Adherence to prehabilitation in adult surgical patients: a systematic review, meta-analysis, meta-regression, and qualitative synthesis
Bibliographic record
Abstract
BACKGROUND: Prehabilitation is hypothesised to play an important role in optimising postoperative outcomes. However, achieving high adherence can be challenging. Our objectives were to synthesise current approaches to adherence measurement and reporting, estimate prehabilitation adherence across trials, identify procedural-, programme-, or patient-level factors associated with adherence, and report barriers and facilitators to adherence. METHODS: Ovid MEDLINE, Embase, the CINAHL, PsycINFO, Web of Science, and the Cochrane CENTRAL Register of Controlled Trials were searched from inception until April 10, 2024. We included randomised trials of adults undergoing major elective surgery allocated to a prehabilitation programme, with at least one binary or continuous measure of adherence to prehabilitation, to an individual component, or both. Random-effects meta-analysis pooled overall adherence rates; meta-regression evaluated predictors of adherence. Qualitative synthesis of reported barriers and facilitators was informed by the Theoretical Domains Framework. RESULTS: =95.4%). Substantial qualitative and statistical heterogeneity existed in defining prehabilitation adherence. Only patient age was significantly associated with adherence (per year older: odds ratio 0.95 [95% CI 0.91-0.99]). Based on qualitative synthesis, common barriers were logistical issues and health conditions; facilitators included supervision by specialists and personalisation. CONCLUSIONS: Prehabilitation adherence metrics are variable across trials and standardisation is required to improve reporting and interpretation of prehabilitation evidence. Little credible evidence identifies factors associated with adherence; however, qualitative barriers and facilitators could inform programme design and implementation. SYSTEMATIC REVIEW PROTOCOL: PROSPERO (CRD42024518851).
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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 | 0.088 | 0.182 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.039 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".