Transitions between social network profiles and their relation with all-cause mortality among older adults

Cheng, Grand H.‐L.; Sung, Pildoo; Chan, Angelique; Ma, Stefan; Malhotra, Rahul

doi:10.1016/j.socscimed.2021.114617

Cited by 10 publications

(13 citation statements)

References 37 publications

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“…Analysis. We carried out LCA to identify patterns of social isolation and low social support (Cheng et al, 2022;Xiao et al, 2019). Multi-trajectory modeling was used to identify frailty trajectories (Jung et al, 2022;Pan et al, 2021).…”

Section: Independent Variablesmentioning

confidence: 99%

“…Although many studies have examined the impact of social support and social isolation on health outcomes among older adults simultaneously (Freak-Poli et al, 2022; Gulledge et al, 2021; Hu et al, 2021; Joyce et al, 2022), these studies typically overlooked the interplay of social support and social isolation. While a few studies have investigated patterns of social relationships, most have focused solely on the structural aspect (Cheng et al, 2022; Windsor et al, 2016). Only one study has identified patterns of both structural and functional social relationships and explored their associations with frailty among European older adults (Lestari et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…One way to determine distinct patterns of social isolation and low social support can be pattern-centered methods such as Latent Class Analysis (LCA) (Cheng et al, 2022). Additionally, identifying sub-patterns of frailty trajectories can be achieved through the multi-trajectory modeling approach (Jung et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Patterns of Social Isolation and Low Social Support and Frailty Trajectories Among Chinese Older Adults

Pan,

Cao

2023

J Appl Gerontol

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Objectives: This study aims to identify patterns of social isolation and low social support, and discover their associations with frailty trajectories among Chinese older adults. Methods: The paper used five waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS, 2005–2018). The latent class analysis, multi-trajectory modeling, and the multinomial logistic regression approaches were used to perform the analysis. Results: The paper identified five types of social isolation and low social support, and three typical frailty trajectories. Patterns of social isolation and low social support were associated with frailty trajectories among older adults. Conclusions: Distinct patterns of social isolation and low social support were significantly correlated with frailty trajectories. To improve frailty trajectories among older adults, interventions such as Healthy China 2030 should prioritize addressing the interplay between social isolation and low social support.

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Section: Independent Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Patterns of Social Isolation and Low Social Support and Frailty Trajectories Among Chinese Older Adults

Pan,

Cao

2023

J Appl Gerontol

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“…In summary, while researchers recognize that different aspects of human connection might affect well-being differently, they have typically made only limited distinctions among them, such as between just family and friends, and have often merged them into global indices (e.g., a “social disconnection” scale, Cornwell and Waite, 2009 ). Other studies, such as Fiori et al (2006) , Giannella and Fischer (2016) , and Cheng et al (2022) pool measures to discover clusters of ego networks inductively. It is somewhat rarer to decompose networks into their distinct components, then compare the effects of these components (with important exceptions, e.g., Nicolini et al, 2021 ).…”

Section: Which Kinds Of Human Connection Matter For Subjective Well-b...mentioning

confidence: 99%

Distinct aspects of human connection associated with subjective well-being

Ruppel

Child²,

Fischer

et al. 2022

SSM - Mental Health

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“…With the rapid development of information technology, e-commerce and social networking sites have become inseparable from people's daily life, coupled with the increasing networkedness and various data resources, the network has received wide attention as a new perspective for information analysis and management research. [1][2][3][4][5][6] Especially since entering the twenty-rst century, Internet technology has been rapidly developed and people have rapidly entered the era of online Internet. With the rise of various social networking sites and communication software such as Weibo, WeChat, TikTok, Alipay, and QQ, they provide people with direct and quick platforms for online friendships and online shopping.…”

Section: Introductionmentioning

confidence: 99%

A Method to Study Group Relationship of College Students Based on Predictive Social Network

Zhang

Shi

2022

Scientific Programming

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With the rise of social network platforms such as WeChat, Weibo, and TikTok, social networks have developed from simple social networks to complex social networks. Researchers have gradually found that the traditional data sampling methods can no longer meet the development needs of the complex social network structure. In order to save network resources, various methods of social relationship prediction have been proposed. In this paper, we propose a BTCS algorithm based on low sampling rate under cognitive model and conduct several sets of comparison experiments under different networks and different sampling rates, and the results show that the BTCS algorithm improves the prediction accuracy and reduces the prediction time under low sampling rate. To address the problems of poor stability and slow prediction speed of random sampling prediction methods, this paper proposes a CCS algorithm in colleges and universities using the characteristics of high awareness among nodes within the same college. It can effectively combine the cognitive characteristics of the nodes with the college attributes and apply them to the relationship prediction to realize the college-oriented relationship prediction. The simulation results show that the CCS algorithm is more stable than other random sampling prediction methods. The results make full use of the cognitive characteristics and college attributes of nodes in social networks; reduce the influence of multiple factors such as response time, data packet loss, and individual behavior on relationship prediction; and improve the efficiency of college student group relationship prediction, which has certain theoretical significance and application prospects.

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Transitions between social network profiles and their relation with all-cause mortality among older adults

Cited by 10 publications

References 37 publications

Patterns of Social Isolation and Low Social Support and Frailty Trajectories Among Chinese Older Adults

Patterns of Social Isolation and Low Social Support and Frailty Trajectories Among Chinese Older Adults

Distinct aspects of human connection associated with subjective well-being

A Method to Study Group Relationship of College Students Based on Predictive Social Network

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