Health behavior models in the age of mobile interventions: are our theories up to the task?

By W. Riley, D. Rivera, Audie A Atienza, W. Nilsen, S. Allison, R. Mermelstein. Translational Behavioral Medicine, 2011

1,049 citations recorded by the source corpus.

Mobile technologies are being used to deliver health behavior interventions. The study aims to determine how health behavior theories are applied to mobile interventions. This is a review of the theoretical basis and interactivity of mobile health behavior interventions. Many of the mobile health behavior interventions reviewed were predominately one way (i.e., mostly data input or informational output), but some have leveraged mobile technologies to provide just-in-time, interactive, and adaptive interventions. Most smoking and weight loss studies reported a theoretical basis for the mobile intervention, but most of the adherence and disease management studies did not. Mobile health behavior intervention development could benefit from greater application of health behavior theories. Current theories, however, appear inadequate to inform mobile intervention development as these interventions become more interactive and adaptive. Dynamic feedback system theories of health behavior can be developed utilizing longitudinal data from mobile devices and control systems engineering models.

Subfields: Applied Psychology, General Health Professions

View Paper: https://www.semanticscholar.org/paper/1d764cae598365e83f6d250713ff08b43d2df9a1

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Citations in the corpus

Putting the ‘app’ in Happiness: A Randomised Controlled Trial of a Smartphone-Based Mindfulness Intervention to Enhance Wellbeing (2016, background): “Acknowledging this distinction, Riley et al. (2011) argue that mobile interventions require their own guiding theory that captures their dynamic nature and that which sets them apart from other forms of intervention.”
Building health behavior models to guide the development of just-in-time adaptive interventions: A pragmatic framework (2015, background): “Advances in mobile technology offer many opportunities for delivering interventions that accommodate an individual’s immediate needs (Riley et al., 2011).”
An Actor-Critic Contextual Bandit Algorithm for Personalized Interventions using Mobile Devices (2014, background): “Advanced technology in mobile devices including smartphones and wearable devices provides a great platform for the delivery of JITAIs (Just in time adaptive intervention, [1]).”
Mind the theoretical gap: interpreting, using, and developing behavioral theory in HCI research (2013, background): “, see [61] for a discussion about this).”
Mobile App Rating Scale: A New Tool for Assessing the Quality of Health Mobile Apps (2015, background): “The use of mobile apps for health and well being promotion has grown exponentially in recent years [1].”
Increasing Physical Activity With Mobile Devices: A Meta-Analysis (2012, background): “It is clear that some of the unique qualities of these devices would be attractive features for physical activity interventions allowing scientists to: collect objective and self-report measures of activity in real time; provide feedback and support at the point of decision; provide interactive, imm”
Mobile Health Technology Evaluation (2013, background): “This is a multi-step process that: (1) defines the classification problem; (2) divides the annotated data set into training and validation data sets; (3) decides what machine-learning classifiers will be tested (e.”
An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions (2017, background): “…eating disorders (Bauer et al. (2010)), drug abuse (Scott and Dennis (2009)), alcohol use (Witkiewitz et al. (2014); Suffoletto et al. (2012); Gustafson et al. (2011)), smoking cessation (Riley et al. (2011)), obesity and weight management (Patrick et al. (2009)), and other chronic disorders.”
The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions (2014, background): “While these models are useful in describing the types of behavioral and cognitive changes required of users in their environments to achieve clinical targets, they offer little information on how to design and implement BITs to ensure that they are useful and usable [1].”
Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support (2017, background): “sary to develop efficacious JITAIs and decide whether a JITAI is warranted in a particular setting [10].”
Mapping mHealth Research: A Decade of Evolution (2013, methodology): “6% of adults worldwide own a mobile phone [2,10], making it the most equitable communication technology [1].”
Empirical Studies on Usability of mHealth Apps: A Systematic Literature Review (2015, background): “Furthermore, mobile technologies have a high adaptability that allows them to be practiced in many different fields [58, 59].”
IDEAS (Integrate, Design, Assess, and Share): A Framework and Toolkit of Strategies for the Development of More Effective Digital Interventions to Change Health Behavior (2016, background): “Researchers may draw from behavioral theories, such as social cognitive theory [52], which are frequently used in academia to guide mobile health interventions [2].”
Can Smartphone Apps Increase Physical Activity? Systematic Review and Meta-Analysis (2019, neutral): “They are equipped with advanced technological features, most notably their connection to the internet, global positioning system, and inbuilt accelerometers [9], and offer the capacity to create individualized and interactive apps that collect real-time data [10].”
Mobile medical and health apps: state of the art, concerns, regulatory control and certification (2014, background): “Furthermore, many smartphone apps are not based on behavioural change theories or guidelines (where these could have made a significant, positive difference, if implemented) [30,44].”