https://orcid.org/0000-0002-5523-5435
ABSTRACT
We used logistic regression to investigate the joint associations of physical activity level (high: ≥3000 MET-min/week, moderate: ≥600 MET-min/week, low: not meeting either criteria) and TV viewing time (low: ≤1 h/day, moderate: 2–3 h/day, high: ≥4 h/day) with COVID-19 mortality risk in UK Biobank. Additional models were performed with adjustment for body mass index (BMI) and waist circumference. Within the 373, 523 included participants, there were 940 COVID-19 deaths between 16 March 2020 and 12 November 2021. Compared to highly active individuals with a low TV viewing time, highly active individuals with a high TV viewing time were at significantly higher risk of COVID-19 mortality (odds ratio = 1.54, 95% confidence interval = 1.11–2.15). However, the greatest risk was observed for the combination of a low physical activity level and a high TV viewing time (2.29, 1.63–3.21). After adjusting for either BMI or waist circumference, only this latter combination remained at a significantly higher risk, although the effect estimate was attenuated by 43% and 48%, respectively. In sum, a high TV viewing time may be a risk factor for COVID-19 mortality even amongst highly active individuals. Higher adiposity appears to partly explain the elevated risk associated with a low physical activity level and a high TV viewing time.
Introduction
Physical inactivity has emerged as a key risk factor for severe COVID-19 (Rowlands et al., Citation2021). Sallis et al. reported physical inactivity to be a stronger risk factor for COVID-19 hospitalisation and mortality than other established modifiable risk factors such as smoking, obesity and cardiometabolic disease (Sallis et al., Citation2021). Although there is a strong evidence base to support physical activity for protection against COVID-19, less is known about the role of sedentary behaviour. Sedentary behaviour is characterised by a low energy expenditure (i.e., a metabolic equivalent of task value (MET) of <1.5) in a sitting or reclined posture during waking hours (Tremblay et al., Citation2017). Television (TV) viewing is a major contributor to leisure sedentary time (Hamer et al., Citation2017). A multitude of studies suggest that independent of physical activity, a high TV viewing time is a significant risk factor for noncommunicable diseases, such as cardiovascular diseases and type II diabetes mellitus (Grøntved & Hu, Citation2011; FB Hu et al., Citation2001)
Although a Mendelian randomisation study indicated that both physical activity and TV viewing may exert causal associations with COVID-19 severity (Chen et al., Citation2022), the current available evidence does not provide insight into how different combinations of these factors may affect risk of severe COVID-19. Research on all-cause mortality suggests that a high physical activity level can attenuate the adverse effects of a high TV viewing time, and that a high TV viewing time may exacerbate the health risks associated with a low physical activity level (Ekelund et al., Citation2016). Whether the same patterns may be observed in the context of COVID-19 is yet to be determined. Furthermore, although the protective association for physical activity against COVID-19 has been shown to be independent of body mass index (BMI),(Hamrouni et al., Citation2021) a proxy for general adiposity, it is unknown whether the same holds true for central adiposity (commonly measured by waist circumference). Determining this is warranted, as a beneficial effect on not just the quantity but the distribution of fat (i.e., reduced visceral fat mass) is proposed to be one of the main mechanisms through which physical activity exerts health benefits (Gleeson et al., Citation2011). Similarly, the extent to which the potential association between TV viewing time and severe COVID-19 may be explained by central adiposity is unknown.
Though the peak of COVID-19 may have passed, the pandemic is not over. Moreover, respiratory viruses with the potential to cause pandemics will continue to arise, making it imperative to learn as much as possible about how modifiable factors may influence our risk of severe COVID-19. The primary aim of this study was to examine the joint associations of physical activity and TV viewing time with COVID-19 mortality risk. A secondary aim of this study was to explore whether and to what extent general and/or central adiposity may explain the associations of physical activity and TV viewing time with COVID-19 mortality.
Methods
Data were provided by UK Biobank, a prospective cohort of more than 500,000 individuals aged 37–73, with baseline assessments being undertaken between 2006 and 2010. The explanatory and confounder variables were assessed at baseline, and the response variable – COVID-19 mortality – was obtained via linkage of the cohort with national death registries. The analysis period for this study covers 16 March 2020 to 12 November 2021. UK Biobank obtained ethical approval from the North West Multi-Centre Research Ethics Committee. All participants gave written informed consent before enrolment in the study. Study funders had no influence over data collection, analysis and/or interpretation or in article preparation.
Participant characteristics
With the exception of current age, participant demographics were assessed at baseline assessment. The current age was estimated as the age on 1 March 2020. Height was measured using a wall-mounted SECA 240 height measure and weight with a Tanita BC 418 body composition analyser at the baseline assessment. BMI was calculated from weight in kilograms divided by height squared (kg/m2). Waist circumference was measured around the midway point between the lower costal margin and the iliac crest after normal expiration. Sex was determined via NHS records at recruitment. At the baseline assessment, participants also completed a touchscreen questionnaire to determine ethnicity, smoking status and alcohol intake frequency. Townsend Index was used as a measure of socioeconomic deprivation and was determined using national census output for each participant’s postcode at the time of recruitment.
Co-morbidities
Co-morbidities were identified through a verbal interview by a trained nurse at baseline. The co-morbidities assessed in the current study included cardiovascular diseases (myocardial infarction, heart failure, angina, stroke), respiratory diseases (asthma, chronic obstructive pulmonary disease – i.e., emphysema and chronic bronchitis), diabetes and cancer. Disease groupings were treated as dichotomous variables for analysis.
Physical activity level and TV viewing
At baseline, the International Physical Activity Questionnaire (IPAQ) short form, a validated survey instrument (Craig et al., Citation2003) was used to classify physical activity level into high: ≥3000 MET-min/week, moderate: ≥600 MET-min/week and low: not meeting either of the aforementioned criteria. Further details on the IPAQ short form can be found in the data processing guidelines developed by the IPAQ Research Committee (IPAQ Research Committee, Citation2005). As part of the touch-screen questionnaire at baseline, participants were asked how many hours they spend watching TV on a typical day. TV viewing time was categorised as low (≤1 h/day), moderate (2–3 h/day) and high (≥4 h/day) as per previous research on the UK Biobank cohort (Morris et al., Citation2018).
COVID-19 mortality data
Mortality from COVID-19 was determined from the presence of ICD-10 codes U071 (virus identified in laboratory testing) or U072 (clinical or epidemiological diagnosis) as the primary or contributory cause of death.
Statistical analysis
Participant demographics were presented as median and interquartile range for continuous and discrete variables (due to non-Gaussian distribution). Categorical variables were presented as number and percentage. Separate logistic regressions were undertaken to identify the associations of physical activity level and TV viewing time category with COVID-19 mortality. These associations were explored in a series of models, whereby we initially adjusted for potential confounders (model 1), followed by mutual adjustment for physical activity and TV viewing time (model 2). As adiposity may link physical inactivity/sedentary behaviour with adverse health outcomes, we added additional models with further adjustment for BMI (model 3) and waist circumference (model 4). The attenuation of the effect estimates upon including either BMI or waist circumference into the regression model was used to determine the degree to which general and central adiposity may explain the associations of physical activity level and TV viewing time category with COVID-19 mortality risk and was presented to supplement important findings. Attenuation was assessed by percentage change, whereby the change in value was divided by the original value and then multiplied by 100 (using odds ratios expressed as percentages; Hamer, Gale et al., Citation2020; Hamrouni et al., Citation2021). We then assessed the joint associations of physical activity level and TV viewing time category (yielding nine groups) with COVID-19 mortality. For this analysis, we first adjusted for potential confounders (model 1) and then further adjusted for BMI (model 2) and waist circumference (model 3). Confounders were selected a priori based on the available literature and included current age, sex, ethnicity, smoking status, alcohol intake frequency, Townsend Index, cardiovascular disease, pulmonary disease, diabetes and cancer. Results from the logistic regression models were presented as odds ratios with 95% confidence intervals. Statistical significance was accepted at p < 0.05 (which corresponds to 95% confidence intervals not crossing 1.00). All statistical analyses were conducted using R (R Core Team, Vienna, Austria).
Results
After excluding participants lost to follow-up, deceased as of 16 March 2020, or with missing data, our final analytical sample was composed of373523individuals. A participant flow diagram is presented in . Within our sample, there were 940 COVID-19 deaths between 16 March 2020 and 12 November 2021. Participant demographics are presented in . Participants who died from COVID-19 were more likely to be older, male, non-white, current or former smokers, more socioeconomically deprived, as well as have a higher BMI and waist circumference, a low physical activity level and a high TV viewing time. Individuals who died from COVID-19 also had a higher prevalence of the included co-morbidities. The number of participants with missing data for each explanatory/confounder variable can be found in Supplementary Table 1. A notable number of participants had missing physical activity data (n = 92 884). The participant demographics of the final analytical sample vs. those with missing physical activity data are presented in Supplementary Table 2. The demographics between these groups were mostly similar, but there was a higher proportion of females and individuals with a high TV viewing time amongst those with missing physical activity data. For all other variables, missing data were minimal (≤1%).
Figure 1. Participant flow diagram.
Table 1. Participant demographics for individuals who did and did not die from COVID-19.
Individual associations of physical activity level and TV viewing time with COVID-19
The individual associations of physical activity level and TV viewing time category are presented in . In model 1, individuals with a high TV viewing time had a significantly greater risk of mortality from COVID-19, but those with a moderate TV viewing time did not. The significantly higher risk persisted following adjustment for physical activity (model 2). Additional adjustment for BMI (model 3) and waist circumference (model 4) both attenuated the effect estimates by 48%, but the association between a high TV viewing time and COVID-19 mortality remained significant. In model 1, a low physical activity level was associated with a significantly higher risk of COVID-19 mortality, whereas a moderate physical activity level was not. Individuals with a low physical activity level were still at a significantly higher risk following adjustment for TV viewing time (model 2), and further adjustment for BMI (model 3) and waist circumference (model 4). Notably, however, adjusting for BMI and waist circumference attenuated the effect estimate by 36% and 48%, respectively.
Table 2. Associations of TV viewing time category and physical activity level with COVID-19 mortality.
Joint associations of physical activity level and TV viewing time COVID-19 mortality
The results of the joint physical activity level-TV viewing category analysis are presented in . In model 1 (no adjustment for BMI or waist circumference), a high TV viewing time was associated with a significantly higher risk of COVID-19 mortality regardless of physical activity level. The combination of a low physical activity level and a high TV viewing time was associated with the highest risk of COVID-19 mortality. In model 2 and model 3 (additional adjustment for BMI and waist circumference, respectively), only the combination of a low physical activity level and a high TV viewing time was associated with a significantly higher risk of COVID-19 mortality. However, within each physical activity level, the highest risk estimate was still consistently observed in individuals with a high TV viewing time. When compared with model 1, the effect estimate for the combination of a low physical activity level and a high TV viewing time was attenuated by 43% following BMI adjustment and 48% following adjustment for waist circumference.
Figure 2. Odds ratios (95% CI) for COVID-19 mortality across the different physical activity levels and TV viewing time category combinations.
Model 1: adjusted for current age, sex, ethnicity, smoking status, alcohol intake frequency, Townsend Index, cardiovascular disease, respiratory disease, diabetes and cancer.
Model 2: model 1 plus BMI.
Model 3: model 1 plus waist circumference.
CI, confidence interval; BMI, body mass index.
Discussion
This analysis yielded several novel findings. Across all physical activity levels, a high TV viewing time was associated with a greater risk of COVID-19 mortality. The risk observed for the combination of a low physical activity level and a high TV viewing time was particularly pronounced and corresponded to a greater than two-fold higher risk when compared to the highly active, low TV viewing time reference group. Moreover, we found that the individual and combined associations of a low physical activity level and a high TV viewing time with COVID-19 mortality were largely explained by higher levels of adiposity.
Our results support the current promotion of physical activity as a means to protect individuals from severe COVID-19, but further indicate that a high TV viewing time requires consideration as a distinct risk factor. Indeed, we found that highly active individuals with a high TV viewing time were still at significantly higher risk of COVID-19 mortality compared to highly active individuals with a low TV viewing time. However, the elevated risk was attenuated and no longer significant following adjustment for BMI and waist circumference, suggesting higher levels of adiposity in such individuals may have largely accounted for the association.
Based on attenuation of the effect estimates after adjusting for BMI and waist circumference, the findings herein suggest adiposity partly explains the higher risk of COVID-19 mortality associated with physical inactivity and a high TV viewing time. Adipose tissue has been suggested to be a key reservoir for viral replication, leukocyte activation and recruitment and pro-inflammatory cytokine production, which in turn propagates COVID-19 severity (Ryan & Caplice, Citation2020). For a low physical activity level in particular, we found that adjusting for waist circumference led to a greater degree of attenuation than BMI in the risk of COVID-19 mortality. Waist circumference is routinely used as a proxy measure for visceral fat, which is known to drive metabolic dysregulation and systemic inflammation to a greater extent than subcutaneous fat (Hotamisligil, Citation2006). Taking together the fact that these are key determinants of COVID-19 severity and that higher physical activity levels preferentially reduce visceral fat (Whitaker et al., Citation2017), this may explain why waist circumference explained the risk associated with a low physical activity level to a greater extent than BMI.
The individual and combined associations of a low physical activity level and a high TV viewing time with higher risk of COVID-19 mortality remained significant following adjustment for our measures of adiposity, suggesting additional mechanisms may underpin these associations. Independent of adiposity, higher levels of physical activity beneficially modulate several inflammatory and cardiometabolic risk factors for COVID-19. For example, physical activity exhibits favourable associations with circulating concentrations of C-reactive protein, interleukin-6, high-density lipoprotein-cholesterol and glucose (Hawkins et al., Citation2012; X Hu et al., Citation2019; Mansikkaniemi et al., Citation2012; Vella et al., Citation2017); pre-pandemic levels of all of which have been shown to predict severe disease and/or mortality from COVID-19 (Hamer, Kivimäki et al., Citation2020; Morys & Dagher, Citation2021; Silberstein, Citation2020). Additionally, TV viewing time has recently been shown to be an independent risk factor for chronic obstructive pulmonary disease mortality (Ukawa et al., Citation2015). Although mechanistic studies are needed to explain the emerging role of sedentary behaviour in respiratory disease pathophysiology, inflammation has been suggested to underpin this relationship (Dogra et al., Citation2018). Therefore, to further explain our findings, impaired pulmonary function driven by inflammation amongst individuals with a high TV viewing time may make them more vulnerable to severe disease and mortality from COVID-19.
This study has several limitations. First, as our exposure and confounder variables were measured between 2006 and 2010 (baseline assessment), over a decade prior to the COVID-19 pandemic, these variables were subject to change in the interim. Whilst BMI has been reported to track well over time (Sattar et al., Citation2020) and Smith et al. found reasonable stability in physical activity levels over a 10-year period in older adults (Smith et al., Citation2015), future research is warranted to confirm the present findings with more recent assessment of the analysed exposure variables. Furthermore, as this is an observational study, we cannot dismiss the risk of residual confounding from unmeasured covariates. Another limitation is that the classifications of physical activity and TV viewing time were based on self-report data, which are liable to recall and social desirability bias. Due to the nature of the questionnaires, the TV viewing time data reflect a typical day for each participant, whereas the physical activity data only reflect the last 7 days, and as such the latter may not be as reflective of their typical patterns. When interpreting the present results, it is also important to note that we observed a high number of participants with missing physical activity data, which may reduce statistical power and bias our findings towards the null. Furthermore, participants in the UK Biobank are of predominately White European ancestry, and the current findings may therefore be less generalisable to different cultures and ethnic backgrounds. The UK Biobank cohort is also healthier and less socioeconomically deprived than the general population (Fry et al., Citation2017). However, whilst this means the study sample may not be representative, this likely does not affect the identification of disease risk factors (Batty et al., Citation2020).
Importantly, the implications of this study may extend beyond the current pandemic. There are many similarities in risk factors and pathophysiology between severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS) and COVID-19 (T Hu et al., Citation2020; Lu et al., Citation2020). It is therefore plausible that continuing to identify risk factors for COVID-19 will allow us to be more prepared for future respiratory disease outbreaks. Even before COVID-19, the global population was gradually becoming less physically active and more sedentary, and the current pandemic may have been a catalyst to exacerbate this trend (Stockwell et al., Citation2021). Given the significant health risks associated with these behaviours, particularly in the context of COVID-19 – as shown in this study – greater public health efforts promoting not just physical activity but also less time spent sedentary are needed to reverse this direction. Highlighting TV viewing time as a distinct risk factor is therefore of particular importance, as many may believe a high physical activity level can offset or even eliminate the adverse health effects of TV viewing and other sedentary behaviours based on previous research (Ekelund et al., Citation2016). However, in the context of COVID-19 mortality risk, our results suggest otherwise.
To summarise, this is the first study to explore the joint associations of physical activity level and TV viewing time with COVID-19 mortality risk. When assessed separately, a low physical activity level and a high TV viewing time were associated with a higher risk of COVID-19 mortality. Although the elevated risks remained significant following adjustment for BMI and waist circumference, the attenuation of the effect estimates suggested that higher levels of adiposity may partly mediate these associations. Regarding the joint analyses, we found that highly active individuals with a high TV viewing time were at greater risk of COVID-19 mortality compared to highly active individuals with a low TV viewing time, but that the elevated risk was attenuated (and no longer significant) following adjustment for BMI and waist circumference. Furthermore, the risk associated with the combination of a low physical activity level and a high TV viewing time was striking and persisted following adjustment for measures of adiposity. Based on the current findings, in addition to continuing to promote physical activity, greater efforts are needed to raise awareness on the emerging role of TV viewing time as a risk factor for COVID-19 mortality, especially for individuals with higher levels of adiposity.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
Data from UK Biobank are not publicly available but may be provided upon reasonable request through a research proposal (https://www.ukbiobank.ac.uk).
Additional information
Funding
References
- Batty, G. D., Gale, C. R., Kivimäki, M., Deary, I. J., & Bell, S. (2020). Comparison of risk factor associations in UK Biobank against representative, general population based studies with conventional response rates: Prospective cohort study and individual participant meta-analysis. British Medical Journal, 368, m131. https://doi.org/10.1136/bmj.m131
- Chen, X., Hong, X., Gao, W., Luo, S., Cai, J., Liu, G., & Huang, Y. (2022). Causal relationship between physical activity, leisure sedentary behaviors and COVID-19 risk: A Mendelian randomization study. Journal of Translational Medicine, 20(1), 216. https://doi.org/10.1186/s12967-022-03407-6
- Craig, C. L., Marshall, A. L., Sjöström, M., BAUMAN, A. E., Booth, M. L., Ainsworth, B. E., Pratt, M., EKELUND, U., Yngve, A., SALLIS, J. F., & Oja, P. (2003). International physical activity questionnaire: 12-country reliability and validity. Medicine & Science in Sports & Exercise, 35(8), 1381–1395. https://doi.org/10.1249/01.MSS.0000078924.61453.FB
- Dogra, S., Good, J., Buman, M. P., Gardiner, P. A., Copeland, J. L., & Stickland, M. K. (2018). Physical activity and sedentary time are related to clinically relevant health outcomes among adults with obstructive lung disease. BMC Pulmonary Medicine, 18(1), 98. https://doi.org/10.1186/s12890-018-0659-8
- Ekelund, U., Steene-Johannessen, J., Brown, W. J., Fagerland, M. W., Owen, N., Powell, K. E., Bauman, A., & Lee, I.-M. (2016). Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women. The Lancet, 388(10051), 1302–1310. https://doi.org/10.1016/S0140-6736(16)30370-1
- Fry, A., Littlejohns, T. J., Sudlow, C., Doherty, N., Adamska, L., Sprosen, T., Collins, R., & Allen, N.E. (2017). Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. American Journal of Epidemiology, 186(9), 1026–1034. https://doi.org/10.1093/aje/kwx246
- Gleeson, M., Bishop, N. C., Stensel, D. J., Lindley, M. R., Mastana, S. S., & Nimmo, M. A. (2011). The anti-inflammatory effects of exercise: Mechanisms and implications for the prevention and treatment of disease. Nature Reviews Immunology, 11(9), 607–615. https://doi.org/10.1038/nri3041
- Grøntved, A., & Hu, F. B. (2011). Television viewing and risk of type 2 diabetes, cardiovascular disease, and all-cause mortality A meta-analysis. JAMA, 305(23), 2448–2455. https://doi.org/10.1001/jama.2011.812
- Hamer, M., Gale, C. R., Kivimäki, M., & Batty, G. D. (2020). Overweight, obesity, and risk of hospitalization for COVID-19: A community-based cohort study of adults in the United Kingdom. Proceedings of the National Academy of Sciences, 117(35), 21011–21013. https://doi.org/10.1073/pnas.2011086117
- Hamer, M., Kivimäki, M., Gale, C. R., & Batty, G. D. (2020). Lifestyle risk factors, inflammatory mechanisms, and COVID-19 hospitalization: A community-based cohort study of 387,109 adults in UK. Brain, Behavior, and Immunity, 87, 184–187. https://doi.org/10.1016/j.bbi.2020.05.059
- Hamer, M., Yates, T., & Demakakos, P. (2017). Television viewing and risk of mortality: Exploring the biological plausibility. Atherosclerosis, 263, 151–155. https://doi.org/10.1016/j.atherosclerosis.2017.06.024
- Hamrouni, M., Roberts, M. J., Thackray, A., Stensel, D. J., & Bishop, N. (2021). Associations of obesity, physical activity level, inflammation and cardiometabolic health with COVID-19 mortality: A prospective analysis of the UK Biobank cohort. BMJ Open, 11(11), e055003. https://doi.org/10.1136/bmjopen-2021-055003
- Hawkins, M., Belalcazar, L. M., Schelbert, K. B., Richardson, C., Ballantyne, C. M., & Kriska, A. (2012). The effect of various intensities of physical activity and chronic inflammation in men and women by diabetes status in a national sample. Diabetes Research and Clinical Practice, 97(1), e6–e8. https://doi.org/10.1016/j.diabres.2012.03.019
- Hotamisligil, G. S. (2006). Inflammation and metabolic disorders. Nature, 444(7121), 860–867. https://doi.org/10.1038/nature05485
- Hu, F. B., Leitzmann, M. F., Stampfer, M. J., Colditz, G. A., Willett, W. C., & Rimm, E. B. (2001). Physical activity and television watching in relation to risk for type 2 diabetes mellitus in men. Archives of Internal Medicine, 161(12), 1542–1548. https://doi.org/10.1001/archinte.161.12.1542
- Hu, T., Liu, Y., Zhao, M., Zhuang, Q., Xu, L., & He, Q. (2020). A comparison of COVID-19, SARS and MERS. PeerJ, 8, e9725. https://doi.org/10.7717/peerj.9725
- Hu, X., Yu, W., Yang, L., Pan, W., Si, Q., Chen, X., Li, Q., & Gu, X. (2019). Inverse association between physical activity and blood glucose is independent of sex, menopause status and first-degree family history of diabetes. Journal of Diabetes Investigation, 10(6), 1502–1509. https://doi.org/10.1111/jdi.13062
- IPAQ Research Committee.(2005) Guidelines for data processing and analysis of the international physical activity questionnaire (IPAQ)-short and long forms. https://biobank.ndph.ox.ac.uk/showcase/ukb/docs/ipaq_analysis.pdf. Published online 2005
- Lu, L., Zhong, W., Bian, Z., Li, Z., Zhang, K., Liang, B., Zhong, Y., Hu, M., Lin, L., Liu, J., Lin, X., Huang, Y., Jiang, J., Yang, X., Zhang, X., & Huang, Z. (2020). A comparison of mortality-related risk factors of COVID-19, SARS, and MERS: A systematic review and meta-analysis. Journal of Infection, 81(4), e18–e25. https://doi.org/10.1016/j.jinf.2020.07.002
- Mansikkaniemi, K., Juonala, M., Taimela, S., Hirvensalo, M., Telama, R., Huupponen, R., Saarikoski, L., Hurme, M., Mallat, Z., Benessiano, J., Jula, A., Taittonen, L., Marniemi, J., Kähönen, M., Lehtimäki, T., Rönnemaa, T., Viikari, J., & Raitakari, O. T. (2012). Cross-sectional associations between physical activity and selected coronary heart disease risk factors in young adults. The cardiovascular risk in young finns study. Annals of Medicine, 44(7), 733–744. https://doi.org/10.3109/07853890.2011.590146
- Morris, J. S., Bradbury, K. E., Cross, A. J., Gunter, M. J., & Murphy, N. (2018). Physical activity, sedentary behaviour and colorectal cancer risk in the UK Biobank. British Journal of Cancer, 118(6), 920–929. https://doi.org/10.1038/bjc.2017.496
- Morys, F., & Dagher, A. (2021). Poor metabolic health increases COVID-19-Related mortality in the UK Biobank sample. Front Endocrinol, 12. Accessed April 8, 2022 https://www.frontiersin.org/article/10.3389/fendo.2021.652765
- Rowlands, A. V., Dempsey, P. C., Gillies, C., Kloecker, D. E., Razieh, C., Chudasama, Y., Islam, N., Zaccardi, F., Lawson, C., Norris, T., Davies, M. J., Khunti, K., & Yates, T. (2021). Association between accelerometer-assessed physical activity and severity of COVID-19 in UK Biobank. Mayo Clinic Proceedings: Innovations, Quality & Outcomes, 5(6), 997–1007. https://doi.org/10.1016/j.mayocpiqo.2021.08.011
- Ryan, P. M., & Caplice, N. M. (2020). Is adipose tissue a reservoir for viral spread, immune activation, and cytokine amplification in coronavirus disease 2019? Obes Silver Spring Md, 28(7), 1191–1194. https://doi.org/10.1002/oby.22843
- Sallis, R., Young, D. R., Tartof, S. Y., Sallis, J. F., Sall, J., Li, Q., Smith, G. N., & Cohen, D. A. (2021). Physical inactivity is associated with a higher risk for severe COVID-19 outcomes: A study in 48 440 adult patients. British Journal of Sports Medicine, 55(19), 1099–1105. https://doi.org/10.1136/bjsports-2021-104080
- Sattar, N., Ho, F. K., Gill, J. M. R., Ghouri, N., Gray, S. R., Celis-Morales, C. A., Katikireddi, S. V., Berry, C., Pell, J. P., McMurray, J. J., & Welsh, P. (2020). BMI and future risk for COVID-19 infection and death across sex, age and ethnicity: Preliminary findings from UK biobank. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(5), 1149–1151. https://doi.org/10.1016/j.dsx.2020.06.060
- Silberstein, M. (2020). Correlation between premorbid IL-6 levels and COVID-19 mortality: Potential role for vitamin D. International Immunopharmacology, 88, 106995. https://doi.org/10.1016/j.intimp.2020.106995
- Smith, L., Gardner, B., Fisher, A., & Hamer, M. (2015). Patterns and correlates of physical activity behaviour over 10 years in older adults: Prospective analyses from the English longitudinal study of ageing. BMJ Open, 5(4), e007423. https://doi.org/10.1136/bmjopen-2014-007423
- Stockwell, S., Trott, M., Tully, M., Shin, J., Barnett, Y., Butler, L., McDermott, D., Schuch, F., & Smith, L. (2021). Changes in physical activity and sedentary behaviours from before to during the COVID-19 pandemic lockdown: A systematic review. BMJ Open Sport & Exercise Medicine, 7(1), e000960. https://doi.org/10.1136/bmjsem-2020-000960
- Tremblay, M. S., Aubert, S., Barnes, J. D., Chastin, S. F. M., Altenburg, T. M., & Chinapaw, M. J. M. (2017). Sedentary behavior research network (SBRN) – terminology consensus project process and outcome. International Immunopharmacology, 14(1), 75. https://doi.org/10.1186/s12966-017-0525-8
- Ukawa, S., Tamakoshi, A., Yatsuya, H., Yamagishi, K., Ando, M., & Iso, H. (2015). Association between average daily television viewing time and chronic obstructive pulmonary disease-related mortality: Findings from the japan collaborative cohort study. Journal of Epidemiology, 25(6), 431–436. https://doi.org/10.2188/jea.JE20140185
- Vella, C. A., Allison, M. A., Cushman, M., Jenny, N. S., Miles, M. P., Larsen, B., Lakoski, S. G., Michos, E. D., & Blaha, M. J. (2017). Physical activity and adiposity-related inflammation: The Mesa. Medicine and Science in Sports and Exercise, 49(5), 915–921. https://doi.org/10.1249/MSS.0000000000001179
- Whitaker, K. M., Pereira, M. A., Jacobs, D. R., Sidney, S., & Odegaard, A. O. (2017). Sedentary behavior, physical activity, and abdominal adipose tissue deposition. Medicine & Science in Sports & Exercise, 49(3), 450–458. https://doi.org/10.1249/MSS.0000000000001112