Mayo Clinic Proceedings Home

Understanding How Much TV is Too Much

A Nonlinear Analysis of the Association Between Television Viewing Time and Adverse Health Outcomes

      Abstract

      Objective

      To inform potential guideline development, we investigated nonlinear associations between television viewing time (TV time) and adverse health outcomes.

      Methods

      From 2006 to 2010, 490,966 UK Biobank participants, aged 37 to 73 years, were recruited. They were followed from 2006 to 2018. Nonlinear associations between self-reported TV time (hours per day) and outcomes explored using penalized cubic splines in Cox proportional hazards adjusted for demographics and lifestyle. Population-attributable and potential impact fractions were calculated to contextualize population-level health outcomes associated with different TV time levels. Nonlinear isotemporal substitution analyses were used to investigate substituting TV time with alternative activities. Primary outcomes were mortality: all-cause, cardiovascular disease (CVD) and cancer; incidence: CVD and cancer; secondary outcomes were incident myocardial infarction, stroke, and heart failure and colon, lung, breast, and prostate cancer.

      Results

      Those with noncommunicable disease (109,867 [22.4%]), CVD (32,243 [6.6%]), and cancer (37,81 [7.7%]) at baseline were excluded from all-cause mortality, CVD, and cancer analyses, respectively. After 7.0 years (mortality) and 6.2 years (disease incidence) mean follow-up, there were 10,306 (2.7%) deaths, 24,388 (5.3%) CVD events, and 39,121 (8.7%) cancer events. Associations between TV time and all-cause and CVD mortality were curvilinear ( P non-linear ≤.003), with lowest risk observed <2 hours per day. Theoretically, 8.64% (95% confidence interval [CI], 6.60-10.73) of CVD mortality is attributable to TV time. Limiting TV time to 2 hours per day might have prevented, or at least delayed, 7.97% (95% CI, 5.54-10.70) of CVD deaths. Substituting TV time with sleeping, walking, or moderate or vigorous physical activity was associated with reduced risk for all outcomes when baseline levels of substitute activities were low.

      Conclusion

      TV time is associated with numerous adverse health outcomes. Future guidelines could suggest limiting TV time to less than 2 hours per day to reduce most of the associated adverse health events.

      Abbreviations and Acronyms:

      CVD ( cardiovascular disease), NCD ( noncommunicable disease), PAF ( population attributable fraction), PIF ( potential impact fraction), TV time ( television viewing time)
      To read this article in full you will need to make a payment

      References

        • Biswas A.
        • Oh P.I.
        • Faulkner G.E.
        • et al.
        Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults a systematic review and meta-analysis.
        Ann Intern Med. 2015; 162: 123-132
        • Eurostat Statistical Office of the European Communities
        The Life of Women and Men in Europe.
        A Statistical Portrait, 2008
      1. The Nielsen Total Audience Report: QI 2018. The Nielsen Company, New York, NY2018
      2. Media Nations 2018 Annual Report. Ofcom, United Kingdom2018
        • Grontved A.
        • Hu F.B.
        Television viewing and risk of type 2 diabetes, cardiovascular disease, and all-cause mortality: a meta-analysis.
        JAMA. 2011; 305: 2448-2455
        • Schmid D.
        • Leitzmann M.F.
        Television viewing and time spent sedentary in relation to cancer risk: a meta-analysis.
        J Natl Cancer Inst. 2014; 106
        • Hu F.B.
        • Li T.Y.
        • Colditz G.A.
        • Willett W.C.
        • Manson J.E.
        Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women.
        JAMA. 2003; 289: 1785-1791
        • Patterson R.
        • McNamara E.
        • Tainio M.
        • et al.
        Sedentary behaviour and risk of all-cause, cardiovascular and cancer mortality, and incident type 2 diabetes: a systematic review and dose response meta-analysis.
        Eur J Epidemiol. 2018; 33: 811-829
        • Pearson N.
        • Biddle S.J.
        Sedentary behavior and dietary intake in children, adolescents, and adults. A systematic review.
        Am J Prev Med. 2011; 41: 178-188
        • Hamilton M.T.
        • Healy G.N.
        • Dunstan D.W.
        • Zderic T.W.
        • Owen N.
        Too little exercise and too much sitting: inactivity physiology and the need for new recommendations on sedentary behavior.
        Curr Cardiovasc Risk Rep. 2008; 2: 292-298
        • Lewis O.
        • Odeyemi Y.
        • Joseph V.
        • Mehari A.
        • Gillum R.F.
        Screen hours and sleep symptoms: the US National Health and Nutrition Examination Survey.
        Family Community Health. 2017; 40: 231-235
        • Stamatakis E.
        • Hillsdon M.
        • Mishra G.
        • Hamer M.
        • Marmot M.
        Television viewing and other screen-based entertainment in relation to multiple socioeconomic status indicators and area deprivation: the Scottish Health Survey 2003.
        J Epidemiol Community Health. 2009; 63: 734-740
        • Healy G.N.
        • Clark B.K.
        • Winkler E.A.
        • Gardiner P.A.
        • Brown W.J.
        • Matthews C.E.
        Measurement of adults' sedentary time in population-based studies.
        Am J Prev Med. 2011; 41: 216-227
        • Hutcheon J.A.
        • Chiolero A.
        • Hanley J.A.
        Random measurement error and regression dilution bias.
        BMJ. 2010; : 340
        • Stamatakis E.
        • Ekelund U.
        • Ding D.
        • Hamer M.
        • Bauman A.E.
        • Lee I.M.
        Is the time right for quantitative public health guidelines on sitting? A narrative review of sedentary behaviour research paradigms and findings.
        Br J Sports Med. 2019; 53: 377-382
        • Altman D.G.
        Categorising continuous covariates (letter to the editor).
        Br J Cancer. 1991; 64: 975
        • Wainer H.
        Visual revelations. Finding what is not there through the unfortunate binning of results: the Mendel effect.
        Chance. 2006; 19: 45-56
        • Sun J.W.
        • Zhao L.G.
        • Yang Y.
        • Ma X.
        • Wang Y.Y.
        • Xiang Y.B.
        Association between television viewing time and all-cause mortality: a meta-analysis of cohort studies.
        Am J Epidemiol. 2015; 182: 908-916
        • Sudlow C.
        • Gallacher J.
        • Allen N.
        • et al.
        UK Biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age.
        PLoS Med. 2015; 12: e1001779
        • Townsend P.
        • Philimore P.
        • Beattie A.
        Health and Deprivation: Inequality and the North.
        Croom Helm, London, UK1988
        • Craig C.L.
        • Marshall A.L.
        • Sjostrom M.
        • et al.
        International physical activity questionnaire: 12-country reliability and validity.
        Med Sci Sports Exerc. 2003; 35: 1381-1395
        • McCance R.
        McCance and Widdowson’s: The Composition of Foods.
        Royal Society of Chemistry, London, UK2002
        • Govindarajulu U.S.
        • Malloy E.J.
        • Ganguli B.
        • Spiegelman D.
        • Eisen E.A.
        The comparison of alternative smoothing methods for fitting non-linear exposure-response relationships with Cox models in a simulation study.
        Int J Biostat. 2009; 5
        • Mekary R.A.
        • Willett W.C.
        • Hu F.B.
        • Ding E.L.
        Isotemporal substitution paradigm for physical activity epidemiology and weight change.
        Am J Epidemiol. 2009; 170: 519-527
        • Morgenstern H.
        • Bursic E.S.
        A method for using epidemiologic data to estimate the potential impact of an intervention on the health status of a target population.
        J Commun Health. 1982; 7: 292-309
        • Van De Vegte Y.J.
        • Said M.A.
        • Rienstra M.
        • Van Der Harst P.
        • Verweij N.
        Genome-wide association studies and Mendelian randomization analyses for leisure sedentary behaviours.
        Nat Commun. 2020; 11: 1770
        • Wijndaele K.
        • Sharp S.J.
        • Wareham N.J.
        • Brage S.
        Mortality risk reductions from substituting screen time by discretionary activities.
        Med Sci Sports Exerc. 2017; 49: 1111-1119
        • Hamer M.
        • Yates T.
        • Demakakos P.
        Television viewing and risk of mortality: exploring the biological plausibility.
        Atherosclerosis. 2017; 263: 151-155
        • Grace M.S.
        • Dillon F.
        • Barr E.L.M.
        • Keadle S.K.
        • Owen N.
        • Dunstan D.W.
        Television viewing time and inflammatory-related mortality.
        Med Sci Sports Exerc. 2017; 49: 2040-2047
        • Doherty A.
        • Smith-Byrne K.
        • Ferreira T.
        • et al.
        GWAS identifies 14 loci for device-measured physical activity and sleep duration.
        Nat Commun. 2018; 9: 5257
        • Bailey D.P.
        • Locke C.D.
        Breaking up prolonged sitting with light-intensity walking improves postprandial glycemia, but breaking up sitting with standing does not.
        J Sci Med Sport. 2015; 18: 294-298
        • Peddie M.C.
        • Bone J.L.
        • Rehrer N.J.
        • Skeaff C.M.
        • Gray A.R.
        • Perry T.L.
        Breaking prolonged sitting reduces postprandial glycemia in healthy, normal-weight adults: a randomized crossover trial.
        Am J Clin Nutr. 2013; 98: 358-366
        • Foster H.M.E.
        • Celis-Morales C.A.
        • Nicholl B.I.
        • et al.
        The effect of socioeconomic deprivation on the association between an extended measurement of unhealthy lifestyle factors and health outcomes: a prospective analysis of the UK Biobank cohort.
        Lancet Public Health. 2018; 3: e576-e585
        • Barendregt J.J.
        • Veerman J.L.
        Categorical versus continuous risk factors and the calculation of potential impact fractions.
        J Epidemiol Commun Health. 2010; 64: 209-212
        • Mansournia M.A.
        • Altman D.G.
        Statistics notes: population attributable fraction.
        BMJ. 2018; : 360
        • Fry A.
        • Littlejohns T.J.
        • Sudlow C.
        • et al.
        Comparison of sociodemographic and health-related characteristics of uk biobank participants with those of the general population.
        Am J Epidemiol. 2017; 186: 1026-1034
        • Shuval K.
        • Gabriel K.P.
        • Leonard T.
        TV viewing and BMI by race/ethnicity and socio-economic status.
        PLoS One. 2013; 8: e63579
        • Cassidy S.
        • Chau J.Y.
        • Catt M.
        • Bauman A.
        • Trenell M.I.
        Cross-sectional study of diet, physical activity, television viewing and sleep duration in 233,110 adults from the UK Biobank: the behavioural phenotype of cardiovascular disease and type 2 diabetes.
        BMJ Open. 2016; 6: e010038
        • Katikireddi S.V.
        • Higgins M.
        • Smith K.E.
        • Williams G.
        Health inequalities: the need to move beyond bad behaviours.
        J Epidemiol Commun Health. 2013; 67: 715-716
        • Billings M.E.
        • Hale L.
        • Johnson D.A.
        Physical and social environment relationship with sleep health and disorders.
        Chest. 2020; 157: 1304-1312
        • Archer E.
        • Pavela G.
        • Lavie C.J.
        The inadmissibility of what we eat in America and NHANES dietary data in nutrition and obesity research and the scientific formulation of national dietary guidelines.
        Mayo Clin Proc. 2015; 90: 911-926
        • Archer E.
        • Marlow M.L.
        • Lavie C.J.
        Controversy and debate: memory based methods paper 3: nutrition's ‘black swans’: our reply.
        J Clin Epidemiol. 2018; 104: 130-135
        • Davy B.M.
        • Estabrooks P.A.
        The validity of self-reported dietary intake data: focus on the “what we eat in America” component of the National Health and Nutrition Examination Survey Research Initiative.
        Mayo Clin Proc. 2015; 90: 845-847
        • Martín-Calvo N.
        • Martínez-González M.Á.
        Controversy and debate: memory-based dietary assessment methods paper 2.
        J Clin Epidemiol. 2018; 104: 125-129
        • Otten J.J.
        • Littenberg B.
        • Harvey-Berino J.R.
        Relationship between self-report and an objective measure of television-viewing time in adults.
        Obesity. 2010; 18: 1273-1275
        • Newell S.A.
        • Girgis A.
        • Sanson-Fisher R.W.
        • Savolainen N.J.
        The accuracy of self-reported health behaviors and risk factors relating to cancer and cardiovascular disease in the general population: a critical review.
        Am J Prev Med. 1999; 17: 211-229
        • Chu G.S.
        • Schramm W.
        Learning From Television: What the Research Says.
        IAP, Alexandria, VA2004
        • Black J.
        • Barnes J.L.
        Fiction and social cognition: the effect of viewing award-winning television dramas on theory of mind.
        Psychology of Aesthetics, Creativity, and the Arts. 2015; 9: 423-429