Prevention

Interactive SBOT for Health Education and Active Living.

Physical inactivity is a global health issue contributing to chronic conditions like obesity and cardiovascular diseases, with regular exercise often hindered by sedentary lifestyles and aging populations. This study introduces SBOT, a gamification-driven chatbot that combines personalized exercise guidance with engaging features, such as challenges and rewards, to promote physical activity (PA). ...

Research Gaps, Challenges, and Opportunities in Automated Insulin Delivery Systems.

BACKGROUND: Since the discovery of the life-saving hormone insulin in 1921 by Dr Frederick Banting in 1921, there have been many critical discoveries and technical breakthroughs that have enabled people living with type 1 diabetes (T1D) to live longer, healthier lives. The development of insulin pumps, continuous glucose monitoring (CGM) systems, and automated insulin delivery (AID) systems have e...

Rethinking arthritis: exploring its types and emerging management strategies.

Arthritis is a complex group of disorders characterized by inflammation of the joints and poses a significant burden on global health. The most preval...

Improved convolutional neural network for precise exercise posture recognition and intelligent health indicator prediction.

This paper presents a novel framework for accurate exercise posture recognition and health indicator prediction based on improved convolutional neural...

Machine learning models integrating dietary data predict all-cause mortality in U.S. NAFLD patients: an NHANES-based study.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease, closely associated with metabolic abnormalities and...

How does social support influence autonomous physical learning in adolescents? Evidence from a chain mediation and latent profile analysis.

PURPOSE: This study examines how social support influences adolescents' autonomous physical learning behavior, exploring the mediating roles of self-e...

Metabolic Phenotypes, Genotypes, and Gut Microbiome Signatures in Obesity: Implications for Precision Nutrition Strategies in Type 2 Diabetes Prevention.

The worldwide prevalence of overweight and obesity has increased rapidly in the last decades. This rise has led to a surge in comorbidities such as ty...

White Box Modeling of Self-Determined Sequence Exercise Program Among Sarcopenic Older Adults: Uncovering a Novel Strategy Overcoming Decline of Skeletal Muscle Area.

BACKGROUND: Resistance exercise, Taichi exercise, and the hybrid exercise program consisting of the two aforementioned methods have been demonstrated ...

Modelling Complex Spatial Distribution in Central Italy: A Random Forest Approach Revealing Underrepresented Lowland Populations Based on Spatially-Explicit Predictors.

Species distribution models are powerful tools to infer ecology and support management of conservation and socio-economic valuable taxa, such as brown...

A hybrid approach to heart disease prediction using a fractional-order mathematical model and machine learning algorithm.

Heart disease remains one of the leading causes of morbidity and mortality worldwide, necessitating the development of more accurate and efficient dia...

Alterations in Gut Microbiota-Brain Axis in Major Depressive Disorder as Identified by Machine Learning.

Major depressive disorder (MDD) is a complex mental health condition whose causes may extend beyond purely biological explanations and are increasingl...

Signature gene expression model for quantitative evaluation of MASH-like liver injury in mice.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a spectrum of chronic pathologic conditions strongly associated with metabolic syn...

Chemical Properties-Based Deep Learning Models for Recommending Rational Daily Diet Combinations to Diabetics Through Large-Scale Virtual Screening of α-Glucosidase Dietary-Derived Inhibitors and Verified In Vitro.

The lack of suitable chemical research methodologies has hindered the discovery of rational daily diet combinations from large-scale dietary-derived c...

Energy consumption analysis and prediction in exercise training based on accelerometer sensors and deep learning.

This study aims to enhance the accuracy and efficiency of energy consumption prediction during exercise training and address the limitations of existi...

The Application of Machine Learning in Warfarin Dose Precision for Diabetic Patients Treated with Statins: A Comparative Study.

PURPOSE: To evaluate the impact of statin therapy on warfarin dose requirements in diabetic patients and to assess the performance of various machine ...

Evaluating and modifying the PHDI for depression prevention: insights from NHANES 2005-2018.

BACKGROUND: Depression is a significant focus in mental health research, emerging as a pressing public health concern globally. The Planetary Health D...

What are we learning with Yoga? Mapping the scientific literature on Yoga using a vector-text-mining approach.

The techniques used in yoga have roots in traditions that precede modern science. Research shows that yoga enhances quality of life and well-being, po...

Machine Learning with Ingredient-Level Food Trees Reveals Contributors to Systemic Inflammation Among Adults in the National Health and Nutrition Examination Survey, 2001-2010 and 2015-2018.

BACKGROUND: Methods for modeling the relationship between self-reported 24-hour dietary recalls and health outcomes are traditionally based on nutrien...

Tilorone attenuates high-fat diet-induced hepatic steatosis by enhancing BMP9-Smad1/5/8 signaling.

The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) is rapidly increasing and is caused by excessive fat deposition in ...

Using Large Language Models to Enhance Exercise Recommendations and Physical Activity in Clinical and Healthy Populations: Scoping Review.

BACKGROUND: Regular exercise recommendations (ERs) and physical activity (PA) are crucial for the prevention and management of chronic diseases. Howev...