JMIR Publications

ABSTRACT

Background:

Multiple types of mobile health (mhealth) technologies are available, such as smartphone health apps, fitness trackers, and digital medical devices. Despite their availability, some individuals don’t own or use these technologies. Others may use the devices, but use varies in tracking health, behaviors, and goals. Examining patterns of mhealth use at the population level can advance our understanding of technology use for health and behavioral tracking. Moreover, investigating sociodemographic and health-related correlates of these patterns can provide direction to researchers about how to target mhealth interventions for diverse audiences.

Objective:

The objective of this study is to identify patterns of mhealth use in the U.S. population and to characterize the population according to those patterns.

Methods:

We combined data from the 2017 and 2018 National Cancer Institute Health Information National Trends Survey (N=6,789) to characterize respondents according to five mutually exclusive reported patterns of mhealth use. (1) “mHealth non-users” report not owning or using devices to track health, behaviors, or goals. (2) “Supertrackers” track health or behaviors and goals using a smartphone/tablet plus other devices (e.g., Fitbit), (3) “app trackers” use only a smartphone/tablet, and (4) “device trackers” use only non-smartphone/non-tablet devices and don’t track goals. Finally, (5) “non-trackers” report having smartphone/tablet health apps but don’t track goals. Weighted logistic regression was used to examine sociodemographic and health-related correlates of patterns of mhealth use.

Results:

mHealth non-users comprised the largest proportion of the U.S. adult population (34.72%), followed by supertrackers (23.93%), app trackers (19.40%), device trackers (12.84%), and non-trackers (8.76%). Characteristics of device trackers and supertrackers were most distinctive. Compared to supertrackers, device trackers have higher odds of being male (OR=2.22, 95% CI=1.55, 3.19), older age (vs. 18-34 years; 50-64 years: OR=2.83, 95% CI=1.52, 5.30; 65+ years: OR=6.28, 95% CI=3.35, 11.79), annual household income $20,000-$49,999 (vs. $75,000+) (OR=2.31, 95% CI=1.36, 3.91), a chronic condition (OR=1.69, 95% CI=1.14, 2.49), and not being health information seekers (OR=2.98, 95% CI=1.66, 5.33). They have lower odds of being non-Hispanic black and “other” race/ethnicity (vs. white, non-Hispanic: OR=0.48, 95% CI=0.31, 0.74 and OR=0.54, 95% CI=0.30, 0.99, respectively), overweight (OR=0.58, 95% CI=0.38, 0.88), and obese (OR=0.55, 95% CI=0.35, 0.86). Being a mhealth non-user (vs. any type of use) was also associated with males, older age, lower income, and not being a health information seeker.

Conclusions:

Findings revealed distinctive sociodemographic and health-related characteristics of the population by pattern of mhealth use, with notable contrasts between those who do and do not use devices to track goals. Several characteristics of individuals who track health or behaviors but not goals (device trackers) are similar to mhealth non-users. Our results suggest patterns of mhealth use may inform how to target mhealth interventions to enhance reach and facilitate healthy behaviors.