Understanding Patient and Physiotherapist Requirements for a Personalized Automated Smartphone Telemonitored App for Posttotal Knee Arthroplasty Rehabilitation: Qualitative Study
Bibliographic record
Abstract
Background: Total knee arthroplasty (TKA) is a cost-effective surgical intervention for painful knee osteoarthritis in older adults, but postsurgery rehabilitation access is limited. Telerehabilitation offers a solution, but existing models require significant therapist involvement and a costly setup. A personalized smartphone-based automated program could be a cost-effective alternative. Objective: This study aimed to understand the requirements of both patients and physiotherapists in developing an automated telemonitored rehabilitation smartphone app for individuals undergoing TKA. To ensure uptake and long-term sustainability, this study adopted a person-based approach. Methods: A multistakeholder qualitative study of user needs was conducted. Physiotherapists and patients who underwent TKA were recruited via purposive sampling. Individual in-depth, hour-long interviews were conducted via Zoom by an experienced, trained female interviewer with a Master of Arts in Sociology. Data were audio-recorded and transcribed by the same interviewer. Two reviewers (ESC and SSK) independently analyzed the data using thematic analysis, with data triangulation achieved through cross-checking of data sources by 3 reviewers (ESC, SSK, and AES). Interviews were conducted to data saturation. Results: Six patients and 4 physiotherapists participated. For the patient interface, patients emphasized ease of use and specified features like a search function and multilingual options. For the physiotherapist interface, physiotherapists stated ease of accessing patient data and outcome measures for effective monitoring as important. Both patients and physiotherapists highlighted the need for timely, condition-specific information, supplemented by visual aids to support exercises, pain management, and recovery goals. They also stressed the significance of progress tracking, feedback, and the ability to access health care professionals for reassurance. Motivational features, including reminders, prompts, and exercise logs, were recommended to improve adherence. Both groups similarly identified the need for initial training to ensure confident use of the app. Conclusions: This study provided insights into the requirements of potential end users of a smartphone app for automated telemonitored rehabilitation following TKA. This is useful for steering the development of a user-centric smartphone 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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".