Changes made to the RHRV package to facilitate analysis in our recent manuscript: "Facilitating Ambulatory Heart Rate Variability Analysis using accelerometry-based Classifications of Physical...

Background Physical activity guidelines recommend accumulating moderate-to-vigorous physical activity but interpreting and monitoring these recommendations remains challenging. Although step-based m...

AbstractAims. To investigate the joint association of accelerometer-measured physical activity (PA) and sleep duration with mortality risk.Methods and resu

Abstract: Parkinson’s disease (PD) motor symptoms are characterized by fluctuations, rendering their monitoring and therapy personalization challenging. Wrist-worn accelerometers serve as objective di...

AbstractStudy Objectives. Sleep is vital for health and development, yet inadequate sleep among children and adolescents has risen in recent decades, const

Abstract: Bradykinesia and rigidity are two of the core motor symptoms of Parkinsonism. Current clinical assessment is confined into in-person clinical setting, involves the element of subjectivity an...

Facilitating ambulatory heart rate variability analysis using accelerometry-based classifications of body position and self-reported sleep, Rietz, Marlene, Schmidt-Persson, Jesper, Gillies Banke Rasmussen, Martin, Overgaard Sørensen, Sarah, Rath Mortensen, Sofie, Brage, Søren, Lund Kristensen, Peter, Grøntved, Anders, Brønd, Jan Christian

Interfaces the 'actinet' Python module &lt;<a href="https://github.com/OxWearables/actinet" target="_top">https://github.com/OxWearables/actinet</a>&gt; for an activity classification model based on self-supervised learning for wrist-worn accelerometer data.

We propose a method for identifying individuals based on their continuously monitored wrist-worn accelerometry during activities of daily living. The method consists of three steps: (1) using Adaptive...

Conflation With Cardiorespiratory Fitness

Background: Maintaining balance is essential for older adults to preserve independence and reduce fall risk. However, empirical evidence linking trunk stability with balance and gait is scarce, partly due to the lack of accessible field tests. Smartphone-based accelerometry field tests offer a promising approach to assess trunk stability outside the laboratory. Objective: The study aimed to determine (1) the association between trunk stability and whole-body balance and gait and (2) the extent to which trunk stability and whole-body balance field tests align with laboratory-based assessments in older adults. Methods: A total of 53 physically active older adults performed 2 trunk stability tests (ie, the lumbopelvic stability [field] and the unstable sitting posturographic [laboratory] tests), 2 whole-body balance tests (ie, the tandem stance posturographic [laboratory] and the whole-body static balance [field] tests), and 2 gait-related tests (ie, the straight-line gait and the modified Timed Up and Go [field] tests) 2 times. A Spearman correlation analysis was performed after assessing the reliability of the variables. Results: Significant positive low-to-high correlations were found between the lumbopelvic stability and the whole-body static balance tests (0.380≤≤0.700;

The six-minute walk test and quantitative muscle testing (QMT) are commonly used skeletal muscle assessments in Duchenne muscular dystrophy; however, …

**Abstract:** This paper introduces a novel framework, DietComplianceAI, for predicting and optimizing dietary adherence in clinical settings. Current methods for monitoring dietary intake are often subjective, time-consuming, and prone to reporting bias. DietComplianceAI leverages a multi-modal data fusion approach, combining wearable sensor data (accelerometry, heart rate variability), image-based food logging (computer vision), and patient-reported outcomes (natural language processing) to provide a robust and objective assessment of dietary compliance.

**Abstract:** This research proposes a novel methodology for high-resolution mapping of subtle gravitational anomalies using a distributed network of micro-accelerometry-based quantum sensors, coupled with a real-time adaptive Kalman filter. Our approach significantly enhances accuracy in detecting subsurface density variations associated with groundwater reservoirs, a critical resource for sustainable water management. This technique surpasses traditional gravimetry through spatial resolution improvements (10x), reduced noise (2x), and automated calibration requiring minimal human intervention.

Abstract. The study of repeatability in behaviour and activity level can be used to evaluate inter-individual differences, which are fundamental to assess

This large-scale study of 73,485 adults, aged 40 to 79 years at accelerometry baseline, provides, to our knowledge, the first examination of physical activity intensity equivalence with a broad range of major outcomes including all-cause, cardiovascular, and cancer mortality. We...