Predictors of Older Adults’ Technology Use and Its Relationship to Depressive Symptoms and Well-being

Older adults’ use of information and communications technology (ICT), including computers, the Internet, and mobile communication devices (Selwyn, Gorard, Furlong, & Madden, 2003) has increased considerably over the past decade. For example, a recent survey estimated that 53% of adults aged 65 and older are Internet users (Zickuhr & Madden, 2012) compared with 22% in 2004 (Fox, 2004) although it is not clear to what extent such growth is due to the movement of higher use cohorts into this age demographic (i.e., cohort effects) versus growing use over time more generally (i.e., period effects). Cognizant of this trend, social scientists have sought to understand the specific purposes for which older adults use ICT, identify the factors that contribute to higher levels of use, and determine whether and how ICT use is related to mental health and well-being in this population. Research examining the sociodemographic correlates of ICT use among older adults has indicated that the average user is younger and has more education and income than the average nonuser (Fazeli, Ross, Vance, & Ball, 2013; Spooner & Rainie, 2000; Wagner, Hassanein, & Head, 2010). Differences across racial/ethnic groups have also been observed, with Whites reporting greater Internet access and use than Blacks/African Americans and Hispanics/Latinos (Czaja et al., 2006; Livingston, Parker, & Fox, 2009; Spooner & Rainie, 2000), a gap that may reflect corresponding disparities in socioeconomic status (SES). In addition, men in this age group have typically reported greater computer experience and Internet use than women (Czaja et al., 2006; Fallows, 2005; Karavidas, Lim, & Katsikas, 2005). Although age and educational attainment have consistently predicted older adults’ ICT use (Wagner et al., 2010), effects of gender and income have been less robust (Carpenter & Buday, 2007; Werner, Carlson, Jordan-Marsh, & Clark, 2011). In addition to sociodemographic characteristics, ICT use in this population has been related to attitudinal variables, including computer-specific anxiety, self-efficacy, and interest (Ellis & Allaire, 1999; Fazeli et al., 2013; Karavidas et al., 2005). Research has also supported a relationship between ICT use and cognitive function among older adults. Although score on a brief test of cognitive impairment was unrelated to the likelihood of computer use (Carpenter & Buday, 2007), multiple studies utilizing more extensive cognitive testing have shown that better performance is associated with greater computer use or experience (Fazeli et al., 2013; Tun & Lachman, 2010). In a longitudinal study (Freese, Rivas, & Hargittai, 2006), performance on a test of cognitive ability in high school predicted Internet use at age 65 although only for men. In contrast, results pertaining to associations with health and social network–related variables have been inconsistent (Carpenter & Buday, 2007; Gracia & Herrero, 2009; Werner et al., 2011). Furthermore, only one study to date has examined activities of daily living (ADLs), reporting a positive association between computer use and functional independence (Carpenter & Buday, 2007). Given several preliminary or inconsistent results and that low statistical power may be responsible for certain null findings, further empirical investigation is needed to understand the factors that promote or support ICT use among older adults (Freese et al., 2006). Findings may have implications for the design of computer/Internet training programs and other efforts to narrow the “digital divide” facing older adults and racial/ethnic minorities in particular (Czaja et al., 2006). However, the broader importance of these findings is also linked to the question of whether ICT use contributes to mental health and well-being in this population. As theorized in the literature, there are several mechanisms by which various uses of ICT may be conducive to the social, mental, and emotional well-being of older adults (Slegers, Boxtell, & Jolles, 2008). E-mail is the most common computer-based activity among older adults although their use of text messaging and social networking sites is increasing (Carpenter & Buday, 2007; Karavidas et al., 2005; Smith, 2011; Zickuhr & Madden, 2012). The use of ICT for communication purposes may provide greater opportunity for supportive social interaction (Cody, Dunn, Hoppin, & Wendt, 1999; Slegers et al., 2008), a consistent predictor of health and well-being in older adulthood (Fiori, Antonucci, & Cortina, 2006; Pinquart & Sorensen, 2000). Similarly, ICT use may help to reduce loneliness, an important risk factor for depressive symptoms in older adults (Barg et al., 2006; Cacioppo, Hughes, Waite, Hawkley, & Thisted, 2006). ICT may also be used for leisure and entertainment, as well as for instrumental purposes (i.e., seeking heath information, banking, and shopping), potentially increasing quality of life and preserving a sense of autonomy (Selwyn et al., 2003; Slegers et al., 2008). Despite the many hypothesized benefits of ICT use for older adults, the results of research examining its relationship to mental health and well-being in this population have been inconclusive. Studies have reported that computer and/or Internet use are related to greater perceived social support and connection (Cody et al., 1999), self-efficacy (Erickson & Johnson, 2011), life satisfaction (Karavidas et al., 2005; Shapira, Barak, & Gal, 2007), and psychological well-being (Chen & Persson, 2002; Koopman-Boyden & Reid, 2009; Shapira et al., 2007), as well as lower depression (Cotten, Ford, Ford, & Hale, 2012; McConatha, McConatha, & Dermigny, 1994; Shapira et al., 2007) and loneliness (Choi, Kong, & Jung, 2012). However, other observational and experimental studies with older adults have failed to find significant associations of ICT use with similar outcome variables (Billipp, 2001; Fazeli et al., 2013; Fokkema & Knipscheer, 2007; White et al., 2002). A randomized controlled study, for instance, found that learning to use a computer and the Internet over a 1-year period had no effect on measures of loneliness, mood, quality of life, and autonomy (Slegers et al., 2008). Dickinson and Gregor (2006) suggest that the inconsistent results in the extant literature are due to small and nonrepresentative study samples, misattributions of causality, and the confounding effect of social interactions in several intervention-based studies. The objective of this study was twofold: to extend the empirical evidence concerning the predictors of older adults’ ICT use and to clarify the relationship of ICT use to mental health and well-being in this population. We sought to build upon the existing literature in several ways. First, we used data from the National Health and Aging Trends Study (NHATS), thereby employing several methodological features that address limitations of prior studies, including the increased statistical power and generalizability afforded by a large and demographically diverse sample, a multifaceted measure of ICT use, and a relatively comprehensive set of predictor variables. Second, previous studies have typically conducted regression analyses examining technology use as either an independent or dependent variable. In this study, we employed a structural equation modeling (SEM) approach to simultaneously estimate the effects of predictor variables on ICT use, and in turn, the effects of use on depressive symptoms and well-being. SEM has several advantages over multiple regression techniques, including iterative model fit solutions, exclusion of error variance, and incorporation of latent variables (Byrne, 2010; Kline, 2011). Third, although prior studies have considered only whether level of ICT use differs by age, gender, and race/ethnicity, we also conducted test of moderation to determine whether the relationships of ICT use to predictor and outcome variables differ across these demographic lines. Finally, we are not aware of any prior studies that have examined interactions of ICT use with other variables in predicting mental health or well-being. Therefore, we examined ICT use as a potential moderator of associations between predictor variables and depressive symptoms.