Feasibility and acceptability of an mHealth app integrating pain-cognitive behavioral therapy and pharmacologic support for patients with advanced cancer.
Bibliographic record
Abstract
275 Background: Up to 60% of patients with advanced cancer and chronic pain experience inadequate relief from analgesics. We developed an mHealth app that augments pharmacologic therapy by integrating CBT-based psychological support for patients with cancer pain, and evaluated its feasibility and acceptability. Methods: Patients were recruited from the Dana-Farber Cancer Institute (Boston, MA) and Stephenson Cancer Center (Oklahoma City, OK). Patients were ≥21 years old, diagnosed with an advanced solid tumor or hematologic malignancy, had moderate-to-severe pain (4 or greater on 0-10 scale) that was related to cancer or their cancer treatment, and were using opioids. Exclusion criteria were cognitive impairment, history of opioid use disorder, enrollment in hospice, and acute pain from a recent surgery. App content integrated pain-CBT treatment with pain education and opioid support, and utilized just-in-time adaptive interventions (e.g. algorithm-based advice for pain, stress, and sleep) in response to once daily symptom surveys, including once daily psychoeducational content to support pain self-management. Patients used the app for 2-4 weeks, after which they completed the E-Acceptability scale and semi-structured debriefing interview. Results: Fifteen patients (female=9, mean age=50.3) enrolled on the study and 93% opted to test the app for 28 days (1 patient chose to test the app for 14 days). Patients who tested the app for 28 days completed a median of 18 of the 28 daily surveys (54% total surveys completed). Within patients who tested the app, 11 patients (73%) completed ≥50% of their daily surveys, with 4 (27%) completing ≥75%, and 4 (27%) completing ≤25% of surveys. Overall satisfaction with the app was high (4.43/5-pt Likert scale). Patients also rated the app as understandable (4.93/5) and easy to use (4.57/5-pt Likert scale). In study interviews, many patients reported that the app improved their ability to self-manage their cancer-related pain by facilitating the creation of more intentional pain management routines, and by helping to modify maladaptive thinking patterns allowing them to better cope with their pain. Patients emphasized how salient, clear, and succinct the information was, particularly in the context of coping with stress and pain. Conclusions: The STAMP+CBT app was an acceptable and feasible method to integrate and deliver psychological/behavioral treatment with opioid support to support cancer pain management. Compliance may have been influenced by the app’s notification structure limiting survey engagement to a 45-minute period each day. A future, larger pilot study will further evaluate feasibility and acceptability in patients with advanced cancer. In addition to additional content, the updated version of the app retains notifications but allows patients to return to missed content for 24 hours.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".