Health Behavior Models in the Age of Mobile Interventions: Are Our Theories Up to the Task?

This study aims to determine how health behaviour theories are applied to mobile interventions used to deliver health behaviour interventions. This is a review of the theoretical basis and interactivity of mobile health behaviour interventions. [Footnotes have been removed by the editor.]

Rapid development of information and communication technology (ICT) has led to its use in behaviour change communication (BCC): "Advances in Internet-based infrastructure and accessibility promoted the migration of computerized health behavior interventions from prototype stand-alone software to robust, scalable, interactive, and tailored web-based programs. As a result, web-based health behavior interventions have proliferated in recent years and appear to be an efficacious method for delivering health behavior interventions in a cost-effective manner....The content and timing of a specific mobile phone intervention delivered via voice, text, resident application, mobile web, or other modality can be driven by a range of variables including (a) the target behavior frequency, duration, or intensity; (b) the effect of prior interventions on the target behavior; and (c) the current context of the individual (time, location, social environment, psychophysiological state, etc.). Such interventions require health behavior models that have dynamic, regulatory system components to guide rapid intervention adaptation based on the individual’s current and past behavior and situational context. Some have argued that current health behavior models are inadequate even for low-tech interventions but the predominately linear and static nature of these models severely limits their ability to guide the dynamic, adaptive interventions possible via mobile technologies."

For this research, current literature was reviewed that included research on mobile phone and pagers and text messaging systems such as personal digital assistants (PDAs), but laptops, netbooks, iPads, and phone-based counselling were not included. Researchers reviewed: seven smoking cessation studies; twelve weight loss, diet, and physical activity interventions; ten treatment adherence studies; and 20 disease management studies (diabetes, asthma, and hypertension). Few included tailored or personalised interventions based on an assessment initially or during the course of the intervention - such as a "just-in-time" immediate intervention adjustment.

The research found that, in addition to voice, text, and data inputs, researchers have begun to use video input via camera features on phones. Automated sensors can also report via mobile device: "Much of the mobile health behavior interventions to date have focused on transmitting data from glucometers and spirometers, but an expanding variety of sensor technologies can be used to assess physiological states, movement, location, and other variables using mobile phones." The researchers found that in addition to audio outputs, data and displays can be sent over mobiles, including: progress charts, animation, videos, and games.

However, there was found to be a limited use of the rapid two-way interaction of inputs and outputs that could be used to deliver just-in-time health behaviour interventions. Researchers suggest that "[a]lthough more complex intervention adjustments might be best reserved for health professional judgment, standard treatment algorithms can be used to automate, with 100% treatment fidelity, many of these treatment adjustments, greatly improving scalability, and reducing professional time and costs."

The study correlated the simple "cue to action" reminders with the Health Belief Model and the various health behaviour theories and models that followed, and it suggests that "a greater reliance on health behavior theories to guide mobile technology intervention development, even for apparently simple interventions, should result in interventions that address more comprehensively the potential mechanisms of behavior change, resulting in more effective interventions." However, current theories are limited in their application to new technological possibilities: "they are particularly limited at informing just-in-time intervention adaptations...", especially adapting and timing individualised intervention responses over the course of an intervention. " [S]ome concepts such as reciprocal determinism in Social Cognitive Theory are explicitly dynamic in nature. Moreover, a rich behavior change process-outcome literature describes dynamic interactions in face-to-face behavioral interventions that could be applied to behavioral interventions via mobile technologies . Adoption of dynamical system models for mobile health behavior interventions does not require that our current health behavior theories and models be discarded, but the predominately static, linear nature of these theories appears to be a poor fit with the intra-individual dynamics of future mobile technology interventions."

The study suggests feedback systems that take in data that is then fed to applications of control systems engineering applied to mobile health behaviour interventions. "With a dynamical systems model, it becomes possible to apply control systems engineering to develop algorithms that use real-time assessments and predicted responses from the model to adaptively decide on the timing and dose of the intervention components. Mobile technology is an enabler to advanced control algorithms such as model predictive control that employ formal optimization methods to decide on current and future doses of intervention components while satisfying clinical practice preferences and restrictions....The opportunity via mobile devices to collect intensive context- and time-dependent (longitudinal) data and to systematically vary intervention components enables researchers to test not only these components but also the theoretical concepts and dynamic models that underlie them."

The sudy concludes: "To meet these challenges, our current health behavior theories and models need to expand from elucidating between-person differences to explaining within-person changes over time and to evolve to incorporate dynamic feedback control systems to “close the loop.” Health behavior interventions delivered via mobile technologies offer not only the impetus to transform our current theories into more dynamic feedback control models but also the potential to provide the intensive longitudinal data necessary to test and improve our theoretical intervention models."