An investigation into the optimal wear time criteria necessary to reliably estimate physical activity and sedentary behaviour from ActiGraph wGT3X+ accelerometer data in older care home residents
Bibliographic record
Abstract
Abstract Background Research protocols regarding the use of ActiGraph wGT3X+ accelerometers in care home residents are yet to be established. The purpose of this study was to identify the minimal wear time criteria required to achieve reliable estimates of physical activity (PA) and sedentary behaviour (SB) in older care home residents. Methods Ninety-four older adults from 14 care homes wore an ActiGraph wGT3X+ accelerometer on the right hip for 7 consecutive days. A pragmatic, staged approach was adopted in order to explore the effect of: monitoring day; minimum daily wear time and number of wear days on estimates of four outcomes derived from the accelerometer data: counts . day − 1 , counts . minute − 1 , PA time and SB time. Results Data from 91 participants (mean age: 84 ± 9 years, 34% male) was included in the analysis. No effect of monitoring day was observed. Lowering the daily wear time to ≥ 8 h (compared to ≥10 h) had no effect on the outcomes of interest. Four days of monitoring was sufficient to provide reliable estimates of all four outcomes. Conclusion In this study, a minimum wear time criterion of ≥ 8 h on any 4 days was required to derive reliable estimates of PA and SB from ActiGraph wGT3X+ accelerometer data in older care home residents.
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".