Randomized controlled trial investigating the effectiveness of a multimodal mobile application for the treatment of chronic pain
Bibliographic record
Abstract
Background Until recently, treatments for chronic pain commonly relied on in-person interventions, and despite more hybrid care options today, capacity for delivery remains challenged. Digital programs focusing on the psychosocial aspects of pain may provide low-barrier alternatives.Aims Through a randomized controlled trial, we investigated the effectiveness of a multimodal mobile application.Methods Participants (n = 198; 82% women, mean age = 46.7 [13.1] years; mean pain duration 13.6 [11.2] years) with nonmalignant chronic pain were randomized to either a 6-week intervention (n = 98) or a wait-listed usual care group (n = 100). The intervention involved regular engagement with a user-guided mobile application (Curable Inc.) informed by the biopsychosocial model of pain that included pain education, meditation, cognitive behavioral therapy, and expressive writing. The co-primary outcomes were pain severity and interference at 6 weeks.Results We observed significant improvements in the intervention group compared to the control group with estimated changes of −0.67 (95% confidence interval [CI] −1.04 to −0.29, P < .001, d = 0.43) and −0.60 (95% CI −1.18 to −0.03, P = .04, d = 0.27) for pain severity and interference, respectively. There were significant improvements across secondary outcomes (Patient-Reported Outcome Measurement Information System pain interference; pain catastrophizing; anxiety, depression; stress). Frequency of app use was correlated with improved pain interference (P < .001) and pain catastrophizing (P = 0.018), and changes from baseline persisted in the intervention group at 12 weeks (P < .05).Conclusions A short-term mobile app intervention resulted in significant improvements across physical and mental health outcomes compared to wait-listed usual care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".