Physical Activity across Frailty Phenotypes in Females with Parkinson’s Disease
Bibliographic record
Abstract
Females with Parkinson's disease (PD) are vulnerable to frailty. PD eventually leads to decreased physical activity, an indicator of frailty. We speculate PD results in frailty through reduced physical activity. Objective. Determine the contribution of physical activity on frailty in PD (n = 15, 65 ± 9 years) and non-PD (n = 15, 73 ± 14 years) females. Methods. Frailty phenotype (nonfrail/prefrail/frail) was categorized and 8 hours of physical activity was measured using accelerometer, global positioning system, and self-report. Two-way ANCOVA (age as covariate) was used to compare physical activity between disease and frailty phenotypes. Spearman correlation assessed relationships, and linear regression determined associations with frailty. Results. Nonfrail recorded more physical activity (intensity, counts, self-report) compared with frail. Self-reported physical activity was greater in PD than non-PD. In non-PD, step counts, light physical activity time, sedentary time, and self-reported physical activity were related to frailty (R = 0.91). In PD, only carbidopa-levodopa dose was related to frailty (r = 0.61). Conclusion. Physical activity influences frailty in females without PD. In PD females, disease management may be a better indicator of frailty than physical activity. Further investigation into how PD associated factors contribute to frailty is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".