THE MY HIP & KNEE APP: OUTCOMES AND EXPERIENCES OF 5,433 PATIENTS
Bibliographic record
Abstract
With the expansion of virtual care since the COVID-19 Pandemic, there has also been increasing interest in the use of digital adjuncts to improve the perioperative patient care experience. We have used a digital application (i.e. myHip&Knee) to facilitate post-operative recovery following total hip replacement (THR) and total knee replacement (TKR) at our centre since 2015. In this study, we aimed to summarize patient outcomes and experience with the App. All patients undergoing primary THR and TKR at our centre were encouraged to download and use the digital ‘App’ on their mobile phones, tablets or via the web application. The App comprises a pre-operative program and a 42-day post-operative program, including daily health checks with personalized feedback that helps the patient track their recovery as well as an educational library (with information provided via text, images, and video media). Self-reported range-of-motion measurements, pain scores, and medication usage were captured daily. We summarized this information utilizing descriptive statistics. A total of 5433 patients used the App since 2015, representing 42.5% of all patients undergoing surgery during that time period. Most patients were between 65 and 74 years of age (40.3%), and 62.2% were female. Of these patients, 2575 (47.4%) underwent THR and 2858 (52.6%) underwent TKR. Among THR patients, resting pain scores peaked on day 1 at a mean score of 2.6 (out of 10) at rest and 5.1 with exercise, while maximum daily opioid use peaked on day 2 at 18.4mg morphine equivalents. Among TKR patients, resting pain scores peaked on day 1 at mean score of 3.7 (out of 10) and pain with exercise peaked at day 1 at 6.2; however, maximum daily opioid use occurred on day 3, with a mean daily use of 34.6mg morphine equivalents. Mean flexion exceeded 100 degrees and began to plateau at 24 days post-operatively. Mean Extension fell below 5 degrees and began to plateau at 12 days post-operatively,. Overall, 98% of patients found the App useful during recovery, and 66% reported that it prevented at least one phone call to their surgeon's office/hospital. Both pain and opioid usage were lower following THR than TKR. Among TKR patients, extension and flexion gains occurred rapidly and plateaued at an average of 2 and 3.5 weeks, respectively. These data provide key insights for health care providers in terms of the ‘normal’ course of recovery following THR and TKR. In addition, high rates of satisfaction, perceived benefit, and data collection can be achieved with the use of well-designed mobile App.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".