Use of actigraphy to measure real-world physical activities in manual wheelchair users
Bibliographic record
Abstract
INTRODUCTION: The benefits of physical activity for manual wheelchair users are well-known. The purpose of this study was to validate actigraphy to objectively measure physical activity intensity among manual wheelchair users. METHOD: An experimental design was used. Adult manual wheelchair users wore a GT3X actigraph on their non-dominant arm while completing eight physical activities of low (reading), moderate (propelling -- flat) and high (propelling -- steep ramp) intensity. Heart rate and rating of perceived exertion were collected at the end of each physical activity. Distribution of data were examined and used to determine the type of repeated measures (parametric vs. non-parametric). A categorical principal component analysis was performed to determine the amount of variability explained by actigraphy, heart rate and rating of perceived exertion. Activity count cut-points were estimated using bootstrapping methods. RESULTS: Twenty-eight manual wheelchair users completed the study. Actigraphy, heart rate and rating of perceived exertion co-varied as physical activity intensity changed. Activity counts for low-intensity and medium-intensity physical activities were estimated to be 0 to 45 and 45 to 100 activity counts per second, respectively. Activity counts' ranges for high-intensity physical activities were not clear. CONCLUSION: Combining actigraphy and rating of perceived exertion could be an easy and reliable method to measure the intensity of real-world activities. Further research is needed confirm cut-points for physical activity intensity.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".