JMIR Publications

ABSTRACT

Background:

Background:

Nearly 83 million Americans experience mental illness, but fewer than 50% accessed past year mental health services. There is a critical need for highly disseminable, potent mental health interventions. Standalone digital interventions offer promise for improving clinical outcomes at scale; however, efficacy of these interventions to date is modest, likely due in part to insufficient personalization to patient heterogeneity, varying context, and the stochastic nature of mental health symptoms.

Objective:

Objective:

This study seeks to improve the efficacy of precision psychiatry digital health interventions by developing real-world, person-specific models for psychopathology using ecological data and dynamical systems modeling. Dynamical systems modeling of ecological momentary assessment (EMA) and smartphone sensor data will be used to understand each individual’s maintenance system for psychopathology in their daily life. Just-in-time adaptive interventions (JITAIs) involve support delivered in-the-moment at specific decision points, often via smartphone, when risk for maladaptive behaviors is determined to be elevated. JITAIs can be conceptualized as ecological quasi-experiments that induce use of specific therapeutic skills at specific times. This study will use micro-randomized assignment (in which the JITAI content is randomly assigned at each decision point) to deliver JITAIs at times of risk for maladaptive behavior based on the individual’s dynamical system with the goal of evaluating the granular effects and mechanisms of specific therapeutic skills. Eating disorders (EDs) are the ideal population in which to test proof-of-concept of these methods for improving efficacy of precision psychiatry digital interventions, as they are characterized by easily measurable maladaptive behaviors, are maintained by a complex intersection of biopsychosocial factors, and their frontline treatment, enhanced cognitive behavioral therapy (CBT-E), is primarily comprised of behavioral skills. The aims are: 1) test the hypothesis that person-centered dynamical models will describe risk for dietary restriction, binge eating, and compensatory behaviors with ≥ 60% average area under the curve (AUC); 2) test hypothesized effects and mechanisms of CBT-E skills using JITAIs; and 3) characterize feasibility and acceptability of the standalone JITAI system.

Methods:

Methods:

The study will enroll N = 170 adults with EDs who will complete four months of ecological data collection and receive JITAIs targeting CBT-E skills for 10 weeks.

Results:

Results:

This project will be funded from August 2025 through July 2030, with recruitment occurring from June 2026 through July 2029 and data collection from June 2026 through November 2029. Results will demonstrate the efficacy of idiographic dynamical systems modeling for accurately describing real time behavioral risk and provide insight into the relative effects and mechanisms of key CBT-E skills under conditions of daily life.

Conclusions:

Conclusions:

The study will set the stage for a future randomized controlled trail to evaluate the optimized, standalone JITAI, which will have the potential to substantially improve access to evidence-based mental healthcare. Clinical Trial: Clinicaltrials.gov NCT Number NCT07600671