Personalized exercise plans are transforming health outcomes! A new study introduces PERFECT, an IoT-based framework leveraging reinforcement learning to recommend tailored walking programs. Results showed significant increases in daily exercise duration and high participant satisfaction.
Key Insights from the Study
- Objective: To improve PA engagement and outcomes by creating a personalized exercise recommendation system powered by IoT and mHealth applications.
- Methods: Smartwatch and smartphone applications collected real-time data to recommend exercises.
- Results:
- Participants significantly increased their average daily exercise duration (P < .001).
- High satisfaction rates with the program (average ratings of 4.31/5 for the walking program and 3.69/5 for the system).
- Confidence in safely performing exercises and the program’s perceived effectiveness rated above 4/5.
At Centralive, we’re proud to support such advancements with tools that enable seamless integration of IoT and mHealth applications for research success.
📖 Read the full study in ACM Digital Library
Authored by: Milad Asgari Mehrabadi, Elahe Khatibi, Tamara Jimah, Sina Labbaf, Holly Borg, Pamela Pimentel, Nikil Dutt, Yuqing Guo, Amir M. Rahmani
