Intelligent mobile support for therapy adherence and behavior change

Abstract

Mobile applications have proven to be promising tools for supporting people in adhering to their health goals. Although coaching and reminder apps abound, few of them are based on established theories of behavior change. In the present work, a behavior change support system is presented that uses a computational model based on multiple psychological theories of behavior change. The system determines the user's reason for non-adherence using a mobile phone app and an online lifestyle diary. The user automatically receives generated messages with persuasive, tailored content. The system was designed to support chronic patients with type 2 diabetes, HIV, and cardiovascular disease, but can be applied to many health and lifestyle domains. The main focus of this work is the development of the model and the underlying reasoning method. Furthermore, the implementation of the system and some preliminary results of its functioning will be discussed.

What makes a mobile app successful in supporting health behaviour change?

“…The inclusion of these drivers in the operational content of mental health apps is recommended by authors and designers and apps that have such drivers tend to do better in facilitating behaviour change than those that do not (Abroms et al, 2011;Chomutare et al, 2011;West et al, 2012;Klein et al, 2014;Middelweerd et al, 2014;Wang et al, 2014;Direito et al, 2014;Lyzwinski, 2014;Middelweerd et al, 2014).…”

E-coaching systems

“…However, from her other remarks in [30], it becomes clear that she is solely concerned with human-computer interaction and that she presupposes more complexity in BCSSs than is inherent in the definition of those systems. For example, in [18], Van Wissen and her colleagues focus on "accessibility, adaptability, and interactiveness" [18, p. 138]. Moreover, they make an explicit distinction between simple, tailored, and model-driven ecoaching systems [18, p. 139].…”

“…For this, the system will need learning abilities to build up and maintain a personalized user model (cf. [18,30]). 5.…”

A Model for an Aggression Discovery Through Person Online Behavior

“… development of machine learning techniques for an analysis of a relationship between behavior in virtual environments and properties of aggressive behavior,  succeeding investigations of dependencies between other psychological patterns and actual person behavior in virtual environments,  enabling a selection and classification of persons, who should be covered by actions of prophylactic either therapeutic nature with respect to discovered aggression types,  development of behavior impact methods using virtual environments similar to [17,18].…”

“… Actual issues influenced by aggressive behavior of persons,  Announced in the end of 2014 and actual call of Defense Advanced Research Projects Agency in the field of Detection and computational analysis of psychological signals [16],  Recent works in the field of classification and interpretation of a behavior in virtual environments [11,12,13,14,15],  Existence of theoretical [17] and practical [18] techniques for implementation of behavioral change. This fact permits to consider a possibility to influence aggressive persons online that can be discovered using the model proposed in the given paper.…”