Physical activity in persons with Parkinson disease: A feasibility study
Bibliographic record
Abstract
Background: Physical activity for persons with Parkinson Disease (PD) is recommended yet little is known about the physical activity levels in this patient population. The primary aim was to assess the feasibility of using a direct measurement and self-report measure of physical activity in patients with PD. Methods: Physical activity was recorded in 11 out-patients with mild to moderate PD. An accelerometer based sensor system (SenseWear Pro Armband?) which was worn continuously over 2 days was used to measure physical activity. Minute by minute energy expenditure and steps per day were recorded. Self-report physical activity was measured using the Short QUestionnaire to ASsess Health-enhancing physical activity (SQUASH) which assessed average weekly activity. Results: Using the accelerometer based sensor system, 83% of the day was spent in sedentary activity with the majority active time spent at a light intensity (2.7 [SD 2.0] hrs/day). Self-reported mean number of hours for activities greater than 2.0 METs was 3.4 (SD 1.5) hrs/day. Although the overall time spent in activity did not differ between the accelerometer and SQUASH, partici- pants reported a higher proportion of activities at the moderate and vigorous intensities than the accelerometer recorded. Conclusions: Measurement of physical activity is a challenge in persons with PD given the disease-related symptoms. We found that, by all accounts, a self-report measure of physical activity should be complemented with a direct measure of physical activity.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".