AIMC Topic: Sedentary Behavior

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Movement- and Posture-based Measures of Sedentary Patterns and Associations with Metabolic Syndrome in Hispanic/Latino and non-Hispanic Adults.

Journal of racial and ethnic health disparities
BACKGROUND: Sedentary behavior has been identified as a significant risk factor for Metabolic Syndrome (MetS). However, it is unclear if the sedentary pattern measurement approach (posture vs. movement) impacts observed associations or if association...

Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity.

Scientific reports
Sociodemographic and lifestyle factors (sleep, physical activity, and sedentary behavior) may predict obesity risk in early adolescence; a critical period during the life course. Analyzing data from 2971 participants (M = 11.94, SD = 0.64 years) wear...

Deep learning of movement behavior profiles and their association with markers of cardiometabolic health.

BMC medical informatics and decision making
BACKGROUND: Traditionally, existing studies assessing the health associations of accelerometer-measured movement behaviors have been performed with few averaged values, mainly representing the duration of physical activities and sedentary behaviors. ...

Machine learning and ontology in eCoaching for personalized activity level monitoring and recommendation generation.

Scientific reports
Leading a sedentary lifestyle may cause numerous health problems. Therefore, passive lifestyle changes should be given priority to avoid severe long-term damage. Automatic health coaching system may help people manage a healthy lifestyle with continu...

Development of a Smart Chair Sensors System and Classification of Sitting Postures with Deep Learning Algorithms.

Sensors (Basel, Switzerland)
Nowadays in modern societies, a sedentary lifestyle is almost inevitable for a majority of the population. Long hours of sitting, especially in wrong postures, may result in health complications. A smart chair with the capability to identify sitting ...

Let's (Tik) Talk About Fitness Trends.

Frontiers in public health
Several factors that follow the development of society affect physical inactivity, which primarily includes the development of technology and digitalization and the increasing choice of unhealthy lifestyle habits. However, certain shifts in the fitne...

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

PloS one
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...

Feature selection for unsupervised machine learning of accelerometer data physical activity clusters - A systematic review.

Gait & posture
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. ...

Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA.

Scientific reports
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...