Ari J Elliot1, Christopher J Mooney2, Kathryn Z Douthit2, Martin F Lynch2. 1. Warner School of Education & Human Development, University of Rochester, New York. aelliot4@u.rochester.edu. 2. Warner School of Education & Human Development, University of Rochester, New York.
Abstract
OBJECTIVE: To extend the empirical evidence regarding the predictors of older adults' use of information and communications technology (ICT) and to further examine its relationship to depressive symptoms and well-being. METHOD: This cross-sectional study utilized a sample of community-dwelling older adults from the National Health and Aging Trends Study (N = 6,443). Structural equation modeling was used to estimate the effects of predictor variables on ICT use and the effects of use on depressive symptoms and well-being. Tests of moderation by demographic characteristics and level of ICT use were also performed. RESULTS: Socioeconomic status (SES), age, and cognitive function accounted for approximately 60% of the variance in ICT use. SES was a stronger predictor for Blacks/African Americans, whereas cognitive function was a stronger predictor for Whites. ICT use was unrelated to depressive symptoms or well-being. However, it acted as a moderator, such that limitations in activities of daily living (ADLs) was a stronger predictor of depressive symptoms for high ICT users, whereas ill-health was a stronger predictor for non/limited users. DISCUSSION: Findings do not support the claim that ICT use directly enhances mental health or well-being among older adults although it may protect against depressive symptoms for individuals coping with health conditions other than ADL impairments.
OBJECTIVE: To extend the empirical evidence regarding the predictors of older adults' use of information and communications technology (ICT) and to further examine its relationship to depressive symptoms and well-being. METHOD: This cross-sectional study utilized a sample of community-dwelling older adults from the National Health and Aging Trends Study (N = 6,443). Structural equation modeling was used to estimate the effects of predictor variables on ICT use and the effects of use on depressive symptoms and well-being. Tests of moderation by demographic characteristics and level of ICT use were also performed. RESULTS: Socioeconomic status (SES), age, and cognitive function accounted for approximately 60% of the variance in ICT use. SES was a stronger predictor for Blacks/African Americans, whereas cognitive function was a stronger predictor for Whites. ICT use was unrelated to depressive symptoms or well-being. However, it acted as a moderator, such that limitations in activities of daily living (ADLs) was a stronger predictor of depressive symptoms for high ICT users, whereas ill-health was a stronger predictor for non/limited users. DISCUSSION: Findings do not support the claim that ICT use directly enhances mental health or well-being among older adults although it may protect against depressive symptoms for individuals coping with health conditions other than ADL impairments.
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