Abstract
Understanding and predicting how people change their behavior after an intervention from time series data is an important task for health recommender systems. This task is especially challenging when the time series data is frequently sampled. In this paper, we develop and propose a novel recommender system that aims to promote physical activeness in elderly people. The main novelty of our recommender system is that it learns how senior adults with different lifestyle change their activeness after a digital health intervention from minute-by-minute fitness data in an automated way. We trained the system and validated the recommendations using data from senior adults. We demonstrated that the low-level information contained in time series data is an important predictor of behavior change. The insights generated by our recommender system could help senior adults to engage more in daily activities.
Highlights
The proportion of the global population aged 60 years or over increases rapidly: from 8.5% in 1980 to 12.7% in 2017 (United Nations, Department of Economic and Social Affairs 2017)
We describe our effort in creating a recommender system that predicts the personalized effects of different behavior interventions on the same user to select one intervention over another
We proposed a novel recommender system that aims to promote physical activeness in senior adults
Summary
The distribution of the fitness data collected from young people and senior adults is different This is why models trained on data from the general population might not work for the elderly subpopulation. Phatak et al (2018) developed a system that generates recommendations based on the median value of steps/day from the baseline period Their system does not take into account the distribution of the physical activity throughout the day as a predictor of the behavior change. We built machine learning methods to predict the change of the physical activity levels after each intervention for new users This allows the system to decide which intervention should be recommended. In our work we focus on the offline evaluation — in the future we plan to perform an online evaluation as well
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