Your browser doesn't support javascript.
Digital behaviors of older adults: An examination of social media characteristics, social support, and depression during the COVID-19 pandemic
Dissertation Abstracts International: Section B: The Sciences and Engineering ; 83(9-B):No Pagination Specified, 2022.
Article in English | APA PsycInfo | ID: covidwho-1958002
ABSTRACT
The population of the United States is aging. Depression in older adults has serious consequences. Social support has long been recognized as a beneficial and protective factor in the prevention of depression in older adults. Social media have allowed older adults to connect with their social networks and social support systems. The use of social media was highlighted during the coronavirus disease (COVID-19) pandemic as social distancing and self-quarantine discouraged face-to-face interactions. While social media behaviors and their influence on mental health is a growing area of research, the digital behaviors of an aging population specific to the relationship between their social media behavior, social network structures, social support, and depression under the context of the Coronavirus pandemic of 2019 (COVID-19) is understudied. This dissertation's overall purpose is to explore the relationship between social media use, social support, and depression in older adults. The First Manuscript is a review of the literature aimed at identifying and synthesizing quantitative studies addressing the relationship between social media use and depression in older adults. The findings of this review showed a dearth of literature and a complicated relationship between social media use and depression in older adults. Furthermore, there is a paucity of studies that use validated measures to measure social media use in the older adult population. Age-related health and social variables could potentially influence the relationship between social media use and depression. Further, studies of current social media use measurements in older adults omit descriptions of social network characteristics to include social network structure and function. The Second Manuscript is a cross-sectional study of 371 older adults. Using multiple mediation models, this study examines the mediating effect of social support in the relationship of social network structures (online and offline) and depression in older adults. This study found that social support does not significantly mediate the relationship between online social network structure and depression. However, social support mediated the relationship between offline social network structure and depression to some extent. The tangible and emotional/informational social support domains did not mediate the known relationship between network structure and depression in older adults. Both structural sizes of online and offline social network size did not show any significant relationship to depression. The size of the offline social network, not the online social network size, predicted higher levels of social support. Higher total social support scores predicted lower depression scores in both online and offline network size models. Online and offline social network size models showed that increased social support predicts lower depression scores on all social support scales except the emotional/informational subscale. Certain domains of social support did not mediate the known relationship between offline social network structure and depression in older adults in the context of the COVID-19 pandemic. The Third Manuscript examined the mediating effect of social support in the relationship between social media use and depression in older adults, using the Social Media Use Integration Scale. This study showed that social support did not significantly mediate the relationship between social media use and depression in older adults in models that controlled for demographic and health covariates. Greater social media use significantly predicted higher depression scores in older adults when including only basic demographic covariates (beta = .070, se = .333, p = .037). (PsycInfo Database Record (c) 2022 APA, all rights reserved)
Keywords
Search on Google
Collection: Databases of international organizations Database: APA PsycInfo Language: English Journal: Dissertation Abstracts International: Section B: The Sciences and Engineering Year: 2022 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS

Search on Google
Collection: Databases of international organizations Database: APA PsycInfo Language: English Journal: Dissertation Abstracts International: Section B: The Sciences and Engineering Year: 2022 Document Type: Article