Modeling Physical Activity Behavior Change

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Hagler

Kurillo

et al. 2015

Self Cite

Optimal health coaching interventions are tailored to individuals’ needs, preferences, motivations, barriers, and readiness to change. Technology approaches are useful in both monitoring a user’s adherence to their behavior change goals and also in providing just-in-time feedback and coaching messages. User models that incorporate dynamically varying behavior change variables with algorithms that trigger tailored messages provide a framework for making health interventions more effective. These principles are applied in the described system for assisting older adults in meeting their physical exercise goals with a tailored interactive video system with just-in-time feedback and encouragement.

Section: Quantitative Modeling Frameworksupporting

confidence: 71%

Remote health coaching for interactive exercise with older adults in a home environment

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Hagler

Kurillo

et al. 2015

Self Cite

“…to develop a dynamic user model and a tailored action plan for health behavior change. We have created and tested modules for cognitive exercise [ 10 ], physical exercise [ 11 , 12 , 13 , 14 , 15 ], sleep management [ 16 ], socialization [ 17 ], and stress management [ 18 , 19 ].…”

Section: Methodsmentioning

confidence: 99%

Adaptive Health Coaching Technology for Tailored Interventions

Shapiro

Pavel

2021

IJERPH

Recent advances in sensor and communications technology have enabled scalable methods for providing continuity of care to the home for patients with chronic conditions and older adults wanting to age in place. In this article we describe our framework for a health coaching platform with a dynamic user model that enables tailored health coaching messages. We have shown that this can improve coach efficiency without a loss of message quality. We also discovered many lessons for coaching technology, most demonstrating the need for more coach input on sample message content, perhaps even requiring that individual coaches be able to modify the message database directly. Overall, coaches felt that the structure of the automated message generation was useful in remembering what to say, easy to edit if necessary and especially helpful for training new health coaches.

Behavioral informatics: Dynamical models for measuring and assessing behaviors for precision interventions

Pavel

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Spring

2016

Poor health-related behaviors represent a major challenge to healthcare due to their significant impact on chronic and acute diseases and their effect on the quality of life. Recent advances in technology have enabled an unprecedented opportunity to assess objectively, unobtrusively and continuously human behavior and have opened the possibility of optimizing individual-tailored, precision interventions within the framework of behavioral informatics. A key prerequisite for this optimization is the ability to assess and predict effects of interventions. This is potentially achievable with computational models of behavior and behavior change. In this paper we describe various approaches to computational modeling and describe a new hybrid model based on a dual process theoretical framework for behavior change. The model leverages cognitive learning theories and is shown to be consistent with mobile intervention data. We also illustrate how system-theoretic approaches can be used to assess the effect of coaching and participants' health behaviors.