Finding the Goldilocks zone for toddler accelerometry: how many days are needed for a reliable estimate of physical activity using machine learning?
Bibliographic record
Abstract
Abstract Accelerometers are used to measure sedentary time (SED) and physical activity (PA) in toddlers, but they may struggle to wear them for extended periods of time (e.g., weeks). Previous studies have investigated the minimum number of days needed to reliably estimate SED and PA using count-based methods. Machine learning (ML) methods use raw data which is more variable, thus potentially requiring more days for a reliable estimate. The objective of this study is to understand how many days and hours per day of accelerometer wear are needed for a reliable estimation of SED and PA using ML. Methods 109 toddlers wore an accelerometer on the right hip at home for 7 days. Time in SED, light PA (LPA), moderate-to-vigorous PA (MVPA), and total PA (TPA) were assessed using a validated ML model for toddlers. Single day intraclass coefficients (ICCs) were calculated for each minimum hours per day of wear time and each outcome. These ICCs were passed to the Spearman-Brown prophecy equation to determine the reliability of each hour per day and days combination (3-12 hours, 1-10 days). Results Predicted reliabilities ranged from 0.32 to 0.98, increasing as both numbers of hours per day and number of days increased. Discussion Our findings support the recommended 6 hours per day of wear for at least 4 days as it balances acceptable reliability with participant retention. This recommendation is valid for ML methods and we anticipate that it can be used to further explore SED and PA in toddlers using ML advances. Key Highlights This study calculates reliability of estimates of toddlers’ physical activity and sedentary time using machine learning method for a range of days (1-10) and hours per day (3-12). Our findings support the recommended 6 hours per day of wear for at least 4 days as it balances acceptable reliability with participant retention. We hope that this recommendation can be used to further explore physical activity and sedentary time in toddlers using machine learning advances.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".