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Purpose: The purpose of this study was to determine whether the published left-wrist cut points for the triaxial Gravity Estimator of Normal Everyday Activity (GENEA) accelerometer are accurate for predicting intensity categories during structured activity bouts.
Methods: A convenience sample of 130 adults wore a GENEA accelerometer on their left wrist while performing 14 different lifestyle activities. During each activity, oxygen consumption was continuously measured using the Oxycon mobile. Statistical analysis used Spearman's rank correlations to determine the relationship between measured and estimated intensity classifications. Cross tabulations were constructed to show the under- or overestimation of misclassified intensities. One-way χ2 tests were used to determine whether the intensity classification accuracy for each activity differed from 80%.
Results: For all activities, the GENEA accelerometer-based physical activity monitor explained 41.1% of the variance in energy expenditure. The intensity classification accuracy was 69.8% for sedentary activities, 44.9% for light activities, 46.2% for moderate activities, and 77.7% for vigorous activities. The GENEA correctly classified intensity for 52.9% of observations when all activities were examined; this increased to 61.5% with stationary cycling removed.
Conclusions: A wrist-worn triaxial accelerometer has modest-intensity classification accuracy across a broad range of activities when using the cut points of Esliger et al. Although the sensitivity and the specificity are less than those reported by Esliger et al., they are generally in the same range as those reported for waist-worn, uniaxial accelerometer cut points.
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http://dx.doi.org/10.1249/MSS.0b013e3182965249 | DOI Listing |
J Imaging Inform Med
September 2025
Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.
Large language models (LLMs) have been successfully used for data extraction from free-text radiology reports. Most current studies were conducted with LLMs accessed via an application programming interface (API). We evaluated the feasibility of using open-source LLMs, deployed on limited local hardware resources for data extraction from free-text mammography reports, using a common data element (CDE)-based structure.
View Article and Find Full Text PDFImmunol Res
September 2025
Department of Immunology and Allergy, Faculty of Medicine, Necmettin Erbakan University, Konya, Türkiye.
Background: Variants of uncertain significance (VUS) represent a major diagnostic challenge in the interpretation of genetic testing results, particularly in the context of inborn errors of immunity such as severe combined immunodeficiency (SCID). The inconsistency among computational prediction tools often necessitates expensive and time-consuming wet-lab analyses.
Objective: This study aimed to develop disease-specific, multi-class machine learning models using in silico scores to classify SCID-associated genetic variants and improve the interpretation of VUS.
Sci Justice
September 2025
Department of Forensic Science, People's Public Security University of China, Beijing 100038, China. Electronic address:
As a critical frontier in forensic science, the profiling of physical evidence characteristics has garnered substantial attention. This study employed gas chromatography-mass spectrometry (GC-MS) to investigate age-related differences in sebaceous fingermark fatty acid compositions. Fingermark samples from 80 volunteers were analyzed to characterize fatty acid profiles across different age groups.
View Article and Find Full Text PDFJ Safety Res
September 2025
National Technical University of Athens, Department of Transportation Planning and Engineering, 5 Iroon Polytechniou St., GR-15773 Athens, Greece.
Introduction: Assessing safety using traffic simulation is becoming increasingly feasible with advancements in methodological frameworks and tools, emphasizing the critical importance of accuracy and reliability. This study aims to bridge the gap between simulation models and real-world safety observations, contributing to the advancement of more robust safety assessment methodologies. It presents a comprehensive comparative analysis of traffic safety metrics derived from both simulated and real-world data, employing clustering technique to identify safety patterns.
View Article and Find Full Text PDFAppl Clin Inform
September 2025
Pediatric Critical Care, Stanford University School of Medicine, Stanford, United States.
Background: Time spent in the electronic health record (EHR) is an important measure of clinical activity. Vendor-derived EHR use metrics may not correspond to actual EHR experience. Raw EHR audit logs enable customized EHR use metrics, but translating discrete timestamps to time intervals is challenging.
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