Objectives: One's personal social network constitutes a contextual framing factor for late-life cognitive function. This study examined the association between network type at baseline and changes in three cognitive measures: immediate recall, delayed recall, and fluency, two years hence, among Europeans aged 50 and older.
Participants: Data were taken from Waves four and five of the Survey of Health, Ageing, and Retirement in Europe of adults aged 50 and above (N = 50,071).
Measurements: The latent class analysis was applied to a set of criterion variables. The procedure yielded five distinct network types: multi-tie (6%), family-rich (23%), close-family (49%), family-poor (12%), and friend-enhanced (10%). The network types were then regressed on the cognition measures at follow-up, controlling for the respective baseline cognition scores, as well as for age, gender, education, self-rated health, mobility difficulty, and country.
Results: Respondents in family-poor network types had poorer cognition scores at follow-up, compared to those in the modal close-family network, while those in multi-tie networks had consistently better scores. The family-rich network and the friend-enhanced network also had a somewhat better cognitive function.
Conclusions: Having varied sources of network ties, e.g. friendship ties and/or several types of family relationships, is beneficial to the cognitive health of older adults over time. Networks based mainly on ties with relatives other than spouse and children, on the other hand, have poorer cognitive outcomes. Older people in this latter group face an increased risk for cognitive decline and should receive assistance in enhancing their interpersonal environments.
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http://dx.doi.org/10.1017/S1041610220003439 | DOI Listing |
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Division of Oncology, Department of Medicine, University of Washington, Seattle, WA.
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Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czech Republic.
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College of Education for the Future, Beijing Normal University, Zhuhai, Guangdong, China.
Personalized sports training plans are essential for addressing individual athlete needs, but traditional methods often need to integrate diverse data types, limiting adaptability and effectiveness. Existing machine learning (ML) and rule-based approaches cannot dynamically generate context-specific training programs, reducing their applicability in real-world scenarios. This study aims to develop a Generative Adversarial Network (GAN)- based framework to create context-specific training plans by integrating numeric attributes (e.
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