Physical fatigue is frequent for heavy manual laborers like construction workers, but it causes distraction and may lead to safety incidents. The purpose of this study is to develop predictive models for monitoring construction workers’ inattention caused by physical fatigue utilizing electrocardiograph (ECG) and galvanic skin response (GSR) sensors. Thirty participants were invited to complete an attention-demanding task under non-fatigued and physically fatigued conditions. Supervised learning algorithms were utilized to develop models predicting their attentional states, with heart rate variability (HRV) features derived from ECG signals and skin electric activity features derived from GSR signals as data inputs. The results demonstrate that using HRV features alone could obtain a prediction accuracy of 88.33%, and using GSR features alone could achieve an accuracy of 76.67%, both through the KNN algorithm. The accuracy increased to 96.67% through the SVM algorithm when combining HRV and GSR features. The findings indicate that ECG sensors used alone or in combination with GSR sensors can be applied to monitor construction workers’ inattention on job sites. The findings would provide an approach for detecting distracted workers at job sites. Additionally, it might reveal the relationships between workers’ physiological features and attention.

Background: With the aging of the population, health issues among older people have drawn increasing societal attention. The rapid development of intelligent technology provides innovative solutions to addressing these challenges. This study aims to explore the impact of smart wearable devices on older people’s health outcomes while discussing the mechanism of physical exercise, to provide a new perspective and exercise strategy for maximizing the positive role of smart wearable devices in older adult health management.Methods: Utilizing data from the China Longitudinal Aging Social Survey (CLASS), this study analyzed 21,881 samples. Descriptive statistics was computed using SPSS, followed by bidirectional fixed effect model analysis and intermediate effect test via Stata.Results: Bidirectional fixed effect model showed that the use of smart wearable devices could significantly improve the physical health of the older people (β = 0.325, p < 0.001). The effect was more significant in those aged 70 years or older (β = 0.385, p < 0.001), those without chronic diseases (β = 0.590, p < 0.001), and those living with their children (β = 0.452, p < 0.001). The mediating mechanism test results reported that exercise frequency (β = 0.037, p < 0.05) and exercise duration (β = 0.063, p < 0.001) were two positive mediating pathways for smart wearable devices to affect the health of the older adults.Conclusion: This study reveals the positive impact of smart wearable devices on the physical health level of the older people. To maximize benefits, we propose a dual-path optimization strategy: (1) Develop age-appropriate functions and use methods to improve device accessibility among older adult people, and (2) Generate a personalized exercise prescription for the older people to promote regular physical activity and exercise habits to improve their health level.

Skuld (skd) is a subunit of the Mediator complex subunit complex. In the heart, skd controls systemic obesity, is involved in systemic energy metabolism, and is closely linked to cardiac function and aging. However, it is unclear whether the effect of cardiac skd on cardiac energy metabolism affects cardiac function. We found that cardiac-specific knockdown of skd showed impaired cardiac function, metabolic impairment, and premature aging. Drosophila was subjected to an exercise and high-fat diet (HFD) intervention to explore the effects of exercise on cardiac skd expression and cardiac function in HFD Drosophila. We found that Hand-Gal₄>skd RNAi (KC) Drosophila had impaired cardiac function, metabolic impairment, and premature aging. Regular exercise significantly improved cardiac function and metabolism and delayed aging in HFD KC Drosophila. Thus, our study found that the effect of skd on cardiac energy metabolism in the heart affected cardiac function. Exercise may counteract age-related cardiac dysfunction and metabolic disturbances caused by HFD and heart-specific knockdown of skd. Skd may be a potential therapeutic target for heart disease.

Disruption of circadian rhythms is related to disorders of glucose metabolism, and the molecular clock also exists in skeletal muscle. The ChIP-derived repressor of network oscillator (Chrono) and brain and muscle ARNT-like 1 (Bmal1) are core circadian components. Chrono is considered to be the repressor of Bmal1, and the Chrono–Bmal1 pathway is important in regulating the circadian rhythm; it has been speculated that this pathway could be a new mechanism for regulating glucose metabolism. The purpose of this study was to investigate the effects of Chrono on glucose metabolism in skeletal muscle and exercise capacity by using mice with skeletal-muscle-specific overexpression of Chrono (Chrono TG) and wild-type (WT) mice as the animal models. The results of this cross-sectional study indicated that the Chrono TG mice had an impaired glucose tolerance, lower exercise capacity, and higher levels of nonfasted blood glucose and glycogen content in skeletal muscle compared to WT mice. In addition, the Chrono TG mice also showed a significant increase in the amount of Chrono bound to Bmal1 according to a co-IP analysis; a remarkable decrease in mRNA expression of Tbc1d1, Glut4, Hk2, Pfkm, Pdp1, Gbe1, and Phka1, as well as in activity of Hk and protein expression of Ldhb; but higher mRNA expression of Pdk4 and protein expression of Ldha compared with those of WT mice. These data suggested the skeletal-muscle-specific overexpression of Chrono led to a greater amount of Chrono bound to Bmal1, which then could affect the glucose transporter, glucose oxidation, and glycogen utilization in skeletal muscle, as well as exercise capacity.

Monitoring the mental workload of construction workers is effective in detecting risky subjects because cognitive overload may threaten their safety. This study aimed to measure workers’ mental workload caused by heat exposure using heart rate variability (HRV) and eye movement features. Inexperienced pipe workers (n = 30) were invited to perform an installation task in a normothermic (26 °C, 50% RH) and a hyperthermic (33 °C, 50% RH) condition. Their HRV and eye movement features were recorded as the inputs of training models classifying mental workload between the two thermal conditions, using supervised machine learning algorithms, including Support Vector Machines (SVM), KNearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Random Forest (RF). The results show that applying eight HRV features through the KNN algorithm could obtain the highest classification accuracy of 90.00% (Recall = 0.933, Precision = 0.875, F1 = 0.903, AUC = 0.887). This study could provide a new perspective for monitoring the mental workload of construction workers, and it could also provide a feasible approach for the construction industry to monitor workers’ mental workload in hot conditions.