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Record W3118334560 · doi:10.21203/rs.2.18993/v1

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

2019· preprint· en· W3118334560 on OpenAlexfundno aff
Ruth F. Hunter, Jennifer Murray, Helen G. Coleman

Bibliographic record

VenueResearch Square · 2019
Typepreprint
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersMedical Research CouncilNorthwest Regional Development AgencyQueen's University BelfastQueen's UniversityBritish Heart FoundationWellcome Trust
KeywordsBiobankProspective cohort studyAssociation (psychology)CohortCohort studyMedicineEnvironmental healthGerontologyDemographyPsychologyInternal medicineBioinformaticsSociologyBiology

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.071
GPT teacher head0.487
Teacher spread0.416 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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