Abstract
BackgroundIt 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.ObjectiveThe 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.MethodsWe 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.ResultsOf 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.ConclusionsArtificial 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.
Highlights
BackgroundStudies have reported the efficacy of physical activity in reducing the risk of disease; physical inactivity is on rise in the United States [1]
One promising solution is wearable fitness trackers that enable individuals to monitor their activity levels and patterns to ensure a healthy level of physical activity [3]
[5] A previous study [6] found two factors associated with the adoption and sustained use of physical activity trackers: (1) the number of digital devices owned by the participants, and (2) the use of activity fitness trackers and other smart devices by the participants’ family members
Summary
BackgroundStudies have reported the efficacy of physical activity in reducing the risk of disease; physical inactivity is on rise in the United States [1]. Continued engagement with fitness trackers is an issue that warrants further investigation [5] A previous study [6] found two factors associated with the adoption and sustained use of physical activity trackers: (1) the number of digital devices owned by the participants, and (2) the use of activity fitness trackers and other smart devices by the participants’ family members. 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. Objective: 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. A follow-up prospective study is ongoing to determine the performance of the engagement algorithm
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.