Participatory development and proof-of-concept of a dyadic-based, AI-driven, just-in-time adaptive intervention mechanism for preventing anxiety and depressive disorders via app

Study protocol for a feasibility study

Bibliographic Data

ID22080673
AuthorsAnna‐Carlotta Zarski (0000-0002-0517-6668, Philipps University of Marburg, corresponding author), Theresa Sextl-Plötz (0009-0007-9690-8492, Philipps University of Marburg), Juliane Schmidt-Hantke (0000-0002-2292-1973, Philipps University of Marburg), Natalie Sarah Hess (Philipps University of Marburg), Claudia Buntrock (0000-0002-4974-5455, Otto-von-Guericke-Universität Magdeburg)
Year2025
Volume13
Pages1626428-1626428
Publication date2025-07-29
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2025.1626428
PMID40800021
OpenAlexW4412734058
LanguageEN
References cited79

Background The growing prevalence of anxiety and depressive disorders highlights the need for transdiagnostic prevention through innovative interventions that outperform existing applications. By delivering personalized, context-sensitive support in moments of need, Just-In-Time-Adaptive-Interventions (JITAIs) have the potential to boost intervention relevance, engagement, and adherence. Aims The study aims to (1) develop a transdiagnostic dyadic, AI-based JITAI (DyAI-JITAI) app through a participatory process, and (2) evaluate its feasibility in terms of acceptability, usability, and preliminary intervention effects. Methods Stage I followed a participatory development process using focus groups, think-aloud tests, and qualitative interviews to explore expectations, ideas, and needs for a preventive DyAI-JITAI app among N = 28 target users with lived experience and potential users without clinical anxiety or depressive symptoms. Stage II involves a randomized-controlled proof-of-concept study ( N = 60) to test the DyAI-JITAI app’s acceptability and clinical usability. Adults without a clinical anxiety or depressive disorder will be randomized to the 4-week JITAI or waitlist control group. The CBT-based app features a seven-day learning phase with Ecological Momentary Assessment (EMA) and optional geo-tracking to identify optimal intervention times and locations, followed by AI-driven JITAIs using reinforcement learning. Users will receive optional motivational support from a self-chosen buddy with shared app access. Feasibility will be evaluated using a formal framework. Assessments will be conducted at four time points: screening, pre-intervention (prior to randomization), mid-intervention (10 days following randomization), and post-intervention (4 weeks following randomization), complemented by qualitative interviews on user perspectives of the DyAI-JITAI. Discussion This study aims to participatorily develop and assess the feasibility of a DyAI-JITAI app that bridges the intention-behavior gap by supporting CBT skill use in daily life and offering optional buddy-based motivation. The goal is to tailor the app to users’ needs and inform the design, procedures, and safety management of a future large-scale efficacy RCT

Anxiety · Human–computer interaction · mHealth · Psychiatry · Psychological intervention · Randomized controlled trial · Usability · Behavioral Health and Interventions · Clinical Psychology · Computer Science · Digital Mental Health Interventions · Medicine · Mental Health Research Topics · Psychology

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