Who Uses Mobile Phone Health Apps and Does Use Matter? A Secondary Data Analytics Approach

Abstract

BackgroundMobile 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.ObjectiveThe 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.MethodsData 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.ResultsFrom 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).ConclusionsThe 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.

Differences in mobile health app use: A source of new digital inequalities?

“…In the case of mobile technologies, there is evidence that certain population segments such as younger, higher educated men are more likely to have access to them (Statistics Netherlands 2016; Rice and Pearce 2015;Carroll et al 2017). On the other hand, it has been argued that mobile technology use may be less associated with the old digital divides than Internet use because it is more affordable (Rice and Katz 2003;James 2009).…”

“…Because individuals' mobile activities and motivations to use mobile health apps are extremely varied, it is challenging to investigate their use through traditional surveys. Consequently, research has primarily measured mobile health app use by examining one specific health app in depth or by looking at aggregated mobile health use, for instance by assessing general usage of health apps as one outcome variable (e.g., Lee and Rho 2013;Carroll et al 2017). Although different mobile health apps might provide similar features, they also offer distinct features addressing different goals and targeting different groups that could affect usage.…”

Media Health Literacy, eHealth Literacy, and the Role of the Social Environment in Context

“…56,57], and similar findings have been summarized for eHealth Literacy and Media Health Literacy [3] in earlier sections of this article. This may be the result of education acting as a SES proxy [22], as well as skill sets developed through educational settings in the lifespan, particularly following secondary school. This standpoint is supported by scholars who closely link the development of health literacy to school health education [5,58].…”

“…Regarding the use of eHealth tools among ethnic minorities, the data is inconclusive. According to recent studies, as opposed to previous ones, no significant differences between groups have been evidenced [22]. Yet the cultural context of eHealth literacy including mobile health (mHealth) has been recognized [23].…”

Oncology Patient Interest in the Use of New Technologies to Manage Their Disease: Cross-Sectional Survey (Preprint)

“…The use of wireless, portable devices (smartphones, tablets, wearables, and smartwatches) facilitates the collection, processing, and transmission of patient health information through mobile applications (apps) and remote monitoring devices. All these features can present several advantages, such as developing the active role of patients in their own health care and affording health professionals real-time access to patient clinical information [5][6][7][8].…”