Evaluating and Optimizing Just-in-Time Adaptive Interventions in a Digital Mental Health Intervention (Wysa for Chronic Pain) for Middle-Aged and Older Adults With Chronic Pain: Protocol for a Series of Randomized Trials
Notice bibliographique
Résumé
BACKGROUND: On a population level, digital mental health interventions effectively reduce depression and anxiety symptoms. However, middle-aged and older adults with chronic pain and coexisting depression or anxiety have not been adequately represented in digital mental health studies. OBJECTIVE: The goal of this study is to refine an existing mobile, digital mental health intervention (Wysa for Chronic Pain) that addresses symptoms of depression, anxiety, and coexisting chronic pain for the unique challenges and technology use patterns of middle-aged and older adults. METHODS: Using a mixed methods, human-centered design approach and a series of randomized trials, we will test and iteratively refine just-in-time adaptive interventions (JITAIs) that are designed to increase engagement with a digital mental health intervention. Participants will be aged 45 years or older, endorse at least moderately severe depression or anxiety symptoms (Patient Health Questionnaire-9 or Generalized Anxiety Disorder-7 score ≥10), and have coexisting chronic pain (ie, pain on most days or every day in the past 3 months), and live in the United States. In this open, web-based trial, participants will all receive Wysa for Chronic Pain (by Wysa), which uses a behavioral activation framework and encourages users to work toward pain acceptance. The fully automated intervention also includes cognitive behavioral therapy, mindfulness, and sleep tools, among others. In each trial, participants will be randomized during a maximum 12-week study period to receive versus not receive novel JITAIs that are intended to reduce navigation burden and improve usability (and subsequent engagement and clinical effectiveness). The JITAIs are being designed with iterative user feedback, guided by the Discover, Design/Build, and Test framework and the Behavioral Intervention Technology model. The proximal outcome for each JITAI is related to engagement with Wysa for Chronic Pain after JITAI delivery (compared to when no JITAI is delivered). The primary distal clinical outcome is the Patient Health Questionnaire Anxiety and Depression Scale. Based on statistical analysis that is triangulated with qualitative feedback from a subsample of trial participants, the JITAIs will be iteratively refined and retested in subsequent microrandomized trials until retesting of refined adaptations no longer yields meaningful improvement in immediate engagement or a maximum of 5 total trials have been completed. RESULTS: Institutional review board approval was obtained on April 11, 2025. The first participant was enrolled on June 2, 2025, and recruitment is expected to conclude in 2026. CONCLUSIONS: Completion of this project will result in iteratively refined JITAIs that are designed to improve usability and engagement with a digital mental health intervention by middle-aged and older adults with depression or anxiety and coexisting chronic pain. TRIAL REGISTRATION: ClinicalTrials.gov NCT06978166; https://clinicaltrials.gov/study/NCT06978166. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/77532.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,032 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,007 | 0,003 |
| Méta-épidémiologie (sens large) | 0,011 | 0,012 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,007 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,064 | 0,011 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».