SEDENTARY BEHAVIOUR PROFILING IN BREAST CANCER SURVIVORS AND IMPLICATIONS FOR

MENTAL HEALTH

by

Jason Lacombe

A thesis submitted in conformity with the requirements for the degree of Masters of Science

Exercise Sciences University of Toronto

© Copyright by Jason Lacombe 2015

ii

Sedentary behavior profiling in breast cancer survivors and implications

for mental health

Jason Lacombe

Masters of Science

Exercise Sciences University of Toronto

2015

Abstract

Background: Symptoms of depression remain under diagnosed but highly prevalent in breast

cancer survivors (BCS). Reducing sedentary behaviour (SED) during the early survivorship

period may be a strategy for treating clinical depression in survivors.

Methods: One hundred and eighty-seven BCS provided baseline demographic, medical, and

SED/physical activity data. Depression was reported three months later. Multiple dimensions of

SED were identified and examined in cluster analysis. The association between cluster

membership and depression was assessed using logistic regression.

Results: Two SED groups were identified representing higher and lower SED. High SED cluster

BCS’s were significantly older, heavier, less physically active, less educated, and were more

likely to have undergone lymph/axial node dissection. In the logistic regression model cluster

membership was a significant predictor of clinical depression.

Conclusions: Reducing dimensions of SED during the early survivorship period could protect

from clinical depression symptoms.

Key Words: Sedentary behaviour, physical activity, breast cancer survivors, depression

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Acknowledgments

I would like to first acknowledge and express my sincerest gratitude to my supervisor,

Dr. Catherine Sabiston. There is not enough room in this document to even begin to express how

much I appreciate everything that you have done for me over these past two years. I have learned

so much under your supervision, and your willingness to provide countless hours of time to help

me with this thesis is greatly appreciated.

Next, I would like to acknowledge the support from my committee members and say

thank you for all your help.

Dr. Faulkner: you were the first mentor that I ever had in academia. I have learned so

much from you, and continue to learn and be inspired by your work. I am grateful for everything

that you have done for me.

Dr. Jones: I first met you in the third year of my undergraduate studies and I can

truthfully say that you are the reason that I am passionate about studying physical activity and

sedentary behavior in breast cancer survivors. When I first started a research placement with you

in September 2011, I was lost. I did not know what I was interested in, nor did I know what I

wanted to do once I graduated from Kinesiology. You opened my eyes and provided me with the

opportunity to work with breast cancer patients. Through working with these wonderful women,

I developed a passion for the doing the research that I am doing today.

Dr. Trinh: Over the past two years you have been a terrific mentor! I learned so much

about research and academia from you. In addition, I really enjoyed going on multiple coffee

breaks (tea for you)! Whenever I was stressed I would come upstairs and no matter what time it

was, or how busy you were, you would make time for me. You were always able to offer great

guidance and support! Thank you so much!

Finally, I would like to say thank you to my family! You have always supported me and

been there for me when I needed you.

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Table of Contents

Abstract……………………………………………………………………………………………i

Acknowledgements………………………………………………………………………………iv

List of Tables…………………………………………………………………………………….vii

List of Appendices………………………………………………………………………………viii

CHAPTER 1………………………………………………………...…………………………….1

Introduction……………..…………………………………………………………………1

CHAPTER 2………………………………………………………………………………………5

Literature Review.....………………………………………………………........................5

Cancer Survivorship ………………………………………………………………………5

Life After Cancer Moving On……………………………………………………………..8

Mental Health in Breast Cancer Survivors: Depression…………………………………10

Physical Activity and Breast Cancer ………………………………………………….....13

Sedentary Behaviour …………………………………………………………………….15

CHAPTER 3……………………………………………………………………………………..23

Manuscript Title Page...………………………………………………………………….23

Abstract...…………………………………………………………………………….…..24

Introduction………………………………………..………..……………………………25

Methods………………………………………………………...………………………...29

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Results……………………………………………………………………………………35

Discussion……………………………………………………………………..................38

CHAPTER 4……………………………………………………………………………………..48

Conclusions and Future Directions………………………………………………………48

References ………………………………………………………………….....................52

TABLES………..………………………………………………………………..........................72

APPENDICIES………………………………………………………………………………..…78

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List of Tables

Table 1: Baseline demographic, medical, sedentary and physical activity descriptives for

participants

Table 2: SITT variables by cluster membership

Table 3: Bivariate correlations by sedentary behaviour cluster membership variables and

self-report/objective moderate-to-vigorous physical activity

Table 4: Bivariate correlations between cluster membership variables and continuous

depression

Table 5: MANOVA and chi square examining medical, demographic and weight status

variables by cluster membership

Table 6: Logistic regression of main Study variables predicting clinical depression

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List of Appendices

Appendix A: Sedentary Behaviour Definitions

Appendix B: Descriptive statistics comparing 500 minute accelerometer wear-time and 600

minute accelerometer wear-time

Appendix C: Bivariate correlations comparing 500 minute accelerometer wear-time and 600

minute accelerometer wear-time

Appendix D: Sedentary behaviour frequency (S in the SITT principle) and interruptions (I in

the SITT principle), time (T in the SIIT principle) and type (T in the SITT

principle) using objective and self-report data

Appendix E: Bivariate correlations between demographic and medical covariates

Appendix F: Bivariate correlations between objective sedentary behaviour and measures of

weight status

Appendix G: Bivariate correlations between self-report sedentary behavior and measures of

weight status

1

Chapter 1 Introduction

One in nine Canadian women will be diagnosed with breast cancer in their lifetime making

breast cancer the most common female cancer diagnosis in women (Canadian Cancer Society,

2013). In conjunction with this, the number of breast cancer survivors (BCS) continues to

increase, with the 5-year survival rate being estimated between 80 and 95% (Coleman et al.,

2011). Unfortunately, cancer survival is associated with increased risk of death from noncancer

causes and poorer overall health (Baade, Fritschi, & Eakin, 2006; Lynch, Dunstan, Vallance, &

Owen, 2013). Moreover, cancer survivors are also at risk of developing many long-term

psychological and physiological complications from the disease itself and/or the cancer treatment

profile (Howard-Anderson, Ganz, Bower, & Stanton, 2012; Zainal, Nik-Jaafar, Baharudin,

Sabki, & Ng, 2013). Fortunately, physical activity (PA) has been consistently reported to

alleviate physical and mental health challenges among breast cancer survivors (Brunet, Sabiston,

& Meterissian, 2011; Courneya et al., 2005; Schmitz et al., 2010). Despite this, few BCS engage

in sufficient PA to gain the associated health benefits. Estimates ranging from 50 to 90% of BCS

are not achieving the American College of Sports Medicine (ACSM; (Schmitz et al., 2010))

recommended 150 minutes of moderate-to-vigorous physical activity (MVPA) per week

(Blanchard, Courneya, & Stein, 2008; Lynch et al., 2010; Schmitz et al., 2010). Given the low

levels of MVPA reported and measured among BCS, and challenges associated with uptake and

adherence to MVPA, it may be that other related behaviours need to be targeted to help improve

mental and physical health in this population. Sedentary behaviour is related to, but a distinct

health behaviour from MVPA. Researchers have found relationships between SED and a host of

physical and mental health problems among healthy adults, including having a direct influence

on bone mineral content, vascular health, metabolism and poorer quality of life (Hamer,

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Coombs, & Stamatakis, 2014; Tremblay, Colley, Saunders, Healy, & Owen, 2010; Wilmot et al.,

2012). While there is limited overall evidence on the health implications of SED among cancer

survivors, high volumes of SED are independently associated with chronic disease-related risk

factors such as central adiposity, elevated blood glucose and insulin as well as poorer physical

functioning and general health in cancer survivors (George et al., 2014; Lynch et al., 2013).

Thus, a new cancer survivorship research agenda is needed to focus on sedentary behaviour

(SED) (Lynch et al., 2013).

SED is defined as any waking behaviour characterized by a low-energy expenditure (i.e.,

≤1.5 resting metabolic equivalents) while in a sitting or reclining posture (Tremblay, 2013).

Researchers on SED have proposed using the SITT principle to describe participant’s SED

(Tremblay et al., 2010). Specifically, SITT refers to Sedentary behaviour frequency (number of

bouts of a certain duration); Interruptions in sedentary behaviour (e.g., getting up from one’s

desk while at work; Healy et al., 2008); Time (the duration of total sitting time); and Type (mode

of sedentary behaviour). As such, SED is multidimensional health behaviour. To date, there is no

evidence of understanding SED using the SITT principle amongst BCS. Thus, one aim of this

current study is to examine and describe the SITT variables in a sample of BCS.

While research on SED is in its infancy, SED has been shown to have a direct influence

on bone mineral content, vascular health, and metabolism among healthy adults (Tremblay et al.,

2010). Increased SED is also associated with increased cancer risk (Matthews et al., 2012). At

this time, there is limited overall evidence on the health implications of SED among cancer

survivors. Nonetheless, it is important to recognize that SED is a distinct entity from PA with

independent and qualitatively different effects on physical function, metabolism, and health

outcomes, which must be treated separately from a lack of PA (Hamilton, Hamilton, & Zderic,

3

2004; Hamilton, Healy, Dunstan, Zderic, & Owen, 2008; Sallis, Owen, & Fotheringham, 2000;

Tremblay et al., 2010). To date, some hypothesized biological mechanisms have been proposed

to better understand the association between SED and various health outcomes. For example,

Lynch (2010) proposed a framework to better understand these complex relationships. Within

this framework and the well-established evidence that SED is independently associated with

central adiposity, elevated blood glucose/insulin, and a number of cardiometabolic outcomes,

Lynch believes that a bidirectional relationship between SED and metabolic attributes exist

which are then responsible for cancer progression. The strongest evidence exists to support a

bidirectional relationship between SED and adiposity when compared to other metabolic

attributes (i.e., sex hormones, inflammation, vitamin D; Lynch, 2010). Using Lynch’s (2010)

proposition, SED may be linked to physical and mental health problems among BCS. Depression

is a prominent mental health factor for BCS that may be linked to SED.

Symptom reporting of depression is a common comorbidity associated with cancer

survival (Harrington, Hansen, Moskowitz, Todd, & Feuerstein, 2010), and is thought to be

under-reported in BCS with an estimated 10-25% of women reporting depression (Burgess et al.,

2005; Qiu et al., 2012; Zainal et al., 2013). Depression is an important mental health factor to

reduce because it has been linked to cardiac mortality, stroke, hypertension, heart disease, type 2

diabetes, anxiety disorders, insomnia and cancer (Archer et al., 2012; Black, Markides, & Ray,

2003; Eaton, Armenian, Gallo, Pratt, & Ford, 1996; Jonas & Lando, 2015; Kim et al., 2009;

Ladwig, Röll, Breithardt, Budde, & Borggrefe, 1994; Mendes de Leon et al., 1998; Penninx et

al., 1998, 2001). The associations between some SED facets and depression in BCS have been

studied with mixed results (Rogers, Markwell, Courneya, McAuley, & Verhulst, 2011; Trinh,

Amireault, Lacombe, & Sabiston, 2015). In a cross-sectional study examining daily sitting time

and fatigue and depression, rural living survivors had a significant increase in fatigue with

4

increased SED but no association between depression and increased SED (Rogers et al., 2011).

Furthermore, Trinh and colleagues (2015) found a MVPA by SED interaction effect such that

lower MVPA and higher SED were associated with higher pain, fatigue, and depression in a

sample of BCS. Based on these findings, there may be some association between specific SED

facets and depression. However, researchers have not explored all dimensions of SED and the

link to depression.

Thus, this study has three objectives. The first objective is to better understand the SED

profile of BCS using the SITT principle. Along with this first objective, the multiple dimensions

of SED will be examined as grouping factors to classify survivors based on their SED. The

second objective is to examine personal and cancer-specific factors that distinguish the clusters

of SED. The third objective is to examine the association between SED and depression among

BCS.

5

Chapter 2 Review of Literature

One in nine Canadian women will be diagnosed with breast cancer in their lifetime

(Canadian Cancer Society, 2013). In 2013, 12,200 new cases of breast cancer in Canada were

reported, and breast cancer continues to be the most common cancer diagnosis in women

(Canadian Cancer Society, 2013). Breast cancer is most prevalent in females aged 50 to 69 years

(52% of diagnoses), with 30% of cases being in women over the age of 69 years (Canadian

Cancer Society, 2013). Despite this, early detection strategies and better treatment opportunities

have led to a substantial increase in the number of breast cancer survivors (BCS) with five-year

survival rates estimated between 80 and 95% (Coleman et al., 2011). Moreover, the female

breast cancer mortality rate has been steadily declining since the mid-1980’s (Canadian Cancer

Society, 2013). A number of hypotheses have been proposed as to why this downward trend is

being observed, but at present, it appears that increased mammography screening (Shields &

Wilkins, 2009) and the use of more effective therapies following surgery (Edwards et al., 2005;

Mariotto, 2002) are the two factors most responsible for the decline. As a result, in recent years

there has been an emphasis placed on the importance of the survivorship period and both the

physiological and psychosocial outcomes for women treated for breast cancer (Lynch et al.,

2013; Lynch, 2010).

Cancer Survivorship

In 1985, Dr. Mullan, a cancer survivor and physician, wrote the first paper in the New

England Journal of Medicine introducing the term “survivor” to describe cancer patients

(Mullan, 1985). A widespread belief of cancer survival at this time was that once an individual

was treated for cancer they were either “cured” or “living with overt or covert disease”

(Feuerstein, 2007; Mullan, 1985). Based on discussions with other cancer survivors and his

6

personal experience, Mullan believed that survivorship was not this binary experience but

instead a process and far more complex experience, he termed “survival” (Feuerstein, 2007;

Mullan, 1985). Furthermore, Mullan was the first to suggest that, “survivorship should be studied

as a phenomenon in itself rather than a by-product or afterthought of basic research or cancer

treatment” (Feuerstein, 2007). Mullan suggested that survival should be divided into three

distinct phases (Ristovski-Slijepcevic & Bell, 2014). First, a survivor enters the acute survival

period, which is immediately following diagnosis and is focused on surviving treatment

(Ristovski-Slijepcevic & Bell, 2014). Next, a survivor enters the extended survival period, which

is the period after treatment completion and focuses on dealing with the physical and

psychological consequences of treatment (Ristovski-Slijepcevic & Bell, 2014). Finally, a

survivor enters the permanent survival phase, where recurrence seems unlikely, and the survivor

focuses on the long-term effects of treatment (Ristovski-Slijepcevic & Bell, 2014). Building off

the work by Mullan, Aziz (2002) noted that survivorship needs to account for both late and long-

term effects of cancer and not just the effects of primary treatment on the well-being and health

of survivors. While the National Coalition for Cancer Survivorship (NCCS) defined a cancer

survivor as any person diagnosed with cancer, from the time of initial diagnosis until his or her

death (Khan, Rose, & Evans, 2012), it is important to examine survivorship in phases when

targeting health behaviours given the unique experiences that occur during treatment compared

to post-treatment. This definition is also extended out from the patient themselves to the family

and social support network surrounding the patient (Khan et al., 2012). In the current study, BCS

are studied in the ‘extended survivor’ phase of the survivorship trajectory (Ristovski-Slijepcevic

& Bell, 2014).

Approximately 90% of women in developed countries, such as Canada, are expected to

survive cancer for at least five years (Canadian Cancer Society, 2013). As such, women with a

7

history of breast cancer are the largest group of cancer survivors (Canadian Cancer Society,

2013). Unfortunately, cancer survival is associated with decrements in health status and

increased risk of death from noncancer causes (Baade et al., 2006; Lynch et al., 2013). The

burden of cancer survival includes premature death from comorbid chronic diseases, such as,

type 2 diabetes and cardiovascular disease (Demark-Wahnefried Pinto, & Gritz, 2006; Lynch et

al., 2013) and generally increased susceptibility to chronic diseases. Additionally, those who are

cancer-free for a minimum of 5 years after their diagnoses may be at a risk of developing long-

term psychological complications from the disease itself and/or the treatments such as

radiotherapy, chemotherapy, mastectomy (Howard-Anderson et al., 2012; Zuraida-Zinal et al.,

2013). For example, Deimling and colleagues (2005) showed that one-third of long-term

survivors (5 + years) reported worries about cancer reoccurrence, worries about developing a

second cancer, and worries that symptoms they experience on a daily basis may be from cancer.

Additionally, cancer-related worries significantly predicted depression and anxiety.

Many BCS experience long-term chronic physical symptoms as a result of the

multifaceted sequelae of breast cancer (Alfano et al., 2007). The period of survival after active

treatment is completed brings a number of unique physical health challenges (Baade et al., 2006;

Alfano et al., 2007; Lynch et al., 2013). These physical symptoms include: fatigue (Barton-

Burke, 2006; Bowen, Alfano, McGregor, Kuniyuki, Bernstien, & Meeske, et al, 2007), hormone-

related symptoms (Carpenter & Andrykowski, 1999; Ganz, Desmond, Leedham, Meyerowitz, &

Wyatt, 1998), chronic pain and sensation’s in the arm or breast that was treated (Erickson,

Pearson, Ganz, Adams, & Kahn, 2001), and sexual dysfunction (Ganz et al., 2002; Kornblith,

Herndon, Weiss, Zhang, Zuckerman, & Rosenberg, et al, 2003; Alfano et al., 2007). These

symptoms are common with fatigue and pain estimated to effect one-third of all survivors

(Kornblith et al., 2003; Bower, Ganz, Desmond, Bernaards, Rowland, Meyerowitz, et al., 2006;

8

Alfano et al., 2007) and sexual dysfunction estimated to effect 20-30% of all survivors

(Kornblith et al., 2003; Alfano et al., 2007). Symptoms that are commonly reported as a result of

undergoing hormone-replacement therapy include vasomotor complaints (sweats, hot flashes,

palpitations), urinary problems, vaginal dryness, and cognitive/mood changes (Carpenter et al.,

1999; Alfano et al., 2007). Although this thesis will focus on psychological symptom reporting,

specifically depression, chronic physical symptoms can serve as a continuous reminder of cancer

and thus result in significant psychological morbidity, including depression, for as much as 20

years post-treatment (Deimling et al., 2002; Kornblith et al., 2003; Alfano et al., 2007). Physical

activity has been shown to consistently improve many of these physical and psychological

symptoms in healthy adults (Alfano et al., 2007; Lynch et al., 2013), however how these

symptoms are associated with SED remains understudied, and inconclusive in BCS.

Life After Cancer Moving On Dataset

To date, 10 research articles have been published using the “Life After Breast Cancer:

Moving On” dataset. This comprehensive longitudinal study examines the physical,

psychological and behavioural activities/symptoms of female breast cancer survivors.

Topics examined using this dataset include:

1. The role of physical activity as a potential mediator between pain and mental health

(Sabiston, Brunet, & Burke, 2012);

2. PA and psychological health explored using Basic Psychological Needs Theory (Mack,

Meldrum, Wilson & Sabiston 2013);

3. The role of social support and stress in examining changes in post-traumatic growth and

subjective well-being (McDonough, Sabiston, & Wrosch, 2015);

9

4. Investigating the relationship between self-presentation process and physical activity

(Brunet, Sabiston, & Gaudreau, 2012);

5. Examining PA and SED using objective measures during the early survivorship period

(Sabiston, Brunet, Vallance, & Meterissian, 2014);

6. Systemic inflammation and the role of goal disengagement and health-related self-

protection (Castonguay, Wrosch, & Sabiston, 2014);

7. Identification and prediction of PA trajectories (Brunet, Amireault, Chaiton, & Sabiston,

2014);

8. Examining how being self-determined increases physical activity and well-being (Brunet,

Burke, & Sabiston, 2013);

9. Validating the Godin-Shephard Leisure-Time PA questionnaire using accelerometry

(Amireault, Godin, Lacombe, & Sabiston, 2015); and

10. Interactions between PA and SED as they relate to physical and psychological health

(Trinh, Amireault, Lacombe, & Sabiston, 2015).

Worth noting; a recent cross-sectional baseline examination using self-report measures of

pain, fatigue and depression, as well as, objective PA and SED found that lower levels of MVPA

and higher levels of SED significantly predicted higher levels of fatigue (p <.001), but not higher

levels of pain (p = .06) or depression (p = .08) (Trinh et al., 2015). When higher levels of MVPA

and higher levels of SED were explored, no association with these symptoms existed (Trinh et

al., 2015). This study provided an important examination into the potential interactive effect

between SED and PA in BCS and how this interaction may be associated with symptom

reporting pain, fatigue and depression. To expand and examine a different research question this

thesis will use a prospective study design opposed to a cross-sectional research design as is seen

in this work. In addition, a cut-off score to indicate clinical depression using a valid measure will

10

be used opposed the continuous measure of depression. The measure of depression is different

(i.e., Trinh et al. reported on dysphoria using the Profile of Mood States compared to the current

study using the Centers for Epidemiological Studies – Depression measure). Moreover,

additional participant data will be added and examined, and all data will be extracted from raw

data and re-cleaned using stringent criteria. Finally, this thesis will focus only on SED, using

both self-report and objective measures and thus will not examine the interactive effect of PA

and SED as is seen in the Trinh and colleagues (2015) study.

Mental health in BCS: Depression

There is a number of different definitions of depression in the literature and the most

commonly used diagnostic criteria tool for clinical depression is that of the Diagnostic and

Statistical Manual of Mental Disorders (DSM, 2013) The DSM criteria required to be diagnosed

with major depressive disorder is as follows:

1. Depressed mood or a loss of interest or pleasure in daily activities for more than 2 weeks,

2. Mood represents a change from the person’s baseline,

3. Impaired function: social, occupational or educational,

4. Specific symptoms of at least 5 of the 8 following,

i) depressed mood or irritable

ii) decreased interest or pleasure

iii) significant weight change (5%) or change in appetite

iv) change in sleep

v) change in activity

vi) fatigue or loss of energy

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vii) guilt/worthlessness

viii) decreased concentration

ix) suicidality (DSM, 2013).

In Western societies, yearly incidence rates of depression for adults are between 3%-5%

with an estimated 17% of people experiencing depression in their lifetime (Andrews, Henderson,

& Hall, 2001; Blazer, Kessler, McGonagle, & Swartz, 1994; Krogh, Nordentoft, Sterne, &

Lawlor, 2011; Lépine, Gastpar, Mendlewicz, & Tylee, 1997; Spaner, Bland, & Newman, 1994).

Moreover, the World Health Organization estimates that unipolar depression is the fourth leading

cause of burden and years lost in healthy adults, with major depression accounting for 12% of all

total years lived with disability (Krogh et al., 2011; Ustün, Ayuso-Mateos, Chatterji, Mathers, &

Murray, 2004). To date, depression is under reported in BCS, with prevalence varying between

10% and 25% (Burgess et al., 2005; Qiu et al., 2012; Zainal et al., 2013). In a recent systematic

review of BCS, Zainal and colleagues (2013) concluded that depression has not been studied

adequately and reported that prevalence varies across the extreme of 1-56% depending on how it

is defined. This is mainly due to the number of different measurement instruments that have been

validated to assess depression in cancer survivors. From this systematic review, Zainal and

colleagues (2013) recommended that the Center for Epidemiological Studies Depression Scale

(CES-D;(Radloff, 1977)), which is a self-report questionnaire, deriving questions from the DSM

is a valid and reliable screening tool for BCS.

There are number of possible biological and psychological mechanisms to explain how

reducing SED and increasing PA might act as an antidepressant (Krogh et al., 2011). In animal

models, increasing PA has led to an increase in neurogenesis (Bjørnebekk, Mathé, & Brené,

2005) and increased levels of serotonin (Gomez-Merino, Béquet, Berthelot, Chennaoui, &

12

Guezennec, 2001), resulting in an antidepressant response (Krogh et al., 2011). When examining

non-biological mechanisms, studies have shown that depressed individuals who exercise

regularly might receive positive feedback/compliments from other individuals who exercise with

them, resulting in increased self-esteem or as a diversion from negative feelings and thoughts

(Krogh et al., 2011; Nolen-Hoeksema & Morrow, 1993). Alternatively, regular exercise in 24

BCS’s has led to a decrease in depression and potential therapeutic effect (Segar et al., 1998).

Among BCS, depression has been correlated with a number of demographic characteristics, such

as, age (Burgess et al., 2005), income level (Casso, Buist, & Taplin, 2004), and education status

(Casso et al., 2004). Furthermore, depression is associated with cancer or treatment (Casso et al.,

2004), symptoms, such as pain (Casso et al., 2004; Kim et al., 2008), and a number of

psychosocial factors, including social support, hopelessness/helplessness, and loss of control

(Bardwell et al., 2006; Kim et al., 2008). Long term effects associated with depression include

cancer treatment, specifically chemotherapy, sexual dysfunction, infertility, menopause-

associated health problems such as cardiovascular disease, and osteoporosis (Azim, de

Azambuja, Colozza, Bines, & Piccart, 2011; Lorizio et al., 2012; Zainal et al., 2013). Moreover,

patients can be prescribed tamoxifen, a drug used for treatment of breast cancer, which has also

been associated with increased risk for depression (Lorizio et al., 2012; Zuraida-Zinal et al.,

2013). Furthermore, inactive and highly sedentary cancer survivors may also be at risk for

depression (Roshanaei-Moghaddam, Katon, & Russo, 2009). Nonetheless, reviews of the

association between physical activity and depression in healthy adults suggest that there is a

protective effect of being physically active on the risk for developing depression (Teychenne and

colleagues (2010); Mammen & Faulkner (2013)). As such, it may be of interest to study the

protective effects of behaviours such as physical activity and sedentary behaviour among BCS to

help reduce the risk for, and burden associated with, depression symptoms.

13

Physical Activity and Breast Cancer

Regular moderate-intensity PA, such as brisk walking, has been shown to be associated

with diminished treatment side effects, enhanced quality of life (Courneya et al., 2005; Lynch,

Cerin, Owen, Hawkes, & Aitken, 2008), and prolonged survival (Haydon, Macinnis, English, &

Giles, 2006; Holmes, Chen, Feskanich, Kroenke, & Colditz, 2005; Meyerhardt et al., 2006).

Furthermore, a systematic review by Speck and colleagues (2010) found that regular PA

decreased depression and reduced fatigue in BCS and, a recent meta-analysis also found that

higher PA levels were correlated with reduced breast cancer mortality and overall mortality in

BCS (Ballard-Barbash et al., 2012). Overall, there is a myriad of positive benefits associated

with PA participation among BCS that have physiological (e.g., improved cardiovascular

function, aerobic fitness, blood pressure, etc.), psychological (e.g., reduced depression, anxiety,

improved body image, etc.), and social (e.g., improved connectedness, support, etc.)

underpinnings (Sabiston & Brunet, 2011).

In spite of the positive outcomes associated with PA, and research that has demonstrated

it is feasible, safe, and effective, a large number of survivors are not meeting physical activity

guidelines recommended to achieve a number of health benefits (Schmitz et al., 2010; Speck et

al., 2010). The American College of Sports Medicine (ACSM; (Schmitz et al., 2010)) has a set of

exercise guidelines specifically designed for individuals with cancer, suggesting that cancer

survivors should aim to complete at least 150 minutes of moderate-to-vigorous physical activity

(MVPA) per week. Specifically, cancer survivors should strive to increase aerobic exercise to 3-

5 times per week and also introduce resistance training into their regime 2-3 times per week, and

flexibility exercises should be done daily. Unfortunately, BCS report lower PA levels compared

to a sample of females without cancer (Ballard-Barbash et al., 2012), and are generally quite

14

inactive with rates ranging from 50 to 90% of women not meeting recommended PA levels of

150 minutes of MVPA each week (Blanchard et al., 2008; Lynch et al., 2010). In the first study

to describe PA using objective measures, Lynch and colleagues (2010) found that after 7 days of

monitoring, BCS (n= 111) spent 31.5% of their day engaged in light activities and only 0.5% of

their day engaged in MVPA. Similarly, a recent imputation-based approach comparing estimates

of activity level amongst BCS (n=398) and healthy controls (n=1120) found that, on average,

BCS spent 31.1% in light activity and 2.6% in MVPA (Phillips et al., 2015). Trinh and

colleagues (2015) also reported low PA of less than 2% of the day spent in MVPA in a sample of

BCS. Notwithstanding these poor rates of PA among BCS, the healthy adult population is no

better. Generally, adults spend less than 5% of their waking hours engaged in MVPA and 25% of

their time engaged in light activities (Healy, Wijndaele, et al., 2008; Matthews et al., 2012).

Light-intensity activities make up the majority of time where older adults find

themselves exercising, spending more time in daily light PA than any other age group (Buman et

al., 2010). Interestingly, when PA and physiological changes/health outcomes are studied across

different intensities (light, moderate and vigorous), a curvilinear relationship is observed for a

variety of diseases with the steepest gradient observed at the lowest end of activity (Powell,

Paluch, & Blair, 2011). This suggests that something is better than nothing and given the large

proportion of older adults who fall into the lowest end of the PA scale, relatively small increases

in daily PA will bring substantial health benefits, even if one does not meet the recommended

daily guidelines (Matthews et al., 2008; Powell et al., 2011; Troiano et al., 2008). In older adults,

easy walking is reported as the most common light PA (Buman et al., 2010; Westerterp, 2008). A

recent meta-analysis examining walking and all-cause mortality, reported that daily walking for

3 hours/week at 3 kilometres/hour results in a 20% risk reduction of all-cause mortality (M

Hamer & Chida, 2008; Woodcock, Franco, Orsini, & Roberts, 2011). In addition, replacing

15

sedentary time with equal amounts 30 minutes/day with light PA yields better physical health

and well-being overall (Buman et al., 2010).

Given the low levels of physical activity reported and measured among BCS, there is a

gap in the understanding of this health behaviour. It is likely that there is too much emphasis on

higher intensity PA at the expense of better understanding other related health behaviours.

Sedentary behaviour (SED) is related to, but distinct health behaviour from MVPA that has only

been recently introduced as an independent behaviour that may have unique health outcomes.

Sedentary Behaviour

Lynch and colleagues (2013) suggested that a new cancer survivorship research agenda is

needed to focus on sedentary behaviour (SED). Sedentary behaviour is defined as any waking

behaviour characterized by a low-energy expenditure (i.e., ≤1.5 resting metabolic equivalents)

while in a sitting or reclining posture (Tremblay, 2013). Researchers have advocated that SED

should be identified as a distinct behaviour from PA with independent and qualitatively different

effects on physical function, metabolism, and health outcomes (Hamilton et al., 2004, 2008;

Sallis et al., 2000; Tremblay et al., 2010). Unfortunately, at this point in time there are no

published guidelines pertaining to SED for adults regardless of health status.

Definition and measurement of SED. Similar to the FITT formula used to study PA

(Frequency, Intensity, Time (duration), and Type of activity), Tremblay and colleagues (2010)

propose using the SITT formula to describe participant’s SED. This acronym corresponds to the

following:

• Sedentary behaviour frequency (number of bouts of a certain duration);

• Interruptions in sedentary behaviour (e.g., getting up from one’s desk while at work;

16

Healy et al., 2008);

• Time (the duration of total sitting time); and

• Type (mode of sedentary behaviour, e.g., sitting at one’s desk, TV viewing, or

driving a car).

Methods used to assess SED using the SITT require the use of both self-report

questionnaires to assess aspects related to Type and Time, and objective tools to assess SED

frequency, Interruptions, and Time. In a systematic review of SED in longitudinal studies, Thorp

and colleagues (2011) recommended the use of device-based measures to provide a clear

understanding of the impact of SED. One method to measure objective SED is to use an

accelerometer. An accelerometer is a device that measures movement of the body in space and

also the individual’s intensity, duration, frequency, and patterns of movement (Tremblay et al.,

2010). Additionally, the inclinometer feature on some accelerometers can indicate whether a

participant is standing, sitting or lying down when the device is worn at the hip. At this time, the

ActiGraph GT3X remains one of the best accelerometers to measure both PA and SED and the

GT3X is an improvement from the previous biaxial, antero-posterior GT1M accelerometer

(Hänggi, Phillips, & Rowlands, 2013). Furthermore, the inclusion of the inclinometer on the

GT3X allows researchers to accurately measure individuals SED (Hänggi et al., 2012). This is

supported by a plethora of recent studies, which have concluded that triaxial accelerometers are

more sensitive than uniaxial and biaxial accelerometers (Coleman et al., 2011; Eston, Rowlands,

& Ingledew, 1998; Trost, Mciver, & Pate, 2005). In a recent report by Verloigne and colleagues

(2012), the authors concluded that accelerometry should be used when studying the possible

effect of SED on health. These researchers sampled 672 children and assessed their sedentary

time using both accelerometry and self-report. Importantly, these authors showed that self-

reported TV and computer time did not effectively reflect total sedentary time compared to

17

accelerometers (Verloigne et al., 2012). Despite these findings, it appears that the best method of

assessing SED is using a combined approach similar to the SITT principle outlined above

(Prince, Saunders, Gresty, & Reid, 2014). For example, when self-report time in SED is

compared to objectively measured SED, only a low to moderate correlation is observed

(Saunders, Prince, & Tremblay, 2011). However, there are stronger associations among

construct-specific associations such as when specific domains of self-reported SED (i.e., TV

viewing) are compared with objective measures of SED (Healy et al., 2011; Saunders et al.,

2011).

SED and health outcomes. While research on SED is in its infancy, in the general

population, SED has been shown to have a direct influence on bone mineral content, vascular

health, and metabolism among healthy adults (Tremblay et al., 2010). Furthermore, a systematic

review of prospective studies (n= 19) examining adult SED and health outcomes found that there

is moderate evidence for a positive association between SED (time spent sitting) and risk for

type 2 diabetes (Proper, Singh, Van Mechelen, & Chinapaw, 2011). Additionally, strong

evidence appears to exist linking SED to all-cause and cardiovascular disease mortality and there

is also evidence of significant associations between SED and overall cancer mortality (Matthews

et al., 2012). Moreover, a systematic review of longitudinal studies (n= 48) between 1996-2011

found a consistent relationship between SED and weight gain from childhood to adulthood

(Thorp et al., 2011). Both of these reviews are supported by a recent systematic review of 24

studies, which also found strong evidence that increased SED is related to all-cause mortality

risk in older adults (Rezende, Rodrigues Lopes, Rey-López, Matsudo, & Luiz, 2014). This

review also found moderate evidence that SED is related to obesity, waist circumference, and

metabolic syndrome (Rezende et al., 2014). Unfortunately, a criticism of these reviews and the

literature to date is that self-report measures of SED tend to be used. For example, of the 48

18

studies included in the Thorp and colleagues review, 46 of these studies used a self-report

measure of SED. Furthermore, many of the studies reviewed focus on a unidimensional SED

construct with little regard to the potential different dimensions as defined using the SITT

principle. As such, there is value in studying the potential multiple dimensions of SED using

objective measures such as inclinometers and accelerometers. Finally, there are few studies

focused on cancer survivors.

At this time the majority of work done pertaining to SED has been completed in children

and adolescents. In these groups there are clear SED guidelines (Tremblay et al., 2011) and

research on various bout and break lengths has been completed, whereby frequently breaking

SED may be a more important overall measure then examining specific bout lengths (Carson &

Janssen, 2011; Cliff et al., 2014; Travis John Saunders et al., 2013). In adults using the NHANES

survey data from 2003-2006, adults reported on average 92.5 breaks (interruptions) and a higher

number of breaks in SED was associated with better C-reactive protein (immune function)

outcomes and waist circumference (Healy, Matthews, Dunstan, Winkler, & Owen, 2011). In a

cross-sectional study of 197 overweight/obese women, researchers collected length of bouts of

SED lasting ≥ 10, ≥ 30 and ≥ 60 minutes as well as the mean length of breaks as different

measures of SED (Baruth, Sharpe, Hutto, Wilcox, & Warren, 2013). On average, women spent

544.1 minutes per day engaged in sedentary pursuits (Baruth et al., 2013). Furthermore, many

women engaged in multiple bouts lasting ≥ 10 and ≥ 30 minutes, however a great deal of data

was not recorded when ≥ 60 minutes bouts were considered, meaning that participants did not

have a large number of SED bouts lasting 60 minutes or longer. Thus, it would appear that when

choosing appropriate bout lengths to use in analysis for women, one should not include 60-

minute bout lengths but instead focus on 10 and 30 minute bout lengths.

19

SED and BCS. Researchers have shown that BCS spend an average of 66 to nearly 80%

of their day sedentary, primarily sitting (Lynch et al., 2013; Phillips et al., 2015; Trinh,

Amireault, et al., 2015). Compared with women and men over 60 years of age from the National

Health and Nutrition Examination Survey (NHANES) population, both breast and prostate

cancer survivors spent less time physically active (light, moderate, and vigorously active) and

more time sedentary, compared to non-cancer controls (Lynch et al., 2013). Using

accelerometers with 14 hours of wear-time per day, survivors accumulated approximately 9.5

hours of daily sedentary time (Lynch et al., 2013). This is slightly more time spent in SED

compared to data from the NHANES survey that found in the general population adults over the

age of 20 years spend approximately 8.4 hours/day sedentary (Healy et al., 2011). These findings

are supported by a recent report by Brunet and colleagues (2014) which followed 177 BCS

during a yearlong period following completion of primary treatment. Objective measures of

sedentary time and MVPA were collected using the ActiGraph GT3X accelerometer. In this

group, survivors spent 78% of their waking hours engaged in SED and only 2% of their day

engaged in MVPA (Brunet et al., 2014). Moreover, a recent study of 398 BCS (Mage = 56.95

years) reported that BCS spent 66.4% of their waking hours engaged in SED (Phillips et al.,

2015). In addition, BCS’s SED remained relatively stable over the 12-month period, and when

trends were observed, these trends were more pronounced in women who were overweight

(Brunet et al., 2014).

Importantly, to date, no work has examined specifically how different bout lengths of

SED might impact physical and/or psychological health symptoms in cancer survivors. While

there is limited overall evidence on the health implications of SED among cancer survivors, high

volumes of SED in this clinical population are independently associated with chronic disease-

related risk factors such as central adiposity, elevated blood glucose and insulin (Lynch et al.,

20

2013). The most detailed systematic review of SED and cancer to date was conducted by Brigid

Lynch (2010). Lynch reviewed all available literature pertaining to SED and cancer and

identified 11 articles that examined the association between SED and cancer risk. One study was

focused on breast cancer risk, whereby the authors assessed SED using TV viewing time and

found null findings between both weekday and weekend viewing time with cancer risk (Mathew

et al., 2009). Overall, at this time there is a lack of SED interventions examining potential

associations with mental health variables in clinical populations such as women diagnosed and

treated for breast cancer.

Specifically related to mental health outcomes in BCS, two American studies and one

Canadian study have considered the associations between SED and mental health. Cross-

sectional work (n=483) examining daily sitting time and fatigue, as well as, depression found

that rural living survivors had a significant increase in fatigue with increased SED but no

association between depression and increased SED (Rogers et al., 2011). Prospective analysis of

710 BCS from the HEAL study found time spent engaged in SED was not associated with

quality of life or fatigue (George et al., 2012). Finally, a recent study of 195 BCS found MVPA

by SED had a significant interaction effect when examining associations with pain, fatigue, and

depression (Trinh, Amireault, et al., 2015). However, in women with lower levels of MVPA,

simple slopes analysis showed that high levels of SED significantly predicted higher levels of

fatigue, and higher levels of depression. Moreover, these associations between SED and health

outcomes were not observed when higher levels of MVPA were considered (Trinh et al., 2015).

Researchers have also suggested that depression and SED have a bidirectional relationship,

however, much more research needs to be conducted (Roshanaei-Moghaddam et al., 2009). For

example, the causality mechanism that links SED and depression remains unknown.

21

To date, no studies have explored the association between SED and clinical depression in

BCS using a validated depression screening tool (Andresen, Malmgren, Carter, & Patrick, 1994)

and this research is imperative. Breast cancer remains the most common cancer diagnosis in

women, with survival estimates increasing every year (Canadian Cancer Society, 2013). With

survivors living longer, much more research needs to be conducted examining how certain

common co-morbidities (i.e., depression) can be reduced through easy to implement, cost-

effective, behavioural change interventions such as reducing SED. Psychosocial researchers have

identified SED as one of the top 10 most important research topics to examine in the field of

physical activity and cancer survivorship (Courneya, Rogers, Campbell, Vallance, &

Friedenreich, 2015) and this prospective study will begin to provide recommendations for SED

and clinical depression in BCS.

Thus, this study has three main research objectives. The first objective is to better

understand the SED profile of BCS using the SITT principle. Along with this first objective, the

multiple dimensions of SED will be examined as grouping factors to classify survivors based on

their SED. The second objective is to examine personal and cancer-specific factors that

distinguish the clusters of SED. Based on previous research with SED and more prominently

with PA, it is expected that age, weight status, and potentially some cancer-related variables such

as treatments endured will be important factors differentiating the SED clusters. It is

hypothesized that those who are most SED will be older and have a higher body mass index

(BMI ( Demark-Wahnefried et al., 2001; Kroenke, Chen, Rosner, & Holmes, 2005; Patterson,

Cadmus, Emond, & Pierce, 2010). Furthermore, women who have undergone surgical removal

of the axillary lymph node have been shown to be less active than those who have not (Sagen,

Kåresen, & Risberg, 2009) and undergoing chemotherapy or radiation therapy during treatment

can make survivors less active due to high levels of fatigue (Demark-Wahnefried, Hars, et al.,

22

1997; Irwin et al., 2003). As a result it was hypothesized that these demographic/medical and

treatment outcomes may be associated with increased SED. The third objective is to examine the

association between SED and depression among BCS during the early survivorship period. This

time period is generally understudied and can be a good time to intervene and teach new health

behaviours (Brunet et al., 2014). It is hypothesized that those reporting higher SED, as measured

through cluster membership and those with the highest SED facets, will be significantly

associated with higher depression.

23

Chapter 3 Thesis Manuscript

Profiling sedentary behaviour in breast cancer survivors: Links with depression during the

early survivorship period

Jason Lacombe, BKIN Faculty of Kinesiology and Physical Education,

University of Toronto, 55 Harbord Street, Toronto, Ontario, Canada, M5S 2W6

Guy Faulkner, PhD Faculty of Kinesiology and Physical Education,

University of Toronto, 55 Harbord Street, Toronto, Ontario, Canada, M5S 2W6

Jennifer Jones, PhD Associate Director, ELLICSR,

Princess Margaret Cancer Centre

Linda Trinh, PhD Faculty of Kinesiology and Physical Education,

University of Toronto, 55 Harbord Street, Toronto, Ontario, Canada, M5S 2W6

Catherine Sabiston, PhD (Corresponding Author) Faculty of Kinesiology and Physical Education,

University of Toronto, 55 Harbord Street, Toronto, Ontario, Canada, M5S 2W6

Email: catherine.sabiston@utoronto.ca

ACKNOWLEDGEMENTS:

This study was funded by a Canadian Institutes of Health (CIHR) Research Operating Grant (grant#186128) awarded to CMS. During the time of data analysis, LT was supported by a research trainee award from the Kidney Cancer Research Network of Canada (KCRNC). JL was supported by a Sir Frederick Banting and Charles Best Canada Graduate Scholarship-Master’s from CIHR and the Psychosocial Oncology Research Training Program (PORT)-Master’s. GF hold a CIHR Applied Public Health Chair. CMS holds a Canada Research Chair in Physical Activity and Mental Health.

24

ABSTRACT

Background: Symptoms of depression remain under diagnosed but highly prevalent in breast

cancer survivors (BCS). Reducing sedentary behaviour (SED) during the early survivorship

period may be a strategy for treating clinical depression in survivors.

Methods: One hundred and eighty-seven BCS provided baseline demographic, medical, and

SED/physical activity data. Depression was reported three months later. Multiple dimensions of

SED were identified and examined in cluster analysis. The association between cluster

membership and depression was assessed using logistic regression.

Results: Two SED groups were identified representing higher and lower SED. High SED cluster

BCS’s were significantly older, heavier, less physically active, less educated, and were more

likely to have undergone lymph/axial node dissection. In the logistic regression model cluster

membership was a significant predictor of clinical depression.

Conclusions: Reducing dimensions of SED during the early survivorship period could protect

from clinical depression symptoms.

25

INTRODUCTION

Breast cancer continues to be the most diagnosed and prevalent cancer in women,

affecting approximately one in nine Canadian females (Canadian cancer society, 2013).

Fortunately, five-year survival rates are estimated to be between 80-95% (Coleman et al., 2011).

Although the number of breast cancer survivors (BCS) is increasing each year, cancer survival is

associated with an increased risk of psychological and physiological complications from the

disease itself and/or the treatment experienced (Howard-Anderson et al., 2012; Zainal et al.,

2013). Specifically, BCS tend to be sedentary, overweight/obese and older (Demark-Wahnefried

et al., 2001; Kroenke, Chen, Rosner, & Holmes, 2005; Patterson et al., 2010). Furthermore, a

diagnosis of breast cancer increases risk for mental health problems such as depression (Fann et

al., 2008). Depression is one of the most common comorbidities associated with cancer survival

(Harrington et al., 2010), and may be under-reported in BCS with an estimated 10 to 25% of

survivors reporting depression (Burgess et al., 2005; Fann et al., 2008; Qiu et al., 2012; Zainal et

al., 2013). Identifying factors that help protect from the deleterious mental health challenges

among BCS is an important public health agenda.

Physical activity (PA) has been consistently shown to alleviate many physical and mental

health challenges among BCS (Courneya et al., 2005; Sabiston & Brunet, 2011; Speck et al.,

2010), including but not limited to, diminished treatment side effects, enhanced quality of life

(Courneya et al., 2005; Lynch et al., 2008), and prolonged survival (Haydon et al., 2006; Holmes

et al., 2005; Meyerhardt et al., 2006). Moreover, compared to those women who do not exercise

regularly, active BCS are significantly less depressed (Segar et al., 1998). Notwithstanding these

findings, BCS are inactive with 50-90% of survivors not meeting healthy activity guidelines

(Blanchard et al., 2008; Lynch et al., 2010, 2013; Speck et al., 2010). Specifically, the American

26

Cancer Society (Rock et al., 2012) and the American College of Sports Medicine (Schmitz et al.,

2010) recommend PA guidelines of 150 minutes of moderate-to-vigorous physical activity

(MVPA), 75 minutes weekly of vigorous aerobic physical activity and muscle-strengthening

exercises 2 times per week. Given the low levels of PA among BCS, and that PA behaviours

only occur during a fraction of one’s day, it may be more effective to intervene with other health

risk behaviours (Lynch et al., 2013). Specifically, Lynch and colleagues (2013) have proposed a

research agenda to highlight the importance of targeting sedentary behaviour.

Sedentary behaviour (SED) is defined as any waking behaviour characterized by a low-

energy expenditure (i.e., ≤1.5 resting metabolic equivalents) while in a sitting or reclining

posture (Tremblay, 2013). Importantly, SED is a distinct entity from PA with independent and

qualitatively different effects on physical function, metabolism, and health outcomes (Hamilton

et al., 2004, 2008; Sabiston, Brunet, Vallance, & Meterissian, 2014; Trinh et al., 2015).

Sedentary behaviour has been shown to have a direct influence on bone mineral content, vascular

health, and metabolism among healthy adults (Tremblay et al., 2010) and high SED is also

associated with increased cancer risk (Matthews et al., 2012). To best define and study SED,

Tremblay and colleagues (2010) propose using a SITT principle. SITT refers to Sedentary

behaviour frequency (number of bouts of a certain duration); Interruptions in sedentary

behaviour (e.g., getting up from one’s desk while at work; Healy et al., 2008); Time (the

duration of total sitting time); and Type (mode of sedentary behaviour) of SED (Tremblay et al.,

2010). This combined approach to measuring SED allows researchers to use both traditional self-

report tools and objective measures of SED/PA to capture a complete picture of individuals daily

SED. In addition, using SITT, researchers are also able to study breaks/interruptions in SED.

Independent of levels of MVPA and total SED, breaks in SED has been shown to be beneficial

for adults BMI, waist circumference, levels of triglycerides and fasting blood glucose levels

27

(Healy et al., 2008). To date, no studies have examined all the SITT principle variables in cancer

survivors. The way in which SED is operationalized is important for targeted efforts aimed at

decreasing SED among BCS.

Using more generalized assessments of SED rather than a multidimensional approach

such as SITT, BCS spend 66-79% of their waking hours engaged in sedentary pursuits versus

only 1-3% of their day engaged in higher intensity PA (Lynch et al., 2010; Phillips et al., 2015;

Sabiston et al., 2014; Trinh, Amireault, et al., 2015; Wrosch & Sabiston, 2013). Unfortunately,

the majority of past research on SED has used self-report measures. For example, of the 48

studies included in the Thorp and colleagues review, 46 of these studies used a self-report

measure of SED. Self-report measures of SED may times do not paint a complete picture. A

recent study comparing objective accelerometer SED and self-report SED in 317 Australian

adults found that after 7 days of monitoring (500 minutes/day wear time for a minimum of 4

days), the self-report estimate of SED was 13% less time sitting than accelerometer estimates

(Celis-Morales et al., 2012). Furthermore, many of the studies reviewed focus on a

unidimensional SED construct with little regard to the potential different dimensions as defined

using the SITT principle. As such, there is value in studying the potential multiple dimensions of

SED using objective measures such as inclinometers and accelerometers. Both self-report

measures and objective measures used on their own have limitations, such as, recall bias for self-

report measures and the ability to only capture time in SED and not type of SED for objective

measures, however when combined using the SITT principle researchers should be able to

capture a more complete understanding of daily SED.

In preliminary evidence, researchers have reported a mix of findings linking SED and

mental health outcomes among cancer survivors. Rogers and colleagues (2011) conducted a

28

cross-sectional study with 483 rural-living BCS and reported increased fatigue, but not

depression, with higher SED. In a study of 195 BCS, Trinh and colleagues (2015) found a

MVPA by SED interaction effect such that low levels of MVPA and high levels of SED

significantly predicted higher levels of fatigue and depression and a trend for increased pain.

These associations between SED and health outcomes were not observed when higher levels of

MVPA were considered (Trinh, Amireault, et al., 2015). Combined, the evidence from emerging

studies demonstrates a possible protective effect of SED on depression. However, researchers

have only explored total SED as either time spent sitting or total time spent in SED with little

regard to the number and length of time in SED, type of SED, and number of breaks from SED.

As such, a multidimensional approach to the study of SED is needed to classify BCS and explore

links to health outcomes. Finally, studying SED as it relates to cancer survivorship has been

identified as one of the 10 most important research questions related to PA and cancer

survivorship, and exploring this question can potentially improve the lives of many cancer

survivors each year (Courneya et al., 2015).

Thus, this study has three objectives. The first objective is to better understand the SED

profile of BCS using the SITT principle. Along with this first objective, the multiple dimensions

of SED will be examined as grouping factors to classify survivors based on their SED. The

second objective is to examine personal and cancer-specific factors that distinguish the clusters

of SED. Based on previous research with SED and more prominently with PA, it is expected that

age, weight status, and potentially some cancer-related variables such as treatments endured will

be important factors differentiating the SED clusters. It is hypothesized that those who are most

SED will be older and have a higher BMI (Demark-Wahnefried et al., 2001; Kroenke, Chen,

Rosner, & Holmes, 2005; Patterson et al., 2010). Furthermore, women who have undergone

surgical removal of the axillary lymph node have been shown to be less active than those who

29

have not (Sagen, Kåresen, & Risberg, 2009) and undergoing chemotherapy or radiation therapy

during treatment can make survivors less active due to high levels of fatigue (Demark-

Wahnefried, Hars, et al., 1997; Irwin et al., 2003). As a result it was hypothesized that these

demographic/medical and treatment outcomes may be associated with increased SED. The third

objective is to examine the association between SED and depression among BCS during the

early survivorship period. This time period is generally understudied compared to the phases of

diagnosis and treatment and can be a good time to intervene and teach new health behaviours

(Brunet et al., 2014). It is hypothesized that those reporting higher SED, as measured through

cluster membership and those with the highest SED facets, will be significantly associated with

higher depression.

METHODS

Study Population

This study is part of a larger and on-going trial exploring the natural developmental

changes in lifestyle behaviours of BCS (Life After Breast Cancer: Moving On). Specifically,

participants who were diagnosed and had undergone treatment for breast cancer were recruited

through advertisements and oncologist referrals from various local medical clinics and hospitals

in Montreal, Quebec. Participants were screened for study eligibility using the following

inclusion criteria: (1) at least 18+ years of age; (2) 0–20 weeks post primary treatment (i.e.,

surgery, chemotherapy, radiation therapy) for stage I to III breast cancer; (3) first cancer

diagnosis; (4) able to provide written informed consent and read/speak in English or French; and

(5) reported no health concerns which prevent them from engaging in PA. The appropriate

university and hospital research ethics committees approved the study protocol, and all

participants provided written informed consent before starting data collection.

30

Procedures

Once screened for eligibility and the completion of the consent process, women visited

the Health Behaviour and Emotion laboratory to complete a baseline questionnaire, have height,

weight, and waist circumference measured, and to receive an accelerometer to wear for seven

days to assess PA and SED. Depression data was collected during the second data collection

three months after baseline, which equated to approximately 6 months after completion of

systemic treatment and approximately one year post treatment. This period of time was targeted

to capture the early transient survivorship period in BCS because it is a good time to intervene

and teach new health behaviours, consistent with the teachable moment hypothesis (Brunet et al.,

2014).

Measures

Data were collected using a combination of reliable and valid self-report and objective measures.

Demographics and Medical Variables. Women self-reported their demographic and

medical history including questions on age in years, ethnicity, education level, income, stage of

cancer, cancer treatments, time since diagnosis and time since treatment. Measures of weight, to

nearest kilogram (kg), height, to nearest .01 cm, and waist circumference, to nearest .01 cm were

taken by a trained research assistant during the first laboratory visit. These measures were used

to calculate body mass index (BMI, measured as weight divided by height in meters squared)

which was a continuous variable and also examined as a categorical variable based on healthy

(<24.9 kg/m2), and being overweight/obese (≥ 25.0 or more kg/m2 (Flegal, Carroll, Kuczmarski,

& Johnson, 1997). Waist circumference was defined as a continuous variable and categorical

based on an established cut-point of greater than 88 centimeters for women representing

overweight (National Institutes Of Health, 1998), and waist-to-height ratio (WHRT) was

31

assessed as waist circumference in centimeters divided by height in centimeters for a total value

and a categorical variable with a cut-point of 0.50 established to be “unhealthy = > 0.50” or

healthy for women with WHRT of less than 0.50 (Ashwell, Gunn, & Gibson, 2012).

Sedentary Behaviour and Physical Activity. Objective SED was assessed using GT3X+

accelerometers (Actigraph, Pensicola, Florida). Participants wore the accelerometer on their hip

during waking hours for a 7-day period, except for periods of bathing/showering or other water

activities. Data were downloaded in 60-second epochs and converted to mean counts per minute

to estimate daily minutes SED as <100 counts•minute-1, adjusted for non-wear time

operationalized as at least 60 minutes of consecutive zeroes with the allowance of 2-minute

intervals of non-zeroes (Troiano et al., 2008). Data were analyzed if there were no extreme

counts (> 20,000) and if data are available for at least 500 minutes on 4 or more days (Celis-

Morales et al., 2012; Trinh et al., 20151).

Using the sedentary analysis tool within the ActiGraph software, participants’ bouts of

SED (10, 15, 20 and 30 minutes), breaks, total time and average time engaged in SED were

calculated and a minimum 5-minute breaks of SED were used as the default. This enabled the

assessment of each participant’s Sedentary behaviour frequency, Interruptions, and objective

Time spent in SED. The 10 and 30 minute bouts used in the main analysis was based on findings

reported by (Baruth et al., 2013) suggesting these are valuable and meaningful bout lengths.

The Type of SED and an additional Time measure was assessed using self-report items.

1 Some researchers have used 600 minutes for minimum wear time (Healy et al., 2011). However, this level may be inappropriate for clinical samples. Therefore, a comparison of the descriptive statistics for 500 and 600 minimum wear time was conducted (See Appendix B) and bivariate correlations were calculated (See Appendix C). Given the findings, the 500 wear time was used for the current study.

32

Participants were asked to report number of times per week and average duration per session of

SED using the question: “during a typical week (7-day period), how many times on average do

you participate in sedentary activity (no effort) (e.g., TV/video watching, video/computer games,

computer use) and for how long”. This question was modeled after common items used to assess

PA among cancer survivors (Amireault, Godin, Lacombe, & Sabiston, 2015; Godin, 2011).

Participants also completed questions on the amount of time (number of times and average

duration) spent engaged in television viewing, computer use and reading for pleasure per week to

get at type of SED. In the absence of validated SED questionnaires among cancer survivors,

these questions were drawn from the Sedentary Behaviour Questionnaire (SBQ) which has been

found to be a valid and reliable tool to use in adults (Rosenberg et al., 2010). For the current

study and consistent with SED analyses, participants’ total television time and computer time

were summed to create a screen-time variable (Stamatakis, Hamer, & Dunstan, 2011; Sugiyama,

Healy, Dunstan, Salmon, & Owen, 2008). A list of detailed definitions of the various SED

variables used in the current study is presented in Appendix A. Cluster membership SED

variables included: 1) self-report SED; 2) screen-time; 3) reading; 4) average number of 10

minute bouts; 5) average time in 10 minute bouts; 6) average number of 30 minute bouts; 7)

average time in 30 minute bouts; 8) objective SED; and 9) average length of breaks. For a

detailed descriptive analysis of all SITT principle variables see Appendix D.

PA was assessed using accelerometers and total minutes of PA were calculated using

mean counts per minute to estimate daily minutes of light (100-2019 counts•minute-1), moderate

(2200-5998 counts•minute-1) and vigorous (> 5998 counts•minute-1) PA based on established

cut-points (Troiano et al., 2008), while controlling for the number of days the accelerometer was

worn. For the current study, and consistent with guidelines (Schmitz et al., 2010) time spent in

moderate and vigorous PA was combined to create a MVPA variable. Self-report PA was

33

assessed using the Godin-Shephard Leisure Time Exercise Questionnaire (GSLTEQ; Godin,

2011). The GSLTEQ is easily administered and asks participants to recall the past 7-days of PA

which they participated in. The GSLTEQ is a 4-item questionnaire with the first 3 questions

assessing participants PA mild, moderate and strenuous PA frequency during a typical week

lasting more than 15 minutes (Amireault et al., 2015; Godin, 2011). Again, consistent with

previous research, guidelines, and the objective assessment of PA, participants’ moderate and

strenuous PA scores were summed to create an MVPA variable.

Depression. Depression symptoms were assessed using the 10-item Center for

Epidemiological Studies Depression Scale ((CES-D; Andresen, Malmgren, Carter, & Patrick,

1994)) approximately three months following the baseline assessment. Adapted from the CES-D

20, the CES- D 10 has 10 items assessing depression symptoms experienced in the last week

(sample question “I felt depressed”) reported on a 4-point (0-3) Likert-type scale ranging from

“Rarely or none of the time [< 1 day]” to “all of the time [5-7 days]”. Higher scores on the CES-

D scale represent higher levels of reporting of symptoms of depression; with a total score range

possible of 0 to 30. Consistent with previous reports (Andresen et al., 1994), a score ≥ 10 was

used to screen participants with depressive symptomology related to clinical depression in the

current study.

Data Analyses

Preliminary Data Analysis. All quantitative data analysis was conducted using the

Statistical Package for the Social Sciences version 21 (SPSS Inc., Chicago, IL). In addition, the

wear time validation and sedentary analysis tools were used in Actigraph to input and clean data.

Inspection of statistical outliers and examination of statistical assumptions for analysis of

variance and regression analyses were conducted. Missing data for all items were explored using

34

frequency distributions. Means and standard deviations were computed and reported for all

continuous study variables, and frequencies were explored among dichotomous variables.

Pearson (and where appropriate Spearman Rho) correlation coefficients were calculated to

explore relationships among all study variables.

Main Analyses. To examine potential groupings of BCS based on the multiple facets of

SED, cluster analysis was conducted. Specifically, the facets of SED included average number of

10 minute bouts of SED, average time in 10 minute bouts of SED, average number of 30 minute

bouts of SED, average time in 30 minute bouts of SED, daily average time in SED breaks, self-

report SED, average screen-time, average time spent reading and accelerometer SED average.

All these results are presented in minutes per day. Based on cluster analysis guidelines (Hair,

Anderson, Tatham, & Black, 1998; Ullrich-French & Cox, 2009), multiple approaches to cluster

analysis were used to accurately assess the stability of the outcome and identify the appropriate

number of clusters. First, a hierarchical cluster-analysis using Ward’s linkage method and

squared Euclidean distance was conducted to determine the appropriate number of clusters

represented by the data (Ullrich-French & Cox, 2009). Second, a k-means (nonhierarchical)

cluster analysis was conducted using simple Euclidean distance in which the specific number of

clusters was specified based on the hierarchical cluster solution (Ullrich-French & Cox, 2009). A

preliminary multivariate analysis of variance (MANOVA) was tested to examine all SED

variables across cluster membership.

Following the cluster membership analysis, an additional MANOVA with follow-up

univariate tests was conducted to examine significant differences across the clusters on

continuous demographic, personal, and medical variables. In the current study, age, time since

diagnosis and time since treatment, body mass index, waist circumference, waist-to-height ratio,

35

and both self-report and objectively assessed MVPA were tested. Chi-square tests were

conducted to examine differences in percentage of women reporting on categorical variables

including ethnicity, marital status, and type of treatment of lymph/axillary node dissection,

lumpectomy, single mastectomy, double mastectomy, reconstructive surgery, chemotherapy,

radiotherapy, hormonal therapy, and cancer stage. If there were significant differences observed

across cluster membership (i.e., p <.05), variables were then used as covariates in follow-up

analyses.

To test the association between SED cluster membership and depression three months

later, a stepwise logistic regression analysis was completed while controlling for significant

variables identified in the MANOVA/ANOVA and chi-square analyses.

RESULTS

Overall, 187 women provided complete self-report and objective data for the study (92%

of total sample). Women were on average 55 years of age (SD = 11), and predominantly married,

white, highly educated, and were stage I or II cancer survivors (see Table 1). Women had an

average BMI of 26.25 (SD = 5.7) kg/m2, and were approximately 10 months (SD = 3.4) since

being diagnosed for breast cancer and 3 months (SD = 2.3) since completing their last treatment.

Based on accelerometer data, participants spent approximately 8.8 hours of their waking

hours a day sedentary and 25 minutes engaged in MVPA. This differed from self-report SED

where BCS reported that only around 2.5 hours of their day were spent engaged in any SED and

approximately 24 minutes a day engaged in MVPA. Surprisingly when specific types of SED

were considered, women reported spending almost 6 hours per day engaged in screen-time

activities and just less than 2 hours per day spent reading (see Table 2). Using the CES-D 10,

36

31.1% of our sample reported a score of ≥ 10, which places individuals at a risk for clinical

depression.

Pearson and/or Spearman Rho bivariate correlations were used to test relationships

among SED variables that were entered in the cluster membership and self-report/objective

MVPA (Table 3) and cluster membership variables and depression (Table 4). The inter-

correlation of MVPA (self-report and objective) was significant (p<.001). Lower self-report

MVPA was significantly associated with higher numbers of 10-minute bouts, objective SED, and

decreased number of breaks. Lower levels of MVPA as measured with accelerometer was

significantly associated with higher self-report and objective SED, higher screen-time, higher

number of 10 and 30 minute bouts, higher average time in 10 minute bouts and lower number of

breaks. As supplementary analyses, Pearson bivariate correlations were also calculated to

examine relationships among demographic and medical variables (Appendix E), objective

(accelerometer-measured) SED and weight status (Appendix F), and self-report SED and weight

status (Appendix G).

Hierarchical cluster analysis was conducted using self-report SED, screen-time, reading,

objective SED time, average number of 10- and 30-minute bouts, average time in 10- and 30-

minute bouts, and average time in SED breaks. The nine SED variables were standardized for

use in the cluster analyses (Ullrich-French and Cox, 2009). Ward’s linkage method and squared

Euclidean distance as a similarity measure was complete to attempt to assess the most stable and

appropriate number of cluster groups (Ullrich-French and Cox, 2009). These findings led to a

recommended 2-cluster solution based on the creation of a high SED group and low SED group.

Next, a non-hierarchical cluster using Euclidean distance as a similarity measure was conducted

specifying two cluster groups. K-means cluster solution confirmed two clusters that were

37

subsequently examined and described as a low SED and a high SED group. The MANOVA

model testing significant differences in SED variables across cluster membership was significant,

F (1,185) = 44.11, p<.001, η2 = .69. Participants in the high SED cluster spent significantly more

time engaged self-report SED η2 = .16; F (1,185)= 34.23, p<.001, screen-time activities η2 =

.67; F (1,185)= 380.56, p<.001, average time in 10 minute bouts η2 = .04; F (1,185)=7.70,

p<.05, and average number of 30 minute bouts η2 = .03; F (1,185)=3.34, p<.05 compared to

participants in the low SED cluster.

To examine cluster membership differences by personal and cancer-specific variables,

the main MANOVA model was significant, F (8,185) = 4.82, p<.001, η2 =.17. The high SED

group was significantly older (F (1, 185) = 6.56, p = <.05, partial η2 = .03), heavier based on

BMI (F (1, 185) = 7.11, p = <.05; partial η2 = .04), had a larger waist circumference (F (1, 185) =

17.64, p = <.001, partial η2 = .09), higher waist-to-height ratio (F (1, 185) = 12.40, p = <.001,

partial η2 = .06), were less active (self-report MVPA F (1, 185) = 4.07, p = <.05, partial η2 = .03

and objective MVPA F (1, 185) = 16.38, p = <.001, partial η2 = .08) compared to those in the

low SED cluster (see Table 5). In addition, women in the high SED group were significantly (p

< 0.05) more likely to be less educated [X2(1) = 11.86], had undergone lymph or axillary node

dissection [X2 (1) = 5.21], overweight [X2 (1) = 4.00], have a waist-to-height ratio >0.50 [X2 (1) =

4.00], and have a waist circumference > 88cm [X2 (1) = 11.09]. These results are presented in

Table 5.

A stepwise logistic regression analysis was conducted to predict symptom reporting of

clinical depression (i.e., scores of > 10 on the CES-D) for BCS three months after baseline,

controlling for age, education, lymph/axillary node dissection, being overweight, self-report

MVPA and objective MVPA based on the results presented above. In the final model, objective

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MVPA (OR = 0.97, 95%CI = .95 to 0.99) and cluster membership (OR = 0.46, 95%CI = 0.22 to

0.98) were significant predictors of clinical depression suggesting that high MVPA and being in

the low SED cluster were protective of self-report symptoms of depression (see Table 6).

DISCUSSION

Owning to recent advances in the operational definition of SED and the possible

independent health effects of SED among BCS, the purpose of this study was threefold. First it

was an aim to explore and describe sedentary behaviour facets among women with breast cancer

and examine how sedentary behaviours cluster. Second personal demographic and cancer-

specific differences on the clustering of SED were explored, and third, the association between

SED clusters and depression was tested, independent of PA. The focus of this work was on

women who were recently finished treatments for breast cancer because this is an important time

in the survivorship trajectory for making health behaviour changes (Brunet et al., 2014). Overall,

it was found that BCS were highly sedentary, spending the majority of their waking hours

engaged in sedentary pursuits. Women who were highly SED were more likely to be overweight,

older, less educated and had undergone surgical removal of the lymph/axillary node. In addition,

the results from the logistic regression revealed that after controlling for significant

demographic/medical and PA variables, cluster membership was a significant predictor of

clinical depression among BCS such that high SED was a risk factor of depression.

The current study is the first to examine the SITT principle in cancer survivors and how

this combined approach using objective and self-report SED measurement can be used to

identify SED frequency, interruptions, time, and type of SED. In the current study, average

number and time in 10-minute bouts, average number and time in 30-minute bouts, average time

in SED breaks, objective SED, self-report SED, screen-time and time spent reading were used to

39

represent the different facets of SITT. There are few to no studies reporting on these multiple

facets of SED, however a common measure used has been total time spent in SED – defined as

less than 100 counts per minute using accelerometers (Lynch et al., 2010; Trinh, Amireault, et

al., 2015). In the current sample, 64% of BCS waking hours were spent engaged in SED pursuits

and 3% of the time was spent engaged in MVPA. This is consistent with national survey

estimates of BCS, where 66% of their waking hours were spent sedentary and 1% of time

engaged in MVPA (Lynch, 2010). Moreover, a recent study of 398 BCS (Mage = 56.95 years)

reported that BCS spent 66.4% of their waking hours engaged in SED and only 2.6% engaged in

MVPA (Phillips et al., 2015). In the first representative population objective measure of

sedentary time, Matthews and colleagues (2008) found that adults over the age of 60 spend

approximately 60% of their waking hours engaged in SED (Matthews et al., 2008). In addition,

women aged 50-59 years spent 7.74 waking hours a day (~56% of the day) engaged in SED and

this differed from women over the age of 60 who spent on average 8.60 waking hours a day

(~62% of the day) engaged in SED (Matthews et al., 2008). These estimates are similar to

Australian objective population data which has shown adults spend on average 58% of their

waking hours sedentary (average age 53.3 years; (Healy et al., 2007)). Given these empirical

results coupled with the findings from the current study, it may be that BCS are more SED (or at

least as sedentary) as healthy adults. Furthermore, based on the cluster membership data in the

current study, there is a group of BCS who are likely targets for SED interventions. Specifically,

women in the high SED cluster reported significantly more SED time, screen-time, average time

spent in 10-minute bouts and average number of 30-minute bouts compared to women in the low

SED group. The biggest discrepancy in cluster membership was seen in screen-time, where

participants in the high SED group reported approximately 9 hours a day engaged in screen-time

activities versus under 4 hours a day spent in screen-time activities observed for the low SED

40

group. As such, screen time may be an early intervention target for women with breast cancer

who are recently finished systemic treatments. Specifically, drawing on a recent qualitative study

for men with prostate cancer, it may be important to help BCS add breaks to their SED screen

time using mobile technology such as alarms that remind them to stand up (Trinh et al., 2015).

While this is a novel study exploring the SITT principle in cancer survivors, the results of

the cluster analysis demonstrate that the SITT variables tend to function similarly such that they

grouped into high and low clusters. In addition, most objective and self-report SED variables

were significantly correlated together, however when individually compared to depression, no

significant associations were found. Researchers are urged to further tease out the uniqueness of

the different facets of SED, especially among clinical samples. Future work should examine how

all SITT principle variables are related and the implications of assessing the different facets of

SED on the health and well-being of BCS. Examining the correlation coefficients, most SED

variables were directly associated with decreased length of breaks. Given that breaks are times in

the day when the participants are not sedentary, these findings may allude to a replacement

proposition such that PA replaces time spent sedentary (Owen, Healy, Matthews, & Dunstan,

2010). Researchers have shown that simply replacing SED with light intensity PA has a number

of independent health benefits and when compared against all other intensities of PA, a switch

from no activity (SED) to light activity has the greatest overall health benefit (Powell, Paluch &

Blair, 2011). Future work should begin to examine in more detail the association and interaction

between SED and light PA in BCS. Numerous studies have shown that breaking SED routinely

and replacing this behaviour with PA have number of unique health benefits, including improved

insulin responses and better resting blood pressure (Dunstan et al., 2012; Larsen et al., 2014;

Barr-Anderson et al., 2011). Transitioning from SED to light PA exhibits health benefits

irrespective of a change in the volume of MVPA (Powell et al., 2011). In addition, a recent

41

systematic examining non-vigorous PA and all-cause mortality found that the largest health

benefit observed was from moving from SED to light PA (Woodcook, Franco, Orsini & Roberts,

2011). Thus, it is recommended that researchers begin to focus on the transition between SED

and light PA rather than focus solely on MVPA. Older adults and BCS spend the majority of

their day engaged in SED and light PA opposed to approximately 2% of their day engaged in

MVPA (Powell et al., 2011; Lynch et al., 2013). This research could have public health and

clinical ramifications that are as important or even more important that promoting the many

benefits of regular MVPA.

Furthermore, the results of the cluster membership in the current study take into account

both self-report and objective measures of SED. To date, the knowledge of the effects of SED

has been driven predominantly by self-report assessments. Among healthy adults, self-report

SED has been consistently linked with a number of negative disease-related outcomes. Using

time spent sitting, adults with the longest time SED were more at risk for total mortality and

cardiovascular morbidity and mortality regardless of PA (Katzmarzyk, Church, Craig, &

Bouchard, 2009; Patel et al., 2010; van der Ploeg, Chey, Korda, Banks, & Bauman, 2012). In a

population sample, screen-time as measured as a combination of television viewing and screen-

based activities was related to heightened risk of mortality and cardiovascular disease

independent of PA level (Stamatakis et al., 2011). Regardless of these findings, self-report

measures may be subject to bias. For example, a recent study comparing objective accelerometer

SED and self-report SED in 317 Australian adults found that after 7 days of monitoring (500

mins/day wear time for a minimum of 4 days), the self-report estimate of SED was 13% less time

sitting than accelerometer estimates (Celis-Morales et al., 2012). In addition, these adults self-

reported spending 7.5 hours/day engaged in SED versus accelerometer derived estimates of

almost 9 hours/day engaged in SED (Celis-Morales et al., 2012). In the current study, there was

42

also a discrepancy between self-report SED/PA and accelerometer derived SED/PA with a nearly

300 minute difference between objective time spent SED and self-report SED time.

Discrepancies in self-report and objective assessment of health behaviours are commonly

reported and are likely based on social desirability, inability to self-monitor behaviours, lack of

knowledge of what constitutes the behaviour (Brunet et al., 2011; Motl, McAuley, & DiStefano,

2005; Patterson et al., 2010; Sabiston et al., 2014). Thus, person-centered analyses such as the

one used here allows one to capture subtle differences in SED using both objective and

subjective measurement, and may be a valuable way to continue to assess SED.

Based on the results of this study, high SED was associated with a number of important

demographic, personal and medical variables. Of particular relevance is the link between SED

and weight status indicators. High SED was significantly associated with higher levels of BMI,

waist circumference and waist-to-height ratio. Being overweight and highly SED combined

could potentially increase BCS’s risk profile for a host of other health problems (Healy et al.,

2011; Owen, Healy, Matthews, & Dunstan, 2010; Tremblay et al., 2010). Older BCS were also

more likely to be in the high SED group. This finding is consistent with previous reports

suggesting that age is a common predictor of low levels of PA among BCS (Fontein et al., 2013).

Nonetheless, the benefit of engaging in PA may be even greater in older BCS compared to

younger BCS (Fontein et al., 2013). Thus, health care practitioners should be encouraging

women to reduce their SED and increase their PA at any intensity that feels comfortable across

the lifespan. In addition, SED cluster membership was associated with having had undergone

surgical removal of the axillary lymph node. Traditionally BCS have been told not to perform

upper body exercises following surgical removal of the axillary lymph node due to the belief that

this exercise would worsen the lymphedema (Kwan, Cohn, Armer, Stewart, & Cormier, 2011).

While this myth has been dispelled by a number of researchers (Ganz, 1999; Kwan et al., 2011;

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Markes, Brockow, & Resch, 2006; Rockson, 1998; Schmitz et al., 2010; Young-McCaughan &

Arzola, 2007), many oncologists, nurses, physiotherapists, and rehabilitation scientists may

continue instruct patients to restrict their daily PA (Sagen et al., 2009) and few discuss PA with

their patients (Karvinen, DuBose, Carney, & Allison, 2010). Thus, like age, health care-

practitioners should encourage BCS to reduce their SED and increase their PA even if they have

undergone this procedure.

Not surprisingly, women in the high SED cluster were also significantly less physically

active. In addition women who spent more time sedentary were less likely to break SED which

can be used as a proxy for physical activity. These findings are common and consistent in adults

and clinical populations such as cancer survivors (Celis-Morales et al., 2012; Gordon-Larsen,

Nelson, & Popkin, 2004; Healy, Dunstan, et al., 2008; Katzmarzyk et al., 2009; Lynch et al.,

2013) As such, there is a constant message that both SED and PA are needed targets in cancer

care. In addition, researchers have begun to tease out the importance of separating SED from

physical inactivity and examining how SED and levels of PA produce unique metabolic profiles

independently (Owen et al., 2010; Tremblay et al., 2010). For example, researchers have

demonstrated that participants can be both sedentary and physically inactive, but also sedentary

and physically active, and these profiles have significantly different metabolic outcomes (Owen

et al., 2010). The ‘active couch potato’ is classified as an individual highly sedentary but also

meeting national physical activity guidelines (Owen et al., 2010). This differs from an inactive

couch potato who is both highly sedentary and highly inactive. The results of the current study

were purposefully limited to define the multiple facets of SED, while controlling for MVPA in

analyses. However, there are multiple ways of exploring the combination of SED and PA that

should be explored among BCS.

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In the current study, SED cluster membership significantly predicted clinical depression

three months after baseline and approximately six months after completion of systemic

treatments. In clinical populations, researchers employing cross-sectional designs have

demonstrated depressed individuals live a more sedentary lifestyle (Roshanaei-Moghaddam et

al., 2009; Weyerer & Kupfer, 1994). Moreover, although only limited work has been done

examining SED and depression in BCS, the current findings are consistent with evidence that

high levels of SED were associated with depressive mood assessed as dysphoria (Trinh et al.,

2015). Nonetheless, when levels of MVPA were assessed independently and in interaction with

SED, women with high levels of MVPA were more protected from dysphoria (Trinh et al.,

2015). In the current study, both SED (assessed as cluster membership) and MVPA were

significant predictors of depression three months later. As such, it is important to continue to

study both PA and SED to best understand mental health outcomes. It is also important to

identify the unique and combined mechanisms that may explain the association between SED,

MVPA, and depression. There are a number of proposed possible linkages that should be

examined. For example, it may be the intensity and frequency of PA combined with low SED

that improve mental health outcomes through neurobiological influences such as monoamine

availability and increased neurotropic factors (Rot, Mathew, & Charney, 2009). Also, PA

opportunities, but not SED, may enhance self-efficacy and self-concept which could explain the

association to depression (Dishman et al., 2006). Furthermore, the associations may be explained

in part by immune functioning such that high SED and low PA are related to poorer immune

function which exacerbates health conditions including depression (Ballard-Barbash et al., 2012;

Lynch, 2010). Lynch’s (2010) model also explored the existing literature on a number of

metabolic markers and how these markers influence the relationship between SED and cancer.

These include inflammation, sex hormones (androgen/estrogen/sex hormone binding globulin),

45

vitamin D and metabolic dysfunction (insulin/glucose). It is possible that these metabolic

markers might also influence the relationship between SED and depression. Thus, future work

should study these markers both independently and concomitantly to explore if and how they

might influence the direction of relationship between SED and depression.

In the current study, the participants were BCS considered in the “extended survivorship”

period (Ristovski-Slijepcevic & Bell, 2014). This is an important time for survivorship outcomes

and could be a possible time for intervention (Demark-Wahnefried, 2005). Furthermore, more

than 88% of breast cancer patients are surviving for 5 years or more after being diagnosed

(Canadian Cancer Society, 2013) and that there is a high prevalence of depression in BCS, which

means it is imperative to explore how behavioural changes such as reducing SED might

influence the sequeale caused by cancer and its related physical, social and psychological

symptoms. Interventions reducing SED represent a cost-effective and non-pharmaceutical

strategy to potentially combat many negative health outcomes among cancer survivors (Lynch et

al., 2013). Given the low levels of MVPA reported and measured among BCS, and challenges

associated with uptake and adherence to regular physical activity, targeting SED may be more

feasible and realistic in this population. A recent qualitative study exploring SED and the

perceptions of SED in prostate cancer survivors found that men would enjoy a web-based/mobile

application to reduce SED and these men were not able to differentiate between the differences

of SED and inactivity (Trinh, Arbour-nicitopoulos, et al., 2015). To the best of our knowledge

this qualitative work has yet to be done in BCS and qualitative work like this could help

researchers identify the best and most effective ways to reduce SED and increase PA in BCS.

Future work could aim to identify what mobile and web-based applications BCS may prefer for

changing SED patterns and how these applications can be best introduced into their daily lives.

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Although the findings from this prospective cohort study offer exciting findings related to

SED and depression in BCS, the results need to be considered with some limitations. The sample

was a convenience sample of BCS with the majority of women being early stage cancer

survivors, highly educated, and white. As a result, these women are not reflective of all BCS, and

future work should examine the replicability in other groups (e.g., nonwhite, low education,

stage III BCS). Nonetheless, the sample characteristics are similar to the characteristic of many

studies exploring factors related to PA in BCS (Brunet et al., 2014). Second, cluster analysis is a

person-centered approach to understanding SED. This analysis renders the current findings to be

sample specific and thus may limit the generalizability of results. However, this approach

enabled a comprehensive understanding of multiple facets of SED simultaneously in light of no

detailed guidelines for understanding SED behaviours in BCS. Also, the use of SITT principle to

operationalize SED has not been used among cancer survivors and these findings should be

replicated. While specific justifiable facets were included in the current study to represent each

of the SITT elements, it is clear that there are a number of additional facets of SED that were not

studied in the current analysis that may also have clinical, practical, and/or theoretical

implications. Finally, although the cut-point for clinical depression was consistent with

recommendations (Andresen et al., 1994), the original CES-D measure was tested for clinical

ramifications and thus replication of these findings with similar cut-points are needed.

Notwithstanding the limitations, this study offers important preliminary evidence that

high SED as measured through a combination of self-report and objective measures may be

related to symptoms of clinical depression during the early survivorship period. Based on these

findings, it is important to encourage BCS to reduce SED to attenuate symptoms of depression.

Introducing health promotion messages encouraging survivors to reduce SED might represent an

easier to follow and more effective tool then instructing survivors to increase daily MVPA,

47

wherefew BCS currently meet the recommended PA guidelines. At the individual level, reducing

SED represents a cost-effective and non-pharmaceutical tool, which could potentially reduce

symptoms of depression. A large number of BCS are currently not active enough to gain health

benefits (Lynch et al., 2010), so promoting reductions in SED, where individuals spend a large

portion of their day, could lead to tremendous benefits, which are probably not limited to

reducing symptoms of depression. From a public health perspective, using health messaging to

inform all survivors about the potential benefits of reducing SED could prove to be an effective

tool that could improve the quality and quantity of life among BCS (Lynch et al., 2013). Also,

reducing symptoms of depression and other negative symptoms associated with cancer survival

through promoting reductions in SED could potentially reduce the financial commitment

required to treat co-morbities associated with cancer survival. Based on our findings and

previous work, the early survivorship period represents an important time to introduce health

behaviour change messaging (Brunet et al., 2014) and can potentially exhibit positive changes to

the lives of the many women living with a diagnosis of breast cancer.

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Chapter 4 Conclusions and Future Directions

This thesis had three main objectives. The first goal was to better understand the SED

profile of BCS using the SITT principle. Along with this first objective, the multiple dimensions

of SED were examined by grouping survivors based on their SED into a high and low SED

group. The preliminary modeling demonstrated that the two clusters were significantly different

on age, level of education, weight-status, PA and having undergone surgical removal of the

lymph/axillary node.

The second objective was to examine personal and cancer-specific factors that distinguish

the groups of SED. Based on previous research with SED and more prominently with PA, it was

expected that age, weight status, and potentially some cancer-related variables would be

important factors differentiating the SED groups. It was hypothesized that high SED would be

associated with being older and have a higher BMI (Demark-Wahnefried et al., 2001; Kroenke,

Chen, Rosner, & Holmes, 2005; Patterson et al., 2010). Furthermore, we hypothesized that those

women who had undergone chemotherapy, radiation and/or had undergone surgical removal of

the axillary lymph node would be highly SED (Harvie, Campbell, Baildam, & Howell, 2004;

Rockson, 1998; Sagen et al., 2009). Based on MANOVA, ANOVA and chi-square results we

confirmed that SED was significantly associated with age, weight status measures, education and

lymph/axillary node dissection but not with chemotherapy or radiation.

Finally, the third objective was to examine the association between SED and depression

among BCS over time. It was hypothesized that cluster membership would significantly predict

symptom reporting consistent with clinical depression. We confirmed this hypothesis through

completing a logistic regression analysis while controlling for a number of significant

demographic, medical and PA covariates. These findings are exciting because they are the first

49

study in cancer survivor’s to explore various SED facets and examine how these relate to

depression however insufficient evidence has accumulated to draw strong conclusions between

SED and depression. Future work should explore this potential relationship in more detail as well

as the direction of this relationship.

Practically, reducing SED and depression should be given important priority by BCS.

Psychosocial oncology researchers have identified SED as one of the most important issues to

consider in PA and cancer survivorship, and exploring this question in more detail can

potentially improve the lives of the many cancer survivors (Courneya et al., 2015). In addition,

compared to MVPA, targeting SED may be a more feasible, realistic, cost-effective for the ever-

increasing number of BCS’s (Gardiner, Eakin, Healy, & Owen, 2011; Lynch et al., 2013). In the

current study of a convenience sample of BCS, the women spent on average 8.8 hours/day

engaged in SED during their waking hours. This corresponds to 64% of one’s waking hours

engaged in SED. Displacing SED and replacing this behaviour with light intensity PA will lead

to a myriad of positive physical and psychological health outcomes (Owen et al., 2012). The

current analysis only began to explore the number of different ways that SED can be measured

through using the SITT principle. Future work should identify the appropriate bout length, break

length and number of breaks required to most effectively reduce symptoms of depression and

also experience a number of additional physical and psychological health benefits. Until this

work can be done, researchers first need to agree on operational definitions of SED, beginning

with adults in general and in clinical populations afterwards (i.e., cancer survivors). From a

clinical perspective, researchers should begin to tease out the impact that different facets of SED

have on both physical and psychological health of adults and clinical populations. Before doing

this work it is imperative to create detailed SED guidelines, which provide recommendations on

the appropriate dose and frequency of daily SED. In addition, these guidelines should also

50

instruct to adults on how often to break SED and for how long they should break SED in order to

receive health benefits. Doing this work will help clinician’s and survivors to better understand

SED and continue to dispel the myth that SED is synonymous with physical inactivity. As of

2013, there are 12,200 new cases of breast cancer in Canada reported annually, and breast cancer

continues to be the most common cancer diagnosis in women (Canadian Cancer Society, 2013).

From a research perspective, a criticism from systematic reviews regarding current SED

measurement is heterogeneity of measurement across studies (Lynch, 2010). Thus in addition to

having operational definitions of SED, researchers should also validate and agree on an

established set of procedures to use when conducting studies and randomized controlled trials

amongst BCS. Based on the directions of the current study, it may be of value to study and assess

SED using the SITT principle in BCS because it allows the researcher to capture multiple facets

of SED using both objective and self-report measures of SED.

Furthermore, it may be of research importance for researchers to direct attention at

developing and testing nomological frameworks explaining the association between SED, PA,

and both physical and mental health outcomes. To accomplish this goal, mechanisms linking

these lifestyle behaviours to health outcomes such as depression need to be tested. There are a

number of proposed possible mechanisms including the type, intensity and frequency of PA and

SED, effects of these features of lifestyle behaviours on neurobiology (including monoamine

availability, neurotrasmitters, and neurotropic factors) and immune function, metabolic

dysfunction, and increases in self-efficacy and mastery (Rot et al., 2009)(Dishman et al.,

2006)(Ballard-Barbash et al., 2012; Lynch, 2010). Lynch’s (2010) model may offer a starting

point for the testing of mechanisms and the development of theoretical or conceptual frameworks

that best explain the association between SED, PA, and health. Researchers are encouraged to

51

design prospective studies to examine proposed associations, and to examine manipulations in

the SED and PA behaviours using randomized controlled trials among BCS.

In conclusion, breast cancer is most prevalent in females aged 50 to 69 years (52% of

diagnoses) and five-year survival rates are now estimated between 80 and 95% (Coleman et al.,

2011). With an ever increasing number of survivors and with survivors living longer, the time is

now for public health researchers, clinicians and policy makers to beginning to explore the

impact the modifiable factors such as reducing SED and increasing daily PA have on the

physical and psychological health of BCS’s. In conclusion, efforts to explore SED and how this

potential negative health behaviour might be linked with various mental health outcomes are of

immediate concern. However, before effective interventions can be tested, researchers first need

to generate a clear and concise set of guidelines for SED beginning in adults and then within

clinical populations. Once these tasks have been completed, researchers are next encouraged to

test the framework suggested by Lynch (2010) and explore if these relationships continue to hold

when different demographic (i.e., ethnicity) and medical variables (i.e., stage of cancer) of BCS

are considered. Overall, this is the first study to explore SED in BCS using the SITT principle

and the first study to examine SED as a predictor clinical depression.

52

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Tables

Table 1. Baseline demographic, medical, sedentary and physical activity descriptives

for participants (N=187).

Demographic and Cancer-Related Data Descriptive Coefficient, Mean (SD) or % Age in years (Mean, SD) 55.04 (10.92) White (%) 85 College or University (%) 70.7 Marital Status (%) Single/separated 37.3 Married/common-law 62.7 Stage of breast cancer (%) I 41.8 II 39.3 III 18.9 Type of Treatment (%) Single mastectomy (yes) 27.9 Double mastectomy (yes) 16.9 Chemotherapy (yes) 64.2 Radiation (yes) 88.6 Lumpectomy (yes) 60.2 Lymph/Axillary node dissection (yes) 58.3 Hormonal therapy (yes) 51.2 Months Since Diagnosis (Mean, SD) 10.63 (3.41) Months Since Treatment (Mean, SD) 3.46 (2.33) Post-menopause (%) 64.7 Weight Status Body Mass Index (Mean, SD) 26.25 (5.65) Waist-to-height ratio (Mean, SD) 0.56 (0.90) Waist Circumference (Mean, SD) 90.10 (15.12)

73

Table 2. SITT variables by cluster membership (n=187). Variable Cluster One

n=64 (M, SD) Cluster Two

n=123 (M, SD) Total

N = 187 (M, SD)

Average number of 10 min bouts 102.28 (22.98) 98.59 (23.86) 99.85 (23.57) Average time in 10 min bouts 23.18 (3.13) 21.86 (3.06) 22.31 (3.14)* Average number of 30 min bouts 22.26 (9.39) 19.10 (9.13) 20.18 (9.32)* Average time in 30 min bouts 47.70 (5.76) 46.13 (5.45) 46.67 (5.59) Average time in SED breaks 33.94 (9.98) 35.17 (10.23) 34.75 (10.14) Self-report SED 195.20 (105.54) 115.14 (78.74) 142.54 (96.38)** Screen-time 572.43 (127.09) 233.12 (104.75) 349.25 (196.79)** Reading 115.37 (77.89) 102.38 (71.96) 106.82 (74.09) Objective SED 539.27 (87.41) 521.83 (88.87) 527.80 (88.53) **. Univariate model significant at the .001 level (2-tailed). *. Univariate model significant at the .05 level (2-tailed).

74

Table 3. Bivariate correlations by sedentary behaviour cluster membership variables and self-report/objective

moderate-to-vigorous physical activity (n=187). Variable 1 2 3 4 5 6 7 8 9 10

1. Self-report MVPA

2. Objective MVPA .45**

3. Self-report SED -.10 -.16*

4. Screen-time -.12 -.28** .37**

5. Reading .02 -.12 .08 .10

6. Avg number 10 minute bouts -.17* -.24** .18* .12 .09

7. Avg time per 10 minute bouts .06 -.24** .26** .23** .17* .23**

8. Avg number of 30 minute bouts -.08 -.31** .22** .19** .18* .67** .81**

9. Avg time per 30 minute bouts .12 -.03 .21** .16* .10 -.04 .64** .29**

10. Objective SED -.15* -.28** .19* .10 .14 .84** .46** .75** .16*

11. Average time of SED breaks .15* .18* -.13 -.05 -.11 -.75** -.11 -.45** .09 -.83**

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

75

Table 4. Bivariate correlations between cluster membership variables and continuous depression

(n=187).

Variable 1 2 3 4 5 6 7 8 9

1. Depression

2. Self-report SED .13

3. Screen-time .15 .37**

4. Reading -.03 .08 .10

5. Avg number of 10 minute bouts

.02 .18* .12 .09

6. Avg time per 10 minute bouts .05 .26** .23** .17* .23**

7. Avg number of 30 minute bouts

.05 .22** .19** .18* .67** .81**

8. Avg time per 30 minute bouts -.05 .21** .16* .10 -.04 .64** .29**

9. Avg time in SED breaks .03 -.13 -.05 -.11 -.75** -.11 -.45** .09

10. Objective SED -.02 .19* .10 .14 .84** .46** .75** .16* -.83**

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

76

Table 5. MANOVA and chi square examining medical, demographic and weight status

variables by cluster membership (n=187). Variable Total

(n=187) Cluster 1 (n=64) Cluster 2 (n=123)

Age (M, SD) 54.44 (10.85)* 57.22 (11.16) 53.00 (10.44) BMI (M, SD) 26.24 (5.60)* 27.73 (5.90) 25.46 (5.30) Waist circumference (M, SD) 90.17 (15.41)** 96.45 (16.68) 86.90 (13.66) Waist-to-height ratio (M, SD) .56 (.09)** .59 (.09) .54 (.09) LTEQ MVPA (M, SD) 23.75 (24.26)* 18.83 (20.50) 26.31 (25.71) Objective MVPA (M, SD) 25.21 (18.28)** 18.00 (13.88) 28.96 (19.20) Time since diagnosis (M, SD) 10.73 (3.35) 10.59 (3.49) 10.81 (3.29) Time since treatment (M, SD) 3.47 (2.32) 3.19 (2.17) 3.61 (2.39) University (% yes) 51.2 ** 32.8 59.3 White ethnicity (% yes) 85.1 85.9 84.6 Lymph/axillary node dissection (% yes) 57.7* 46.9 64.2 Lumpectomy (% yes) 60.2 65.6 59.3 Single mastectomy (% yes) 27.9 23.4 28.5 Double mastectomy (% yes) 16.9 14.1 16.3 Reconstructive surgery (% yes) 7.0 3.1 8.9 Chemotherapy (% yes) 64.2 62.5 65.9 Radiotherapy (% yes) 88.6 92.2 87.8 Hormonal therapy (% yes) 51.2 48.4 51.2 BMI overweight (% yes) 50.2* 60.9 45.5 Waist circumference over 88 cm (% yes) 53.2** 70.3 44.7 Waist-to-height ratio (% yes) 72.1* 81.3 67.5 Married (% yes) 64.2 59.4 69.1 Cancer stage 1 or 2 (% yes) 81.1 23.4 18.7 Post-menopausal (% yes) 64.7 71.9 57.7 *Indicates significance at the .05 level. **Indicates significance at the .001 level.

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Table 6. Logistic regression of main Study variables predicting clinical

depression (n=187). Variable β SE OR 95% CI University Education 0.02 0.38 1.02 0.49-2.13 Age (years) -0.02 0.02 0.98 0.95-1.02 Lymph/axillary node dissection 0.60 0.38 1.82 0.87-3.82 Overweight -0.03 0.37 0.98 0.47-2.02 Self-report MVPA 0.00 0.01 1.00 0.99-1.02 Objective MVPA -0.03 0.01 0.97* 0.95-0.99 SED cluster membership -0.77 0.39 0.46* 0.22-0.98 *Indicates significance at the .05 level. **Indicates significance at the .001 level.

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Appendices

Appendix A Sedentary Behaviour Definitions

Bouts Time spent in sedentary behaviour that is <100 counts per minute and meets the criteria for valid wear-time (4 or more days a week and minimum 500 minutes of wear time per week).

5 Minute Bout Any bout of sedentary behaviour lasting more than 5 minutes in duration and meeting the criteria for valid wear-time.

10 Minute Bout Same as above but lasting more than 10 minutes in duration. 15 Minute Bout Same as above but lasting more than 15 minutes in duration. 20 Minute Bout Same as above but lasting more than 20 minutes in duration. 30 Minute Bout Same as above but lasting more than 30 minutes in duration. Number of Sedentary Bouts - 5 minutes - 10 minutes - 15 minutes - 20 minutes - 30 minutes

Number of sedentary bouts occurring over a period of time during data collection.

Total Time in Bouts - 5 minutes - 10 minutes - 15 minutes - 20 minutes - 30 minutes

The total time a participant spent in sedentary bouts (in minutes) over a specified time during data collection.

Average Time per Bout - 5 minutes - 10 minutes - 15 minutes - 20 minutes - 30 minutes

Average length of time (in minutes) of sedentary bouts for participants during data collection.

Daily average number of SED bouts

The total number of sedentary bouts divided by the total number of valid days for all participants.

Daily Average time of SED bouts

The total length of sedentary bouts (in minutes) divided by the total number of valid days for all participants.

Break Any measure of activity/movement that is above 100 counts per minute and meets the criteria for valid wear-time (essentially this is any physical activity for a participant).

Total Sedentary Breaks The total number of sedentary breaks for each participant during data collection.

Average Length of Sedentary Breaks

An average (in minutes) of all the time when a participant is not engaged in a sedentary bout across data collection. This is total length of sedentary breaks (in minutes/total number of sedentary breaks).

Daily average number of SED breaks

The total number of sedentary breaks for each participant divided by the number of valid days.

Daily average time of SED breaks

The total number of sedentary breaks (in minutes) for each participant divided by the number of valid days.

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Appendix B

Descriptive statistics comparing 500 minute accelerometer wear-time (n=187) and 600 minute

accelerometer wear-time (n=184).

Variable Minimum Maximum Mean Std. Deviation 500 minute wear-time

Average number of 10 minute bouts 30.00 160.00 100.05 23.66

Average time per 10 minute bouts 15.20 31.50 22.30 3.13

Average number 30 minute bouts 1.00 44.00 20.20 9.30

Average time per 30 minute bouts 33.50 62.50 46.65 5.58

Average length of SED breaks 14.70 64.50 34.69 10.14

Daily average of SED breaks 323.00 2014.00 958.51 161.98

600 minute wear-time

Average number of 10 minute bouts 12.00 176.00 98.30 25.04

Average time per 10 minute bouts 15.20 33.30 22.31 3.20

Average number of 30 minute bouts .00 68.00 20.06 9.97

Average time per 30 minute bouts .00 84.50 46.57 7.15

Average length of SED breaks 14.70 100.80 35.23 11.93

Daily average of SED breaks 323.00 2041.50 977.25 186.29

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Appendix C

Bivariate correlations comparing 500 minute accelerometer wear-time (n=187) and 600 minute accelerometer wear-time (n=184). Variable 1 2 3 4 5 6 7 8 9 10 11 1. Average number of 10 minute bouts -

2. Average time per 10 minute bouts .23**

3. Average number 30 minute bouts .67** .81**

4. Average time per 30 minute bouts -.04 .64** .29**

5. Average length of SED breaks -.75** -.11 -.45** .09

6. Daily average of SED breaks -.54** -.31** -.48** -.09 .79**

7. Average number of 10 minute bouts _600 .97** .23** .65** -.06 -.74** -.51**

8. Average time per 10 minute bouts _600 .24** .99** .80** .64** -.13 -.34** .23**

9. Average number of 30 minute bouts _600 .67** .78** .97** .26** -.47** -.51** .69** .78**

10. Average time per 30 minute bouts_600 .04 .55** .27** .83** .01 -.16* .02 .58** .25**

11. Average length of SED breaks_600 -.71** -.09 -.41** .13 .94** .76** -.73** -.09 -.45** .06

12. Daily average of SED breaks_600 -.53** -.25** -.44** -.02 .76** .84** -.56** -.25** -.48** -.06 .88**

**. Correlation is significant at the .001 level (2-tailed). *. Correlation is significant at the .05 level (2-tailed).

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Appendix D

Sedentary behaviour frequency (S in the SITT principle) and interruptions (I in the SITT

principle), time (T in the SIIT principle) and type (T in the SITT principle) using objective

and self-report data (n=187). Variable Minimum Maximum Mean Std. Deviation SED frequency/interupptions Avg number of 10 minute bouts 30.00 160.00 100.0479 23.66077 Avg time in 10 minute bouts 15.20 31.50 22.2989 3.13242 Avg number of 15 minute bouts 7.00 129.00 61.1755 18.19050 Avg time in 15 minute bouts 19.90 40.80 29.0000 3.80777 Avg number of 20 minute bouts 3.00 108.00 41.2074 14.73272 Avg time in 20 minute bouts 24.30 47.50 34.9686 4.43063 Avg number of 30 minute bouts 1.00 44.00 20.2048 9.29992 Avg time in 30 minute bouts 33.50 62.50 46.6500 5.58132 Avg time SED Breaks 14.70 64.50 34.6947 10.13974 Time/Type Objective SED 321.00 778.14 528.19 88.46 LTEQ SED .00 432.00 144.82 98.55 Screen-time .00 898.00 348.77 195.85 Reading .00 315.00 108.24 73.47 * NOTE: all values are presented in minutes/day.

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Appendix E

Bivariate correlations between demographic and medical covariates (n=187). Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1. Age -

2. Income .05

3. Marital status .29** -.09

4. Education -.17* .04 -.11

5. Lymph/axillary node -.13 -.06 -.02 .07

6. Lumpectomy .09 .07 .04 .05 -.04

7. Single mastectomy .04 .09 -.06 .01 .04 -.49**

8. Double mastectomy -.28** .01 -.13 .11 .01 -.37** -.16*

9. Reconstructive surgery -.17* -.04 .05 .003 .04 -.14 -.04 .45**

10. Chemotherapy -.36** -.02 -.19** .10 .22** -.16* .09 .25** .16*

11. Radiotherapy .14 .03 .11 -.08 -.09 .19** -.09 -.38** -.33** -.27**

12. Hormone therapy -.06 .14 -.11 .20** -.03 .02 -.04 .18* .03 .04 -.04

13. Time since diagnosisa -.21** -.02 -.13 -.05 .20** -.02 .06 .10 .09 .39** .10 .05

14. Time since treatmenta .07 -.01 -.02 -.04 -.02 .06 .01 -.07 .01 -.13 -.04 .06 .36**

15. Cancer stage -.23** .11 -.16* .16* .18** -.14* .06 .21** -.05 .44** -.05 .03 .31** -.09

**. Correlation is significant at the .001 level (2-tailed). *. Correlation is significant at the .05 level (2-tailed). aRefers to time in months.

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Appendix F

Bivariate correlations between objective sedentary behaviour and measures of weight status (n=187). Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14

1. Body mass index -

2. Waist circumference .72**

3. Waist-to-height ratio .84** .87**

4. Average number of 10 min bouts .09 .06 .10

5. Average time in 10 min bouts .09 .14 .10 .23**

6. Average number of 15 min bouts .06 .08 .08 .92** .49**

7. Average time in 15 min bouts .13 .13 .12 .14 .94** .32**

8. Average number of 20 min bouts .08 .12 .18 .84** .64** .96** .48**

9. Average time in 20 min bouts .08 .06 .06 .06 .85** .21** .94** .31**

10. Average number of 30 min bouts .09 .14 .12 .67** .81** .83** .71** .90** .59**

11. Average time in 30 min bouts .09 .00 .02 -.04 .64** .07 .73** .16* .80** .29**

12. Average length of SED breaks -.01 .02 -.02 -.75** -.11 -.67** -.02 -.59** .02 -.45** .09

13. Daily average time in SED breaks .00 -.03 -.03 -.54** -.31** -.56** -.20** -.55** -.16* -.48** -.09 .79**

14. Objective SED .10 .06 .10 .84** .46** .85** .37** .81** .30** .75** .16* -.83** -.61**

15. Percent time SED .13 .11 .11 .73** .52** .80** .38** .80** .29** .75** .12 -.69** -.55** .82**

**. Correlation is significant at the .001 level (2-tailed). *. Correlation is significant at the .05 level (2-tailed).

84

Appendix G

Bivariate correlations between self-report sedentary behavior and measures of weight status (n=187). Variable 1 2 3 4 5 1. Body mass index -

2. Waist circumference .72**

3. Waist-to-height ratio .84** .87**

4. LTEQ SED .14* .24** .21**

5. LTEQ Screen-time .18* .28** .25** .37**

6. LTEQ Reading .01 -.02 .02 .08 .10

**. Correlation is significant at the .001 level (2-tailed). *. Correlation is significant at the .05 level (2-tailed).