The Design and Development of a Personalized Leisure Time Physical Activity Application Based on Behavior Change Theories, End-User Perceptions, and Principles From Empirical Data Mining

Bibliographic Data

ID22082209
AuthorsKarlijn Sporrel (0000-0002-2757-8553, Utrecht University, corresponding author), R De Boer (0000-0003-1062-4290, Amsterdam University of Applied Sciences), Rémi D D De Boer, Shihan Wang (0000-0001-6854-9217, Utrecht University), Nicky Nibbeling (0000-0002-3982-2349, Amsterdam University of Applied Sciences), Monique Simons (0000-0002-6475-4616, Wageningen University & Research), Marije Deutekom (0000-0001-5280-5204, Inholland University of Applied Sciences), Dick Ettema (0000-0001-8404-5510, Utrecht University), Paula Costa Castro (0000-0002-0363-0871, Universidade Federal de São Carlos), Victor Zuniga Dourado (0000-0002-6222-3555, Universidade Federal de São Paulo), Ben Kröse (0000-0003-1237-0618, Amsterdam University of Applied Sciences)
Year2021
Volume8
Pages528472-528472
Publication date2021-02-02
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2020.528472
PMID33604321
OpenAlexW3127451197
LanguageEN
Citations received5
References cited70

Introduction: Many adults do not reach the recommended physical activity (PA) guidelines, which can lead to serious health problems. A promising method to increase PA is the use of smartphone PA applications. However, despite the development and evaluation of multiple PA apps, it remains unclear how to develop and design engaging and effective PA apps. Furthermore, little is known on ways to harness the potential of artificial intelligence for developing personalized apps. In this paper, we describe the design and development of the Playful data-driven Active Urban Living (PAUL): a personalized PA application. Methods: The two-phased development process of the PAUL apps rests on principles from the behavior change model; the Integrate, Design, Assess, and Share (IDEAS) framework; and the behavioral intervention technology (BIT) model. During the first phase, we explored whether location-specific information on performing PA in the built environment is an enhancement to a PA app. During the second phase, the other modules of the app were developed. To this end, we first build the theoretical foundation for the PAUL intervention by performing a literature study. Next, a focus group study was performed to translate the theoretical foundations and the needs and wishes in a set of user requirements. Since the participants indicated the need for reminders at a for-them-relevant moment, we developed a self-learning module for the timing of the reminders. To initialize this module, a data-mining study was performed with historical running data to determine good situations for running. Results: The results of these studies informed the design of a personalized mobile health (mHealth) application for running, walking, and performing strength exercises. The app is implemented as a set of modules based on the persuasive strategies “monitoring of behavior,” “feedback,” “goal setting,” “reminders,” “rewards,” and “providing instruction.” An architecture was set up consisting of a smartphone app for the user, a back-end server for storage and adaptivity, and a research portal to provide access to the research team. Conclusions: The interdisciplinary research encompassing psychology, human movement sciences, computer science, and artificial intelligence has led to a theoretically and empirically driven leisure time PA application. In the current phase, the feasibility of the PAUL app is being assessed

Behavior change · End user · Focus group · Human–computer interaction · Perception · World Wide Web · Computer Science · Innovative Human-Technology Interaction · Mobile Health and mHealth Applications · Physical Activity and Health · Psychology

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