Background: Physical inactivity (PA) and low fi tness levels are known to initiate early in life, and negatively influence physical, mental and emotional wellbeing. The comparison between PA, fitness and self-esteem in young Hispanic children has not been documented. Purpose: To test for differences in PA by gender and grade ...
Background: Blood pressure (BP) reactivity in response to mental stress increases with age and contributes to vascular damage manifesting as increased carotid intima-media thickness (IMT). This increases risk for cardiovascular (CV) events. Participation in regular physical activity (PA) may lead to a favorable CV response to stress. Purpose: To examine ...
Background: Pregnancy researchers use various physical activity (PA) measurement techniques. However, few studies have evaluated the validity of these techniques in free-living environments, and there is little agreement regarding which may be the best to use. Consensus on this issue would be valuable to future researchers wishing to compare results ...
Background: Physical activity has been objectively measured using hip-worn accelerometers for decades. However, wrist-worn accelerometers are currently used in large-scale studies. Differences in wrist and hip dynamics during locomotion affect monitor output, which may impact how prediction models are built. Purpose: To compare ActiGraph™ wrist and hip accelerations (g’s) ...
Background: Accelerometers objectively assess physical activity (PA) and are increasingly used in epidemiologic studies. However, processing techniques are not standardized and limit data comparability across studies. Purpose: To compare the impact of wear-time assessment method and filter choice on accelerometer output in a large cohort. Methods: Participants (7,650 women, mean age 71.4...
Background: Physical activity patterns captured by accelerometers have been used to classify activity type with machine learning (ML) algorithms. ML may also be applied to accelerometer data for predicting cardiovascular (CV) health risk directly. Decision trees are efficient constructive search algorithms that develop rules for categorizing the data based ...
Purpose: To compare activity type recognition rates of machine learning algorithms trained on laboratory versus free-living accelerometer data in free-living older adults. Methods: Thirty-seven older adults (21F and 14M ; 70.8 ± 4.9 y) performed selected activities (total of 35 min) in the lab while wearing three ActiGraph GT3X+ activity monitors (dominant hip, wrist, ...
Background: Physical function is an important factor for health and well-being of older adults. Performance-based measures of physical function have been related to an elevated risk of frailty, disability, and mortality. Poor sleep quality has been associated with reduced physical function in older adults. However, little is known about the ...
Background: Accelerometry paradata (administrative data related to collection/management/treatment) are inconsistently reported or limited to accounts of valid days and average wear time. Purpose: To present a model for reporting accelerometry paradata collected from children (mean age 10 years) at the Baton Rouge, USA site of the International Study of ...
Background: Active school travel provides a convenient, daily opportunity to contribute to meeting physical activity guidelines. Accelerometer-based studies of children’s active commuting often use a standardized 60-min estimation method, in which commuting is assumed to occur for an hour before and after school. We developed an individualized method in ...