Experiences With a Multicomponent Digital Behavioral Pain Management Intervention for Adults With Sickle Cell Disease: Qualitative Analysis of the CaRISMA Trial
Bibliographic record
Abstract
BACKGROUND: Chronic pain is prevalent among adults with sickle cell disease (SCD) and can be worsened by psychosocial factors such as depression and inadequate social support. Effective behavioral interventions (eg, cognitive behavioral therapy [CBT]) exist for chronic pain in various populations; however, few have been developed to address chronic pain in SCD. Several barriers have restricted the development and dissemination of CBT pain interventions in SCD, such as limited accessibility and time constraints. Digital interventions provide accessible and cost-effective pain management tools, offering self-management strategies, real-time monitoring, and personalized treatment options. Yet, there are limited data regarding patients' experiences with such interventions within the SCD population. The Cognitive Behavioral Therapy and Real-Time Pain Management Intervention for Sickle Cell Via Mobile Applications (CaRISMA) trial evaluated the effectiveness of a digital CBT intervention compared with a digital educational intervention for pain management in SCD. Evaluating participants' experiences can guide refinement of digital pain interventions in SCD. OBJECTIVE: This study aimed to gain a deeper understanding of the lived experiences of participants in the CaRISMA trial and to determine how to better adapt this intervention to the SCD population. The study examined individuals' overall experience with the trial and their perspectives of the trial components: a health coach, a chatbot-delivered digital CBT program, and an electronic pain diary. METHODS: Respondents were randomly selected to participate in semistructured interviews at (1) baseline, (2) the end of the intervention period at 3 months, and (3) the postintervention time point at 6 months or beyond. Interviews were audiotaped, transcribed verbatim, and analyzed using conventional content analysis. RESULTS: A total of 48 participants (women: 33/48, 69%) completed the interviews, with 24 and 19 completing midpoint and postintervention interviews, respectively. Participants generally had a positive experience in the trial. Many found value in learning about the connection between pain and mental health, considering it an important aspect of their well-being. The health coach played a key role in offering personalized support and guidance. Although the chatbot reinforced pain management strategies, its usefulness and engagement varied based on participants' prior knowledge of SCD. The pain diary helped increase self-awareness of pain patterns but was perceived as tedious and irrelevant by those without current pain episodes. CONCLUSIONS: This qualitative substudy of the CaRISMA trial showed that participants valued the personalized support of the health coach, education about the connection between stress and pain, and the self-reflection fostered by the pain diary. These findings highlight the potential of digital, patient-centered approaches to address the multifaceted needs of SCD care. For digital interventions, the inclusion of personalized support with ongoing communication appears to be a critical component that can influence treatment adherence and effectiveness. TRIAL REGISTRATION: ClinicalTrials.gov NCT04419168; https://clinicaltrials.gov/study/NCT04419168. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/29014.
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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 | 0.038 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".