Dolodoc, an App to Leverage Self-Management of Chronic Pain: Design, Development, and Implementation Report
Bibliographic record
Abstract
Background: Chronic pain affects approximately 19% of the European population and presents major challenges, both in terms of individual impact and the economic burden on health care systems. While clinical expertise remains essential, patient empowerment through self-management tools has become a key component in the long-term management of chronic pain. Objective: This report describes the development and implementation of Dolodoc, a mobile app designed to support patients with chronic pain in monitoring and managing their condition. Methods: Developed by a research and development team at the University Hospitals of Geneva, Dolodoc enables users to track their pain across 7 dimensions of daily life. A digital coach provides personalized guidance, drawing from a corpus of over 80 evidence-based recommendations elaborated by clinical experts. The project was conducted over 4 years with the early involvement of stakeholders, including pain specialists and end users, to ensure alignment with user needs. Emphasis was placed on both the scientific validity and accessibility of the recommendations. Results: The project was completed on time and within budget. The app was made freely available to patients identified as likely to benefit. However, a notable limitation is the absence of predefined key performance indicators to assess the impact of the intervention quantitatively. Conclusions: This implementation report illustrates how mobile technology can be leveraged in a university hospital context to address the needs of patients with chronic pain and promote self-management. Early and sustained collaboration with stakeholders was instrumental in aligning the solution with both clinical evidence and user expectations.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".