This study examined the associations between adherence to 24-hour movement behavior guidelines (24-HMB) and the mental-health-related outcomes of depressive symptoms and anxiety in Chinese children. Data on movement behavior from 5357 children (4th and 5th grades), including physical activity, recreational screen time and sleep, were self-reported using the Health Behavior School-Aged Children Survey. Depressive symptoms and anxiety were self-reported using the Chinese version of the nine-item Patient Health Questionnaire and the Generalized Anxiety Disorder Scale, respectively. Depressive symptoms and anxiety were treated as categorical variables. Only 3.2% of the participants met physical activity, screen time, and sleep 24-HMB guidelines. Ordinal logistic regressions showed that, compared with participants who met the 24-HMB guidelines, participants who met none (odds ratio (OR) = 2.62, 95% CI: 1.76–3.90) or any one of the guidelines (OR = 1.88, 95% CI: 1.27–2.77) had higher odds of depressive symptoms. Similarly, there were higher odds of anxiety in participants who met none (OR = 2.32, 95% CI: 1.45–3.70) or any one of the recommendations (OR = 1.62, 95% CI: 1.03–2.57) compared with participants who met all the 24-HMB guidelines. Meeting the 24-HMB guidelines is associated with better mental-health-related outcomes in Chinese children. Because of the low prevalence of Chinese children meeting the 24-HMB recommendations, the present findings highlight the need to encourage children to regularly engage in physical activity, decrease their time spent sitting, and improve their sleep patterns.

Objectives: the current study aimed to investigate the relationship between physical activity (PA) level and inhibitory control performance and then to determine whether this association was mediated by multiple sleep parameters (i.e., subjective sleep quality, sleep duration, sleep efficiency, and sleep disturbance). Methods: 180 healthy university students (age: 20.15 ± 1.92 years) from the East China Normal University were recruited for the present study. PA level, sleep parameters, and inhibitory control performance were assessed using the International Physical Activity Questionnaire (IPAQ), the Pittsburgh Sleep Quality Index Scale (PSQI), and a Stroop test, respectively. The data were analyzed using structural equation modeling. Results: A higher level of PA was linked to better cognitive performance. Furthermore, higher subjective sleep quality and sleep efficiency were associated with better inhibitory control performance. The mediation analysis revealed that subjective sleep quality and sleep efficiency mediated the relationship between PA level and inhibitory control performance. Conclusion: our results are in accordance with the literature and buttress the idea that a healthy lifestyle that involves a relatively high level of regular PA and adequate sleep patterns is beneficial for cognition (e.g., inhibitory control performance). Furthermore, our study adds to the literature that sleep quality and sleep efficiency mediates the relationship between PA and inhibitory control performance, expanding our knowledge in the field of exercise cognition.

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.

To reduce the burden of clinicians diagnosing a large number of depressive symptoms, the field of artificial intelligence researchers are increasingly interested in designing automatic recognition systems for depression. Depressed patient have different speech signal from normal people. Here, we present a deep model, Depression AudioNet, which encodes depression-related features in the vocal tract and provides a more comprehensive audio representation. Firstly, the Mel-frequency cepstral coefficients (MFCCs) were extracted from raw audio data. Secondly, the robust emotions features were acquired by Multiscale Audio Delta Normalization (MADN), which is a data processing algorithm we proposed. Finally, the MFCCs and the emotions features of two adjacent segments of local audio were fed into the Depression AudioNet in turn to train the network. This method solves the problem of less training data and low precision by increasing the length information of the sample without reducing the number of samples. Experiments are conducted on AVEC2014 dataset, and the results shows that the proposed method is more effective and accurate than the existing speech depression recognition algorithms.