The Design and Development of a Personalized Leisure Time Physical Activity Application Based on Behavior Change...

Area of Science:

• Digital Health
• Behavioral Science
• Computer Science

Background:

• Many adults do not meet recommended physical activity (PA) guidelines, increasing health risks.
• Smartphone applications are a promising method to increase PA, but effective design remains challenging.
• Harnessing artificial intelligence (AI) for personalized PA apps is an emerging area.

Purpose of the Study:

• To describe the design and development of the Playful data-driven Active Urban Living (PAUL) personalized PA application.
• To explore the integration of location-specific PA information and AI-driven personalized reminders.
• To create an engaging and effective mobile health (mHealth) application for leisure time PA.

Main Methods:

• A two-phased development process based on the IDEAS framework and BIT model.
• Literature study and focus groups to establish theoretical foundations and user requirements.
• Development of a self-learning module for personalized reminder timing using data-mining.

Main Results:

• Informed the design of a personalized mHealth app for running, walking, and strength exercises.
• App modules utilize persuasive strategies: monitoring, feedback, goal setting, reminders, rewards, and instruction.
• Architecture includes a smartphone app, adaptive back-end server, and research portal.

Conclusions:

• An interdisciplinary approach combining psychology, human movement sciences, computer science, and AI.
• The PAUL app is a theoretically and empirically driven leisure time PA application.
• Feasibility of the PAUL app is currently under assessment.