Dataset Information

Social Inequalities and Loneliness as Predictors of Ageing Well: A Trend Analysis Using Mixed Models.

ABSTRACT: Background: This study examines if education, income, and loneliness are associated with physical functioning and optimism in an ageing population in Germany. Furthermore, time trends of physical functioning and optimism as well as of associations with social inequality and loneliness are analyzed. Methods: The German Ageing Survey (DEAS), a longitudinal population-based survey of individuals aged 40 years and older, was used (four waves between 2008 and 2017, total sample size N = 23,572). Physical functioning and optimism were introduced as indicators of ageing well. Educational level, net equivalent income, and loneliness were used as predictors in linear mixed models for longitudinal data. Results: Time trends show that physical functioning decreases over time, while optimism slightly increases. Education and income are positively associated with physical functioning, while higher loneliness correlates with lower physical functioning. Higher optimism was associated with higher income and particularly with lower loneliness. Income and notable educational inequalities in physical functioning increase over time. Time trends of the associations with optimism show decreasing income inequalities and increasing disparities in loneliness. Conclusions: Increasing educational inequalities in physical functioning and a strong association of loneliness with optimism provide information for further interventions. Targeted health promotion among the aged and addressing maladaptive social cognition are options to tackle these issues. Key areas for action on healthy ageing include, for instance, the alignment of health systems to the needs of older populations or the creation of age-friendly environments.

SUBMITTER: Klein J

PROVIDER: S-EPMC7432820 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Social Inequalities and Loneliness as Predictors of Ageing Well: A Trend Analysis Using Mixed Models.

Klein Jens J von dem Knesebeck Olaf O Lüdecke Daniel D

International journal of environmental research and public health 20200723 15

Background: This study examines if education, income, and loneliness are associated with physical functioning and optimism in an ageing population in Germany. Furthermore, time trends of physical functioning and optimism as well as of associations with social inequality and loneliness are analyzed. Methods: The German Ageing Survey (DEAS), a longitudinal population-based survey of individuals aged 40 years and older, was used (four waves between 2008 and 2017, total sample size ...[more]

PMID: 32718010

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Project description:BackgroundLoneliness and social isolation are associated with multiple health problems, including depression, functional impairment, and death. Mobile sensing using smartphones and wearable devices, such as fitness trackers or smartwatches, as well as ambient sensors, can be used to acquire data remotely on individuals and their daily routines and behaviors in real time. This has opened new possibilities for the early detection of health and social problems, including loneliness and social isolation.ObjectiveThis scoping review aimed to identify and synthesize recent scientific studies that used passive sensing techniques, such as the use of in-home ambient sensors, smartphones, and wearable device sensors, to collect data on device users' daily routines and behaviors to detect loneliness or social isolation. This review also aimed to examine various aspects of these studies, especially target populations, privacy, and validation issues.MethodsA scoping review was undertaken, following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Studies on the topic under investigation were identified through 6 databases (IEEE Xplore, Scopus, ACM, PubMed, Web of Science, and Embase). The identified studies were screened for the type of passive sensing detection methods for loneliness and social isolation, targeted population, reliability of the detection systems, challenges, and limitations of these detection systems.ResultsAfter conducting the initial search, a total of 40,071 papers were identified. After screening for inclusion and exclusion criteria, 29 (0.07%) studies were included in this scoping review. Most studies (20/29, 69%) used smartphone and wearable technology to detect loneliness or social isolation, and 72% (21/29) of the studies used a validated reference standard to assess the accuracy of passively collected data for detecting loneliness or social isolation.ConclusionsDespite the growing use of passive sensing technologies for detecting loneliness and social isolation, some substantial gaps still remain in this domain. A population heterogeneity issue exists among several studies, indicating that different demographic characteristics, such as age and differences in participants' behaviors, can affect loneliness and social isolation. In addition, despite extensive personal data collection, relatively few studies have addressed privacy and ethical issues. This review provides uncertain evidence regarding the use of passive sensing to detect loneliness and social isolation. Future research is needed using robust study designs, measures, and examinations of privacy and ethical concerns.

Dataset Information

Social Inequalities and Loneliness as Predictors of Ageing Well: A Trend Analysis Using Mixed Models.

Publications

Social Inequalities and Loneliness as Predictors of Ageing Well: A Trend Analysis Using Mixed Models.

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