Engagement with Manage My Pain mobile health application among patients at the Transitional Pain Service
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Mobile health platforms have become an important component of pain self-management programs and hundreds of mobile applications are commercially available for patients to monitor pain. However, few of these applications have been developed in collaboration with healthcare professionals or have been critically evaluated. Manage My Pain is a user-driven mobile health platform developed by ManagingLife in collaboration with clinician researchers. Manage My Pain allows patients to keep a "pain record" and supports communication of this information with clinicians. The current report describes a user engagement study of Manage My Pain among patients at the Transitional Pain Service (TPS) at Toronto General Hospital, a multidisciplinary clinic for patients at high risk of developing postsurgical pain. METHODS: Patients at the TPS were encouraged to register on Manage My Pain as one component of a larger, non-randomized prospective study of treatment predictors and treatment enhancement. Uptake of the application and rates of registration, use, and retention were tracked for 90 days. RESULTS: Of the 196 patients who consented to the larger study, 132 (67%) also provided consent to the Manage My Pain component, indicating that they found this to be an acceptable treatment adjunct, and 119 (61%) completed registration. Of those who used the app, 67.9% and 43.2% continued to use Manage My Pain beyond 30 and 90 days, respectively. On average, users engaged with the app for 93.14 days (SD = 151.9 days) logged an average of 47.39 total records (SD = 136.1). CONCLUSIONS: Manage My Pain was found acceptable by a majority of patients at an academic pain management program. Rates of user registration and retention were favorable compared to those reported by other applications. Further research is needed to develop strategies to retain users and maximize patient benefit.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it