Patients’ Experiences of Digital Health Interventions for the Self-Management of Chronic Pain: Protocol for a Systematic Review and Thematic Synthesis
Bibliographic record
Abstract
BACKGROUND: Chronic pain is a highly prevalent condition that requires multidisciplinary treatment. However, in the United Kingdom, access to specialist pain clinics where patients can receive medical multidisciplinary treatment is limited, and provision varies between health boards. As such, self-management of chronic pain using digital tools has been gaining traction recently, but evidence of its effectiveness from clinical-based trials focuses mainly on quantitative outcomes. OBJECTIVE: This systematic review aims to identify, appraise, and synthesize qualitative evidence on patients' experiences with digital health interventions (DHIs) for the management of chronic pain. METHODS: This systematic review will consider qualitative and mixed methods studies that explore the experience of patients (aged 18 years and older) with chronic pain engaging in DHIs to manage their pain. MEDLINE Ovid, PubMed, Embase, CINAHL, PsycINFO, and Scopus databases will be searched for published studies. The systematic review will be conducted in accordance with the ENTREQ (Enhancing Transparency in Reporting the Synthesis of Qualitative Research) guidelines. Following the 3-step thematic synthesis methodology of Thomas and Harden, titles and abstracts will be screened by 2 independent reviewers (AM and HM), and a third reviewer (MI or FM) will resolve any conflict that arises before the full-text screening. The Critical Appraisal Skills Programme checklist tool will be used to critically appraise the included studies. The extracted data will be imported to NVivo (QSR International), where thematic synthesis will be used to derive analytical themes from the included studies. RESULTS: Themes that encapsulate the patient experience will be identified from qualitative evidence, and these themes will shed light on the perceived benefits and disadvantages, usability, acceptability, and the overall impact digital tools can have on the lives of those with chronic pain. CONCLUSIONS: This systematic review will identify, appraise, and synthesize the overall experience of patients engaging in DHI to manage a diverse range of chronic pain conditions. By elaborating the patient experience through qualitative analysis, the findings from this review will enhance our current understanding of the experiences of patients with chronic pain using digital tools for the self-management of their pain and highlight what person-centered elements are essential for future DHI development. TRIAL REGISTRATION: PROSPERO CRD42023445100; http://tinyurl.com/4z77khfs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52469.
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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 | 0.122 | 0.115 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.019 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.066 | 0.008 |
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".