Exercise & Fitness

The multiple uses of artificial intelligence in exercise programs: a narrative review.

BACKGROUND: Artificial intelligence is based on algorithms that enable machines to perform tasks and activities that generally require human intelligence, and its use offers innovative solutions in various fields. Machine learning, a subset of artificial intelligence, concentrates on empowering computers to learn and enhance from data autonomously; this narrative review seeks to elucidate the util...

The triglyceride-glucose index and its obesity-related derivatives as predictors of all-cause and cardiovascular mortality in hypertensive patients: insights from NHANES data with machine learning analysis.

BACKGROUND: Hypertension (HTN) is a global public health concern and a major risk factor for cardiovascular disease (CVD) and mortality. Insulin resistance (IR) plays a crucial role in HTN-related metabolic dysfunction, but its assessment remains challenging. The triglyceride-glucose (TyG) index and its derivatives (TyG-BMI, TyG-WC, and TyG-WHtR) have emerged as reliable IR markers. In this study,...

Classification system for Ai-enabled consumer-grade wearable technologies aiming to automatize decision-making about individualization of exercise procedures.

Acute effect of endurance exercise on human milk insulin concentrations: a randomised cross-over study.

INTRODUCTION: Insulin is present in human milk and its concentration correlates with maternal circulating levels. Studies on the association between h...

A robust and generalized framework in diabetes classification across heterogeneous environments.

Diabetes mellitus (DM) represents a major global health challenge, affecting a diverse range of demographic populations across all age groups. It has ...

Machine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007-2016.

Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of...

Psychophysiological foundations of human physical activity behavior and motivation: theories, systems, mechanisms, evolution, and genetics.

Physical activity is a meaningful part of life that starts before birth and lasts until death. There are many health benefits to be derived from physi...

A comprehensive analysis of stroke risk factors and development of a predictive model using machine learning approaches.

Stroke is a leading cause of death and disability globally, particularly in China. Identifying risk factors for stroke at an early stage is critical t...

Accelerated enzyme engineering by machine-learning guided cell-free expression.

Enzyme engineering is limited by the challenge of rapidly generating and using large datasets of sequence-function relationships for predictive design...

A machine learning-based model for predicting the risk of cognitive frailty in elderly patients on maintenance hemodialysis.

Elderly patients undergoing maintenance hemodialysis (MHD) face a heightened risk of cognitive frailty (CF), which significantly compromises quality o...

EFL learners' motivation and acceptance of using large language models in English academic writing: an extension of the UTAUT model.

Large language models (LLMs), represented by ChatGPT, are one of the most significant technological breakthroughs in generative AI and have begun to b...

Single-Cell Identification and Characterization of Viable but Nonculturable Using Raman Optical Tweezers and Machine Learning.

is a leading foodborne pathogen that may enter a viable but nonculturable (VBNC) state to survive under environmental stresses, posing a significant ...

A Deep Learning Approach for Mental Fatigue State Assessment.

This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate ...

Early Prediction of Cardio Vascular Disease (CVD) from Diabetic Retinopathy using improvised deep Belief Network (I-DBN) with Optimum feature selection technique.

Cardio Vascular Disease (CVD) is one of the leading causes of mortality and it is estimated that 1 in 4 deaths happens due to it. The disease prevalen...

Task-oriented robotic rehabilitation for back mobility and functioning in a post-intensive care unit obese patient: A case report.

BackgroundIntensive care unit (ICU) acquired weakness is a detrimental condition characterized by muscle weakness, difficulty in weaning from mechanic...

Optimized assessment of physical rehabilitation exercises using spatiotemporal, sequential graph-convolutional networks.

Rehabilitation is the process of helping people regain or improve lost or impaired function due to injury, illness, or disease. To assist in tracking ...

Explainable post hoc portfolio management financial policy of a Deep Reinforcement Learning agent.

Financial portfolio management investment policies computed quantitatively by modern portfolio theory techniques like the Markowitz model rely on a se...

Development and evaluation of interpretable machine learning regressors for predicting femoral neck bone mineral density in elderly men using NHANES data.

Osteoporotic femoral neck fractures (OFNFs) pose a significant orthopedic challenge in the elderly population, accounting for up to 40% of all osteopo...

Exploring the triglyceride-glucose index's role in depression and cognitive dysfunction: Evidence from NHANES with machine learning support.

BACKGROUND: Depression and cognitive impairments are prevalent among older adults, with evidence suggesting potential links to obesity and lipid metab...

Tailoring industrial enzymes for thermostability and activity evolution by the machine learning-based iCASE strategy.

The pursuit of obtaining enzymes with high activity and stability remains a grail in enzyme evolution due to the stability-activity trade-off. Here, w...