Objective: There is growing evidence that in adults, higher levels of handgrip strength (HGS) are linked to better cognitive performance. However, the relationship between HGS and cognitive performance has not been sufficiently investigated in special cohorts, such as individuals with hypertension who have an intrinsically higher risk of cognitive decline. Thus, the purpose of this study was to examine the relationship between HGS and cognitive performance in adults with hypertension using data from the Global Ageing and Adult Health Survey (SAGE). Methods: A total of 4486 Chinese adults with hypertension from the SAGE were included in this study. Absolute handgrip strength (aHGS in kilograms) was measured using a handheld electronic dynamometer, and cognitive performance was assessed in the domains of short-term memory, delayed memory, and language ability. Multiple linear regression models were fitted to examine the association between relative handgrip strength (rHGS; aHGS divided by body mass index) and measures of cognitive performance. Results: Overall, higher levels of rHGS were associated with higher scores in short-term memory (β = 0.20) and language (β = 0.63) compared with the lowest tertiles of rHGS. In male participants, higher HGS was associated with higher scores in short-term memory (β = 0.31), language (β = 0.64), and delayed memory (β = 0.22). There were no associations between rHGS and cognitive performance measures in females. Conclusion: We observed that a higher level of rHGS was associated with better cognitive performance among hypertensive male individuals. Further studies are needed to investigate the neurobiological mechanisms, including sex-specific differences driving the relationship between measures of HGS and cognitive performance in individuals with hypertension.

Few studies have focused on the specific effects of trait mindfulness on physical health status, especially from a resilient aging perspective. This study examined the mediating role of mental health in the association between trait mindfulness and physical health status among middle-older adults in urban China. The participants included 188 individuals who were recruited from a community senior university and five community senior care centers. The findings reveal: (1) trait mindfulness has a strong effect on two physical health indicators (self-rated physical health and subjective sleep quality); (2) mental health is a significant mediator in the relationship between trait mindfulness and physical health status; and (3) the mediation role of mental health is more evident in the self-rated physical health model (24.15%) than subjective sleep quality (18.10%). This study improves our knowledge of how trait mindfulness can lead to a better physical health in middle-older adults and could lead to the development of social value communication and effective prevention.

PurposeThis study aimed to investigate the association between participation in community seniors' organizations (CSOs) and mental health among retired adults in urban China, and illustrate the causal mechanism.MethodsWe collected data on participation from a community seniors' organization and mental health survey in Shenzhen City, China, in July 2022. The survey used a two-stage cluster sample design, based on administrative divisions as the primary sampling unit and communities as the second sampling unit, where retired adults were randomly sampled. The inclusion criteria were individuals >60 years old (the mandatory age for retirement in China) or women who retired early at the age of 45 years. We used three multivariable regression models to estimate the effects of participation in CSOs on mental health. Furthermore, we used structural equation models to examine the mediator of acquired interpersonal needs in the association between CSOs participation and mental health.ResultsThe study examined the values of CSOs, generated explicitly for older adults and explained how participation in such organizations benefits retired adults' mental health. Mental health is defined in three dimensions: aggregate mental health, positive emotions, and negative emotions. The results show that constituting social networks with like-minded individuals and perceiving interpersonal needs are the two main benefits of CSOs on mental health. The retired individual who participated in CSOs with a higher level of diversity and frequency, joined specific types such as health- and study-related CSOs, could receive more interpersonal needs and were probably mentally healthier. The mediating mechanism of interpersonal needs associated with participation in mental health was significant. Besides, mental health is generally affected by physical health, sleep quality, and socioeconomic status.ConclusionThis study suggested that CSOs have expanded the social interaction channels of retired adults and affected their mental health by providing basic interpersonal needs such as inclusion, dominance, and affection. Among the types of CSOs, health and study organizations might enhance mental health most effectively, while semi-official organizations have no effect.

The domain of computer vision's most popular study area has always been face recognition which aims to identify different face images and predict the corresponding identity information through feature analysis and modeling. In practical applications, a number of variables, including lighting, posture, and clarity, have an impact on facial recognition accuracy. Among them, the most challenging scene is occlusion face recognition, which will cause feature loss, local coherence and alignment errors to greatly inhibit the accuracy and generalization ability of the face model. Based on detailed literature research and analysis, this paper provides a comprehensive evaluation of the research progress of occluded face recognition. Specifically, based on the introduction of classical face recognition technology, we further discuss the design ideas, basic framework, advantages and disadvantages of representative occlusion face recognition methods from two aspects: robust feature extraction and robust classifier. Finally, we summarize the main challenges and give an outlook on the future research development of object detection.