MyPainPal, a Novel mHealth App to Improve Pain in Patients With Advanced Cancer: Single-Arm Pilot Study
Bibliographic record
Abstract
BACKGROUND: Pain is common among patients with advanced cancer and is often inadequately controlled. Opioids are central to treatment; yet, self-management is challenging, and clinicians lack scalable tools to monitor and support patients between visits. OBJECTIVE: This study aimed to evaluate the feasibility and acceptability of MyPainPal (Dana-Faber Cancer Institute), a novel mobile health app designed to optimize cancer pain management. MyPainPal combines daily surveys assessing symptoms and analgesic use, algorithmic self-management support, tailored psychoeducation, and clinician monitoring. Secondary objectives were to explore preliminary clinical impact and identify priorities for refinement. METHODS: This single-arm pilot study enrolled adults with advanced malignancies using opioids for moderate-to-severe pain from an outpatient palliative care clinic at a comprehensive cancer center. Participants used MyPainPal for 28 days while nurses monitored symptom responses via a secure portal, and also completed structured surveys at end-of-study. Primary assessment of usability and acceptability included the System Usability Scale (SUS; range 0-100), the Acceptability E-Scale (range 6-30), and ratings of satisfaction using a 5-point Likert scale. Semistructured debriefing interviews explored user experience, perceived impact, and suggestions for optimization. RESULTS: Twenty participants with advanced cancer enrolled, with a mean age of 57 (SD 12.3) years, 55% (11/20) female, 80% (16/20) non-Hispanic White, with mixed cancer types. Over the 28-day study, patients logged into MyPainPal a median of 14 (IQR 8-17) times, and completed a median of 8 (IQR 5-14) symptom surveys, reflecting mean of 36% (SD 20%) of eligible (out-of-hospital) days on study. Usability and acceptability ratings of MyPainPal were high (mean SUS 78.3, SD 16.2; mean Acceptability E-Scale 24.0, SD 4.4); 79% rated overall satisfaction of greater than or equal to 4/5. Twenty percent of surveys generated an alert, prompting nurse outreach. In response, 5 participants had symptom medications changed and 2 had medication errors corrected. In debriefing interviews, many participants described that the intervention reduced barriers to pain reporting and facilitated timely and constructive interactions with care teams for symptom management. Several noted that the intervention validated their pain experience, reduced stigma around opioid use, enabled constructive conversations with providers, and promoted self-management. Patients recommended several survey modifications, including reducing their frequency and enabling more nuanced pain assessments. Participants underused the educational resources and suggested that they be featured more prominently. Some patients suggested that the MyPainPal app should be introduced earlier in patients' cancer pain trajectory when pain needs are higher and opioid management is novel. CONCLUSIONS: In this pilot study, MyPainPal demonstrated feasibility, acceptability, and preliminary evidence of potential clinical impact among patients with advanced cancer receiving palliative care. The app has been rebuilt and optimized with attention to patient feedback and in preparation for a future efficacy study. TRIAL REGISTRATION: ClinicalTrials.gov NCT03717402; https://clinicaltrials.gov/study/NCT03717402.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".