Dual-Facing Digital Health Systems to Support Self-Management of Chronic Pain: Protocol for a Scoping Review
Bibliographic record
Abstract
Background: Chronic pain affects approximately 1 in 5 adults worldwide and imposes a major personal and societal burden. Supported self-management, increasingly delivered through digital health systems, is recommended to improve quality of life and function. Dual-facing digital health systems (DDHSs), which engage both patients and professionals, show promise but remain underexplored, with limited use of established frameworks to guide their design and evaluation. Objective: This protocol outlines the planned methods for conducting a scoping review that aims to map existing DDHSs for chronic pain self-management; examine the frameworks used to inform their design, implementation, and evaluation; and identify gaps to guide future research and practice. Methods: The review will follow the Joanna Briggs Institute methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guidelines. Peer-reviewed studies (2010-present) on DDHSs for use by health care professionals and adults with chronic pain will be included. Searches will be conducted across MEDLINE, CINAHL, Embase, Scopus, Web of Science, and APA PsycInfo, supplemented by citation tracking. Two reviewers will independently screen and extract data using a structured template. Results will be synthesized narratively, with emphasis on system features, populations, and applied frameworks. The quality of the included studies will be assessed using the Quality Assessment for Diverse Studies tool to support the interpretation of the findings. Results: Searches identified 1377 records, with 822 (59.7%) screened after deduplication. Following full-text assessment, 19 studies were included. The scoping review is expected to be completed by June 2026. The first search was conducted in September 2025, and screening was completed in November 2025. The results are expected to be published in June to August 2026. The evidence base is anticipated to be heterogeneous, with variation in system features, populations, and outcomes. Early indications suggest inconsistent use of theoretical and implementation frameworks, limited integration of dual-facing components, and a predominance of feasibility and acceptability outcomes. Conclusions: This review will provide a structured overview of DDHSs for chronic pain self-management and the frameworks informing their design and evaluation, with findings intended to guide the development and implementation of future digital interventions.
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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 | 0.102 | 0.112 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.106 | 0.018 |
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".