Exercise-based rehabilitation for major non-communicable diseases in low-resource settings: a scoping review
Bibliographic record
Abstract
INTRODUCTION: While there is substantial evidence for the benefits of exercise-based rehabilitation in the prevention and management of non-communicable disease (NCD) in high-resource settings, it is not evident that these programmes can be effectively implemented in a low-resource setting (LRS). Correspondingly, it is unclear if similar benefits can be obtained. The objective of this scoping review was to summarise existing studies evaluating exercise-based rehabilitation, rehabilitation intervention characteristics and outcomes conducted in an LRS for patients with one (or more) of the major NCDs. METHODS: The following databases were searched from inception until October 2018: PubMed/Medline, Embase, CINAHL, Cochrane Library, PsycINFO and trial registries. Studies on exercise-based rehabilitation for patients with cardiovascular disease, diabetes, cancer or chronic respiratory disease conducted in an LRS were included. Data were extracted with respect to study design (eg, type, patient sample, context), rehabilitation characteristics (eg, delivery model, programme adaptations) and included outcome measures. RESULTS: The search yielded 5930 unique citations of which 60 unique studies were included. Study populations included patients with cardiovascular disease (48.3%), diabetes (28.3%), respiratory disease (21.7%) and cancer (1.7%). Adaptations included transition to predominant patient-driven home-based rehabilitation, training of non-conventional health workers, integration of rehabilitation in community health centres, or triage based on contextual or patient factors. Uptake of adapted rehabilitation models was 54%, retention 78% and adherence 89%. The majority of the outcome measures included were related to body function (65.7%). CONCLUSIONS: The scope of evidence suggests that adapted exercise-based rehabilitation programmes can be implemented in LRS. However, this scope of evidence originated largely from lower middle-income, urban settings and has mostly been conducted in an academic context which may hamper extrapolation of evidence to other LRS. Cost-benefits, impact on activity limitations and participation restrictions, and subsequent mortality and morbidity are grossly understudied.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".