Do adolescent sedentary behavior levels predict type 2 diabetes risk in adulthood?
Bibliographic record
Abstract
BACKGROUND: The objective was to determine whether time spent in different types of sedentary behavior during adolescence are associated with the risk of developing type 2 diabetes in adulthood. METHODS: Participants were 3942 adolescents aged 16 years who were part of the 1970 British Cohort Study. Sedentary behavior was assessed using a questionnaire that asked participants to indicate how much time they spent watching TV and videos, using the computer, reading, and doing homework. Incident cases of type 2 diabetes were determined quadrennially until 46 years of age. The association between adolescent sedentary behaviors and type 2 diabetes was determined using Cox proportional hazards regression that controlled for sex, body mass index, sugary beverage consumption, smoking status, physical activity at baseline, and physical activity in adulthood . RESULTS: There were 91 incident cases of type 2 diabetes with an incidence rate of 9 cases/10,000 person-years. By comparison to those who watched TV and videos for 2 or less hours/day, type 2 diabetes risk was not different in those who watched for 2.1-4.0 h/day (HR = 0.89, 95% CI = 0.54, 1.47) but was increased by 2.06-fold (95% CI = 1.24, 3.43) in those who watched for more than 4 h/day. Time spent using a computer, reading, and doing homework were not significantly associated with type 2 diabetes. CONCLUSION: Spending more than 4 h/day watching television and videos at age 16 was associated with an increased risk of type 2 diabetes. Conversely, using a computer and non-screen based sedentary behaviors were not associated with type 2 diabetes risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".