JMIR Publications

ABSTRACT

Background:

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 methods to improve adherence to goals is to motivate individuals to improve on their historic performance metrics.

Objective:

The aim of this work was to build a machine learning model to dynamically adjust the activity target for the forthcoming week.

Methods:

We built a neural network model (NNs) that prescribes an activity target for the forthcoming week that can be realistically achieved by the activity-tracker users. The inputs to the model are user-specific personal, social, and environmental factors, daily steps for the 7 days of the current week, and an entropy measure that characterizes the pattern of daily step count for the current week. Data for training the machine learning model was collected from 30 participants over a duration of 9 weeks.

Results:

The model predicted target daily count with a mean absolute error of 1545 (95% CI: 1383 – 1706) steps.

Conclusions:

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