A systematic review of compositional analysis studies examining the associations between sleep, sedentary behaviour, and physical activity with health indicators in early childhood
Bibliographic record
Abstract
BACKGROUND: This systematic review examined if the composition of time spent in sleep, sedentary behaviour, and physical activity of different intensities is associated with health and developmental indicators in children aged 0-5 years. METHODS: Four electronic databases (MEDLINE, EMBASE, PsycINFO, and SPORTDiscus) were searched in January 2022. Studies were eligible for inclusion if they were peer-reviewed, the average age of participants was < 6 years, and compositional data analysis was used to examine the associations between the composition of time spent in movement behaviours and health and developmental indicators. RESULTS: Eight studies (7 cross-sectional, 1 prospective cohort) of < 2070 unique participants were included. Only a single study included children < 3 years old and 37% of the associations examined in the literature were based on indicators of body composition. The 24-h movement behaviour composition was associated with mental health indicators (3 of 4 associations examined in the literature), motor skills and development (6 of 7 associations), and physical fitness (3 of 3 associations). Reallocating time from light physical activity into moderate-to-vigorous physical activity was favourable for motor skills and development. Reallocating time from light physical activity into sleep was unfavourable for mental health. Reallocating time from light physical activity into sedentary behaviour or sleep was favourable for motor skills and development. CONCLUSIONS: This review provides some evidence that the composition of movement behaviours is important for the health of young children. Future research should consider including infants and toddlers, larger sample size, and measures of health and development other than body composition. (PROSPERO registration no.: CRD42022298370.).
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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 | 0.016 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".