Sedentary (sitting) behaviors occupy a large proportion of adults’ waking hours (11). There is consistent epidemiological evidence for a detrimental association of overall and context-specific sedentary behaviors with all-cause and cardiovascular disease (CVD) mortality (6,16,36). A more modest association with cancer risk has also been observed, with evidence suggesting that sedentary time may increase mortality risk from certain types of cancer (including lung, colorectal, colon, endometrial, breast, and ovarian cancers) (6,32). Television (TV) viewing is the most prevalent leisure-time sedentary behavior in industrialized countries (4,16,30), and has been the context-specific type of sedentary behavior most consistently shown to be associated with increased all-cause, cancer, and CVD mortality risk (10,23,27,34). Though inherently confounded by other factors such as social disadvantage, snacking behavior and unemployment, TV viewing is characterized physiologically by lack of muscle contraction while sitting, and metabolically by low energy expenditure (≤1.5 METs), consistent with published definitions of sedentary behavior and inactivity (1,33).
Previous studies have reported overall sitting time and TV viewing time to be detrimentally associated with key inflammatory markers (C-reactive protein, fibrinogen, leptin, interleukin 6, and adiponectin) (17,18,32,40), providing a potential mechanistic link to increased risk of inflammation-related mortality such as cancer and CVD. A recent study also showed that excessive TV viewing was associated with a wide range of other noncancer and non-CVD causes of death, including several that have underlying inflammatory pathologies, such as diabetes, chronic obstructive pulmonary disease, Parkinson’s disease, influenza, and pneumonia (22). Support for an inflammatory component to the deleterious effects of prolonged sitting, such as that which typically accompanies TV viewing, can also be inferred from acute experimental studies, where uninterrupted sitting is associated with greater postprandial glucose and lipid excursions, compared with conditions in which sitting is regularly interrupted by light or moderate activities (9,12,29). This postprandial dysmetabolism can lead to oxidative stress and inflammation, which may increase disease risk and progression (39). Howard and colleagues also observed an increase in plasma fibrinogen (an important hemostatic and inflammatory marker) over 5 h of prolonged uninterrupted sitting, but not when sitting was regularly interrupted with brief bouts of light intensity activity (19). Moreover, breaks in sitting have been shown to acutely increase muscle expression of genes involved in anti-inflammatory and anti-oxidative pathways (e.g., N-methyltransferase and dynein light chain LC8-type 1), compared with prolonged sitting (24). Therefore, repeated bouts of prolonged sitting may initiate acute deleterious effects on inflammatory mediators and postprandial dysmetabolism, leading to the promotion or augmentation of chronic inflammation. These laboratory-based studies control for confounding factors and experimentally isolate the adverse impacts of sitting (involving lack of muscle contraction and low energy expenditure) on low-grade inflammation. In real-life settings, other behaviors such as snacking, often associated with time spent watching TV, may compound the adverse effects of prolonged sitting associated with TV viewing.
Associations of TV viewing and overall sitting time with cancer and CVD mortality have been previously studied, including in the Australian Diabetes, Obesity and Lifestyle Study (AusDiab) cohort (6,10). We examined the associations of TV viewing time (not including time when the TV was switched on but other activities were being undertaken concurrently) with inflammatory-related mortality not attributable to cancer or CVD in Australian adults participating in the AusDiab study. We hypothesized that high TV viewing time would increase the risk of mortality from diseases for which inflammation is a predominant pathophysiological factor. Because smoking is associated with inflammation and oxidative stress, we also repeated these analyses among those who reported never smoking.
Study design and population
AusDiab is a population-based study undertaken in 1999 to 2000 (wave 1, 11,247 respondents), with longitudinal follow-up in 2004 to 2005 (wave 2, 8798 respondents) and 2011–2012 (wave 3, 6186 respondents). The study was approved by the International Diabetes Institute ethics committee. All responders gave written informed consent to participate in the survey. The lower response rates in waves 2 and 3 do not impact on the main analyses in this study, because associations have been determined based on baseline risk factors and their impact on mortality outcomes. The study design, population, and measurement of cardiometabolic parameters have been previously described (14). Briefly, between 1999 and 2000, a stratified clustered sample was drawn from 42 randomly selected census districts, six in each of the states and in the Northern Territory of Australia. Of the 17,129 eligible households, 20,347 participants age ≥25 yr from the 11,479 households completed the household interview, and 11,247 adults (55.3%) also undertook a biomedical examination after an overnight fast (minimum 9 h).
TV viewing time
Total time spent watching TV or videos in the previous 7 d was collected as a continuous variable, using an interviewer-administered questionnaire (10,13). This did not include time when the TV was switched on but other activities (such as preparing a meal or doing other household chores) were being undertaken concurrently. The TV time measure has been shown to provide a reliable (intraclass correlation, 0.82; 95% confidence interval [CI], 0.75–0.87) and reasonably valid (criterion validity, 0.3) estimate of TV time among adults (31). Three categories of TV time (<2, ≥2 to <4, and ≥4 h·d−1) were also created based on previously identified associations with biomarkers of cardiometabolic risk and premature mortality (10). Baseline TV viewing time for the 1999–2000 AusDiab survey was used in the main analyses. Changes in TV time were calculated using data from waves 2 and 3. A sensitivity analysis excluded participants whose TV time changed by more than 2 h·d−1 at any time point, as changes in TV viewing behavior can modify mortality risk (25).
Demographic attributes, history of diabetes mellitus, smoking, highest level of educational attainment, level of household income, previous history of CVD (myocardial infarction, or stroke), and lipid medication use were assessed with interviewer-administered questionnaires. Never-smokers were defined as not currently smoking, and reporting smoking <100 cigarettes over their lifetime. Self-reported leisure time physical activity (LTPA) was measured using the Active Australia questionnaire, which asks respondents about their participation in predominantly leisure-time physical activities during the previous week (5). This measure has been shown to provide a reliable and valid estimate of LTPA among adults (7). Total energy intake, and energy intake from alcohol were assessed with a self-administered validated food frequency questionnaire (usual eating habits over the past 12 months) (20). Data were considered valid and included in the analysis if total energy intake was between 500 and 3500 kcal·d−1 for women, and 800 and 4000 kcal·d−1 for men. Diet quality was assessed with the dietary guideline index (DGI), which has been previously described (28). The DGI was developed to reflect Dietary Guidelines for Australian Adults. Indicators were based on the Australian Guide to Healthy Eating dietary guidelines, cut-points, and food groupings. Each of 13 items was scored from 0 to 10, where 10 indicated that a participant was meeting the recommendation or had optimal intake. Therefore, the diet score had a possible range of 0–130, with a higher score reflecting increased compliance with the dietary guidelines (28).
Oral glucose tolerance testing was performed, and categories of abnormal glucose metabolism were determined according to World Health Organization criteria (37). Fasting and 2-h plasma glucose, fasting serum triglycerides, total cholesterol and high-density lipoprotein cholesterol (HDL-C) levels were obtained by enzymatic methods, measured on an Olympus AU600 analyzer (Olympus Optical, Tokyo, Japan) at a central laboratory (10). Waist circumference and resting blood pressure were measured by trained personnel as reported previously (10). Hypertension was defined as treatment with blood pressure-lowering medication or blood pressure ≥140/90 mm Hg.
Ascertainment of mortality
Follow-up was to date of death or November 30, 2013. Mortality status and underlying contributory causes of death (International Classification of Diseases, 10th revision [ICD10]) were determined by linking the AusDiab cohort to the Australian National Death Index (NDI). Those who were not matched to the NDI were assumed to be alive. Deaths were classified according to their ICD10 codes (38); for CVD and cancer as described in (10); all other deaths were classified as inflammatory-related or noninflammatory-related. For inflammatory-related diseases, selected a priori where inflammation has been documented as a significant underlying cause: infectious diseases; endocrine, nutritional and metabolic diseases; nervous system disorders; respiratory system diseases; digestive system diseases; musculoskeletal and connective tissue disorders; and genitourinary diseases (as described previously) (3,21). Noncancer, non-CVD causes of death that were not included in the inflammatory category were: injury; acute organ failure; psychoses; substance abuse; unspecified immunodeficiency; chronic skin ulcer; secondary systemic amyloidosis; organ-limited amyloidosis; and vesicointestinal fistula (3,21).
For this analysis, we excluded those who reported at baseline that they had a previous history of CVD (coronary heart disease or stroke; n = 643). We further excluded those who were pregnant at baseline (n = 60); did not fast for ≥9 h (n = 25); had missing data on TV viewing time (n = 92); had missing data on leisure-time physical activity (LTPA; n = 108); extreme values for total energy intake (<500 or >3500 kcal·d−1 for women, and <800 and >4000 kcal·d−1 for men; n = 275 ); had missing data for one or more of the other variables under consideration (n = 1405); could not be matched to the Australian NDI or for which the cause of death was unknown (n = 21); 8933 participants remained in the main analysis.
For the analysis in never-smokers, only participants who at baseline reported no history of smoking (n = 4953) were included. Sensitivity analyses excluded those who died in the first 2 yr (n = 68 overall population, 6 deaths; n = 30 never-smokers, 3 deaths), and those who reported >2 h·d−1 changes in TV viewing habits over follow-up (n = 919 overall population, 14 deaths; n = 511 never-smokers, 7 deaths).
Cox proportional hazards models were used to estimate the hazard ratios (HR) and 95% CI of inflammatory (excluding cancer and CVD deaths) and noninflammatory-related mortality according to TV time as a continuous measure, and as a categorical variable (<2, ≥2 to <4, and ≥4 h·d−1). Assumptions required for proportional hazards were met. Model 1 adjusted for age and gender. Model 2 additionally adjusted for education ≥12 yr, Diet Quality Index, alcohol intake and smoking status. Model 3 additionally adjusted for waist circumference, level of household income (≥US $400 per week), hypertension (blood pressure ≥140/90 mm Hg, or antihypertensive medication use), total plasma cholesterol (mmol·L−1), HDL-C (mmol·L−1), serum triglycerides (mmol·L−1), lipid-lowering medication use, and glucose tolerance status (impaired fasting glucose, impaired glucose tolerance, undiagnosed diabetes mellitus, known diabetes mellitus according to 1999 World Health Organization criteria) (37). Model 4 additionally adjusted for LTPA. The interaction between baseline TV time and waist circumference, BMI, LTPA or smoking status was assessed by the likelihood ratio (LR) test of a model that contained the interaction term for TV time and the variable of interest, nested within a model not including the interaction term. Sensitivity analyses excluding participants who died in the first 2 yr, and for those who changed their TV viewing habits by more than 2 h·d−1 were undertaken separately. Participants with missing follow-up TV viewing time data for both waves 2 and 3 were excluded from the sensitivity analyses. Analyses were performed using Stata V.14 for Windows (StataCorp LP, Texas, United States of America).
Participant characteristics are shown in Table 1. Those who spent more time watching TV were older, less likely to have completed at least 12 yr of education, had lower household income, were more likely to be current or ex-smokers, more likely to have diabetes or hypertension, and had a more adverse cardiometabolic profile. High TV viewers also had a lower diet quality, and participated in less LTPA.
Over 117,835 person-years (median, 13.6 yr) of follow-up, 896 deaths occurred. Of these, 248 were attributable to CVD and 346 to cancer; additionally, 130 were classed as noncancer and non-CVD inflammatory-related (subsequently referred to as “inflammatory-related”) deaths according to previously published criteria (3,21); and, 172 were classed as noninflammatory-related. The top four World Health Organization ICD10 categories contributing to inflammatory-related mortality were consistent for both the overall population and never-smokers: diseases of the respiratory system (n = 58 and 21 deaths, respectively); diseases of the nervous system (n = 30 and 18 deaths, respectively); endocrine, nutritional and metabolic diseases (n = 18 and 14 deaths, respectively); and, diseases of the genitourinary system (n = 12 and 9 deaths, respectively) (see Table, Supplemental Digital Content 1, summary of cause of inflammatory-related death by World Health Organization ICD10 code classification, https://links.lww.com/MSS/A949).
Survival curves according to TV time category are presented in Figure 1. In the overall population, each additional hour per day of TV time was associated with a 12% greater risk of inflammatory-related mortality in model 3, additional adjustment for LTPA attenuated this effect to nonsignificance (model 4; Table 2). For categorical TV time, after adjusting for age and sex (model 1), we observed a 54% higher risk of inflammatory-related death in the 2 to <4 h·d−1 and twofold higher risk in the ≥4 h·d−1 TV time groups, compared with those watching <2 h·d−1. The risk for inflammatory-related death remained elevated for the ≥4 h·d−1 group in models 2, 3 and 4, compared with <2 h·d−1 of TV viewing time. Greater continuous or categorical TV time was not associated with risk of noninflammatory-related mortality in the overall population (Table 2).
For never-smokers, each additional hour per day of TV time was associated with 18% greater risk of inflammatory-related mortality in model 3, but was not significant in all other models (Table 2). For categories of TV time, never-smokers were at more than twofold greater risk of inflammatory-related death in the ≥2 to <4 h·d−1 and ≥4 h·d−1 TV time groups compared with <2 h·d−1 in models 1 and 2. However, the association for ≥4 h·d−1 of TV viewing time was attenuated for models 3 and 4. In contrast, each additional hour per day of TV time tended to be associated with reduced risk of noninflammatory-related mortality for never-smokers in all models (Table 2). Lower multivariate-adjusted categorical TV time HR were also observed for noninflammatory-related mortality in all models, for those watching ≥2 to <4 h·d−1 compared with <2 h·d−1. However, the association was less clear for those watching ≥4 h·d−1 compared with <2 h·d−1. The lack of power to definitively detect differences in the ≥4 h·d−1 group for never-smokers most likely reflects the small numbers included in the analysis, where few deaths occurred. Including the interaction of TV time and smoking status (LR χ2 (2) = 0.96, P = 0.62) in the model did not improve the goodness of fit for inflammatory-related mortality.
Stratifying by waist circumference (based on International Diabetes Federation criteria for central obesity; <94 cm for men and <80 cm for women) (2) revealed that the associations for TV viewing and inflammatory-related mortality were lower for those without central obesity (model 4: continuous TV time hours per day HR, 1.04; 95% CI, 0.82–1.32) compared with those with central obesity (HR, 1.11; 95% CI, 0.99–1.26), although the interaction was not significant (LR χ2(1) = 0.67, P = 0.18). In contrast, stratifying by BMI revealed that TV-inflammatory-related mortality associations were stronger for those who are normal weight (BMI <25 kg·m−2) (model 4: continuous TV time hours per day HR, 1.20; 95% CI, 0.96–1.50) compared with those who are overweight or obese (BMI ≥25 kg·m−2; HR, 1.11; 95% CI, 0.97–1.26), although the interaction was not significant (LR χ2(1) = 2.05, P = 0.15). Stratifying by physical activity showed that those who met the recommended physical activity guidelines (≥150 min·wk−1 moderate-to-vigorous intensity exercise) had a higher risk of inflammatory-related mortality (model 4: continuous TV time hours per day HR, 1.25; 95% CI, 1.00–1.57) than are those who did not meet the guidelines (HR, 1.06; 95% CI, 0.93–1.21), although the interaction was not significant (LR χ2(1) = 0.22, P = 0.64).
A sensitivity analysis excluding participants who died in the first 2 yr had minimal effect on the association of TV time with inflammatory and noninflammatory-related mortality in the overall population (68 excluded, 6 inflammatory deaths, 14 noninflammatory deaths). However, the associations were strengthened for inflammatory (model 4: continuous TV time hours per day HR, 1.19; 95% CI, 1.01–1.41) and noninflammatory-related mortality (HR, 0.80, 95% CI, 0.66–0.97) in never-smokers (30 excluded, 3 inflammatory deaths; 6 noninflammatory deaths). Excluding participants who changed their TV time habits by >2 h·d−1 strengthened the association of TV time with inflammatory-related mortality in the overall population (model 4: continuous TV time hours per day HR, 1.14; 95% CI, 1.02–1.29) and never-smokers (HR, 1.22; 95% CI, 1.02–1.47), but had minimal effect on noninflammatory-related mortality in the overall population (919 excluded, 14 inflammatory deaths; 10 noninflammatory deaths) and never-smokers (511 excluded, 7 inflammatory deaths; 6 noninflammatory deaths). A sensitivity analysis adjusting for baseline plasma fibrinogen (as a continuous variable) did not attenuate the association between TV time and inflammatory-related mortality.
We found higher TV viewing time to be associated with greater risk of inflammatory-related mortality (excluding cancer and CVD mortality). These associations remained even after accounting for several potential confounding factors including waist circumference, participation in LTPA, diet quality, energy intake, socioeconomic status, and concomitant conditions, including diabetes. Smoking is associated with chronic inflammation and oxidative stress, and these physiological processes might explain the elevated risk of inflammatory-related causes of death. However, we were able to minimize the potential effects of smoking on the relationships observed between higher TV viewing time and inflammatory deaths by repeated analyses in participants reporting having never smoked at baseline, and though the sample sizes were smaller, we observed similar associations for TV viewing time. Our findings provide additional support for chronic inflammatory processes playing a role in the increased mortality risks associated with high levels of TV viewing. Interestingly, our findings also suggest that even moderate amounts of TV viewing may be detrimental.
Inflammatory-related mortality accounted for a relatively large proportion (14.5%) of deaths in the current cohort, which is consistent with what has been previously reported (3,21). The diseases contributing the most to mortality included chronic lower respiratory diseases, influenza/pneumonia, Alzheimer’s disease, Parkinson disease, motor neuron disease, diabetes mellitus (type 1, type 2, and unspecified), and kidney disease. Our results confirm and expand on the findings of a recent study which investigated the association of TV viewing time and leading causes of death in the United States, several of which have a substantial inflammatory component (22).
Fibrinogen plays an important role in both coagulation and inflammation, and is mechanistically linked with diseases that have an inflammatory component. High TV viewing time has previously been shown to be associated with increased plasma fibrinogen in women, but not men, in the AusDiab cohort (18). In the current study, additionally adjusting for baseline fibrinogen levels did not attenuate the observed association of TV time with inflammatory-related mortality in the overall population. However, there are limitations to using only one inflammatory marker as an indication of overall inflammatory status, and it’s relevance to disease. This is exemplified by the small additional clinical value of adding single inflammatory variables to standard risk scores for predicting CVD, and it has been suggested that the use of multiple biomarkers may increase their predictive value (26). C-reactive protein is also used as a marker of general inflammatory status. Unfortunately this marker was not measured at baseline for AusDiab, and only in a subset of participants.
Due to the substantial inflammatory and oxidative stress stimulus associated with smoking, we performed a subanalysis in participants who reported never smoking. Never-smokers who watched ≥2 to <4 h·d−1 and ≥4 h·d−1 of TV were at higher risk of inflammatory-related mortality compared with those watching <2 h·d−1. However, this effect was attenuated with adjustment for multiple potential confounders in the high TV viewing group, possibly owing to the small number of deaths. Indeed, in sensitivity analyses excluding participants who died in the first 2 yr, or who reported a change of more than 2 h·d−1 in their TV viewing habits over the follow up period, we observed a greater risk of inflammatory-related mortality in high TV viewers compared with low TV viewers in the multivariate adjusted models. In contrast, higher TV time tended to be negatively associated with noninflammatory-related mortality in never-smokers. This was particularly evident in the ≥2 to <4 h·d−1 category (compared with <2 h·d−1 of TV time), where a robust 40% reduction in risk of noninflammatory mortality was observed across all models.
Adipose tissue is a major site of production of inflammatory mediators, and stratifying by waist circumference revealed that those who are centrally obese may be at higher risk of inflammatory-related mortality with increasing TV viewing time. In contrast, when stratifying by BMI, those who were normal weight tended to be at greater risk of inflammatory-related mortality in the presence of high TV viewing time, compared with those who were overweight. The difference in these two outcomes could reflect the differing contributions of these two measures of adiposity. Waist circumference reflects central or abdominal obesity, which is more reliably associated with disease risk, as opposed to BMI which reflects total adiposity (2). Also of note, when stratifying by level of physical activity, we observed that those who reported meeting the recommended exercise levels (≥150 min·wk−1 of moderate-to-vigorous physical activity) tended to be at greater risk of inflammatory-related mortality in the presence of high levels of TV viewing time, compared with their inactive counterparts. These results are counter-intuitive, as regular participation in exercise is thought to be anti-inflammatory. Indeed, it was recently shown in a harmonized meta-analysis of over 1 million men and women, that regular participation in increasing levels of physical activity incrementally attenuated the increased risk of all-cause and CVD mortality associated with high overall sitting, up to where very high levels (60–75 min·d−1) of moderate activity all-but eliminated the risks associated with total sitting time (15). In contrast, high activity levels attenuated, but did not eliminate, the increased mortality risk associated with high TV viewing time (15). The difference in effects between total sitting and TV viewing in this meta-analysis could be due to other factors associated with TV viewing, such as snacking, socioeconomic status and employment status. Considering the specific findings that we report, it could be that those who are regularly physically active might be more likely to sit for prolonged periods while watching TV, or partake in unhealthy snacking behaviors while doing so. However, it is worth noting that the confidence intervals in the stratified analyses for the current study overlap; therefore, further exploration in larger data sets is warranted.
Strengths of our study include the conservative approach in models 3 and 4, adjusting for several factors that may be mediators (rather than confounders) in the pathway between inflammation and mortality. Limitations include the assessment of a single self-reported sedentary behavior (TV viewing time), which has been reported to only weakly correlate with objectively measured total sitting time, and therefore may not be generalizable to overall sedentary behavior patterns (8). Although we adjusted for several potential confounders, it is possible that other unmeasured or unknown confounding factors may have accounted for the associations reported. Furthermore, although we controlled for dietary and socioeconomic variables, these can be poorly captured and may represent a source of residual confounding; therefore, the results should be appropriately interpreted, with consideration of the potential influence of other deleterious behaviors known to be associated with TV viewing, in addition to prolonged sitting time. However, TV viewing is the most prevalent discretionary sedentary behavior, and both in the context of prolonged sitting and other behaviors, reducing TV viewing remains an important behavioral target for intervention. Reverse causality, whereby diagnosed or undiagnosed illness at study induction may have been responsible for elevated TV viewing time cannot be ruled out. Self-reported cancer was not measured at baseline, therefore, these participants could not be excluded from the analyses. However, we excluded those who reported a previous history of CVD, and adjusted for baseline health status in our models. We also performed a sensitivity analysis excluding any deaths that occurred in the first 2 yr after baseline assessments, which did not substantially affect the results. The small number of deaths for the never-smokers cohort, particularly in the high TV viewing time category, limit the ability to interpret our findings from this group. Indeed, adjustment for multiple potential confounders in models 3 and 4 attenuated the association of TV time with inflammatory-related mortality in this group. Despite this, robust associations were observed for never-smokers in the moderate TV viewing category, and the continuous and categorical TV time associations were strengthened when excluding participants who died in the first 2 yr, and who changed their TV viewing habits by >2 h·d−1 over the follow-up period.
TV viewing time was found to be associated with a higher risk of noncancer and non-CVD inflammatory-related mortality, both in the overall study population and in never-smokers. Our results suggest that even healthy, active individuals may benefit from reducing their TV viewing time. Further research is needed to understand the relationships between TV viewing, its associated behaviors and contexts (e.g., sitting, snacking, socioeconomic status and employment status) and inflammation, and the potential effects on health and mortality.
This work was supported by a National Health and Medical Research Council (NHMRC) project grant (NHMRC 233200) and by in-kind support from the Australian Institute of Health and Welfare, which collected the mortality data. In addition, the AusDiab study has received financial support from the Australian Government Department of Health and Ageing; Abbott Australasia; Alphapharm; AstraZeneca; Aventis Pharma; Bio-Rad Laboratories; Bristol-Myers Squibb; City Health Centre Diabetes Service Canberra; Department of Health and Community Services Northern Territory; Department of Health and Human Services Tasmania; Department of Health New South Wales; Department of Health Western Australia; Department of Human Services South Australia; Department of Human Services Victoria; Diabetes Australia; Diabetes Australia Northern Territory; Eli Lilly Australia; Estate of the Late Edward Wilson; GlaxoSmith-Kline; Highpoint Shopping Centre; Jack Brockhoff Foundation; Janssen-Cilag; Kidney Health Australia; Marian & EH Flack Trust; Menzies Research Institute; Merck Sharp & Dohme; Multiplex; Novartis Pharmaceuticals; Novo Nordisk Pharmaceuticals; Pfizer Pty Ltd; Pratt Foundation; Queensland Health; Roche Diagnostics Australia; Royal Prince Alfred Hospital Sydney; and Sanofi-Synthelabo. M. G. is supported by a Marian and EH Flack Trust Fellowship; N. O. by a NHMRC Senior Principal Research Fellowship (APP1003960) and NHMRC Centre of Research Excellence Grant (APP1057608) and the Victorian Government OIS scheme; D. D. by a NHMRC Senior Research Fellowship (NHMRC 1078360) and the Victorian Government’s Operational Infrastructure Support Program; and S. K. by the Intramural Research Program at the National Cancer Institute in the United States.
The authors have no conflict of interest to declare. The results of the present study do not constitute endorsement by the American College of Sports Medicine. The results are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.
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