The association between non-occupational TV and computer screen-time viewing and cancer risk: Findings from the UK Biobank, a large prospective cohort study

doi:10.21203/rs.2.18993/v1

Background

Evidence is suggestive of sedentary behaviour being associated with an increased risk of endometrial cancer, but the evidence base is too limited to draw any conclusions for other cancers. The aim of the study was to investigate the association between sedentary behaviour and total cancer incidence and site-specific cancer incidence.

Methods

This prospective population-based cohort study involved data from the UK Biobank (470 578 adults; 53.8% females; mean age 56.3 years). Sedentary behaviours including television viewing time, computer use time and daily total screen time were the exposure variables. Primary and secondary outcome measures included incident total cancer, and site-specific cancers identified from the International Classification of Diseases, 9th and 10th revisions (ICD-9 and ICD-10). Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) showing the relationship between sedentary behaviour and cancer using continuous (hours/day) and categorical exposure variables. Partition models and isotemporal substitution models were used to investigate the impact of substituting sedentary behaviour with physical activity.

Results

During a mean follow-up time of 7.6 years, 28 992 incident cancers were identified. A 1-hour increase in daily TV screen time was associated with higher risk of oropharyngeal cancer (HR 1.06, 95% CI: 1.02, 1.11), stomach cancer (HR 1.06, 95% CI: 1.001, 1.13), oesophagus and stomach cancer (HR 1.04, 95% CI: 1.005, 1.09), and colon cancer (HR 1.04, 95% CI: 1.01, 1.06) in fully adjusted models. Participants who reported ≤1 hour/day of TV screen time had a lower risk of lung cancer (HR 0.85, 95% CI: 0.73, 0.997), breast (female only) cancer (HR 0.92, 95% CI: 0.85, 0.996), stomach cancer (HR 0.66, 95% CI: 0.45, 0.97), and oesophagus and stomach cancer (HR 0.78, 95% CI: 0.62, 0.98) compared to participants who reported 1-≤3 hours/day of TV screen time. Isotemporal substitution models showed reduced risk of total cancer (HR 0.97, 95% CI: 0.95, 0.99) and some site-specific cancers when replacing 1-hour/day of TV viewing with moderate-intensity physical activity or walking.

Conclusions

Our findings show that sedentary behaviours were associated with some site-specific cancers (including oropharyngeal, oesophagus and stomach, colon and lung cancer), particularly for TV viewing time. Our findings were less consistent for time spent on computer and daily total screen time. Substitution models showed that replacing 1-hour per day of TV viewing with 1-hour of moderate-intensity physical activity or walking was associated with lower risk of total cancer and lower risk of several site-specific cancers. Health promotion strategies should endorse the message to minimise sedentary behaviour, replacing it with health-enhancing physical activity, and to particularly target TV viewing.

Physical Medicine & Rehab

Nutrition & Dietetics

Sedentary behaviour

Cancer

Cohort study

Epidemiology

Research in sedentary behaviours has grown rapidly over recent years (1). Such behaviours are seen as distinct from physical inactivity or sleep, and have been defined as “any waking behaviour characterised by an energy expenditure ≤ 1.5 metabolic equivalents (METs), while in a sitting, reclining or lying posture” (1,2). This definition is typically operationalised as self-reported sitting (including in recreational and occupational activities), television (TV) viewing or other screen-time. The most recent UK Chief Medical Officers' Physical Activity Guidelines lists behaviours such as TV viewing and computer-use as examples of sedentary behaviour, highlighting that self-reported screen time is among the most common measures of sedentary behaviour cited in the literature (3). Screen-time can take many forms including social media use, internet use, gaming, general Smartphone use, watching TV and computer use (regardless of what these devices are used for) (4).

The UK Government guidance on sedentary behaviours, published in 2011 and 2019, suggests that we should minimise time spent in prolonged sedentary behaviours for health benefits (3,5). However, owing to the relative early stage of the evidence base, no further recommendations were provided around a timeframe for what would be deemed a ‘harmful’ level of sedentary time exposure. Even the most recent US guidance published in 2018 does not provide more specific recommendations for minimising sedentary time (6).

Evidence demonstrates that prolonged sedentary time is associated with increased risk of non-communicable diseases (NCDs). Mechanistically, sedentary behaviour is thought to impact particularly on cardio-metabolic diseases through adverse effects on lipid and glucose metabolism (7,8). Recent evidence from a meta-analysis has demonstrated a significant direct association between 6–8 hours daily sedentary time and increased all-cause mortality, cardiovascular disease mortality and Type 2 Diabetes Mellitus risk (9). Prolonged sedentary behaviour is therefore a significant burden on our healthcare systems. In 2016–2017, for example, it was estimated to cost the UK National Health Service £0.8 billion (10).

However, much less is known about sedentary behaviour and cancer, and known biological mechanisms are less well understood (11). The World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) global report in 2018 stated that evidence on sedentary behaviours is limited but is suggestive as being associated with an increased risk of endometrial cancer (pooled risk estimate from three studies comparing the highest versus lowest levels of sitting time was 1.46, 95% CI: 1.21, 1.76, cases = 1579) (11–14). The evidence base was deemed too limited to draw any conclusions for other cancers (11). However, in a 2018 meta-analysis, Patterson et al. demonstrated significant linear associations of TV viewing with cancer mortality (N = 4 studies; relative risk [RR] 1.02, 95% CI: 1.01, 1.03 per 1-hour increase in TV viewing per day) (9).

More recent evidence from analyses of the large, prospective UK Biobank cohort shows mixed evidence for an association between sedentary behaviour and cancer outcomes (15). Celis-Morales et al (2018) found significant associations of discretionary screen-time (time spent in TV viewing or computer screen use during leisure time) exposure and all-cause mortality (hazard ratio [HR] 1.06, 95% CI: 1.05, 1.07), and cancer incidence (HR 1.04, 95% CI: 1.03, 1.04). This study also found that these results were substantially attenuated by physical activity, cardiorespiratory fitness and grip strength (15). Our research group have previously found no association between non-occupational screen-based sedentary behaviour levels and oesophago-gastric cancer risk within the UK Biobank cohort (16). In contrast, higher levels of TV viewing time were associated with a greater risk of colon cancer in the same study population (HR for ≥ 5 hours per day vs ≤ 1 hour per day = 1.32, 95% CI: 1.04, 1.68) (17), although time spent using computers (excluding using a computer at work) was not associated with colorectal cancer risk in the UK Biobank cohort (17). The findings of a 2017 meta-analysis including six studies also demonstrated significant associations between the highest compared with the lowest levels of occupational sedentary behaviour, and risk of colon cancer (pooled RRs 1.44, 95% CI: 1.28, 1.62) (18). On the other hand, there was little evidence of an association between sedentary behaviour and rectal cancer risk (18).

Many of the previous studies investigating the association between sedentary behaviour and health outcomes have attempted to adjust for physical activity levels in their analysis. A recent US Government report has highlighted limited evidence on the role of physical activity in displacing the mortality risks associated with sedentary behaviour (6). An improved understanding of these interactive effects would enable more specific recommendations to be made regarding quantifying prolonged sedentary time. Analytical techniques such as partition models and isotemporal substitution models (19) could help to model such predictions, but have yet to be extensively applied in large cohort analyses.

Therefore, this study aimed to investigate sedentary behaviour (including TV viewing, computer use and total screen-use) in relation to total cancer risk and risk of site-specific cancers (including endometrial, colorectal, pre- and post-menopausal breast, prostate, lung, and other cancers) in the large UK Biobank cohort study. The study investigated whether associations varied by gender, age, socio-economic status, smoking and excess body weight. Finally, partition and isotemporal substitution models were used to investigate the impact of substituting sedentary behaviour with physical activity.

Study design

Between 2006 and 2010, UK Biobank recruited a cohort of 502 619 adults (5.5% response rate) aged 40–69 years from the general population (20,21). Approximately 9.2 million invitations were mailed to potential participants who were registered with the National Health Service (NHS) and living within a 25-mile radius of one of the 22 assessment centres across England, Scotland and Wales.

From this overall cohort, we excluded participants if: (1) they had been diagnosed with malignant cancer (excluding non-melanoma skin cancer) at baseline (n = 26 868); and (2) they did not complete the self-report assessments of their TV screen time (n = 5078), computer time (n = 8000) or total screen time (n = 11232); (3) they requested to be removed from the UK Biobank dataset as per General Data Protection Regulation (GDPR) (n = 95). This resulted in 470 578 participants being included in the analysis for TV screen time, 467 656 participants being included in the analysis for computer screen time and 464 424 participants being included in the analysis for total screen time. All participants provided informed consent.

Screen time assessment

Relevant screen-time exposure variables were assessed by self-reported time spent watching TV, time spent using the computer outside of work, which were used to derive total screen time. Self-reported TV screen time was assessed for all participants by asking the following question: “In a typical DAY, how many hours do you spend watching TV? (Put 0 if you do not spend any time doing it)?”. Self-reported computer screen time was assessed for all participants by asking the following question: “In a typical DAY, how many hours do you spend using the computer? (Do not include using a computer at work; put 0 if you do not spend any time doing it).” Durations of < 0 hours were set to missing, as were responses of "Do not know" or "Prefer not to answer". If the respondent replied "Less than an hour a day", this was recoded to 0.5 hours. Total screen time was then computed as the sum of hours spent watching TV and hours spent using the computer. If the summation of total hours spent watching TV and hours spent using the computer was greater than 24, this was set to missing (n = 35).

Physical activity assessment

Self-report physical activity was assessed for all participants using the validated short-form International Physical Activity Questionnaire (IPAQ) (22) on which participants reported the frequency (i.e. days/week) and duration (i.e. minutes/day) of walking, moderate- and vigorous-intensity physical activity in the past seven days. For each domain (walking, moderate, vigorous), durations of < 10 minutes/day were recoded to 0 and durations of > 180 minutes were truncated at 180 minutes/day in line with IPAQ processing rules. This was used to derive hours/day spent in walking, moderate- and vigorous-intensity physical activity. All data processing was carried out according to official IPAQ rules (23).

Assessment of covariates

Height (m), weight (kg), and waist and hip circumference (cm) were measured by staff at the UK Biobank study centre. Body mass index (BMI) was then calculated from the weight and height measurements (kg/m²). Waist circumference measurements were taken from the level of the umbilicus and regarded as a measure of central obesity, using official cut-off values established by the International Diabetes Federation (> 94 cm in men and > 80 cm in women) (24). Age, sex and postcodes were acquired from a central registry for all participants and updated by the participant. Participants also self-reported their ethnicity, educational attainment, lifestyle behaviours (smoking status, alcohol consumption, dietary intake and sunscreen/ultraviolet (UV) protection use) and medical history using electronic questionnaires. Townsend deprivation scores were derived from postcodes (25). Core confounders for all models included socio-demographic factors (i.e. age, sex, ethnicity, educational attainment and deprivation index), smoking status, alcohol consumption, fruit and vegetable consumption, BMI, height and waist-hip ratio. Cancer site-specific confounders included use of sun/UV protection (melanoma), self-reported oesophageal reflux (oesophagus cancer), diabetes at baseline (pancreatic and colorectal cancers), aspirin use (colorectal cancers), red and processed meat intake (colorectal cancers), hormone replacement therapy (HRT) use (breast, uterus and colorectal cancers), oral contraceptive use (breast and uterus cancers), number of live births (breast and uterus cancers), age at menarche (breast and uterus cancers), age at menopause (breast and uterus cancers), hysterectomy status (breast and uterus cancers) and self-reported family history of cancer (total cancer, lung, prostate, and breast cancers), based on known aetiological risk factors for these tumours.

Proportions of missing data were less than 1% for all variables apart from aspirin use (1.9%), red meat intake (1.1%), age at menarche (1.6%), age at menopause (2.1%), education (1.5%), fruit and vegetable consumption (2.6%), hysterectomy status (5.9%), family history of cancer (1.5%), minutes of moderate-intensity physical activity (14.0%), vigorous-intensity physical activity (10.5%) and walking (12.7%).

Cancer ascertainment

For the present analysis, the main outcomes were incident total cancer (excluding non-melanoma skin cancer) and site-specific cancers. Incident cancers for participants in the UK Biobank cohort were identified through records maintained at national cancer registries (Health and Social Care Information Centre and the NHS Central Register) and identified from the International Classification of Diseases, 9th and 10th revisions (ICD-9 and ICD-10 (26)). Cancer outcomes were coded according to ICD-9 and ICD-10 as follows: all cancers excluding non-melanoma skin cancer (ICD-10: C00-C97 excluding C44; ICD9: 140–209 excluding 173), melanoma (ICD-10: C43; ICD-9: 172), oropharyngeal cancers (ICD-10: C00-C14; ICD-9: 140–149), lung (ICD-10: C33-C34; ICD-9: 162), breast: female only (ICD-10: C50; ICD-9: 174), uterus (ICD-10: C54; ICD-9: 182), ovary (ICD-10: C56; ICD-9: 183), prostate (ICD-10: C61; ICD-9: 185), oesophagus, (ICD-10: C15; ICD-9: 150) stomach (ICD-10: C16; ICD-9: 151), hepatobiliary tract cancers (ICD-10: C22-C24; ICD-9: 155–156), pancreatic (ICD-10: C25; ICD-9: 157), kidney (ICD-10: C64-C65; ICD-9: 189.0-189.1), bladder (ICD-10: C66-C67; ICD-9: 188, 189.2), colorectal (ICD-10: C18-C21; ICD-9: 153–154), colon (ICD-10: C18; ICD-9: 153), rectum (ICD-10: C19-C20; ICD-9: 1540–1541), brain tumours (ICD-10: C71; ICD-9: 191), thyroid (ICD-10: C73; ICD-9: 193), and haematological malignancies (ICD-10: C81-C96; ICD-9: 200–208), including separate analysis of non-Hodgkin’s lymphoma (ICD-10: C82-C85; ICD-9: 200, 202).

Statistical analyses

Descriptive statistics for all covariates are presented according to participants’ total daily TV screen time. Categorical variables are presented as participant numbers and percentages. Means and standard deviations (SDs) are presented for continuous variables. Follow-up time in days from baseline was used as the timescale and for each participant end of follow-up occurred at: (1) cancer diagnosis date; (2) date of emigration; (3) date of death; or (4) end of follow-up (14th December 2016), whichever came first.

Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) showing the relationship between a 1-hour increase/day in TV screen time and cancer. All analyses were adjusted for age and sex in the baseline model. Additional covariates were added in the second adjusted model and included ethnicity (white/other), deprivation index (quintiles), education (University degree, A-levels/HNC/HND/NVQ, GCSE/O-level/CSE, Other, None), BMI (kg/m²), height (m), smoking status (never, former light smoker [< 20 pack-years], former heavy smoker [≥ 20 pack-years], current light smoker [< 20 pack-years], current heavy smoker [≥ 20 pack-years]), alcohol intake (never, former, current [< once/week], current [≥ once/week]) and fruit and vegetable intake (< 5 portions/day, ≥ 5 portions/day). Cancer site-specific covariates were included in the third adjusted model for each type of cancer (details included in the footnotes of Tables 2–6). These included use of sun/UV protection, HRT use, oral contraceptive use, number of live births, age at menarche, age at menopause, hysterectomy status, diabetes at baseline, aspirin use, red meat intake, processed meat intake. For analyses including gender-specific covariates (e.g. incident total cancers, colorectal cancer, colon cancer and rectum cancer), separate models were run for males and females and HRs were combined using inverse variance meta-analysis and a fixed-effects model (27–29). Participants were excluded from the analysis if they did not have the complete exposure and covariate data required for each model. We did not adjust for total dietary energy intake as the large amount of missing data (for 57.6% of participants) made this unfeasible. Further analyses were conducted to investigate the role of anthropometric factors by running all models with and without adjustment for waist-hip ratio, as a proxy for central adiposity. Models including incident total cancers, breast cancer, prostate cancer and lung cancer were run with and without adjustment for self-report family history (mother, father, siblings). The oesophageal cancer model was also run with and without adjustment for self-reported gastro-oesophageal reflux Disease (GORD).

These analyses were repeated separately to investigate the relationship between a 1-hour increase/day in (1) time spent using the computer; (2) total screen time; and cancer risk. Since the association between time spent in sedentary behaviour and cancer risk may not be linear (9,30), we repeated these analyses with categorised independent variables as follows: daily TV screen time (1-≤3 hours [reference category]; ≤1 hour; 3-≤5 hours; >5 hours), computer screen time (≤ 1 hour [reference category]; none; 1-≤3 hours; >3 hours) and total screen time (1-≤4 hours [reference category]; ≤1 hour; 4-≤8 hours; >8 hours) categorised based on previously published categories (17). The analysis including total screen time was not considered to be the primary analysis since summing the time spent watching TV and the time spent using computers may overestimate total screen time through double counting (if participants watched TV and used computers at the same time). Therefore, the daily TV screen time analysis was considered to be the primary analysis.

A series of partition models and isotemporal substitution models (19) were used for each type of cancer to examine the associations of daily TV screen time, time spent walking/day, time spent in moderate-intensity physical activity/day, time spent in vigorous-intensity physical activity/day and cancer incidence (19,31–34). Partition models examined all behaviours simultaneously, without adjusting for total physical activity time. Therefore, the HR for one type of physical activity represented the effect of increasing this type of physical activity (by 1-hour/day) while holding the other physical activities constant. Since total physical activity time is not included in the model (and thus is not held constant), these results represent the effect of adding a physical activity type whilst holding the others constant. The effects of substituting one physical activity type by another for the same amount of time (i.e. replacing 1-hour/day of TV screen time for 1-hour/day of walking, moderate-intensity physical activity or vigorous-intensity physical activity) was investigated using isotemporal substitution models which adjusted for time spent walking/day, time spent in moderate-intensity physical activity/day, time spent in vigorous-intensity physical activity/day and total activity time/day (i.e. the summation of walking, moderate activity, vigorous activity and TV screen time). In this case, since total physical activity is included the model (and thus is held constant), these results represent the effect of replacing TV screen time with the same amount of another physical activity type (i.e. walking, moderate- or vigorous- activity) while holding the others constant.

Sensitivity analyses were conducted by confining the analysis to cancers diagnosed at least two years following baseline to examine the impact of removing prevalent disease. Subgroup analyses were conducted by selected baseline characteristics. These included sex, age, deprivation index, smoking status and BMI with reference to obese/non-obese thresholds defined for various ethnic groups by gender in a previous UK Biobank study (35), assuming that participants with mixed backgrounds or ‘other’ ethnicities had the same obesity thresholds as white participants since cut-off points were not available for this group. Further analyses were conducted by creating four categories based on body fat percentage and physical activity levels defined according to the IPAQ. Body fat percentage cut-points were derived from previously established thresholds defined by age, gender, ethnicity and BMI (36). We assumed participants with mixed backgrounds or ‘other’ ethnicities had the same body fat percentage thresholds as white participants. Subgroup analyses were also conducted by menopausal status for female-specific cancers (i.e. breast, uterus, ovary cancers). Interactions were tested using the Wald test for homogeneity and declared significant if p < 0.01 in line with previous studies (37).

The proportional hazards assumption was tested for each model formally using Schoenfeld residuals (p < 0.05 indicated potential violation of the proportional hazards assumption), and by visual inspection of scaled Schoenfeld residual plots (38) and log-log plots (parallel curves indicated that there was no evidence for violation of the proportional hazards assumption). Analyses were carried out using Stata 13 (39).

Participant characteristics according to total daily screen time are shown in Table 1. Among the 470 578 participants included in this analysis, 53.8% were women and the mean age was 56.3 years. Most participants reported that they spent between 2 and 8 hours/day watching TV or using the computer. During a mean follow-up time of 7.6 (SD 1.4) years (median 7.8 years, interquartile range 7.0-8.5), 28 992 incident cancers were identified.

Table 1

Baseline characteristics by self-report TV screen time. Values are numbers and percentages unless otherwise stated.
			Total TV screen time
	Total		≤ 1 hour per day		1-≤3 hours per day		3-≤5 hours per day		> 5 hours/day
	No./mean	%/SD	No./mean	%/SD	No./mean	%/SD	No./mean	%/SD	No./mean	%/SD
Total participants	470 578	100.0%	97 419	20.7%	236 988	50.4%	110 334	23.5%	25 837	5.5%
Self-report total screen time (hours/day; mean/SD)	3.9	2.1	2.0	1.5	3.5	1.4	5.3	1.4	7.9	2.4
Time spent watching TV (hours/day; mean/SD)	2.8	1.7	0.7	0.4	2.5	0.5	4.3	0.5	6.9	1.8
Time spent using computers (hours/day; mean/SD)	1.1	1.4	1.2	1.5	1.1	1.3	1.0	1.3	1.1	1.7
IPAQ physical activity (mean/SD)
Minutes of walking	314.0	325.6	297.3	309.3	320.9	330.6	321.7	331.4	280.9	311.9
Minutes of moderate-intensity physical activity	234.5	304.4	226.5	294.8	238.3	307.4	242.4	310.6	195.1	281.1
Minutes of vigorous-intensity physical activity	83.2	146.7	90.3	143.7	85.9	147.5	76.3	148.6	58.4	138.0
Age at baseline (mean/SD)	56.3	8.1	54.2	8.0	55.9	8.1	58.5	7.6	59.0	7.6
Height (m) (mean/SD)	1.7	0.1	1.7	0.1	1.7	0.1	1.7	0.1	1.7	0.1
Sex
Female	253 188	53.8%	53 500	54.9%	127 135	53.7%	59 553	54.0%	13 000	50.3%
Male	217 390	46.2%	43 919	45.1%	109 853	46.4%	50 781	46.0%	12 837	49.7%
Ethnicity
White	443 484	94.6%	90 405	93.3%	224 142	94.9%	104 922	95.4%	24 015	93.3%
Black	7505	1.6%	1549	1.6%	3358	1.4%	1846	1.7%	752	2.9%
South Asian	9395	2.0%	2582	2.7%	4721	2.0%	1639	1.5%	453	1.8%
Chinese	1501	0.3%	465	0.5%	717	0.3%	266	0.2%	53	0.2%
Mixed background or others	7069	1.5%	1949	2.0%	3299	1.4%	1354	1.2%	467	1.8%
Townsend deprivation quintile
1 (Least deprived)	94 590	20.1%	19 860	20.4%	51 164	21.6%	20 497	18.6%	3069	11.9%
2	93 950	20.0%	18 854	19.4%	49 804	21.0%	21 691	19.7%	3601	14.0%
3	94 166	20.0%	18 857	19.4%	48 706	20.6%	22 379	20.3%	4224	16.4%
4	94 118	20.0%	20 209	20.8%	46 505	19.7%	22 081	20.0%	5323	20.6%
5 (Most deprived)	93 165	19.8%	19 530	20.1%	40 495	17.1%	23 561	21.4%	9579	37.1%
Education
University degree	153 223	33.1%	52 250	54.3%	79 041	33.9%	19 257	17.8%	2675	10.6%
A-levels/HNC/HND/NVQ	83 315	18.0%	15 934	16.6%	44 649	19.1%	18 921	17.5%	3811	15.1%
GCSE/O-level/CSE	124 765	26.9%	17 518	18.2%	66 180	28.3%	34 257	31.6%	6810	27.0%
Other	24 018	5.2%	4230	4.4%	12 530	5.4%	6091	5.6%	1167	4.6%
None	78 028	16.8%	6346	6.6%	31 125	13.3%	29 800	27.5%	10 757	42.7%
Smoking status^a
Never	257 696	55.0%	58 981	60.7%	132 976	56.3%	54 858	49.9%	10 881	42.4%
Former light smoker	119 085	25.4%	24 556	25.3%	61 147	25.9%	27 891	25.4%	5491	21.4%
Former heavy smoker	42 251	9.0%	5350	5.5%	19 256	8.2%	13 521	12.3%	4124	16.1%
Current light smoker	27 794	5.9%	5535	5.7%	13 706	5.8%	6646	6.1%	1907	7.4%
Current heavy smoker	22 082	4.7%	2735	2.8%	9094	3.9%	6972	6.3%	3281	12.8%
Alcohol intake
Never	20 749	4.4%	4873	5.0%	9428	4.0%	4868	4.4%	1580	6.1%
Former drinker	16 659	3.5%	3330	3.4%	7128	3.0%	4342	3.9%	1859	7.2%
Current drinker: <once/week	106 020	22.6%	19 466	20.0%	50 958	21.5%	27 939	25.3%	7657	29.7%
Current drinker: ≥once/week	326 759	69.5%	69 676	71.6%	169 324	71.5%	73 089	66.3%	14 670	56.9%
Dietary intake (mean/SD)
Fruits and vegetables (portion/day)	4.7	3.1	5.1	3.2	4.7	3.0	4.5	3.0	4.2	3.3
Red meat (portion/week)	2.1	1.5	2.0	1.4	2.1	1.4	2.2	1.5	2.4	1.7
Processed meat (portion/week)	1.5	1.4	1.3	1.4	1.5	1.4	1.6	1.4	1.9	1.6
Body Mass Index (Kg/m²) (mean/SD)	27.4	4.8	26.0	4.3	27.3	4.6	28.5	4.9	29.7	5.8
Body Mass Index (Kg/m²)
< 18.5	2418	0.5%	825	0.9%	1113	0.5%	352	0.3%	128	0.5%
18.5-<25	152 533	32.6%	44 075	45.5%	77 507	32.9%	26 157	23.8%	4794	18.8%
25-<30	199 212	42.6%	37 528	38.7%	103 141	43.7%	48 586	44.3%	9957	39.1%
30+	113 922	24.3%	14 489	15.0%	54 197	23.0%	34 626	31.6%	10 610	41.6%
Body fat percentage (mean/SD)	31.3	8.5	28.9	8.3	31.1	8.4	33.2	8.4	34.3	8.8
Waist:hip ratio^b
Waist:hip ratio (mean/SD)	0.9	0.1	0.9	0.1	0.9	0.1	0.9	0.1	0.9	0.1
Below IDF guideline	202 545	43.2%	54 750	56.4%	104 482	44.2%	36 717	33.4%	6596	25.7%
Above IDF guideline	266 443	56.8%	42 333	43.6%	131 829	55.8%	73 249	66.6%	19 032	74.3%
Health status
Diabetes^c	24 347	5.2%	3085	3.2%	10 404	4.4%	7687	7.0%	3171	12.4%
Gastro-oesophageal reflux^d	22 495	4.8%	3233	3.3%	10 672	4.5%	6648	6.0%	1942	7.5%
Family history^e
Prostate cancer	37 225	8.0%	8431	8.8%	18 607	8.0%	8332	7.7%	1855	7.3%
Breast cancer	49 524	10.7%	10 520	10.9%	24 986	10.7%	11 360	10.5%	2658	10.5%
Lung cancer	59 042	12.7%	9596	10.0%	29 218	12.5%	16 107	14.9%	4121	16.3%
Bowel cancer	52 109	11.2%	10 181	10.6%	25 851	11.1%	12 943	11.9%	3134	12.4%
Use of sun/UV protection
Never/rarely/sometimes	203 968	43.7%	43 450	44.9%	99 121	42.1%	48 286	44.1%	13 111	51.4%
Most of the time/always	260 241	55.7%	52 699	54.5%	135 033	57.4%	60 493	55.3%	12 016	47.1%
Do not go out in sunshine	2770	0.6%	538	0.6%	1136	0.5%	717	0.7%	379	1.5%
Aspirin use
Regularly uses aspirin^f	64 822	14.0%	9711	10.1%	29 908	12.9%	19 246	17.8%	5957	23.7%
HRT use^g
Ever used HRT	95 369	37.8%	14 791	27.8%	46 587	36.8%	27 639	46.6%	6352	49.1%
Oral contraceptive use^g
Ever taken oral contraceptive pill	205 528	81.4%	44 285	83.0%	104 772	82.7%	46 695	78.7%	9776	75.6%
Number of live births (0, 1, 2, 3 + live births)^g(mean/SD)	1.8	1.2	1.8	1.2	1.8	1.2	1.9	1.2	2.0	1.3
Age at menarche (mean/SD)^g	13.0	1.6	13.0	1.6	13.0	1.6	13.0	1.7	13.0	1.7
Age at menopause (mean/SD)^g	49.8	5.1	50.0	4.7	49.9	5.0	49.6	5.4	49.0	5.8
Menopausal status^g
Had menopause	151 101	59.8%	27 736	51.9%	74 075	58.4%	40 399	68.0%	8891	68.6%
Not had menopause	62 570	24.8%	18 659	34.9%	33 174	26.1%	9075	15.3%	1662	12.8%
Unsure	39 065	15.5%	7002	13.1%	19 684	15.5%	9969	16.8%	2410	18.6%
Hysterectomy status^g
Had hysterectomy	17 530	7.8%	2458	5.0%	8193	7.2%	5483	10.6%	1396	12.7%
Not had hysterectomy/unsure	207 953	92.2%	46 846	95.0%	105 238	92.8%	46 232	89.4%	9637	87.4%
CSE: Certificate of Secondary Education; GCSE: General Certificate of Secondary Education; HNC: Higher National Certificate; HND: Higher National Diploma; HRT: hormone-replacement therapy; IDF: International Diabetes Federation; MVPA: moderate-vigorous intensity physical activity; NVQ: National Vocational Qualifications; UV: ultraviolet.
^aDefined in terms of pack-years: light (< 20 pack-years), heavy (≥ 20 pack-years).
^bBased on IDF criteria (waist circumference > 94 cm in men; >80 cm in women).
^cDiagnosed by doctor.
^dSelf-reported.
^eBased on self-reported illnesses of father, mother and siblings.
^fRegular use defined as most days of the week for the last 4 weeks.
^gFemale participants only.

Association of cancer risk and daily TV screen time

Table 2 and Fig. 1 show the association between daily TV screen time and total cancer risk and site-specific cancer risk. A 1-hour increase in daily TV screen time was associated with higher risk of oropharyngeal cancer (HR 1.06, 95% CI: 1.02, 1.11), stomach cancer (HR 1.06, 95% CI: 1.001, 1.13), oesophagus and stomach cancer (HR 1.04, 95% CI: 1.005, 1.09), and colon cancer (HR 1.04, 95% CI: 1.01, 1.06) in fully adjusted models. In addition, the categorical analysis showed that participants who reported > 5 hours/day of TV screen time had a higher risk of oropharyngeal cancer (HR 1.48, HR: 1.09, 2.01) and a lower risk of uterus cancer (HR 0.61, 95% CI: 0.42, 0.88) compared to participants who reported 1-≤3 hours/day of TV screen time. Participants who reported 3-≤5 hours/day of TV screen time had a higher risk of bladder cancer (HR 1.21, 95% CI: 1.002, 1.45) compared to participants who reported 1-≤3 hours/day of TV screen time, but no dose-response association was evident for greater duration of screen time.

Table 2. Results of Cox proportional hazards analyses investigating the association between self-report TV screen time and cancer incidence.

		1 hour increase in TV screen time	p-value	≤1 hour	1-≤3 hours (reference)	3-≤5 hours	>5 hours
Person-years		3 526 324		736 537	1 781 542	818 674	189 571
All cancers excluding non-melanoma skin cancer	Cases	28 992		4971	14 105	7856	2060
All cancers excluding non-melanoma skin cancer	HR (95% CI)*	1.03 (1.02 1.03)	<0.001**	0.96 (0.92 0.99)	1.00	1.04 (1.01 1.07)	1.13 (1.08 1.19)
	HR (95% CI)†	1.01 (0.999 1.01)	0.07**	0.98 (0.94 1.01)	1.00	1.003 (0.97 1.03)	1.02 (0.97 1.07)
	HR (95% CI)^{a, b, c, d, e, g, h}	1.01 (0.999 1.02)	0.07**	0.97 (0.94 1.01)	1.00	1.01 (0.97 1.04)	1.01 (0.96 1.07)
Skin, melanoma	Cases	1635		315	831	404	85
Skin, melanoma	HR (95% CI)*	0.98 (0.95 1.01)	0.24**	0.99 (0.87 1.12)	1.00	0.96 (0.85 1.08)	0.84 (0.67 1.06)
	HR (95% CI)†	1.01 (0.97 1.04)	0.74	0.99 (0.87 1.13)	1.00	1.004 (0.89 1.14)	1.01 (0.80 1.29)
	HR (95% CI)^a	1.004 (0.97 1.04)	0.84	1.01 (0.88 1.15)	1.00	1.001 (0.88 1.13)	1.02 (0.81 1.30)
Oropharyngeal	Cases	557		86	263	148	60
	HR (95% CI)*	1.12 (1.08 1.17)	<0.001	0.83 (0.65 1.06)	1.00	1.17 (0.96 1.43)	1.99 (1.50 2.63)
	HR (95% CI)†	1.06 (1.02 1.11)	0.009	0.83 (0.64 1.07)	1.00	1.07 (0.87 1.32)	1.48 (1.09 2.01)
	HR (95% CI)	1.06 (1.02 1.11)	0.009	0.83 (0.64 1.07)	1.00	1.07 (0.87 1.32)	1.48 (1.09 2.01)
Lung	Cases	2076		236	901	656	283
Lung	HR (95% CI)*	1.17 (1.14 1.19)	<0.001**	0.74 (0.64 0.86)	1.00	1.29 (1.17 1.43)	2.28 (1.99 2.61)
	HR (95% CI)†	1.02 (0.995 1.04)	0.12**	0.87 (0.75 1.01)	1.00	0.98 (0.88 1.09)	1.09 (0.93 1.26)
	HR (95% CI)^h	1.02 (0.997 1.05)	0.09**	0.85 (0.73 0.997)	1.00	0.98 (0.88 1.09)	1.09 (0.94 1.27)
Breast (female only)	Cases	5702		1097	2903	1386	316
Breast (female only)	HR (95% CI)*	1.01 (0.99 1.02)	0.43	0.93 (0.87 1.002)	1.00	0.97 (0.91 1.03)	1.003 (0.89 1.13)
	HR (95% CI)†	1.003 (0.98 1.02)	0.77**	0.94 (0.88 1.01)	1.00	0.96 (0.90 1.03)	0.99 (0.87 1.12)
	HR (95% CI)^{b, h}	1.01 (0.99 1.03)	0.59**	0.92 (0.85 0.996)	1.00	0.95 (0.88 1.02)	1.01 (0.87 1.16)
Uterus	Cases	872		151	411	264	46
	HR (95% CI)*	1.04 (0.999 1.08)	0.053	0.97 (0.81 1.17)	1.00	1.21 (1.03 1.41)	0.95 (0.70 1.28)
	HR (95% CI)†	0.97 (0.93 1.02)	0.21	1.05 (0.86 1.27)	1.00	1.05 (0.89 1.24)	0.63 (0.44 0.88)
	HR (95% CI)^c	0.97 (0.93 1.02)	0.24	1.03 (0.84 1.27)	1.00	1.04 (0.87 1.24)	0.61 (0.42 0.88)
Ovary	Cases	578		105	287	155	31
	HR (95% CI)*	1.002 (0.95 1.05)	0.93	0.95 (0.76 1.19)	1.00	1.02 (0.84 1.24)	0.91 (0.63 1.32)
	HR (95% CI)†	1.02 (0.96 1.08)	0.53	0.90 (0.71 1.15)	1.00	1.04 (0.84 1.27)	0.93 (0.63 1.38)
	HR (95% CI)	1.02 (0.96 1.08)	0.53	0.90 (0.71 1.15)	1.00	1.04 (0.84 1.27)	0.93 (0.63 1.38)
Prostate	Cases	5979		1116	2957	1562	344
	HR (95% CI)*	0.96 (0.95 0.98)	<0.001**	1.08 (1.01 1.15)	1.00	0.96 (0.90 1.02)	0.81 (0.73 0.91)
	HR (95% CI)†	0.99 (0.97 1.004)	0.12**	1.05 (0.98 1.13)	1.00	1.01 (0.94 1.07)	0.94 (0.83 1.06)
	HR (95% CI)^h	0.99 (0.97 1.01)	0.17**	1.04 (0.97 1.12)	1.00	1.01 (0.95 1.08)	0.95 (0.84 1.07)
Oesophagus	Cases	541		70	246	176	49
	HR (95% CI)*	1.10 (1.05 1.15)	<0.001	0.80 (0.61 1.04)	1.00	1.30 (1.07 1.58)	1.44 (1.06 1.96)
	HR (95% CI)†	1.03 (0.98 1.08)	0.31**	0.85 (0.64 1.13)	1.00	1.09 (0.89 1.34)	1.02 (0.73 1.42)
	HR (95% CI)^f	1.02 (0.97 1.08)	0.34**	0.86 (0.65 1.14)	1.00	1.09 (0.88 1.34)	1.02 (0.73 1.42)
Stomach	Cases	356		36	164	121	35
	HR (95% CI)*	1.14 (1.08 1.20)	<0.001	0.61 (0.43 0.88)	1.00	1.34 (1.06 1.70)	1.55 (1.08 2.24)
	HR (95% CI)†	1.06 (1.001 1.13)	0.045	0.66 (0.45 0.97)	1.00	1.12 (0.87 1.44)	1.03 (0.69 1.53)
	HR (95% CI)	1.06 (1.001 1.13)	0.045	0.66 (0.45 0.97)	1.00	1.12 (0.87 1.44)	1.03 (0.69 1.53)
Oesophagus and stomach	Cases	891		105	405	297	84
	HR (95% CI)*	1.12 (1.08 1.15)	<0.001	0.73 (0.59 0.90)	1.00	1.33 (1.15 1.55)	1.50 (1.19 1.90)
	HR (95% CI)†	1.04 (1.005 1.09)	0.03	0.78 (0.62 0.98)	1.00	1.12 (0.95 1.31)	1.04 (0.81 1.34)
	HR (95% CI)	1.04 (1.005 1.09)	0.03	0.78 (0.62 0.98)	1.00	1.12 (0.95 1.31)	1.04 (0.81 1.34)
Hepatobiliary tract	Cases	456		74	203	130	49
	HR (95% CI)*	1.08 (1.03 1.14)	0.002	1.02 (0.78 1.33)	1.00	1.15 (0.93 1.44)	1.77 (1.30 2.43)
	HR (95% CI)†	1.01 (0.96 1.07)	0.62	1.08 (0.82 1.43)	1.00	0.98 (0.78 1.24)	1.26 (0.90 1.77)
	HR (95% CI)	1.01 (0.96 1.07)	0.62	1.08 (0.82 1.43)	1.00	0.98 (0.78 1.24)	1.26 (0.90 1.77)
Pancreatic	Cases	615		97	283	187	48
	HR (95% CI)*	1.07 (1.02 1.11)	0.004	0.96 (0.76 1.21)	1.00	1.19 (0.99 1.43)	1.25 (0.92 1.70)
	HR (95% CI)†	1.04 (0.99 1.09)	0.15	0.99 (0.78 1.27)	1.00	1.12 (0.92 1.36)	1.07 (0.77 1.49)
	HR (95% CI)^d	1.03 (0.98 1.08)	0.20	0.996 (0.78 1.27)	1.00	1.11 (0.92 1.36)	1.03 (0.74 1.44)
Kidney	Cases	779		113	390	206	70
	HR (95% CI)*	1.06 (1.02 1.10)	0.007	0.79 (0.64 0.98)	1.00	0.99 (0.83 1.17)	1.37 (1.06 1.76)
	HR (95% CI)†	0.996 (0.95 1.04)	0.86**	0.92 (0.74 1.14)	1.00	0.88 (0.74 1.05)	1.08 (0.82 1.42)
	HR (95% CI)	0.996 (0.95 1.04)	0.86**	0.92 (0.74 1.14)	1.00	0.88 (0.74 1.05)	1.08 (0.82 1.42)
Bladder	Cases	677		92	295	221	69
	HR (95% CI)*	1.10 (1.05 1.14)	<0.001	0.90 (0.71 1.14)	1.00	1.32 (1.10 1.57)	1.62 (1.25 2.11)
	HR (95% CI)†	1.04 (0.99 1.09)	0.13**	1.04 (0.81 1.32)	1.00	1.21 (1.002 1.45)	1.29 (0.97 1.73)
	HR (95% CI)	1.04 (0.99 1.09)	0.13**	1.04 (0.81 1.32)	1.00	1.21 (1.002 1.45)	1.29 (0.97 1.73)
Colorectal	Cases	3358		538	1643	936	241
	HR (95% CI)*	1.03 (1.01 1.05)	0.001**	0.90 (0.82 0.99)	1.00	1.05 (0.97 1.14)	1.11 (0.97 1.28)
	HR (95% CI)†	1.02 (0.999 1.04)	0.07**	0.93 (0.84 1.03)	1.00	1.03 (0.94 1.12)	1.05 (0.90 1.22)
	HR (95% CI)^{e, g, f (males)}	1.02 (0.995 1.04)	0.13**	0.93 (0.83 1.03)	1.00	1.02 (0.93 1.11)	1.03 (0.88 1.20)
Colon	Cases	2155		329	1041	614	171
	HR (95% CI)*	1.05 (1.02 1.08)	<0.001**	0.87 (0.77 0.99)	1.00	1.08 (0.98 1.19)	1.24 (1.05 1.45)
	HR (95% CI)†	1.04 (1.01 1.07)	0.007**	0.92 (0.81 1.05)	1.00	1.05 (0.94 1.17)	1.19 (1.003 1.42)
	HR (95% CI)^{e, g, f (males)}	1.04 (1.01 1.06)	0.02**	0.93 (0.81 1.06)	1.00	1.05 (0.94 1.16)	1.17 (0.98 1.41)
Rectum	Cases	1127		196	556	307	68
	HR (95% CI)*	1.01 (0.98 1.05)	0.53**	0.96 (0.81 1.13)	1.00	1.04 (0.90 1.20)	0.94 (0.73 121)
	HR (95% CI)†	0.996 (0.96 1.04)	0.84**	0.98 (0.82 1.16)	1.00	1.04 (0.89 1.20)	0.84 (0.63 1.11)
	HR (95% CI)^{e, g}	0.99 (0.95 1.03)	0.67	0.96 (0.81 1.14)	1.00	1.01 (0.87 1.18)	0.82 (0.62 1.10)
Brain tumours	Cases	463		82	237	114	30
	HR (95% CI)*	1.03 (0.98 1.08)	0.29	0.92 (0.72 1.19)	1.00	0.93 (0.74 1.16)	1.003 (0.69 1.47)
	HR (95% CI)†	1.04 (0.98 1.10)	0.20	0.87 (0.66 1.13)	1.00	0.92 (0.73 1.17)	0.96 (0.63 1.46)
	HR (95% CI)	1.04 (0.98 1.10)	0.20	0.87 (0.66 1.13)	1.00	0.92 (0.73 1.17)	0.96 (0.63 1.46)
Thyroid	Cases	242		48	124	57	13
	HR (95% CI)*	0.99 (0.91 1.07)	0.75	0.95 (0.68 1.32)	1.00	0.97 (0.70 1.33)	0.97 (0.55 1.73)
	HR (95% CI)†	1.001 (0.92 1.09)	0.98	0.92 (0.65 1.30)	1.00	0.93 (0.66 1.32)	1.14 (0.63 2.06)
	HR (95% CI)	1.001 (0.92 1.09)	0.98	0.92 (0.65 1.30)	1.00	0.93 (0.66 1.32)	1.14 (0.63 2.06)
Haematological malignancies	Cases	2468		438	1208	652	170
	HR (95% CI)*	1.01 (0.98 1.03)	0.52	0.995 (0.89 1.11)	1.00	0.99 (0.90 1.09)	1.06 (0.91 1.25)
	HR (95% CI)†	1.002 (0.98 1.03)	0.89	0.97 (0.87 1.09)	1.00	0.97 (0.88 1.08)	0.97 (0.82 1.16)
	HR (95% CI)	1.002 (0.98 1.03)	0.89	0.97 (0.87 1.09)	1.00	0.97 (0.88 1.08)	0.97 (0.82 1.16)
Non-Hodgkin’s lymphoma	Cases	1193		197	586	337	73
	HR (95% CI)*	1.01 (0.98 1.05)	0.44	0.92 (0.78 1.08)	1.00	1.06 (0.93 1.21)	0.95 (0.74 1.21)
	HR (95% CI)†	1.01 (0.98 1.05)	0.48	0.89 (0.75 1.05)	1.00	1.08 (0.93 1.24)	0.85 (0.65 1.11)
	HR (95% CI)	1.01 (0.98 1.05)	0.48	0.89 (0.75 1.05)	1.00	1.08 (0.93 1.24)	0.85 (0.65 1.11)

*Models adjusted for age and sex (total observations=470 578).

†Models adjusted for age, sex, ethnicity (white/other), deprivation index (quintiles), education (University degree, A-levels/HNC/HND/NVQ, GCSE/O-level/CSE, OTHER, None), fruit and vegetable intake (<5 portions/day, ≥5 portions/day), BMI (kg/m2), height (m), smoking status (never, former light smoker [<20 pack-years], former heavy smoker [≥20 pack-years], current light smoker [<20 pack-years], current heavy smoker [≥20 pack-years]) and alcohol intake (never, former, current [<once/week], current [≥once/week]).

^aAdditional site-specific covariates in the final model include use of sun/UV protection (Never/rarely/sometimes; most of the time/always; do not go out in sunshine).

^bAdditional site-specific covariates in the final model include HRT use (ever used/never used), oral contraceptive use (ever used/never used), number of live births (0, 1, 2, 3+ live births), age at menarche (early menarche [<12 years], menarche at 12-14 years, late menarche [≥15 years]), age at menopause (<40 years, 40-44 years, 45-49 years, 50-54 years, 55-59 years, 60-64 years, ≥65 years, not had menopause/unsure), hysterectomy status (had hysterectomy, not had hysterectomy/unsure).

^cAdditional site-specific covariates in the final model include HRT use (ever used/never used), oral contraceptive use (ever used/never used), number of live births (0, 1, 2, 3+ live births), age at menarche (early menarche [<12 years], menarche at 12-14 years, late menarche [≥15 years]), age at menopause (<40 years, 40-44 years, 45-49 years, 50-54 years, 55-59 years, 60-64 years, ≥65 years, not had menopause/unsure), hysterectomy status (had hysterectomy, not had hysterectomy/unsure).

^dAdditional site-specific covariates in the final model include diabetes at baseline (yes/no).

^eAdditional site-specific covariates in the final model include diabetes at baseline (yes/no), aspirin use (regular use/non-regular use or no use), HRT use (ever used/never used), red meat intake (portion/week), processed meat intake (portion/week).

^fFinal model also adjusted for waist-hip ratio (>94cm in men, >80cm in women).

^gResults for males and females combined using meta-analysis as covariates are different.

^hFinal model also adjusted for family history of cancer (mother/father/sibling had cancer, no family history).

**Schoenfeld test indicated potential violation of the proportional hazards assumption (p<0.05).

Participants who reported ≤ 1 hour/day of TV screen time had a lower risk of lung cancer (HR 0.85, 95% CI: 0.73, 0.997), breast (female only) cancer (HR 0.92, 95% CI: 0.85, 0.996), stomach cancer (HR 0.66, 95% CI: 0.45, 0.97), and oesophagus and stomach cancer (HR 0.78, 95% CI: 0.62, 0.98) compared to participants who reported 1-≤3 hours/day of TV screen time.

After excluding cancers diagnosed within the first 2 years following baseline, all associations were attenuated except those for oesophagus and stomach cancers, and colon cancers (Table 3). Whilst the results of the Schoenfeld residual tests indicated that some of our models may not have been in line with the proportional hazards assumption, our visual inspection of log-log plots and Schoenfeld residual plots showed no serious violations. Therefore, we proceeded with the analyses as planned.

Table 3

Results of Cox proportional hazards analyses investigating the association between self-report TV screen time and cancer incidence (excluding cancers diagnosed within the first 2 years following baseline).
		1 hour increase in TV screen time	p-value	≤ 1 hour	1-≤3 hours (reference)	3-≤5 hours	> 5 hours
All cancers excluding non-melanoma skin cancer	Cases	18190		3,213	9,003	4,802	1,172
	HR (95% CI)^{a, b, c, d, e, g, h}	1.01(0.999,1.02)	0.07**	0.96(0.92,1.001)	1.00	1.01(0.97,1.04)	1.002(0.94,1.07)
Skin, melanoma	Cases	1,192		222	613	299	58
	HR (95% CI)^a	1.02(0.98,1.06)	0.39**	0.93(0.80,1.09)	1.00	1.04(0.90,1.20)	1.004(0.76,1.33)
Oropharyngeal	Cases	410		69	197	105	39
	HR (95% CI)	1.04(0.99,1.10)	0.12	0.90(0.68,1.19)	1.00	1.02(0.80,1.30)	1.27(0.89,1.84)
Lung	Cases	1,461		165	638	467	191
	HR (95% CI)^h	1.02(0.99,1.05)	0.21**	0.87(0.73,1.03)	1.00	0.97(0.86,1.10)	1.10(0.93,1.30)
Breast (female only)	Cases	3,288		657	1,724	756	151
	HR (95% CI)^{b, h}	1.004(0.98,1.03)	0.76**	0.90(0.82,0.99)	1.00	0.96(0.87,1.04)	0.94(0.79,1.11)
Uterus	Cases	567		102	270	165	30
	HR (95% CI)^c	0.98(0.93,1.04)	0.56**	1.05(0.83,1.33)	1.00	1.06(0.87,1.29)	0.75(0.50,1.10)
Ovary	Cases	404		69	204	109	22
	HR (95% CI)	1.02(0.95,1.09)	0.60	0.88(0.67,1.16)	1.00	1.01(0.80,1.29)	0.93(0.59,1.46)
Prostate	Cases	4,235		804	2,127	1,069	235
	HR (95% CI)^h	0.99(0.97,1.01)	0.34**	1.02(0.94,1.10)	1.00	0.98(0.91,1.06)	0.94(0.82,1.08)
Oesophagus	Cases	392		46	182	125	39
	HR (95% CI)^f	1.04(0.98,1.10)	0.20**	0.79(0.57,1.10)	1.00	1.08(0.86,1.37)	1.11(0.77,1.60)
Stomach	Cases	250		24	125	78	23
	HR (95% CI)	1.06(0.99,1.14)	0.09	0.57(0.37,0.89)	1.00	0.996(0.74,1.33)	0.96(0.60,1.53)
Oesophagus and stomach	Cases	638		70	303	203	62
	HR (95% CI)	1.05(1.011.10)	0.03**	0.71(0.54,0.92)	1.00	1.07(0.89,1.28)	1.07(0.80,1.42)
Hepatobiliary tract	Cases	348		51	156	100	41
	HR (95% CI)	1.03(0.97,1.10)	0.29	0.9999(0.72,1.38)	1.00	1.001(0.77,1.29)	1.36(0.94,1.95)
Pancreatic	Cases	463		75	215	140	33
	HR (95% CI)^d	1.01(0.95,1.06)	0.85	1.02(0.78,1.33)	1.00	1.09(0.88,1.36)	0.91(0.62,1.34)
Kidney	Cases	583		90	280	161	52
	HR (95% CI)	0.99(0.94,1.04)	0.75	1.02(0.80,1.30)	1.00	0.95(0.77,1.15)	1.11(0.81,1.51)
Bladder	Cases	461		57	208	155	41
	HR (95% CI)	1.03(0.98,1.09)	0.25**	0.88(0.65,1.19)	1.00	1.19(0.96,1.47)	1.10(0.77,1.55)
Colorectal	Cases	2,281		383	1,118	621	159
	HR (95% CI)^{e, g, f (males)}	1.03(0.998,1.05)	0.07**	0.95(0.85,1.07)	1.00	1.03(0.93,1.14)	1.10(0.93,1.31)
Colon	Cases	1,478		246	712	407	113
	HR (95% CI)^{e, g, f (males)}	1.04(1.01,1.07)	0.02**	0.97(0.84,1.13)	1.00	1.05(0.92,1.19)	1.22(0.99,1.50)
Rectum	Cases	754		134	373	201	46
	HR (95% CI)^{e, g}	1.000(0.96,1.05)	0.995	0.98(0.80,1.20)	1.00	1.03(0.87,1.23)	0.99(0.72,1.36)
Brain tumours	Cases	333		58	178	80	17
	HR (95% CI)	1.03(0.96,1.11)	0.38	0.85(0.62,1.15)	1.00	0.89(0.68,1.17)	0.81(0.48,1.35)
Thyroid	Cases	161		32	85	35	9
	HR (95% CI)	0.99(0.89,1.09)	0.78	0.91(0.60,1.38)	1.00	0.88(0.59,1.32)	0.998(0.49,2.03)
Haematological malignancies	Cases	1,786		315	888	470	113
	HR (95% CI)	1.003(0.97,1.03)	0.86**	0.96(0.84,1.10)	1.00	0.98(0.87,1.10)	0.95(0.78,1.17)
Non-Hodgkin’s lymphoma	Cases	886		138	431	254	43
	HR (95% CI)	1.02(0.98,1.07)	0.26	0.84(0.69,1.02)	1.00	1.12(0.95,1.31)	0.78(0.57,1.08)
^aAdditional site-specific covariates in the final model include use of sun/UV protection (Never/rarely/sometimes; most of the time/always; do not go out in sunshine).
^bAdditional site-specific covariates in the final model include HRT use (ever used/never used), oral contraceptive use (ever used/never used), number of live births (0, 1, 2, 3 + live births), age at menarche (early menarche [< 12 years], menarche at 12–14 years, late menarche [≥ 15 years]), age at menopause (< 40 years, 40–44 years, 45–49 years, 50–54 years, 55–59 years, 60–64 years, ≥ 65 years, not had menopause/unsure), hysterectomy status (had hysterectomy, not had hysterectomy/unsure).
^cAdditional site-specific covariates in the final model include HRT use (ever used/never used), oral contraceptive use (ever used/never used), number of live births (0, 1, 2, 3 + live births), age at menarche (early menarche [< 12 years], menarche at 12–14 years, late menarche [≥ 15 years]), age at menopause (< 40 years, 40–44 years, 45–49 years, 50–54 years, 55–59 years, 60–64 years, ≥ 65 years, not had menopause/unsure), hysterectomy status (had hysterectomy, not had hysterectomy/unsure).
^dAdditional site-specific covariates in the final model include diabetes at baseline (yes/no).
^eAdditional site-specific covariates in the final model include diabetes at baseline (yes/no), aspirin use (regular use/non-regular use or no use), HRT use (ever used/never used), red meat intake (portion/week), processed meat intake (portion/week).
^fFinal model also adjusted for waist-hip ratio (> 94 cm in men, > 80 cm in women).
^gResults for males and females combined using meta-analysis as covariates are different.
^hFinal model also adjusted for family history of cancer (mother/father/sibling had cancer, no family history).
**Schoenfeld test indicated potential violation of the proportional hazards assumption (p < 0.05).

Subgroup analyses and tests of effect modification (supplement 1, tables 1.2–1.8) showed that HR estimates for total cancer risk in females differed according to BMI (p = 0.0002) and that HR estimates differed for lung cancer risk according to socio-economic status (p = 0.004). HR estimates differed according to the combined body fat percentage and physical activity level category for lung cancer (p = 0.003) and haematological malignancies (p = 0.0004). The results of all other subgroup analyses were non-significant.

Results of partition models and isotemporal substitution models

Partition models showed there was an association between a 1-hour increase in daily TV screen time and a higher risk of total cancer (HR 1.01, 95% CI: 1.002, 1.02), oropharyngeal cancer (HR 1.11, 95% CI: 1.05, 1.17), and lung cancer (HR 1.04, 95% CI: 1.01, 1.07) when holding daily time spent in moderate-intensity physical activity, vigorous-intensity physical activity and walking constant. There was an association between a 1-hour increase in daily time spent in moderate-intensity physical activity and a lower risk of breast (female only) cancer (HR 0.91, 95% CI: 0.86, 0.96), and colon cancer (HR 0.89, 95% CI: 0.81, 0.97) when holding daily TV screen time, and time spent in vigorous-intensity physical activity and walking constant (supplement 1, Table 1.1).

Isotemporal substitution models showed there was an association between replacing 1-hour of daily TV screen time with 1-hour of moderate-intensity physical activity and a lower risk of total cancer (HR 0.97, 95% CI: 0.95, 0.99), breast (female only) cancer (HR 0.90, 95% CI: 0.85, 0.96), colorectal cancer (HR 0.92, 95% CI: 0.86, 0.99), and colon cancer (HR 0.87, 95% CI: 0.79, 0.95) when holding time spent in vigorous-intensity physical activity and walking constant. There was an association between replacing 1-hour of daily TV screen time with 1-hour of walking and a lower risk of oropharyngeal cancer (HR 0.79, 95% CI: 0.67, 0.92), and lung cancer (HR 0.89, 95% CI: 0.82, 0.97) when holding time spent in moderate- and vigorous-intensity physical activity constant (Table 4).

Table 4. Results of isotemporal substitution models showing the impact on cancer incidence of replacing a 1-hour of total daily TV screen time with the same amount of daily moderate activity, daily vigorous activity or daily walking time, holding the other activities constant.


	1-hour increase in daily moderate activity	1-hour increase in daily vigorous activity	1-hour increase in daily walking time
	HR (95% CI)	HR (95% CI)	HR (95% CI)
All cancers excluding non-melanoma skin cancer [cases=19 167]^{a b c d e g h}	0.97 (0.95, 0.99)	1.001 (0.96, 1.05)	0.98 (0.96, 1.01)
Skin melanoma [cases=1256]^a	0.98 (0.89, 1.09)	0.97 (0.81, 1.17)	1.03 (0.94, 1.12)
Oropharyngeal [cases=411]	0.91 (0.77, 1.08)	0.86 (0.63, 1.18)	0.79 (0.67, 0.92)
Lung [cases=1355]^h	1.0003 (0.92, 1.09)	0.84 (0.71, 1.004)	0.89 (0.82, 0.97)
Breast (female only) [cases=3454]^{b h}	0.90 (0.85, 0.96)	1.02 (0.89, 1.16)	0.99 (0.94, 1.05)
Uterus [cases=570]^c	1.001 (0.86, 1.17)	1.05 (0.76, 1.46)	0.99 (0.86, 1.13)
Ovary [cases=405]	1.09 (0.93, 1.28)	1.12 (0.81, 1.55)	0.97 (0.83, 1.13)
Prostate [cases=4629]^h	1.01 (0.96, 1.06)	1.05 (0.97, 1.15)	0.9997 (0.95, 1.05)
Oesophagus [cases=386]^f	1.09 (0.93, 1.28)	1.06 (0.80, 1.42)	0.91 (0.77, 1.06)
Stomach [cases=264]	1.06 (0.87, 1.29)	0.77 (0.52, 1.15)	0.91 (0.76, 1.10)
Oesophagus and stomach [cases=644]	1.08 (0.95, 1.22)	0.94 (0.74, 1.18)	0.90 (0.80, 1.02)
Hepatobiliary tract [cases=331]	0.84 (0.69, 1.02)	1.01 (0.71, 1.43)	1.03 (0.87, 1.21)
Pancreatic [cases=467]^d	1.07 (0.92, 1.24)	0.92 (0.69, 1.23)	0.95 (0.82, 1.09)
Kidney [cases=559]	1.01 (0.88, 1.17)	1.12 (0.87, 1.44)	0.95 (0.83, 1.09)
Bladder [cases=502]	0.98 (0.85, 1.13)	0.83 (0.62, 1.09)	1.03 (0.90, 1.17)
Colorectal [cases=2405]^{e g f (males)}	0.92 (0.86, 0.99)	0.997 (0.87, 1.14)	1.01 (0.95, 1.08)
Colon [cases=1530]^{e g f (males)}	0.87 (0.79, 0.95)	0.96 (0.81, 1.14)	1.001 (0.92, 1.09)
Rectum [cases=821]^{e g}	0.99 (0.88, 1.12)	1.06 (0.86, 1.30)	1.01 (0.90, 1.12)
Brain tumours [cases=345]	0.85 (0.70, 1.03)	0.85 (0.59, 1.23)	1.04 (0.88, 1.23)
Thyroid [cases=181]	0.94 (0.72, 1.23)	0.80 (0.46, 1.40)	1.07 (0.85, 1.35)
Haematological malignancies [cases=1794]	0.98 (0.90, 1.06)	1.07 (0.93, 1.24)	0.99 (0.92, 1.07)
Non-Hodgkin’s lymphoma [cases=864]	0.99 (0.88, 1.11)	1.07 (0.87, 1.32)	0.95 (0.85, 1.06)

All models were adjusted for age, sex, ethnicity (white/other), deprivation index (quintiles), education (University degree, A-levels/HNC/HND/NVQ, GCSE/O-level/CSE, OTHER, None), fruit and vegetable intake (<5 portions/day, ≥5 portions/day), BMI (kg/m2), height (m), smoking status (never, former light smoker [<20 pack-years], former heavy smoker [≥20 pack-years], current light smoker [<20 pack-years], current heavy smoker [≥20 pack-years]) and alcohol intake (never, former, current [<once/week], current [≥once/week]).