Background The aim of this study was to investigate the relationship of addictive use of social media (AUSM) with depressive symptoms, perceived social support and demographic variables among people aged 65 years and older. Methods People aged 65 years and older who use social media constituted the study sample. The data were obtained from social networking sites via Google survey link. Bergen social media addiction scale (BSMAS) for determining AUSM, Multidimensional Scale of Social Support for determining social support, Geriatric Depression Scale to identify depressive symptoms and sociodemographic data form were administered to the participants. Results The mean age of the sample was 68.86 ± 2.0 years. AUSM showed significant differences depending on gender, marital status, economic status, educational level, settlement, occupation, and time spent in social media (P = 0.00). AUSM had correlations with both sub‐dimensions of perceived social support and depressive symptoms (P < 0.01). In the regression analysis, it was found that the depressive symptoms, social support from family (P = 0.00) and from a significant other (P = 0.001) had significant effects on AUSM. Conclusions When evaluating elderly individuals with depressive symptoms, it is important to evaluate these individuals in terms of social media addiction. Interventions to improve social support systems, especially for older people with little perceived social support can help prevent the development of AUSM.
Background The aim of the present study was to explore the relationship between addictive smartphone use (ASU) and depressive symptoms, anxiety and sleep quality in elderly adults. Methods The study sample included smartphone users over the age of 65 years. The research data were obtained from social networking sites via a Google survey link. In addition to filling out a sociodemographic data form, the participants were also assessed with Smartphone Addiction Scale (SAS), Geriatric Depression Scale, Beck Anxiety Inventory and Pittsburgh Sleep Quality Index tools. Results The correlation analysis revealed the SAS score to be positively correlated with depression and anxiety, and negatively correlated with sleep quality. In the regression analysis, depressive symptoms, anxiety level and sleep quality were all found to have an effect on the SAS total score. Furthermore, the SAS score was found to have an effect on depressive symptoms, anxiety and sleep quality. Conclusions Our findings reveal a bidirectional relationship between ASU and depressive, anxiety symptoms and impaired sleep quality in elderly adults. It is important to question smartphone use patterns in people with sleep problems, symptoms of depression or anxiety.
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