How Effective Are Interventions to Increase Physical Activity Levels among Older Inpatients Receiving Rehabilitation, without Increasing the Amount of Therapy? A Systematic Review
Bibliographic record
Abstract
Purpose: We systematically reviewed the effectiveness of interventions to increase physical activity in older adults (aged ≥ 60 y), admitted for inpatient rehabilitation, without increasing the amount of therapy. Method: Five electronic databases were systematically searched to identify English-language articles reporting controlled trials of interventions to increase the physical activity (through participation or behavioural change) of older adults receiving inpatient rehabilitation. Trials were excluded if an intervention increased the intensity of usual care, either during the week or on the weekend. Two reviewers independently completed trial selection, quality assessment, and data extraction. Data were synthesized descriptively, and effect sizes with 95% CIs were calculated. Results: Of the 316 articles identified, 3 were included. Two were activity-based, and 1 was a behavioural change intervention. Physical activity was significantly improved in the behavioural change intervention trial that occurred during therapy ( d = 0.27; 95% CI: 0.02, 0.52) and non-therapy time ( d = 0.43; 95% CI: 0.19, 0.68). Participants in all trials were sedentary for the vast majority of the day. Conclusions: Older adults in inpatient rehabilitation have a high level of inactivity. Evidence is lacking that interventions increase physical activity in older adults admitted to inpatient rehabilitation without increasing the amount of time in therapy. Evidence from one trial indicates that behaviour-based strategies are effective for increasing physical activity levels in the inpatient rehabilitation setting.
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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 | 0.016 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".