Background: Cognition is multidimensional, and each domain plays a unique and crucial part in successful daily life engagement. However, less attention has been paid to multi-domain cognitive health for the elderly, and the role of lifestyle factors in each domain remains unclear.

Specific disparities exist in the association of sleep quality with sleep duration and QOL in T2DM patients. Failures to take into account the effect of sleep quality when evaluating the impact of sleep on QOL significantly bias the results. It is important to integrate duration and quality of sleep as a composite sleep index when assessing sleep of patients with T2DM.

Background Smartphone use has become an increasingly pervasive part of our daily lives, and as a portable media device, smartphones provide good support for cognitive training during aging. However, little is known about the joint association of smartphone use and gender on the cognitive health of older adults, particularly with regard to multi-domain cognition. Methods A face-to-face survey of 3230 older adults aged 60+ years was conducted in Xiamen, China, in 2016. The Montreal Cognitive Assessment (MoCA) score was used to measure both general and multi-domain cognition. Smartphone use was self-reported and the number of the smartphone functions used (NSFU) was classified as 0, 1, and 2+. General and subdomain cognitive functions were modelled on NSFU only, gender only, and NSFU and gender combined by using a series of proportional-odds cumulative logit models. Furthermore, joint associations of gender and NSFU on both general and multi-domain cognition were estimated, and a four-category quantile classification was used to evaluate the total MoCA score. Results Among all 3230 respondents, 2600 remained after exclusion of respondents with very low MoCA scores (below the education-adjusted cut-offs for dementia). Only 29.96% of older adults used smartphones, 473 (60.72%) of which were men. Respondents who had a higher NSFU maintained a better general and sub-domain cognition except for memory and orientation. Although women had lower values compared to men in visuospatial ability (OR (95% CI): 0.46 (0.37–0.57)), they outperformed their male counterparts in memory (OR (95% CI): 1.38 (1.10–1.73)). The results of the joint association showed that women’s inferiority in visuospatial ability diminished when they had a NSFU of 2+. However, a significantly better improvement in memory for male was achieved when they had a NSFU of 1 rather than 2 + . Conclusions A higher NSFU was positively associated with increased general and partial subdomain cognitive functions. However, gender differences were found in visuospatial ability and memory, which could be alleviated by smartphone use.

Aim: A better income condition has always been associated with better cognition; however, studies that have demonstrated the pathway of this relationship are limited. We aim to evaluate the mediation effect of depression in this association, and whether this mediation is moderated by the place of residence. Methods: We conducted a face-to-face study, including 3230 older adults aged >60 years in Xiamen, China, in 2016. The income condition of participants was categorized into three groups: income less than expenditure, income equals expenditure and income more than expenditure. Depression was measured using the Geriatric Depression Scale and cognition was evaluated using the Montreal Cognitive Assessment tool. We first examined a simple mediation model where depression was a mediator between income condition and cognition. Furthermore, residence was systematically integrated into the model as a moderator, and the model was adjusted for age, gender, number of year of education, hypertension and diabetes. All mediation and moderated mediation effects were estimated by the plug-in "PROCESS" in SPSS. Results: In total, 2852 participants were finally included. Depression partially mediated the relationship between income condition and cognition (indirect effect = 0.25, total effect = 0.72). Moderated mediation analyses indicated that a direct effect only existed among urban older adults (B = 0.92; 95% confidence interval [CI]: [0.47-1.38]), whereas an indirect effect was stronger for individuals in urban (B = 0.28; 95% CI: [0.18-0.41]) rather than rural environments (B = 0.17; 95% CI: [0.11-0.26]). Conclusions: A better income condition is a protective factor for cognition and it partially benefits work through milder depressive symptoms, particularly in older adults in urban residences.

As one of the most remarkable indicators of physiological health, heart rate (HR) has become an unfailing investigation for researchers. Unlike many existing methods, this article proposes an approach to implement short-time HR estimation from electrocardiography in time series missing patterns. Benefiting from the rapid development of deep learning, we adopted a bidirectional long short-term memory model (Bi-LSTM) and temporal convolution network (TCN) to recover complete heartbeat signals from those with durations are less than one cardiac cycle, and the estimated HR from recovered segment combining the input and the predicted output. We also compared the performance of Bi-LSTM and TCN in PhysioNet dataset. Validating the method over a resting heart rate range of 60–120 bpm in the database without significant arrhythmias and a corresponding range of 30–150 bpm in the database with arrhythmias, we found that networks provide an estimated approach for incomplete signals in a fixed format. These results are consistent with real heartbeats in the normal heartbeat dataset (γ > 0.7, RMSE < 10) and in the arrhythmia database (γ > 0.6, RMSE < 30), verifying that HR could be estimated by models in advance. We also discussed the short-time limits for the predictive model. It could be used for physiological purposes such as mobile sensing in time-constrained scenarios, and providing useful insights for better time series analyses in missing data patterns.