Long-term trajectories of physical activity behavior in adults with physical disabilities and/or chronic diseases following rehabilitation: the prospective cohort study ReSpAct 2.0
Bibliographic record
Abstract
PURPOSE: This study aimed to identify trajectories of physical activity behavior from discharge up to 6-8 years after rehabilitation among adults with physical disabilities and/or chronic diseases, and to determine modifiable determinants associated with trajectory membership. MATERIAL AND METHODS: 390 Adults with physical disabilities and/or chronic diseases participated in the Rehabilitation, Sports and Active lifestyle (ReSpAct) 2.0 study with measurements at 3-6 weeks before discharge (T0), and 14 (T1), 33 (T2), and 52 weeks (T3), and 6-8 years (T4) after discharge from rehabilitation. Physical activity behavior and its determinants were assessed using questionnaires. Latent class growth modeling was used to identify trajectories of physical activity behavior. Associations between determinants at T0 and trajectory membership were analyzed using logistic regression analyses. RESULTS: = 22; baseline total physical activity: 1755 (461:2415) min/week) trajectory. Barriers regarding physical activity (OR = 0.71, 95%CI 0.53-0.95) and perceived fatigue (OR = 0.75, 95%CI 0.57-0.98) were significantly associated with trajectory membership in univariable models, corrected for age and sex. CONCLUSIONS: Targeting barriers regarding physical activity and perceived fatigue early in rehabilitation seem crucial for membership of a trajectory resulting in a more favorable development of physical activity behavior after rehabilitation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".