Stay Connected and Keep Motivated: Modeling Activity Level of Exercise in an Online Fitness Community

Recent years have witnessed a growing popularity of activity tracking applications. Previously work has focused on three major types of social interaction features in such applications: cooperation, competition and community. Such features motivate users to be more active in exercise and stay within the track of positive behavior change. Online fitness communities such as Strava encourage users to connect to peers and provide a rich set of social interaction features. Utilizing a large-scale behavioral trace data set, this work aims to analyze the dynamics of online fitness behaviors and network subscription as well as the relationship between them. Our results indicate that activeness of fitness behaviors not only has seasonal variations, but also vary by user group and how well users are connected in an online fitness community. These results provide important implications for studies on network-based health and design of application features for health promotion.

[1]  Lieven De Marez,et al.  Understanding persistence in the use of Online Fitness Communities: Comparing novice and experienced users , 2016, Comput. Hum. Behav..

[2]  Pearl Pu,et al.  HealthyTogether: exploring social incentives for mobile fitness applications , 2014, Chinese CHI '14.

[3]  Tyler Sax,et al.  Just a Fad? Gamification in Health and Fitness Apps , 2014, JMIR serious games.

[4]  Hyewon Chung,et al.  Applying the Technology Acceptance Model to Social Networking Sites (SNS): Impact of Subjective Norm and Social Capital on the Acceptance of SNS , 2013, Int. J. Hum. Comput. Interact..

[5]  Minna Isomursu,et al.  Designing social features for mobile and ubiquitous wellness applications , 2009, MUM.

[6]  J. Kulik,et al.  Social support and recovery from surgery. , 1989, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[7]  J. West,et al.  There ’ s an App for That : Content Analysis of Paid Health and Fitness Apps , 2018 .

[8]  Jane D. Brown,et al.  A social media-based physical activity intervention: a randomized controlled trial. , 2012, American journal of preventive medicine.

[9]  Tim Althoff.  Population-Scale Pervasive Health , 2017, IEEE Pervasive Computing.

[10]  James A. Landay,et al.  Design requirements for technologies that encourage physical activity , 2006, CHI.

[11]  R. Wing,et al.  Benefits of recruiting participants with friends and increasing social support for weight loss and maintenance. , 1999, Journal of consulting and clinical psychology.

[12]  David W. McDonald,et al.  Activity sensing in the wild: a field trial of ubifit garden , 2008, CHI.

[13]  J. Sallis,et al.  The determinants of physical activity and exercise. , 1985, Public health reports.

[14]  P. Killworth,et al.  The Problem of Informant Accuracy: The Validity of Retrospective Data , 1984 .

[15]  M. Tremblay,et al.  A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review , 2008, The international journal of behavioral nutrition and physical activity.

[16]  Damon Centola,et al.  Choosing your network: social preferences in an online health community. , 2015, Social science & medicine.

[17]  Juho Hamari,et al.  "Working out for likes": An empirical study on social influence in exercise gamification , 2015, Comput. Hum. Behav..

[18]  Ben Kirman,et al.  Motivating physical activity at work: using persuasive social media for competitive step counting , 2010, MindTrek.

[19]  K. Patrick,et al.  Technologies to measure and modify physical activity and eating environments. , 2015, American journal of preventive medicine.

[20]  James Fogarty,et al.  Game design principles in everyday fitness applications , 2008, CSCW.