In recent years, the spread of mobile communication devices such as smartphones has been markedly rapid. With this technological diffusion, mobile health (mHealth) has become an increasingly important issue. In particular, there is an increasing interest in smartphone apps improving public health. Although there is increased availability of mobile devices and health apps, little is known about motivational factors predicting health app adoption and use. The aim of this study was to identify motivational factors that predict the adoption and use of health apps (i.e. health app engagement). To identify the motivational factors, 391 college students were surveyed and survey questions considered the effects of media exposure to health information, interpersonal communication on health issues, and psychological factors (e.g. attitude, usefulness, peer norm, and self-efficacy) on health app engagement. Our results confirm the effect of attitude (β = 0.36) and usefulness are (β = 0.33) on mHealth App usage. Furthermore, we found that age (β = 0.11) and reading news articles about health (β = 0.13) predict mHealth App usage. Theoretical and practical implications and suggestions for future research are discussed.
AjzenI (1991) The theory of planned behavior. Organizational behavior and human decision processes50: 179–211.
2.
AlagözFValdezACWilkowskaW. (2010) From cloud computing to mobile internet, from user focus to culture and hedonism: the crucible of mobile health care and wellness applications. In: 5th International Conference on Pervasive Computing and Applications (eds HuBLiXYanJ), Maribor, Slovenia, 1–3 December 2010, pp. 38–45. New York: IEEE.
3.
AnckerJSCarpenterKMGreeneP. (2009) Peer-to-peer communication, cancer prevention, and the internet. Journal of Health Communication14: 38–46.
4.
ÅrsandETufanoJTRalstonJD. (2008) Designing mobile dietary management support technologies for people with diabetes. Journal of Telemedicine and Telecare14(7): 329–332.
5.
BanduraA (1977) Self-efficacy: toward a unifying theory of behavioral change. Psychological Review84: 191–215.
6.
BealeDAMansteadASR (1991) Predicting mothers intentions to limit frequency of infants’ sugar intake: testing the theory of planned behavior. Journal of Applied Social Psychology21: 409–431.
7.
BorlandROwenNHillD. (1991) Predicting attempts and sustained cessation of smoking after the introduction of workplace smoking bans. Health Psychology10: 336–342.
8.
BoulosMNKYangSP (2013) Exergames for health and fitness: the roles of GPS and geosocial apps. International Journal of Health Geographics12(1): 18–24.
9.
BraunerPValdezACSchroederU. (2013) Increase physical fitness and create health awareness through exergames and gamification: the role of individual factors, motivation and acceptance. In: 1st International Conference on Human Factors in Computing and Informatics (eds HolzingerAZiefleMHitzM, et al.), 1–3 July 2013, pp. 349–362. New York: Springer.
10.
CarterMCBurleyVJNykjaerC. (2013) Adherence to a smartphone application for weight loss compared to website and paper diary: pilot randomized controlled trial. Journal of Medical Internet Research15(4). Epub ahead of print 10 June 2014. DOI: 10.2196/jmir.2283.
11.
CheungCMKLeeMKO (2010) A theoretical model of intentional social action in online social networks. Decision Support Systems49(1): 24–30.
12.
ChoJParkDLeeHE (2014) Cognitive factors of using health apps: systematic analysis of relationships among health consciousness, health information orientation, eHealth literacy, and health app use efficacy. Journal of Medical Internet Research16(5). Epub ahead of print 10 June 2014. DOI: 10.2196/jmir.3283.
13.
ChungJEParkNWangH. (2010) Age differences in perceptions of online community participation among non-users: an extension of the technology acceptance model. Computers in Human Behavior26(6): 1674–1684.
14.
CialdiniRBTrostMR (1998) Social influence: social norms, conformity and compliance. In: GilbertDTFiskeSTLindzeyG (eds) The Handbook of Social Psychology. New York: McGraw-Hill, pp. 151–192.
15.
ConnerMNormanP (1994) Comparing the health belief model and theory of planned behavior in health screening. In: RutterDRQuineL (eds) Social Psychology and Health: European perspectives. Brookfied: Avebury/Ashgate Publishing Co., pp. 1–24.
16.
CuriosoWHKurthAE (2007) Access, use and perceptions regarding ìnternet, cell phones and PDAs as a means for health promotion for people living with HIV in Peru. BMC Medical Informatics and Decision Making7(1). Epub ahead of print 10 June 2014. DOI: 10.1186/1472-6947-7-24.
17.
DavisFD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly13(3): 319–340.
18.
DavisFDBagozziRPWarshawPR (1989) User acceptance of computer technology: a comparison of two theoretical models. Management Science35(8): 982–1003.
19.
DengZMoXLiuS (2014) Comparison of the middle-aged and older users’ adoption of mobile health services in China. International Journal of Medical Informatics83(3): 210–224.
20.
DevellisBMBlalockSJSandlerR (1990) Predicting participation in cancer screening: the role of perceived behavioral control. Journal of Applied Social Psychology20(8): 639–660.
21.
EllisonNBSteinfieldCLampeC (2007) The benefits of Facebook “friends:” social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication12(4): 1143–1168.
22.
FinkelsteinEATrogdonJGCohenJW (2009) Annual medical spending attributable to obesity: payer-and service-specific estimates. Health Affairs28(5): w822–w831.
23.
FishbeinM (1967) Attitude and the prediction of behavior. In: FishbeinM (ed) Readings in Attitude Theory and Measurement. New York: Wiley, pp. 477–490.
24.
FishbeinMAjzenI (1975) Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Reading: Addison-Wesley Publishing Co.
25.
FishbeinMAjzenI (2010) Predicting and Changing Behavior: The Reasoned Action Approach. New York: Taylor & Francis Group.
26.
FoxSDugganM (2012) Mobile health 2012. Report, Pew Research Center, USA, November.
27.
FoxSDugganM (2013) Tracking for health. Report, Pew Research Center, USA, January.
28.
GodinGBoyerRDuvalB. (1992) Understanding physicians decision to perform a clinical examination on an HIV seropositive patient. Medical Care30(3): 199–207.
29.
GoodenRJWinefieldHR (2007) Breast and prostate cancer online discussion boards: a thematic analysis of gender differences and similarities. Journal of Health Psychology12(1): 103–114.
30.
GuntherACBoltDBorzekowskiDLG. (2006) Presumed influence on peer norms: how mass media indirectly affect adolescent smoking. Journal of Communication56(1): 52–68.
31.
HauserBKKoontzTMBruskotterJT (2011) Volunteer participation in collaborative watershed partnerships: insights from the theory of planned behaviour. Journal of Environmental Planning and Management55(1): 77–94.
32.
HolzingerADornerSFödingerM. (2010) Chances of increasing youth health awareness through mobile wellness applications. In: 65th Symposium of the Workshop Human-Computer Interaction and Usability Engineering (eds LeitnerGHitzMHolzingerA), Klagenfurt, Austria, 4–5 November 2010, pp. 71–81. New York: Springer.
33.
HolzingerARöckerCZiefleM (2015) From smart health to smart hospitals. In: HolzingerARöckerCZiefleM (eds) Smart Health: Open Problems and Future Challenges. New York: Springer, pp. 1–20.
34.
HolzingerASearleGWernbacherM (2011) The effect of previous exposure to technology on acceptance and its importance in usability and accessibility engineering. Universal Access in the Information Society10(3): 245–260.
35.
HydeMKWhiteKM (2009) To be a donor or not to be? Applying an extended theory of planned behavior to predict posthumous organ donation intentions. Journal of Applied Social Psychology39(4): 880–900.
36.
JenW-Y (2010) The adoption of mobile weight management services in a virtual community: the perspective of college students. Telemedicine and e-Health16(4): 490–497.
37.
JohnsenJ-AKRosenvingeJHGammonD (2002) Online group interaction and mental health: an analysis of three online discussion forums. Scandinavian Journal of Psychology43(5): 445–449.
38.
JonesKODenhamBESpringstonJK (2007) Differing effects of mass and interpersonal communication on breast cancer risk estimates: an exploratory study of college students and their mothers. Health Communication21(2): 165–175.
39.
KamerowD (2013) Regulating medical apps: which ones and how much?British Medical Journal. Epub ahead of print 10 June 2014. DOI: 10.1136/bmj.f6009.
40.
KjellgrenA,BoodSÅAxelssonK. (2007) Wellness through a comprehensive Yogic breathing program: a controlled pilot trial. BMC Complementary and Alternative Medicine7. Epub ahead of print 10 June 2014. DOI: 10.1186/1472-6882-7-43.
41.
KristjánsdóttirÓBForsEAEideE. (2013) A smartphone-based intervention with diaries and therapist-feedback to reduce catastrophizing and increase functioning in women with chronic widespread pain: randomized controlled trial. Journal of Medical Internet Research15(1). Epub ahead of print 10 June 2014. DOI: 10.2196/jmir.2249.
42.
KwonM-WYooWAguilarM (2013) Taking the die out of diet: media and interpersonal communication’s impact on healthy eating habits. Journal of Communication in Healthcare6(1): 11–13.
43.
LeeWChaeYMKimS (2010) Evaluation of a mobile phone-based diet game for weight control. Journal of Telemedicine and Telecare16(5): 270–275.
44.
LewisMADeVellisBMSleathB (2002) Social influence and interpersonal communication in health behavior. In: GlanzKRimerBKLewisFM (eds) Health Behavior and Health Education: Theory Research and Practice. San Francisco: Jossey-Bass, pp. 240–264.
45.
LinT-CHuangC-C (2008) Understanding knowledge management system usage antecedents: an integration of social cognitive theory and task technology fit. Information & Management45(6): 410–417.
46.
LvYZhaoHLiangZ. (2012) A mobile phone short message service improves perceived control of asthma: a randomized controlled trial. Telemedicine and e-Health18(6): 420–426.
47.
NischelwitzerAPintofflKLossC. (2007) Design and development of a mobile medical application for the management of chronic diseases: methods of improved data input for older people. In: 3rd Symposium of the Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society (ed HolzingerA), Graz, Austria, 22 November 2007, pp. 119–132. New York: Springer.
48.
NoarSMCarlyleKColeC (2006) Why communication is crucial: meta-analysis of the relationship between safer sexual communication and condom use. Journal of Health Communication11(4): 365–390.
49.
OwenNFotheringhamMJMarcusBH (2002) Communication technology and health behavior change. In: GlanzKRimerBKLewisFM (eds) Health Behavior and Health Education: Theory Research and Practice. San Francisco: Jossey-Bass, pp. 510–529.
50.
ParkN (2010) Adoption and use of computer based voice over internet protocol phone service: toward an integrated model. Journal of Communication60(1): 40–72.
51.
ParkNLeeKMCheongPH (2007) University instructors acceptance of electronic courseware: an application of the technology acceptance model. Journal of Computer-Mediated Communication13(1): 163–186.
52.
PenaVWatsonAJKvedarJC. (2009) Mobile phone technology for children with type 1 and type 2 diabetes: a parent survey. Journal of Diabetes Science and Technology3(6): 1481–1489.
QinLKimYHsuJ. (2011) The effects of social influence on user acceptance of online social networks. International Journal of Human-Computer Interaction27(9): 885–899.
55.
RedmondNBaerHJClarkCR. (2010) Sources of health information related to preventive health behaviors in a national study. American Journal of Preventive Medicine38(6): 620–627.
56.
RogersEM (1995) Diffusion of Innovations. New York: Free Press.
57.
RojasHShahDVFriedlandLA (2011) A communicative approach to social capital. Journal of Communication61(4): 689–712.
58.
SchifterDEAjzenI (1985) Intention, perceived control, and weight loss: an application of the theory of planned behavior. Journal of Personality and Social Psychology49(3): 843–851.
ShahDVMcLeodJMLeeN-J (2009) Communication competence as a foundation for civic competence: processes of socialization into citizenship. Political Communication26(1): 102–117.
61.
SnuggsSMcRobbieHMyersK. (2012) Using text messaging to prevent relapse to smoking: intervention development, practicability and client reactions. Addiction107(S2): 39–44.
62.
SparksPHedderleyDShepherdR (1992) An investigation into the relationship between perceived control, attitude variability and the consumption of two common foods. European Journal of Social Psychology22(1): 55–71.
63.
SvendsenGBSøholtYMunch-EllingsenA. (2009) The importance of being useful and fun: factors influencing intention to use a mobile system motivating for physical activity. In: 42nd Hawaii International Conference on System Sciences (ed SpragueRHJr), Big Island, USA, 5–8 January 2009, pp. 1–10. New York: IEEE.
64.
SwansonEB (1988) Information System Implementation: Bridging the Gap Between Design and Utilization. Homewood: Irwin.
65.
TsaiCCLeeGRaabF. (2007) Usability and feasibility of PmEB: a mobile phone application for monitoring real time caloric balance. Mobile Networks and Applications12(2–3): 173–184.
66.
ValenzuelaSParNKeeKF (2009) Is there social capital in a social network site?: facebook use and college students life satisfaction, trust, and participation. Journal of Computer-Mediated Communication14(4): 875–901.
67.
Van den PutteBYzerMSouthwellBG. (2011) Interpersonal communication as an indirect pathway for the effect of antismoking media content on smoking cessation. Journal of Health Communication16(5): 470–485.
68.
VenkateshVMorrisMGDavisGB. (2003) User acceptance of information technology: toward a unified view. MIS Quarterly27(3): 425–478.
69.
WartellaERideoutVZupancicH. (2015) Teens, health, and technology: a national survey. Report, Northwest University, USA, June.
70.
WeinerJP (2012) Doctor-patient communication in the e-health era. Israel Journal of Health Policy Research1: 33.
71.
WillemsRAWillemsenMCNagelhoutGE. (2013) Understanding smokers’ motivations to use evidence-based smoking cessation aids. Nicotine & Tobacco Research15(1): 167–176.
72.
WuJ-HWangS-CLinL-M (2007) Mobile computing acceptance factors in the healthcare industry: a structural equation model. International Journal of Medical Informatics76(1): 66–77.
73.
XueLYenCCChangL. (2012) An exploratory study of ageing women’s perception on access to health informatics via a mobile phone-based intervention. International Journal of Medical Informatics81(9): 637–648.
74.
YordyGALentRW (1993) Predicting aerobic exercise participation: social cognitive, reasoned action, and planned behavior models. Journal of Sport & Exercise Psychology15(4): 363–374.
75.
ZiefleMKlackLWilkowskaW. (2013) Acceptance of telemedical treatments: a medical professional point of view. In: 15th International Conference on Human Interface and the Management of Information: Information and Interaction for Health, Safety, Mobility and Complex Environments (ed YamamotoS), Las Vegas, USA, 21–26 July 2013, pp. 325–334. New York: Springer.
