AIMC Topic: Exercise

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Is personality associated with the lived experience of the NHS England low calorie diet programme: A pilot study.

Clinical obesity
This pilot study explored the use of a novel behavioural artificial intelligence (AI) tool to examine whether personality is associated with the lived experience of the NHS England launched a low calorie diet (LCD). A cross-sectional survey was disse...

The predictive role of sedentary behavior and physical activity on adolescent depressive symptoms: A machine learning approach.

Journal of affective disorders
OBJECTIVE: This study aims to investigate the predictive value of sedentary behavior and physical activity in adolescent depressive symptoms.

Machine learning modeling for predicting adherence to physical activity guideline.

Scientific reports
This study aims to create predictive models for PA guidelines by using ML and examine the critical determinants influencing adherence to the PA guidelines. 11,638 entries from the National Health and Nutrition Examination Survey were analyzed. Variab...

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

Frontiers in public health
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 artifi...

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

Physiological reviews
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 physical activity; hence, regular engagement is recommended on a weekly basis. However, these recommendat...

What factors influence the willingness and intensity of regular mobile physical activity?- A machine learning analysis based on a sample of 290 cities in China.

Frontiers in public health
INTRODUCTION: This study, based on Volunteered Geographic Information (VGI) and multi-source data, aims to construct an interpretable macro-scale analytical framework to explore the factors influencing urban physical activities. Using 290 prefecture-...

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

Computers in biology and medicine
Rehabilitation is the process of helping people regain or improve lost or impaired function due to injury, illness, or disease. To assist in tracking the progress of patients undergoing rehabilitation, this paper proposes a lightweight graph-based de...

Application of Additive Manufacturing and Deep Learning in Exercise State Discrimination.

Sensors (Basel, Switzerland)
With the rapid development of sports technology, smart wearable devices play a crucial role in athletic training and health management. Sports fatigue is a key factor affecting athletic performance. Using smart wearable devices to detect the onset of...

Estimating cardiovascular mortality in patients with hypertension using machine learning: The role of depression classification based on lifestyle and physical activity.

Journal of psychosomatic research
PURPOSE: This study aims to harness machine learning techniques, particularly the Random Survival Forest (RSF) model, to assess the impact of depression on cardiovascular disease (CVD) mortality among hypertensive patients. A key objective is to eluc...