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Altieri M, Maggi G, Giacobbe C, Santangelo G. Psychometric properties and normative data of the Italian version of the Cognitive Function at Work Questionnaire: a screening tool for detecting subjective cognitive complaints at work. Neurol Sci 2024; 45:2593-2603. [PMID: 38155286 DOI: 10.1007/s10072-023-07265-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 12/12/2023] [Indexed: 12/30/2023]
Abstract
INTRODUCTION Considering the extension of working life due to socioeconomic and political factors, many people may experience cognitive complaints (CC) at their workplace, with severe consequences on their quality of life. The identification of workers reporting significative SCC is crucial to eventually address them to an objective neuropsychological evaluation and implement cognitive interventions to guarantee workers' well-being. Since no Italian questionnaires for detecting CC were designed for occupational settings, the aim of the study was to validate the Italian version of the Cognitive Function at Work Questionnaire (CFWQ) and to provide its normative data. MATERIALS AND METHODS Internal consistency, convergent and divergent validity, and factorial structure of the CFWQ were evaluated. A regression-based procedure served to compute percentiles of CFWQ and its subscales. RESULTS Four hundred twenty-one participants without psychiatric and/or neurological disorders completed the survey. We found that the Italian CFWQ included 26 items, with a good internal consistency (Cronbach's alpha = 0.897) and a six-factor structure (memory, language, processing speed, abstract thinking/behavioral control, behavioral inertia, planning ability). CFWQ score did not correlate with empathy but correlated strongly with memory scores and moderately with anxiety and depression scores. CONCLUSIONS The Italian CFWQ showed good psychometric properties, in analogy with the original English scale. Therefore, it can be successfully employed in organizational contexts to possibly identify workers with CC and therefore with possible co-occurrent psychological, behavioral, and cognitive consequences.
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Affiliation(s)
- Manuela Altieri
- Department of Psychology, University of Campania "Luigi Vanvitelli", Caserta, Italy
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Gianpaolo Maggi
- Department of Psychology, University of Campania "Luigi Vanvitelli", Caserta, Italy
| | - Chiara Giacobbe
- Department of Psychology, University of Campania "Luigi Vanvitelli", Caserta, Italy
| | - Gabriella Santangelo
- Department of Psychology, University of Campania "Luigi Vanvitelli", Caserta, Italy.
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Xie XY, Huang LY, Cheng GR, Liu D, Hu FF, Zhang JJ, Han GB, Liu XC, Wang JY, Zhou J, Zeng DY, Liu J, Nie QQ, Song D, Yu YF, Hu CL, Fu YD, Li SY, Cai C, Cui YY, Cai WY, Li YQ, Fan RJ, Wan H, Xu L, Ou YM, Chen XX, Zhou YL, Chen YS, Li JQ, Wei Z, Wu Q, Mei YF, Tan W, Song SJ, Zeng Y. Association Between Long-Term Exposure to Ambient Air Pollution and the Risk of Mild Cognitive Impairment in a Chinese Urban Area: A Case-Control Study. J Alzheimers Dis 2024; 98:941-955. [PMID: 38489185 DOI: 10.3233/jad-231186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
Background As a prodromal stage of dementia, significant emphasis has been placed on the identification of modifiable risks of mild cognitive impairment (MCI). Research has indicated a correlation between exposure to air pollution and cognitive function in older adults. However, few studies have examined such an association among the MCI population inChina. Objective We aimed to explore the association between air pollution exposure and MCI risk from the Hubei Memory and Aging Cohort Study. Methods We measured four pollutants from 2015 to 2018, 3 years before the cognitive assessment of the participants. Logistic regression models were employed to calculate odds ratios (ORs) to assess the relationship between air pollutants and MCI risk. Results Among 4,205 older participants, the adjusted ORs of MCI risk for the highest quartile of PM2.5, PM10, O3, and SO2 were 1.90 (1.39, 2.62), 1.77 (1.28, 2.47), 0.56 (0.42, 0.75), and 1.18 (0.87, 1.61) respectively, compared with the lowest quartile. Stratified analyses indicated that such associations were found in both males and females, but were more significant in older participants. Conclusions Our findings are consistent with the growing evidence suggesting that air pollution increases the risk of mild cognitive decline, which has considerable guiding significance for early intervention of dementia in the older population. Further studies in other populations and broader geographical areas are warranted to validate these findings.
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Affiliation(s)
- Xin-Yan Xie
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Lin-Ya Huang
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Gui-Rong Cheng
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Dan Liu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Fei-Fei Hu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Jing-Jing Zhang
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Gang-Bin Han
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Xiao-Chang Liu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Jun-Yi Wang
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Juan Zhou
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - De-Yang Zeng
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Jing Liu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Qian-Qian Nie
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Dan Song
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Ya-Fu Yu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Chen-Lu Hu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Yi-Di Fu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Shi-Yue Li
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Cheng Cai
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Yu-Yang Cui
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Wan-Ying Cai
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Yi-Qing Li
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Ren-Jia Fan
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Hong Wan
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Lang Xu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Yang-Ming Ou
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Xing-Xing Chen
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Yan-Ling Zhou
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Yu-Shan Chen
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Jin-Quan Li
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Zhen Wei
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Qiong Wu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Yu-Fei Mei
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Wei Tan
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Shao-Jun Song
- Reproductive Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Zeng
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
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Xue C, Li J, Hao M, Chen L, Chen Z, Tang Z, Tang H, Fang Q. High prevalence of subjective cognitive decline in older Chinese adults: a systematic review and meta-analysis. Front Public Health 2023; 11:1277995. [PMID: 38106895 PMCID: PMC10722401 DOI: 10.3389/fpubh.2023.1277995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 11/13/2023] [Indexed: 12/19/2023] Open
Abstract
Background Subjective cognitive decline (SCD) is considered a preclinical stage of Alzheimer's disease. However, reliable prevalence estimates of SCD in the Chinese population are lacking, underscoring the importance of such metrics for policymakers to formulate appropriate healthcare strategies. Objective To systematically evaluate SCD prevalence among older Chinese adults. Methods PubMed, Web of Science, The Cochrane Library, Embase, CNKI, Wanfang, VIP, CBM, and Airiti Library databases were searched for studies on SCD in older Chinese individuals published before May 2023. Two investigators independently screened the literature, extracted the information, and assessed the bias risk of the included studies. A meta-analysis was then conducted using Stata 16.0 software via a random-effects model to analyze SCD prevalence in older Chinese adults. Results A total of 17 studies were included (n = 31,782). The SCD prevalence in older Chinese adults was 46.4% (95% CI, 40.6-52.2%). Further, subgroup analyzes indicated that SCD prevalence was 50.8% in men and 58.9% among women. Additionally, SCD prevalence in individuals aged 60-69, 70-79, and ≥ 80 years was 38.0, 45.2, and 60.3%, respectively. Furthermore, SCD prevalence in older adults with BMI <18.5, 18.5-24.0, and > 24.0 was 59.3, 54.0, and 52.9%, respectively. Geographically, SCD prevalence among older Chinese individuals was 41.3% in North China and 50.0% in South China. In terms of residence, SCD prevalence was 47.1% in urban residents and 50.0% among rural residents. As for retired individuals, SCD prevalence was 44.2% in non-manual workers and 49.2% among manual workers. In the case of education, individuals with an education level of "elementary school and below" had an SCD prevalence rate of 62.8%; "middle school, "52.4%; "high school, "55.0%; and "college and above, "51.3%. Finally, SCD prevalence was lower among married individuals with surviving spouses than in single adults who were divorced, widowed, or unmarried. Conclusion Our systematic review and meta-analysis identified significant and widespread SCD prevalence in the older population in China. Therefore, our review findings highlight the urgent requirement for medical institutions and policymakers across all levels to prioritize and rapidly develop and implement comprehensive preventive and therapeutic strategies for SCD.Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023406950, identifier: CRD42023406950.
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Affiliation(s)
- Chao Xue
- School of Nursing, Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China
- Department of Nursing, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
| | - Juan Li
- Department of Nursing, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
| | - Mingqing Hao
- Department of Nursing, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
| | - Lihua Chen
- Department of Nursing, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
| | - Zuoxiu Chen
- Department of Nursing, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
| | - Zeli Tang
- School of Nursing, Zunyi Medical University, Zunyi, Guizhou, China
| | - Huan Tang
- School of Nursing, Zunyi Medical University, Zunyi, Guizhou, China
| | - Qian Fang
- Department of Nursing, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
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