1. Conclusions & Implications
Technology Use and Depressive Symptoms in Older Adults
Ari J. Elliot, M.S., Christopher J. Mooney, M.A., M.P.H., Kathryn Z. Douthit, Ph.D
Warner School of Education & Human Development, University of Rochester
Data and measures:
Data from the National Health and Aging Trends study (NHATS)
A continuous variable representing use of Information & Communications Technology (ICT) over the past month combined:
reported frequency of ing/texting (0=not at all; 1=rarely, 2=sometimes, 3=often)
use of computer/Internet (0=did not have or use a computer; 1=used computer, did not go on the Internet; 2=went on the Internet; 3=used Internet for both shopping/banking and health-related purposes).
Binary variables: frequency of /text (never/rarely vs. sometimes/ often) and whether or not went online in past month.
Data analysis:
Using wave 1 data (unweighted N=6,451), linear regression analysis was used to investigate effects of technology use on depressive symptoms, as well as interactions with ill-health and ADL limitations. Analyses were also stratified by gender and race/ethnicity. A linear regression analysis (N=5,034, 78% of initial sample) predicting wave 2 depressive symptoms, controlling for wave 1, was also conducted
Binary logistic regressions predicting odds of depression (defined as exceeding the clinical cutoff on the PHQ-2) at Time 1, as well as odds of incident depression at wave 2 (among the 87% of sample not meeting criterion at Time 1), were conducted. Cox regression analysis was also employed given varying time to event.
Covariates included age, gender, race/ethnicity, education, income, ill-health (self-rated health and chronic disease), having 1 or more limitations ADLs, self-rated memory, and social integration.
Cross sectional analyses:
Technology use was stable over a 1-year period (r = .90)
A continuous measure of technology use was not related to depressive symptoms at Time 1
High technology use ( ed/texted sometimes or often and went online in past month) predicted fewer depressive symptoms and reduced odds of depression at Time 1
Depressogenic effects of ill-health were considerably smaller for high technology users
Technology use, measured continuously and categorically, was marginally associated with lower odds of depression at Time 2
To further investigate interactions of technology use with other variables in predicting depressive symptoms by examining different types of use
To investigate whether technology use is related to odds of baseline and incident depression
Background
Use of technology may protect or enhance older adults’ mental health (e.g., Slegers et al., 2008):
use for communication purposes may result in increased social support and engagement and reduced loneliness
other uses (e.g., shopping, health information or services) may preserve a sense of autonomy
may provide opportunities for leisure and entertainment
Findings as to effects of technology use on depression and well- being in older adults are inconsistent
computer and/or Internet use related to greater well-being, lower depression and loneliness (Chen & Persson, 2002; Choi, Kong, & Jung, 2012; Cotten, Ford, Ford, & Hale, 2012; Karavidas et al., 2005; Koopman-Boyden & Reid, 2009)
a recent longitudinal study using the Health & Retirement Study (HRS; Cotton, Ford, Ford, & Hale, 2014) reported that regular Internet use reduced the probability of depression by about 33%
other correlational and intervention studies have found no effects on depression (Billipp, 2001; Choi et al., 2012; Fokkema & Knipscheer, 2007; Slegers et al., 2008; White et al., 2002)
Generalizability of several study results is limited by small, non-representative samples
Technology use may interact with other variables in predicting depressive symptoms
In a recent study using NHATS data (Elliot, Mooney, Douthit, & Lynch, 2014), limitations in activities of daily living (ADLs) had stronger effects on depressive symptoms for high technology users ( ed/texted and went online in the past month), ill-health had stronger effects for non/limited users.
ICT use interacted with ill-health, though not with having at least one ADL limitation. Interaction of high technology use with ill-health shown in Figure 1. Interaction was significant in both genders and in Whites and Hispanics, but not African-Americans
Conclusions & Implications
Findings converge with other recent studies in suggesting that technology use reduces odds of depression in older adults
Technology use may be most protective of depressive symptoms among individuals in poorer health
Further research is needed to examine whether technology use is related to risk of depression over longer time periods, the specific uses that are most beneficial, and the underlying mechanisms
Limitations
Use of PHQ-2, a two-item depression screener
Examined changes in depression over a limited period of time
Causality can not be directly inferred
High technology use associated with reduced odds of depression at Time 1 (Table 2)
Results
Results
Longitudinal analyses:
Round 2 nonrespondents were older (p<.001) and had lower ICT scores (p<.001, d=.33). ICT use at waves 1 and 2, r = .90.
No effect of technology use (continuous or binary) on depressive symptoms (continuous) at wave 2 controlling for wave 1 (not shown)
ICT use and high technology use were marginally associated with lower odds of depression at Time 2 (Table 2). Similar results were obtained in a Cox proportional hazards model.
Approximately 33.5% of the sample ed/texted at least sometimes and went online in the past month (“high technology users”)
One-way ANOVAs indicated expected mean differences in ICT use across categories by age and education (p < .001). High technology users had higher income (Cohen’s d = .78), self-rated health (d=.55), self-rated memory (d=.43) and social integration (d=.75), and lower levels of chronic disease (d=.20), p < .001.