An Ecological Monitoring and Management App (EMMA) for Older Adults With Chronic Pain: Protocol for a Design and Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Chronic pain is a complex problem for many older adults that affects both physical functioning and psychological well-being. Mobile health (mHealth) technologies have shown promise in supporting older persons in managing chronic conditions. Cognitive behavior therapy is recommended for older people with chronic pain. However, web-based treatment programs for chronic pain are not aimed at the needs of older people and offer standard therapies without providing tailored treatment for this population. OBJECTIVE: To address this problem, we aim to develop a psychological web-based intervention for ecological monitoring of daily life experiences with chronic pain called EMMA to support self-management of chronic pain in older adults. METHODS: The key clinical and engagement features of the intervention were established through the integration of evidence-based material from cognitive behavioral therapy for the treatment of chronic pain in older adults. The development process uses a co-design approach and actively involves end-users in the design process by incorporating feedback from focus groups with older adults in order to inform a user-centered intervention design. For the co-design process, we will include 10 older adults with chronic pain, who will discuss the requirements for the app in workshops in order to ensure suitability of the app for older adults with chronic pain. In order to test the feasibility and acceptability of the intervention, we will include a sample of 30 older adults with chronic pain who will test all features of the intervention for a period of 8 consecutive weeks. After the trial period, validated instruments will be used to assess usability and acceptability, as well as influence on pain levels and associated physical and psychological symptoms. Participants will be invited to take part in a semistructured telephone interviews after the trial period to explore their experiences using the app. RESULTS: Digitalization of the pain diary and psychotherapeutic content has started. Recruitment of participants for the co-design workshops will start as soon as we have a functioning prototype of the electronic pain diary and EMMA intervention, which is expected to be in September 2021. The feasibility study will start as soon as the co-design process is finished and required changes have been implemented into the pain diary and the EMMA intervention. We expect to start the feasibility study early in 2022. CONCLUSIONS: Required changes to assure usability and acceptability will be directly implemented in the app. EMMA brings together a strong body of evidence using cognitive behavioral and self-management theory with contemporary mHealth principles, allowing for a cost-effective intervention that can be used to target chronic pain anywhere and anytime by older adults. Given the ubiquity of mHealth interventions for chronic conditions, the results of this study may serve to inform the development of tailored self-management interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/26930.
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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 | 0.049 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.060 | 0.014 |
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".