A Mobile App to Support Self-Management in Patients with Multiple Myeloma or Chronic Lymphocytic Leukemia: Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Patients with blood cancer experience serious physical and emotional symptoms throughout their cancer journey. OBJECTIVE: Building on previous work, we aimed to develop an app designed to help patients with multiple myeloma and chronic lymphocytic leukemia self-manage symptoms and test it for acceptability and preliminary efficacy. METHODS: We developed our Blood Cancer Coach app with input from clinicians and patients. Our 2-armed randomized controlled pilot trial recruited participants from Duke Health and nationally in partnerships with the Association of Oncology Social Work, Leukemia and Lymphoma Society, and other patient groups. Participants were randomized to the attention control (Springboard Beyond Cancer website) arm or the Blood Cancer Coach app intervention arm. The fully automated Blood Cancer Coach app included symptom and distress tracking with tailored feedback, medication reminders and adherence tracking, multiple myeloma and chronic lymphocytic leukemia education resources, and mindfulness activities. Patient-reported data were collected at baseline, 4 weeks, and 8 weeks for both arms through the Blood Cancer Coach app. Outcomes of interest were global health (Patient Reported Outcomes Measurement Information System Global Health), posttraumatic stress (Posttraumatic Stress Disorder Checklist for DSM-5), and cancer symptoms (Edmonton Symptom Assessment System Revised). Among participants in the intervention arm, satisfaction surveys and usage data were used to evaluate acceptability. RESULTS: Among 180 patients who downloaded the app, 49% (89) of them consented to participate and 40% (72) of them completed baseline surveys. Of those who completed baseline surveys, 53% (38) of them completed week 4 surveys (16 intervention and 22 control) and 39% (28) of them completed week 8 surveys (13 intervention and 15 control). Most participants found the app at least moderately effective at helping manage symptoms (87%), feeling more comfortable seeking help (87%), increasing awareness of resources (73%), and reported being satisfied with the app overall (73%). Participants completed an average of 248.5 app tasks over the 8-week study period. The most used functions within the app were medication log, distress tracking, guided meditations, and symptom tracking. There were no significant differences between the control and intervention arms at week 4 or 8 on any outcomes. We also saw no significant improvement over time within the intervention arm. CONCLUSIONS: The results of our feasibility pilot were promising in which most participants found the app to be helpful in managing their symptoms, reported satisfaction with the app, and that it was helpful in several important areas. We did not, however, find significantly reduced symptoms or improved global mental and physical health over 2 months. Recruitment and retention were challenging for this app-based study, an experience echoed by others. Limitations included a predominantly White and college educated sample. Future studies would do well to include self-efficacy outcomes, target those with more symptoms, and emphasize diversity in recruitment and retention. TRIAL REGISTRATION: ClinicalTrials.gov NCT05928156; https://clinicaltrials.gov/study/NCT05928156.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".