Claire Rosalie van Genugten ^1,2* Melissa Thong ^2,3,4 Wouter Van Ballegooijen ^1,2,5 Annet Kleiboer ^1,2 Donna Spruijt-Metz ⁶ Arnout C Smit ⁷ Mirjam Sprangers ^2,3 Yannik Terhorst ^8,9 Heleen Riper ^1,2,5*

• ¹ Department of Clinical, Neuro and Developmental Psychology, Faculty of Behavioural and Movement Sciences, VU Amsterdam, Amsterdam, Netherlands
• ² Amsterdam Public Health Research Institute, VU Medical Center, Amsterdam, Netherlands
• ³ Department of Medical Psychology, University Medical Center Amsterdam, Amsterdam, Noord-Holland, Netherlands
• ⁴ Unit of Cancer Survivorship, German Cancer Research Center (DKFZ, Heidelberg, Baden-Württemberg, Germany
• ⁵ Department of Psychiatry, Amsterdam Neuroscience, VU Medical Center, Amsterdam, Netherlands
• ⁶ Center for Economic and Social Research, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California, United States
• ⁷ Interdisciplinary Center Psychopathology and Emotion Regulation, University Center of Psychiatry, Faculty of Medical Sciences, University of Groningen, Groningen, Netherlands
• ⁸ Department of Psychology, Faculty of Psychology and Education, Ludwig Maximilian University of Munich, Munich, Bavaria, Germany
• ⁹ German Center for Mental Health, Center for Intervention and Research On Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Halle-Jena-Magdeburg, Germany

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.Methods: 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.Results: 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.Conclusions: JITAIs for mental health are still in their early stages of development, with opportunities for improvement in 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.