Exploring the Needs of People With Chronic Low Back Pain and Health Care Professionals for mHealth Devices to Support Self-Managed Physical Activity and Pain: User-Centered Design Approach
Bibliographic record
Abstract
BACKGROUND: Chronic low back pain (CLBP) is a major economic and social problem worldwide. Despite the variety of recommended treatments, long-term self-management of this condition is complex and requires the development of innovative interventions. Mobile health (mHealth) technologies hold great promise for the management of chronic pain, particularly to support physical activity. However, their implementation is challenged by a lack of user compliance and limited engagement, which may be due to insufficient consideration of the needs of potential users during development. OBJECTIVE: This study aims to explore the needs of people with CLBP and health care professionals regarding mHealth technologies to support self-managed physical activity, and to delineate design recommendations based on identified needs. METHODS: A participatory study was conducted using a 3-phase, user-centered design approach: needs investigation with a group of experts in a workshop (phase 1), needs exploration with end users in focus groups (phase 2), and validation of needs using Delphi questionnaires followed by the development of a set of recommendations (phase 3). RESULTS: A total of 121 people with CLBP, expert patients, health care professionals, rehabilitation researchers, and biomechanical engineers participated in this study. The results indicated how technology could help people with CLBP overcome their difficulties with managing physical activity. Specific needs were formulated concerning device objectives, expected strategies, functionalities, technical features, conditions of use, and potential facilitators and barriers to use. These needs were validated by consensus from the potential end users and translated into design recommendations. CONCLUSIONS: This study provides design recommendations for the development of an mHealth device specifically adapted for people with CLBP.
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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 | 0.024 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".