Identifying characteristics of resistance-based therapeutic exercise interventions for Achilles tendinopathy: A scoping review
Bibliographic record
Abstract
OBJECTIVE: This scoping review describes resistance-based therapeutic exercise intervention characteristics for Achilles tendinopathy (AT) treatment (e.g., therapeutic dose, underlying mechanisms targeted by exercise) and assesses participant reporting characteristics. METHODS: Seven electronic databases were searched; studies delivering a resistance exercise-focused treatment for individuals with AT were included. The Template for Intervention Description and Replication (TIDieR) and the ICON 2019 'Recommended standards for reporting participant characteristics in tendinopathy research' checklists framed data extraction, and study quality was assessed using the Mixed Methods Appraisal Tool 2018 version. RESULTS: 68 publications (describing 59 studies and 72 exercise programs) were included. Results demonstrate that therapeutic exercise interventions for AT are well reported according to the TIDieR checklist, and participant characteristics are well reported according to the ICON checklist. Various underlying therapeutic mechanisms were proposed, with the most common being increasing tendon strength, increasing calf muscle strength, and enhancing collagen synthesis. CONCLUSIONS: While evidence suggests that resistance-based therapeutic exercise interventions are effective in treating AT, more reporting on program fidelity, adherence, and compliance is needed. By summarizing currently published AT exercise programs and reporting key intervention characteristics in a single location, this review can assist clinicians in developing individualized resistance training programs for people with AT.
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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 | 0.023 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".