Mental fatigue is a major public health issue worldwide that is common among both healthy and sick people. In the literature, various modern technologies, together with artificial intelligence techniques, have been proposed. Most techniques consider complex biosignals, such as electroencephalogram, electro-oculogram or classification of basic heart rate variability parameters. Additionally, most studies focus on a particular area, such as driving, surgery, etc. In this paper, a novel approach is presented that combines electrocardiogram (ECG) signal feature extraction, principal component analysis (PCA), and classification using machine learning algorithms. With the aim of daily mental fatigue recognition, an experiment was designed wherein ECG signals were recorded twice a day: in the morning, i.e., a state without fatigue, and in the evening, i.e., a fatigued state. PCA analysis results show that ECG signal parameters, such as Q and R wave amplitude values, as well as QT and T intervals, presented with the largest differences between states compared to other ECG signal parameters. Furthermore, the random forest classifier achieved more than 94.5% accuracy. This work demonstrates the feasibility of ECG signal feature extraction for automatic mental fatigue detection.

Healthy lifestyle is one of the most important factors in the prevention of premature deaths, chronic diseases, productivity loss, obesity, and other economic and social aspects. The workplace plays an important role in promoting the physical activity and wellbeing of employees. Previous studies are mostly focused on individual interviews, various questionnaires that are a conceptual information about individual health state and might change according to question formulation, specialist competence, and other aspects. In this paper the work ability was mostly related to the employee’s physiological state, which consists of three separate systems: cardiovascular, muscular, and neural. Each state consists of several exercises or tests that need to be performed one after another. The proposed data transformation uses fuzzy logic and different membership functions with three or five thresholds, according to the analyzed physiological feature. The transformed datasets are then classified into three stages that correspond to good, moderate, and poor health condition using machine learning techniques. A three-part Random Forest method was applied, where each part corresponds to a separate system. The obtained testing accuracies were 93%, 87%, and 73% for cardiovascular, muscular, and neural human body systems, respectively. The results indicate that the proposed work ability evaluation process may become a good tool for the prevention of possible accidents at work, chronic fatigue, or other health problems.

Objective: The prevalence of arterial hypertension in adult men is 34% worldwide and 52% in Lithuania. This paper aims to provide an overview of the prevalence trends of arterial hypertension and its clinical forms in middle-aged Lithuanian men and to assess the risk profiles of the different groups. Methods: This retrospective research study collected data from 52,012 Lithuanian male patients. The study population consisted of men aged 40–55 who participated in the Lithuanian High Cardiovascular Risk Program between 2009 and 2019. Results: Of the 52,012 participants, 47.2% (n = 24,531) were men with arterial hypertension. The prevalence of arterial hypertension in Lithuanian men decreased between 2009 and 2019 (p < 0.001). Before the study visit, 12.6% (n = 6583) of men were unaware of their diagnosis, and 8.8% (n = 4555) of diagnosed cases were untreated. In addition, 2.6% (n = 1334) of the men had resistant arterial hypertension. Significant differences in most general characteristics were found between the groups with arterial hypertension. Waist circumference increased from 92.8 ± 10.7 cm in men without arterial hypertension to 109 ± 13.3 cm and in men with resistant arterial hypertension (p < 0.001), and mean triglyceride levels increased from 1.55 ± 1.22 mmol/L to 2.32 ± 1.82 mmol/L in these groups (p < 0.001). Obesity (63.6%), unhealthy diet (74.7%), physical inactivity (62.9%), and diabetes (25.2%) were more common in the group with resistant arterial hypertension than in all other groups (p < 0.001). Meanwhile, dyslipidaemia was the most common risk factor in all groups (from 83.2% in men without arterial hypertension to 92.6% in men with resistant hypertension). Conclusions: Almost half of middle-aged men had arterial hypertension, with prevalence decreasing from 2009 to 2019. Significant changes in cardiometabolic characteristics were observed in newly diagnosed cases of arterial hypertension. These changes are even more notable in men with resistant hypertension compared to a non-resistant form. Most cardiovascular risk factors were most prevalent in over 50% of resistant hypertension cases, with dyslipidaemia being the most common risk factor in the entire male sample.

Background: Hypertension is one of the most common cardiovascular risk factors worldwide. Additionally, epidemiological studies show a worryingly high prevalence of treatment-resistant hypertension. Especially concerning is the frequent co-occurrence of other cardiovascular risk factors, including dyslipidaemia, smoking, and diabetes mellitus. Objectives: The aim of this study is to investigate the prevalence of arterial hypertension and other cardiovascular risk factors in patients aged 50–54 years. Methods: A retrospective study was conducted on patients participating in the Lithuanian High Cardiovascular Risk Primary Prevention Programme. Data were collected from self-report questionnaires, laboratory tests, and clinical assessment. Hypertension was confirmed if systolic blood pressure was ≥140 mmHg and/or diastolic blood pressure was ≥90 mmHg or the patient had been previously diagnosed. Results: In total, 49155 patients—32018 (62.4%) women and 17137 (37.6%) men—were enrolled in this study. A total of 24549 (49.9%) patients were diagnosed with arterial hypertension. The prevalence of non-resistant primary hypertension was 45.9%, while the prevalence of resistant primary hypertension was 4.1%. The prevalence of dyslipidaemia was 92.79% in the non-resistant primary arterial hypertension group and was 94.59% in the resistant primary arterial hypertension group. The prevalence of smoking was higher in the non-resistant primary arterial hypertension group compared to patients with resistant hypertension (22.43% and 17.09%, respectively). A total of 23.06% of patients with resistant primary arterial hypertension had diabetes mellitus. Conclusions: The prevalence of primary arterial hypertension in middle-aged Lithuanians was high, reaching almost 50% in both sexes. Patients tended to have many cardiovascular risk factors simultaneously, with dyslipidaemia being the most common (prevalence > 90%).

Background/Objectives: Hypertension (HTN) is a significant risk factor for cardiovascular disease (CVD), and a subset of patients exhibits apparently resistant hypertension (aRHTN), where blood pressure remains inadequately controlled despite treatment. This study aims to assess the prevalence of cardiovascular risk factors in middle-aged Lithuanian men with HTN and aRHTN, as well as to evaluate the effectiveness of hypertension management in these groups. Methods: Data from 52,012 men participating in the Lithuanian High Cardiovascular Risk Programme (LitHiR) between 2009 and 2019 were analysed. Participants were categorised into two groups: treated hypertension (HTN) and apparent resistant hypertension (aRHTN). Despite treatment, the aRHTN group included those who failed to achieve their target blood pressure. The prevalence of cardiovascular risk factors (dyslipidaemia, diabetes, metabolic syndrome, obesity, physical inactivity, and an unbalanced diet) was compared between the groups. Results: The overall prevalence of HTN was 47%, with 9.9% of treated hypertensive men having aRHTN. Dyslipidemia was both groups’ most prevalent risk factor (94.1% in HTN vs. 95.5% in aRHTN, p < 0.001). Men with aRHTN exhibited higher rates of diabetes (25.9% vs. 18.5%, p < 0.001), metabolic syndrome (75.3% vs. 66.3%, p < 0.001), and left ventricular hypertrophy (59.4% vs. 43.1%, p < 0.001). Treatment success was significantly lower in the aRHTN group (7.57% vs. 28.4%, p < 0.001). Conclusions: Hypertension affects almost half of the studied population, with 10% of treated hypertensives exhibiting aRHTN. The aRHTN group had a higher number of additional cardiovascular risk factors and lower treatment success rates. Improved management of cardiovascular risk factors is crucial, especially in the aRHTN population, to reduce the burden of CVD.