Usability and Acceptability of an App-Based Approach to Treat Low Back Pain: Preplanned Secondary Analysis of a Randomized Controlled Trial
Bibliographic record
Abstract
Background: Low back pain (LBP) is a major cause of disability worldwide. To tackle issues such as long wait times and limited access to conventional care, telemedicine is emerging as a viable alternative. It offers benefits such as reduced travel and increased flexibility, with evidence showing comparable effectiveness to in-person care. However, usability remains a key challenge, impacting patient compliance. objectives: In a 3-arm randomized controlled trial, our preplanned secondary analysis aimed to assess the usability and patient perceptions toward an autonomous app-based intervention ("NOLA") for LBP to improve clinical practice of telemedicine interventions. Objectives included evaluating app usability, assessing perceptions toward telemedicine, and exploring app usage, adherence, and motivation. Methods: Patients with LBP were recruited from May to August 2022 and randomized into App, Physio+App, or Physio groups. App and Physio+App groups were included in this subanalysis. Intervention duration was 6 weeks. Data on baseline characteristics, System Usability Scale, Telemedicine Perception Questionnaire, app usage, adherence, and motivation were collected via web-based questionnaires. Results: A total of 64 participants were randomized to use the app with available data for 38 participants. The mean age of participants who completed was 49.9 (SD 13.6) years, with 78% (29/38) experiencing LBP for more than 2 years. Usability scores (0-100) were good (Physio+App: median 78, IQR 58-92, app: median 86, IQR 65-91). Positive telemedicine perceptions were noted, with 84% (15/20) rating it an adequate addition to usual care. App usage varied, with 43% (16/35) using it 3 to 5 days per week, and 64% (21/33) reported motivation to use the app. The dropout rates (App: 14/32, 44%; Physio+App: 12/32, 38%; Physio: 11/29, 38%) were similar, but participants who dropped out had statistically significantly less pain (completers: mean 3.9, SD 2.0; dropouts: mean 3.0, SD 2.0; P=.02). Reasons for dropout were mostly not reported. Conclusions: The app "NOLA" demonstrated good usability, and participants expressed positive perceptions toward telemedicine in those who completed the intervention. Despite concerns about the lack of physical contact, the majority considered telemedicine a convenient form of health care delivery. App usage and motivation were favorable, emphasizing the potential of app-based interventions in managing LBP.
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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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".