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.

Supplementary Material

PDF File (p707-rabbi-sup.pdf)

Screenshots from the control phase

Download
436.98 KB

MP4 File (p707-rabbi.mp4)

Download
35.46 MB

References

[1]

B. E. Ainsworth, W. L. Haskell, S. D. Herrmann, N. Meckes, D. R. Bassett, C. Tudor-Locke, J. L. Greer, J. Vezina, M. C. Whitt-Glover, and A. S. Leon. 2011 compendium of physical activities: a second update of codes and met values. Medicine and science in sports and exercise, 43(8): 1575--1581, 2011.

[2]

I. Ajzen. Theory of planned behavior. Handb Theor Soc Psychol Vol One, 1: 438, 2011.

[3]

Amazon Mechanical Turk. http://www.mturk.com/, 2013. {Online; accessed 19 March 2013}.

[4]

A. Bandura and D. C. McClelland. Social learning theory. 1977.

[5]

Behavioral Intentions. http://chirr.nlm.nih.gov/behavioral-intention.php, 2013. {Online; accessed 26 February 2015}.

[6]

A. Biglan, D. Ary, and A. C. Wagenaar. The value of interrupted time-series experiments for community intervention research. Prevention Science, 1(1): 31--49, 2000.

[7]

S. Bubeck and N. Cesa-Bianchi. Regret analysis of stochastic and nonstochastic multi-armed bandit problems. arXiv preprint arXiv:1204.5721, 2012.

[8]

M. N. Burns, M. Begale, J. Duffecy, D. Gergle, C. J. Karr, E. Giangrande, and D. C. Mohr. Harnessing context sensing to develop a mobile intervention for depression. Journal of medical Internet research, 13(3), 2011.

[9]

B. Caballero. The global epidemic of obesity: an overview. Epidemiologic reviews, 29(1): 1--5, 2007.

[10]

A. Cnaan, N. Laird, and P. Slasor. Tutorial in biostatistics: Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data. Stat Med, 16: 2349--2380, 1997.

[11]

S. Consolvo, D. W. McDonald, T. Toscos, M. Y. Chen, J. Froehlich, B. Harrison, P. Klasnja, A. LaMarca, L. LeGrand, R. Libby, et al. Activity sensing in the wild: a field trial of ubifit garden. In Proceedings of the twenty-sixth annual SIGCHI conference on Human factors in computing systems, pages 1797--1806. ACM, 2008.

Digital Library

[12]

J. Dallery, R. N. Cassidy, and B. R. Raiff. Single-case experimental designs to evaluate novel technology-based health interventions. Journal of medical Internet research, 15(2), 2013.

[13]

P. Diggle, P. Heagerty, K.-Y. Liang, and S. Zeger. Analysis of longitudinal data. Oxford University Press, 2002.

[14]

D. Estrin. Small data, where n = me. Commun. ACM, 57(4): 32--34, Apr. 2014.

Digital Library

[15]

Fitbit, Inc. http://www.fitbit.com/, 2013. {Online; accessed 19 March 2013}.

[16]

B. Fogg. A behavior model for persuasive design. In Proceedings of the 4th international Conference on Persuasive Technology, page 40. ACM, 2009.

Digital Library

[17]

W. M. Grove. Thinking clearly about psychology.

[18]

J. Harris and F. Benedict. Biometric studies of basal metabolism. Washington, DC: Carnegie Institution, 1919.

[19]

D. Haytowitz, L. Lemar, P. Pehrsson, J. Exler, K. Patterson, R. Thomas, M. Nickle, J. Williams, B. Showell, M. Khan, et al. Usda national nutrient database for standard reference, release 24, 2011.

[20]

G. Hochbaum, I. Rosenstock, and S. Kegels. Health belief model. United States Public Health Service, 1952.

[21]

D. Kahneman. Thinking, fast and slow. Macmillan, 2011.

[22]

P. Klasnja, S. Consolvo, T. Choudhury, R. Beckwith, and J. Hightower. Exploring privacy concerns about personal sensing. In Pervasive Computing, pages 176--183. Springer, 2009.

Digital Library

[23]

P. Klasnja, S. Consolvo, and W. Pratt. How to evaluate technologies for health behavior change in hci research. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 3063--3072. ACM, 2011.

Digital Library

[24]

R. Kukafka. Tailored health communication. Consumer Health Informatics: Informing Consumers and Improving Health Care, pages 22--33, 2005.

[25]

N. D. Lane, M. Mohammod, M. Lin, X. Yang, H. Lu, S. Ali, A. Doryab, E. Berke, T. Choudhury, and A. T. Campbell. Bewell: A smartphone application to monitor, model and promote wellbeing. In 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth2011), 2011.

[26]

J. Lester, C. Hartung, L. Pina, R. Libby, G. Borriello, and G. Duncan. Validated caloric expenditure estimation using a single body-worn sensor. In Proceedings of the 11th international conference on Ubiquitous computing, Ubicomp '09, pages 225--234, New York, NY, USA, 2009. ACM.

Digital Library

[27]

L. Li, W. Chu, J. Langford, and R. E. Schapire. A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on World wide web, pages 661--670. ACM, 2010.

Digital Library

[28]

H. Lu, J. Yang, Z. Liu, N. D. Lane, T. Choudhury, and A. T. Campbell. The jigsaw continuous sensing engine for mobile phone applications. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, pages 71--84. ACM, 2010.

Digital Library

[29]

T. Lu, D. Pl, and M. Pl. Showing relevant ads via lipschitz context multi-armed bandits. In Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.

[30]

S. McCallum. Gamification and serious games for personalized health. Stud Health Technol Inform, 177: 85--96, 2012.

[31]

C. D. Mohr, M. S. Schueller, E. Montague, N. M. Burns, and P. Rashidi. The behavioral intervention technology model: An integrated conceptual and technological framework for ehealth and mhealth interventions. J Med Internet Res, 16(6): e146, Jun 2014.

[32]

L. G. Morrison, C. Hargood, S. X. Lin, L. Dennison, J. Joseph, S. Hughes, D. T. Michaelides, D. Johnston, M. Johnston, S. Michie, et al. Understanding usage of a hybrid website and smartphone app for weight management: A mixed-methods study. Journal of medical Internet research, 16(10), 2014.

[33]

MyFitnessPal, LLC. http://www.myfitnesspal.com/, 2013. {Online; accessed 19 March 2014}.

[34]

I. Nahum-Shani, S. N. Smith, A. Tewari, K. Witkiewitz, L. M. Collins, B. Spring, and S. Murphy. Just in time adaptive interventions (jitais): An organizing framework for ongoing health behavior support. Methodology Center technical report, (14--126), 2014.

[35]

National Heart, Lung and Blood Institute, National Institutes of Health. Getting started and staying active. http://www.nhlbi.nih.gov/health/public/heart/obesity/lose_wt/calories.htm, 2011. {Online; accessed 19 March 2013.

[36]

National Heart, Lung and Blood Institute, National Institutes of Health. Healthy eating plan. http://www.nhlbi.nih.gov/health/public/heart/obesity/lose_wt/calories.htm, 2013. {Online; accessed 19 March 2013}.

[37]

Netflix. http://www.netflix.com/, 2013. {Online; accessed 19 March 2014}.

[38]

J. Noronha, E. Hysen, H. Zhang, and K. Z. Gajos. Platemate: crowdsourcing nutritional analysis from food photographs. In Proceedings of the 24th annual ACM symposium on User interface software and technology, pages 1--12. ACM, 2011.

Digital Library

[39]

C. L. Ogden, M. D. Carroll, B. K. Kit, and K. M. Flegal. Prevalence of obesity in the united states, 2009--2010, 2012.

[40]

P. Onghena and E. S. Edgington. Customization of pain treatments: Single-case design and analysis. The Clinical journal of pain, 21(1): 56--68, 2005.

[41]

R. L. Ott and M. T. Longnecker. An Introduction to Statistical Methods and Data analysis, 4th. New York: Duxbury Press, 1993.

[42]

T. Philipson and R. Posner. Is the obesity epidemic a public health problem? a decade of research on the economics of obesity. Technical report, National Bureau of Economic Research, 2008.

[43]

J. Pinheiro and D. Bates. Nlme: Software for mixed-effects models, 2000.

[44]

J. P. Pollak, P. Adams, and G. Gay. Pam: a photographic affect meter for frequent, in situ measurement of affect. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 725--734. ACM, 2011.

Digital Library

[45]

W. B. Powell. Approximate Dynamic Programming: Solving the curses of dimensionality, volume 703. John Wiley & Sons, 2007.

Digital Library

[46]

J. O. Prochaska and W. F. Velicer. The transtheoretical model of health behavior change. American journal of health promotion, 12(1): 38--48, 1997.

[47]

M. Rabbi, A. Pfammatter, M. Zhang, B. Spring, and T. Choudhury. Automated personalized feedback for physical activity and dietary behavior change with mobile phones: A randomized controlled trial on adults. JMIR mHealth uHealth, 3(2): e42, May 2015.

[48]

M. C. Roberts, A. S. Dizier, and J. Vaughan. Multiobjective optimization: Portfolio optimization based on goal programming methods.

[49]

E. M. Rogers. Diffusion of innovations. Simon and Schuster, 2010.

[50]

J. D. Singer and J. B. Willett. Applied longitudinal data analysis: Modeling change and event occurrence. Oxford university press, 2003.

[51]

L. Springvloet, L. Lechner, H. de Vries, M. J. Candel, and A. Oenema. Short-and medium-term efficacy of a web-based computer-tailored nutrition education intervention for adults including cognitive and environmental feedback: Randomized controlled trial. Journal of medical Internet research, 17(1): e23, 2015.

[52]

R. Sriraghavendra, K. Karthik, and C. Bhattacharyya. Fréchet distance based approach for searching online handwritten documents. In Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on, volume 1, pages 461--465. IEEE, 2007.

Digital Library

[53]

A. Tashakkori and C. Teddlie. Mixed methodology: Combining qualitative and quantitative approaches, volume 46. SAGE Publications, Incorporated, 1998.

[54]

B. E. Wilde, C. L. Sidman, and C. B. Corbin. A 10,000-step count as a physical activity target for sedentary women. Research Quarterly for Exercise and Sport, 72(4): 411--414, 2001.

[55]

T. Zhang, R. Ramakrishnan, and M. Livny. Birch: an efficient data clustering method for very large databases. In ACM SIGMOD Record, volume 25, pages 103--114. ACM, 1996.

Digital Library

[56]

L. M. Zhou and C. Gurrin. A survey on life logging data capturing. SenseCam 2012, 2012.

Cited By

De Croon RSegovia-Lizano DFinglas PVanden Abeele VVerbert K(2025)An Explanation Interface for Healthy Food Recommendations in a Real-Life Workplace Deployment: User-Centered Design StudyJMIR mHealth and uHealth10.2196/5127113(e51271-e51271)Online publication date: 11-Feb-2025
https://doi.org/10.2196/51271
Oda NShoji YKim JYamamoto Y(2025)Yes-No Flowchart Generation for Interactive Exploration of Personalized Health Improvement ActionsDatabase Systems for Advanced Applications. DASFAA 2024 International Workshops10.1007/978-981-96-0914-7_24(302-316)Online publication date: 23-Jan-2025
https://doi.org/10.1007/978-981-96-0914-7_24
Ghosh SGuo YHung PCoughlin LBonar ENahum-Shani IWalton MMurphy SLarson K(2024)ReBanditProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/805(7278-7286)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/805
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)

Recommendations

User-Based Collaborative Filtering Mobile Health System

Mobile health systems predict health conditions based on multimodal signals. Users are often reluctant to provide their health status over privacy concerns. It is challenging to make health predictions without sufficient historical data from the users. ...
Wireless networks, physician handhelds use, and medical devices in U.S. hospitals
WH '15: Proceedings of the conference on Wireless Health

As wireless technologies such as smartphones, tablets, and other handheld devices continue to permeate into every aspect of everyday life, hospitals have attempted to integrate them into the clinical workflow. Similarly, wireless sensor devices are ...
Development of a Point-of-Care HIV/Aids Medication Dosing Support System Using the Android Mobile Platform

Medication dosing errors can greatly reduce HIV treatment effectiveness as incorrect dosing leads to drug resistance and non-adherence. In order to dose correctly, HIV therapy providers must balance several patient characteristics such as renal ...

Comments

Information & Contributors

Information

Published In

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
,
Marc Langheinrich
Università della Svizzera italiana (USI), CH
,
Daniel Gatica-Perez
IDIAP-EPFL, CH
,
Program Chairs:
Hans Gellersen
Lancaster University, UK
,
Tanzeem Choudhury
Cornell University
,
Koji Yatani
Univerisity of Tokyo, JP

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Yahoo! Japan: Yahoo! Japan
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
FX Palo Alto Laboratory, Inc.: FX Palo Alto Laboratory, Inc.
Google Inc.
ACM: Association for Computing Machinery
Rakuten Institute of Technology: Rakuten Institute of Technology
Microsoft: Microsoft
Bell Labs: Bell Laboratories
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
KDDI
Panasonic: Panasonic Corporation
NTT DoCoMo
Telefónica: Telefónica
ISTC-PC: Intel Science and Technology Center for Pervasive Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

UbiComp '15

Sponsor:

Yahoo! Japan
SIGMOBILE
FX Palo Alto Laboratory, Inc.
ACM
Rakuten Institute of Technology
Microsoft
Bell Labs
SIGCHI
Panasonic
Telefónica
ISTC-PC

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

September 7 - 11, 2015

Osaka, Japan

Acceptance Rates

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

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

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

180
Total Citations
View Citations
2,488
Total Downloads

Downloads (Last 12 months)192
Downloads (Last 6 weeks)22

Reflects downloads up to 09 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

De Croon RSegovia-Lizano DFinglas PVanden Abeele VVerbert K(2025)An Explanation Interface for Healthy Food Recommendations in a Real-Life Workplace Deployment: User-Centered Design StudyJMIR mHealth and uHealth10.2196/5127113(e51271-e51271)Online publication date: 11-Feb-2025
https://doi.org/10.2196/51271
Oda NShoji YKim JYamamoto Y(2025)Yes-No Flowchart Generation for Interactive Exploration of Personalized Health Improvement ActionsDatabase Systems for Advanced Applications. DASFAA 2024 International Workshops10.1007/978-981-96-0914-7_24(302-316)Online publication date: 23-Jan-2025
https://doi.org/10.1007/978-981-96-0914-7_24
Ghosh SGuo YHung PCoughlin LBonar ENahum-Shani IWalton MMurphy SLarson K(2024)ReBanditProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/805(7278-7286)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/805
Dwyer BFlathers MBurns JMikkelson JPerlmutter EChen KRam NTorous J(2024)Assessing Digital Phenotyping for App Recommendations and Sustained Engagement: Cohort StudyJMIR Formative Research10.2196/627258(e62725)Online publication date: 19-Nov-2024
https://doi.org/10.2196/62725
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
Chung CChiang PTan CWu CSchmidt HKotarski AGuise D(2024)Opportunities to design better computer vison-assisted food diaries to support individuals and experts in dietary assessment: An observation and interview study with nutrition expertsPLOS Digital Health10.1371/journal.pdig.00006653:11(e0000665)Online publication date: 27-Nov-2024
https://doi.org/10.1371/journal.pdig.0000665
Adler DYang YViranda TXu XMohr DVan Meter ATartaglia JJacobson NWang FEstrin DChoudhury T(2024)Beyond Detection: Towards Actionable Sensing Research in Clinical Mental HealthcareProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997558:4(1-33)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699755
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: 12-Nov-2024
https://dl.acm.org/doi/10.1145/3696425
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten