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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Phys Act Health. Author manuscript; available in PMC 2014 March 1.
Published in final edited form as:
J Phys Act Health. 2013 March; 10(3): 350–358.
Published online 2012 July 9.
PMCID: PMC3794705
NIHMSID: NIHMS507389

Sedentary behavior, health-related quality of life and fatigue among breast cancer survivors

Abstract

Background

Many cancer survivors experience declines in health-related quality of life (HRQOL) and increases in fatigue as a result of cancer and its treatment. Exercise is linked to improvements in these outcomes, but little is known about the role of sedentary behavior. In a large, ethnically-diverse cohort of breast cancer survivors, we examined the relationship between sedentary time, HRQOL, and fatigue, and examined if that relationship differed by recreational moderate-vigorous physical activity (MVPA) level.

Methods

Participants were 710 women diagnosed with stage 0-IIIA breast cancer in the Health, Eating, Activity, and Lifestyle Study. Women completed questionnaires at approximately 30-months postdiagnosis (sedentary time; recreational MVPA) and 41-months postdiagnosis (HRQOL; fatigue). In multivariate models, we regressed these outcomes linearly on quartiles of daily sedentary time, and a variable jointly reflecting sedentary time quartiles and MVPA categories (0; > and <; ≥9 MET-hrs/wk).

Results

Sedentary time was not independently related to subscales or summary scores of HRQOL or fatigue. Additionally, comparisons of women with high vs. low (Q4:Q1) sedentary time by MVPA level did not result in significant differences in HRQOL or fatigue.

Conclusion

In this breast cancer survivor cohort, self-reported sedentary time was not associated with HRQOL or fatigue, 3.5 years postdiagnosis.

Keywords: health behavior, survivorship, epidemiology

Introduction

Improved prognosis based on early detection and new treatments for breast cancer has resulted in over 2.5 million women living with a personal history of breast cancer in the United States.12 As a result of cancer and its treatment, this growing population of cancer survivors face persistent physical and psychological challenges.3 For example, even five years after diagnosis, many survivors still experience impaired physical functioning, 45, psychosocial distress67, and fatigue8 ,the latter of which is often reported as the most frequent and distressing cancer symptom.9 Cancer diagnoses may prompt survivors to seek lifestyle change to improve long-term physical and psychological health and reduce dysfunction.10 Thus, it is important to understand modifiable factors that may impact these outcomes in order to develop better interventions for survivors.

There is strong evidence that exercise after a breast cancer diagnosis can improve survivors’ HRQOL and fatigue.1113 Less attention has been paid to sedentary time— prolonged periods of sitting or reclining without whole body movement like television watching. Many long-term breast cancer survivors not only fail to get enough exercise (e.g., recreational moderate-vigorous intensity physical activity (MVPA)) 14 but also spend the majority of their time sedentary.15 Being that too much sitting is increasingly becoming known as a distinct health behavior16 with its own negative health effects,17 there is a need for research in this area to inform behavioral interventions for survivors.

We previously reported that in the Health Eating Activity and Lifestyle (HEAL) study of breast cancer prognosis, three years after diagnosis, higher postdiagnosis recreational aerobic MVPA was associated with better HRQOL outcomes (including physical functioning18, vitality,19 social functioning 19) and less fatigue.18 There is little known about the independent role sedentary time might have in affecting these outcomes among cancer survivors. The only study to date observed a inverse relationship between television watching time and HRQOL among colorectal cancer survivors 20.

To build on our previous research in the HEAL study1819 and address this gap in the literature regarding sedentary behavior among breast cancer survivors, we investigated relationships between sedentary time, independently and stratified by recreational MVPA level, and subsequent HRQOL and fatigue among women with early-stage breast cancer.

Materials and Methods

Study participants

The HEAL study is a multi-ethnic prospective cohort study that has enrolled 1,183 women with first primary breast cancer drawn from Surveillance, Epidemiology, and End Results (SEER) population-based cancer registries in New Mexico, Los Angeles County, and Western Washington. The study was designed to determine whether lifestyle, hormones, and other exposures affect breast cancer prognosis. Details of the study have been published.2123 We obtained written informed consent from all study participants. The study was approved by the institutional review board at each participating center, in accord with assurances filed with and approved by the US Department of Health and Human Services.

In New Mexico, we recruited 615 women aged 18 years or older, diagnosed with in situ to regional breast cancer between July 1996 and March 1999, and living in Bernalillo, Santa Fe, Sandoval, Valencia, or Taos counties. In Western Washington, we recruited 202 women between ages 40 and 64 years, diagnosed with in situ to regional breast cancer between September 1997 and September 1998, and living in King, Pierce, or Snohomish counties. The age range for the Washington patients was restricted due to other ongoing breast cancer studies. In Los Angeles County, we recruited 366 black women diagnosed with in situ to regional breast cancer between May 1995 and May 1998 who had participated in the Los Angeles portion of the Women’s Contraceptive and Reproductive Experiences (CARE) Study or who had participated in a parallel case-control study of in situ breast cancer. The Women’s CARE Study restricted eligibility to women ages 35 to 64 years at diagnosis.

Data for the current study derive from three self-report assessments of the HEAL cohort, at 6 months, 30-months, and 41-months postdiagnosis. A total of 819 participants completed the 41-month assessment. We excluded women who may have been receiving treatment for subsequent recurrences or new primaries that occurred before the 41-month assessment (n=58), because active treatment may be associated with changes in sedentary behavior, physical activity, and the physical symptoms in which we were interested. We further excluded women missing data on sedentary behavior (n=3), diet (n=12), and body mass index (BMI) (n=36). Our final sample included 710 women.

Data Collection

HRQOL and fatigue. At approximately 41-months postdiagnosis, we measured HRQOL using the SF-36 health survey, version 1, a 36 item, valid and reliable short-form instrument 2425 that is widely used among medically ill and healthy populations 26. The SF36® yields eight subscales (physical functioning, role-physical, bodily pain, general health, social functioning, role-emotional, mental health, vitality) and two component summary scores (physical and mental). For all scales, a higher score represents better functioning and well-being. All scales are standardized on a T-score metric, with a score of 50 representing the U.S. general population average (standard deviation= 10).

Given fatigue’s complex, subjective, multicausal, multidimensional nature27, we measured fatigue using the Piper Fatigue Scale28 which has been shown to be a reliable and valid measure of subjective fatigue.22 The 22 items were coded into four scales, each with a range of 1–10): 1) behavioral severity: the observable behavioral changes in activities of daily living resulting from fatigue; 2) affective meaning: the emotional meaning attributed to fatigue; 3) sensory: the physical symptoms of fatigue; and 4) cognitive/mood: the mental and emotional symptoms related to fatigue. We changed the response time frame to assess fatigue over the past month rather than the past week to minimize the effect of acute situational events and to enhance our assessment of each survivor’s general state of fatigue The scale’s four factor structure was confirmed in the HEAL cohort (Cronbach’s alphas for the subscales ranged from 0.92–0.97).

Sedentary behavior

At the 30-month assessment, participants reported the number of hours they spent sitting while watching television or videos and while engaging in other sedentary pursuits (0, <1, 1–2, 3–4, 5–6, 7–8, 9+) during a typical 24-hour period on weekdays and weekends in the past year. To create an overall variable for continuous sitting time/day, we used the following formula ((weekday median daily time spent watching television + weekday median daily time spent in other sedentary pursuits)×5/7 + (weekend median time spent watching television + weekend median time spent in other sedentary pursuits)×2/7)). We classified daily sedentary time into quartiles (Q1–Q4). We did not have direct evidence of validity or reliability of the sedentary behavior questions we developed and asked.

Physical activity

Given the associations we previously observed in HEAL for recreational MVPA with physical functioning 18 fatigue,18 vitality,19 and social functioning19 we chose to focus on the recreational domain of MVPA for this analysis. At the 30-month assessment, we collected information on aerobic recreational MVPA in the last year using the Modifiable Activity Questionnaire developed by Kriska which has high validity and reliability.29 Participants reported the type, duration, and frequency of recreational physical activities in a typical week in the past year (e.g., brisk walking, biking, dancing, swimming, jogging, etc.). We classified each activity according to its corresponding metabolic equivalent of task (MET) value in the “Compendium of Physical Activities.”30 For all activities with MET values ≥3, we summed the products of activity MET values and hours spent in each activity to arrive at MET-hrs/week spent in MVPA for each participant. For joint association analyses, similar to Irwin et al. 31, we classified recreational physical activity into three categories (inactive: 0; somewhat active: >0 to <9; active: ≥9 MET-hours/week), with 9 MET-hours/week approximately equal to 150 min/week of moderate-intensity physical activity, and meeting the general population guidelines for health promotion.32

Additional risk factors

Height was measured 6-months postdiagnosis and weight at 30-months postdiagnosis. BMI was calculated as weight (kg)/ height (m2) and was categorized into the World Health Organization’s (WHO) BMI categories (underweight <18.5; normal: ≥18.5 to <25; overweight: ≥25 to <30; obese: ≥30 to <40; very obese: ≥40 kg/m2). Because diet quality was associated with physical and mental functioning in HEAL 23 we considered diet in this analysis. At the 30-month assessment, women completed a 122-item self-administered food-frequency questionnaire (FFQ) developed and validated for the Women’s Health Initiative.33 New Mexico participants reported their usual dietary intake for the previous year, whereas participants at the other two centers reported usual intake for the previous month. We measured diet quality with the Healthy Eating Index-20053437 which aligns with the U.S. Dietary Guidelines for Americans-2005 and uses an energy-adjusted density approach.38 For each participant, we scored each component, calculated a total score (100 possible points), and classified scores into quartiles.

For participants’ breast cancer diagnoses, disease stage was obtained from cancer registry records, and detailed information on treatment and surgical procedures was obtained from cancer registry, physician, and hospital records. At 6-months postdiagnosis, information was collected on recruitment site, date of birth, race, and education level. At the 30-month assessment, participants reported current use of tamoxifen, anti-depressants, and anti-anxiety medications. They also reported physician-diagnosed medical conditions and whether any of their current activities of daily living were limited by any of these conditions. A comorbidity summary score was generated based on the number of activity-limiting comorbidities (0; 1; ≥2). Also at the 30-month assessment, participants reported their employment status and current smoking status. Menopausal status at 30-months postdiagnosis was determined from medical records, hormone levels, and questionnaires. We considered each of these potential confounders in model development.

Statistical Analysis

We calculated means, standard deviations, and frequencies of demographic, clinical, and health behavior characteristics of our study population by quartiles of daily sedentary time.

In multivariate linear regression models, we regressed self-reported HRQOL and fatigue on quartiles of daily sedentary time and a combined variable jointly reflecting quartiles of daily sedentary time and MET-h/week MVPA (0; >0 and <9; ≥9). We calculated adjusted least square means for all HRQOL subscale and summary scores as well as fatigue scores. We controlled for confounders that improved model fit for any outcome and changed the magnitude of the beta coefficients by at least 10% (postdiagnosis MVPA, postdiagnosis diet quality, number of activity-limiting comorbidities, postdiagnosis BMI, antidepressant use, menopausal status, and race). Age and treatment did not qualify as confounders but were retained in our models for comparability with the literature, and their inclusion did not alter estimates obtained. We also analyzed data only among women diagnosed with invasive breast cancer (n=533), and associations were similar, so we report on the entire sample (n=710).

In analyses of sedentary time, we also explored whether any observed associations differed by MVPA, BMI, or race/ethnicity, by examining likelihood ratio tests for both the interaction of sedentary time with these factors (alpha=0.05) and the difference in model fit of full and reduced models.

All statistical analyses were conducted using SAS (version 9.2, Cary, NC) and used an alpha of 0.05.

Results

Women with higher daily sedentary time were more likely to be White, have higher BMIs, have lower recreational MVPA, and not be current users of antidepressants (Table 1). Sedentary time was only weakly correlated with recreational physical activity (r=0.09) (data not shown)., Women with higher sedentary time did not have significantly different scores for self-reported overall physical HRQOL (ptrend=0.771), physical functioning (ptrend=0.513),role-physical (ptrend=0.719), bodily pain (ptrend= 0.352), general health (ptrend=0.530), overall mental HRQOL (ptrend=0.703), vitality (ptrend=0.744), social functioning (ptrend=0.625), role-emotional (ptrend=0.936), mental health (ptrend=0.724), or dimensions of fatigue (behavioral severity: ptrend= 0.180; affective meaning: ptrend=0.988; sensory: ptrend=0.828; cognitive: ptrend=0.738) (Table 2). The associations between sedentary time, HRQOL and fatigue among survivors did not differ by recreational MVPA level (Tables 3a and 3b) or by BMI or race (data not shown).

Table 1
Demographic, Clinical, and Lifestyle Characteristics of 710 Breast Cancer Survivors in the Health, Eating, Activity, and Lifestyle Study
Table 2
Multivariate-adjusted HRQOL and fatigue scores1 by quartiles of sedentary time2

Discussion

Our study fills an important gap in the literature by investigating how self-reported sedentary time, both independently and stratified by physical activity level, relates to subsequent HRQOL and fatigue among breast cancer survivors. Our data suggest that self-reported sedentary time is not independently related to long-term survivors’ HRQOL or symptoms of fatigue. Further, the null associations between sedentary time, HRQOL, and fatigue did not vary by recreational MVPA level.

It is important to understand findings within the context of established cut-offs as well as existing literature.Different criteria have been used in the literature to indicate the minimally important difference (MID) in the SF-36 necessary to signify a meaningful or clinical effect.39 Cohen’s (1992) criteria suggest that a small effect is indicated by a 0.20 SD and a 0.50 SD is a medium effect size.40 There is support in the literature that MIDs fall within this range.4142. We observed between 0.1 and 0.7 point differences in HRQOL scores for those in Q4 vs. Q1 of sedentary time. Not surprisingly, these non-significant differences are also not meaningful. There are no published guidelines on MIDs for the Piper Fatigue Scale, but differences in fatigue scores for those in Q4 vs. Q1 of sedentary time were small (0–0.3) and non-significant. Among older adults without cancer, gradual and inverse relationships have been observed between sedentary behavior and scores on most of the SF-36 scales.43 Being that these outcomes were not related to sedentary time but were strongly related to recreational MVPA among HEAL breast cancer survivors1819, it is possible that after cancer and its treatment, recreational MVPA has stronger effects than sedentary time on biologically-relevant pathways affecting physical and mental health.

Key strengths of this study included our large, diverse group of survivors recruited through population-based cancer registries, comprehensive data on covariates related to sedentary behavior and our outcomes of interest, and validated, commonly-used self-report outcome measures. It may be that the multidimensional Piper fatigue scale captures a different construct than do the four items on vitality subscale of the SF-36, and our study had the advantage of looking at both of these constructs for the first time in relation to sedentary behavior.

The results of this study should be interpreted in the context of its limitations. Our results are only generalizable to women who have completed treatment, survived at least 41 months after diagnoses of breast cancer, and have similar demographic, clinical, and behavioral characteristics. Due to the study design, we were also not able to capture whether participants’ active and sedentary behavior changed between the 30-month postdiagnosis exposure assessment and the 41-month postdiagnosis outcome assessment, nor whether their HRQOL or fatigue changed in that timeframe.

First, if a weak or moderate association between sedentary behavior and these cancer-related comorbidities truly exists, there are a few explanations for our null results. First, measurement error from misclassification of self-reported sedentary behavior may have precluded us from observing a moderate or weak association. Our measure of sedentary behavior assessed only time spent television/video watching and time spent in all other sedentary pursuits. While the former may account for up to 50% of sedentary time 44 and is the most common domain measured in the literature 45, the latter may be cognitively challenging for participants to recall. Second, like most studies, we chose to assess HRQOL and fatigue using patient self-administered questionnaires,26 and it is possible that sedentary behavior might have a different association with objectively-measurable aspects of HRQOL, like physical functioning.

Third, sedentary behavior may not have as strong of a role, independently or jointly, in reducing the risk of physical or mental comorbidities 41-months into the cancer experience as it might in the acute period during or after treatment. In HEAL, survivors reported substantially worse physical functioning than the general population,46 and a large proportion of survivors (40%) reported experiencing fatigue.47 Nevertheless, if HRQOL and symptoms are worse during or right after treatment, the relationship between sedentary behavior and outcomes may differ by timing along the cancer continuum and we were not able to examine this in our study. Last, a ceiling effect could also explain our null findings, as survivors in our study did not score extremely poorly on HRQOL or fatigue measures.

Even if sedentary time is not a strong predictor of HRQOL or fatigue, it remains possible that it might be an important, independent indicator of other health outcomes after cancer, like survival and metabolic biological factors associated with survival.. Among adults without cancer, irrespective of participation in MVPA, higher levels of objectively-measured sedentary time have been associated with biomarkers of postmenopausal breast cancer risk—BMI, waist circumference, C-reactive protein, fasting insulin, and insulin resistance48—and mortality4952. Among a nationally representative sample of breast cancer survivors, objectively-measured sedentary behavior has been associated with overall and abdominal obesity. 15

In this cohort of breast cancer survivors, self-reported sedentary time was not associated with HRQOL or fatigue, 3.5 years postdiagnosis. To improve our understanding of public health benefits for survivors associated with reducing sedentary time, longitudinal studies on this topic are needed. Future research may benefit from recruiting survivors with poorer HRQOL, including validated self-report measures of sedentary time that span multiple domains (home, workplace, transportation, social settings), and complementing self-report questionnaires or recalls with objective monitoring of sedentary time.

Acknowledgements

We would like to thank Dr. Charles L. Wiggins, HEAL study managers, Todd Gibson of Information Management Systems, and the HEAL study participants.

Funding support

National Cancer Institute Grants N01-CN-75036-20, NO1-CN-05228, NO1-PC-67010.

Contributor Information

Stephanie M. George, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD.

Catherine M. Alfano, Office of Cancer Survivorship, National Cancer Institute, Bethesda, MD.

Ashley Wilder Smith, Applied Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD.

Melinda L. Irwin, Dept of Epidemiology and Public Health, Yale University, New Haven, CT.

Anne McTiernan, Division of Public Health, Fred Hutchinson Cancer Research Center, Seattle, WA.

Leslie Bernstein, Dept of Population Sciences, City of Hope Medical Center and Beckman Research Center, Duarte, CA.

Kathy B. Baumgartner, Dept of Epidemiology and Public Health, University of Louisville, Louisville, KY.

Rachel Ballard-Barbash, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD.

References

1. Altekruse S, Kosary C, Krapcho M, et al., editors. SEER Cancer Statistics Review 1975–2007. Bethesda, MD: National Cancer Institute; 2010.
2. American Cancer Society. Breast Cancer Facts and Figures 2009–2010. Atlanta, GA: 2009.
3. Hewitt M, Greenfield S, Stovall E, editors. From cancer patient to cancer survivor: lost in tranition. Washington, DC: National Academies Press; 2006.
4. Michael YL, Kawachi I, Berkman LF, Holmes MD, Colditz GA. The persistent impact of breast carcinoma on functional health status: prospective evidence from the Nurses' Health Study. Cancer. 2000 Dec 1;89(11):2176–2186. [PubMed]
5. Sweeney C, Schmitz KH, Lazovich D, Virnig BA, Wallace RB, Folsom AR. Functional limitations in elderly female cancer survivors. J Natl Cancer Inst. 2006 Apr 19;98(8):521–529. [PubMed]
6. Newport DJ, Nemeroff CB. Assessment and treatment of depression in the cancer patient. J Psychosom Res. 1998 Sep;45(3):215–237. [PubMed]
7. Stark DP, House A. Anxiety in cancer patients. Br J Cancer. 2000 Nov;83(10):1261–1267. [PMC free article] [PubMed]
8. Bower JE, Ganz PA, Desmond KA, et al. Fatigue in long-term breast carcinoma survivors: a longitudinal investigation. Cancer. 2006 Feb 15;106(4):751–758. [PubMed]
9. Curt GA, Breitbart W, Cella D, et al. Impact of cancer-related fatigue on the lives of patients: new findings from the Fatigue Coalition. Oncologist. 2000;5(5):353–360. [PubMed]
10. Demark-Wahnefried W, Aziz NM, Rowland JH, Pinto BM. Riding the Crest of the Teachable Moment: Promoting Long-Term Health After the Diagnosis of Cancer. Journal of Clinical Oncology. 2005 Aug 20;23(24):5814–5830. 2005; [PMC free article] [PubMed]
11. Schmitz K. Physical activity and breast cancer survivorship. Recent Results Cancer Res. 2011;186:189–215. [PubMed]
12. Speck RM, Courneya KS, Masse LC, Duval S, Schmitz KH. An update of controlled physical activity trials in cancer survivors: a systematic review and meta-analysis. J Cancer Surviv. 2010 Jun;4(2):87–100. [PubMed]
13. Speed-Andrews AE, Courneya KS. Effects of exercise on quality of life and prognosis in cancer survivors. Curr Sports Med Rep. 2009 Jul-Aug;8(4):176–181. [PubMed]
14. Pinto BM, Trunzo JJ, Reiss P, Shiu SY. Exercise participation after diagnosis of breast cancer: trends and effects on mood and quality of life. Psychooncology. 2002 Sep-Oct;11(5):389–400. [PubMed]
15. Lynch B, Dunstan D, Healy G, Winkler E, Eakin E, Owen N. Objectively measured physical activity and sedentary time of breast cancer survivors, and associations with adiposity: findings from NHANES (2003–2006) Cancer Causes and Control. 2010;21(2):283–288. [PubMed]
16. Owen N, Healy GN, Matthews CE, Dunstan DW. Too Much Sitting: The Population Health Science of Sedentary Behavior. Exercise & Sport Sciences Reviews. 2010;38(3):105–113. [PMC free article] [PubMed]
17. Thorp AA, Owen N, Neuhaus M, Dunstan DW. Sedentary Behaviors and Subsequent Health Outcomes in Adults: A Systematic Review of Longitudinal Studies, 1996–2011. American journal of preventive medicine. 2011;41(2):207–215. [PubMed]
18. Alfano CM, Smith AW, Irwin ML, et al. Physical activity, long-term symptoms, and physical health-related quality of life among breast cancer survivors: a prospective analysis. J Cancer Surviv. 2007 Jun;1(2):116–128. [PMC free article] [PubMed]
19. Smith AW, Alfano CM, Reeve BB, et al. Race/ethnicity, physical activity, and quality of life in breast cancer survivors. Cancer Epidemiol Biomarkers Prev. 2009 Feb;18(2):656–663. [PMC free article] [PubMed]
20. Lynch B, Cerin E, Owen N, Hawkes A, Aitken J. Television viewing time of colorectal cancer survivors is associated prospectively with quality of life. Cancer Causes and Control. 2011;22(8):1111–1120. [PubMed]
21. Irwin ML, McTiernan A, Bernstein L, et al. Physical activity levels among breast cancer survivors. Med Sci Sports Exerc. 2004 Sep;36(9):1484–1491. [PMC free article] [PubMed]
22. McTiernan A, Rajan KB, Tworoger SS, et al. Adiposity and sex hormones in postmenopausal breast cancer survivors. J Clin Oncol. 2003 May 15;21(10):1961–1966. [PMC free article] [PubMed]
23. Wayne SJ, Baumgartner K, Baumgartner RN, Bernstein L, Bowen DJ, Ballard-Barbash R. Diet quality is directly associated with quality of life in breast cancer survivors. Breast Cancer Research and Treatment. 2006;96(3):227–232. [PubMed]
24. Hays RD, Sherbourne CD, Mazel RM. The RAND 36-Item Health Survey 1.0. Health Econ. 1993 Oct;2(3):217–227. [PubMed]
25. Ware JE., Jr . The SF-36 Health Survey. In: Spilker B, editor. Quality of life and pharmacoeconomics in clinical trials. 2nd ed. Philadelphia: Lippincott-Raven; 1996. pp. 337–345.
26. Jacobsen PB, Jim HSL. Consideration of Quality of Life in Cancer Survivorship Research. Cancer Epidemiology Biomarkers & Prevention. 2011 Oct 1;20(10):2035–2041. 2011; [PubMed]
27. Nail LM. Fatigue in patients with cancer. Oncol Nurs Forum. 2002 Apr;29(3):537. [PubMed]
28. Piper BF, Dibble SL, Dodd MJ, Weiss MC, Slaughter RE, Paul SM. The revised Piper Fatigue Scale: psychometric evaluation in women with breast cancer. Oncol Nurs Forum. 1998 May;25(4):677–684. [PubMed]
29. Kriska A. Modifiable Activity Questionnaire. Medicine & Science in Sports & Exercise. 1997;29(No. 6) Supplement:73–78.
30. Ainsworth BE, Haskell WL, Whitt MC, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc. 2000 Sep;32(9 Suppl):S498–S504. [PubMed]
31. Irwin ML, Smith AW, McTiernan A, et al. Influence of Pre- and Postdiagnosis Physical Activity on Mortality in Breast Cancer Survivors: The Health, Eating, Activity, and Lifestyle Study. J Clin Oncol. 2008 Aug 20;26(24):3958–3964. 2008; [PMC free article] [PubMed]
32. U.S. Department of Health and Human Services. Physical Activity Guidelines for Americans: Be Active, Healthy, and Happy. Washington, DC: 2008.
33. Patterson RE, Kristal AR, Tinker LF, Carter RA, Bolton MP, Agurs-Collins T. Measurement Characteristics of the Women's Health Initiative Food Frequency Questionnaire. Annals of Epidemiology. 1999;9(3):178–187. [PubMed]
34. Guenther PM, Krebs-Smith SM, Reedy J, et al. Healthy Eating Index-2005. Beltsville, MD: Center for Nutrition Policy and Promotion, United States Department of Agriculture; 2008.
35. Guenther PM, Reedy J, Krebs-Smith SM. Development of the Healthy Eating Index-2005. J Am Diet Assoc. 2008 Nov;108(11):1896–1901. [PubMed]
36. Guenther PM, Reedy J, Krebs-Smith SM, Reeve BB. Evaluation of the Healthy Eating Index-2005. J Am Diet Assoc. 2008 Nov;108(11):1854–1864. [PubMed]
37. Guenther PM, Reedy J, Krebs-Smith SM, Reeve BB, Basiotis PP. Development and Evaluation of the Healthy Eating Index-2005: Technical Report. Center for Nutrition Policy and Promotion, US. Department of Agriculture; 2007.
38. U.S. Department of Health and Human Services and, U. S. Department of Agriculture. 6th Edition ed. Washington, DC: U.S. Government Printing Office; 2005. Dietary Guidelines for Americans, 2005.
39. Guyatt G, Walter S, Norman G. Measuring change over time: assessing the usefulness of evaluative instruments. J Chronic Dis. 1987;40(2):171–178. [PubMed]
40. Cohen J. A power primer. Psychol Bull. 1992 Jul;112(1):155–159. [PubMed]
41. Hays RD, Farivar SS, Liu H. Approaches and recommendations for estimating minimally important differences for health-related quality of life measures. COPD. 2005 Mar;2(1):63–67. [PubMed]
42. Kosinski M, Zhao SZ, Dedhiya S, Osterhaus JT, Ware JE., Jr Determining minimally important changes in generic and disease-specific health-related quality of life questionnaires in clinical trials of rheumatoid arthritis. Arthritis Rheum. 2000 Jul;43(7):1478–1487. [PubMed]
43. Balboa-Castillo T, Leon-Munoz LM, Graciani A, Rodriguez-Artalejo F, Guallar-Castillon P. Longitudinal association of physical activity and sedentary behavior during leisure time with health-related quality of life in community-dwelling older adults. Health Qual Life Outcomes. 2011;9:47. [PMC free article] [PubMed]
44. Sugiyama T, Healy GN, Dunstan DW, Salmon J, Owen N. Is television viewing time a marker of a broader pattern of sedentary behavior? Ann Behav Med. 2008 Apr;35(2):245–250. 2008. [PubMed]
45. Clark BK, Sugiyama T, Healy GN, Salmon J, Dunstan DW, Owen N. Validity and reliability of measures of television viewing time and other non-occupational sedentary behaviour of adults: a review. Obes Rev. 2009 Jul 8;10(1):7–16. 2008. [PubMed]
46. Bowen D, Alfano C, McGregor B, et al. Possible socioeconomic and ethnic disparities in quality of life in a cohort of breast cancer survivors. Breast Cancer Research and Treatment. 2007;106(1):85–95. [PMC free article] [PubMed]
47. Meeske K, Smith AW, Alfano CM, et al. Fatigue in breast cancer survivors two to five years post diagnosis: a HEAL Study report. Qual Life Res. 2007 Aug;16(6):947–960. [PubMed]
48. Lynch BM, Friedenreich CM, Winkler EA, et al. Associations of objectively assessed physical activity and sedentary time with biomarkers of breast cancer risk in postmenopausal women: findings from NHANES (2003–2006) Breast Cancer Res Treat. 2011 May 8; [PubMed]
49. Dunstan DW, Barr EL, Healy GN, et al. Television viewing time and mortality: the Australian Diabetes, Obesity and Lifestyle Study (AusDiab) Circulation. 2010 Jan 26;121(3):384–391. [PubMed]
50. Katzmarzyk PT, Church TS, Craig CL, Bouchard C. Sitting Time and Mortality from All Causes, Cardiovascular Disease, and Cancer. Medicine & Science in Sports & Exercise. 2009;41(5):998–1005. [PubMed]
51. Patel AV, Bernstein L, Deka A, et al. Leisure time spent sitting in relation to total mortality in a prospective cohort of US adults. Am J Epidemiol. 2010 Aug 15;172(4):419–429. [PMC free article] [PubMed]
52. Wijndaele K, Brage S, Besson H, et al. Television viewing time independently predicts all-cause and cardiovascular mortality: the EPIC Norfolk Study. Int. J. Epidemiol. 2010 Jun 23;2010 dyq105. [PubMed]