van Genugten Claire R, Thong Melissa S Y, van Ballegooijen Wouter, Kleiboer Annet M, Spruijt-Metz Donna, Smit Arnout C, Sprangers Mirjam A G, Terhorst Yannik, Riper Heleen
Clinical, Neuro-, and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
Amsterdam Public Health, Mental Health, Amsterdam, Netherlands.
Front Digit Health. 2025 Jan 28;7:1460167. doi: 10.3389/fdgth.2025.1460167. eCollection 2025.
Just-In-Time Adaptive Interventions (JITAIs) are interventions designed to deliver timely tailored support by adjusting to changes in users' internal states and external contexts. To accomplish this, JITAIs often apply complex analytic techniques, such as machine learning or Bayesian algorithms to real- or near-time data acquired from smartphones and other sensors. Given the idiosyncratic, dynamic, and context dependent nature of mental health symptoms, JITAIs hold promise for mental health. However, the development of JITAIs is still in the early stages and is complex due to the multifactorial nature of JITAIs. Considering this complexity, Nahum-Shani et al. developed a conceptual framework for developing and testing JITAIs for health-related problems. This review evaluates the current state of JITAIs in the field of mental health including their alignment with Nahum-Shani et al.'s framework.
Nine databases were systematically searched in August 2023. Protocol or empirical studies self-identifying their intervention as a "JITAI" targeting mental health were included in the qualitative synthesis if they were published in peer-reviewed journals and written in English.
Of the 1,419 records initially screened, 9 papers reporting on 5 JITAIs were included (sample size range: 5 to an expected 264). Two JITAIs were for bulimia nervosa, one for depression, one for insomnia, and one for maternal prenatal stress. Although most core components of Nahum-Shani's et al.'s framework were incorporated in the JITAIs, essential elements (e.g., adaptivity and receptivity) within the core components were missing and the core components were only partly substantiated by empirical evidence (e.g., interventions were supported, but the decision rules and points were not). Complex analytical techniques such as data from passive monitoring of individuals' states and contexts were hardly used. Regarding the current state of studies, initial findings on usability, feasibility, and effectiveness appear positive.
JITAIs for mental health are still in their early stages of development, with opportunities for improvement in both development and testing. For future development, it is recommended that developers utilize complex analytical techniques that can handle real-or near-time data such as machine learning, passive monitoring, and conduct further research into empirical-based decision rules and points for optimization in terms of enhanced effectiveness and user-engagement.
即时自适应干预（JITAIs）是旨在通过适应用户内部状态和外部环境的变化来提供及时的量身定制支持的干预措施。为实现这一目标，JITAIs通常将复杂的分析技术，如机器学习或贝叶斯算法应用于从智能手机和其他传感器获取的实时或近实时数据。鉴于心理健康症状具有独特性、动态性且依赖于具体情境性质，JITAIs在心理健康领域具有前景。然而，JITAIs的开发仍处于早期阶段，并且由于其多因素性质而较为复杂。考虑到这种复杂性，纳胡姆 - 沙尼等人开发了一个用于开发和测试针对健康相关问题的JITAIs的概念框架。本综述评估了JITAIs在心理健康领域的当前状态，包括它们与纳胡姆 - 沙尼等人框架的一致性。
于2023年8月系统检索了九个数据库。如果将其干预自我认定为针对心理健康的“JITAI”的方案或实证研究发表在同行评审期刊且为英文撰写，则纳入定性综合分析。
在最初筛选的1419条记录中，纳入了9篇报告5种JITAIs的论文（样本量范围：5至预期的264）。两种JITAIs针对神经性贪食症，一种针对抑郁症，一种针对失眠症，一种针对孕妇产前压力。虽然纳胡姆 - 沙尼等人框架的大多数核心组件被纳入了JITAIs中，但核心组件中的基本要素（如适应性和接受性）缺失，并且核心组件仅部分得到实证证据的支持（例如，干预措施得到了支持，但决策规则和要点未得到支持）。几乎未使用来自对个体状态和情境进行被动监测的数据等复杂分析技术。关于研究的当前状态，关于可用性、可行性和有效性的初步结果似乎是积极的。
用于心理健康的JITAIs仍处于早期开发阶段，在开发和测试方面都有改进的机会。对于未来的发展，建议开发者利用能够处理实时或近实时数据的复杂分析技术，如机器学习、被动监测，并对基于实证的决策规则和要点进行进一步研究，以在提高有效性和用户参与度方面进行优化。
