Neural Network-Based Algorithm for Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers: Retrospective Observation and Algorithm Development Study.

Ramin Mohammadi Mursal Atif Amanda Jayne Centi Stephen Olusegun Agboola Kamal Jethwani Joseph C Kvedar Sagar Kamarthi

Published in: JMIR mHealth and uHealth (2020)

Artificial intelligence applied to physical activity data combined with behavioral data can be used to set personalized goals in accordance with the individual's level of activity and thereby improve adherence to a fitness tracker; this could be used to increase engagement with activity trackers. A follow-up prospective study is ongoing to determine the performance of the engagement algorithm.

Keyphrases

artificial intelligence
neural network
machine learning
physical activity
deep learning
big data
electronic health record
body mass index
metabolic syndrome
cross sectional
public health
high intensity
global health
data analysis