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Cited by 540Mobile Health and mHealth ApplicationsHealth, Technology, Consumer BehaviorMobile Learning in Education

Who uses mobile phone health apps and does use matter? A secondary data analytics approach

Open access
Carroll, Jennifer K, Moorhead, Anne, Bond, Raymond, LeBlanc, William G, Petrella, Robert J, Fiscella, Kevin·2017·Journal of Medical Internet Research
DOI 10.2196/jmir.5604PMC PMC5415654
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Abstract

BACKGROUND: Mobile phone use and the adoption of healthy lifestyle software apps ("health apps") are rapidly proliferating. There is limited information on the users of health apps in terms of their social demographic and health characteristics, intentions to change, and actual health behaviors. OBJECTIVE: The objectives of our study were to (1) to describe the sociodemographic characteristics associated with health app use in a recent US nationally representative sample; (2) to assess the attitudinal and behavioral predictors of the use of health apps for health promotion; and (3) to examine the association between the use of health-related apps and meeting the recommended guidelines for fruit and vegetable intake and physical activity. METHODS: Data on users of mobile devices and health apps were analyzed from the National Cancer Institute's 2015 Health Information National Trends Survey (HINTS), which was designed to provide nationally representative estimates for health information in the United States and is publicly available on the Internet. We used multivariable logistic regression models to assess sociodemographic predictors of mobile device and health app use and examine the associations between app use, intentions to change behavior, and actual behavioral change for fruit and vegetable consumption, physical activity, and weight loss. RESULTS: From the 3677 total HINTS respondents, older individuals (45-64 years, odds ratio, OR 0.56, 95% CI 0.47-68; 65+ years, OR 0.19, 95% CI 0.14-0.24), males (OR 0.80, 95% CI 0.66-0.94), and having degree (OR 2.83, 95% CI 2.18-3.70) or less than high school education (OR 0.43, 95% CI 0.24-0.72) were all significantly associated with a reduced likelihood of having adopted health apps. Similarly, both age and education were significant variables for predicting whether a person had adopted a mobile device, especially if that person was a college graduate (OR 3.30). Individuals with apps were significantly more likely to report intentions to improve fruit (63.8% with apps vs 58.5% without apps, P=.01) and vegetable (74.9% vs 64.3%, P<.01) consumption, physical activity (83.0% vs 65.4%, P<.01), and weight loss (83.4% vs 71.8%, P<.01). Individuals with apps were also more likely to meet recommendations for physical activity compared with those without a device or health apps (56.2% with apps vs 47.8% without apps, P<.01). CONCLUSIONS: The main users of health apps were individuals who were younger, had more education, reported excellent health, and had a higher income. Although differences persist for gender, age, and educational attainment, many individual sociodemographic factors are becoming less potent in influencing engagement with mobile devices and health app use. App use was associated with intentions to change diet and physical activity and meeting physical activity recommendations.

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Crossref · 10.2196/jmir.5604

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  • Having a better education level, such as high school education or higher and college degree or higher, was associated with better engagement with health and well-being apps.

    “Living in an urban area [42,44,55]; having a better education level, such as having high school education or higher [41,42,44,64] and college degree or higher [41,53]; and having a higher income [44] were also associated with better engagement with health and well-being apps.”

    From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [44]

    The Results section, “Predicting Health App Adoption Only” (page unavailable), reports higher odds of health-app adoption for people with some college education or a college degree, and lower odds for those with less than a high-school education. It also says that completing high school only had no predictive ability, so the claim is too broad if “high school education or higher” implies an association at every level in that group. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Living in an urban area was associated with better engagement with health and well-being apps.

    “Living in an urban area [42,44,55]; having a better education level, such as having high school education or higher [41,42,44,64] and college degree or higher [41,53]; and having a higher income [44] were also associated with better engagement with health and well-being apps.”

    From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [44]

  • Being younger than 44 years was associated with a higher level of uptake and engagement than older adults.

    “Being younger than 44 years was associated with a higher level of uptake and engagement [38,41,42,44,49,53,55,63,64] than older adults.”

    From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [44]

  • Having a higher income was associated with better engagement with health and well-being apps.

    “Living in an urban area [42,44,55]; having a better education level, such as having high school education or higher [41,42,44,64] and college degree or higher [41,53]; and having a higher income [44] were also associated with better engagement with health and well-being apps.”

    From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [44]

  • Volitional processes are especially important for mHealth apps because users typically report preexisting intentions to change.

    “Volitional processes are especially important with regard to mobile health (mHealth) apps because users of such apps typically report preexisting intentions to change (11).”

    From Which Components of a Smartphone Walking App Help Users to Reach Personalized Step Goals? Results From an Optimization Trial (2020) · cites this paper as [11]

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Authors
Carroll, J. K., Moorhead, A., Bond, R., LeBlanc, W. G., Petrella, R. J., & Fiscella, K.
Affiliations
  • University of Colorado Anschutz (US)
  • University of Ulster (GB)
  • Western University (CA)
  • University of Rochester Medicine (US)
Corresponding author
Carroll, Jennifer K
ORCID
Venue
Journal of Medical Internet Research
Publisher
JMIR Publications Inc.
Published
2017-04-19
Volume
19
Issue
4
Pages
e125
ISSN
1438-8871 (ISSN-L)
DOI
10.2196/jmir.5604
PMC
PMC5415654
OpenAlex
W2607303733
Cited by
540
References
15
Citations by year
2026: 28 · 2025: 46 · 2024: 49 · 2023: 56 · 2022: 81 · 2021: 78 · 2020: 82 · 2019: 60 · 2018: 49 · 2017: 11
FWCI
89.24
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Medical Internet Research (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Ulster University Research Portal (Ulster University) (submittedVersion, cc-by)
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
  • Physical Sciences › Computer Science › Information Systems › Mobile Learning in Education
Keywords
mobile health apps, sociodemographic predictors, fruit and vegetable intake, physical activity, weight loss intentions, health behavior change, mobile device use, educational attainment, age differences, Health Information National Trends Survey, digital health equity, health disparities, health information seeking
MeSH terms
Adolescent, Adult, Humans, Male, Health Behavior, Internet, Cell Phone, Young Adult, Mobile Applications

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