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http://dx.doi.org/10.1002/nau.70011 | DOI Listing |
J Am Heart Assoc
August 2025
The Research Unit for Exercise Epidemiology, Centre of Research in Childhood Health, Department of Sports Science and Clinical Biomechanics University of Southern Denmark Odense Denmark.
Background: Limited evidence exists on the link between sedentary activity and blood pressure (BP), especially using combined thigh- and back-worn accelerometry, which offers accurate posture classification. This study examined the association between device-measured sedentary activity patterns and BP levels and hypertension and explored whether obesity explained these associations.
Methods: This cross-sectional study included 3127 adults (≥18 years) from the Danish Lolland-Falster Health Study (2017-2020).
Neurourol Urodyn
August 2025
Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Scand J Med Sci Sports
January 2025
Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark.
Physical activity (PA) reduces the risk of negative mental and physical health outcomes in older adults. Traditionally, PA intensity is classified using METs, with 1 MET equal to 3.5 mL O·min·kg.
View Article and Find Full Text PDFOsteoarthritis Cartilage
August 2024
Department of Physical Therapy, Boston University, Boston, MA, USA; Division of Rheumatology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA. Electronic address:
Objective: Individuals with chronic pain due to knee osteoarthritis (OA) are insufficiently physically active, and alterations of facilitatory and inhibitory nociceptive signaling are common in this population. Our objective was to examine the association of these alterations in nociceptive signaling with objective accelerometer-based measures of physical activity in a large observational cohort.
Design: We used data from the Multicenter Osteoarthritis Study.
JMIR Form Res
May 2024
Novartis Biomedical Research, Novartis Pharma AG, Basel, Switzerland.
Background: Wrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing.
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