Exercise & Fitness

Behavioral Inhibition Network Predicts Alcohol Use in Men and Stress in Women.

OBJECTIVE: Impulsivity, a complex construct linked to addictions, is often inconsistently assessed and conceptualized, making it difficult to effectively target in addiction treatment. The current study aimed to identify neural substrates underlying distinct impulsivity domains and explore their relationships with alcohol use and stress in both women and men. METHOD: We utilized a whole-brain mach...

Can AI chatbots effectively respond inquiries for patients with Pulmonary Arterial Hypertension about exercise and physical activity: A comparative study of three chatbots.

OBJECTIVE: The utilization of artificial intelligence-based chatbots in the healthcare field is increasingly prevalent. Nonetheless, the quality of responses for these chatbots on this topic is unknown, particularly in considering the sparse data about exercise and physical activity in patients with pulmonary arterial hypertension (PAH). The aim was to evaluate and compare the accuracy and readabi...

A Flexible Wireless Passive Platform for Decoupled Electrolyte and Temperature Sensing Toward Heat‑Stress Assessment.

Strenuous exercise and heat exposure can significantly elevate core body temperature and induce profuse sweating, leading to dehydration, electrolyte ...

Digital and Blended Lifestyle Interventions for Preschool-Aged Children and Families With a Low Socioeconomic Position and the General Population: Scoping Review.

BACKGROUND: Many unhealthy habits develop in early childhood and can lead to long-term health risks, which disproportionately affect children with low...

Structural damage assessment in the spine of patients with axial spondyloarthritis - results from an international OMERACT multi-reader exercise using MRI-based synthetic CT.

OBJECTIVE: To develop and perform preliminary cross-sectional validation of a magnetic resonance imaging (MRI)-based outcome measure for assessing spi...

PMB-NN: Physiology-centered hybrid AI for personalized hemodynamic monitoring from photoplethysmography.

BACKGROUND AND OBJECTIVE: Continuous monitoring of blood pressure (BP) and hemodynamic parameters such as peripheral resistance (R) and arterial compl...

Longitudinal serum uric acid transition patterns and their clinical, genetic, and dietary determinants: prospective prediction of incident hyperuricemia and gout in a Korean population-based cohort.

BACKGROUND: Longitudinal serum uric acid (SUA) transition patterns and their clinical, genetic, and dietary determinants remain poorly characterized. ...

Development of an artificial intelligence prediction model for moderate-to-severe COPD exacerbations using continuous multiple unobtrusive sensors: protocol of a multicentre prospective observational study.

INTRODUCTION: Exacerbations, impaired health-related quality of life (HRQoL) and reduced exercise capacity increase the risk of hospitalisations and d...

Students' engagement with AI-supported learning and its association with academic interest and career intentions in business analytics education.

Artificial intelligence (AI) tools are increasingly embedded in higher education, yet limited research has examined how sustained AI usage intentions ...

Body composition phenotyping of obesity in children aged 6-18 years: multi-strategy clustering and interpretable machine learning.

BACKGROUND: Body composition heterogeneity in childhood obesity is not fully captured by BMI, motivating operational phenotyping using non-invasive me...

A systematic review of molecular signaling in the muscle-brain-gut axis: exercise-induced myokines and microbial metabolites as key mediators.

Exercise physiology is evolving from an organ-based framework toward a systems-level understanding, where molecular interactions between muscle, brain...

Personalized Type 1 Diabetes Management: Reinforcement Learning-Based Insulin Dosing and Glucose Forecasting.

BACKGROUND: Optimizing insulin dosing and predicting future glucose levels for people with type 1 diabetes is challenging due to the dynamic nature of...

A data-informed multidimensional composite score for stress assessment.

Occupations such as the military, emergency responders, and athletes demand acute physical, cognitive and psychosocial effort. Individuals in these ro...

Training effect of a deep learning-based blended teaching model on ECMO transport for ICU nurses: a prospective, parallel-group, randomized controlled trial.

OBJECTIVE: Given the high alignment between deep learning and blended teaching objectives, blended teaching provides a feasible pathway for achieving ...

Advances in body composition imaging for diagnosis and monitoring: From radiologic assessment to precision medicine.

Body composition (BC) is a key determinant of metabolic risk, treatment tolerance, and long-term outcomes. The distribution of skeletal muscle, viscer...

Artificial intelligence readiness in Pakistan's medical and dental education: training-phase decline, a knowledge-practice paradox, and the role of digital determinants of health.

BACKGROUND: We assessed AI knowledge, attitudes, and use among medical and dental students in Pakistan, a lower-middle-income country (LMIC), and iden...

Mental health in early pregnancy: Interplay of objectively measured lifestyles, gut microbiota, and metabolomics.

Prenatal anxiety symptoms (AS) and depression symptoms (DS) in early pregnancy substantially impact maternal-infant health, but their pathophysiologic...

Employee perceptions of AI adoption across service domains in a Finnish public health and social care organization: a cross-sectional mixed-methods study.

BACKGROUND: Empirical evidence on how employees across different service domains within a single multi-sector public organization perceive AI adoption...

Predictive and interactive roles of motivation and situational learning activities on emotional and cognitive engagement.

BACKGROUND: Emotions and cognitions are important factors of learning engagement. While extensive research focuses on in-class activity, there is a ga...

Data-Driven Prediction of Microbiological Quality in Oregano-Active Packaged Gilthead Sea Bream Fillets using Non-Invasive Spectroscopy and Machine Learning.

This study investigated the potential of FTIR spectroscopy and multispectral imaging (MSI) combined with machine learning to monitor microbiological q...