Improving Physical Activity mHealth Interventions: Development of a Computational Model of Self-Efficacy Theory to Define Adaptive Goals for Exercise Promotion

Dario Baretta,Marco D’Addario,Riccardo Melen,Patrizia Steca,Andrea Greco,Fabio Sartori

doi:10.1155/2019/3068748

Abstract

The practice of regular physical exercise is a protective factor against noncommunicable diseases and premature mortality. In spite of that, large part of the population does not meet physical activity guidelines and many individuals live a sedentary life. Recent technological progresses and the widespread adoption of mobile technology, such as smartphone and wearables, have opened the way to the development of digital behaviour change interventions targeting physical activity promotion. Such interventions would greatly benefit from the inclusion of computational models framed on behaviour change theories and model-based reasoning. However, research on these topics is still at its infancy. The current paper presents a smartphone application and wearable device system called Muoviti! that targets physical activity promotion among adults not meeting the recommended physical activity guidelines. Specifically, we propose a computational model of behaviour change, grounded on the social cognitive theory of self-efficacy. The purpose of the computational model is to dynamically integrate information referring to individuals’ self-efficacy beliefs and physical activity behaviour in order to define exercising goals that adapt to individuals’ changes over time. The paper presents (i) the theoretical constructs that informed the development of the computational model, (ii) an overview of Muoviti! describing the system dynamics, the graphical user interface, the adopted measures and the intervention design, and (iii) the computational model based on Dynamic Decision Network. We conclude by presenting early results from an experimental study.

Highlights

Noncommunicable diseases such as cardiovascular and respiratory diseases, cancer, diabetes, and obesity are the main cause of mortality in Western countries and cause unimaginable costs for public health [1]
In spite of that, existing physical activity (PA) apps are characterized by a lack of adherence to behaviour change theories [11] and relatively little attention has been paid to the adoption of specific computational models grounded in behaviour change theories [12]
The current paper presents an innovative computational model that is conceptually framed in self-efficacy theory with a particular emphasis on self-efficacy beliefs and goal setting constructs

Summary

Introduction

Noncommunicable diseases such as cardiovascular and respiratory diseases, cancer, diabetes, and obesity are the main cause of mortality in Western countries and cause unimaginable costs for public health [1]. With regards to physical activity (PA) behaviour, mobile sensors can perform direct, intense, and longitudinal measurements of physical parameters (e.g., the heartbeat) and may produce detailed records of the individual behaviour (e.g., exercise) that are immediately available for analysis [7] Thanks to such opportunities for data collection, new technologies can rapidly manage and combine different input datasets, provide accurate predictions about the influence pattern among interested variables (e.g., behavioural, psychological), Advances in Human-Computer Interaction and deliver behaviour change interventions that are adaptive to individual and context changes over time [8]. Even though digital interventions that made extensive use of behaviour change theories produce larger effects on behaviour [13], Cowan and colleagues [11] evidenced that Health & Fitness apps mostly included only minimal theoretical content

Methods

Results

Conclusion