AIMC Topic: Exercise
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MoveMentor-examining the effectiveness of a machine learning and app-based digital assistant to increase physical activity in adults: protocol for a randomised controlled trial.
Trials
Jul 1, 2025
BACKGROUND: Physical inactivity is prevalent, leading to a high burden of disease and large healthcare costs. Thus, there is a need for affordable, effective and scalable interventions. However, interventions that are affordable and scalable are bese...
Improved convolutional neural network for precise exercise posture recognition and intelligent health indicator prediction.
Scientific reports
Jul 1, 2025
This paper presents a novel framework for accurate exercise posture recognition and health indicator prediction based on improved convolutional neural networks. We propose a multi-scale feature fusion architecture incorporating spatiotemporal attenti...
How does social support influence autonomous physical learning in adolescents? Evidence from a chain mediation and latent profile analysis.
PloS one
Jul 1, 2025
PURPOSE: This study examines how social support influences adolescents' autonomous physical learning behavior, exploring the mediating roles of self-efficacy and exercise motivation, and the moderating effects of gender and behavioral typologies. The...
Energy consumption analysis and prediction in exercise training based on accelerometer sensors and deep learning.
Scientific reports
Jun 3, 2025
This study aims to enhance the accuracy and efficiency of energy consumption prediction during exercise training and address the limitations of existing methods in terms of data feature extraction, model complexity, and adaptability to practical appl...
Using Large Language Models to Enhance Exercise Recommendations and Physical Activity in Clinical and Healthy Populations: Scoping Review.
JMIR medical informatics
May 27, 2025
BACKGROUND: Regular exercise recommendations (ERs) and physical activity (PA) are crucial for the prevention and management of chronic diseases. However, creating effective exercise programs demand substantial time and specialized expertise from both...
Deep learning to promote health through sports and physical training.
Frontiers in public health
May 27, 2025
BACKGROUND: Physical activity plays a crucial role in maintaining health and preventing chronic diseases. However, accurately assessing the impact of sports and physical training on health improvement remains a challenge. Recent advancements in deep ...
Characterising physical activity patterns in community-dwelling older adults using digital phenotyping: a 2-week observational study protocol.
BMJ open
May 24, 2025
INTRODUCTION: Physical activity (PA) is crucial for older adults' well-being and mitigating health risks. Encouraging active lifestyles requires a deeper understanding of the factors influencing PA, which conventional approaches often overlook by ass...
[Cluster predictors of trajectories of leisure-time physical activity intensity in men and women from ELSA-Brasil].
Cadernos de saude publica
May 19, 2025
The maintenance of physical activity over time is a challenge for public health. Predictors of different physical activity intensities have not been sufficiently analyzed. This study aimed to identify clusters of trajectories of physical activity int...
Potential Modulatory Roles of Gut Microbiota and Metabolites in the Associations of Macronutrient-to-Physical Activity Ratios With Dyslipidemia.
Journal of the American Heart Association
May 15, 2025
BACKGROUND: Lifestyle factors toward diet and physical activity (PA) may directly influence the pathophysiology of dyslipidemia. However, the associations of the specific macronutrient-to-PA ratio with dyslipidemia, and the underlying mechanisms rega...
Uncovering key factors in weight loss effectiveness through machine learning.
International journal of obesity (2005)
May 6, 2025
BACKGROUND/OBJECTIVES: One of the main challenges in weight loss is the dramatic interindividual variability in response to treatment. We aim to systematically identify factors relevant to weight loss effectiveness using machine learning (ML).
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