Exercise & Fitness

Cardiopulmonary exercise testing in contemporary cardiology: physiological insights, novel applications and evolving algorithms.

Cardiopulmonary exercise testing (CPET) allows for the study of the pathophysiology of exercise intolerance through assessment of exercise integrative physiology of the pulmonary, cardiovascular, muscular and cellular oxidative systems. Over the years, key gas exchange variables have shown a role in the interpretative translation of physiology to clinical decision making. CPET is a standard when a...

Digital twin technologies for supporting self-care in adults with diet-related chronic conditions: a systematic review.

BACKGROUND: Diet-related chronic conditions are major contributors to global morbidity and mortality. Effective management of these conditions requires consistent engagement in self-care behaviours such as healthy eating, physical activity, and medication adherence. However, behavioural interventions often lack personalisation, limiting their impact, whereas digital twin (DT) systems, which use di...

Development and validation of a risk prediction model for diabetic peripheral neuropathic pain in type 2 diabetes: A machine learning and statistical approach.

OBJECTIVE: To identify independent risk factors for diabetic peripheral neuropathic pain (DPNP), construct a nomogram prediction model, and quantify t...

Identification of key genes in the pathogenesis of hepatic ischemia-reperfusion injury based on bioinformatics and experimental verification.

Hepatic ischemia-reperfusion injury (HIRI) is a frequently encountered complication during liver surgical procedures, characterized by ischemia-induce...

From algorithms to action: Risk stratification for arteriovenous fistula failure in hemodialysis.

BACKGROUND: Arteriovenous fistula (AVF) is the preferred vascular access for hemodialysis, yet primary failure and early dysfunction remain common, th...

Development and validation of an AI use scale for sport and exercise science students.

A sequential deep learning model for predicting people with obesity in adults aged 18-64 using physical fitness variables.

BACKGROUND/OBJECTIVES: Health of people with obesity is a global concern. We developed an explainable sequential deep learning model using nationally ...

Environmental supportiveness, physical activity, and sedentary time in children with cerebral palsy.

AIM: To describe how movement behaviours (sedentary time, light-intensity physical activity, moderate-to-vigorous physical activity [MVPA]) and parent...

Exploration of a Multimodal Machine Learning Model Integrating Ultrasound and Clinical Indicators for the Diagnosis of Diabetic Peripheral Neuropathy.

OBJECTIVES: Based on ultrasound technology and clinical indicators, this study intends to develop multiple risk prediction models for diabetic periphe...

The Influence of the COVID-19 Pandemic on Current Teaching Methods, Training, and Perception among Romanian Surgery-Oriented Students: Observational, Cross-Sectional Multicentric Study.

BACKGROUND: The COVID-19 pandemic prompted rapid changes in medical education, accelerating the adoption of online and distance learning methods as al...

Fuzzy logic for preanesthetic risk assessment in cataract surgery patients.

We aimed to build a fuzzy logic preanaesthetic risk score tailored to cataract surgery. By fusing systemic comorbidities with key patient attributes i...

Advancing diagnostic equity through artificial intelligence chest radiograph screening for osteoporosis in Asian populations.

Early identification of abnormal bone mineral density (BMD) through opportunistic screening is critical for preventing osteoporotic fractures. We vali...

Behavioural, cognitive, and computational risk factors for type 2 diabetes: A systematic review.

BACKGROUND: Type 2 diabetes mellitus represents a global public health challenge, with rising prevalence driven by complex interactions between lifest...

The relationship between sarcopenia and all-cause and cardiovascular mortality risk among middle-aged and older adults across stages 0-3 of cardiovascular-kidney-metabolic syndrome: evidence from NHANES and CHARLS.

BACKGROUND: Sarcopenia has been proved to be associated with cardiovascular diseases, chronic kidney disease, and metabolic disorders, but the relatio...

Personalized Glucose Management With AI: Pilot Study Using a Multiarmed Bandit Approach.

BACKGROUND: Personalized behavioral recommendations through mobile apps have proven effective in preventing serious chronic diseases such as diabetes....

Explainable artificial intelligence for personalized prognosis in pancreatic cancer: A nationwide study from Taiwan.

Pancreatic cancer is highly aggressive with poor outcomes; current artificial intelligence (AI) prognostic models often lack interpretability and unde...

Artificial intelligence-based prediction of boar reproductive fitness and health: Current status in research and practice.

The development of artificial intelligence (AI) and machine learning (ML) is transforming reproductive management in boar studs by providing objective...

Associations between 12 insulin resistance surrogates with metabolic dysfunction-associated steatotic liver disease risk and all-cause mortality: data from the NHANES III (1988-1994).

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) has been shown to be intimately linked to the presence of insulin resista...

Risk-prediction models for postpartum depression: Assessment of risk of bias using PROBAST+AI.

OBJECTIVE: The postpartum depression (PPD) risk prediction model is an effective risk stratification tool and is expected to play a significant role i...

Data-Driven Phenotypic Clusters of Gestational Diabetes Mellitus and Associations With Risk of Perinatal Complications and Postpartum Diabetes.

OBJECTIVE: Management of gestational diabetes mellitus (GDM) largely follows a uniform approach, despite growing recognition of GDM heterogeneity. We ...