Physical activity is well established as a lifestyle component contributing to longevity (22). Sedentary behavior (i.e., awake time sitting/reclining with low energy expenditure ) is increasingly recognized as an additional risk factor for chronic disease (39). TV viewing in particular has been most strongly and consistently associated with incident diabetes, cardiovascular disease, specific cancers, and premature mortality from a multitude of causes in adults, irrespective of levels of moderate- to vigorous-intensity physical activity (MVPA) (10,20,21,29,38). Consequently, separate public health guidelines focusing on TV viewing in adults have been advocated (20,35,38). Early intervention work focusing on recreational sedentary screen time has shown promising results (27). Nevertheless, at a population level, this behavior still amounts to a substantial proportion of people's time. English adults, for example, on average watch TV for 3 h·d−1, making it the single most prevalent leisure time pursuit (similar to the US ), and although total sedentary time has shown a slight decrease in recent years, TV viewing has not declined (30). This high prevalence together with reasonably strong associations with premature mortality has resulted in sizeable estimates of public health effect (14,21,35,38).
Recommendations on reducing screen time should involve replacement by nonsitting rather than other sitting activities. The most feasible behavioral change options likely involve replacement of screen time by nonsitting activities in the home and leisure time domain (i.e., discretionary time) rather than by nonsitting activities at work or during transportation (i.e., nondiscretionary time). It is however largely unknown which types of such discretionary activities would be healthy alternatives. This is partially because current estimates for mortality risk have only considered the potential additive effect of screen time reductions per se, i.e., while keeping all other activities constant. Total discretionary time is however fairly constant in this age-group and mortality risk reductions from decreasing screen time depend on the activity that displaces screen time (25). Higher-intensity physical activities are associated with greater longevity but are generally less amenable to change (19,28). Activities that can be easily incorporated into daily life (e.g., home maintenance and improvement activities) are perceived as more attractive compared with activities which require more organization and costs (e.g., sports) (19).
Isotemporal substitution modeling allows the estimation of the mortality benefits of replacing screen time with another specific type of activity for the same duration, while keeping other activities constant (25). This approach, therefore, provides a more realistic insight into the potential effect of behavioral change using observational data (39), which is of interest given the lack of intervention studies with mortality as an outcome. We therefore aimed to estimate the differential mortality risk reductions associated with substituting leisure screen time with different discretionary physical activity types, in a large sample of UK middle-age adults, by means of isotemporal substitution modeling. To further inform public health guidance, we also estimated the proportional reduction in all-cause mortality incidence associated with each of these behavioral change scenarios.
UK Biobank is a large-scale prospective cohort study of half a million middle-age UK adults, established with a main aim to determine the etiological role of various genetic and lifestyle factors in the development of chronic disease (1,37). Eligible individuals 40–69 yr old and living within a convenient distance (up to ≈25 miles) from one of 22 assessment centers located throughout the United Kingdom were identified from NHS registers and invited to participate in a baseline assessment visit (2006–2010) (1,34). Those who reported a history of stroke, myocardial infarction, or cancer at baseline were excluded from the current analyses (n = 55,401), as well as those with missing data for any of the covariates (n = 26,960). As a result, 423,659 participants were included in the present analysis (45.3% men). The UK Biobank was approved by the North West Research Ethics Committee and is monitored by the UK Biobank Ethics and Governance Council. All procedures performed were in accordance with the 1964 Helsinki declaration and its later amendments. Informed consent was obtained from all individual participants included in the study.
All UK Biobank participants were followed up for vital status by linkage to national data sets (NHS Information Centre and Scottish Morbidity Record) until September 2016. This resulted in a median (interquartile range [IQR]) follow-up time of included participants of 7.6 (1.4) yr.
Screen time and discretionary physical activity
As part of an electronic touch screen questionnaire, participants were asked to indicate how many hours they spend watching TV and subsequently how many hours they spend using the computer (not including occupational computer use) on a typical day. The sum of both estimates was calculated to estimate average daily screen time (h·d−1). Questions were open-ended, and screen time was truncated at 9 h·d−1.
Participants were also asked about their participation in the last 4 wk in five different types of activities in their leisure time or at home. Activities included walking for pleasure (not as a means of transport), light do-it-yourself (DIY, i.e., home maintenance and improvement and gardening) activities (e.g., pruning) and heavy DIY (e.g., digging and carpentry), strenuous sports that make you sweat or breathe hard, and other exercises (e.g., swimming). Average time (min·d−1) spent in each of these activities was calculated by multiplying the reported frequency and average session duration. All frequency and duration questions were categorical. Strenuous sports and other exercises were combined into an indicator of structured exercise, and time spent in walking for pleasure, light and heavy DIY were combined into an indicator of daily life activities, indicating the greater ease of embedding these activities into daily life. A screenshot with the exact wording for each of the screen time and discretionary activity questions can be found online (e.g., TV viewing: http://biobank.ctsu.ox.ac.uk/crystal/refer.cgi?id=100277).
The Townsend deprivation index, an indicator of material deprivation, was included as a proxy for socio-economic status. It was calculated at recruitment, based on the location of participants' postcode relative to the national census output areas, with higher scores indicating higher levels of deprivation. Ethnicity (White, mixed, Asian, Black, Chinese, and others) and employment status (unemployed, in paid employment, or self-employed) were self-reported through the electronic questionnaire.
Participants also reported their alcohol intake (never, former, current less than three times per week, and current three or more times per week), smoking status (never, former, and current), salt adding behavior (never/rarely, sometimes, usually, and always), oily fish consumption (never, less than once a week, once a week, and more than once a week), fruit and vegetable intake (score ranging from 0 to 4 based on fresh and dried fruit intake and raw and cooked vegetable intake), processed and red meat intake (n, d·wk−1), and sleep duration (categorized as <7 h per night, 7–8 h per night, and >8 h per night). Sleep time was not included in the isotemporal framework because of the nonlinearity of the association between sleep time and all-cause mortality; this is similar to the approach taken by others (17).
They were also asked about their chronic disease status at baseline. This included doctor diagnosis of stroke (yes, no), myocardial infarction (yes, no), and cancer (yes, no) and intake of antihypertensive (yes, no) and lipid-lowering medication (yes, no). They were considered to have diabetes (yes, no) if they reported a physician confirmed diagnosis and/or treatment with insulin. Finally, parental history of cardiovascular disease or diabetes (yes, no) was defined as self-reported paternal or maternal history of heart disease, stroke, hypertension, or diabetes. A similar definition was used for parental history of cancer (yes, no) based on bowel, lung, paternal prostate, or maternal breast cancer.
Participants with baseline history of stroke, myocardial infarction, or cancer were excluded from analyses (as described earlier), and all other covariates were included as confounders in the models, chosen a priori based on the relevant literature.
Baseline characteristics were summarized by vital status and by screen time and discretionary physical activity tertiles. Cox regression with age as the underlying timescale was used to estimate the association between screen time, activity types, and all-cause mortality. The linearity of the associations between all exposures and all-cause mortality was assessed by fitting cubic spline regression models with 5 knots. As no substantial deviations from linearity were suggested, all exposures were modeled linearly as continuous variables and hazard ratios (HR) and 95% confidence interval (CI) were estimated for 30-min·d−1 increments, deemed to be feasible changes for both sedentary and activity behaviors. The proportional hazards assumption for each covariate was examined using Schoenfeld residuals and graphical checks and found to be appropriate. Partition models were fitted first, which estimate the additive effect of screen time and each type of activity on all-cause mortality risk, holding all other exposures constant (25). Multiplicative interactions between sex and screen time and physical activity were tested by including the relevant parameters in the models.
We then estimated the effect of substituting screen time by each of the physical activity types using isotemporal substitution models. The resulting HR (95% CI) for each physical activity type from this model provides an estimate of the potential effect on mortality of increasing that type of activity by 30 min·d−1 while decreasing screen time by the same duration and holding other activity types constant (25).
To examine the potential public health effect of these substitutional effect estimates on all-cause mortality, we then calculated potential impact fractions (PIF) using the “distribution shift” method described by Barendregt et al. (3), based on a normal distribution for screen time. The 95% CI values were derived from bootstrap analysis with 1000 replications. Each PIF represents the proportionate change in the incidence of mortality in the population if average screen time in that population decreased by 30 min·d−1, whereas average physical activity (of the type being examined) increased by the same amount. For calculation of the PIF, the mean and the SD of the screen time variables were estimated from the UK Biobank sample. In a sensitivity analysis, prevalence estimates of TV viewing time from the nationally representative Health Survey for England 2012 (HSE 2012)  were used to overcome any potential selection bias of the UK Biobank sample. Questions for television viewing time in HSE 2012 data closely resembled those asked in UK Biobank, with the only difference that HSE 2012 questions were interview based with a reference frame of the last 4 wk, and asked for weekdays and weekend days separately. We calculated average daily television viewing estimates from the separate estimates for weekdays and weekend days (30 min·d−1).
To examine the possibility of reverse causality (i.e., when participants are on the chronic disease pathway but not yet diagnosed at baseline and therefore show or report high levels of screen time and/or low levels of discretionary activity) influencing the estimated HR (95% CI), we performed a second sensitivity analysis excluding those who died in the first 2 yr of follow-up (in addition to exclusion of those who reported baseline history of stroke, myocardial infarction, or cancer). All analyses were performed using Stata version 14 (Stata Statistical Software; StataCorp LP, College Station, TX).
During 3,202,105 person-years of follow-up, 8928 participants died (3466 women and 5462 men), a crude mortality rate of 27.9 per 10,000 person-years. Baseline descriptive characteristics by vital status and by screen time and discretionary activity tertiles are shown in Tables 1 and 2, respectively.
Associations with All-Cause Mortality
As shown in Table 3, each 30-min·d−1 difference in screen time was associated with a 1% higher hazard of all-cause mortality, independent of time spent in activity and all other confounding variables included in the model. Furthermore, each 30-min·d−1 difference in daily life activities and in structured exercise was independently associated with a 4% and 12% lower hazard for all-cause mortality. When examining the two screen time variables separately, associations for TV viewing time were the strongest, showing a 2% higher mortality hazard for each 30 min·d−1 difference, whereas associations for computer use were nonsignificant. A significant interaction between computer use and sex was identified (P = 0.001). Examining the independent associations with all-cause mortality for computer use separately by sex, there was a positive association (1.015, 1.001–1.031) in women, whereas the association was negative (0.986, 0.975–0.996) in men. Therefore, isotemporal substitution for computer use was modeled in men and women separately.
Isotemporal substitution models
As shown in Figure 1, each 30-min·d−1 difference in screen time was associated with a lower mortality hazard when modeling substitution of screen time by an equal amount of daily life activities as well as by an equal amount of structured exercise. Although modeling 30-min·d−1 replacements of screen time by structured exercise resulted in the lowest mortality hazard (0.87, 0.84–0.90), modeling substitution by daily life activities also suggested a relevant protective effect (0.95, 0.94–0.97). When looking at more specific subtypes of daily life activities, the lowest mortality hazards were found when modeling 30 min·d−1 reallocations from screen time into walking for pleasure (0.95, 0.92–0.98) and into heavy DIY (0.93, 0.90–0.96), whereas potential mortality benefits were more limited when modeling reallocation into light DIY (0.97, 0.94–1.00). However, CI did overlap between estimates for these activity subtypes. As expected, the modeled effects of replacing 30 min of screen time with an equivalent duration of strenuous sports (0.87, 0.79–0.95) and other types of exercise (0.88, 0.84–0.91) were stronger.
Results when modeling substitution of TV viewing time were similar to those when modeling substitution of screen time (Fig. 1). The modeled effects of substitution of computer time by different activity types were less consistent, with generally weaker effect estimates than for screen and TV viewing time. In both men and women, a potential protective effect was found when modeling substitution of computer time by daily life activities (men, 0.98, 0.96–1.00; women, 0.94, 0.91–0.97) and structured exercise (men, 0.89, 0.85–0.93; women, 0.90, 0.84–0.96). For daily life activities in women, this was mainly driven by walking for pleasure (0.93, 0.89–0.98) and heavy DIY (0.84, 0.76–0.94). Modeling reallocation of computer time into other exercises showed a potential protective effect in men (0.88, 0.84–0.93) and women (0.90, 0.84–0.97), whereas results for reallocation into strenuous sports only reached significance in men (0.90, 0.81–0.99).
Results were similar when those who died within the first 2 yr of follow-up were excluded (see Table, Supplemental Digital Content 1, Hazard ratios (95% CI) for all-cause mortality from partition models for screen time and different types of discretionary activity, http://links.lww.com/MSS/A847; see Table, Supplemental Digital Content 2, Hazard ratios (95% CI) for all-cause mortality when modeling 30-min·d−1 substitutions of screen time, http://links.lww.com/MSS/A848).
Figure 2 displays PIFs for time reallocation of overall screen and TV viewing time (i.e., the sedentary behaviors that consistently showed associations with mortality) into the different discretionary activity types. For example, if UK Biobank participants were to decrease their screen time by an average of 30 min·d−1 in exchange for a daily 30-min walk, the incidence of all-cause mortality would decrease by 5.9%, assuming causality. The estimated PIFs were highest when replacing TV viewing time by either strenuous sports or other exercises (14.9% and 14.6%, respectively) and lowest when replacing screen time with light DIY (4.3%). Sensitivity analysis using the weighted HSE 2012 distribution for TV viewing time (restricting the age range to that of UK Biobank participants, i.e., 40–69 yr) gave similar PIF estimates to those obtained using the UK Biobank distribution (Fig. 2).
The findings of this study provide novel insights into the potential beneficial effects on mortality risk of substituting recreational screen time by different types of discretionary active alternatives in a large population-based sample of UK middle-age adults. They suggest that replacing small amounts of screen time (i.e., 30 min·d−1) by everyday activities such as DIY and walking, which are generally more easily adopted (19), results in important mortality benefits. Replacement by sports and other exercise provides additional benefits. The substantial differences in effect estimates arising from different substitution scenarios highlight the importance of using isotemporal substitution modeling to more fully inform public health guidance on effective behavior change to increase longevity.
The direction and strength of the associations from partition models found for screen time (32) and TV viewing time (14) confirm those found previously, as well as the stronger and more consistent associations for TV viewing compared with computer time (2,4,26). Previous meta-analytical work has also indicated protective mortality effects for exercise/sports as well as daily life activities, with stronger effect estimates for the former (28). However, none of these studies examined the substitutional effects between screen time and these different discretionary activity types. Indeed, the few studies that have used an isotemporal substitution approach in relation to mortality have focused on non-domain-specific sedentary time and/or non-domain-specific physical activity (11,23,33). We chose to specifically focus on leisure-screen time, given its high prevalence across all age-groups, stronger associations with health compared with other sedentary behaviors, and responsiveness to change through intervention [8,20,21,27,29,30,35,38]. We specifically focused on discretionary activity types in the leisure and home domain, as these are likely more realistic and feasible alternatives for replacing leisure screen time in a middle-age population compared with activities in the occupational and transportation domain.
Our findings suggested that both replacement by lower (i.e., walking for pleasure and light DIY) and higher-intensity activities (i.e., heavy DIY, strenuous sports, and other exercise) was found to be beneficial; however, the latter conferred the largest benefit. This is in line with findings for intermediate health risk factors. Some intermediate cardiometabolic risk factors (such as adiposity) require substitution of sedentary time by higher-intensity activities, whereas others (such as glucose and lipid metabolism markers) may respond beneficially from substitution into both lower (i.e., as low as standing) and higher-intensity activity (7,17). For risk factors reliant on energy balance, this may be due to the higher energy cost associated with higher-intensity activities for the same duration. Other risk factors may be influenced via other protective pathways related to features such as more fragmented accumulation patterns or an upright posture, which may be applicable to both low- and high-intensity activities. Enzymes regulating glucose and lipid metabolism, for example, may be upregulated with muscle activity related to standing and nonsedentary activities and with higher activity fragmentation (13,15). In terms of mental health as an intermediate risk factor, replacements of sitting time into both lower- and higher-intensity activity have been shown to be beneficial, which is likely also due to differential pathways, such as increased socialization and increased β-endorphines (6,24).
We have estimated that 4.3% to 14.9% of premature deaths in the United Kingdom could be avoided through substitution of 30 min·d−1 of total screen or TV viewing time by discretionary active alternatives, with the highest potential shift in mortality cases to be gained from substituting TV viewing by sports and exercise. Lee et al.  estimated that physical inactivity (i.e., not achieving 150 min·wk−1 of MVPA) causes 9% of premature mortality globally (an effect on par with smoking and obesity). The latter is an estimate of the excess proportion of deaths that could be avoided through the increase in MVPA to prescribed levels, keeping all other activities constant. A direct comparison of study results is challenging because of differences in populations and methodology. However, most of our PIF estimates for discretionary activities, which would be classified as moderate to vigorous in terms of intensity, exceeded 9% (e.g., 30-min·d−1 reallocation of TV viewing into heavy DIY [10%], strenuous sports [15%], or other exercises [15%]). Although caution is needed when comparing these study results, this may be partially due to our PIF estimates reflecting the combined effect of reducing screen time and increasing activity levels. The latter provides more realistic estimates of the potential public health effect of behavioral change, as reducing one type of activity necessarily results in increased engagement in another type of activity (39). Future studies should, therefore, aim to also incorporate such estimates.
There are several strengths of this study. A wide range of potential confounding variables was controlled for. These included several dietary variables, confounding the associations for physical activity. However, these dietary covariates may be on the causal pathway between screen time, especially TV viewing time, and premature mortality (12,16), potentially resulting in overadjustment and, therefore, underestimation of the effect estimates for screen time. Inclusion of a large population-based sample of UK adults enabled us to exclude all those with baseline chronic conditions, and in sensitivity analyses additionally those who died in the first 2 yr of follow-up. This helped us to minimize the risk of reverse causality influencing our estimates and potentially also the risk of severe recall bias by those with chronic disease influencing our findings. We minimized the potential risk of selection bias influencing the PIF estimates, by recalculating these estimates in a separate population-representative sample of English adults (30). Finally, we examined the substitution effects for both types of screen time separately. However, there were also limitations to the study. Isotemporal substitution modeling estimates are based on statistical modeling rather than actual behavioral change. It is also unlikely that the self-report instrument captured all screen time and activity behaviors in the domestic and leisure domain. Screen time and physical activity were self-reported through questions that were categorical and have not been directly examined for criterion validity, and social desirability bias may have caused overreporting of physical activity and underreporting of screen time. However, most self-report instruments have similar validity (18), and effect estimates were comparable with those found previously in comparable populations using similar adjustment strategies (14,28,32). We examined potential bias associated with differences in education level, by adjusting for education level instead of Townsend index, which resulted in very similar effect estimates (results not shown). The questionnaire did not distinguish between weekdays and weekend days, and the time frame of reference for screen time (“typical day”) was different to that for physical activity (“last 4 wk”), although it is unlikely the latter would have substantially affected the results (9). Objective activity measures are currently unable to classify activities by type and domain, which makes subjective measures particularly valuable for these types of research questions (23,33). Finally, although we aimed to increase internal validity by excluding those with baseline conditions, this may have enhanced the healthy cohort effect on observed associations. Replication in populations with different health status, age, ethnicity, and lifestyle profiles is needed. Ideally, these studies would also incorporate repeated objective measures for screen time and physical activity, allowing consideration of the patterns of sitting and activity, and have longer follow-up.
In conclusion, replacing small amounts of screen time (i.e., 30 min·d−1) by everyday activities such as DIY or walking could result in considerable public health benefits. These may be important targets for adults for whom taking up more structured activities or higher-intensity sports (which show stronger mortality benefits) to replace screen time initially is less feasible or compromised through ill health. Given the ubiquitous nature of screen time, the achievability of the examined behavioral change options, and the substantial mortality benefits estimated, specific guidelines on reductions in screen time, so far mainly implemented for pediatric populations (36), could be considered for adult age-groups to complement emerging guidelines on occupational sitting (5). These could not only recommend reductions in screen time but also substitution by alternative healthy activities, which can take place in the home and leisure domain.
This work was conducted using the UK Biobank resource and was supported by the British Heart Foundation (Intermediate Basic Science Research Fellowship grant no. FS/12/58/29709) and the Medical Research Council (Unit Programme nos. MC_UU_12015/1 and MC_UU_12015/3). The results of this study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. The results of the present study do not constitute endorsement by the American College of Sports Medicine. The authors declare that they have no conflict of interest.
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