Background: Actigraphic data during simulated participant movements were evaluated to differentiate among patient behavior states.
Methods: Arm and leg actigraphic data were collected on 30 volunteers who simulated 3 behavioral states (calm, restless, agitated) for 10 minutes; counts of observed participant movements (head, torso, extremities) were documented.
Results: The mean age of participants was 34.7 years, and 60% were female. Average movement was significantly different among the states (P < .0001; calm [mean = .48], restless [mean = 2.16], agitated [mean = 3.75]). Mean actigraphic measures were significantly different among states for both arm (P < .0001; calm [mean = 6.8], restless [mean = 28.5], agitated [mean = 52.6]) and leg (P < .0001; calm [mean = 3.5], restless [mean = 18.7], agitated [mean = 37.7]).
Conclusion: Distinct levels of behavioral states were successfully simulated. Actigraphic data can provide an objective indicator of patient activity over a variety of behavioral states, and these data may offer a standard for comparison among these states.
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http://dx.doi.org/10.1016/j.hrtlng.2009.12.013 | DOI Listing |
Digit Biomark
December 2024
VivoSense, Inc., Newport Coast, CA, USA.
Introduction: Wrist-worn accelerometers can capture stepping behavior passively, continuously, and remotely. Methods utilizing peak detection, threshold crossing, and frequency analysis have been used to detect steps from wrist-worn accelerometer data, but it remains unclear how different approaches perform across a range of walking speeds and free-living activities. In this study, we evaluated the performance of four open-source methods for deriving step counts from wrist-worn accelerometry data, when applied to data from a range of structured locomotion and free-living activities.
View Article and Find Full Text PDFBehav Sleep Med
January 2025
Centre for Sport Research within the Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Burwood, Victoria, Australia.
Objectives: This study sought to characterize the sleep of youth athletes and investigate relationships between sleep measures and cognitive function.
Method: Youth netball athletes ( = 19, age; 16.58 ± 1.
BMC Genomics
January 2025
Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway.
Background: Few studies have explored the association between DNA methylation and physical activity. The aim of this study was to evaluate the association of objectively measured hours of sedentary behavior (SB) and moderate physical activity (MPA) with DNA methylation. We further aimed to explore the association between SB or MPA related CpG sites and cardiometabolic traits, gene expression, and genetic variation.
View Article and Find Full Text PDFFront Psychol
January 2025
Comprehensive Research Organization, Waseda University, Tokyo, Japan.
Background: Dietary management in diabetic patients is affected by psychosocial factors and the social-environmental context. Ecological momentary assessment (EMA) allows patients to consistently report their experiences in real-time over a certain period and across different contexts. Despite the importance of dietary management, only a few EMA studies have been conducted on dietary management and psychosocial factors in patients with type 2 diabetes; further evidence must be gathered.
View Article and Find Full Text PDFCureus
December 2024
Nursing, Tokyo Women's Medical University, Tokyo, JPN.
Circadian rhythms develop from an ultradian to a circadian rhythm during a few months in the early human life stage. One of the strong factors in promoting the development of circadian rhythms during infancy is maternal rest-activity rhythms. However, few studies have examined comparing the rest-activity rhythms of parents and infants.
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