AIMC Topic: Sedentary Behavior

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Generation of a Free-Living Ground-Truth Validation Dataset for Wearable Measures of Physical Activity, Sedentary Behavior, Sleep, and Heart Rate in Adults (OxWEARS): Protocol for a Cross-Sectional Study.

JMIR research protocols
BACKGROUND: Wearable devices enable continuous measurement of physical activity, sedentary behavior, sleep, and heart rate under free-living conditions. However, most validation studies rely on small, homogeneous samples; are conducted under laborato...

Site-specific pain dynamics: associations between accelerometer-measured physical activity patterns and pain in older adults.

The journal of headache and pain
BACKGROUND: Physical activity (PA) has emerged as a promising non-pharmacological intervention for pain management, the relationship between objectively measured PA patterns and multi-site pain remains poorly understood. This exploratory study invest...

Reducing annotation burden in physical activity research using vision language models.

Scientific reports
Data from wearable devices collected in free-living settings, and labelled with physical activity behaviours compatible with health research, are essential for both validating existing wearable-based measurement approaches and developing novel machin...

Identifying subjective life expectancy risk factors in physically active and inactive middle-aged and older adults using machine learning models.

BMC public health
BACKGROUND: Physical activity is a key focus in the field of public health, and subjective life expectancy is closely associated with individuals' physical and psychological well-being. This study aimed to identify the risk factors for subjective lif...

Application of machine learning models in predicting physical literacy in 4-6-year-old children: A comprehensive analysis of individual and family factors.

PloS one
Physical literacy in children has become a significant research topic in both education and psychology. Recently, machine learning, as a cutting-edge AI technology, has started to play a crucial role in these fields. This study aimed to apply machine...

Associations between the 24-h Activity Daily Cycle and Incident Dementia.

Medicine and science in sports and exercise
BACKGROUND: Physical activity, sedentary behavior (SB), and sleep all impact the risk of incident dementia, however, engagement in these activities is constrained by the 24-h day. Increasing time spent in one activity necessarily reduces time spent i...

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...

Social Robots and Sensors for Enhanced Aging at Home: Mixed Methods Study With a Focus on Mobility and Socioeconomic Factors.

JMIR aging
BACKGROUND: Population aging affects society, with a profound impact on daily activities for those of a low socioeconomic status and with motor impairments. Social assistive robots (SARs) and monitoring technologies can improve older adults' well-bei...

Characterizing daily physical activity patterns with unsupervised learning via functional mixture models.

Journal of behavioral medicine
Physical inactivity is a significant public health concern. Consideration of inter-individual variations in physical activity (PA) trends can provide additional information about the groups under study to aid intervention design. This study aims to i...