Mobile health (mHealth) and other highly pervasive, interactive and adaptive digital systems offer an enormous opportunity for enabling theory-based health behavior-change interventions that are replicable, scalable, and sustainable. Such advances could have great impact, and they offer great opportunities for advances in artificial intelligence (AI), psychology, and social science. Unfortunately, the technical challenges of developing AI to support healthier behaviors generally fall into the category of “not suitable for machine learning.” This presentation will summarize recent research with the Fittle+ mHealth systems and predictive models of the daily effects of individual-level mHealth interventions. The models are developed in a computational cognitive architecture and rely on Instance Based Learning Theory and ACT-R learning mechanisms.
Introduction
Mobile health (mHealth) systems offer an opportunity for
pervasive support of health behavior change in the actual
ecology of people’s everyday environments. Such advances
could have vast impact since individual and social behavior,
including poor diet, sedentary lifestyle, and social isolation
are central to the etiology and management of many health
outcomes, and yet are typically resistant to change
However, the mHealth field is still relatively new and
lacks integrated theories of long-term behavior change that
address multiple interventions and a multiplicity of
individual, social, and environmental factors. As with digital health
interventions in general, mHealth systems suffer high
attrition rates (Eysenbach, 2005). However, recent research
suggests that mHealth systems that adapt to the individual
(Konrad et al., 2015; Pirolli, 2016), use evidence-based
interventions (Pirolli et al., 2018), and include online social
support
Novel computational theories of the psychological and social mechanisms of behavior change are needed in order to accelerate the development of mHealth systems so that they can help people build healthier lifestyles (Riley et al., 2011; Spruijt-Metz et al., 2015). In turn, we could use mHealth systems as experimental platforms for accelerated development of such theories. These theoretical and empirical foundations would then provide a basis for intelligent interactive agents that reason about causal models of the dynamics of individual human behavior--models that enable the planning and delivery of interventions and assessments that optimize the acquisition and maintenance of healthy habits.
Human Goal-Striving and Habit Formation
An unhealthy lifestyle can be viewed, in part, as a complex
set of interrelated habits that need to be switched out for
healthy ones, a few tiny habits at a time
Unfortunately, modeling human behavior change and
intervening in ways that shape healthier habits is enormously
challenging and not suitable for current machine learning
approaches
I will present an overview of the Fittle+ mHealth systems (Pirolli et al., 2018) that have been used to study several evidence-based behavior change interventions. These systems provide scaffolding interventions: Behavior-change techniques and associated mHealth interactions (e.g., SMS reminders; chatbot dialogs; user interface functionality; etc.) that support the acquisition and maintenance of healthy habits.
I will present models developed in the ACT-R
computational cognitive architecture
The motivation for developing these models is that they may serve as a foundation for intelligent mHealth interaction algorithms. By developing these in a cognitive architecture, the models are not only predictive, but provide insight as to the underlying causal mechanisms, which is necessary for reasoning about optimal personalized interventions that help people achieve healthy lifestyles.