Screen Time and Bone Status in Children and Adolescents: A Systematic Review
Bibliographic record
Abstract
Introduction: Technological advances over the last 2 decades have led to an increase in the time spent by children and youth engaged in screen-based activities, and growing recognition of deleterious effects on health. In this systematic review of cohort and cross-sectional studies, we assess current data on the relationship between screen time and bone status in children and teenagers. Methods: We searched PUBMED and SCOPUS databases for studies of children and adolescents that assessed screen time and bone status, determined by measuring bone mineral content or density, bone stiffness index, bone speed of sound, bone broadband ultrasound attenuation, or frame index. Searches were limited to studies published between 1900 and 2020, and performed in accordance with Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. The studies included were evaluated using the Newcastle-Ottawa quality assessment scale. Results: Ten cohort and cross-sectional studies including pediatric population were selected. The combined study population was 20,420 children/adolescents, of whom 18,444 participated in cross-sectional studies. Four studies assessed the effects of total screen time, seven the consequences of TV viewing time, and six the effects of recreational computer use on bone health. Our findings indicate an inverse association between total and weekly screen time and bone health in children and adolescents. In 57% of the studies included also a negative correlation between television viewing time and bone status was observed, while recreational computer time did not have a significant impact on bone health. According to the only four studies that included dietetic factors, no relevant differences were found between calcium intake and screen time or bone broadband ultrasound attenuation and bone speed of sound. Conclusions: Review of the literature of the past three decades provides strong support for comprehensive education of screen time on bone status. The findings of this systematic review support a negative association between screen time and bone status in children and adolescents, with a different impact when considering the different technological devices. As peak bone mass in adolescents is the strongest predictor of osteoporosis risk, strategies aimed at improving bone health should incorporate conscious use of digital technology.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".