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Background: Insufficient physical activity (IPA) is a significant risk factor for various non-communicable diseases. The Iran action plan is a 20% reduction in IPA. Therefore, we aimed to describe the age and sex pattern of physical activity domains, IPA, the intensity of physical activity, sedentary behavior, and their associates at Iran’s national and provincial levels in 2021. Methods: This study used the data of the STEPwise Approach to NCD Risk Factor Surveillance (STEPS) 2021 in Iran. The STEPS study used the Global Physical Activity Questionnaire (GPAQ) version two developed by WHO for the assessment of physical activity, which included work, transport, and recreational activities domains. We showed and compared demographic and clinical characteristics of participants between males and females, using t-test and Chi-square test. A logistic regression model adjusted for residential areas, years of schooling, wealth index, age, marital status, and occupation has also been implemented. The results were presented as percentages and 95% confidence intervals (CI). Results: We included 27,874 participants with a mean (SD) age of 45.69 (15.91), among whom 12,479 (44.77%) were male. The mean prevalence of IPA for the whole population for all ages was 51.3% (50.62–51.98%). By sex, 41.93% (40.88–42.98%) and 57.87% (56.99–58.75%) of men and women had IPA, respectively. According to the physical activity domains, the age-standardized prevalence of no recreational activity was 79.40% (78.80–79.99%), no activity at work was 66.66% (65.99–67.32%), and no activity at transport was 49.40% (48.68–50.11%) for both sexes combined. Also, the overall age-standardized prevalence of sedentary behaviors was 50.82% (50.11–51.53%). Yazd province represented the highest prevalence of IPA (63.45%), while West Azerbaijan province represented the lowest prevalence (39.53%). Among both sexes, living in the urban area vs. rural area [adjusted OR: 1.44; (1.31–1.58)], married vs. single

The study aimed to estimate the prevalence of lipid abnormalities in Iranian adults by demographic characterization, geographical distribution, and associated risk factors using national and sub-national representative samples of the STEPs 2021 survey in Iran. In this population-based household survey, a total of 18,119 individuals aged over 25 years provided blood samples for biochemical analysis. Dyslipidemia was defined by the presence of at least one of the lipid abnormalities of hypertriglyceridemia (≥ 150 mg/dL), hypercholesterolemia (≥ 200 mg/dL), high LDL-C (≥ 130 mg/dL), and low HDL-C (< 50 mg/dL in women, < 40 mg/dL in men), or self-reported use of lipid-lowering medications. Mixed dyslipidemia was characterized as the coexistence of high LDL-C with at least one of the hypertriglyceridemia and low HDL-C. The prevalence of each lipid abnormality was determined by each population strata, and the determinants of abnormal lipid levels were identified using a multiple logistic regression model. The prevalence was 39.7% for hypertriglyceridemia, 21.2% for hypercholesterolemia, 16.4% for high LDL-C, 68.4% for low HDL-C, and 81.0% for dyslipidemia. Hypercholesterolemia and low HDL-C were more prevalent in women, and hypertriglyceridemia was more prevalent in men. The prevalence of dyslipidemia was higher in women (OR = 1.8), obese (OR = 2.8) and overweight (OR = 2.3) persons, those residents in urban areas (OR = 1.1), those with inappropriate physical activity (OR = 1.2), patients with diabetes (OR = 2.7) and hypertension (OR = 1.9), and participants with a history (OR = 1.6) or familial history of CVDs (OR = 1.2). Mixed dyslipidemia prevalence was 13.6% in women and 11.4% in men (P < 0.05). The prevalence of lipid abnormalities was highly heterogeneous among provinces, and East Azarbaijan with 85.3% (81.5–89.1) and Golestan with 68.5% (64.8–72.2) had the highest and lowest prevalence of dyslipidemia, respectively. Although the prevalence of high

This study aimed to investigate the diabetes mellitus (DM) and prediabetes epidemiology, care cascade, and compliance with global coverage targets. We recruited the results of the nationally representative Iran STEPS Survey 2021. Diabetes and prediabetes were two main outcomes. Diabetes awareness, treatment coverage, and glycemic control were calculated for all population with diabetes to investigate the care cascade. Four global coverage targets for diabetes developed by the World Health Organization were adopted to assess the DM diagnosis and control status. Among 18,119 participants, the national prevalence of DM and prediabetes were 14.2% (95% confidence interval 13.4–14.9) and 24.8% (23.9–25.7), respectively. The prevalence of DM treatment coverage was 65.0% (62.4–67.7), while the prevalence of good (HbA1C < 7%) glycemic control was 28.0% (25.0–31.0) among all individuals with diabetes. DM diagnosis and statin use statics were close to global targets (73.3% vs 80%, and 50.1% vs 60%); however, good glycemic control and strict blood pressure control statistics, were much way behind the goals (36.7% vs 80%, and 28.5% vs 80%). A major proportion of the Iranian population are affected by DM and prediabetes, and glycemic control is poorly achieved, indicating a sub-optimal care for diabetes and comorbidities like hypertension.

Accurate diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) is a significant challenge. Misdiagnosis has significant negative medical side effects. Due to the complex nature of this disorder, there is no computational expert system for diagnosis. Recently, automatic diagnosis of ADHD by machine learning analysis of brain signals has received an increased attention. This paper aimed to achieve an accurate model to discriminate between ADHD patients and healthy controls by pattern discovery. Event-Related Potentials (ERP) data were collected from ADHD patients and healthy controls. After pre-processing, ERP signals were decomposed and features were calculated for different frequency bands. The classification was carried out based on each feature using seven machine learning algorithms. Important features were then selected and combined. To find specific patterns for each model, the classification was repeated using the proposed patterns. Results indicated that the combination of complementary features can significantly improve the performance of the predictive models. The newly developed features, defined based on band power, were able to provide the best classification using the Generalized Linear Model, Logistic Regression, and Deep Learning with the average accuracy and Receiver operating characteristic curve > %99.85 and > 0.999, respectively. High and low frequencies (Beta, Delta) performed better than the mid, frequencies in the discrimination of ADHD from control. Altogether, this study developed a machine learning expert system that minimises misdiagnosis of ADHD and is beneficial for the evaluation of treatment efficacy.