Background: The wrist has become a standard location for accelerometry (ACC) data collection, primarily to optimize compliance, yet interpretation of wrist ACC data is limited due to a lack of calibration studies. This study aimed to establish cutpoints for a wrist-mounted Actical accelerometer in 6- to 11-year-old children using 2 methods.

Methods: Metabolic and ACC data (15-sec epoch) were collected during 8 activities in 22 children ages 6-11. Linear regression (LR) and Receiver Operator Characteristics (ROC) were used to examine the relationship between METs and ACC counts. Cutpoints were established at < 1.5, 1.5-2.99, 3-5.99, and ≥ 6 METs for sedentary, light, moderate, and vigorous activity, respectively. Cutpoints were applied to a large, multiday sample of children (n = 269) to examine differences in cutpoints on minutes of moderate to vigorous PA (MVPA).

Results: LR and ROC yielded moderate cutpoints of 574 and 388, respectively. When applied to the large sample, LR and ROC cutpoints resulted in an estimated 83 and 140 minutes of daily MVPA, respectively.

Conclusions: This study established wrist-mounted Actical cutpoints for children using 2 methods. The differences in cutpoints and their effect on estimates of MVPA in an independent sample highlight challenges associated with establishing cutpoints, suggesting that standardized calibration procedures be developed.

Download full-text PDF

Source
http://dx.doi.org/10.1123/jpah.2011-0411DOI Listing

Publication Analysis

Top Keywords

acc data
12
cutpoints
10
actical accelerometer
8
wrist-mounted actical
8
moderate vigorous
8
applied large
8
differences cutpoints
8
children
5
establishing wrist-based
4
wrist-based cutpoints
4

Similar Publications

Aerolysin Nanopore Electrochemistry.

Acc Chem Res

January 2025

Molecular Sensing and Imaging Center, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.

ConspectusIons are the crucial signaling components for living organisms. In cells, their transportation across pore-forming membrane proteins is vital for regulating physiological functions, such as generating ionic current signals in response to target molecule recognition. This ion transport is affected by confined interactions and local environments within the protein pore.

View Article and Find Full Text PDF

Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers' pneumoconiosis.

Biomed Eng Online

January 2025

Department of Pulmonary and Critical Care Medicine, National Health Commission Key Laboratory of Pneumoconiosis, Shanxi Key Laboratory of Respiratory Diseases, First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Taiyuan, 030001, Shanxi, People's Republic of China.

Background: Coal workers' pneumoconiosis is a chronic occupational lung disease with considerable pulmonary complications, including irreversible lung diseases that are too complex to accurately identify via chest X-rays. The classification of clinical imaging features from high-resolution computed tomography might become a powerful clinical tool for diagnosing pneumoconiosis in the future.

Methods: All chest high-resolution computed tomography (HRCT) medical images presented in this work were obtained from 217 coal workers' pneumoconiosis (CWP) patients and dust-exposed workers.

View Article and Find Full Text PDF

To enhance the intelligent classification of computer vulnerabilities and improve the efficiency and accuracy of network security management, this study delves into the application of a comprehensive classification system that integrates the Memristor Neural Network (MNN) and an improved Temporal Convolutional Neural Network (TCNN) in network security management. This system not only focuses on the precise classification of vulnerability data but also emphasizes its core role in strengthening the network security management framework. Firstly, the study designs and implements a neural network model based on memristors.

View Article and Find Full Text PDF

Backgrounds/objective: Deep brain stimulation (DBS) has proved the viability of alleviating depression symptoms by stimulating deep reward-related nuclei. This study aims to investigate the abnormal connectivity profiles among superficial, intermediate, and deep brain regions within the reward circuit in major depressive disorder (MDD) and therefore provides references for identifying potential superficial cortical targets for non-invasive neuromodulation.

Methods: Resting-state functional magnetic resonance imaging data were collected from a cohort of depression patients (N = 52) and demographically matched healthy controls (N = 60).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!