research-article

MyBehavior: automatic personalized health feedback from user behaviors and preferences using smartphones

Authors:

Mashfiqui Rabbi,

Tanzeem ChoudhuryAuthors Info & Claims

UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Pages 707 - 718

https://doi.org/10.1145/2750858.2805840

Published: 07 September 2015 Publication History

Abstract

Mobile sensing systems have made significant advances in tracking human behavior. However, the development of personalized mobile health feedback systems is still in its infancy. This paper introduces MyBehavior, a smartphone application that takes a novel approach to generate deeply personalized health feedback. It combines state-of-the-art behavior tracking with algorithms that are used in recommendation systems. MyBehavior automatically learns a user's physical activity and dietary behavior and strategically suggests changes to those behaviors for a healthier lifestyle. The system uses a sequential decision making algorithm, Multi-armed Bandit, to generate suggestions that maximize calorie loss and are easy for the user to adopt. In addition, the system takes into account user's preferences to encourage adoption using the pareto-frontier algorithm. In a 14-week study, results show statistically significant increases in physical activity and decreases in food calorie when using MyBehavior compared to a control condition.

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Cited By

Fiedler JBergmann MSell SWoll AStetter B(2024)Just-in-Time Adaptive Interventions for Behavior Change in Physiological Health Outcomes and the Use Case for Knee Osteoarthritis: Systematic ReviewJournal of Medical Internet Research10.2196/5411926(e54119)Online publication date: 27-Sep-2024
https://doi.org/10.2196/54119
Mullick TShaaban SRadovic ADoryab A(2024)Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized ModelingJMIR AI10.2196/478053(e47805)Online publication date: 20-May-2024
https://doi.org/10.2196/47805
Brons AWang SVisser BKröse BBakkes SVeltkamp R(2024)Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity: Scoping Review and Categorization OverviewJournal of Medical Internet Research10.2196/4777426(e47774)Online publication date: 15-Nov-2024
https://doi.org/10.2196/47774
Show More Cited By

Index Terms

MyBehavior: automatic personalized health feedback from user behaviors and preferences using smartphones
1. Computing methodologies
  1. Machine learning
2. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Conferences

UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 2015

1302 pages

ISBN:9781450335744

DOI:10.1145/2750858

General Chairs:
Kenji Mase
Nagoya University, JP
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Marc Langheinrich
Università della Svizzera italiana (USI), CH
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Program Chairs:
Hans Gellersen
Lancaster University, UK
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Tanzeem Choudhury
Cornell University
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Koji Yatani
Univerisity of Tokyo, JP

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Published: 07 September 2015

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UbiComp '15: The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 7 - 11, 2015

Osaka, Japan

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UbiComp '15 Paper Acceptance Rate 101 of 394 submissions, 26%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Fiedler JBergmann MSell SWoll AStetter B(2024)Just-in-Time Adaptive Interventions for Behavior Change in Physiological Health Outcomes and the Use Case for Knee Osteoarthritis: Systematic ReviewJournal of Medical Internet Research10.2196/5411926(e54119)Online publication date: 27-Sep-2024
https://doi.org/10.2196/54119
Mullick TShaaban SRadovic ADoryab A(2024)Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized ModelingJMIR AI10.2196/478053(e47805)Online publication date: 20-May-2024
https://doi.org/10.2196/47805
Brons AWang SVisser BKröse BBakkes SVeltkamp R(2024)Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity: Scoping Review and Categorization OverviewJournal of Medical Internet Research10.2196/4777426(e47774)Online publication date: 15-Nov-2024
https://doi.org/10.2196/47774
Asgari Mehrabadi MKhatibi EJimah TLabbaf SBorg HNarvaez LPimentel PTurner ADutt NGuo YRahmani A(2024)PERFECT: Personalized Exercise Recommendation Framework and architECTureACM Transactions on Computing for Healthcare10.1145/36964255:4(1-25)Online publication date: 3-Oct-2024
https://dl.acm.org/doi/10.1145/3696425
Adler DChoudhury TKostakos VKay JHoang T(2024)Ubiquitous Computing in Action: Infrastructure to Support Sensing and Mental Health Research in PracticeCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678372(301-306)Online publication date: 5-Oct-2024
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Clark MDoryab A(2024)Changing Your Tune: Lessons for Using Music to Encourage Physical ActivityProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596118:2(1-24)Online publication date: 15-May-2024
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Yan XBaroudi LBei RBoudalia LCain SBarton KShorter KNewman M(2024)Proposing a Context-informed Layer-based Framework: Incorporating Context into Designing mHealth Technology for Fatigue ManagementProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661615(571-583)Online publication date: 1-Jul-2024
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Figueiredo MAnkrah EPowell JEpstein DChen Y(2024)Powered by AIProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314147:4(1-24)Online publication date: 12-Jan-2024
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Orzikulova AXiao HLi ZYan YWang YShi YGhassemi MLee SDey AXu X(2024)Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse InterventionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642747(1-20)Online publication date: 11-May-2024
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Chen YWoodward JBista DZhang XSingh IObajemu OShankar MRoss KRuiz JAnthony L(2024)Investigating Contextual Notifications to Drive Self-Monitoring in mHealth Apps for Weight MaintenanceProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641993(1-21)Online publication date: 11-May-2024
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