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@ARTICLE{vanGenugten:298915,
      author       = {C. R. van Genugten and M. S. Y. Thong$^*$ and W. van
                      Ballegooijen and A. M. Kleiboer and D. Spruijt-Metz and A.
                      C. Smit and M. A. G. Sprangers and Y. Terhorst and H. Riper},
      title        = {{B}eyond the current state of just-in-time adaptive
                      interventions in mental health: a qualitative systematic
                      review.},
      journal      = {Frontiers in digital health},
      volume       = {7},
      issn         = {2673-253X},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {DKFZ-2025-00351},
      pages        = {1460167},
      year         = {2025},
      abstract     = {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.},
      subtyp        = {Review Article},
      keywords     = {JITAI (Other) / digital mental health (Other) /
                      intervention development (Other) / just-in-time adaptive
                      intervention (Other) / smartphone intervention (Other)},
      cin          = {C071},
      ddc          = {610},
      cid          = {I:(DE-He78)C071-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39935463},
      pmc          = {pmc:PMC11811111},
      doi          = {10.3389/fdgth.2025.1460167},
      url          = {https://inrepo02.dkfz.de/record/298915},
}