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

• Published in: JMIR mHealth and uHealth Vol. 8; no. 9; p. e18142
• Main Authors: Mohammadi, Ramin, Atif, Mursal, Centi, Amanda Jayne, Agboola, Stephen, Jethwani, Kamal, Kvedar, Joseph, Kamarthi, Sagar
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 08.09.2020
• Subjects: Datasets; Exercise; Feature selection; Female; Fitness Trackers; Humans; Machine learning; Male; Medical screening; Middle Aged; Neural networks; Neural Networks, Computer; Original Paper; Physical fitness; Principal components analysis; Prospective Studies; Questionnaires; Retrospective Studies; Training; Variables; dynamic activity target; machine learning; exercise engagement; neural network; activity tracker; activity target prediction
• ISSN: 2291-5222; 2291-5222
• DOI: 10.2196/18142

It is well established that lack of physical activity is detrimental to the overall health of an individual. Modern-day activity trackers enable individuals to monitor their daily activities to meet and maintain targets. This is expected to promote activity encouraging behavior, but the benefits of activity trackers attenuate over time due to waning adherence. One of the key approaches to improving adherence to goals is to motivate individuals to improve on their historic performance metrics. The aim of this work was to build a machine learning model to predict an achievable weekly activity target by considering (1) patterns in the user's activity tracker data in the previous week and (2) behavior and environment characteristics. By setting realistic goals, ones that are neither too easy nor too difficult to achieve, activity tracker users can be encouraged to continue to meet these goals, and at the same time, to find utility in their activity tracker. We built a neural network model that prescribes a weekly activity target for an individual that can be realistically achieved. The inputs to the model were user-specific personal, social, and environmental factors, daily step count from the previous 7 days, and an entropy measure that characterized the pattern of daily step count. Data for training and evaluating the machine learning model were collected over a duration of 9 weeks. Of 30 individuals who were enrolled, data from 20 participants were used. The model predicted target daily count with a mean absolute error of 1545 (95% CI 1383-1706) steps for an 8-week period. 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.