Telerehabilitation to Address the Rehabilitation Gap in Anterior Cruciate Ligament Care: Survey of Patients
Bibliographic record
Abstract
BACKGROUND: Evidence shows that after anterior cruciate ligament (ACL) reconstruction, patients may have varied access to physical therapy. In particular, physical therapy input may end many months before patients reach full recovery. Telerehabilitation may provide an opportunity to address this rehabilitation gap and improve access to evidence-based rehabilitation alongside physical therapy at all stages of care. OBJECTIVE: This study aims to understand the opinions of patients who have undergone ACL surgery and rehabilitation on the use of telerehabilitation as part of ACL care and define the population and explore their experiences and views on the acceptability of telerehabilitation after ACL reconstruction. METHODS: This study was a cross-sectional, voluntary, web-based survey combining both closed and open questions. Ethical approval was obtained from the Yale School of Medicine Institutional Review Board. Participants were aged 16 years or older at the time of recruitment and had undergone ACL reconstruction within the past 5 years. A 26-item survey was developed using the Qualtrics survey platform. No items were mandatory. Responses were multiple choice, binary, and qualitative. The CHERRIES (Checklist for Reporting Results of Internet E-Surveys) was used to ensure the quality of reporting of surveys in the medical literature. Data were analyzed using Stata version 15. Qualitative data were analyzed using NVivo 11. The theoretical framework for this analysis is based on the Capability, Opportunity, and Motivation-Behavior model of behavior change. RESULTS: A total of 100 participants opened the survey. All completers were unique. The participation and completion rates were each 96% (96/100). Patients reported their physical therapy care ended at an average of 6.4 months and that they felt fully recovered at an average of 13.2 months. Only 26% (25/96) of patients felt fully recovered at the end of physical therapy. Of these 96 patients, 54 (60%) were younger than 30 years, 71 (74%) were recreational athletes, 24 (24%) were competitive athletes, 72 (75%) had private insurance, 74 (77%) were not familiar at all with telerehabilitation, and 89% (85/96) felt capable. They preferred to use telerehabilitation at different stages of care. Reported benefits included resource saving, improved access to care, improved learning, and greater engagement. Concerns included incorrect performance of exercises or unmanaged pain being missed and less access to manual therapy, motivation, and opportunities to ask questions. Participants' priorities for a future telerehabilitation intervention included its use as an adjunct to physical therapy rather than a replacement, with content available for each stage of care, especially return to sports. Participants stressed that the intervention should be personalized to them and include measures of progress. CONCLUSIONS: These findings helped understand and define the ACL reconstruction population. Participants found telerehabilitation acceptable in principle and highlighted the key user requirements and scope of future interventions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".