Who are mobile app users from healthy lifestyle websites? Analysis of patterns of app use and user characteristics
Jazyk angličtina Země Anglie, Velká Británie Médium print
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
28929368
PubMed Central
PMC5684086
DOI
10.1007/s13142-017-0525-x
PII: 10.1007/s13142-017-0525-x
Knihovny.cz E-zdroje
- Klíčová slova
- Healthy lifestyle websites, Individual differences, Mobile app users, Smartphones,
- MeSH
- cvičení MeSH
- dospělí MeSH
- index tělesné hmotnosti MeSH
- internet * MeSH
- lidé MeSH
- logistické modely MeSH
- mladiství MeSH
- mladý dospělý MeSH
- mobilní aplikace * MeSH
- osobnost MeSH
- průřezové studie MeSH
- průzkumy a dotazníky MeSH
- telemedicína * metody MeSH
- tělesná hmotnost MeSH
- věkové faktory MeSH
- zdravý životní styl * MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
The use of online communities and websites for health information has proliferated along with the use of mobile apps for managing health behaviors such as diet and exercise. The scarce evidence available to date suggests that users of these websites and apps differ in significant ways from non-users but most data come from US- and UK-based populations. In this study, we recruited users of nutrition, weight management, and fitness-oriented websites in the Czech Republic to better understand who uses mobile apps and who does not, including user sociodemographic and psychological profiles. Respondents aged 13-39 provided information on app use through an online survey (n = 669; M age = 24.06, SD = 5.23; 84% female). Among users interested in health topics, respondents using apps for managing nutrition, weight, and fitness (n = 403, 60%) were more often female, reported more frequent smartphone use, and more expert phone skills. In logistic regression models, controlling for sociodemographics, web, and phone activity, mHealth app use was predicted by levels of excessive exercise (OR 1.346, 95% CI 1.061-1.707, p < .01). Among app users, we found differences in types of apps used by gender, age, and weight status. Controlling for sociodemographics and web and phone use, drive for thinness predicted the frequency of use of apps for healthy eating (β = 0.14, p < .05), keeping a diet (β = 0.27, p < .001), and losing weight (β = 0.33, p < .001), whereas excessive exercise predicted the use of apps for keeping a diet (β = 0.18, p < .01), losing weight (β = 0.12, p < .05), and managing sport/exercise (β = 0.28, p < .001). Sensation seeking was negatively associated with the frequency of use of apps for maintaining weight (β = - 0.13, p < .05). These data unveil the user characteristics of mHealth app users from nutrition, weight management, and fitness websites, helping inform subsequent design of mHealth apps and mobile intervention strategies.
Zobrazit více v PubMed
Pew. PEW Research Center/CHCF Health Survey. 2013. http://www.pewinternet.org/2013/02/12/the-internet-and-health/.
European Commission D-G for C, Networks C and T. Flash Eurobarometer 404 “European Citizens’ Digital Health Literacy.” 2014. http://ec.europa.eu/public_opinion/flash/fl_404_sum_en.pdf.
Kontos E, Blake KD, Chou W-YS, Prestin A. Predictors of eHealth usage: insights on the digital divide from the Health Information National Trends Survey 2012. J Med Internet Res. 2014;16(7):e172. doi: 10.2196/jmir.3117. PubMed DOI PMC
Graham AL, Papandonatos GD, Erar B, Stanton CA. Use of an online smoking cessation community promotes abstinence: results of propensity score weighting. Health Psychol. 2015;34(Suppl):1286–1295. doi: 10.1037/hea0000278. PubMed DOI PMC
Leist AK. Social media use of older adults: a mini-review. Gerontology. 2013;59(4):378–384. doi: 10.1159/000346818. PubMed DOI
Antypas K, Wangberg SC. An internet- and mobile-based tailored intervention to enhance maintenance of physical activity after cardiac rehabilitation: short-term results of a randomized controlled trial. J Med Internet Res. 2014;16(3):78–95. doi: 10.2196/jmir.3132. PubMed DOI PMC
Statista. Worldwide Mobile App Revenues 2020|Statistic. https://www.statista.com/statistics/269025/worldwide-mobile-app-revenue-forecast/.
Yang C-H, Maher JP, Conroy DE. Implementation of behavior change techniques in mobile applications for physical activity. Am J Prev Med. 2015;48(4):452–455. doi: 10.1016/j.amepre.2014.10.010. PubMed DOI
Conroy DE, Yang C-H, Maher JP. Behavior change techniques in top-ranked mobile apps for physical activity. Am J Prev Med. 2014;46(6):649–652. doi: 10.1016/j.amepre.2014.01.010. PubMed DOI
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. PubMed DOI PMC
Pew Research Center. Few Americans Track Their Weight, Diet or Exercise Online | Pew Research Center. 2014. http://www.pewresearch.org/fact-tank/2014/01/08/few-americans-track-their-weight-diet-or-exercise-online/.
Purcell K. Pew Internet Research. Half of adult cell phone owners have apps on their phones Americans’ appetite for apps continues to grow. 2011. http://www.pewinternet.org/files/old-media/Files/Reports/2011/PIP_Apps-Update-2011.pdf. Accessed December 1, 2016.¨.
European Commission. Green Paper on Mobile Health (“mHealth”); 2014. file:///C:/Users/SterianiElavsky/Downloads/GreenPaperonmobilehealth (1).pdf.
Schnall R, Rojas M, Bakken S, Brown W, Carballo-Dieguez A, Carry M, Gelaude D, Mosley JP, Travers J. A user-centered model for designing consumer mobile health (mHealth) applications (apps) J Biomed Inform. 2016;60:243–251. doi: 10.1016/j.jbi.2016.02.002. PubMed DOI PMC
Kim Y, Briley DA, Ocepek MG. Differential innovation of smartphone and application use by sociodemographics and personality. Comput Hum Behav. 2015;44:141–147. doi: 10.1016/j.chb.2014.11.059. DOI
Peñas-Lledó E, Bulik CM, Lichtenstein P, Larsson H, Baker JH. Risk for self-reported anorexia or bulimia nervosa based on drive for thinness and negative affect clusters/dimensions during adolescence: a three-year prospective study of the TChAD cohort. Int J Eat Disord. 2015;48(6):692–699. doi: 10.1002/eat.22431. PubMed DOI PMC
Dobmeyer AC, Stein DMA. Prospective analysis of eating disorder risk factors: drive for thinness, depressed mood, maladaptive cognitions, and ineffectiveness. Eat Behav. 2003;4(2):135–147. doi: 10.1016/S1471-0153(03)00013-8. PubMed DOI
Krebs P, Duncan DT. Health app use among US mobile phone owners: a national survey. JMIR mHealth uHealth. 2015;3(4):e101. doi: 10.2196/mhealth.4924. PubMed DOI PMC
Bhuyan SS, Lu N, Chandak A, et al. Use of mobile health applications for health-seeking behavior among US adults. J Med Syst. 2016; 40(6) 10.1007/s10916-016-0492-7. PubMed
Talmon J, Ammenwerth E, Brender J, de Keizer N, Nykänen P, Rigby M. STARE-HI—statement on reporting of evaluation studies in health informatics. Int J Med Inform. 2009;78(1):1–9. doi: 10.1016/j.ijmedinf.2008.09.002. PubMed DOI
Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13(3):319. doi: 10.2307/249008. DOI
Venkatesh V, Smith RH, Morris MG, Davis GB, Davis FD, Walton SM. User acceptance of information and technology: toward a unified view. MIS Q 2003; 27(3):425–478.
Kwon M-W, Mun K, Lee JK, Mcleod DM, D’angelo J. Is mobile health all peer pressure? The influence of mass media exposure on the motivation to use mobile health apps. Convergence: Int J Res New Media. 2016. 10.1177/1354856516641065.
Higgins O, Sixsmith J, Barry MM. A literature review on health information- seeking behaviour on the web: a health consumer and health professional perspective. A literature review on health information-seeking behaviour on the web. Stockholm: ECDC; 2011.10.2900/5788.
Viswanath K, Finnegan JR Jr, Gollust S. Communication and health behavior in a changing media environment. In: Glanz K, Rimer BK, Viswanath K, editors. Health Behavior: Theory, Research, and Practice. San Francisco, CA: Jossey-Bassy A Wiley Brand; 2015:327–48.
Baumgartner SE, Hartmann T. The role of health anxiety in online health information search. Cyberpsychol Behav Soc Netw. 2011;14(10):613–618. doi: 10.1089/cyber.2010.0425. PubMed DOI
Lahey BB. Public health significance of neuroticism. Am Psychol. 2009;64(4):241–256. doi: 10.1037/a0015309. PubMed DOI PMC
Kelders SM, Kok RN, Ossebaard HC, Van Gemert-Pijnen JE. Persuasive system design does matter: a systematic review of adherence to web-based interventions. J Med Internet Res. 2012;14(6):e152. doi: 10.2196/jmir.2104. PubMed DOI PMC
Ginters N. A review: how user characteristics affect the effectiveness of persuasive strategies in the health promotion domain of online interventions. (Master´s Thesis). The Netherlands: University of Twente, Positive Psychology and Technology; 2016.
Verkasalo H, López-Nicolás C, Molina-Castillo FJ, Bouwman H. Analysis of users and non-users of smartphone applications. Telematics Inform. 27:242–55. 10.1016/j.tele.2009.11.001.
The internet society. The global internet report. https://www.internetsociety.org/globalinternetreport/2016/wp-content/uploads/2016/11/ISOC_GIR_2016-v1.pdf. Accessed 1 April 2017.
Eurostat data. http://ec.europa.eu/eurostat/statistics-explained/index.php/Internet_access_and_use_statistics_-_households_and_individuals. Accessed 1 April 2017.
PEW data. http://www.pewglobal.org/2016/02/22/smartphone-ownership-and-internet-usage-continues-to-climb-in-emerging-economies/. Accessed 1 April 2017.
Czech Republic Just Tops Russia for Mobile Penetration—eMarketer. https://www.emarketer.com/Article/Czech-Republic-Just-Tops-Russia-Mobile-Penetration/1012047. Accessed 14 Dec 2016.
Lupac P, Chrobakova A, Sladek J. Internet in the Czech Republic 2014. Prague; 2015. http://worldinternetproject.com/_files/_//234_report_wip_czr2014_eng_fin.pdf. Accessed 12 Dec 2016.
Organisation for Economic Co-operation and Development (OECD). 2014. OECD Statistics. http://stats.oecd.org/Index.aspx?DataSetCode=HEALTH_STAT#. Accessed 1 April 2017.
World Health Organization. Nutrition, physical activity, and obesity: Czech Republic; 2013. http://www.euro.who.int/__data/assets/pdf_file/0005/243293/Czech-Republic-WHO-Country-Profile.pdf?ua=1. Accessed 1 April 2017.
Cole TJ, Lobstein T. Extended international (IOTF) body mass index cut-offs for thinness, overweight and obesity. Pediatr Obes. 2012;7(4):284–294. doi: 10.1111/j.2047-6310.2012.00064.x. PubMed DOI
Garner DM. Eating Disorder Inventory-3. Professional Manual. In: Lutz FL, ed. Psychological Assessment Resources, Inc. 2004.
Forbush KT, Wildes JE, Pollack LO, et al. Development and validation of the Eating Pathology Symptoms Inventory (EPSI) Psychol Assess. 2013;25(3):859–878. doi: 10.1037/a0032639. PubMed DOI
Stephenson MT, Hoyle RH, Palmgreen P, Slater MD. Brief measures of sensation seeking for screening and large-scale surveys. Drug Alcohol Depend. 2003;72(3):279–286. doi: 10.1016/j.drugalcdep.2003.08.003. PubMed DOI
Lang FR, John D, Lüdtke O, Schupp J, Wagner GG. Short assessment of the Big Five: robust across survey methods except telephone interviewing. Behav Res Methods. 2011;43(2):548–567. doi: 10.3758/s13428-011-0066-z. PubMed DOI PMC
Cook B, Engel S, Crosby R, Hausenblas H, Wonderlich S, Mitchell J. Pathological motivations for exercise and eating disorder specific health-related quality of life. Int J Eat Disord. 2014;47(3):268–272. doi: 10.1002/eat.22198. PubMed DOI PMC
Cook B, Hausenblas H, Crosby RD, Cao L, Wonderlich SA. Exercise dependence as a mediator of the exercise and eating disorders relationship: a pilot study. Eat Behav. 2015;16:9–12. doi: 10.1016/j.eatbeh.2014.10.012. PubMed DOI PMC
Claes L, Vandereycken W, Luyten P, Soenens B, Pieters G, Vertommen H. Personality prototypes in eating disorders based on the big five model. J Personal Disord. 2006;20(4):401–416. doi: 10.1521/pedi.2006.20.4.401. PubMed DOI