being in general and clinical settings.
Keywords: Sleep quality; restful sleep; physical activity; fruit and vegetable intake; quality of life; subjective health; multiple health behaviors; health and well-being.
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Introduction
Non-communicable diseases (NCDs), and especially cardiovascular diseases (CVDs) account for most premature deaths globally, followed by cancers, respiratory diseases, diabetes and obesity (World Health Organization, WHO, 2014). While an unhealthy lifestyle like physical inactivity and an unhealthy diet are the most common behaviors that increase the incidence of NCDs (Lachat et al., 2013; WHO, 2014), sleep problems, such as inadequate sleep duration and poor sleep quality are also becoming more common and recognizable as unhealthy since numerous studies showed their associations with NCDs, especially, hypertension, type 2 diabetes and obesity (Shan et al., 2015; Cassidy et al., 2016; Ferranti et al., 2016; Montag et al., 2017). Accordingly, a systematic review revealed that sleep problems should be mentioned during the visit to healthcare providers, as sleep problems may severely influence recovery from the diseases, control of NCDs, and may affect individuals’ quality of life in general (Surani et al., 2015).
Sleep has gained attention in research and it has been strongly suggested to raise awareness of sleep as an important health behavior (Perry et al., 2013; Chaput & Dutil, 2016).
In particular, good quality and restful sleep not only reduce health risks with the chances for recovery and rejuvenation (Eek et al., 2012; Barber, 2014), but also might facilitate quality of life by enhancing well-being (Palmer & Alfano, 2017).
A healthy lifestyle contains more than single health behaviors, with the balance of energy expenditure (i.e., physical activity) and energy intake (i.e., healthy food intake), and the success in one behavior could facilitate changes in other behaviors (Lippke, 2014; Geller et al., 2017). In fact, physical activity and fruit and vegetable intake are two of the most important and easiest to influence health behaviors which effectively reduce the risks of NCDs
(Lachat et al., 2013; WHO, 2014), while physical activity and fruit and vegetable intake were
108 Chapter 4 positively interrelated within a multiple health behavior study (Fleig et al., 2015). Evidently, physical activity and fruit and vegetable intake are associated with lower mortality (Tian et al., 2017), higher self-rated health (Södergren et al., 2012), and better health-related quality of life (Kwon et al., 2015).
In addition, many previous studies found that different behaviors are related. For example, inadequate sleep duration and poor sleep quality are associated with unhealthy diet and physical inactivity (Grandner et al., 2015; Kittle et al., 2016). A study showed that sufficient sleep and good sleep quality were positively associated with a higher intake of dietary fiber and fruit and vegetable consumption (Ferranti et al., 2016). Furthermore, a systematic review identified that sleep and diet facilitate each other with carry-over mechanisms (Frank et al., 2017), including positive experiences, skills, cognitions like motivation, positive emotion and self-regulatory strategies, and thus have impacts throughout the life course (Lippke, 2014). However, inadequate sleep duration also changed circadian rhythms and hormonal levels and contributed to obesity, diabetes and CVDs (Frank et al., 2017). An online survey study in a web-based platform found that individuals who had more physical activity, fruit and vegetable intake, and slept well were more likely to report better mood (Liu et al., 2012). Therefore, another study strongly argued that sleep should also be taken into consideration for health improvements, and given as much attention as physical activity and fruit and vegetable intake as it seems reasonable that these multiple health behaviors influence overall health (Chaput & Dutil, 2016). However, the interrelations of physical activity and fruit and vegetable intake with sleep are lacking. Consequently, it is worthwhile to examine the interrelations of these health behaviors altogether.
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Multiple Health Behaviors and Age Groups
With increasing age, the number of people suffering from NCDs also increases. It is known that most premature deaths attributed to NCDs occur between the ages of 30 and 70, and thus, NCDs most likely affect people of all age groups (WHO, 2014; WHO, 2017). However, motivation to and success in health behavior change differs by age and subjective health with distinct health perceptions and habits (Deeks et al., 2009), while different age groups might yield noticeably different approaches in improving health. A recent study showed that, in comparison to younger adults, older adults were more likely to meet fruit and vegetable recommendations but were less likely to meet the physical activity recommendations (Alley et al., 2017).
Moreover, a study reported that the predictors of sleep quality differ between young adults and older adults: sleep duration was shorter with increased age, and older adults reported less restful sleep than younger adults (Zilli et al., 2009). Previous studies revealed that differences in health behaviors and sleep quality can be documented across age groups.
For instance, a good sleep was positively associated with fruit and vegetable intake among young adolescents (Ferranti et al., 2016). Additionally, middle-aged and older adults reported poor sleep quality with low levels of physical activity and therefore yielded poorest health characteristics (Rayward et al., 2017). Moreover, a previous study asked people about their perceptions of sleep quality and they defined one of the main indicators of good sleep quality is feeling rested with few or no sleep disruption during the night (Harvey et al., 2008). Sleep problems such as unrestful sleep increase with age. Thus, there is a growing interest in understanding the role of restful sleep to many important health outcomes beyond sleep duration across age groups (Kittle et al., 2016).
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A healthy lifestyle should be initiated earlier throughout young adulthood as it is related to lower risks of CVDs in middle age and later life (Liu et al., 2012). While sleep might be one of the body’s most important mechanisms, little about it has been studied in relation to other health behaviors. Consequently, more scientific evidence of age-group differences among these multiple health behaviors is needed. Thus, this present study examined underlying behavioral and psychological mechanisms across age groups.
Looking at the associations with underlying mechanisms, sleep has been found to be associated with physical activity and fruit and vegetable intake with increased life satisfaction and perceived health, as well as decreased high blood pressure among participants who were employees and mostly middle-aged (Merrill et al., 2011). Within this study, the outcomes highlighted that the multiple health behavior change interventions were effective and showed significant improvements among middle-aged employees with self-rated better health. Compared to older adults who reported having good sleep quality, older adults with poor sleep quality rated poorer subjective health and more frequently experienced restless sleep (Abraham et al., 2017). Among young adults, sleep problems were associated with lower quality of life and subjective health (Chen et al., 2014), but physical activity was not related to better quality of life (Häyrinen & Tarkka, 2016). Poor sleep was related to a decreased likelihood of maintaining healthy behavior change (Hui & Grandner, 2015), and it is thus essential to examine sleep quality and restful sleep further to provide more information on how sleep improvement, together with physical activity and fruit and vegetable intake could be considered.
Theoretical Background
Most theoretical frameworks describe only single behaviors, for example, the Health
Action Process Approach (HAPA, Schwarzer, 2008; Schwarzer et al., 2011) describes single
111 Chapter 4 health behavior change of adoption, initiation, and maintenance of health behavior via motivation and volition phases, including factors such as intention, self-efficacy, outcome expectancies, risk perceptions, action planning and etc.. However, to promote a healthy lifestyle and to prevent NCDs, it is necessary to take multiple health behaviors into account
(Prochaska et al., 2010). Like the Compensatory Carry-Over Action Model (CCAM) originated by Lippke (2014) is a novel approach for understanding the multiple health behavior change process with the underlying mechanisms that promote a healthy lifestyle and prevent non- communicable diseases. The CCAM addresses multiple health behaviors via carry-over mechanisms, and their associations with social-cognitive factors, such as intention, planning, and self-efficacy. Although these social-cognitive factors are essential in coping with tempting situations and compensatory cognitions that occur during the process of behavior change, these factors are not the purposes of this study. The research questions of this study are driven by the assumptions of the CCAM which can be studied individually: (1) different health behaviors interrelate, in this case, restful sleep, physical activity, and fruit and vegetable intake interrelate with carry-over mechanisms; (2) a healthy lifestyle contains more than a single health behavior, which decreases the stress reaction while it increases an individuals’ well-being (in this case, sleep quality), and associates with higher-level goal (in this case, quality of life and subjective health).
People experience different kinds of stressors while adopting and maintaining healthy behaviors. For example, if a person has set a goal and fails to reach this goal (e.g., due to work stress, daily hassles, or chronic stressors), their well-being might be affected. This is especially the case for work stress which has been identified as one of the main risk factors for hypertension and CVDs among young adults (Mucci et al., 2016). Thus, good sleep quality may result in the decrease of the stress reaction and the increase of well-being. This is especially
112 Chapter 4 true when sleep has been portrayed as an approach for achieving a sufficient recovery state to reenergize for future tasks or cope with chronic diseases, and for revitalizing self-control, which increases self-regulatory behavior and consequently decreasing stress symptoms
(Barber, 2014).
Research Questions
Engagements in multiple health behaviors may lead to increased well-being (Geller et al., 2017), thus, many studies examined the associations between health behaviors and underlying behavioral and psychological mechanisms (de Vries et al., 2008; Prochaska et al.,
2010; Schulz et al., 2014; Lippke et al., 2015; Reinwand et al., 2016; Storm et al., 2016; Duan et al., 2017). For example, a study examined students’ health and well-being and found that low-level of engagement in health-promoting lifestyle related behaviors, such as physical activity and nutrition, and stress management were associated with poor sleep quality and poor subjective health (Araújo et al., 2017). Nonetheless, little is known about the interrelations of restful sleep, physical activity and fruit and vegetable intake, and their associations with sleep quality, quality of life and subjective health, for the general population and participants that were motivated to decrease their risk for a cardiovascular disease.
Therefore, based on the CCAM, the current study aims:
(1) To identify the interrelations among restful sleep, physical activity, fruit and
vegetable intake, sleep quality, quality of life and subjective health.
(2) To examine to what extent restful sleep, physical activity, and fruit and vegetable
intake are associated with increased sleep quality, quality of life and subjective
health.
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(3) To investigate age-group differences in health behaviors (i.e., restful sleep,
physical activity and fruit and vegetable intake), and sleep quality, quality of life
and subjective health among young adults, middle-aged adults, and older adults.
(4) To understand the associations between the health behaviors (i.e., restful sleep,
physical activity, and fruit and vegetable intake), and underlying mechanisms (i.e.,
sleep quality, quality of life and subjective health) across age groups.
Methods
Study Design, Settings, Participants and Procedures
This study is cross-sectional study dataset, which stems from a randomized control trial (RCT) initially, to investigate whether a web-based, computer-tailored intervention is effective in increasing self-reported physical activity and fruit and vegetable intake. The results on the effectiveness of the intervention have been published elsewhere (Reinwand et al., 2015; Storm et al., 2016; Duan et al., 2017), and are distinct from the present study. Only the cross-sectional data from the self-report baseline questionnaires have been used to answer the innovative research questions of this current study. The baseline questionnaire was the same for the intervention and waiting control group; thus, we expect no differences in results due to group condition.
A study protocol with detailed information of the study has been previously published
(Reinwand et al., 2013). Thus, only a summary of the study methodology is mentioned in the following. This study has received ethical approval from the German Society for Psychology
(Deutsche Gesellschaft für Psychologie, DGPs; EK-A-SL 022013) in Germany, and the Medical
Ethics Committee of Atrium Medical Centre Heerlen (METC; 12-N-124) in the Netherlands. A clinical trial registration was conducted with ClinicalTrials.gov (Identifier: NCT01909349). All participants in this study gave informed consent online and obtained the access to the web-
114 Chapter 4 based program and the questionnaires. All participants participated voluntarily, and the data were anonymized.
A total of 1,010 participants were initially recruited from cardiac rehabilitation facilities and heart training groups, as well as internet platforms and online panels in Germany and the Netherlands from 2013 to 2015. Only participants who met the following inclusion criteria were approached to take part in this study: (a) aged 20 years and older; (b) German or Dutch language proficiency; (c) having an interest to reduce cardiac risk behavior in terms of being able to be physically active at least 150 minutes per week and being able to eat at least five portions of fruit and vegetables a day; (d) no complications and restrictions for physical activity and fruit and vegetable intake; and (e) and having internet access.
The research team excluded a total of 220 datasets because of double registration
(n=5), non-available dataset (n=128), inadequate age (n=1 younger than 20 years), and missing gender information (n=86). The final sample size is 790 participants.
Measurement Instruments
Sociodemographic variables. All socio-demographic characteristics were self- reported by the participants: year of birth, gender, country, employment status, marital status, and education levels in the baseline questionnaire. Based on participant’s information about body height and body weight, the body mass index (BMI) was calculated. The age of the participants is categorized into three age groups—young adults (1=aged 20 to 35), middle- aged adults (2=aged 36 to 55), and older adults (3=aged 56 and older). This was done by the previous study which explored health behavior change and subjective health among persons at risk for NCDs (Zanjani et al., 2006).
Health behaviors: physical activity and fruit and vegetable intake. To provide reliable individual health information from the web-based study, the assessment of participants’
115 Chapter 4 physical activity and fruit and vegetable intake was done with the items “During the last weeks, did you engage in physical activity at least 5 days a week for 30 minutes or more, in such a way that you were moderately exhausted?” and “During the last weeks, did you eat five portions of fruit and vegetables per day?” The answers were based on a 5-point Likert scale ranging from 1=No, and I do not intend to do so; 2=No, but I'm thinking about it; 3=No. but I intend to do so; 4=Yes, for a short period of time to 5=Yes, for a long period of time.
These items access the stages of change of health behaviors which combine intention and action in ordered categorical form, based on theoretical assumptions of the Transtheoretical
Model (Prochaska & DiClemente, 1983) and HAPA model. The reliability and validity of these measures were found in these previous studies (Lippke et al., 2009).
Restful sleep. With one of the items from the Center for Epidemiologic Studies Short
Depression Scale (CES-D10) (Eaton et al., 2004), participants were asked to assess their level of restful sleep: “In the past week, my sleep was restless,” on a scale from 1=rarely or none of the time (less than 1 day) to 4=most or all the time (5-7 days) (Andresen et al., 1994). As this present study focuses on the positive aspects of sleep, this item has been reverse coded to measure restful sleep. This item has been included individually in the previous health- related studies (Kutner et al., 2001; El Ansari et al., 2011; Bassett & Moore, 2014).
Sleep quality. To determine sleep quality, one item from the short version of the
World Health Organization Quality of Life (WHOQOL-BREF) Questionnaire (Group WHOQOL.,
1993; Skevington et al., 2004; Trompenaars et al., 2005; Hsiao et al., 2014) was used. The participants rated the item “How satisfied are you with your sleep?” on a scale from 1=very dissatisfied to 5=very satisfied. This item measures sleep quality for the physical health domain, but it has been examined individually in a previous study (Lu et al., 2011).
Quality of life and subjective health. This web-based study included two items to
116 Chapter 4 assess the overall quality of life and subjective health which were taken from the World
Health Organization Quality of Life (WHOQOL-BREF). The participants were asked to answer the item “Please keep in mind your standards, hopes, pleasures, and outcomes, in the last four weeks, how would you rate your quality of life?” on a scale from 1=very poor to 5=very good and “How satisfied are you with your health?” on a scale from 1=very dissatisfied to
5=very satisfied (Group WHOQOL., 1993; Skevington et al., 2004; Trompenaars et al., 2005; ;
Lu et al., 2011; Hsiao et al., 2014). Based on a recent review, well-being overlaps and interrelates highly with subjective health and quality of life (Diener, Pressman, Hunter, &
Delgadillo-Chase, 2017). Thus, these two items were combined into one index to measure participants’ aggregated quality of life and subjective health overall (Diener et al., 2017). This was also done in previous research (Johnson et al., 2016).
Statistical Analysis
All statistical analyses were performed using IBM SPSS 24 and AMOS 24. In the preliminary analysis which described the key characteristics of the main study variables which are important for further analysis, chi-square tests were performed to detect differences in the categorical variables gender and country among the three age groups. Descriptive statistics and bivariate correlation determined the interrelations among the main continuous variables: physical activity, fruit and vegetable intake, restful sleep, sleep quality, quality of life and subjective health, age, and BMI. Pearson’s r values were used to measure the effect size of the relationships among variables, with the purpose to avoid incorrect inferences, bias results and less precise estimates (Field, 2009).
To answer the research questions two, three and four, the main analyses contain three parts: (1) a path analysis was conducted to generate a proposed model based on the
CCAM; (2) comparing age-group differences on the means of main variables by using one-way
117 Chapter 4 multivariate analysis of covariance (MANCOVA); (3) and a multi-group analysis in structural equation modelling was conducted to examine the proposed model across age-groups.
The analyses confirmed that multicollinearity did not affect any of the significant effects reported below, and that multivariate normality was assumed. Homogeneity of variances was not assumed for the dependent variables: sleep quality, F(2, 787) = 5.41, p =
.005; restful sleep, F(2, 787) = 4.96, p = .007; quality of life and subjective health, F(2, 787) =
5.16, p = .006; fruit and vegetable intake, F(2, 787) = 20.11, p < .001; except physical activity,
F(2, 787) = 1.65, p = .194.
Analysis of variance (ANOVA) was employed to analyze the variations of the dependent variables for each age group. Games-Howell post-hoc test was selected for group comparisons as recommended (Games & Howell, 1976; Jaccard et al., 1984), and Robust Tests of Equality of Means (Welch) with asymptotically F distributed was used (Field, 2009).
Polynomial contrast analyses were adopted to examine both linear and nonlinear trends (i.e., quadratic terms) and the trends were examined with weighted terms for unequal sample sizes for each age group. BMI, country of origin, gender, employment status, marital status, and education levels were included as control variables. The level of two-tailed statistical significance was set at p < .05. We used no statistical measures to correct for multiple testing.
Results
Sample Characteristics
A total of 790 participants with a mean age of 50.9. Among them, 62.9% of the participants were female, 50.1% were working full-time, and 66.3% of the participants were married or in a relationship. Middle-aged adults were the majority in both countries, with
49.1% in Germany, and 50.9% in the Netherlands. Table 7 provides an overview of the
118 Chapter 4 sociodemographic variables in this study, except education levels due to more than one cell showing frequencies below 1, which may fail to detect a genuine effect (Field, 2009).
Table 7
Descriptive Statistics on Sociodemographic Variables (Chapter 4).
Categorical Variables c2 p Young Middle- Older Total adults aged adults adults (n=102) (n=422) (n=266) (N=790) (20-35 yrs) (36-55 yrs) (56-84 yrs) (20-84 yrs) n (%) n (%) n (%)
Country 8.74 .013
German 34 (33.3) 207 (49.1) 130 (48.9) 371 (47.0)
Dutch 68 (66.7) 215 (50.9) 136 (51.1) 419 (53.0)
Gender 40.50 <.001
Male 17 (16.7) 142 (33.6) 134 (50.4) 293 (37.1)
Female 85 (83.3) 280 (66.4) 132 (49.6) 497 (62.9)
Employment status 273.12 <.001
Working full-time 44 (43.1) 271 (64.2) 81 (30.5) 396 (50.1)
Working part-time 28 (27.5) 101 (23.9) 44 (16.5) 173 (21.9)
Vocational Training 11 (10.8) 3 (0.7) 1 (0.4) 15 (1.9)
Unemployed 10 (9.8) 22 (5.2) 17 (6.4) 49 (6.2)
Retired 3 (2.9) 6 (1.4) 106 (13.4) 115 (14.6)
Housewife /-husband 6 (5.9) 19 (4.5) 17 (6.4) 42 (5.3)
Marital Status 98.23 <.001
Single 24 (23.5) 38 (9.0) 16 (6.0) 78 (9.9)
Close relationship not 16 (15.7) 20 (4.7) 10 (3.8) 46 (5.8)
living together
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Close relationship & 25 (24.5) 36 (8.5) 15 (5.6) 76 (9.6)
living together
Married 35 (34.3) 294 (69.7) 195 (73.3) 524 (66.3)
Divorced 2 (2.0) 28 (6.6) 24 (9.0) 54 (6.8)
Widowed 0 (0.0) 6 (1.4) 6 (2.3) 12 (1.5)
Note. N = sample size; c2 = Chi-square.
Correlations of Main Variables
Table 8
Means, Standard Deviations, and Correlations of the Major Study Variables (Chapter 4).
Variables 1 2 3 4 5 6 7
1. SQ ------
2. RS .56** ------
3. QOL&SH .49** .30** - - - - -
4. PA .21** .02 .18** - - - -
5. FV .10** -.03 .18** .23** - - -
6. Age -.06 -.06 <-.01 .18** .06 - -
7. BMI -.10** -.04 -.28** -.08* -.11** .12** -
M 3.32 3.74 7.13 3.45 3.18 50.85 27.55
SD 1.01 0.53 1.42 1.12 1.18 12.15 5.41
Range 1.00- 2.00- 2.00- 1.00- 1.00- 20-84 15.20- 5.00 4.00 10.00 5.00 5.00 55.00 Note. SQ=sleep quality; RS=restful sleep; QOL&SH=overall quality of life and subjective health; PA=physical activity; FV=fruit and vegetable intake; BMI=body mass index (kg/m2);
M = mean; SD = standard deviation; Sample size, N = 790.
** p < .01. * p < .05, significance levels of correlations.
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For continuous variables, inter-correlation analysis (Pearson’s r) was used to inspect the interrelations of the main study variables as displayed in Table 8. In correlational findings, main study variables were interrelated, except restful sleep only significantly correlated with sleep quality and overall quality of life and subjective health (p < .05).
Path Analysis of a Proposed Model
To assess to what extent restful sleep, physical activity, and fruit and vegetable intake were associated with increased quality of life and subjective health, via sleep quality, a path analysis model is displayed in Figure 8. Control variables like age and gender (dichotomous variable) were included as covariates to be able to understand the model as a whole. The overall model fitted the data well, with chi-square, χ2 (2, N = 790) = 3.77, p = .15; normed chi- square, χ2/df = 1.89, comparative fit index (CFI) = .998, Tucker-Lewis index (TLI) = .974, root mean squared error of approximation (RMSEA) = .033.
Although restful sleep is not significantly interrelated with physical activity and fruit and vegetable intake, physical activity is positively interrelated with fruit and vegetable intake
(b = .23, B = .30, SE = .05, p < .001). Restful sleep (b = .55, B = 1.04, SE = .06, p < .001), physical activity (b = .19, B = .17, SE = .03, p < .001) and fruit and vegetable intake (b = .08, B = .07, SE
= .03, p = .011) were associated with increased sleep quality. Sleep quality was associated with increased quality of life and subjective health (b = .47, B = .66, SE = .04, p < .001). Further, physical activity and fruit and vegetable intake yielded positive associations with quality of life and subjective health, with b = .07, B = .09, SE = .04, p = .04, and b = .12, B = .15, SE = .04, p < .001, respectively. The examined variables accounted for 36% of the variance of sleep quality and 26% of the variance of quality of life and subjective health.
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Figure 8. Conceptual path analysis model with standardized regression coefficients showing the associations among restful sleep, physical activity (PA), fruit and vegetable intake (FVI), sleep quality, quality of life and subjective health (QOL&SH), with ***p < .001, ** p < .01, and
* p < .05 (Chapter 4).
Age Differences among Multiple Health Behaviors and Underlying Mechanisms
The outcome of one-way MANCOVA yielded significant differences among age groups,
2 with Wilks λ = .95, F(10, 1554) = 3.831, p < .001, with partial eta squared, ηp = .024. The p values of significance levels, Games-Howell post-hoc comparisons, linear and quadratic terms are displayed in Table 9. In quadratic trends, middle-aged adults yielded the lowest sleep quality, in which the mean is 3.20 and quality of life, and subjective health and the mean is
6.96. Physical activity showed significant differences in both linear and quadratic trends across age groups, in which older adults are having the most intention or action of being physically active and the mean is 3.70 and consuming fruit and vegetables with the mean
3.30.
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Table 9
Analysis of Age-Group Differences on Main Study Variables (Chapter 4).
Total Young Middle- Older Fa Games- Linear Quadratic
adults aged adults (p) Howell term, term,
adults post-hoc F(p) F(p) test (N=790) (n=102) (n=422) (n=266)
M (SE) M (SE) M (SE) M (SE)
SQ 3.40 3.61 3.20 3.40 8.72 YA > MA 0.77 17.49
(0.42) (0.10) (0.05) (0.07) (.00) < OA (.38) (<.001)
RS 3.75 3.79 3.74 3.73 1.99 - 3.07 0.04
(0.22) (0.05) (0.03) (0.03) (.14) (.08) (.85)
QOL 7.22 7.39 6.96 7.31 9.02 YA > MA 0.16 17.83
&SH (0.06) (0.14) (0.07) (0.07) (.00) < OA (.69) (<.001)
PA 3.45 3.31 3.32 3.70 18.56 YA < OA 24.19 12.45
(0.05) (0.11) (0.06) (0.07) (.00) > MA (<.001) (<.001)
FVI 3.20 3.20 3.10 3.30 2.74 OA > MA 1.58 4.22
(0.05) (0.12) (0.06) (0.08) (.07) (.21) (.04)
Note. SQ=sleep quality; RS=restful sleep; QOL&SH=overall quality of life and subjective health; PA=physical activity; FVI=fruit and vegetable intake; BMI=body mass index; M = mean; SE = standard error; N = sample size; CE = Contrast Estimate; Fa= Asymptotically F distributed, Welch; For Games-Howell post-hoc test, display with significance level at p < .05.
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Path Analysis of Underlying Mechanisms across Age Groups
To compare age-group differences across the main study variables, the multi-group analysis in structural equation modelling was performed. Gender (dichotomous variable) was included as the covariate, without including age since age differences are explored. The fully unconstrained path model provided an adequate fit to the data, with χ2 (3, N = 790) = 6.07, p
= .11; χ2/df = 2.03, CFI = .995, TLI = .926, RMSEA = .036. With model comparison, χ2 = 23.54; df = 16, p = .10, with p < .05 which could be the indications for differences among the age groups in the whole model, with the structural weights model fit acceptably well too as χ2 (19,
N = 790) = 29.62, p = .06; χ2/df = 1.56, CFI = .983, TLI = .960, RMSEA = .027. See Figure 9.
For young adults, restful sleep (b = .66, B = 1.71, SE = .19, p < .001) and physical activity
(b = .23, B = .22, SE = .07, p = .002) were positively correlated with sleep quality. Sleep quality was associated with increased quality of life and subjective health (b = .41, B = .55, SE = .12, p < .001).
For middle-aged adults, restful sleep (b = .57, B = .99, SE = .07, p < .001), physical activity (b = .14, B = .12, SE = .03, p < .001), and fruit and vegetable intake (b = .12, B = .10, SE
= .04, p = .003) were significantly positively associated with sleep quality. Sleep quality was associated with increased quality of life and subjective health (b = .46, B = .65, SE = .06, p <
.001). Physical activity and fruit and vegetable intake were significantly interrelated with each other (b = .21, B = .24, SE = .06, p < .001). Physical activity showed significant positive association with quality of life and subjective health, with b = .09, B = .12, SE = .05, p = .03.
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Figure 9. Conceptual path analysis model with standardized regression coefficients showing the associations among restful sleep, physical activity (PA), fruit and vegetable intake (FVI), sleep quality, quality of life and subjective health (QOL&SH) across age groups, begin with young adults, middle-aged adults and older adults, with *** p < .001, ** p < .01, and * p < .05
(Chapter 4).
For older adults, restful sleep (b = .51, B = .995, SE = .10, p < .001) and physical activity
(b = .19, B = .19, SE = .05, p < .001) were associated with sleep quality. Sleep quality was associated with quality of life and subjective health (b = .50, B = .70, SE = .08, p < .001). Physical activity and fruit and vegetable intake were interrelated with each other (b = .25, B = .36, SE
= .09, p < .001). Further, fruit and vegetable intake was related with quality of life and subjective health (b = .18, B = .20, SE = .06, p = .001).
Discussions
The purpose of this web-based study was to investigate age-group differences in the interrelations of multiple health behaviors (i.e., restful sleep, physical activity, and fruit and
125 Chapter 4 vegetable intake) and their associations with sleep quality, quality of life and subjective health among participants who were motivated to reduce their cardiovascular risk.
Summary of the Main Findings
To summarize, in the present study we could show there were interrelations between different health behaviours and that the strength of these interrelations differed among age groups. Regarding the first research question, we found that physical activity was associated with fruit and vegetable intake but neither of them was related to restful sleep. Nevertheless, with regards to our second research question our results show that restful sleep, physical activity, and fruit and vegetable intake were associated with increased sleep quality, and that physical activity and fruit and vegetable intake were positively related to quality of life and subjective health. Sleep quality was also associated with increased quality of life and subjective health. To address our third research question, we investigated age-group differences and could show that older adults had the highest intention or action of being regularly physically active and consuming sufficient fruit and vegetables. With respect to sleep quality, quality of life and subjective health, middle-aged adults yielded the lowest values.
Finally, the intention or action in engaging multiple health behaviors was associated with sleep quality, quality of life and subjective health across age groups, predominantly among middle-aged adults.
Correlation of Main Variables
Physical activity and fruit and vegetable intake were interrelated which aligned to a previous study (Fleig et al., 2015), however, restful sleep was not interrelated with physical activity and fruit and vegetable intake which was inconsistent with a previous study (El Ansari et al., 2011). This may be due to the single item to measure sleep may not be sufficient. Some studies suggested alternative measurements like The Pittsburgh Sleep Quality Index (PSQI)
126 Chapter 4
(Buysse et al., 1989), may be more suitable for measuring sleep as health behavior, as PSQI includes sleep duration, sleep quality, habitual sleep efficiency, sleep disturbances, use of sleep medications, and daytime dysfunction. Restful sleep was only correlated with sleep quality, quality of life and subjective health. This outcome is partly consistent with a recent study that showed poor sleep quality was related to poor subjective health and less restful sleep (Merrill et al., 2011; Abraham et al., 2017).
Path Analysis of Multiple Health Behaviors and Quality of Life and Subjective Health
Based on one of the assumptions of the CCAM, a path model replicated the results from the earlier descriptive analyses which showed that that physical activity was associated with fruit and vegetable intake but neither of them was related to restful sleep. Nonetheless, restful sleep, physical activity and fruit and vegetable intake were all associated with increased sleep quality, which is consistent with previous studies (McKnight-Eily et al., 2011;
Chan et al., 2015; Ferranti et al., 2016). The finding may indicate that other than multiple health behaviors of physical activity and fruit and vegetable intake, restful sleep should also be integrated into a healthy lifestyle, as suggested in past studies (Perry et al., 2013; Chaput
& Dutil, 2016). In these studies, especially those persons who reported poor sleep had a lower likelihood to maintain their health behavior change (Hui & Grandner, 2015).
Our path models also show that physical activity and fruit and vegetable intake were associated with quality of life and subjective health. This can also be found in previous research (Södergren et al., 2012; Kwon et al., 2015). In the present study, sleep quality was related to the increased quality of life and subjective health, which is also consistent with findings in previous studies (Merrill et al., 2011; Chen et al., 2014; Abraham et al., 2017). Given the associations between these multiple health behaviors as well as quality of life and subjective health, the findings highlight that multiple health behaviors play a role in health
127 Chapter 4 and well-being (Geller et al., 2017), which may then also be addressed in the prevention of
NCDs (Shan et al., 2015; Ferranti et al., 2016; Montag et al., 2017).
Age Differences in Multiple Health Behaviors and Underlying Mechanisms
Health behavior change differs by age and health status (Zanjani et al., 2006), however, NCDs can affect people of all age groups (WHO, 2014; WHO, 2017). This present study found significant age-differences regarding the motivation to change one’s health behaviors, the actual behavior and well-being: Middle-aged adults showed lower intention or action of multiple health behavior engagements than older adults, had poorest sleep quality, as well as the lowest quality of life and subjective health among age groups. In line with past research, middle-aged adults reported not only poor sleep quality but also yielded poor health characteristics (Haseli-Mashhadi et al., 2009; Rayward et al., 2017; Tan et al., 2015).
Among age groups, most middle-aged adults in this study were working full-time and were married. Therefore, time constraints with a tight schedule might be a consequence of the multiple roles they fulfill in their daily lives, hindering them to engage in regular health behaviors (Kelly et al., 2017). Together with the decline and limitations of physical functions, adopting and maintaining healthy behaviors is exceptionally challenging for middle-aged adults. Web-based applications should be used to assist these persons in overcoming this challenge as they have proven effective among these age groups (Merrill et al., 2011; Schulz et al., 2014; Chan et al., 2015; Lippke et al., 2015; Duan et al., 2017).
In previous studies, older adults were less likely to meet the recommended level of physical activity although they were more likely to meet the recommendation of a healthy diet (Alley et al., 2017). However, in the present study, we found that older adults had more intention or action of being physically active and consuming sufficient fruit and vegetables than the younger groups and that the quadratic trend increased across age groups. This
128 Chapter 4 finding may imply that older adults were more likely to successfully engage in multiple health behaviors simultaneously compared to younger adults, which could be due to a self-selection process in which older adults were more likely to participate in this study because of their awareness of aging with health declination (Davis et al., 1994). Since the risks of morbidity and mortality caused by NCDs are high in this age group, they are likely to have higher intention or action in engaging multiple health behaviors (Shaw & Agahi, 2012; WHO, 2014).
Another reason could be that the reduced daily stress due to retirement. Retirement might provide more flexibility and time in contrast to those adults who work or study. However, these assumptions should be evaluated further in future research.
Health Behaviors and Underlying Mechanisms across Age Groups
To identify the associations of both behavioral and psychological mechanisms, the proposed path analysis model demonstrated significant age-groups differences on multiple health behaviors, sleep quality, quality of life and subjective health.
Physical activity was not directly associated with quality of life and subjective health for most age-groups, which is in contrast to previous studies (Merrill et al., 2011; Pucci et al.,
2012). This may be due to most of the previous studies have adopted measurements of frequency or intensity levels of physical activity, such as the International Physical Activity
Questionnaire (IPAQ) (Craig et al., 2003; Pucci et al., 2012). In the present study, we used a measure of stages of behavior change instead, which might be useful in extending beyond the frequency but also their intention to initiate and maintain the actual action of health behavior. The stages of behavior change have been considered as linear with increasing intention and action, which also has been included in path analysis in previous studies
(Marcus et al., 1994; Lippke et al., 2009; Plotnikoff et al., 2009; Lippke et al., 2010).
129 Chapter 4
For young adults, physical activity was neither significantly associated with fruit and vegetable intake, nor with quality of life and subjective health, in contrast to middle-aged and older adults. These findings are similar to previous studies that showed that these missing associations between multiple health behaviors and quality of life and subjective health among young adults may imply that multiple health behaviors are less relevant for quality of life and subjective health than among older adults (Ferranti et al., 2016; Alley et al., 2017).
This may be the case because younger adults usually perceive themselves as healthier with a higher quality of life or the health outcomes they could gain from the health behaviors were less apparent. This is not the case with middle-aged and older adults, where the opposite is rather the case and where health outcomes are more prospective when they engaged in health behaviors. Indeed, older adults who showed having more intention or action of engaging in multiple health behaviors yielded significant associations with increased sleep quality, quality of life and subjective health. Especially fruit and vegetable intake was significantly associated with quality of life and subjective health among older adults, this may be consuming fruit and vegetable intake is more manageable than other health behavior such as physical activity, which highly rely on the health conditions and physical functionality.
Although causal relationships can not be drawn in this study, the findings suggest that fruit and vegetable intake may be mediated the relationship between physical activity and sleep quality, which strengthened the concept of multiple health behaviors increased sleep quality. This is an important indicator for coping well with stress and improving well-being
(Merrill et al., 2011; Palmer & Alfano, 2017; Lippke, 2014; Geller et al., 2017). Similarly, the findings suggest that sleep quality seems to be a potential mediator of the relationship between health behavior and quality of life and subjective health, yet further analyses are required to validate this assumption. This may bring in another possible perspective on good-
130 Chapter 4 quality sleep, because decreases stress levels and increased well-being may mediate the relationship between health behavior and achieving quality of life and subjective health, as suggested in the CCAM by Lippke (2014).
Limitations
There are limitations of this current study which need to be considered. First, based on the cross-sectional study design, it is not possible to draw any conclusions about causality between health behaviors and sleep measures as well as psychological measures. Second, a self-report measure was used to obtain reliable outcomes with a minimum of subject response bias. However, the outcomes may suffer from a lack of accuracy due to over- or underestimation of the participants’ actual health behaviors, in which a previous study suggests that people tend to overestimate their physical activity and fruit and vegetable intake (Watkinson et al., 2010). Third, due to the recruitment strategies, selection bias might have occurred since people who had a high motivation to change their health behavior may have registered themselves for participation. A fourth limitation is the unequal sample size of each age group (with middle-aged adults being the majority) which may distort the outcomes.
A fifth limitation is the adoption of binary category for gender which excluded some participants from the study. Therefore, more inclusive gender options should be included in the future study. Moreover, the items of physical activity and fruit and vegetable intake were used to access the stages of change that combined both intention and action may yield different outcomes. Thus, the measurements of the health behaviors should be further investigated separately as either intention or actual behavior. Lastly, such health behaviors as sleep, physical activity and fruit and vegetable intake are complex, and the model proposed here most likely does not account for all relevant factors. For example, future studies should investigate more complex health factors, such as a high-fat diet, which may affect the
131 Chapter 4 intensity level of physical activity on eating behavior (Beaulieu, Hopkins, Blundell, & Finlayson,
2017), as well as genetic predispositions and stable personality characteristics (Conner et al.,
2017). Nonetheless, the findings suggested constructive outcomes with distinctive patterns.
Strengths
To our knowledge, the current study seems to be one of the few studies examining restful sleep, physical activity, and fruit and vegetable intake, as well as sleep quality, quality of life and subjective health in combination across age groups. So far, most previous studies investigated these variables separately. In the current body of literature, most previous studies investigated sleep duration and daytime sleepiness whereas in this study we investigated restful sleep as a more subjective outcome of sleep quality that might be more relevant to people’s subjective health. The application of path analysis and multi-group analysis in structural equation modelling should be mentioned as a strength because of the findings may provide more information regarding behavioral and psychological mechanisms based on the CCAM. And finally, we compared three age groups in contrast to previous studies that compared only young and older adults (Zilli et al., 2009; Alley et al., 2017). This study also investigated middle-aged adults as this particular age group has been suggested to be examined further in improving health outcomes with multiple health behavior change interventions (Rayward et al., 2017).
Conclusions
Combining multiple health behaviors such as regular physical activity and healthy diet has a greater impact on health and well-being as single health behavior only (Geller et al.,
2017). Notwithstanding, sleep seems to play a role in living a healthy lifestyle as well, especially when sleep is embedded together with other health behaviors. Altogether, a multiple health behavior approach may be effective in preventing lifestyle-related diseases
132 Chapter 4 and may lead to better quality of life. The findings of this present study add value to the existing literature by providing more information to health-related and medical internet research. The findings should be considered in lifestyle management interventions to enhance health and well-being, which may be useful for public health, health care system and policy.
Although the findings from this web-based study are applicable to all age groups, knowing the age-group differences is beneficial to take action by customizing and planning strategic interventions to reduce health risks and improve health in general. For example, the associations of multiple health behaviors with sleep quality, quality of life and subjective health were less relevant and apparent to young adults; middle-aged adults seemed to be more inclined to achieve better sleep quality, quality of life and subjective health if they engaged in all multiple health behaviors; and older adults were more in favor of consuming fruit and vegetables. Knowing such differences are informative in adopting the distinct approach of multiple health behavior change, since different age groups might yield noticeably different approaches in improving health.
As suggestions for future studies, sociodemographic factors, such as gender and income may yield distinct health outcomes. For instance, a study showed that insufficient sleep was more likely among females and those with lower income (Grandner et al., 2015).
To understand more of the underlying psychological mechanisms, social-cognitive factors should be investigated further. For example, a study identified that outcome expectancy and self-efficacy were the main predictors for successful behavior change (Klusmann et al., 2016).
133 Chapter 4
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Chapter 5:
Discussion
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5. Discussion
Deterioration of health could efficiently be prevented with a healthy lifestyle including behaviors such as physical activity, healthy diet, and proper sleep (Arena et al.,
2015; Hui & Grandner, 2015; Geller, Lippke, & Nigg, 2017). Many individuals, especially rehabilitation patients and older adults with chronic diseases, still strugge to initiate and/or maintain healthy behaviors necessary for achieving optimum quality of life and self-perceived ideal health (Bowling, 2017). Although there is an extensive amount of research evidence that engagement in health behaviors is essential for individuals’ health and well-being (Conn,
Hafdahl, & Brown, 2009; Hui & Grandner, 2015; Bishwajit, Tang, Yaya, He & Feng, 2017), translating the knowledge into such lifestyle improvements remains a prevalent task in health psychology. It is therefore essential to understand health behaviors and the associations with underlying psychological and behavioral mechanisms of physical and mental health (Schmidt,
Ziemer, Piontkowski & Raque-Bogdan, 2013; Lippke, 2014).
Although non-communicable diseases (NCDs) account for 70% of all deaths globally and affect people of all age groups, the decline of mortality rate due to (NCDs) indicates the success of health promotion and prevention in reducing the lifestyle-related risk factors, such as sedentary behavior and an unhealthy diet (World Health Organization, WHO, 2017). Sleep problems are also clinically related to NCDs, however, sleep deficiency has not yet been recognized by the WHO as a risk factor for NCDs. Rehabilitation often also neglects the importance of sleep. Sleep is a complex health behavior (St-Onge, 2017) and needs to be better understood from a psychological point of view.
Among various theoretical approaches to enhancing health and well-being, the
Compensatory Carry-Over Action Model (CCAM, Lippke, 2014) suggests the carry-over mechanisms of multiple health behaviors, in this case, being physically active, consuming a
147 Chapter 5 healthy diet (including low-fat diet and sufficient fruit and vegetable intake) and getting sufficient, and restful good quality sleep, are associated with stress management, which increases well-being, and the higher-level goals of subjective health and quality of life. Thus, this thesis examined the associations between lifestyle-related health behaviors and their underlying mechanisms, to enhance understanding of the theory-based constructs of multiple health behaviors that could have useful for implications for health promotion and rehabilitation. The age group differences would be worthy of examination to enhance the empirical evidence with additional relevant insights.
In this chapter, the summary of the main findings from Chapters 2 to 4 is first presented. This is followed by a general discussion, including integration with the theoretical framework, practical implications, methodological matters of the study, and suggestions for future research. Lastly, the overall conclusion is outlined.
5.1. Summary of the Main Findings
This thesis includes original contributions to knowledge concerning single health behavior, namely physical activity (Chapter 2), followed by multiple health behaviors of physical activity, healthy diets, and sleep (Chapter 3 and 4), and their associations with increased subjective health (all chapters), quality of life (Chapter 3 and 4) and sleep quality
(Chapter 4). Below is the summary of the main findings (Table 10), followed by a general discussion.
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Table 10
Summary of the Main Findings and Conclusions.
Research Questions Findings Conclusions
How does physical Physical activity is associated The outcomes from a activity associate with with increased subjective health longitudinal study may subjective health in the long run. be informative to across time? customize Participants who were interventions, by
2 What are the physically active were more improving physical predictors of likely to perceive their health as function and enhancing subjective health? better, over eight years. well-being to achieve a Chapter Chapter successful and Predictors of subjective health sustainable are being physically active, rehabilitation care. more positive affect, less negative affect, younger age, and lower BMI.
How do sleep Sleep duration, sleep quality Multiple health duration, sleep and daytime functioning behaviors of a healthy quality, and daytime interrelated with each other, diet and proper sleep functioning interrelate while only daytime functioning seem to play a role for
with a low-fat diet? interrelated with a low-fat diet. older adults in maintaining a How do sleep and the Daytime functioning is functioning, healthy Chapter 3 Chapter consumption of a low- associated with increased lifestyle with improved fat diet associate with quality of life and subjective quality of life and a quality of life and health. Consuming a low-fat positive perception of subjective health? diet is only related to increased health. quality of life.
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How does restful sleep Physical activity interrelated A healthy lifestyle of interrelate with with fruit and vegetable intake. multiple health physical activity, and behaviors of physical fruit and vegetable Both were associated with activity, healthy diet, intake? increased sleep quality, and sleep should be subjective health, and quality of integrated distinctively How do sleep, physical life. Restful sleep was into age groups, to activity and fruit and associated with increased sleep improve health and vegetable intake quality, which is then associated well-being successfully. associate with with increased subjective health Predominantly relevant
subjective health, and quality of life. to middle-aged adults. quality of life and
Chapter 4 Chapter sleep quality? Significant age group The findings may be differences, with middle-aged useful for public health, Are there age group adults, showed less intention or rehabilitation, health differences for restful action in engaging multiple care system and policy, sleep, physical activity, health behaviors, with the as a basis for more and fruit and lowest level of sleep quality, appropriate relevant vegetable intake, and quality of life and subjective interventions for each their associations with health. age groups. subjective health, quality of life and sleep quality?
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5.2. General Discussion
Most NCDs could be prevented or treated by lifestyle improvements (WHO, 2017).
This thesis targeted lifestyle-related health behaviors, including physical activity, a healthy diet, and sleep. Adoption of multiple health behaviors remains a challenge for some individuals, especially rehabilitation patients and older adults with chronic diseases (Nigg &
Long, 2012; Geller et al., 2017). Therefore, this thesis aimed to explore multiple health behaviors and their associations with subjective health, quality of life, and sleep quality.
Based on the findings of these empirical studies, one of the primary outcomes is that single or multiple health behaviors are associated with increased subjective health, quality of life, and sleep quality. This is strengthened by the assumptions of the theoretical framework of CCAM (Lippke, 2014), and the evidence in the existing literature (Conn et al., 2009; Fleig,
Lippke, Pomp, & Schwarzer, 2011; Hui & Grandner, 2015; Bishwajit et al., 2017; Geller et al.,
2017; Muros, Salvador Pérez, Zurita Ortega, Gámez Sánchez, & Knox, 2017). The carry-over mechanisms of multiple health behaviors may relate to self-regulation factors that are potentially embedded in stress management that increase well-being and higher-level goals
(Lippke, 2014).
Single health behavior of physical activity is associated with the higher-level goal of subjective health. To begin with a single lifestyle-related health behavior, a longitudinal study (in Chapter 2) used survey research and investigated orthopedic rehabilitation patients at baseline and follow-ups after being discharged. First, participants self-reported that their subjective health and positive affect increased after baseline, at six-month and three-year follow-ups. Although there were signs of a slight decrease at eight-year follow-up, it remained higher than at baseline. Similar trends in the reverse direction were presented for negative affect. The findings strengthen the theoretical framework of subjective well-being (Diener &
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Chan, 2011; Diener, Pressman, Hunter, & Delgadillo-Chase, 2017). Second, the findings show that former rehabilitation patients who were physically active were more likely to perceive their health as better in the long run, compared to those who were physically inactive. These findings are similar to previous studies (Gunzelmann, Hinz, & Brähler, 2006; Dodge & Lambert,
2009; Lu, Yen, Lei, Cheng, & Wang, 2012; Pino et al., 2013; Hogan, Catalino, Mata, & Fredrickson,
2014; Realo, Johannson, & Schmidt, 2017; Notthoff, Reisch, & Gerstorf, 2017).
The findings reflect the fact that although health and well-being is not always optimal, it is still possible for healthy and unhealthy individuals to obtain long-lasting subjective well- being, if they maintain an active lifestyle and perceive more positive affect as critical aspects in buffering against negative affective states in their daily living, for example, stress (Diener et al., 2017). Physical activity is one of the most common and long-established effective health behaviors, with numerous outcomes in preventing disease and improving well-being (Conn et al., 2009; WHO, 2017; Warburton & Bredin, 2017). Several studies suggest that individual resources and perceptions, such as positive affect and subjective health, as well as subjective experiences and changes over time, are essential elements in providing consistent, valuable information to improve sustainable health and well-being, and to ensure the success of health behavior change.
The findings are potentially informative for health promotion and designing practical behavioral interventions by including physical activity in rehabilitation to initiate or maintain a healthy lifestyle throughout the human lifespan. Any positive development or constructive change across time could be beneficial in alleviating difficulties during or after rehabilitation, and in improving rehabilitation patients’ quality of life and well-being (WHO, 2011; Dunn,
Ehde, & Wegener, 2016; Notthoff et al., 2017; Warburton & Bredin, 2017).
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The predictors of the higher-level goal of subjective health. Within the same study in Chapter 2, former rehabilitation patients who were physically active, and previous measurements of subjective health predicted greater subjective health at a last measurement point at eight-year follow-up. Other predictors of increased subjective health were higher means of positive affect and lower means of negative affect, as well as younger age and lower body mass index (BMI). This finding matches theoretically-grounded assumptions and aligns with previous results (Gunzelmann et al., 2006; Dodge & Lambert, 2009; Pino et al., 2013;
Steptoe, Deaton, & Stone, 2014; Hogan et al., 2014).
The findings suggest that higher means of positive affect and lower means of negative affect predict increased subjective health. This information could be useful and used to learn how to fully utilize the personal resource of positive affect that could potentially assist in adopting changes necessary for improving health and well-being. This could be beneficial in encouraging individuals to initiate and maintain a healthy lifestyle (Wright, 1972; Nierenberg et al., 2016; Diener et al., 2017).
Although increasing age commonly predicts lower subjective health, continuously being physically active in the long-term predicts increased subjective health at eight-year follow-up (Gunzelmann et al., 2006; Dodge & Lambert, 2009; Lu et al., 2012; Hogan et al.,
2014). Therefore, it is essential to consider aging factors, especially in rehabilitation settings, where patients are more prone to chronic disease with advancing age. In addition, the finding of increased subjective health is associated with physical activity show participants’ subjective health improved after rehabilitation in which physical activity was introduced. Moreover, changes in how the participants perceived their health status over time could predict subjective well-being, as suggested in a previous study (Realo et al., 2017).
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Multiple health behaviors and underlying mechanisms. Based on the assumptions of the CCAM model (Lippke, 2014), the findings from Chapter 3 and Chapter 4 raised awareness that multiple health behaviors (1) interrelated with each other, and (2) are associated with improved subjective health, quality of life and sleep quality. Engagements in multiple health behaviors could potentially encourage and motivate individuals to maintain a functional, healthy lifestyle.
Nonetheless, although many individuals do put effort into being physically active and consuming fruit and vegetables, not many individuals will decide to simply go to bed earlier to ensure sufficient restful sleep. Therefore, other than physical activity and a healthy diet, this thesis includes sleep, as it plays a role in health maintenance, restoration, and improvement (Perry, Patil, & Presley-Cantrell, 2013;), and is associated with enhanced quality of life that can improve stress management through better self-regulation (Hagger, 2014;
Grandner, 2014; Filip et al., 2017; Itani, Jike, Watanabe & Kaneita 2017). These positive outcomes became more apparent when sleep is integrated into a healthy lifestyle of physical activity and/or consuming a healthy diet (Khan, Chu, Kirk, & Veugelers, 2015; Flueckiger, Lieb,
Meyer, Witthauer, & Mata, 2016a). Prior to further investigations of multiple health behaviors, the thesis first identified the interrelations among sleep, physical activity and a healthy diet (in Chapter 3 and 4).
Interrelations among multiple health behaviors – physical activity, a healthy diet, and sleep. First, physical activity and a healthy diet interrelated with each other, which aligned with most findings in the literature (Fleig, Lippke, Pomp and Schwarzer, 2011; Lippke,
Nigg & Maddock, 2012; Lachat et al., 2013; Reinwand et al., 2016; Duan, Wienert, Hu, Si, &
Lippke, 2017). This may be because adoption of one behavior can potentially facilitate engagement in another health behavior (Fleig et al., 2011; Lippke et al., 2012; Lippke, 2014),
154 Chapter 5 due to carry-over (or transfer) mechanisms, including self-regulatory strategies (Lippke et al.,
2012; Spring, Moller, & Coons, 2012; Lippke, 2014).
Second, among older adults aged 50 and above in Chapter 3, the findings revealed a positive correlation between sleep duration and sleep quality, in line with the previous study that showed sufficient sleep of 7 to 8 hours duration was related to better sleep quality
(Bayán-Bravo et al., 2017). Beyond sleep, consumption of a low-fat diet, together with sleep duration and sleep quality, interrelated with better daytime functioning, consistent with previous studies showing that dietary patterns are associated with sleep measure of daytime alertness (de Souza, de Sousa, Belísio, & Macêdo de Azevedo, 2012; St-Onge & Shechter,
2014; Cao, Wittert, Taylor, Adams, & Shi , 2016; Flueckiger et al., 2016a; Bayán-Bravo et al.,
2017). With sleep patterns playing a key role in energy metabolism (Markwald et al., 2013;
Calvin et al., 2013; Patterson et al., 2014; Murphy, Holmes, & Brooks, 2017), a systematic review examined the impact of sleep on energy intake, and fat intake was underlined as a preference by individuals who reported insufficient sleep (Patterson et al., 2014; St-Onge, Mikic
& Pietrolungo, 2016a).
Another sleep measurement—restful sleep— is included in the investigation, together with physical activity and a healthy diet of fruit and vegetables (in Chapter 4). Although restful sleep was not interrelated with physical activity and a healthy diet of fruit and vegetables, these three variables were significantly related to increased sleep quality, which aligned with previous studies (McKnight-Eily et al., 2011; Merrill, Anderson, & Thygerson, 2011). Sleep is an essential daily activity, and one within the capacity and capability of most individuals, thus, good sleep may encourage other health behaviors, with the carry-over mechanism between health behaviors (Lippke, 2014). Since multiple health behaviors have a greater impact on health and well-being than those targeting single health behavior (Prochaska, Spring, & Nigg,
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2008; Arena et al., 2015; Geller et al., 2017), health-related empirical investigations targeting these multiple health behaviors are essential (Kwan, Faulkner, Arbour-Nicitopoulos &
Cairney, 2013).
Among multiple health behaviors, sleep measures of daytime functioning and sleep quality yielded significant associations with physical activity and a healthy diet from investigations in this thesis. A prior study showed that poor sleep quality was associated with an increased tendency to contemplate or initiate behavior change, but a decreased tendency to maintain healthy behavior changes (Hui & Grandner, 2015). Moreover, this finding agrees with recent evidence suggesting that replacing sedentary habits with sleep and physical activity may reinforce numerous significant realms of self-regulatory behavior and executive functioning (Fanning et al., 2017).
A systematic review by Boehm and Kubzansky (2012) suggested restful sleep, sleep quantity and sleep quality, to be the key health behaviors together with physical activity and consuming a healthy diet. Although the evidence regarding the association between sleep and energy expenditure through physical activity is varied, sleep problems have been established as associated with the imbalance of energy intake and energy expenditure, which lead to various health problems like NCDs (Markwald et al., 2013; Calvin et al., 2013;
Patterson et al., 2014; Murphy et al., 2017). However, the association between multiple health behaviors and the underlying mechanisms is yet to be understood further.
Multiple health behaviors and their associations with subjective health, quality of life, and sleep quality. Despite the fact that the findings in Chapters 3 and 4 showed that not all sleep measurements interrelated with physical activity and healthy diet, there is still a substantial number of studies which show that multiple lifestyle-related health behaviors are associated with improved subjective health (Bishwajit et al., 2017), quality of life (Conn et al.,
156 Chapter 5
2009; Muros et al., 2017), and sleep quality (Hui & Grandner, 2015), which are beneficial in health promotion and preventing NCDs (Ferranti et al., 2017; Montag et al., 2017). Yet, this remains as challenge for most individuals, due to health complaints that increase with age, high-levels of stress, and unhealthy lifestyles. Thus, this thesis carried out several investigations.
An empirical investigation (Chapter 3) among older adults aged 50 and above discovered that increased sleep duration, improved sleep quality and consumption of a low- fat diet were related to better daytime functioning, which contributed to better subjective health and quality of life (Magee, Caputi, & Iverson, 2011; Grandner, 2014; Duncan et al.,
2014; Frange, de Queiroz, da Silva Prado, Tufik, & de Mello, 2014). This finding strengthens the results from earlier analysis (Slater & Steier, 2012; Boehm & Kubzansky, 2012; Cao et al.,
2016). The findings may indicate that possible carry-over mechanisms within multiple health behaviors not only facilitate each other, but also relate to motivating self-regulation or stress management, which is associated with improved subjective health and quality of life (Lippke,
2014; Cao et al., 2016). Yet, more investigations are needed.
Thus, a cross-sectional study in Chapter 4 went beyond sleep measures and a healthy diet, and included physical activity. The summary of the findings are as follows. First, physical activity and fruit and vegetable intake were associated with increased quality of life and subjective health. Being physically inactive and consuming an unhealthy diet have been identified as factors that lead to increased health risks from NCDs, and linked to poor quality of life and subjective health (Hill, Wyatt, & Peters, 2012; Lachat et al., 2013; Kwon et al., 2015;
WHO, 2017). Second, restful sleep, fruit and vegetable intake and physical activity were associated with increased sleep quality, which was in turn associated with increased quality of life and subjective health. These findings align with previous studies (McKnight-Eily et al.,
157 Chapter 5
2011; Merrill et al., 2011; Chen, Gelaye, & Williams, 2014; Chan, Yen, Fu, & Hwang, 2015;
Ferranti et al., 2016; Abraham, Pu, Schleiden, & Albert, 2017).
Nevertheless, this may be not the case in reality, which has evolved due to urbanization and globalization of unhealthy lifestyles, with more advanced technology accompanying more internet use and sedentary behaviors (Rezende, Lopes, Rey-Lopez,
Matsudo, & Luiz, 2014; WHO, 2017). Many individuals give up their sleep for work, study, or leisure purposes, the common reason being time constraints in daily living commitments.
They thus neglect their sleep quality and are unable to function during daytime due to sleepiness, which is related to unhealthy behaviors, more adverse lifestyle choices, and poor subjective health and quality of life (Arena et al., 2015; Kelly et al., 2017). Knowledge of these associations between multiple health behaviors and underlying mechanisms is beneficial, however, sleep is a complex behavior, and its associations with other behavioral and psychological mechanisms of behavior change, such as self-efficacy and the health outcome of sleep, remains to be investigated in future research.
Age group differences on multiple health behaviors and underlying mechanisms.
NCDs are affecting people earlier than before due to changes in lifestyle (WHO, 20 17). Health behavior change differences exist in age and health status (Zanjani, Schaie, & Willis, 2006).
Individual perspectives are changeable as well, and usually unstable across time. Therefore, it is essential that individuals start to engage in healthy behaviors earlier and continue throughout their entire lives to achieve optimal health and functioning (Michael, Newton &
Kirkwood, 2013). From the overview of the proposed path analysis model in the multi-group analysis (refer to Figure 9), among the health behaviors, restful sleep and physical activity were related to increased sleep quality across all age groups. All age groups showed similar trends: sleep quality was associated with improved quality of life and subjective health, which
158 Chapter 5 aligns with past studies (Merrill et al., 2011; Chen et al., 2014; Abraham et al., 2017).
Nevertheless, evidence of the age-group differences in young, mid-life and old age on health behaviors and their associations with underlying mechanisms is limited, which leads to the purpose of this study: to explore this further by examining distinctiveness among different age groups.
Young adults. Unlike other age groups, physical activity was not significantly associated with fruit and vegetable intake, and neither health behavior was significantly interrelated with restful sleep or quality of life and subjective health. Previous findings suggest the possible justification that the proposed path analysis model based on CCAM (refer to Figure 9) may be less apparent and relevant to young adults for several reasons. For example, young adults were less likely to engage in a healthy lifestyle, which may be because they usually perceive themselves as healthier with a better quality of life, or the health outcomes they could gain from the health behaviors are less apparent. This can be compared to middle-aged and older adults, where the opposite scenarios with health outcomes were more prospective when they engaged in health behaviors (Ferranti et al., 2016; Alley, Duncan,
Schoeppe, Rebar, & Vandelanotte, 2017). A recent study showed 80% of young students reported more than two multiple lifestyle risk behaviors, including excessive screen time, skipping breakfast, low levels of physical activity and short sleep duration, which were related to being overweight and obesity (Continente, Pérez, Espelt, Ariza & López, 2017).
Sleep measurements, including restful sleep and sleep quality, yielded more results, which suggest sleep seems to be more prominent than physical activity and healthy diet in this age group. Most young adults, however, experienced sleep disturbances and habitual short sleep duration, which were significantly related to low quality of life (Chen et al., 2014).
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Therefore, more information about the actual lifestyle of young adults may be useful in providing more insights into how to improve their health and quality of life.
Middle-aged adults. The findings of the cross-sectional study in Chapter 4 showed significant age-group differences. Middle-aged adults showed less intention or action in engaging multiple health behaviors, in particular consuming fruit and vegetables, and restful sleep. Middle-aged adults also reported the lowest level of sleep quality, quality of life and subjective health among the age groups. This is similar to previous studies, where middle- aged adults reported poor sleep quality and poor health characteristics (Haseli-Mashhadi et al., 2009; Tan et al., 2015).
The associations that appeared in the proposed path analysis model (see Figure 9) suggested the findings may be most relevant to this age group. This result may signify that middle-aged adults may need a healthy lifestyle of multiple health behaviors the most, or multiple health behaviors may be most useful for this age-group since they quite often reported poor sleep quality (Haseli-Mashhadi et al., 2009). Many unhealthy behaviors are developed in mid-life and cause chronic disease and mortality in later life (Daw, Margolis, &
Wright, 2017).
One possible explanation for this situation could be that the lifestyle in mid-life is usually occupied with family and work commitments, and responsibilities with multiple roles in daily life. Thus, lack of time is often the primary barrier to engaging in a healthy lifestyle
(Kelly et al., 2017). This could be seen in the sample characteristics described in Table 7; most middle-aged adults worked full-time and were married, which may expose them to higher- levels of stress with their multiple roles. Thus, the question of how to overcome this barrier should be investigated further in future research, especially since this group is within the age range of premature deaths which can be attributed to CVDs (WHO, 2017).
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Older adults. Although older adults reported more intention or action of being physically active and consuming sufficient fruit and vegetables, they were more in favor of fruit and vegetable intake among other age groups. The findings are partially consistent with a previous study, which showed that although older adults were more likely to meet the recommendation of a healthy diet, they were less likely to meet the recommended level of physical activity (Alley et al., 2017).
Among health behaviors, the path between fruit and vegetable intake and quality of life and subjective health was significant, while physical activity was associated with improved sleep quality, in particular for older adults. An explanation for this finding may be that consuming a healthy diet, like fruit and vegetable intake, is more manageable than other health behaviors like physical activity and restful sleep, due to limited physical functionality and increased health problems, which usually increase with age (Alley et al., 2017). Besides, the previous study showed that level of physical activity is consistently linked to how older adults perceived their health (Notthoff et al., 2017). With poor health as one of the barriers to engaging a healthy lifestyle among older adults (Moschny, Platen, Klaaßen-Mielke,
Trampisch & Hinrichs, 2011), it is essential to identify ways to improve not only the physical health, but also the subjective mechanism that may influence well-being and quality of life.
Older adults have been found to be more likely to successfully engage in multiple health behaviors simultaneously compared to younger adults, which could be due to their consciousness of aging and health declination (Davis et al., 1994). The risks of chronic dieases and mortality caused by NCDs are common in this age group (WHO, 2017), and people have acquired more mastery experience over time. An additional reason could be their retirement lifestyle may have less stress from daily hassles, with more flexibility and time compared to
161 Chapter 5 younger adults, who are mostly still working or studying (Hagen, Barnet, Hale, & Peppard,
2016). These assumptions could be evaluated further in future research.
The variances accounted for quality of life, and subjective health increased from young adults to older adults, which may be due to retirement transitions that may yield higher level of quality of life (Eurostats, 2015). Although the evaluation of aging is not possible from the outcomes, the model suggests that variances accounting for sleep quality showed a declining trend from young adults to older adults. Past studies show that poor sleep is very common among older adults, alongside with physical and mental illnesses, as well as the use of medication, which is especially common in rehabilitation settings (Gudberg & Johansen-Berg,
2015; Martin, Jouldjian, Mitchell, Josephson, & Alessi, 2012). Aligned with the findings in this study, older adults showed they had the least restful sleep, followed by middle-aged adults, and young adults had the most restful sleep, a trend that is consistent with a previous study
(Zilli, Ficca, & Salzarulo, 2009). With sleep patterns changing in the aging process, the findings may suggest that the subject of healthy aging should be investigated further.
Possible reasons for inconsistent findings. Although the multiple health behaviors were interrelated as hypothesized, not all findings were as expected. For example, although daytime functioning interrelated with consuming a low-fat diet, other sleep measurements such as sleep duration and sleep quality did not interrelate with consuming a low-fat diet, which is inconsistent with past studies (Peuhkuri, Sihvola, & Korpela, 2012; St-Onge &
Shechter, 2014). This may be due to insufficient information in defining healthy diet and eating patterns, or by measuring only a low-fat diet. Thus, more measurements of a healthy diet should be included in future research, such as a high-fat diet, fruit and vegetable intake, and carbohydrates consumption, as suggested in prior studies (Peuhkuri et al., 2012; St-Onge
& Shechter, 2014). Other research has found that sleep duration does not differ with energy
162 Chapter 5 intake, but fiber and saturated fat intake are related to light and less restorative sleep with more sleep interruptions and less slow wave sleep (St-Onge, Roberts, Shechter, & Choudhury,
2016b). This indicates that other sleep measurements should be included in the future research.
Restful sleep was not interrelated with physical activity and a healthy diet of fruit and vegetables, which contradicts a previous study, which suggested that restful sleep, physical activity and a healthy diet of fruit and vegetables were interrelated (El Ansari et al., 2011).
This inconsistency may be due to the single item used to measure sleep not being sufficient, and some studies suggest that measurements such as the Pittsburgh Sleep Quality Index
(PSQI, Buysse, Reynolds, Monk, Berman, & Kupfer, 1989), may be more comprehensive in measuring sleep as a health behavior.
Unlike previous evidence, the findings in Chapter 4 showed that physical activity was not directly associated with quality of life and subjective health for most age-groups (Merrill et al., 2011; Pucci, Reis, Rech, & Hallal, 2012). Other alternative measurements, such as the
International Physical Activity Questionnaire (IPAQ) (Pucci et al., 2012; Craig et al., 2003), may improve the outcomes. Many studies have demonstrated consistent findings with insufficient sleep being highly associated with increased energy food intake (Patterson et al., 2014; St-
Onge et al., 2016a; Murphy et al., 2017), yet the findings about energy expenditure such as physical activity were not satisfied (Murphy et al., 2017), or changed (Calvin et al., 2013).
Thus, this should be examined further in future research.
5.3. Connotations for Theoretical Frameworks
Foundational Principles. By highlighting personal or psychological assets and self- perception of bodily states, these findings could be beneficial for personal motivation to maintain and/or develop positive perspectives for the future, which is essential for general
163 Chapter 5 populations and particularly rehabilitation patients. As personal assets refer to existing characteristics of individuals, it would be advantageous if they were recognized and identified. This includes positive affect, resilience, self-regulation, self-management, self- affirmation, self-discipline, etc., which should be included in empirical studies in the future, as these are potential practical directions to improve health and well-being, which would buffer against the development of negative affective states (Wright, 1972; Nierenberg et al.,
2016).
Given that the experiences of bodily states are often subjective perspectives of the circumstances, these self-perceptions thus may not reflect the actual occurrence. Still, the subjective perceptions shape how individuals think, feel and behave. Hence, modifying environmental conditions at different life stages may alter personal outlooks and mindsets, as well as lifestyle and health outcomes. Thus, these aspects in foundational principles should be considered in research and practice, with the focus of being mindful of the effectiveness of individuals’ psychological assets and subjective perceptions, in addition to any unsatisfactory conditions, including health and well-being (Dunn et al., 2016; Nierenberg et al., 2016).
Subjective Well-Being (SWB). Besides personal assets and self-perception of bodily states as suggested above, subjective well-being (SWB) underlines a combination of cognitive and affective components (Diener, 1984; Kashdan, Biswas-Diener, & King, 2008). Diener and his colleagues (2017) have recently reviewed this theory regarding health aspects, and suggested that SWB is advantageous in improving physical and mental health. Instead of the cognitive component of life satisfaction, this thesis used satisfaction with health, as it contributes to life satisfaction as a whole through its role in the satisfaction of needs, suggested by the bottom-up approach of the needs conceptual model of subjective well-being
164 Chapter 5
(Gataūlinas & Banceviča, 2014). The longitudinal study in this thesis examined these components (i.e., subjective health, positive affect and negative affect) together for up to eight years, and showed consistent trends of SWB being related to life satisfaction (Kashdan et al., 2008; Diener & Chan, 2011). This may signify that self-perceived ideal health is an indicator of well-being, and the higher-level goal of most individuals, which will be discussed next.
Compensatory Carry-Over Action Model (CCAM). The main theoretical framework of this thesis is the Compensatory Carry-Over Action Model (CCAM), created by Lippke (2014).
It is a novel approach comprising multiple health behavior changes and psychological and behavioral mechanisms to promote a healthy lifestyle and prevent NCDs.
Multiple health behaviors. The findings of this thesis strengthened the assumptions of CCAM, with a healthy lifestyle containing more than single health behaviors being associated with stress-management that can increase well-being and higher-level goals. As the CCAM model adopted mostly long-established health behaviors of physical activity and healthy diet in the existing literature, this thesis included sleep, explored its interrelations with other health behaviors, and investigated its relationship with underlying psychological mechanisms.
The first part of the model, which includes social-cognitive factors and compensatory cognitions, and model-testing as a whole, have not been considered in this thesis. This is because this thesis focuses on the second part of the model due to the goals in examining multiple health behaviors with their carry-over mechanisms and self-regulation in relation to stress-management that increase well-being (e.g. good sleep quality), and higher-level goals
(e.g. good subjective health or quality of life).
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Carry-over mechanisms. Including experiences, skills, and cognitions, such as motivation and self-regulation, may be carried over from one health behavior to another among multiple health behaviors. Among the carry-over mechanisms, the advances in the psychological study of self-regulation contribute to improve our understanding of the elements that lead individuals to set goals, or considerations that undermine individuals’ efforts at achieving their goals (Mann, Ridder, & Fujita, 2013). Thus, the primary outcomes of this thesis strengthened the assumptions of CCAM: engagement in a healthy lifestyle with multiple health behaviors relates to coping well with stress that increases well-being, which is presumably associated with the higher-level goals of self-perceived improved health and quality of life (Minkel et al., 2012; Flueckiger, Lieb, Meyer, Witthauer & Mata, 2016b).
Higher-level goals. Otherwise known as life goals, are mental representations of desired outcomes, which are more constant, clear, long-lasting and less complex than specific health goals. Moreover, higher-level goals consist of individual values with psychological purpose, which potentially motivate individuals to engage in and maintain different health behaviors (Mann et al., 2013; Lippke, 2014). Among higher-level goals, this thesis study suggests and emphasizes quality of life and subjective health as essential psychological mechanisms in achieving successful health behavior change and improving health and well- being (Lippke, 2014; Bowling, 2017), since both capture constant subjective perception and evaluation of individuals’ general health and well-being (Meiselman, 2016; Bowling, 2017).
Quality of life contains various domains of life, including health, which could be studied in relation to an individual’s goals and motivation with self-regulation and management of one’s life priorities (Efklides & Moraitou, 2013). In some cases, self-perceived better quality of life comprises how an individual perceives the ‘goodness’ of different features of their life (Bowling, 2017), which could also diminish stress levels, and thus increase
166 Chapter 5 well-being. Therefore, in this thesis, quality of life has been used as both a higher-level goal and stress-management technique that could increase well-being.
Stress-management that increase well-being. With physical and mental health often influencing each other, the engagements of health behaviors may facilitate a decrease in stress reactions and result in an increase in well-being, such as sleep quality. Some individuals engage in health behaviors, including physical activity, a healthy diet, and sleep, to cope with stress and for this reason, these health behaviors are quite often included in stress- management. For example, low-levels of stress are often linked to good sleep quality (Hui &
Grandner, 2015). Although causal relationships are not possible in this study, the conceptual path analysis model in Chapters 3 and 4, which is based on the CCAM (Lippke, 2014), suggests that stress management increases well-being (e.g., sleep quality) and may be a potential mediator of the relationship between health behavior and higher-level goals (e.g., quality of life and subjective health). In other words, the engagement in more than a single health behavior may decrease stress levels and increase well-being of having good quality sleep, and thus relate to improved quality of life and self-perceived health. This suggestion supports the concept of multiple health behaviors increasing sleep quality, which is an essential indicator of coping well with stress and improving well-being that enhances the quality of life and subjective health (Merrill et al., 2011; Lippke, 2014; Palmer & Alfano, 2017; Geller et al.,
2017). This may point to another possible perspective on stress management that increases well-being, for example, good quality of sleep, into a multiple health behavior change approach. Further investigations of the model as a whole or lifespan changes in the behavioral and psychological mechanisms are strongly recommended.
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5.4. Practical Implications
From primary to tertiary disease prevention, at different levels of health promotion and rehabilitation, it is essential to access individual health-related lifestyle and behavioral differences to develop useful interventions and environmental modifications, to achieve optimal physical and mental health (Owen, Splett, & Owen, 1999). Thus, the findings of this thesis regarding lifestyle-related health behaviors with associations of subjective health, quality of life, and sleep quality, are relatively informative in developing practical and effective interventions based on disease prevention across some levels of health promotion from the individual and community to the system (Kelly, Charlton, & Hanlon, 1993).
To begin with primary prevention, the relevant fields of health psychology could use this information to create awareness and promote health in different settings, including health and rehabilitation care, to prevent exposure to risk factors that lead to health problems throughout the human lifespan. This refers mainly to the importance of multiple health behaviors—physical activity, healthy diet, and sleep, which are associated with health benefits (Warburton & Bredin, 2017) and quality of life outcomes (Conn et al., 2009).
While sleep has been suggested as an essential health behavior in previous studies
(Perry et al., 2013; Irish et al., 2013; Doku, Koivusilta & Rimpelä, 2013), this thesis expanded on the topic of sleep, with other long-established health behaviors like physical activity and a healthy diet. The health behavior of sleep requires more attention, this is especially the case when lack of time is a standard barrier to engagement in health behaviors (Kelly et al., 2017).
For instance, among healthy individuals, the effort required to perform physica activity or consume a healthy diet requires more effort than consistently going to bed earlier or proper sleep hygiene.
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Regarding reducing or removing risk factors by changing individual health-related behaviors, it is essential to be aware of the importance of health behaviors, and initiate and maintain a healthy lifestyle, through training or education with comprehensive and useful guidance. For example, several studies suggest that a health promotion program for college students is feasible and acceptable to implement, and can constructively create awareness of a healthy lifestyle and improve knowledge about health behaviors, including good sleep habits, to improve physical and mental health (Byrd et al., 2014; Levenson et al., 2016).
For secondary prevention, it is important to consider strategies to slow or even stop the progression of the disease, including health screening and detection for early diagnosis
(enhancing early detection of diseases), and prompt treatment. A previous study suggested that health experts, policymakers, and rehabilitation care should encourage and pay attention to evidence-based strategies to promote healthy living through not only physical activity and healthy diet, but also sleep (Shade, Berger, Dizona, Pozehl & Pullen, 2016). Moreover, health experts should translate outcomes into practice, especially in rehabilitation settings, to include health assessment, screening, and management of sleep problems (Shade et al.,
2016). Particularly for older adults, health care providers should pay attention to sleep health, by pinpointing sleep problems and raising early awareness of the need to sleep well, which is associated with better functioning and general better subjective health and quality of life.
The change process or maintaining lifestyle-related health behaviors is complex and continuously challenged by external stressors. Stress management still requires greater clinical attention, in primary or secondary preventions targeting both mental and physical health (Clark et al., 2011; McKenzie & Harris, 2013). In this thesis, sleep quality plays a role in stress management that increases well-being. Sleep measurement should be included in health assessments, to meet the needs of every individual and enhance their overall health
169 Chapter 5 and well-being. Moreover, a previous study suggested incorporating evaluations that measure an individual’s stress level and designing a program that meets specific needs to prevent dropout rates and enhance the effectiveness of wellness programs (Clark et al.,
2011). More studies are needed to examine this complex issue further.
For tertiary prevention, the rehabilitation sector and health care services should implement strategic interventions from health psychology standpoints, to manage individuals who have already been diagnosed with NCDs, to prevent a stable illness from becoming worse. Individuals who engage in healthy behavior have greater adaptability to physical health challenges, which often occur for rehabilitation patients. These outcomes were drawn from the studies which involved participants with rehabilitation experiences (in Chapters 2 and 4). Engagements in health behaviors are a common suggestion or practice during rehabilitation. Sleep should be included in these suggestions to improve their overall functioning during daytime activity, already includes physical activity and consuming a healthy diet. A more holistic approach including these multiple health behaviors could ease the symptoms of illnesses or injuries.
The Center for Disease Control and Prevention (Perry et al., 2013) has mentioned the need to raise awareness of sleep as a health behavior, and empirical studies of sleep could increase the advantages from interventions that are theorized and implemented at the community level (Grandner, 2012). Therefore, as suggested in many previous studies, there is a demand for further development and enhancement of policies that have a direct impact on multiple health behaviors, including sleep (Pedersen et al., 2015). It is necessary to examine whether such approaches are effective, more cost-effective, or can result in resource savings and thus better investment, compared to single health behavior change approaches
(Nigg & Long, 2012).
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The motivation to engage or disengage in a healthy lifestyle varies among different age groups, with distinct perceptions of obstacles and priorities (Kelly et al., 2017). This was also found in this thesis. This scenario can be seen in various disciplines, for example, age- related changes have been captured in work motivation research. Previous studies suggest age-differentiated task-specific design to encourage older workers’ motivation development throughout their working lives, not only productively, but also sustainably in able to work for longer (Stamov-Roßnagel & Hertel, 2010; Stamov-Roßnagel & Biemann, 2012). This may be applicable to engagement in a healthy lifestyle. The current findings suggest that sleep seems to play a more important part of young adults’ healthy and functioning lifestyle, meanwhile, middle-aged adults may need to adopt a more holistic approach in including variety of multiple health behaviors into the healthy lifestyle, and older adults may be more in favor of consuming healthy diet, age-differentiated health behaviors could be suggested or customized for specific age group more appropriately.
5.5. Strengths, Limitations and Suggestions for Future Research
Strengths. The main strength of this study is that the core hypotheses were based on the assumptions of a theoretical framework, CCAM and subjective well-being. To the author’s knowledge, it is one of the few studies that have investigated part of the premises of CCAM, to examine multiple health behaviors and their underlying mechanisms and contributes to strengthening the existing knowledge. As CCAM mainly describes the health behaviors of physical activity and a healthy diet, this thesis included sleep as a health behavior, which should be part of a healthy lifestyle, and examined its interrelations with other health behaviors, and its associations with the underlying mechanisms.
Another strength of this thesis is that a variety of research study designs have been implemented, which include longitudinal and cross-sectional studies in various settings, from
171 Chapter 5 convenient platforms to clinical settings in rehabilitation. Multiple sophisticated statistical analyses have been conducted, including LMM and SEM, which have several advantages in delivering reliable and valid statistical outcomes.
To understand the distinctiveness of lifestyle among age groups, the findings provided more sociodemographic data, which are potentially informative for a wide range of recipients, from research to policy-makers and health-care practitioners.
Limitations and suggestions for future research. There are also limitations of this thesis, which should be recognized. First, it did not include complete model testing of the
CCAM, which would have been very useful in understanding the full picture scientifically and the clustering effects are not available. This thesis mainly explores the associations between selected health behaviors and subjective health and quality of life. However, the reverse might be applicable. Therefore, the other limitation of this study is that it does not include bidirectional hypotheses to test this possibility. Although many individuals are aware of the health benefits of health behaviors, many do not engage in health-promoting behaviors, and motivating this group of individuals is a constant challenge to health psychologists and health practitioners. Thus, for future research, the entire CCAM framework should be investigated, with all additional theorized behavioral and psychological variables included (Lippke, 2014;
Hardcastle et al., 2015), which will be useful in research and theory development.
This thesis did not include interventional data or a randomized controlled trial, and thus causal outcomes are not possible, which implies that an investigation of the effectiveness of interventions aimed at improving health and well-being is not possible. This would be very much informative in knowing the effects of the lifestyle-related health behaviors on sleep quality, subjective health and quality of life, which are the possible positive health outcomes.
Thus, more studies should be carried out to identify the effectiveness of interventions and
172 Chapter 5 important casual effects. In addition to multiple health behavior approaches, more prospective studies that include more accurate measurements will be needed to clarify causal pathways. This is especially true since health behaviors are complex behaviors that require more comprehensive methodology and measurements to access actual behaviors and the associations with other health outcomes.
Additionally, this thesis did not include objective measures, but used self-reported measurements, which can under- or overestimated the actual behaviors. Thus, the accuracy of measurements may be limited, although this scenario is similar to a previous study
(Cassidy, Chau, Catt, Bauman & Trenell, 2016). Objective measures like wearable devices, can provide accurate outcomes of health behaviors with more detailed information (Farooq &
Sazonov, 2016; Chen et al., 2014). A previous study suggests that self-rated sleep quality is distinct from objective actuality (Landry, Best, & Liu-Ambrose, 2015). Moreover, various methodologies, such as mixed-method studies, may be useful to move beyond quantitative studies to include qualitative studies, which would provide more real-life scenarios and insights.
In addition, for the studies in Chapters 3 and 4, the measurement items of subjective health and quality of life were listed one after another in the questionnaires, which could make it difficult for participants to distinguish these items as separate aspects. To deal with this, these two items were combined into one variable as the overall quality of life and subjective health for the study in Chapter 4, to minimize any carry-over effects or response biases.
Many single measurement items were adopted, which may not be sufficient in measuring the complex health behaviors. This may result in the accuracy and
173 Chapter 5 representativeness of the findings not being ensured. Thus, measurements with high in reliability and validity should be adopted instead.
In future studies, sociodemographic variables including socioeconomic status should be investigated further in multiple health behavior studies. A recent study suggested that income is presumed to accelerate access to health-enhancing facilities, such as a gym, or individuals with higher-level of education are better at developing constant and stronger intentions (Schüz, 2017). Gender differences, which are not available in this thesis, may be a factor in the engagement of health behaviors. For example, a past study demonstrated that female university students consumed more fruit and vegetables, while male students reported a higher level of physical activity, and more restful sleep (El Ansari et al., 2011).
Health condition should also be included in the future studies, for example, individuals who reported cardiometabolic disease are three times more likely to have had an unhealthy lifestyle, compared to healthy individuals (Cassidy et al., 2016).
Despite these limitations, this thesis makes valuable contributions in multiple health behaviors, including not only physical activity and healthy diets, but also sleep, and their assocaitions with increased sleep quality, subjective health and quality of life, with significant age-group differences.
5.6. Conclusions
Adopting a healthy lifestyle has been suggested as a possible solution in preventing
NCDs. Besides physical activity and a healthy diet, sleep should also be included, especially in multiple health behaviors paradigm. This thesis gathered theory-based and empirical investigations, in combining the psychological and behavioral mechanisms based on the
CCAM, such as carry-over mechanisms and self-regulation of multiple health behaviors in relating to stress management that increase well-being and higher-level goals. The outcomes
174 Chapter 5 could be informative for guiding policy and strengthening health behaviors, in preventing timely and costly treatments which burden the health system. Although the findings of this thesis may not be able to suggest concrete interventions to prevent and control NCDs, it is essential to address multiple health behaviors in living a healthy functioning lifestyle, to prevent and reduce physical and mental health problems. The findings of age-group differences provide essential health-related information which highlights future research areas to inform age-appropriate health promotion, and rehabilitation strategies and policies within community-based settings. To tailor interventions to individual and population aging, it is essential to enable and encourage all ages, including older people, to experience the positive outcomes of physical and mental health and well-being to their full potential, via multiple health behavior approach. Reducing lifestyle-related risk factors can go a long way in increasing general health and well-being.
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188 Appendix
Sample Questionnaire – Chapter 3 (IROHLA) Please kindly read the questions carefully, and answer all the questions according to what is true for you. There are no right or wrong answers to any of the questions below. Personal Information Please kindly fill in the blanks or choose one answer that fits you the best. In which year were you born? 19_____
Gender q Male q Female q Others
In which country were you born? q Germany q Other: ______
For how many years were you attending school? What is, or was your occupation?
What is your marital status? Single / Widowed / Married / Long-term Divorced relationship
q q
What is your housing situation? Are you living …
by yourself together with your in different living condition (shared partner or family apartment, care or retirement home)
q q q
Health Status & Quality of Life
How would you describe your health state right now? Very bad Not good Satisfactory Good Very good q q q q q How would you describe your quality of life right now? Very bad Not good Satisfactory Good Very good q q q q q
Chronical diseases
Are you currently suffering chronical diseases, such as diabetes or cardiac insufficiency? q No q Yes, which one: ______How often do you see your general practitioner?
189 Appendix
At least once a Once in two Monthly Once in a Once in six Less than once week weeks quarter months a year q q q q q q
Stages of Health Behavior Change
In the last week, Do you visit a sport course regularly or are you member of a sports club? No, and I don’t No, but I’m No, but I intend to Yes, for a short Yes, for a long intend to do so thinking about it do so time time q q q q q Have you been physically active for more than 30 minutes on five days of the last week (or more than 2.5 hours in the last week) on an exhausting level? No, and I don’t No, but I’m No, but I intend to Yes, for a short Yes, for a long intend to do so thinking about it do so time time q q q q q Do you eat fat reduced (for example less fat from animals, less peanuts and chips)? No, and I don’t No, but I’m No, but I intend to Yes, for a short Yes, for a long intend to do so thinking about it do so time time q q q q q Do you eat with less sugar (for example by don’t drinking Lemonades or Softdrinks, eating less sweets and candy)? No, and I don’t No, but I’m No, but I intend to Yes, for a short Yes, for a long intend to do so thinking about it do so time time q q q q q Do you eat 5 servings of fruit and vegetable everyday? (One serving is a handful). No, and I don’t No, but I’m No, but I intend to Yes, for a short Yes, for a long intend to do so thinking about it do so time time q q q q q Do you drink at least 1.5 liters of non-alcoholic and drinks without caffeine (Water, Juice, Tea)? No, and I don’t No, but I’m No, but I intend to Yes, for a short Yes, for a long intend to do so thinking about it do so time time q q q q q Have you ever smoked or are you smoking right now?
190 Appendix
I smoke regularly I smoke I once smoked but I never smoked or smoked less than occasionally stopped 100 cigarettes in my life q q q q
Compensatory Health Beliefs Scale
You can compensate less physical activity by eating healthier. Not true at all Barely true Fairly true Exactly true q q q q You can compensate a bad nutrition by more physical activity. Not true at all Barely true Fairly true Exactly true q q q q If you are not enough physical activity in one week, you can compensate that with more physical activity in the next week. Not true at all Barely true Fairly true Exactly true q q q q If you haven’t eaten healthy on one day, you can compensate by eating right the next days. Not true at all Barely true Fairly true Exactly true q q q q
Work-Life Balance
Range from 1 = I don’t agree at all to 6 = I absolutely agree 1 2 3 4 5 6 I’m happy with the balance of my private life and
work life. I’m hardly able to balance my work and private
life. I can fulfill all requirements of my private life
and my work life equally good. I can compensate exhausting and relaxing
activities in my life very good. I’m happy with my priorities regarding my job
and my private life. Sleep Quality
191 Appendix
How many hours of actual sleep do you get at night? (This may be different than the number of hours you spend in bed) hours minutes When have you usually gone to bed? Estimated time: During the past month, how would you rate your sleep quality overall? Very good Fairly good Fairly bad Very bad q q q q During the past month, how often have you taken medicine (prescribed or “over the counter”) to help you sleep? Always Frequent Seldom Never q q q q During the past month, how much of a problem has it been for you to keep up enthusiasm to get things done? Very easy Fairly easy Fairly difficult Very difficult q q q q
Optimism (Life Orientation – Revised Test)
0 = Strongly Disagree 1 = Disagree 2 = Neutral 3 = Agree 4 = Strongly Agree 0 1 2 3 4 In uncertain times, I usually expect the best. It’s easy for me to relax. If something can go wrong for me, it will. I’m always optimistic about my future. I enjoy my friends a lot. It’s important for me to keep busy. I hardly ever expect things to go my way. I don’t get upset too easily I rarely count on good things happening to me. Overall, I expect more good things to happen to me than bad.
Thank you very much for your participation.
192 Curriculum Vitae – Shu Ling Tan
Experience
04/2018 – present Lecturer (Lehrkraft für Besondere Aufgaben), Institute for Sport and Exercise Science, Department for Social Sciences of Sport, Westfälische Wilhelms-Universität Münster, Germany
03/2018 Visiting Researcher, School of Applied Psychology, University College Cork, Ireland
06/2015 – 01/2018 Project Manager, Department of Psychology and Methods, Jacobs University Bremen, Germany
• Rehabilitation-Aftercare for an optimal Transfer into Autonomous daily life (RENATA) • Health @ Jacobs, students’ health behaviors, stress & performance • Intervention Research on Health Literacy among Ageing Population (IROHLA)
02/2016 – 11/2017 Substitute Teaching & Teaching Assistant, Department of Psychology and Methods, Jacobs University Bremen, Germany
09/2016 – 12/2016 Assistant Lecturer, in Leadership and Management in a Digitalized World, Executive Education, Jacobs University Bremen, Germany
03/2015 – 09/2016 Training Facilitator, Intercultural Competence for international students and employees from Daimler AG, Jacobs University Bremen, Germany
03/2014 – 01/2015 Training Facilitator & Interpreter, Malaysia
02/2012 – 02/2014 Graduate Teaching Assistant, Nursing & Midwifery, Middlesex University, London, U.K.
Education
03/2015 – 12/2017 Ph.D. in Psychology, Jacobs University Bremen, Germany
09/2010 – 10/2011 M.Sc. in Applied Positive Psychology, University of East London, U.K.
193 Curriculum Vitae – Shu Ling Tan
05/2007 – 09/2010 B.Sc. (Hons) Psychology, Middlesex University, London, U.K. & HELP University College, Kuala Lumpur, Malaysia
Scholarships & Awards
12/2016 – 01/2018 PhD scholarship, Wilhelm-Stiftung für Rehabilitationsforschung, a foundation for rehabilitation research
12/2015 – 11/2016 PhD scholarship, work group of Prof. Dr. Sonia Lippke
06/2015 – 11/2015 PhD scholarship, German Federal Ministry of Education and Research (BMBF)
08/2013 Achieved the status of Associate Fellow of the Higher Education Academy, (AFHEA title), U.K.
10/2011 Qualified for Level 2 Award in Community Sports Leadership by Sports Leaders in U.K.
List of Publications Tan, S.L., Whittal, A. & Lippke, S. (2018). Associations among sleep, diet, quality of life and subjective health. Health Behavior and Policy Review, 5(2), 46-58. doi:10.14485/HBPR.5.2.5 Tan, S.L., Duan, Y.P. & Lippke, S. (2018, in press). A non-randomized longitudinal study of rehabilitation patients: Physical activity, subjective health and positive affect. Clinical Applications of Positive Psychology: An International Perspective Tan, S.L., Storm, V., Reinwand, D.A., Wienert, J., de Vries, H., & Lippke, S. (Resubmitted & Under- Review). Understanding the associations of sleep, physical activity, fruit and vegetable intake with quality of life and subjective health: A cross-sectional web-based study. Frontiers in Psychologys Storm, V., Reinwand, D.A., Wienert, J., Tan, S.L. & Lippke, S. (Resubmitted & Under-Review). Welche Rolle spielt die wahrgenommene soziale Unterstützung in der Beziehung zwischen körperlicher Aktivität und Depressivität bei Risikopatienten für Herz-Kreislauf-Erkrankungen? Zeitschrift für Sportpsychologie van't Jagt, R.K., Tan, S.L., Hoeks, J., Reijneveld, S.A., de Winter, A., Paech, J., Lippke, S., & Jansen, C. (Resubmitted & Under-Review). Using photo stories to support doctor-patient communication. Three studies into a communication health literacy intervention for older adults. Journal of Health Communication
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