Mobile app-based monitoring of recovery after knee osteotomy: Patients take approximately five months to return to preoperative step counts despite limited app uptake
Bibliographic record
Abstract
Introduction This study aimed to assess the feasibility of using mobile application (app) technology for monitoring recovery after knee osteotomy and to determine the time required for patients to return to their preoperative step counts. Methods This retrospective study included 329 patients who underwent coronal realignment surgery, including high tibial osteotomy (HTO) or distal femoral osteotomy (DFO) with a minimum follow-up of 1 year. The patients were grouped based on the type of osteotomy performed, i.e., HTO and DFO groups. Step count data were collected using the myrecovery app and analyzed preoperatively and at 1 month, 3 months, 6 months, and 12 months postoperatively. Statistical analyses included univariate linear regression models to assess the relationship between step counts at each time point and the duration required to return to their preoperative step counts. Results Of the 329 patients included in the study, a total of 62 patients (19%) downloaded the app and 24 patients (7%) had complete step count data. Of the 24 patients with complete data, 18 were included in the HTO group and 6 were included in the DFO group. It took patients an average of 153 ± 112 days to return to their preoperative step counts, with the patients in the HTO group taking 174 ± 121 days and those in the DFO group taking 113 ± 77 days. Step counts increased significantly over time, with percentages of preoperative step counts reaching 108% at 12 months postoperatively. A statistically significant correlation was found between step counts at 3 months postoperatively and the time to return to preoperative step counts (R 2 = 0.240, P = 0.015). Discussion This study found that patients took approximately 5 months to return to their preoperative step counts after knee osteotomy. However, the adoption of the app was limited, with only 19% of patients downloading the app and just 7% providing complete data, posing a significant barrier to the feasibility of mobile apps for tracking recovery. Conclusion The mobile app is effective for tracking recovery progress following knee osteotomy, but strategies to increase patient adoption are essential for enhancing its practical application. Level of evidence Level IV, Retrospective Case Series.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".