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Context-Aware Probabilistic Models for Predicting Future Sedentary Behaviors of Smartphone Users

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Abstract

Sedentary behaviors are now prevalent as most modern jobs are done while seated. However, such sedentary behaviors have been found to increase the risk of several ailments including diabetes, cardiovascular disease, and all-cause mortality. Current interventions are mostly reactive and are triggered after the user has already been sedentary. Behavior change theory suggests that preventive sedentary interventions, which are triggered before a person becomes sedentary, are more likely to succeed. In this paper, we characterize user patterns of sedentary behaviors by analyzing smartphone-sensor data in a real-world dataset. Our work reveals location types (where), times of day/week (when), and smartphone contexts in which sedentary behaviors are most likely. Leveraging our findings, we then propose a set of context-aware probabilistic models that can predict sedentary behaviors in advance by analyzing smartphone sensor data. Our Context-Aware Predictive (CAP) models leverage smartphone-sensed contextual variables and the user’s history of sedentary behaviors to predict their future sedentary behaviors. We rigorously analyze the performance of our models and discuss the implications of our work.

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Notes

  1. On the website of StudentLife project and in [11], the authors state that 48 students completed the study. The StudentLife dataset, however, contains 49 students’ data.

  2. Hereafter, without explicit mention, superscripts used in notations are time indexes, not powers.

  3. On the website of StudentLife project, the authors mentioned a “user_info.csv” file which might contain subjects’ demographic information. However, in the data publicly provided, such a file does not exist.

  4. Dell PowerEdge T20 Owner’s Manual, https://dl.dell.com/topicspdf/poweredge-t20_owners-manual_en-us.pdf

  5. Intel® Xeon® Processor E3-1225 v3 https://ark.intel.com/content/www/us/en/ark/products/75461/intel-xeon-processor-e3-1225-v3-8m-cache-3-20-ghz.html

  6. “Don’t kill apps, make them work!” website, https://dontkillmyapp.com/

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  1. Fitbit, Google LLC, 199 Fremont Street, Floor 14, San Francisco, CA, 94105, USA

    Qian He

  2. Department of Computer Science, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA, 01609, USA

    Emmanuel O. Agu

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He, Q., Agu, E.O. Context-Aware Probabilistic Models for Predicting Future Sedentary Behaviors of Smartphone Users. J Healthc Inform Res 6, 112–152 (2022). https://doi.org/10.1007/s41666-021-00107-6

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