TY - GEN
T1 - Multimodal Time-Series Activity Forecasting for Adaptive Lifestyle Intervention Design
AU - Mamun, Abdullah
AU - Leonard, Krista S.
AU - Buman, Matthew P.
AU - Ghasemzadeh, Hassan
N1 - Funding Information:
This work was supported in part by the National Science Foundation, under grants CNS-2210133, CNS-2227002, IIS-1954372, and IIS-1852163, and also by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under the award number R18DK109516. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Physical activity is a cornerstone of chronic conditions and one of the most critical factors in reducing the risks of cardiovascular diseases, the leading cause of death in the United States. App-based lifestyle interventions have been utilized to promote physical activity in people with or at risk for chronic conditions. However, these mHealth tools have remained largely static and do not adapt to the changing behavior of the user. In a step toward designing adaptive interventions, we propose BeWell24Plus, a framework for monitoring activity and user engagement and developing computational models for outcome prediction and intervention design. In particular, we focus on devising algorithms that combine data about physical activity and engagement with the app to predict future physical activity performance. Knowing in advance how active a person is going to be in the next day can help with designing adaptive interventions that help individuals achieve their physical activity goals. Our technique combines the recent history of a person's physical activity with app engagement metrics such as when, how often, and for how long the app was used to forecast the near future's activity. We formulate the problem of multimodal activity forecasting and propose an LSTM-based realization of our proposed model architecture, which estimates physical activity outcomes in advance by examining the history of app usage and physical activity of the user. We demonstrate the effectiveness of our forecasting approach using data collected with 58 prediabetic people in a 9-month user study. We show that our multimodal forecasting approach outperforms single-modality forecasting by 2.2% to 11.1% in mean-absolute-error.
AB - Physical activity is a cornerstone of chronic conditions and one of the most critical factors in reducing the risks of cardiovascular diseases, the leading cause of death in the United States. App-based lifestyle interventions have been utilized to promote physical activity in people with or at risk for chronic conditions. However, these mHealth tools have remained largely static and do not adapt to the changing behavior of the user. In a step toward designing adaptive interventions, we propose BeWell24Plus, a framework for monitoring activity and user engagement and developing computational models for outcome prediction and intervention design. In particular, we focus on devising algorithms that combine data about physical activity and engagement with the app to predict future physical activity performance. Knowing in advance how active a person is going to be in the next day can help with designing adaptive interventions that help individuals achieve their physical activity goals. Our technique combines the recent history of a person's physical activity with app engagement metrics such as when, how often, and for how long the app was used to forecast the near future's activity. We formulate the problem of multimodal activity forecasting and propose an LSTM-based realization of our proposed model architecture, which estimates physical activity outcomes in advance by examining the history of app usage and physical activity of the user. We demonstrate the effectiveness of our forecasting approach using data collected with 58 prediabetic people in a 9-month user study. We show that our multimodal forecasting approach outperforms single-modality forecasting by 2.2% to 11.1% in mean-absolute-error.
KW - machine learning
KW - physical activity
KW - time-series forecasting
KW - wearables
UR - http://www.scopus.com/inward/record.url?scp=85142243694&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142243694&partnerID=8YFLogxK
U2 - 10.1109/BSN56160.2022.9928521
DO - 10.1109/BSN56160.2022.9928521
M3 - Conference contribution
AN - SCOPUS:85142243694
T3 - BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings
BT - BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, BSN 2022
Y2 - 27 September 2022 through 30 September 2022
ER -