Do pain management apps use evidence-based psychological components? A systematic review of app content and quality
Bibliographic record
Abstract
With hundreds of pain management apps on the Canadian marketplace, it can be challenging for patients and clinicians to select effective and evidence-based mobile health (mHealth) apps that address pain from a biopsychosocial perspective. The aim of this study is to identify pain management apps within the Canadian app marketplaces to aid clinicians in recommending apps. The iOS and Android marketplaces were systematically searched to identify pain management apps that included at least one core component of cognitive behavioral therapy (CBT) or mindfulness- and acceptance-based therapies. Selected apps were assessed using a researcher developed psychological components checklist, and the Mobile App Rating Scale (MARS). These two measures provided a robust assessment of the apps’ technical abilities and psychological principles being implemented. Five hundred eight pain management apps were identified, yet only 12 included a psychological component and were available for evaluation. On average, apps contained 8.10 out of 18 psychological components (SD = 2.77) with a MARS quality rating of 4.02 out of 5 (SD = 0.32). The most common psychological components were grounded in CBT, including psychoeducation, sleep hygiene, behavioral activation, coping skills training, and social support. Among the least commonly included components were goal setting, values, and culture/diversity. Two-thirds of the apps involved health care practitioners in their development, but independent scientific review of apps was scarce. The highest scoring apps (Curable, Pathways, Vivify) are highlighted for health care practitioners who may wish to recommend mHealth technologies to their patients for pain management. Future directions for research and app development are discussed.
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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 | 0.028 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".