Considerations in Processing Accelerometry Data to Explore Physical Activity and Sedentary Time in Older Adults
Bibliographic record
Abstract
Processing decisions for accelerometry data can have important implications for outcome measures, yet little evidence exists exploring these in older adults. The aim of the current study was to investigate the impact of three potentially important criteria on older adults, physical activity, and sedentary time. Participants (n = 222: mean age 71.75 years [SD = 6.58], 57% male) wore ActiGraph GT3X+ for 7 days. Eight data processing combinations from three criteria were explored: low-frequency extension (on/off), nonwear time (90/120 min), and intensity cut points (moderate-to-vigorous physical activity ≥1,041 and >2,000 counts/min). Analyses included Wilcoxon signed-rank test, paired t tests, and correlation coefficients (significance, p < .05). Results for low-frequency extension on 90-min nonwear time and >1,041 counts/min showed significantly higher light and moderate-to-vigorous physical activity and lower sedentary time. Cut points had the greatest impact on physical activity and sedentary time. Processing criteria can significantly impact physical activity and/or sedentary time, potentially leading to data inaccuracies, preventing cross-study comparisons and influencing the accuracy of population surveillance.
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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 | 0.164 | 0.320 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".