Effect of Accelerometer Cut-Points on Preschoolers’ Physical Activity and Sedentary Time: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
This systematic review and meta-analysis aimed to compare estimated levels of physical activity (PA) and sedentary time (ST) of preschool-aged children (3–5 years old) based on different published accelerometer cut-points used in this age group. Four electronic databases were searched to identify studies estimating levels of PA or ST (ST, light PA [LPA], and moderate to vigorous PA [MVPA]) using multiple accelerometer cut-points, in a sample of preschool-aged children. Data were extracted and risk of bias assessed for all included studies. Random-effects meta-analysis was used to estimate pooled effects for unique combinations of accelerometer cut-points for each outcome. Twenty-four studies, reporting on 18 unique samples, were included. Results demonstrated substantial variability in estimates of PA and ST across different cut-points, with significant differences in estimates of the behaviors between most cut-points. Few cut-points showed similarity; Evenson and Pate were some of the most similar for the assessment of PA and ST of young children. However, when calculating the differences in ST, LPA, and MVPA between the cut-points, the Evenson cut-point estimates approximately 60 min more LPA per day and the Pate 148CPM cut-points estimates 23 and 37 min more ST and MVPA each day, respectively. Given that these were the most similar estimates, this highlights the magnitude of differences between the accelerometer cut-points when estimating preschool-aged children’s movement behavior. This review provides an illustration on the limitations of accelerometer cut-points used to determine PA and ST of preschool-aged children; in that they often produce substantially different estimates. This review provides a compelling rationale as to why further research moving toward alternative data processing methodologies is required, including to identify an optimal approach to estimating movement behavior outcomes in young children that considers congruence with past and future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.009 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".