Acceptability of a Digital Care App in Patients Undergoing Hip and Knee Arthroplasty: Prospective Cohort Study
Bibliographic record
Abstract
Background: Mobile health (mHealth) apps have become more commonly used in orthopedics. However, for these apps to be efficient, patients should be willing to use them, making it essential to understand patients' perspectives of mHealth interventions. Objective: The aim of this single-center, intent-to-treat, preoperative single-cohort study of 100 patients was to evaluate the acceptability of mymobility (Zimmer-Biomet), an mHealth app designed for the postoperative care of total hip arthroplasty (THA) and total knee arthroplasty (TKA). Methods: We measured acceptability using the theoretical framework for acceptability (TFA) preoperatively and at 3 months post operation. We also measured satisfaction with app use postoperatively using the Usefulness, Satisfaction, and Ease of Use questionnaire as well as patient-reported outcome measures preoperatively and postoperatively using the Oxford hip and knee scores and the visual analog scale for pain. Patients included were 18 years or older; underwent unilateral primary total hip, total knee, or partial knee arthroplasty; spoke and read French or English; and had a smartphone with internet access. Participants used mymobility in addition to standard government-funded physiotherapy. Results: The preoperative overall TFA result was 4.2 out of 5, but results decreased significantly postoperatively. There was higher self-efficacy in preoperative TFAs with higher education, and lower acceptability in postoperative TFAs with TKA. The Usefulness, Satisfaction, and Ease of Use questionnaire revealed a good level of satisfaction with the use of the app. Patient-reported outcome measures showed earlier improvement in THA (31.2 d) than in TKA (89.4 d), whereas the visual analog scale showed a rapid decrease in pain with both procedures. Only 1 patient expressed privacy concerns with the use of the app. Conclusions: There was a good level of acceptability with the use of mymobility for the postoperative management of THA and TKA, although acceptability decreased postoperatively. This decrease could signify high expectations toward the app preoperatively or higher than expected difficulty and pain in the early postoperative period. Acceptability tended to increase with higher education and decrease with TKA. These trends are consistent with prior literature and constitute a potential gap to address for app developers. The influence of the natural recovery process on acceptability remains unclear. Future studies could explore this gap by comparing results in cohorts using the app to cohorts with standard care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".