Is excessive smartphone use associated with weight status and self-rated health among youth? A smart platform study
Bibliographic record
Abstract
BACKGROUND: In Canada, it is recommended that youth limit screen time to less than two hours per day, yet, the majority of youth are reportedly spending a significantly higher amount of time in front of a screen. This is particularly concerning given that these recommendations do not take into account smartphone devices, which is the most common screen time technology of choice for the younger generations. This study implements an innovative approach to understanding screen time behavior and aims to investigate the unique relationship between smartphone specific screen time and physical health outcomes. METHODS: This cross-sectional study is part of the Smart Platform, a digital epidemiological and citizen science initiative. 436 youth citizen scientists, aged 13-21 years, provided all data via their own smartphones using a custom-built smartphone application. Participants completed a 124-item baseline questionnaire which included validated self-report surveys adapted to collect data specifically on smartphone use (internet use, gaming, and texting), demographic characteristics, and physical health outcomes such as weight status and self-rated health. Binary regression models determined the relationship between smartphone use and physical health outcomes. RESULTS: Overall participants reported excessive smartphone use in all categories. 11.4% and 12% of the 436 youth participants reported using their smartphone excessively (greater than 2 h per day) during the week and weekend respectively for gaming and were over 2 times more likely than their peers to fall within an overweight/obese BMI status. Excessive weekend gaming was also associated with self-rated health where participants were over 2 times more likely than their peers to report poor self-rated health. CONCLUSIONS: The results indicate that excessive screen time on smartphones does have complex associations with youth health. Further investigation with more robust study designs is needed to inform smartphone-specific screen time guidelines for youth.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".