Challenges During Implementation of a Patient-Facing Mobile App for Surgical Rehabilitation: Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Translating research into practice, especially the implementation of digital health technologies in routine care, is increasingly important. Yet, there are few studies examining the challenges of implementing patient-facing digital technologies in health care settings. OBJECTIVE: The aim of this study was to report challenges experienced when implementing mobile apps for patients to support their postsurgical rehabilitation in an orthopedic setting. METHODS: A mobile app was tailored to the needs of patients undergoing rotator cuff repair. A 30-min usability session and a 12-week feasibility study were conducted with patients to evaluate the app in routine care. Implementation records (observation reports, issues log, and email correspondence) explored factors that hindered or facilitated patient acceptance. Interviews with clinicians explored factors that influenced app integration in routine care. RESULTS: Participant completion was low (47%, 9/19). Factors that affected patient acceptance included digital literacy, health status, information technology (IT) infrastructure at home, privacy concerns, time limitations, the role of a caregiver, inconsistencies in instruction received from clinicians and the app, and app advice not reflective of patient progress over time. Factors that negatively influenced app integration in routine care included competing demands among clinicians, IT infrastructure in health care settings, identifying the right time to introduce the app to patients, user interface complexity for older patients, lack of coordination among multidisciplinary clinicians, and technical issues with app installation. CONCLUSIONS: Three insights were identified for mobile app implementation in routine care: (1) apps for patients need to reflect their journey over time and in particular, postoperative apps ought to be introduced as part of preoperative care with opportunities for patients to learn and adopt the app during their postoperative journey; (2) strategies to address digital literacy issues among patients and clinicians are essential; and (3) impact of the app on patient outcomes and clinician workflow needs to be communicated, monitored, and reviewed. Lastly, digital health interventions should supplement but not replace patient interaction with clinicians.
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 | 0.022 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".