ASPO Abstracts
Examining Patterns of mHealth Use Among Individuals Diagnosed with Cancer: Results from the 2017 and 2018 NCI Health Information National Trends Survey (HINTS)
Category: Behavioral Science & Health Communication
Conference Year: 2019
Abstract Body:
Purpose. The purpose of this study was to determine whether pattern of mhealth use differsamong individuals diagnosed with cancer versus those never diagnosed with cancer given thepenetration of mhealth technologies in the US and relevancy of mhealth for cancer care andsupport. Methods. HINTS data (2017 & 2018; N=6789) were examined to characterizerespondents from a nationally representative sample by reported mhealth usage patterns.Respondents ever diagnosed with cancer (n=1097) and those never diagnosed with cancer(n=5654) were categorized into 1 of 5 mhealth user types: nonusers (no health apps & don’ttrack health; n=1861); nontrackers (have health apps & don’t track health; n=508); apptrackers (track health goals by apps; n=882); device trackers (track health by otherdevices, e.g., Fitbit, glucometer; n=840); and supertrackers (track health goals by apps &track health by other devices; n=1287). Data were weighted and analyzed in SAS usinglogistic regression. Results. Compared to those never diagnosed, respondents diagnosedwith cancer were nearly twice as likely to be device trackers (OR=1.84, 95% CI=1.38, 2.44,p<.001) but less likely to be app trackers (OR=0.68, 95% CI=0.46, 0.10, p=.049). Therewere nonsignificant differences for all other patterns of mhealth use by cancer diagnosis.After sociodemographic and health-related covariates were included in regression models,cancer diagnosis no longer significantly predicted being a device tracker or an apptracker. Instead, being male, nonwhite, older, overweight/obese, having a chroniccondition (e.g., diabetes), poorer perceived health, and not being a health informationseeker predicted being a device tracker. In contrast, older age and having a chroniccondition negatively predicted being an app tracker. Conclusions. mHealth may beparticularly relevant to people diagnosed with cancer, however we found that diagnosedindividuals’ patterns of mhealth use were similar to usage patterns of those neverdiagnosed. Findings suggest that pattern of mheath use may be an important audiencesegmentation variable. Future mhealth interventions could consider acceptability andbenefit of these interventions by pattern of use since characteristics of mhealth usersvaries according to their usage pattern.
Keywords: mhealth, healthcommunication