Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support

By Inbal Nahum-Shani, Shawna N. Smith, B. Spring, Linda M. Collins, Katie Witkiewitz, Ambuj Tewari, Susan A. Murphy. Annals of Behavioral Medicine, 2017

1,886 citations recorded by the source corpus.

BackgroundThe just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual’s changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual’s state can change rapidly, unexpectedly, and in his/her natural environment.PurposeDespite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap.MethodsBuilding on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration.ConclusionAs we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implications of providing timely and ecologically sound support for intervention adherence and retention.

Subfields: Applied Psychology, Physiology

View Paper: https://www.semanticscholar.org/paper/766133888c58c949de2e9bf086fb49a2cb3d295f

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In Applied Psychology

1. Helena Nguyen
2. Markus Groth
3. Anya Johnson
4. Robert J. DeRubeis
5. Louis-philippe Morency

In Physiology

1. Percy Liang

Citations in the corpus

• Theories of physical activity behaviour change: A history and synthesis of approaches (2019, neutral): “Specifically, there has been a growing body of work using dynamic models by leveraging mobile technology to develop Just-In-Time Adaptive Interventions (Dunton, 2017, 2018; Nahum-Shani et al., 2016; Spruijt-Metz et al., 2015).”
• Digital twins for health: a scoping review (2024, background): “Academic research platforms, such as Beiwe 70 and Mindlamp 71 are now being used to develop personalized modelsthatenable behavioral and psychological interventions that can be delivered in real-time 72 .”
• A review and agenda for examining how technology-driven changes at work will impact workplace mental health and employee well-being (2020, methodology): “For example, detecting when mental health is deteriorating and using Just-in-Time adaptive interventions to provide support, promote behavioural change (Lind et al., 2018; Nahum-Shani et al., 2017) or prompt early help seeking behaviour (Gärtner et al., 2013) can be of enormous value.”
• Remote patient monitoring using artificial intelligence: Current state, applications, and challenges (2023, background): “This can be achieved by adaptive learning of dynamic health changes in a patient (Nahum-Shani et al., 2017).”
• An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions (2017, background): “Maximizing the average reward V ∗(θ) subject to the stochasticity constraint (1) is a chance constrained optimization problem, an active research area in recent years (Nemirovski and Shapiro [2006], Campi and Garatti [2011]).”
• Think Locally, Act Globally: Federated Learning with Local and Global Representations (2020, background): ““Just-in-time” adaptive interventions delivered via mobile health applications provide a platform of exciting developments in low-intensity, high-impact interventions [43].”
• The digital revolution and its impact on mental health care. (2019, methodology): “JITAI is a methodology that aims to provide the right type and amount of support, at the right time by adapting to one' s changing internal and contextual state (Nahum-Shani et al., 2017).”
• Automated assessment of psychiatric disorders using speech: A systematic review (2019, methodology): “35 With monitoring via these real-time methods, patients and clinicians have the potential to more reliably observe behavior, perform early detection of episodes, request unscheduled evaluations, and/or change the course of treatment in a personalized way.”
• The promise of machine learning in predicting treatment outcomes in psychiatry (2021, background): “They could also be used to drive just­in­time adaptive interventions 79 .”