| Full text | |
| Author(s): |
Sporrel, Karlijn
[1]
;
De Boer, Remi D. D.
[2]
;
Wang, Shihan
[3, 4]
;
Nibbeling, Nicky
[5]
;
Simons, Monique
[6]
;
Deutekom, Marije
[7]
;
Ettema, Dick
[1]
;
Castro, Paula C.
[8]
;
Dourado, Victor Zuniga
[9]
;
Krose, Ben
[10]
Total Authors: 10
|
| Affiliation: | [1] Univ Utrecht, Dept Human Geog & Spatial Planning, Utrecht - Netherlands
[2] Univ Appl Sci Amsterdam, Dept Software Engn, Digital Life Ctr, Amsterdam - Netherlands
[3] Univ Utrecht, Dept Informat & Comp Sci, Utrecht - Netherlands
[4] Univ Appl Sci Amsterdam, Fac Digital Media & Creat Ind, Digital Life Ctr, Amsterdam - Netherlands
[5] Amsterdam Univ Appl Sci, Fac Sports & Nutr, Amsterdam - Netherlands
[6] Wageningen Univ & Res, Consumpt & Hlth Lifestyles, Wageningen - Netherlands
[7] Inholland Univ Appl Sci, Dept Hlth Sport & Welf, Haarlem - Netherlands
[8] Univ Fed Sao Carlos, Ctr Biol & Hlth Sci, Dept Gerontol, Sao Paulo - Brazil
[9] Fed Univ Sao Paulo UNIFESP, Dept Human Movement Sci, Sao Paulo - Brazil
[10] Univ Appl Sci Amsterdam, Digital Life Ctr, Fac Digital Media & Creat Ind, Amsterdam - Netherlands
Total Affiliations: 10
|
| Document type: | Journal article |
| Source: | FRONTIERS IN PUBLIC HEALTH; v. 8, FEB 2 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 16/50249-3 - Playful data-driven active urban living |
| Grantee: | Victor Zuniga Dourado |
| Support Opportunities: | Regular Research Grants |