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

Study design

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

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

Screen time assessment

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

Physical activity assessment

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

Assessment of covariates

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

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

Cancer ascertainment

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

Statistical analyses

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

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

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

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

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

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

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.

*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.

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.

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.

 *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.