Effectiveness of telerehabilitation and home-based falls prevention programs for community-dwelling older adults: a systematic review and meta-analysis protocol
Bibliographic record
Abstract
Introduction Falls among older adults are associated with adverse sequelae including fractures, chronic pain and disability, which can lead to loss of independence and increased risks of nursing home admissions. The COVID-19 pandemic has significantly increased the uptake of telehealth, but the effectiveness of virtual, home-based fall prevention programmes is not clearly known. We aim to synthesise the trials on telerehabilitation and home-based falls prevention programmes to determine their effectiveness in reducing falls and adverse outcomes, as well as to describe the safety risks associated with telerehabilitation. Methods and analysis This protocol was developed using the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P). Database searches from inception to August 2022 will be conducted without language restrictions of MEDLINE, EMBASE, Ovid HealthSTAR, CINAHL, SPORTDiscus, Physiotherapy EvidenceDatabase (PEDro) and the Cochrane Library. Grey literature including major geriatrics conference proceedings will be reviewed. Using Covidence software, two independent reviewers will in duplicate determine the eligibility of randomised controlled trials (RCTs). Eligible RCTs will compare telerehabilitation and home-based fall prevention programmes to usual care among community-dwelling older adults and will report at least one efficacy outcome: falls, fractures, hospitalisations, mortality or quality of life; or at least one safety outcome: pain, myalgias, dyspnoea, syncope or fatigue. Secondary outcomes include functional performance in activities of daily living, balance and endurance. Risk of bias will be assessed using the Cochrane Collaboration tool. DerSimonian-Laird random effects models will be used for the meta-analysis. Heterogeneity will be assessed using the I 2 statistic and Cochran’s Q statistic. We will assess publication bias using the Egger’s test. Prespecified subgroup analyses and univariate meta-regression will be used. Ethics and dissemination Ethics approval is not required. The results will be disseminated through peer-reviewed publications and conference presentations. PROSPERO registration number CRD42022356759.
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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 | 0.054 | 0.085 |
| Meta-epidemiology (narrow) | 0.008 | 0.006 |
| Meta-epidemiology (broad) | 0.030 | 0.033 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.060 | 0.006 |
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".