Exercise & Fitness

Development and validation of a machine learning model for identifying liver fibrosis in MAFLD: a retrospective model development study.

BACKGROUND: Metabolic dysfunction-associated fatty liver disease (MAFLD), as the most prevalent chronic liver disease globally, is closely related to the prevalence of metabolic syndrome. Liver fibrosis is a core stage in the progression of this disease, significantly increasing the risk of liver cirrhosis, liver failure, and hepatocellular carcinoma. Early identification of high-risk patients is ...

Technology-enabled telerehabilitation for Parkinson's disease: a scoping review of digital rehabilitation systems, delivery architectures, and implementation challenges.

INTRODUCTION: Digital technologies are increasingly integrated into neurorehabilitation programs for Parkinson's Disease (PD), enabling remote delivery of therapy, continuous monitoring of motor performance, and adaptive feedback during rehabilitation training. Telerehabilitation systems incorporating wearable sensors, virtual reality platforms, mobile applications, and artificial intelligence (AI...

Assessing the association of type 2 diabetes with skin health status: a study of the Northern Finland Birth Cohort 1966.

OBJECTIVES: There is evidence to suggest that alterations in skin health often co-occur with type 2 diabetes (T2D). However, the underlying mechanisms...

Development and validation of an AI-driven multimodal system for assessing aerobic gymnastics training using video analysis, motion capture, and physiological signals.

This research developed and validated a comprehensive multimodal assessment system integrating artificial intelligence for aerobic gymnastics training...

C-reactive protein-triglyceride-glucose index as a novel biomarker for type 2 diabetes mellitus association in women with a history of gestational diabetes mellitus.

OBJECTIVE: To explore the relationship between the C-reactive protein-triglyceride-glucose index (CTI) and the risk of type 2 diabetes mellitus (T2DM)...

Evaluating AI in postoperative rehabilitation of axillary web syndrome after breast cancer surgery: ChatGPT vs DeepSeek.

OBJECTIVE: To systematically compare two generative artificial intelligence (AI) models, ChatGPT and DeepSeek in the rehabilitation management for axi...

Anthropometry-adjusted TyG indices predict diabetes, graft failure, and mortality in kidney transplant recipients.

CONTEXT: The triglyceride-glucose (TyG) index is a widely used surrogate marker of insulin resistance and associated with cardiometabolic outcomes. Ho...

Association and predictive value of preoperative high-sensitivity C-reactive protein for postoperative atrial fibrillation after video-assisted thoracoscopic lobectomy: a cohort study using the INSPIRE database.

BACKGROUND: Postoperative atrial fibrillation (POAF) is a common and serious complication following video-assisted thoracoscopic surgery (VATS), which...

Large language model-driven Socratic questioning for endodontic case analysis: A randomised controlled trial.

OBJECTIVE: Endodontic education requires structured clinical reasoning practise, but resource constraints often limit the delivery of case-based instr...

Performance of AI in Predicting the Progression of Gestational Diabetes to Type 2 Diabetes: Systematic Review and Meta-Analysis.

BACKGROUND: Gestational diabetes mellitus (GDM) significantly increases the risk of developing type 2 diabetes mellitus (T2DM) post partum, with up to...

Effects of an AI-VR enhanced flipped classroom on English achievement, metacognitive self-regulation, and learning motivation among EFL learners: the moderating role of technological literacy.

Traditional flipped classrooms often fail to sustain young EFL learners' engagement due to passive pre-class tasks and high self-regulation demands. W...

Explainable machine learning integrating environmental chemical biomarkers and maternal clinical factors for prediction of hypertensive disorder of pregnancy.

Hypertensive disorders of pregnancy (HDP) represent a leading cause of maternal morbidity, yet environmental chemical contributors to HDP risk remain ...

Determinants of intention to use generative AI fitness assistants: Integrating the Theory of Planned Behavior, second-order information system quality, and perceived value.

As generative artificial intelligence (GAI) becomes embedded in sport and health management, GAI fitness assistants can generate and iteratively refin...

Cardio-respiratory deep learning model to predict hospital admission from emergency department electrocardiogram images and respiration videos: a prospective study.

BACKGROUND: Early prediction of hospital admission at the emergency department (ED) triage can improve patient flow and resource allocation. Most exis...

RLAnOxPeptide: An Integrated Framework Combining Transformer and reinforcement learning for Efficient Antioxidant Peptide Prediction and Innovative Design.

MOTIVATION: Bioactive peptides exhibit immense potential in pharmaceutical and food science domains, with antioxidant peptides (AOPs) garnering signif...

Precision or Paradox? AI-Driven Adiposity Imaging in Women With Overweight and Obesity: A Systematic Review and Meta-Analysis.

Artificial intelligence (AI) enables automated, high-throughput adiposity quantification, offering refined risk stratification for women with overweig...

Advances in wearable sensing technologies and intelligent systems for healthcare and fitness.

Wearable sensing technologies and intelligent systems have gained significant attention due to their potential to revolutionize healthcare and fitness...

Should AI Be Your Personal Trainer?

From providing fitness information to correcting form in real time, AI applications for physical fitness are rapidly evolving. In this News and Perspe...

Artificial Intelligence Diagnosis of Obstructive Sleep Apnea Using Overnight Pulse Oximetry: A Systematic Review and Bayesian Meta-Analysis.

BACKGROUND: Obstructive sleep apnea (OSA) affects 38% of the population, yet over 90% of cases remain undiagnosed. The gold standard for diagnosis, po...

Explainable machine learning revealing the impact of mental and physical health on arthritis.

OBJECTIVES: To develop a robust and interpretable machine learning framework for arthritis risk prediction and to identify important risk factors asso...