AIMC Topic: Sedentary Behavior

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A machine learning approach to predict extreme inactivity in COPD patients using non-activity-related clinical data.

PloS one Aug 19, 2021
Facilitating the identification of extreme inactivity (EI) has the potential to improve morbidity and mortality in COPD patients. Apart from patients with obvious EI, the identification of a such behavior during a real-life consultation is unreliable...
Pulmonary Disease, Chronic Obstructive Male ROC Curve Decision Making Machine Learning Algorithms Female Aged Humans Life Style Sedentary Behavior
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Feature selection for unsupervised machine learning of accelerometer data physical activity clusters - A systematic review.

Gait & posture Aug 13, 2021
BACKGROUND: Identifying clusters of physical activity (PA) from accelerometer data is important to identify levels of sedentary behaviour and physical activity associated with risks of serious health conditions and time spent engaging in healthy PA. ...
Exercise Cluster Analysis Unsupervised Machine Learning Accelerometry Sedentary Behavior Humans
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Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA.

Scientific reports Mar 26, 2021
Type 2 diabetes mellitus (T2D) prevalence in the United States varies substantially across spatial and temporal scales, attributable to variations of socioeconomic and lifestyle risk factors. Understanding these variations in risk factors contributio...
Diabetes Mellitus, Type 2 United States Socioeconomic Factors Obesity Spatial Analysis Humans Diet Machine Learning Quality of Life Risk Factors Life Style Exercise Geography Prevalence Sedentary Behavior
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Improving energy expenditure estimates from wearable devices: A machine learning approach.

Journal of sports sciences Apr 6, 2020
A means of quantifying continuous, free-living energy expenditure (EE) would advance the study of bioenergetics. The aim of this study was to apply a non-linear, machine learning algorithm (random forest) to predict minute level EE for a range of act...
Middle Aged Exercise Body Mass Index Body Composition Algorithms Female Adult Male Body Temperature Accelerometry Fitness Trackers Calorimetry, Indirect Sedentary Behavior Walking Activities of Daily Living Heart Rate Energy Metabolism Humans Machine Learning Bicycling Galvanic Skin Response Jogging
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Detecting prolonged sitting bouts with the ActiGraph GT3X.

Scandinavian journal of medicine & science in sports Dec 22, 2019
The ActiGraph has a high ability to measure physical activity; however, it lacks an accurate posture classification to measure sedentary behavior. The aim of the present study was to develop an ActiGraph (waist-worn, 30 Hz) posture classification to ...
Adult Male Middle Aged Actigraphy Time Sedentary Behavior Sitting Position Humans Machine Learning Algorithms Female
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Calibration and validation of accelerometer-based activity monitors: A systematic review of machine-learning approaches.

Gait & posture Dec 5, 2018
BACKGROUND: Objective measures using accelerometer-based activity monitors have been extensively used in physical activity (PA) and sedentary behavior (SB) research. To measure PA and SB precisely, the field is shifting towards machine learning-based...
Humans Energy Metabolism Calibration Machine Learning Accelerometry Exercise Sedentary Behavior
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Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk.

Sensors (Basel, Switzerland) Sep 24, 2018
With the recent advancement in wearable computing, sensor technologies, and data processing approaches, it is possible to develop smart clothing that integrates sensors into garments. The main objective of this study was to develop the method of auto...
Cardiovascular Diseases Risk Assessment Wearable Electronic Devices Telemedicine Activities of Daily Living Sedentary Behavior Machine Learning Female Adult Male Humans
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Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants.

Scientific reports May 21, 2018
Current public health guidelines on physical activity and sleep duration are limited by a reliance on subjective self-reported evidence. Using data from simple wrist-worn activity monitors, we developed a tailored machine learning model, using balanc...
Adult Male Middle Aged Exercise Biological Specimen Banks Activities of Daily Living Sleep Adolescent Self Report Female Aged Aged, 80 and over Young Adult Humans Machine Learning Phenotype United Kingdom Accelerometry Sedentary Behavior
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Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults.

PloS one Nov 20, 2017
Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promis...
Humans Sedentary Behavior Machine Learning Female Thigh Aged Monitoring, Ambulatory Energy Metabolism Male Accelerometry Middle Aged Calorimetry, Indirect Exercise
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High Amounts of Sitting, Low Cardiorespiratory Fitness, and Low Physical Activity Levels: 3 Key Ingredients in the Recipe for Influencing Metabolic Syndrome Prevalence.

American journal of health promotion : AJHP Dec 22, 2016
PURPOSE: Limited research has evaluated the independent and additive associations of moderate-to-vigorous physical activity (MVPA), sedentary behavior (SB), and cardiorespiratory fitness (CRF) with metabolic syndrome, which was the purpose of this st...
Sitting Position Prevalence Cross-Sectional Studies Adiposity Blood Glucose Male Logistic Models Cardiorespiratory Fitness Middle Aged Accelerometry Nutrition Surveys Exercise Sedentary Behavior Humans Blood Pressure Metabolic Syndrome Female Oxygen Consumption Lipids Young Adult Exercise Test Adult
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