Validity, feasibility, and effectiveness of a voice-recognition based digital cognitive screener for dementia and mild cognitive impairment in community-dwelling older Chinese adults: A large-scale implementation study.

INTRODUCTION: We investigated the validity, feasibility, and effectiveness of a voice recognition-based digital cognitive screener (DCS), for detecting dementia and mild cognitive impairment (MCI) in a large-scale community of elderly participants. METHODS: Eligible participants completed demographic, cognitive, functional assessments and the DCS. Neuropsychological tests were used to assess domain-specific and global cognition, while the diagnosis of MCI and dementia relied on the Clinical Dementia Rating Scale. RESULTS: Among the 11,186 participants, the DCS showed high completion rates (97.5%) and a short administration time (5.9 min) across gender, age, and education groups. The DCS demonstrated areas under the receiver operating characteristics curve (AUCs) of 0.95 and 0.83 for dementia and MCI detection, respectively, among 328 participants in the validation phase. Furthermore, the DCS resulted in time savings of 16.2% to 36.0% compared to the Mini-Mental State Examination (MMSE) and Montral Cognitive Assessment (MoCA). DISCUSSION: This study suggests that the DCS is an effective and efficient tool for dementia and MCI case-finding in large-scale cognitive screening. HIGHLIGHTS: To our best knowledge, this is the first cognitive screening tool based on voice recognition and utilizing conversational AI that has been assessed in a large population of Chinese community-dwelling elderly. With the upgrading of a new multimodal understanding model, the DCS can accurately assess participants' responses, including different Chinese dialects, and provide automatic scores. The DCS not only exhibited good discriminant ability in detecting dementia and MCI cases, it also demonstrated a high completion rate and efficient administration regardless of gender, age, and education differences. The DCS is economically efficient, scalable, and had a better screening efficacy compared to the MMSE or MoCA, for wider implementation.

Green space modified the association between air pollutants and hypertension in China.

Evidence regarding the combined effects of green space and air pollutants on hypertension remains limited and complex. This study aims to investigate the varying effects of greenness under different air pollution levels in China, using data from the wave 2018 China Health and Retirement Longitudinal Study (CHARLS) involving 17 468 adults (aged ≥ 45 years). As a result, the prevalence rate of hypertension was 42.04%. Logistic regression analyses revealed the positive associations between air pollution concentrations at the city level and prevalent hypertension and the negative associations between NDVI and prevalent hypertension, all of which were more prominent in the populations of the eastern and rural regions. Notably, the negative effect of green space was greater at the lowest quartiles of each air pollutant (OR for PM2.5 quartiles = 0.724, 0.792, 0.740, and 0.931) . Improving air quality and greenness could potentially reduce hypertension risk, and minimizing air pollution might optimize the protective effects of greenness.

Effects of green spaces on alleviating mortality attributable to PM2.5 in China.

Increasing research suggested that green spaces are associated with many health benefits, but evidence for the quantitative relationship between green spaces and mortality attributable to particulate matter with an aerodynamic diameter of 2.5 µm or less (PM2.5) is limited. We collected disease-specific mortality and PM2.5 data for a period of 4 years (2015-2018) along with green space data for an 8-year duration (2010-2017) in 31 provincial-level administrative regions of China. First, this study used the Integrated Exposure-Response model to estimate the mortality of four diseases attributable to PM2.5, including chronic obstructive pulmonary diseases (COPD), lung cancer (LC), ischemic heart disease (IHD), and cerebrovascular disease (CBVD). Then we performed linear regression and mixed-effects model to investigate the counteracting effect of green spaces on death caused by PM2.5 exposure. The differences in impacts among the Eastern, Central, and Western regions were explored using stratified analysis. The most significant results from linear regression analysis indicated that per 100 km2 of green spaces increase, there was a decreased total mortality (10-5) (COPD, LC, IHD, and CBVD) attributable to PM2.5 by - 4.012 [95% confidence interval (CI): - 5.535, - 2.488], while the reduction by mixed-linear regression analysis was - 2.702/105 (95% CI = - 3.645, - 1.759). Of all hysteresis analyses, the effect estimates (ß) at lag3 and lag4 were the largest. The effect of green spaces was more advantageous when targeting CBVD and the Eastern region. We found a negative correlation between green space exposure and mortality attributable to PM2.5, which can provide further support for city planners, government personnel, and others to build a healthier city and achieve national health goals.

A voice recognition-based digital cognitive screener for dementia detection in the community: Development and validation study.

Introduction: To facilitate community-based dementia screening, we developed a voice recognition-based digital cognitive screener (digital cognitive screener, DCS). This proof-of-concept study aimed to investigate the reliability, validity as well as the feasibility of the DCS among community-dwelling older adults in China. Methods: Eligible participants completed demographic, clinical, and the DCS. Diagnosis of mild cognitive impairment (MCI) and dementia was made based on the Montreal Cognitive Assessment (MoCA) (MCI: MoCA < 23, dementia: MoCA < 14). Time and venue for test administration were recorded and reported. Internal consistency, test-retest reliability and inter-rater reliability were examined. Receiver operating characteristic (ROC) analyses were conducted to examine the discriminate validity of the DCS in detecting MCI and dementia. Results: A total of 103 participants completed all investigations and were included in the analysis. Administration time of the DCS was between 5.1-7.3 min. No significant difference (p > 0.05) in test scores or administration time was found between 2 assessment settings (polyclinic or community center). The DCS showed good internal consistency (Cronbach's alpha = 0.73), test-retest reliability (Pearson r = 0.69, p < 0.001) and inter-rater reliability (ICC = 0.84). Area under the curves (AUCs) of the DCS were 0.95 (0.90, 0.99) and 0.77 (0.67, 086) for dementia and MCI detection, respectively. At the optimal cut-off (7/8), the DCS showed excellent sensitivity (100%) and good specificity (80%) for dementia detection. Conclusion: The DCS is a feasible, reliable and valid digital dementia screening tool for older adults. The applicability of the DCS in a larger-scale community-based screening stratified by age and education levels warrants further investigation.