Preventing hospital-associated disability in older adults: a living systematic review
Bibliographic record
Abstract
Background During hospitalisation, older adults are at risk of experiencing functional decline unrelated to their initial condition, known as hospital-associated disability (HAD). HAD affects 37% of hospitalised older adults. The population of older adults is expected to grow in the coming years, likely leading to an increase in cases of HAD. Published systematic reviews have certain limitations, leaving several questions unanswered about the prevention of HAD. Randomised controlled studies (RCT) with multiple training components (such as different strengthening and balance or walking components) are difficult to compare against single-component programmes. The aim of the study was to systematically evaluate the effectiveness of (single or multi-component) physical exercise programmes on the prevention of HAD in people over 65 years of age hospitalised in an acute care setting measured at hospital discharge. Methods Living systematic review with network meta-analysis of RCTs. As this study represents a living systematic review, findings are continuously updated. Physical exercise programmes were analysed as multi-component interventions. Risk of bias was evaluated using the ROBUST-RCT tool. Results In total 5967 records were screened for eligibility; 546 full texts were assessed, and 75 studies were included in the evidence synthesis. We analysed 19 pair-wise comparisons at discharge. Inconsistency in the network-analysis was low with I2 = 8.6%. The most effective programme was strengthening compared against usual care (SMD: 0.65; 95% CI: 0.06; 1.24). The exercise programme with the highest precision was balance+strengthening+walking (SMD: 0.50; 95% CI: 0.33; 0.67). Training focused on a single-component of basic daily activities was not more effective than usual care (SMD: –0.13; 95% CI –0.44; 0.18). Conclusion This work enhances the understanding of how to prevent HAD by providing detailed insights into specific components of physical exercise. Currently, the intervention with the highest precision suggests using a multi-component approach involving balance, strengthening, and walking components to prevent HAD in an acute care setting. A major issue with traditional systematic reviews is the lengthy publication process, which often results in findings becoming outdated by the time they are published. This living systematic review enables clinicians to stay current by rapidly integrating new findings into the evidence synthesis of HAD prevention.
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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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".