Who are mobile app users from healthy lifestyle websites? Analysis of patterns of app use and user characteristics
- PMID: 28929368
- PMCID: PMC5684086
- DOI: 10.1007/s13142-017-0525-x
Who are mobile app users from healthy lifestyle websites? Analysis of patterns of app use and user characteristics
Abstract
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.
Keywords: Healthy lifestyle websites; Individual differences; Mobile app users; Smartphones.
Conflict of interest statement
Conflict of interest
The authors declare that they have no conflicts of interest.
Human and animal rights and informed consent
Informed consent (implied by survey submission) was obtained from all individual participants included in the study.
This article does not contain any studies with animals performed by any of the authors.
Similar articles
-
Use of Weight-Management Mobile Phone Apps in Saudi Arabia: A Web-Based Survey.JMIR Mhealth Uhealth. 2019 Feb 22;7(2):e12692. doi: 10.2196/12692. JMIR Mhealth Uhealth. 2019. PMID: 30794205 Free PMC article.
-
Individual and Parental Factors of Adolescents' mHealth App Use: Nationally Representative Cross-sectional Study.JMIR Mhealth Uhealth. 2022 Dec 16;10(12):e40340. doi: 10.2196/40340. JMIR Mhealth Uhealth. 2022. PMID: 36525286 Free PMC article.
-
Who Uses Mobile Phone Health Apps and Does Use Matter? A Secondary Data Analytics Approach.J Med Internet Res. 2017 Apr 19;19(4):e125. doi: 10.2196/jmir.5604. J Med Internet Res. 2017. PMID: 28428170 Free PMC article.
-
The multi-faceted usage patterns of nutrition apps: a survey on the appropriation of nutrition apps among German-speaking users of MyFitnessPal.BMC Med Inform Decis Mak. 2020 Oct 28;20(1):279. doi: 10.1186/s12911-020-01294-9. BMC Med Inform Decis Mak. 2020. PMID: 33115444 Free PMC article.
-
Data Collection Mechanisms in Health and Wellness Apps: Review and Analysis.JMIR Mhealth Uhealth. 2022 Mar 9;10(3):e30468. doi: 10.2196/30468. JMIR Mhealth Uhealth. 2022. PMID: 35262499 Free PMC article. Review.
Cited by
-
Users' Perspective on the AI-Based Smartphone PROTEIN App for Personalized Nutrition and Healthy Living: A Modified Technology Acceptance Model (mTAM) Approach.Front Nutr. 2022 Jul 1;9:898031. doi: 10.3389/fnut.2022.898031. eCollection 2022. Front Nutr. 2022. PMID: 35879982 Free PMC article.
-
Effect of mHealth With Offline Antiobesity Treatment in a Community-Based Weight Management Program: Cross-Sectional Study.JMIR Mhealth Uhealth. 2020 Jan 21;8(1):e13273. doi: 10.2196/13273. JMIR Mhealth Uhealth. 2020. PMID: 31961335 Free PMC article.
-
Long-term Effectiveness of a Smartphone App Combined With a Smart Band on Weight Loss, Physical Activity, and Caloric Intake in a Population With Overweight and Obesity (Evident 3 Study): Randomized Controlled Trial.J Med Internet Res. 2022 Feb 1;24(2):e30416. doi: 10.2196/30416. J Med Internet Res. 2022. PMID: 35103609 Free PMC article. Clinical Trial.
-
New organisation for follow-up and assessment of treatment efficacy in sleep apnoea.Eur Respir Rev. 2019 Sep 11;28(153):190059. doi: 10.1183/16000617.0059-2019. Print 2019 Sep 30. Eur Respir Rev. 2019. PMID: 31511256 Free PMC article. Review.
-
The Use of Digital Platforms for Adults' and Adolescents' Physical Activity During the COVID-19 Pandemic (Our Life at Home): Survey Study.J Med Internet Res. 2021 Feb 1;23(2):e23389. doi: 10.2196/23389. J Med Internet Res. 2021. PMID: 33481759 Free PMC article.
References
-
- 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.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical