Measuring physical activity in older adults: calibrating cut-points for the MotionWatch 8©
Bibliographic record
Abstract
Given the world's aging population, the staggering economic impact of dementia, the lack of effective treatments, and the fact a cure for dementia is likely many years away - there is an urgent need to develop interventions to prevent or at least delay dementia's progression. Thus, lifestyle approaches to promote healthy aging are an important line of scientific inquiry. Good sleep quality and physical activity (PA) are pillars of healthy aging, and as such, are an increasing focus for intervention studies aimed at promoting health and cognitive function in older adults. However, PA and sleep quality are difficult constructs to evaluate empirically. Wrist-worn actigraphy (WWA) is currently accepted as a valid objective measure of sleep quality. The MotionWatch 8(©) (MW8) is the latest WWA, replacing the discontinued Actiwatch 4 and Actiwatch 7. In the current study, concurrent measurement of WWA and indirect calorimetry was performed during 10 different activities of daily living for 23 healthy older adults (aged 57-80 years) to determine cut-points for sedentary and moderate-vigorous PA - using receiver operating characteristic curves - with the cut-point for light activity being the boundaries between sedentary and moderate to vigorous PA. In addition, simultaneous multi-unit reliability was determined for the MW8 using inter-class correlations. The current study is the first to validate MW8 activity count cut-points - for sedentary, light, and moderate to vigorous PA - specifically for use with healthy older adults. These cut-points provide important context for better interpretation of MW8 activity counts, and a greater understanding of what these counts mean in terms of PA. Hence, our results validate another level of analysis for researchers using the MW8 in studies aiming to examine PA and sleep quality concurrently in older adults.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".