Using the Unified Theory of Acceptance and Use of Technology (UTAUT) to Investigate the Intention to Use Physical Activity Apps: Cross-Sectional Survey

Di Liu, Remina Maimaitijiang, Jing Gu, Shuyi Zhong, Mengping Zhou, Ziyue Wu, Ao Luo, Cong Lu, Yuantao Hao, Di Liu, Remina Maimaitijiang, Jing Gu, Shuyi Zhong, Mengping Zhou, Ziyue Wu, Ao Luo, Cong Lu, Yuantao Hao

Abstract

Background: Many university students are lacking adequate physical exercise and are failing to develop physical activity (PA) behaviors in China. PA app use could improve this situation.

Objective: The aim of this study was to use the unified theory of acceptance and use of technology (UTAUT) to investigate the intention to use PA apps among university students in Guangzhou, China, and how body mass index (BMI) moderates the effects of UTAUT in explaining PA app use intention.

Methods: A cross-sectional study was conducted among 1704 university students from different universities in Guangzhou, China. The UTAUT model was used to measure the determinants of intention to use PA apps.

Results: Of the participants, 41.8% (611/1461) intended to use PA apps. All three UTAUT-related scales (performance expectancy, effort expectancy, and social influence) were positively associated with the intention to use PA apps after adjusting for background variables (adjusted odds ratio 1.10-1.31, P<.001). The performance expectancy scale had stronger associations with the intention to use PA apps among those whose BMI were beyond normal range compared with those whose BMI were within normal range (P<.001).

Conclusions: UTAUT is useful for understanding university students' intention to use PA apps. Potential moderating effects should be kept in mind when designing UTAUT-based interventions to improve PA via app use.

Keywords: UTAUT; intention; physical activity apps; university students.

Conflict of interest statement

Conflicts of Interest: None declared.

©Di Liu, Remina Maimaitijiang, Jing Gu, Shuyi Zhong, Mengping Zhou, Ziyue Wu, Ao Luo, Cong Lu, Yuantao Hao. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 22.08.2019.

Figures

Figure 1
Figure 1
Modified model of unified theory of acceptance and use of technology.
Figure 2
Figure 2
Interaction effect between body mass index and performance expectancy scale. BMI: body mass index.

References

    1. World Health Organization. 2010. [2018-01-19]. Global recommendations on physical activity for health
    1. Blair SN. Physical inactivity: the biggest public health problem of the 21st century. Br J Sports Med. 2009 Jan;43(1):1–2.
    1. Hancox RJ, Milne BJ, Poulton R. Association between child and adolescent television viewing and adult health: a longitudinal birth cohort study. Lancet. 2004;364(9430):257–262. doi: 10.1016/S0140-6736(04)16675-0.
    1. Kimm SYS, Glynn NW, Obarzanek E, Kriska AM, Daniels SR, Barton BA, Liu K. Relation between the changes in physical activity and body-mass index during adolescence: a multicentre longitudinal study. Lancet. 2005;366(9482):301–307. doi: 10.1016/S0140-6736(05)66837-7.
    1. Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012 Jul 21;380(9838):247–257. doi: 10.1016/S0140-6736(12)60646-1.
    1. Pate RR, Stevens J, Webber LS, Dowda M, Murray DM, Young DR. Age-related change in physical activity in adolescent girls. J Adolesc Health. 2009 Mar;44(3):275–282. doi: 10.1016/j.jadohealth.2008.07.003.
    1. Sallis JF. Age-related decline in physical activity: a synthesis of human and animal studies. Med Sci Sports Exerc. 2000 Sep;32(9):1598–1600.
    1. Kimm SYS, Glynn NW, Kriska AM, Barton BA, Kronsberg SS, Daniels SR, Crawford PB, Sabry ZI, Liu K. Decline in physical activity in black girls and white girls during adolescence. N Engl J Med. 2002 Sep 05;347(10):709–715. doi: 10.1056/NEJMoa003277.
    1. Telama R. Tracking of physical activity from childhood to adulthood: a review. Obes Facts. 2009;2(3):187–195. doi: 10.1159/000222244.
    1. American College Health Association–National College Health Assessment II: Thompson Rivers University Executive Summary Spring 2013. Hanover: American College Health Association; 2013. [2018-01-20]. .
    1. Bongaarts J. U.S. health in international perspective: shorter lives, poorer health. Popul Dev Rev. 2013;39(1):165–167.
    1. Balon R, Beresin EV, Coverdale JH, Louie AK, Roberts LW. College mental health: a vulnerable population in an environment with systemic deficiencies. Acad Psychiatry. 2015 Oct;39(5):495–497. doi: 10.1007/s40596-015-0390-1.
    1. Anxious students strain college mental health centers. New York: New York Times; 2015. May 27, [2018-01-23]. .
    1. General Administration of Sport in China [National Students' Physical Health Survey results] Chin J School Health. 2015;12:4. doi: 10.16835/j.cnki.1000-9817.2015.12.001.
    1. Dobbins M, Husson H, DeCorby K, LaRocca RL. School-based physical activity programs for promoting physical activity and fitness in children and adolescents aged 6 to 18. Cochrane Database Syst Rev. 2013;2:CD007651. doi: 10.1002/14651858.CD007651.pub2.
    1. Tate EB, Spruijt-Metz D, O'Reilly G, Jordan-Marsh M, Gotsis M, Pentz MA, Dunton GF. mHealth approaches to child obesity prevention: successes, unique challenges, and next directions. Transl Behav Med. 2013 Dec;3(4):406–415. doi: 10.1007/s13142-013-0222-3.
    1. International Telecommunication Union. 2016. [2018-01-21]. Percentage of individuals using the internet .
    1. Ma C. Media use and media literacy of urban and rural youth in the digital media era: an empirical investigation of the youth in S Province. J Sichuan Univ Sci Eng Soc Sci Ed. 2018;33(5):79–100. doi: 10.11965/xbew20180506.
    1. Middelweerd A, Mollee JS, van der Wal CN, Brug J, Te Velde SJ. Apps to promote physical activity among adults: a review and content analysis. Int J Behav Nutr Phys Act. 2014;11:97. doi: 10.1186/s12966-014-0097-9.
    1. Direito A, Jiang Y, Whittaker R, Maddison R. Smartphone apps to improve fitness and increase physical activity among young people: protocol of the Apps for IMproving FITness (AIMFIT) randomized controlled trial. BMC Public Health. 2015;15(1):635. doi: 10.1186/s12889-015-1968-y.
    1. Woolford SJ, Clark SJ, Strecher VJ, Resnicow K. Tailored mobile phone text messages as an adjunct to obesity treatment for adolescents. J Telemed Telecare. 2010;16(8):458–461. doi: 10.1258/jtt.2010.100207.
    1. Noar SM, Benac CN, Harris MS. Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions. Psychol Bull. 2007 Jul;133(4):673–693. doi: 10.1037/0033-2909.133.4.673.
    1. Klasnja P, Pratt W. Healthcare in the pocket: mapping the space of mobile-phone health interventions. J Biomed Inform. 2012 Feb;45(1):184–198. doi: 10.1016/j.jbi.2011.08.017.
    1. Turner-McGrievy GM, Beets MW, Moore JB, Kaczynski AT, Barr-Anderson DJ, Tate DF. Comparison of traditional versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an mHealth weight loss program. J Am Med Inform Assoc. 2013 May 1;20(3):513–518. doi: 10.1136/amiajnl-2012-001510.
    1. Maher C, Ferguson M, Vandelanotte C, Plotnikoff R, De Bourdeaudhuij I, Thomas S, Nelson-Field K, Olds T. A web-based, social networking physical activity intervention for insufficiently active adults delivered via facebook app: randomized controlled trial. J Med Internet Res. 2015;17(7):e174. doi: 10.2196/jmir.4086.
    1. Flores MG, Granado-Font E, Ferré-Grau C, Montaña-Carreras X. Mobile phone apps to promote weight loss and increase physical activity: a systematic review and meta-analysis. J Med Internet Res. 2015;17(11):e253. doi: 10.2196/jmir.4836.
    1. Compeau DR, Higgins CA. Application of social cognitive theory to training for computer skills. Inform Syst Res. 1995 Jun;6(2):118–143. doi: 10.1287/isre.6.2.118.
    1. Ajzen I. The theory of planned behaviour: reactions and reflections. Psychol Health. 2011 Sep;26(9):1113–1127. doi: 10.1080/08870446.2011.613995.
    1. Venkatesh V, Morris M, Davis G, Davis F. User acceptance of information technology: toward a unified view. MIS Q. 2003;27(3):425. doi: 10.2307/30036540.
    1. Liu L, Miguel CA, Rios RA, Buttar V, Ranson Q, Goertzen D. What factors determine therapists' acceptance of new technologies for rehabilitation—a study using the Unified Theory of Acceptance and Use of Technology (UTAUT) Disabil Rehabil. 2015;37(5):447–455. doi: 10.3109/09638288.2014.923529.
    1. Education Examinations Authority of Guangdong Province. [2019-07-16]. .
    1. National Technical Supervision Bureau. 2006. [2018-02-15]. [Classification and code disciplines of China] .
    1. Yang ML, Lou XM, Peng YL. [BMI and health-related physical fitness in Henan undergraduate students] Chin J School Health. 2013;34(9):1093–1095. doi: 10.16835/j.cnki.1000-9817.2013.09.026.
    1. Zhao W, Zhou C, Li S. [The influence of study of nutriology on undergraduate' nutritional knowledge-attitude-practice and BMI] J Molecular Imag. 2015;38(4):409–412. doi: 10.3969/j.issn.1674-4500.2015.04.32.
    1. Xiang J, Song Z, Tian C. [Research on the step count and its correlation with BMI and WHR of college students] Zhejiang Sport Sci. 2016;38(2):104–109.
    1. Choukas-Bradley S, Giletta M, Cohen G, Prinstein M. Peer influence, peer status, and prosocial behavior: an experimental investigation of peer socialization of adolescents' intentions to volunteer. J Youth Adolesc. 2015;44(12):2197–2210.
    1. Helmer S, Sebena R, McAlaney J, Petkeviciene J, Salonna F, Lukacs A. Perception of high alcohol use of peers is associated with high personal alcohol use in first-year university students in three central and eastern European countries. Subst Use Misuse. 2016;51(9):1224–1231.

Source: PubMed

スポンサーと協力者

医学的状態

薬物療法

3
購読する