A Randomized Clinical Trial Testing the Effect of Digital Behavioral Interventions on Pain and Mental Health Among Adults with Sickle Cell Disease
Bibliographic record
Abstract
Background: Patients with sickle cell disease (SCD) suffer a significant disease burden that affects their psychosocial well-being. Digital cognitive-behavioral therapy (CBT) has been utilized in other patient populations and shown to have clinical benefits. Although evidence-based, non-pharmacological interventions for pain management are widely used in other populations, this is not well studied in SCD. There are currently no large-scale, adequately powered clinical trials that evaluate the effectiveness and dissemination potential of digital behavioral pain management interventions for adults with SCD. Objective: The primary goal was to compare the effectiveness of two mobile-phone-delivered programs: 1) digital CBT program tailored for adults with SCD (CBT); or 2) pain and SCD education (Education) for reducing pain symptoms. The secondary goal was to assess whether baseline depression symptoms moderated the effect of these interventions on pain outcomes. Methods: Cognitive Behavioral Therapy and Real-Time Pain Management Intervention for Sickle Cell via Mobile Application (CaRISMA) is a multisite, randomized, pragmatic, comparative effectiveness trial conducted at seven comprehensive sickle cell centers and several community-based organizations in the U.S. The study enrolled adults with SCD who reported chronic pain or using short or long-acting opioids daily. Participants were randomized in a 1:1 ratio to receive either the CBT or Education programs. Both programs utilized identical Facebook Messenger chatbot apps, only the content differed. All intervention participants received health coach support involving weekly phone calls or text messages for a duration of 12 weeks. Participants completed follow up assessments at 3 and 6 months, and daily e-diary entries of 0-10 pain numerical rating scale, mood, and opioid use. The primary outcome was the 6-month change in the PROMIS pain interference. Secondary outcomes included average daily pain intensity for a 2-week period at each time point, change in mean % body area affected by pain (measured by a ‘paintable’ body image within the mobile app), PHQ-9 depression, GAD-7 anxiety, Adult Sickle Cell Quality of Life Measurement Information System (ASCQ-Me) quality of life (social functioning and emotional impact), and Sickle Cell Self-Efficacy Scale (SCSES). Generalized linear mixed models were used to compare changes in 6-month outcomes between study arms after accounting for design variables (study site and baseline depression). Results: Of the 574 participants screened for eligibility, 359 (63%) were consented and randomized (178 to CBT and 181 to Education). Seventy-five percent of participants connected with the chatbot but only 47% completed at least one CBT/education lesson. However, 80% of participants had at least one text message, phone or video session with a health coach. At 6-months, there was a significant decline in pain interference within each arm (CBT [-2.13, 95%CI (-3.42, -0.84)] and Education [-2.66, 95%CI (-3.97, -1.36)]); however, this decline did not differ between arms (p=0.57). There was not a significant 6-month change in daily pain intensity for either arm, however, for % body area covered in pain, both CBT and education conferred a similar relative decrease, 19.2% and 17.1%, respectively. There were significant 6-month improvements within-arms for PHQ-9, GAD-7, and both ASCQ-Me measures, but no between-arm differences emerged. (Table 1). Of these, only the 6-month change in ASCQ-Me emotional impact approached a significant difference between arms with digital CBT conferring slightly greater improvement in scores, 3.51 (95%CI: 2.29, 4.73) compared to education,1.79 (95%CI: .55, 3.04; p=.05). Baseline PHQ depression score (>= 10 vs < 10) did not moderate the effect of treatments on pain interference (p=0.52). Conclusions: Preliminary trial findings suggest that both digital CBT and Education, with health coach support, are effective approaches for management of SCD pain and mental health symptoms. Most participants connected with a health coach and may have derived benefit from this support, however, poor engagement with the digital CBT and education component of the study may have limited the study's ability to detect between arm differences. Secondary analyses will examine the effect of intervention engagement on treatment outcomes.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".