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: https://doi.org/10.21203/rs.2.18993/v1

Abstract

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.

Introduction

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.

Methods

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 26). 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).

Results

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 statusa
                   
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 ratiob
                   
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
                   
Diabetesc
24 347
5.2%
3085
3.2%
10 404
4.4%
7687
7.0%
3171
12.4%
Gastro-oesophageal refluxd
22 495
4.8%
3233
3.3%
10 672
4.5%
6648
6.0%
1942
7.5%
Family historye
                   
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 aspirinf
64 822
14.0%
9711
10.1%
29 908
12.9%
19 246
17.8%
5957
23.7%
HRT useg
                   
Ever used HRT
95 369
37.8%
14 791
27.8%
46 587
36.8%
27 639
46.6%
6352
49.1%
Oral contraceptive useg
                   
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 statusg
                   
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 statusg
                   
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

 

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

 

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

 

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

 

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]).

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).

 

Association of cancer risk and daily time spent on the computer

Table 5 shows the association between a 1-hour increase in daily time spent using computers and total cancer risk and site-specific cancer risk. A 1-hour increase in daily computer screen time was associated with lower risk of oropharyngeal cancer (HR 0.93, 95% CI: 0.87, 0.998). The categorical analysis showed that participants who reported that they spent no hours using computers had a higher risk of oropharyngeal cancer (HR 1.27, 95% CI: 1.03, 1.56), and ovary cancer (HR 1.23, 95% CI: 1.01, 1.50) compared to participants who reported ≤ 1 hour of daily time spent using the computer.

Participants who reported > 3 hours using computers had a higher risk of lung cancer (HR 1.36, 95% CI: 1.12, 1.65) compared to participants who reported ≤ 1 hour of daily time spent using the computer.

Table 5. Results of Cox proportional hazards analyses investigating the association between self-report computer screen time and cancer incidence.



1 hour increase in computer screen time

p-value

None

≤1 hour (reference)

1-≤3 hours

>3 hours

 

Person-years

 

3 498 487

 

969 721

1 744 785

582 168

201 813

 

All cancers excluding non-melanoma skin cancer

Cases

28 697

 

9012

13 124

4992

1569

 

HR (95% CI)*

1.0004 (0.99 1.01)

0.92**

1.06 (1.03 1.09)

1.00

1.03 (0.99 1.06)

1.06 (1.01 1.12)

 

 

HR (95% CI)†

0.998 (0.99 1.01)

0.65**

1.03 (0.996 1.06)

1.00

1.002 (0.97 1.04)

1.02 (0.97 1.08)

 

 

HR (95% CI)a, b, c, d, e, g, h

0.998 (0.99 1.01)

0.73**

1.02 (0.99 1.06)

1.00

0.99 (0.96 1.03)

1.03 (0.97 1.09)

 

Skin, melanoma

Cases 

1621

 

404

852

276

89

 

HR (95% CI)*

1.01 (0.98 1.05)

0.43**

0.77 (0.68 0.86)

1.00

0.90 (0.79 1.04)

0.92 (0.74 1.14)

 

 

HR (95% CI)†

1.01 (0.97 1.05)

0.72

0.90 (0.79 1.02)

1.00

0.97 (0.85 1.12)

0.99 (0.79 1.24)

 

 

HR (95% CI)a

1.01 (0.97 1.05)

0.73

0.91 (0.80 1.03)

1.00

0.98 (0.85 1.12)

0.996 (0.79 1.25)

 

Oropharyngeal

Cases 

561

 

209

239

88

25

 

 

HR (95% CI)*

0.90 (0.84 0.97)

0.004

1.56 (1.29 1.88)

1.00

0.97 (0.76 1.23)

0.81 (0.54 1.23)

 

 

HR (95% CI)†

0.93 (0.87 0.998)

0.04

1.27 (1.03 1.56)

1.00

0.91 (0.71 1.17)

0.77 (0.51 1.17)

 

 

HR (95% CI)

0.93 (0.87 0.998)

0.04

1.27 (1.03 1.56)

1.00

0.91 (0.71 1.17)

0.77 (0.51 1.17)

 

Lung

Cases 

2040

 

894

700

316

130

 

HR (95% CI)*

0.97 (0.93 1.003)

0.08**

1.84 (1.66 2.03)

1.00

1.17 (1.03 1.34)

1.68 (1.39 2.03)

 

 

HR (95% CI)†

1.02 (0.99 1.06)

0.16**

1.10 (0.99 1.23)

1.00

1.01 (0.88 1.16)

1.33 (1.10 1.62)

 

 

HR (95% CI)h

1.02 (0.99 1.06)

0.16**

1.11 (0.99 1.24)

1.00

0.996 (0.87 1.15)

1.36 (1.12 1.65)

 

Breast (female only)

Cases 

5650

 

1728

2931

762

229

 

HR (95% CI)*

1.01 (0.99 1.03)

0.27**

0.93 (0.88 0.99)

1.00

1.02 (0.94 1.10)

0.997 (0.87 1.14)

 

 

HR (95% CI)†

1.003 (0.98 1.03)

0.83**

0.97 (0.91 1.04)

1.00

0.999 (0.92 1.08)

1.001 (0.87 1.15)

 

 

HR (95% CI)b, f, h

1.01 (0.98 1.03)

0.57**

0.96 (0.90 1.04)

1.00

0.998 (0.91 1.09)

1.03 (0.89 1.20)

 

Uterus

Cases 

863

 

315

389

127

32

 

 

HR (95% CI)*

1.01 (0.95 1.07)

0.74

1.18 (1.02 1.38)

1.00

1.25 (1.03 1.53)

1.12 (0.78 1.60)

 

 

HR (95% CI)†

0.96 (0.90 1.02)

0.20

1.18 (0.999 1.39)

1.00

1.04 (0.84 1.28)

0.91 (0.63 1.33)

 

 

HR (95% CI)c

0.98 (0.92 1.04)

0.47

1.16 (0.98 1.37)

1.00

1.08 (0.87 1.35)

0.95 (0.64 1.40)

 

Ovary

Cases 

567

 

211

266

63

27

 

 

HR (95% CI)*

0.98 (0.91 1.05)

0.51

1.16 (0.96 1.39)

1.00

0.91 (0.69 1.19)

1.36 (0.91 2.02)

 

 

HR (95% CI)†

0.96 (0.89 1.04)

0.36

1.23 (1.01 1.50)

1.00

0.91 (0.69 1.21)

1.36 (0.91 2.04)

 

 

HR (95% CI)

0.96 (0.89 1.04)

0.36

1.23 (1.01 1.50)

1.00

0.91 (0.69 1.21)

1.36 (0.91 2.04)

 

Prostate

Cases 

5933

 

1543

2699

1298

393

 

 

HR (95% CI)*

1.005 (0.99 1.02)

0.61**

0.91 (0.85 0.97)

1.00

0.97 (0.91 1.03)

0.97 (0.87 1.07)

 

 

HR (95% CI)†

0.998 (0.98 1.02)

0.85**

0.98 (0.92 1.06)

1.00

0.99 (0.93 1.06)

0.9998 (0.90 1.12)

 

 

HR (95% CI)h

0.997 (0.98 1.02)

0.73**

0.99 (0.92 1.06)

1.00

0.99 (0.93 1.06)

0.99 (0.89 1.11)

 

Oesophagus

Cases 

530

 

174

221

108

27

 

 

HR (95% CI)*

0.97 (0.90 1.03)

0.32

1.20 (0.98 1.47)

1.00

1.13 (0.90 1.42)

0.94 (0.63 1.41)

 

 

HR (95% CI)†

0.97 (0.90 1.04)

0.37

0.99 (0.79 1.23)

1.00

1.02 (0.81 1.30)

0.78 (0.51 1.19)

 

 

HR (95% CI)f

0.97 (0.90 1.04)

0.35

0.99 (0.79 1.23)

1.00

1.02 (0.80 1.30)

0.77 (0.51 1.18)

 

Stomach

Cases 

349

 

133

133

61

22

 

 

HR (95% CI)*

0.98 (0.90 1.06)

0.60

1.50 (1.18 1.92)

1.00

1.09 (0.80 1.47)

1.30 (0.83 2.05)

 

 

HR (95% CI)†

0.98 (0.91 1.07)

0.71

1.16 (0.88 1.51)

1.00

0.98 (0.72 1.35)

1.04 (0.64 1.69)

 

 

HR (95% CI)

0.98 (0.91 1.07)

0.71

1.16 (0.88 1.51)

1.00

0.98 (0.72 1.35)

1.04 (0.64 1.69)

 

Oesophagus and stomach

Cases 

873

 

305

352

168

48

 

 

HR (95% CI)*

0.97 (0.92 1.02)

0.25

1.31 (1.12 1.53)

1.00

1.11 (0.93 1.34)

1.06 (0.79 1.44)

 

 

HR (95% CI)†

0.97 (0.92 1.03)

0.33

1.05 (0.89 1.25)

1.00

1.01 (0.83 1.22)

0.86 (0.62 1.19)

 

 

HR (95% CI)f

0.97 (0.92 1.03)

0.32

1.05 (0.89 1.25)

1.00

1.01 (0.83 1.22)

0.86 (0.62 1.19)

 

Hepatobiliary tract

Cases 

451

 

170

168

91

22

 

 

HR (95% CI)*

0.97 (0.90 1.04)

0.42

1.49 (1.20 1.85)

1.00

1.38 (1.07 1.79)

1.14 (0.73 1.77)

 

 

HR (95% CI)†

0.99 (0.92 1.06)

0.74

1.21 (0.95 1.53)

1.00

1.30 (0.997 1.69)

1.02 (0.65 1.61)

 

 

HR (95% CI)

0.99 (0.92 1.06)

0.74

1.21 (0.95 1.53)

1.00

1.30 (0.997 1.69)

1.02 (0.65 1.61)

 

Pancreatic

Cases 

606

 

189

276

114

27

 

 

HR (95% CI)*

0.98 (0.92 1.05)

0.62

1.01 (0.83 1.21)

1.00

1.07 (0.86 1.34)

0.87 (0.59 1.29)

 

 

HR (95% CI)†

0.98 (0.92 1.05)

0.62

0.90 (0.73 1.11)

1.00

1.05 (0.84 1.31)

0.76 (0.50 1.15)

 

 

HR (95% CI)d

0.98 (0.92 1.05)

0.61

0.90 (0.73 1.10)

1.00

1.05 (0.84 1.31)

0.76 (0.50 1.14)

 

Kidney

Cases 

783

 

251

333

149

50

 

 

HR (95% CI)*

1.01 (0.96 1.07)

0.60

1.17 (0.995 1.39)

1.00

1.12 (0.92 1.36)

1.23 (0.91 1.66)

 

 

HR (95% CI)†

1.02 (0.97 1.08)

0.39**

1.04 (0.87 1.25)

1.00

1.05 (0.86 1.29)

1.19 (0.88 1.61)

 

 

HR (95% CI)

1.02 (0.97 1.08)

0.39**

1.04 (0.87 1.25)

1.00

1.05 (0.86 1.29)

1.19 (0.88 1.61)

 

Bladder

Cases 

670

 

227

271

142

30

 

 

HR (95% CI)*

0.98 (0.93 1.04)

0.54

1.22 (1.02 1.46)

1.00

1.18 (0.96 1.44)

0.85 (0.59 1.25)

 

 

HR (95% CI)†

0.97 (0.92 1.04)

0.41**

1.09 (0.89 1.32)

1.00

1.08 (0.87 1.33)

0.76 (0.51 1.13)

 

 

HR (95% CI)

0.97 (0.92 1.04)

0.41**

1.09 (0.89 1.32)

1.00

1.08 (0.87 1.33)

0.76 (0.51 1.13)

 

Colorectal

Cases 

3312

 

1059

1512

556

185

 

 

HR (95% CI)*

0.99 (0.96 1.02)

0.45**

1.07 (0.99 1.16)

1.00

0.95 (0.87 1.05)

1.05 (0.90 1.23)

 

 

HR (95% CI)†

0.99 (0.96 1.01)

0.31**

1.08 (0.99 1.18)

1.00

0.95 (0.86 1.05)

1.03 (0.88 1.21)

 

 

HR (95% CI)e, g, f (males)

0.98 (0.96 1.01)

0.28**

1.06 (0.97 1.16)

1.00

0.95 (0.86 1.05)

1.02 (0.87 1.20)

 

Colon

Cases 

2124

 

681

980

348

115

 

 

HR (95% CI)*

0.99 (0.96 1.03)

0.63**

1.04 (0.94 1.15)

1.00

0.94 (0.83 1.06)

1.04 (0.86 1.26)

 

 

HR (95% CI)†

0.99 (0.95 1.02)

0.50**

1.04 (0.93 1.15)

1.00

0.93 (0.82 1.06)

1.02 (0.83 1.24)

 

 

HR (95% CI)e, g, f (males)

0.99 (0.95 1.02)

0.42**

1.03 (0.92 1.14)

1.00

0.93 (0.82 1.06)

1.02 (0.83 1.25)

 

Rectum

Cases 

1115

 

354

501

195

65

 

 

HR (95% CI)*

0.98 (0.94 1.03)

0.42**

1.12 (0.98 1.29)

1.00

0.97 (0.82 1.14)

1.04 (0.80 1.35)

 

 

HR (95% CI)†

0.97 (0.93 1.02)

0.28

1.20 (1.03 1.39)

1.00

0.97 (0.82 1.15)

1.03 (0.79 1.35)

 

 

HR (95% CI)e, g

0.97 (0.93 1.02)

0.28

1.16 (0.999 1.36)

1.00

0.96 (0.81 1.15)

0.999 (0.76 1.32)

 

Thyroid

Cases

237

 

82

106

35

14

 

 

HR (95% CI)*

1.02 (0.92 1.12)

0.76

1.31 (0.98 1.76)

1.00

1.10 (0.75 1.61)

1.32 (0.76 2.31)

 

 

HR (95% CI)†

1.01 (0.91 1.11)

0.86

1.36 (0.99 1.87)

1.00

1.08 (0.73 1.59)

1.28 (0.73 2.25)

 

 

HR (95% CI)

1.01 (0.91 1.11)

0.86

1.36 (0.99 1.87)

1.00

1.08 (0.73 1.59)

1.28 (0.73 2.25)

 

Brain tumours

Cases 

463

 

130

221

87

25

 

 

HR (95% CI)*

1.02 (0.95 1.09)

0.62

0.95 (0.77 1.19)

1.00

1.02 (0.79 1.31)

0.93 (0.61 1.40)

 

 

HR (95% CI)†

1.03 (0.96 1.10)

0.39

0.92 (0.72 1.17)

1.00

1.03 (0.80 1.34)

0.96 (0.63 1.47)

 

 

HR (95% CI)f

1.03 (0.96 1.10)

0.38

0.92 (0.72 1.17)

1.00

1.04 (0.80 1.34)

0.97 (0.63 1.48)

 

Haematological malignancies

Cases 

2446

 

714

1137

445

150

 

 

HR (95% CI)*

1.03 (0.998 1.06)

0.06

0.95 (0.87 1.05)

1.00

1.02 (0.92 1.14)

1.14 (0.96 1.36)

 

 

HR (95% CI)†

1.02 (0.99 1.05)

0.24

0.95 (0.86 1.06)

1.00

0.997 (0.89 1.12)

1.11 (0.93 1.32)

 

 

HR (95% CI)

1.02 (0.99 1.05)

0.24

0.95 (0.86 1.06)

1.00

0.997 (0.89 1.12)

1.11 (0.93 1.32)

 

Non-Hodgkin’s lymphoma

Cases 

1182

 

349

545

225

63

 

 

HR (95% CI)*

1.02 (0.98 1.06)

0.37

0.97 (0.85 1.11)

1.00

1.09 (0.94 1.28)

1.02 (0.78 1.32)

 

 

HR (95% CI)†

1.01 (0.97 1.06)

0.65

0.996 (0.86 1.15)

1.00

1.07 (0.91 1.26)

1.03 (0.79 1.34)

 

 

HR (95% CI)

1.01 (0.97 1.06)

0.65

0.996 (0.86 1.15)

1.00

1.07 (0.91 1.26)

1.03 (0.79 1.34)

 

 *Models adjusted for age and sex (total observations=467 656).

†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).

Association of cancer risk and daily total screen time

Table 6 shows the association between a 1-hour increase in total daily screen time and total cancer risk and site-specific cancer risk. A 1-hour increase in daily total screen time was associated with a higher risk of lung cancer (HR 1.03, 95% CI: 1.004, 1.05).

Participants who reported > 8 hours of daily total screen time had a higher risk of lung cancer (HR 1.45, 95% CI: 1.19, 1.77) but a lower risk of oesophagus cancer (HR 0.54, 95% CI: 0.29, 0.99) compared to participants who reported 1-≤4 hours of daily total screen time.

Table 6. Results of Cox proportional hazards analyses investigating the association between self-report total screen time and cancer incidence.



1 hour increase in total screen time

p-value

≤1 hour

1-≤4 hours (reference)

4-≤8 hours

>8 hours

 

Person-years

 

3 474 425

 

254 147

2 111 765

997 699

110 815

 

All cancers excluding non-melanoma skin cancer

Cases

28 475

 

1751

16 402

9348

974

 

HR (95% CI)*

1.02 (1.01 1.02)

<0.001**

0.97 (0.93 1.02)

1.00

1.05 (1.03 1.08)

1.14 (1.07 1.22)

 

 

HR (95% CI)†

1.003 (0.997 1.01)

0.31**

0.998 (0.95 1.05)

1.00

1.01 (0.98 1.04)

1.04 (0.97 1.12)

 

 

HR (95% CI)a, b, c, d, e, g, h

1.004 (0.997 1.01)

0.24**

0.98 (0.93 1.04)

1.00

1.01 (0.98 1.04)

1.04 (0.97 1.12)

 

Skin, melanoma

Cases 

1614

 

101

960

504

49

 

HR (95% CI)*

0.99 (0.97 1.02)

0.64**

0.93 (0.76 1.14)

1.00

1.02 (0.91 1.13)

0.98 (0.73 1.30)

 

 

HR (95% CI)†

1.01 (0.98 1.03)

0.62

0.97 (0.78 1.19)

1.00

1.07 (0.96 1.20)

1.06 (0.78 1.44)

 

 

HR (95% CI)a

1.01 (0.98 1.03)

0.70

0.99 (0.80 1.22)

1.00

1.07 (0.95 1.19)

1.07 (0.79 1.45)

 

Oropharyngeal

Cases 

552

 

24

312

191

25

 

 

HR (95% CI)*

1.05 (1.01 1.09)

0.009

0.69 (0.46 1.05)

1.00

1.18 (0.99 1.42)

1.39 (0.92 2.09)

 

 

HR (95% CI)†

1.02 (0.98 1.06)

0.41

0.69 (0.45 1.04)

1.00

1.10 (0.91 1.32)

1.09 (0.71 1.67)

 

 

HR (95% CI)

1.02 (0.98 1.06)

0.41

0.69 (0.45 1.04)

1.00

1.10 (0.91 1.32)

1.09 (0.71 1.67)

 

Lung

Cases 

2014

 

119

995

774

126

 

HR (95% CI)*

1.11 (1.09 1.13)

<0.001**

1.13 (0.93 1.37)

1.00

1.37 (1.25 1.50)

2.49 (2.07 3.00)

 

 

HR (95% CI)†

1.02 (1.003 1.04)

0.03**

1.13 (0.92 1.38)

1.00

1.05 (0.95 1.16)

1.42 (1.16 1.72)

 

 

HR (95% CI)h

1.03 (1.004 1.05)

0.02**

1.12 (0.91 1.37)

1.00

1.05 (0.95 1.16)

1.45 (1.19 1.77)

 

Breast (female only)

Cases 

5609

 

418

3526

1522

143

 

HR (95% CI)*

1.01 (0.996 1.02)

0.16**

0.91 (0.82 1.004)

1.00

0.97 (0.92 1.04)

1.08 (0.91 1.27)

 

 

HR (95% CI)†

1.003 (0.99 1.02)

0.64**

0.94 (0.85 1.04)

1.00

0.95 (0.90 1.02)

1.08 (0.91 1.29)

 

 

HR (95% CI)b, h

1.01 (0.99 1.02)

0.37**

0.93 (0.83 1.04)

1.00

0.97 (0.90 1.04)

1.11 (0.92 1.34)

 

Uterus

Cases 

856

 

70

504

264

18

 

 

HR (95% CI)*

1.03 (0.99 1.06)

0.14

1.11 (0.87 1.43)

1.00

1.12 (0.96 1.30)

0.99 (0.62 1.58)

 

 

HR (95% CI)†

0.97 (0.93 1.004)

0.08

1.15 (0.88 1.50)

1.00

0.93 (0.80 1.09)

0.57 (0.33 0.97)

 

 

HR (95% CI)c

0.97 (0.94 1.01)

0.17

1.16 (0.89 1.53)

1.00

0.93 (0.79 1.09)

0.66 (0.38 1.12)

 

Ovary

Cases 

561

 

44

354

149

14

 

 

HR (95% CI)*

0.99 (0.95 1.04)

0.75

0.99 (0.72 1.35)

1.00

0.90 (0.74 1.09)

1.08 (0.63 1.85)

 

 

HR (95% CI)†

0.997 (0.95 1.04)

0.91

0.98 (0.71 1.35)

1.00

0.90 (0.74 1.11)

1.14 (0.66 1.95)

 

 

HR (95% CI)

0.997 (0.95 1.04)

0.91

0.98 (0.71 1.35)

1.00

0.90 (0.74 1.11)

1.14 (0.66 1.95)

 

Prostate

Cases 

5898

 

335

3340

2032

191

 

 

HR (95% CI)*

0.98 (0.97 0.99)

<0.001**

1.07 (0.95 1.19)

1.00

0.96 (0.91 1.01)

0.86 (0.74 0.99)

 

 

HR (95% CI)†

0.99 (0.98 1.004)

0.16**

1.06 (0.94 1.19)

1.00

1.01 (0.95 1.07)

0.94 (0.81 1.10)

 

 

HR (95% CI)h

0.99 (0.98 1.005)

0.21**

1.05 (0.93 1.18)

1.00

1.01 (0.95 1.07)

0.94 (0.81 1.10)

 

Oesophagus

Cases 

528

 

28

272

216

12

 

 

HR (95% CI)*

1.05 (1.02 1.10)

0.006

1.02 (0.69 1.50)

1.00

1.34 (1.12 1.60)

0.75 (0.42 1.34)

 

 

HR (95% CI)†

1.003 (0.96 1.05)

0.89

1.13 (0.77 1.68)

1.00

1.13 (0.94 1.36)

0.54 (0.29 0.99)

 

 

HR (95% CI)f

1.001 (0.96 1.04)

0.95

1.15 (0.77 1.69)

1.00

1.12 (0.93 1.36)

0.54 (0.29 0.99)

 

Stomach

Cases 

348

 

14

177

141

16

 

 

HR (95% CI)*

1.08 (1.04 1.13)

<0.001

0.77 (0.45 1.33)

1.00

1.37 (1.09 1.71)

1.58 (0.95 2.63)

 

 

HR (95% CI)†

1.03 (0.98 1.08)

0.19

0.75 (0.43 1.32)

1.00

1.12 (0.89 1.42)

1.07 (0.61 1.85)

 

 

HR (95% CI)

1.03 (0.98 1.08)

0.19

0.75 (0.43 1.32)

1.00

1.12 (0.89 1.42)

1.07 (0.61 1.85)

 

Oesophagus and stomach

Cases 

870

 

42

444

356

28

 

 

HR (95% CI)*

1.07 (1.04 1.10)

<0.001

0.93 (0.68 1.28)

1.00

1.36 (1.18 1.57)

1.09 (0.74 1.59)

 

 

HR (95% CI)†

1.02 (0.98 1.05)

0.31

0.99 (0.72 1.36)

1.00

1.14 (0.98 1.32)

0.76 (0.50 1.14)

 

 

HR (95% CI)f

1.02 (0.98 1.05)

0.34

0.99 (0.72 1.37)

1.00

1.13 (0.98 1.31)

0.76 (0.50 1.14)

 

Hepatobiliary tract

Cases 

446

 

22

249

156

19

 

 

HR (95% CI)*

1.05 (1.01 1.10)

0.02

0.84 (0.54 1.29)

1.00

1.11 (0.90 1.35)

1.45 (0.91 2.31)

 

 

HR (95% CI)†

1.01 (0.96 1.05)

0.73

0.82 (0.52 1.30)

1.00

0.95 (0.77 1.18)

1.05 (0.65 1.72)

 

 

HR (95% CI)

1.01 (0.96 1.05)

0.73

0.82 (0.52 1.30)

1.00

0.95 (0.77 1.18)

1.05 (0.65 1.72)

 

Pancreatic

Cases 

604

 

30

333

220

21

 

 

HR (95% CI)*

1.04 (1.004 1.08)

0.03

0.85 (0.58 1.23)

1.00

1.17 (0.99 1.39)

1.22 (0.78 1.89)

 

 

HR (95% CI)†

1.02 (0.98 1.06)

0.37

0.86 (0.58 1.26)

1.00

1.10 (0.92 1.31)

1.01 (0.64 1.60)

 

 

HR (95% CI)d

1.02 (0.98 1.06)

0.45

0.86 (0.58 1.26)

1.00

1.08 (0.91 1.30)

0.99 (0.63 1.57)

 

Kidney

Cases 

771

 

43

417

279

32

 

 

HR (95% CI)*

1.04 (1.01. 1.08)

0.01

0.98 (0.71 1.34)

1.00

1.19 (1.02 1.38)

1.38 (0.96 1.98)

 

 

HR (95% CI)†

1.01 (0.97 1.04)

0.67**

1.03 (0.74 1.44)

1.00

1.07 (0.91 1.25)

1.14 (0.79 1.66)

 

 

HR (95% CI)

1.01 (0.97 1.04)

0.67**

1.03 (0.74 1.44)

1.00

1.07 (0.91 1.25)

1.14 (0.79 1.66)

 

Bladder

Cases 

662

 

24

351

259

28

 

 

HR (95% CI)*

1.06 (1.02 1.09)

0.001

0.69 (0.46 1.04)

1.00

1.21 (1.03 1.42)

1.36 (0.93 2.00)

 

 

HR (95% CI)†

1.01 (0.98 1.05)

0.50**

0.76 (0.50 1.16)

1.00

1.10 (0.93 1.30)

0.99 (0.65 1.53)

 

 

HR (95% CI)

1.01 (0.98 1.05)

0.50**

0.76 (0.50 1.16)

1.00

1.10 (0.93 1.30)

0.99 (0.65 1.53)

 

Colorectal

Cases 

3290

 

180

1896

1096

118

 

 

HR (95% CI)*

1.02 (1.002 1.04)

0.03**

0.88 (0.76 1.03)

1.00

1.04 (0.97 1.12)

1.16 (0.97 1.40)

 

 

HR (95% CI)†

1.01 (0.99 1.03)

0.41**

0.92 (0.79 1.08)

1.00

1.003 (0.93 1.08)

1.11 (0.91 1.35)

 

 

HR (95% CI)e, g, f (males)

1.01 (0.99 1.02)

0.58**

0.89 (0.76 1.04)

1.00

0.99 (0.91 1.07)

1.08 (0.88 1.32)

 

Colon

Cases 

2110

 

111

1202

721

76

 

 

HR (95% CI)*

1.03 (1.01 1.05)

0.003**

0.86 (0.70 1.04)

1.00

1.08 (0.99 1.19)

1.21 (0.96 1.53)

 

 

HR (95% CI)†

1.02 (0.998 1.04)

0.08**

0.91 (0.75 1.11)

1.00

1.04 (0.95 1.15)

1.16 (0.91 1.48)

 

 

HR (95% CI)e, g, f (males)

1.02 (0.99 1.04)

0.15**

0.90 (0.73 1.10)

1.00

1.04 (0.94 1.15)

1.12 (0.88 1.44)

 

Rectum

Cases 

1107

 

64

645

358

40

 

 

HR (95% CI)*

0.998 (0.97 1.03)

0.89**

0.94 (0.73 1.21)

1.00

0.99 (0.87 1.13)

1.10 (0.80 1.51)

 

 

HR (95% CI)†

0.99 (0.96 1.02)

0.38

0.95 (0.73 1.25)

1.00

0.95 (0.83 1.09)

1.04 (0.74 1.46)

 

 

HR (95% CI)e, g

0.98 (0.95 1.02)

0.31

0.91 (0.69 1.20)

1.00

0.92 (0.80 1.06)

1.01 (0.72 1.43)

 

Brain tumours

Cases 

458

 

28

269

145

16

 

 

HR (95% CI)*

1.01 (0.97 1.06)

0.50

0.96 (0.65 1.42)

1.00

0.99 (0.81 1.22)

1.07 (0.65 1.77)

 

 

HR (95% CI)†

1.02 (0.98 1.07)

0.31

0.98 (0.66 1.46)

1.00

1.001 (0.81 1.24)

1.13 (0.67 1.91)

 

 

HR (95% CI)f

1.03 (0.98 1.07)

0.28

0.97 (0.65 1.45)

1.00

1.01 (0.81 1.25)

1.13 (0.67 1.92)

 

Thyroid

Cases

236

 

15

154

62

5

 

 

HR (95% CI)*

0.99 (0.93 1.06)

0.86

0.78 (0.46 1.32)

1.00

0.87 (0.65 1.18)

0.71 (0.29 1.72)

 

 

HR (95% CI)†

0.999 (0.93 1.07)

0.97

0.76 (0.44 1.32)

1.00

0.88 (0.64 1.20)

0.70 (0.28 1.72)

 

 

HR (95% CI)

0.999 (0.93 1.07)

0.97

0.76 (0.44 1.32)

1.00

0.88 (0.64 1.20)

0.70 (0.28 1.72)

 

Haematological malignancies

Cases 

2427

 

142

1396

806

83

 

 

HR (95% CI)*

1.02 (0.998 1.04)

0.09

0.95 (0.80 1.12)

1.00

1.04 (0.96 1.14)

1.12 (0.90 1.40)

 

 

HR (95% CI)†

1.01 (0.99 1.03)

0.43

0.96 (0.80 1.15)

1.00

1.02 (0.93 1.12)

1.07 (0.85 1.34)

 

 

HR (95% CI)

1.01 (0.99 1.03)

0.43

0.96 (0.80 1.15)

1.00

1.02 (0.93 1.12)

1.07 (0.85 1.34)

 

Non-Hodgkin’s lymphoma

Cases 

1174

 

68

675

392

39

 

 

HR (95% CI)*

1.02 (0.99 1.04)

0.28

0.93 (0.72 1.19)

1.00

1.06 (0.93 1.20)

1.10 (0.80 1.52)

 

 

HR (95% CI)†

1.01 (0.98 1.04)

0.43

0.93 (0.72 1.20)

1.00

1.08 (0.95 1.23)

1.06 (0.75 1.48)

 

 

HR (95% CI)

1.01 (0.98 1.04)

0.43

0.93 (0.72 1.20)

1.00

1.08 (0.95 1.23)

1.06 (0.75 1.48)

 

 *Models adjusted for age and sex (total observations=464,424).

†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).

Discussion

Overview of key findings

This large, prospective cohort study indicates that sedentary behaviours were associated with some site-specific cancers (notably oropharyngeal, oesophagus and stomach, colon and lung cancer), particularly for TV viewing time. Results for oesophagus and stomach cancers, and colon cancers were robust to the omission of cancers occurring within the first two years of follow-up. Our study provides no evidence for an association between sedentary behaviour and total cancer risk. However, the results of our isotemporal substitution models revealed a benefit in terms of reduced total cancer risk and reduced risk of several site-specific cancers when replacing 1-hour of TV viewing per day with 1-hour of moderate-intensity physical activity or walking. Results were less consistent for time spent on computer and daily total screen time. This may suggest that the mechanism of action is more nuanced and complex than the act of being sedentary, but the specific activity that is being undertaken during sedentary time (i.e. watching TV or using the computer) is an important mechanistic driver. Indeed, Patterson F et al (2018) suggested that sedentary behaviour was not a homogenous behaviour and found that different sedentary behaviours had different determinants (40). This will be explored further below.

TV viewing and cancer risk

Television viewing was the most common sedentary behaviour in this population. Our results showed that a 1-hour increase in TV viewing time was associated with higher risk of oropharyngeal, stomach, oesophagus and stomach, and colon cancers. Compared with our reference group of 1–3 hours of TV viewing per day, reporting less than 1-hour TV viewing per day was associated with decreased risk of lung, breast, stomach, and oesophagus and stomach cancers. Thus our analytical approach has allowed us to contribute a novel finding to the literature, highlighting the benefits of zero TV screen-time hours for these cancers. There is some evidence in the literature that higher levels of physical activity may reduce lung cancer. Mechanistically, this is likely to be due to increased respiratory ventilation, reducing the concentration of carcinogenic agents in the lungs (41). Previous research also provides evidence for a relationship between higher levels of physical activity and lower risk of incident breast cancer due to decreased sex and metabolic hormone levels, decreased adiposity, reductions in insulin resistance and reduced inflammation (37,42–45). It is plausible that similar mechanisms could be applied to the relationship between these cancers and sedentary behaviour.

Previous research has suggested that individuals who have increased TV viewing time tend to have poor lifestyle behaviours, such as being more likely to smoke, eating a poor diet, doing little, if any, physical activity, and being overweight or obese (7). Further, Ogden et al (2013) discussed the concept of ‘mindless eating’, where the distraction of watching the TV led to individuals consuming more calories (46). A review of the literature on sedentary behaviour and biological pathways by Lynch (2010) supported the hypothesised role of adiposity and metabolic dysfunction as mechanisms operant in the association between sedentary behaviour and cancer (7). Our findings and other evidence would suggest that sedentary behaviour is much more than an act of not being ‘active’ or being in a stationary position for a prolonged period, but rather a range of sedentary behaviours where the ‘activity’ being undertaken while sedentary is very important. Subsequently, mechanisms of action are likely to act via a number of complex pathways, such as indirectly. For example, TV viewing has been associated with increased risk of being obese or overweight (47), and there is also a strong evidence base associating being overweight or obese to increased cancer risk (7,48). However we adjusted for BMI in our models to try to account for this. Known mechanisms associated with body fatness, such as sex hormones, insulin, and inflammation, may explain part of the association between sedentary behaviours and cancer risk. The association between prolonged TV viewing time and lower levels of vitamin D have also been hypothesised as a possible mechanistic pathway (7,11).

Computer use and cancer risk

The mean computer use time was 1.1 hours/day, which is almost three times less prevalent as a sedentary behaviour than daily TV viewing time within this UK population. Paradoxically, our findings showed that a 1-hour/day increase in computer use was associated with lower risk of oropharyngeal cancer and the results of the categorical analysis showed that 0 hours/day of computer use was associated with higher risk of oropharyngeal and ovary cancers compared with ≤ 1 hour/day. Reporting > 3 hours/day of computer use was also associated with increased risk of lung cancer. It is difficult to compare the findings for computer use with other literature given the explicit exclusion of ‘using a computer at work’ from our measure. Most of the previous literature is focused on occupational sedentary time which largely encompasses computer use [17].

Daily total screen-time and cancer risk

The mean daily total screen-time was almost 4 hours/day, reflecting combined TV and computer screen time. The most notable associations were observed for an increased risk of lung cancer in both continuous and categorical analysis. Previous literature has demonstrated that household air pollution exposure from solid fuel is associated with high rates of lung cancer, especially in low- and middle-income countries, such as China (49). However, this seems an unlikely mechanistic pathway in the UK. It is plausible that indoor sedentary behaviour may be linked to increased residential radon exposure which is known to be associated with an increased risk of lung cancer, particularly in European populations (50). Results were somewhat mixed for other cancers which may be due to the combined nature of essentially two different behaviours (i.e. TV viewing and computer use).

Findings in relation with other literature

Our observations are somewhat mixed to those previously reported for total cancer incidence (15), oesophago-gastric cancer risk (16) and colon cancer risk (17) in relation to sedentary behaviour. However, it is difficult to draw direct comparisons between these studies and our current analysis, since each of those used the lowest category of screen-time exposure as their reference category. Due to our a priori hypothesis that individuals with less than 1-hour of screen time may have different characteristics, we chose 1–3 hours of screen-time as our reference category. This revealed some novel associations not previously identified, such as protective associations for lung, breast, oesophageal, stomach, and oropharyngeal cancers in individuals with the lowest screen-time exposure, and increased risks of lung cancer in individuals with higher levels of exposure to screen time.

Implications of findings

Our findings would support the continued promotion of public health messages and interventions to minimise and reduce sedentary behaviours. However, rather than broad messaging and strategies to simply ‘sit less’, our findings suggest that there is a need to tackle specific sedentary behaviours, in particular TV viewing. Such messages should not only promote the need to reduce sitting time but to also be mindful of the unhealthy behaviours, such as mindless eating, associated with watching TV.

Public health practitioners should also think about what activities they should promote while displacing sedentary behaviour. Results from our partition and substitution models show the benefits of replacing 1-hour of TV viewing time with 1-hour of moderate-vigorous intensity physical activity or 1-hour of walking, particularly for total cancer, breast, colorectal, colon, oropharyngeal, and lung cancers. So rather than messages and interventions to ‘sit less’, such strategies should also promote healthy, displacement physical activity.

Strengths and limitations

This study provides a comprehensive overview of sedentary behaviours for total cancer risk and site-specific cancers. The findings from the partition and isotemporal substitution models are the first, to our knowledge, to model the impact of displacing 1-hour of sedentary behaviour with more physically active behaviours. The UK Biobank has previously been criticised for not being a representative sample for physical activity levels, obesity prevalence and other co-morbidities, indicating a healthy volunteer bias. However, the cohort is representative of the UK population in terms of age, sex, ethnicity and deprivation for the targeted age group (15,51) and a recently published generalisability study suggest that the results of UK Biobank studies can be generalised to England and Scotland (52). All models were adjusted for important socio-demographic, health and behavioural variables, including BMI which is hypothesised to be on the causal pathway between sedentary behaviour and cancer incidence. Some have argued that this may lead to over-adjustment and therefore underestimation of the strength of the tested associations (15). Due to the large amount of missing data, the analyses were not adjusted for total calorie consumption or dietary habits other than total fruit and vegetable intake, red and processed meat consumption. Further, we have interpreted our results of effect modification with caution owing to the number of cancer sites and number of subgroups which have been investigated.

The analysis uses self-report sedentary behaviour data, which may be subject to social desirability and recall bias, and the measure has not been investigated for criterion validity (15). However, the estimates are in line with previous population estimates (53,54). Although the UK Biobank cohort does measure sedentary behaviour using accelerometers, we were unable to use this data to examine the association with cancer incidence as the follow-up time was too short (mean follow-up time 1.9 years). The nature of the observational study means that we cannot attribute causal interpretations to our results owing to the potential for residual confounding. Finally, some associations were attenuated when excluding cancers diagnosed within the first two years of follow-up, suggesting that our results could have been affected by a possible reverse causation bias.

Future research

Given the contrasting findings for TV viewing time and computer use time, future research should take a more nuanced approach to exploring sedentary behaviours. This might help provide a better understanding of the underlying mechanisms of action. The literature to date is dominated by daily and weekly duration of sedentary behaviours. Increasing our knowledge about the role of bouts of sedentary behaviour and the impact of breaks in sedentary behaviour could help us develop more specific time-based recommendations and contribute to the development of much needed cancer prevention strategies. Analysing accelerometer data in large prospective cohorts in future will allow such analysis to be conducted. Accelerometer data has been assessed in UK Biobank during secondary waves of data collection, and so this will be possible given longer follow-up in due course. In addition, the current analysis focussed on cancer risk, but much remains unknown about the interactive effects of physical activity and sedentary behaviour on cancer mortality. These areas of research have been highlighted as important evidence gaps in the US 2018 physical activity guidelines (6).

Conclusions

In summary, the current study adds to the much-needed evidence base on sedentary behaviours and cancer risk, including total cancer risk and site specific cancers (particularly lung cancer). 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 healthy physical activities, and to particularly target TV viewing.

Abbreviations

AICR

American Institute for Cancer Research

BMI

Body mass index

CI

confidence intervals

GCSE

General Certificate of Secondary Education

GDPR

General Data Protection Regulation

GORD

Gastro-Oesophageal Reflux Disease

HNC

Higher National Certificate

HND

Higher National Diploma

HR

Hazard Ratio

HRT

hormone replacement therapy

ICD

International Classification of Diseases

IPAQ

International Physical Activity Questionnaire

METs

Metabolic equivalents

NCDs

Non-communicable diseases

NHS

National Health Service

NVQ

National Vocational Qualification

RR

Relative Risk

SD

Standard Deviation

TV

Television

UK

United Kingdom

US

United States

UV

Ultraviolet

WCRF

World Cancer Research Fund

Declarations

Ethics approval and consent to participate: UK Biobank received ethical approval from the North West Multi-centre Research Ethics Committee (REC reference: 11/NW/03820). All participants gave written informed consent before enrolment in the study, which was conducted in accord with the principles of the Declaration of Helsinki.

Consent for publication: Not applicable.

Availability of data and materials: The data that support the findings of this study are available from UK Biobank but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of UK Biobank.

Competing interests: The authors declare that they have no competing interests.

Funding: The UK Biobank was supported by the Wellcome Trust, Medical Research Council, Department of Health, Scottish government, and Northwest Regional Development Agency. It has also had funding from the Welsh Assembly government and British Heart Foundation. The research was designed, conducted, analysed, and interpreted by the authors entirely independently of the funding sources. All authors had full access to all of the data (including statistical reports and tables) in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Authors’ contributions: RH had the initial idea for the study. RH JM and HC contributed to the design of the study, advised on all statistical aspects, and interpreted the data. JM performed the statistical analysis. RH and JM drafted the manuscript. RH, JM and HC reviewed the manuscript and approved the final version to be published. RH, JM and HC had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. RH is the guarantor.

Acknowledgements: This research was conducted using the UK Biobank resource. We thank the participants of the UK Biobank. The authors would like to thank Dr. Christopher R. Cardwell (Queen's University Belfast) for providing on-going advice on conducting and interpreting statistical analyses.

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