BackgroundThis paper has two objectives. Firstly, it provides an overview of the social network module, data collection procedures, and measurement of ego-centric and complete-network properties in the Korean Social Life, Health, and Aging Project (KSHAP). Secondly, it directly compares the KSHAP structure and results to the ego-centric network structure and results of the National Social Life, Health, and Aging Project (NSHAP), which conducted in-home interviews with 3,005 persons 57 to 85 years of age in the United States.MethodsThe structure of the complete social network of 814 KSHAP respondents living in Township K was measured and examined at two levels of networks. Ego-centric network properties include network size, composition, volume of contact with network members, density, and bridging potential. Complete-network properties are degree centrality, closeness centrality, betweenness centrality, and brokerage role.ResultsWe found that KSHAP respondents with a smaller number of social network members were more likely to be older and tended to have poorer self-rated health. Compared to the NSHAP, the KSHAP respondents maintained a smaller network size with a greater network density among their members and lower bridging potential. Further analysis of the complete network properties of KSHAP respondents revealed that more brokerage roles inside the same neighborhood (Ri) were significantly associated with better self-rated health. Socially isolated respondents identified by network components had the worst self-rated health.ConclusionsThe findings demonstrate the importance of social network analysis for the study of older adults’ health status in Korea. The study also highlights the importance of complete-network data and its ability to reveal mechanisms beyond ego-centric network data.

The social brain hypothesis proposes that large neocortex size evolved to support cognitively demanding social interactions. Accordingly, previous studies have observed that larger orbitofrontal and amygdala structures predict the size of an individual's social network. However, it remains uncertain how an individual's social connectedness reported by other people is associated with the social brain volume. In this study, we found that a greater in-degree network size, a measure of social ties identified by a subject's social connections rather than by the subject, significantly correlated with a larger regional volume of the orbitofrontal cortex, dorsomedial prefrontal cortex and lingual gyrus. By contrast, out-degree size, which is based on an individual's self-perceived connectedness, showed no associations. Meta-analytic reverse inference further revealed that regional volume pattern of in-degree size was specifically involved in social inference ability. These findings were possible because our dataset contained the social networks of an entire village, i.e. a global network. The results suggest that the in-degree aspect of social network size not only confirms the previously reported brain correlates of the social network but also shows an association in brain regions involved in the ability to infer other people's minds. This study provides insight into understanding how the social brain is uniquely associated with sociocentric measures derived from a global network.

ObjectiveSleep disturbance is common in the elderly, which is result from multi-factorial causes encompassing socio-demographic, behavioral, and clinical factors. We aimed to identify factors associated with insomnia among the elderly in a rural community in South Korea, a country with a rapidly growing aged population.MethodsThis cross-sectional study used the data from the second wave of the Korean Social life, Health and Ageing Project, which is a cohort study of individuals living in a typical rural community in South Korea. Socio-demographic, behavioral, and clinical characteristics were obtained through face-to-face interviews. Various factors suspected to be associated with insomnia were compared between elderly participants with and without insomnia, and multiple logistic regression analyses were conducted to identify independent risk factors for insomnia.ResultsWe found that 32.4% of 509 participants (72.8±7.7 years old) had insomnia. Female sex [odds ratio (OR)=2.19], low education level (OR=2.44), current smoking (OR=2.26), number of chronic diseases (OR=2.21 for 2–3 chronic diseases; OR=2.06 for 4 or more chronic diseases), and depression (OR=2.53) were independently associated with insomnia.ConclusionWe found that sex, education, chronic disease, and depression independently increase the risk of insomnia of the elderly in a Korean rural community. To overcome the elderly's insomnia, interventions should target modifiable factors such as depression. To promote active aging, longitudinal studies of factors associated with insomnia among the elderly should be performed in different regions and communities.

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