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An explainable machine learning-based approach to predicting treatment response for neurofeedback in ADHD.

Scientific reports
Attention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder with serious long-term effects if untreated, emphasizing the need for early treatment given its neurobiological heterogeneity. This study introduces a novel expla...

Evaluation of various traditional machine learning techniques for predicting the acute effect of different hamstring muscle stretching methods among male soccer players.

Scientific reports
This study investigated the acute effects of static (SS), dynamic (DS), and ballistic (BS) hamstring stretching on performance in male soccer players and applied machine learning (ML) to predict protocol efficacy. A total of 249 players with and with...

Faecal metabolites as a readout of habitual diet capture dietary interactions with the gut microbiome.

Nature communications
The interplay between diet and gut microbiome composition is complex. Faecal metabolites, the end products of human and microbial metabolism, provide insights into these interactions. Here, we integrate faecal metabolomics, metagenomics, and habitual...

Oura Ring Behavioral Feedback Intervention for Alcohol Reduction in Young Adults: User Experience Evaluation of a Pilot Randomized Trial.

Journal of medical Internet research
BACKGROUND: Wearable fitness technologies, like the Oura Ring (Oura Health Oy), provide physiological metrics, like sleep and heart rate data, to a growing user base of young adults. However, these technologies and connected mobile apps do not measur...

The Validity of Generative Artificial Intelligence in Evaluating Medical Students in Objective Structured Clinical Examination: Experimental Study.

JMIR formative research
BACKGROUND: The Objective Structured Clinical Examination (OSCE) has been widely used to evaluate students in medical education. However, it is resource-intensive, presenting challenges in implementation. We hypothesized that generative artificial in...

Automated Speech Analysis for Screening and Monitoring Bipolar Depression: Machine Learning Model Development and Interpretation Study.

JMIR medical informatics
BACKGROUND: Depressive episodes in bipolar disorder are frequent, prolonged, and contribute substantially to functional impairment and reduced quality of life. Therefore, early and objective detection of bipolar depression is critical for timely inte...

Machine Learning-Based Prediction of In-Hospital Falls in Adult Inpatients: Retrospective Observational Multicenter Study.

JMIR medical informatics
BACKGROUND: Falls among hospitalized patients are a critical issue that often leads to prolonged hospital stays and increased health care costs. Traditional fall risk assessments typically rely on standardized scoring systems; however, these may fail...

Efficacy of FiberMore, an AI-Based mHealth Intervention to Increase Dietary Fiber Intake Among Type 2 Diabetes Patients: Protocol for a Pilot Randomized Controlled Trial.

JMIR research protocols
BACKGROUND: A high intake of dietary fiber has been shown to improve glycemic control and decrease hyperinsulinemia in people living with type 2 diabetes (T2D). T2D patients in Japan consume less than the recommended amount of fiber. Based on finding...

Comparing Human and AI Therapists in Behavioral Activation for Depression: Cross-Sectional Questionnaire Study.

JMIR formative research
BACKGROUND: Large language models (LLMs) have rapidly advanced across numerous fields, including mental health care. A shortage of trained therapists and mental health care providers has driven informal use of LLMs for therapeutic support. However, t...