Supplemental Material for Environment and Cognitive Aging: A Cross-Sectional Study of Place of Residence and Cognitive Performance in the Irish Longitudinal Study on Aging

Background: Several kinds of motor dysfunction can predict future cognitive impairment in elderly individuals. However, the ability of the fine motor index (FINEA) and gross motor index (GROSSA) to predict the risk of cognitive impairment has not been assessed. Objective: We investigated the associations between FINEA/GROSSA and cognitive impairment. Methods: The data of 4,745 participants from The Irish Longitudinal Study on Ageing (TILDA) were analyzed. Cognitive function was assessed using the Mini-Mental State Examination (MMSE). We first assessed the correlation between the FINEA GROSSA and MMSE in a cross-sectional study. Then, we further investigated the predictive role of the incidence of cognitive impairment in a prospective cohort study. Results: We found that both FINEA and GROSSA were negatively correlated with MMSE in both the unadjusted (FINEA: B = –1.00, 95%confidence intervals (CI): –1.17, –0.83, t = –11.53, p < 0.001; GROSSA: B = –0.85, 95%CI: –0.94, –0.76, t = –18.29, p < 0.001) and adjusted (FINEA: B = –0.63, 95%CI: –0.79, –0.47, t = –7.77, p < 0.001; GROSSA: B = –0.57, 95%CI: –0.66, –0.48, t = –12.61, p < 0.001) analyses in a cross-sectional study. In a prospective cohort study, both high FINEA and high GROSSA were associated with an increased incidence of cognitive function impairment (FINEA: adjusted odds ratios (OR) = 2.35, 95%CI: 1.05, 5.23, p = 0.036; GROSSA adjusted OR = 3.00, 95%CI: 1.49, 6.03, p = 0.002) after 2 years of follow-up. Conclusion: Higher FINEA and GROSSA scores were both associated with an increased incidence of cognitive impairment. FINEA or GROSSA might be a simple tool for identifying patients with cognitive impairment.

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Association of depressive symptoms with ambient PM2.5 in middle-aged and elderly Chinese adults: A cross-sectional study from the China health and Retirement Longitudinal Study wave 4

Environmental Research ◽

10.1016/j.envres.2021.111889 ◽

2022 ◽

Vol 203 ◽

pp. 111889

Author(s):

Yifan Yao ◽

Tianjun Lu ◽

Yisi Liu ◽

Qiujun Qin ◽

Jie Jiang ◽

...

Keyword(s):

Depressive Symptoms ◽

Longitudinal Study ◽

Cross Sectional Study ◽

Sectional Study ◽

Middle Aged ◽

Chinese Adults ◽

Cross Sectional ◽

Elderly Chinese

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Exploring the multi-dimensional influencing factors associated with sub-health status for residents: a cross-sectional study from China

10.21203/rs.2.19224/v1 ◽

2019 ◽

Author(s):

Jiaojiao Ren ◽

Xiling Yin ◽

Guangyou Li ◽

Jun Li ◽

Liju Zhang ◽

...

Keyword(s):

Health Status ◽

Influencing Factors ◽

Intervention Program ◽

Cross Sectional Study ◽

Health Intervention ◽

Sectional Study ◽

Place Of Residence ◽

Health Measurement ◽

Cross Sectional ◽

Factors Associated

Abstract Background: The high incidence of sub-health and its impact on life and work have attracted wide attention. Sub-health status has been studied in China; however, there remains a lack of studies on multi-dimensional factors affecting sub-health status. This study aims to explore the sub-health status of residents, and its influencing factors in Zhuhai city of Guangdong Province of China. Methods: Data were originated from the baseline survey of Zhuhai WHO Healthy Cities Index System in 2015, which was a cross-sectional study for the influencing factors associated with sub-health status. Finally, 3,313 participants aged 16-65 years were recruited. The study used the Sub-health Measurement Scale (SHMS V1.0), and the multivariate logistic regression model was to examine their possible associations with sub-health status. Data were analyzed using the SPSS version 22.0. Results: Sub-health and non-sub-health groups accounted for 56.8% and 43.2% of the study population, respectively. There existed significant differences in terms of all items of SHMS V1.0 between the two groups. In the multivariate model, the place of residence was statistically significantly associated with sub-health, followed by having many close neighbors, relatives or friends, and happy feelings. Conclusion: There are significant differences in many items of SHMS V1.0 between sub-health and non-sub-health groups. The leading determinants of sub-health included place of residence; having close neighbors, relatives or friends; having happy feelings; and negative emotions. To develop an effective sub-health intervention program, these factors should be taken into consideration. To develop an effective sub-health intervention program, the influencing factors should be taken into consideration.

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