Tong Su

This study examined how one night of 5 h partial sleep restriction affects mental fatigue and cognitive control and compared the magnitude of change between subjective and objective measures. In a within-subject crossover design, 32 healthy university students completed the Stanford Sleepiness Scale (SSS), a visual analog scale (VAS) of fatigue, the brief Psychomotor Vigilance Task (PVT-B), and a modified Stroop task under a normal-sleep condition and a one-night 5 h sleep-restriction condition. Data were analyzed with linear and generalized linear mixed models. Sleep restriction significantly increased subjective sleepiness and fatigue (both p < 0.001; ηp² = 0.69 and 0.71, respectively), and these increases were already present before the task. It also produced a small decline in Stroop accuracy for congruent trials (95.94% vs. 93.86%; OR = 0.65, p < 0.001) and special trials (80.40% vs. 76.62%; OR = 0.80, FDR-corrected p = 0.038), with no significant change in incongruent-trial accuracy or reaction time. Objective vigilance and overall reaction time did not change significantly. One night of mild sleep restriction was therefore sufficient to induce subjective mental fatigue; the only objective effect detected was a small decline in accuracy for congruent and special trials, whereas objective vigilance and overall reaction time showed no significant change. The early effects of mild sleep loss appear subjectively sensitive, while the objective cognitive effects, if any, are small and confined to congruent- and special-trial accuracy.

Background: During the COVID-19 pandemic, population mental health and sleep problems have become increasingly prominent. Social support, resilience, and quality of life are key determinants of sleep quality; however, their complex interrelationships remain insufficiently understood. This study used network analysis to examine the connections among social support, resilience, quality of life, and insomnia symptoms. Methods: We recruited 662 participants between April and May 2022. Participants completed the Social Support Rating Scale (SSRS), the Connor–Davidson Resilience Scale (CD-RISC), the World Health Organization Quality of Life—Brief (WHOQOL-BREF), and the Athens Insomnia Scale (AIS). We constructed network models with the dimensions of each scale and individual insomnia symptoms as nodes and computed centrality metrics (strength, expected influence) and bridge expected influence (BEI). Results: The network showed negative associations between insomnia symptoms and social support, resilience, and quality of life. Q2 (psychological) and R2 (strength) had the highest strength and high expected influence. R1 (tenacity), Q2 (psychological), and S1 (objective support) demonstrated the greatest positive bridge expected influence in the network, whereas I6 (sense of well-being during the day) had the greatest negative bridge expected influence within the insomnia symptom community. Conclusion: This study illuminates the complex interconnections among social support, resilience, quality of life, and insomnia symptoms in the context of prolonged pandemic control measures and identifies core and bridge nodes through network analysis. These findings provide a theoretical foundation for improving population mental health and sleep quality. Clinicians should prioritize interventions that target core nodes and bridge symptoms to enhance effectiveness.

Background: This study investigated predictive factors and personality determinants of kinesiophobia (fear of movement) in dialysis patients. Methods: Using the Chinese versions of the Ten-Item Personality Inventory (TIPI-C) and Tampa Scale for Kinesiophobia Heart (TSK-SV heart), we assessed 329 patients from December 2022 to February 2023. The influencing factors of fear of movement were identified by univariate analysis and multiple linear regression.Results: The mean score of fear of movement for dialysis patients was 45.128 (45.128 ± 7.023). Multiple linear regression revealed that advanced age,local medical insurance, and lower education significantly predicted higher kinesiophobia scores, while exercise habits, being married, alcohol and tobacco use served as protective factors. Personality analysis demonstrated that lower extraversion, emotional stability, and conscientiousness were associated with greater kinesiophobia.Conclusions: Personality determinants were significantly associated with fear of movement. These findings emphasize the clinical utility of personality assessment in identifying high-risk patients and personalizing rehabilitation strategies for this vulnerable population.

This study aims to analyze the correlation between post-traumatic stress levels and positive/negative emotion among the public during public health emergency, and examine the mediating effect of sleep quality. Our survey relies on an online research platform using the Post-traumatic stress disorder Checklist for DSM-5 (PCL-C), Positive and Negative emotion Schedule (PANAS), and Pittsburgh Sleep Quality Index (PSQI), which was conducted among individuals in Guangdong and other regions to assess post-traumatic stress levels, emotional status, and sleep conditions. A total of 729 participants were included in the study. The detection rates for Post-traumatic stress disorder (PTSD) and sleep disorders were 6.84% and 8.78%, respectively. There are significant differences in positive and negative emotion scores across different age groups and residential areas (P < 0.05). Mediation analysis indicated that sleep quality partially mediates the effects of post-traumatic stress on both positive and negative emotions, with a greater impact on positive emotion than on negative emotion. Relevant departments can enhance positive emotion, reduce negative emotion, and decrease the incidence of PTSD by improving sleep quality.

Fatigue-induced incidents in transportation, aerospace, military, and other areas have been on the rise, posing a threat to human life and safety. The determination of fatigue states holds significant importance, especially through reliable and conveniently available physiological indicators. Here, a portable custom-built fNIRS system was used to monitor the fatigue state caused by nap deprivation. fNIRS signals in ten channels at the prefrontal cortex were collected, changes in blood oxygen concentration were analyzed, followed by a deep learning model to classify fatigue states. For the high-dimensionality and multi-channel characteristics of the fNIRS signal data, a novel 1D revised CNN-ResNet network was proposed based on the double-layer channel attenuation residual block. The results showed a 97.78% accuracy in fatigue state classification, significantly superior than several conventional methods. Furthermore, a fatigue-arousal experiment was designed to explore the feasibility of forced arousal of fatigued subjects through exercise stimulation. The fNIRS results showed a significant increase in brain activity with the conduction of exercise. The proposed method serves as a reliable tool for the evaluation of fatigue states, potentially reducing fatigue-induced harms and risks.

Purpose This study explored the relationship between naps and memory among habitual nappers in China. Methods Medical college students participated and were divided into 30-min, 60-min, and 90-min time-in-bed groups. To evaluate declarative and procedural memory performance, A–B and A–C interfering word pair and interfering finger tapping tasks were employed. Results Among 60 students, a significant decrease in the correct recall rate in the declarative task after having a nap was found only in the 30-min group (p = 0.005). After learning interference (A-C word pairs), the correct recall rate for the declarative task decreased significantly in all interference tests (ps < 0.001). In the procedural task, the speed of sequence A in the retests increased after having a nap in all three groups (ps < 0.048), with a significant decrease in accuracy only in the 30-min group (p = 0.042). After learning interference (sequence B) in the procedural task, the speed of sequence A increased in the 60-min group after 1 h (p = 0.049), and both the 60-min and 90-min groups showed increased speed after one night (ps < 0.022). No significant improvement in speed was found in the 30-min group (ps > 0.05), and this group showed the lowest accuracy for sequence A (ps < 0.16). Conclusion A habitual nap time-in-bed of 60 or 90 min had better effects on declarative and procedural memory consolidation and better memory resistance against interference in procedural memory.

Military personnel live in operating environments in which poor sleep is common. In this cross-temporal meta-analysis (CTMA), 100 studies (144 data sets, N = 75,998) were identified to examine changes in sleep quality among Chinese active service personnel from 2003 to 2019. Participants were divided into three groups: the navy, the non-navy, and the unknown service. The Pittsburgh Sleep Quality Index (PSQI) was used as the measure of sleep quality; it contains a global score and seven component scores, with higher scores indicative of poorer sleep. Among all active military personnel, the PSQI global and seven component scores decreased from 2003 to 2019. In examining the results by military type, the PSQI global and seven component scores increased in the navy group. Conversely, both the non-navy and unknown-service groups showed decreased PSQI global scores over time. Similarly, all PSQI component scores decreased over time for both the non-navy and unknown service groups, except for the use of sleeping medication (USM), which increased in the non-navy group. In conclusion, the sleep quality of Chinese active service personnel showed a positive trend. Further research should focus on improving the navy’s sleep quality.