Background: Wearable motion sensors are gaining popularity for monitoring free-living physical activity among people with Parkinson disease (PD), but more evidence supporting the accuracy and precision of motion sensors for capturing step counts is required in people with PD.
Objective: This study aimed to examine the accuracy and precision of 3 common consumer-grade motion sensors for measuring actual steps taken during prolonged periods of overground and treadmill walking in people with PD.
Methods: A total of 31 ambulatory participants with PD underwent 6-min bouts of overground and treadmill walking at a comfortable speed. Participants wore 3 devices (Garmin Vivosmart 3, Fitbit One, and Fitbit Charge 2 HR), and a single researcher manually counted the actual steps taken. Accuracy and precision were based on absolute and relative metrics, including intraclass correlation coefficients (ICCs) and Bland-Altman plots.
Results: Participants walked 628 steps over ground based on manual counting, and Garmin Vivosmart, Fitbit One, and Fitbit Charge 2 HR devices had absolute (relative) error values of 6 (6/628, 1.0%), 8 (8/628, 1.3%), and 30 (30/628, 4.8%) steps, respectively. ICC values demonstrated excellent agreement between manually counted steps and steps counted by both Garmin Vivosmart (0.97) and Fitbit One (0.98) but poor agreement for Fitbit Charge 2 HR (0.47). The absolute (relative) precision values for Garmin Vivosmart, Fitbit One, and Fitbit Charge 2 HR were 11.1 (11.1/625, 1.8%), 14.7 (14.7/620, 2.4%), and 74.4 (74.4/598, 12.4%) steps, respectively. ICC confidence intervals demonstrated low variability for Garmin Vivosmart (0.96 to 0.99) and Fitbit One (0.93 to 0.99) but high variability for Fitbit Charge 2 HR (-0.57 to 0.74). The Fitbit One device maintained high accuracy and precision values for treadmill walking, but both Garmin Vivosmart and Fitbit Charge 2 HR (the wrist-worn devices) had worse accuracy and precision for treadmill walking.
Conclusions: The waist-worn sensor (Fitbit One) was accurate and precise in measuring steps with overground and treadmill walking. The wrist-worn sensors were accurate and precise only during overground walking. Similar research should inform the application of these devices in clinical research and practice involving patients with PD.
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http://dx.doi.org/10.2196/14059 | DOI Listing |
Ying Yong Sheng Tai Xue Bao
October 2024
Ningxia Helan Mountain National Nature Reserve Administration, Yinchuan 750021, China.
subsp. is an important resource plant with considerable medicinal, economic, and ecological value, and an indicator species in the transition zones between forests and grasslands. Predicting the potential geographic distribution of subsp.
View Article and Find Full Text PDFAnn Neurol
December 2024
Department of Neurology, Jewish Hospital Berlin, Berlin, Germany.
Objective: Among patients with acute stroke, we aimed to identify those who will later develop central post-stroke pain (CPSP) versus those who will not (non-pain sensory stroke [NPSS]) by assessing potential differences in somatosensory profile patterns and evaluating their potential as predictors of CPSP.
Methods: In a prospective longitudinal study on 75 acute stroke patients with somatosensory symptoms, we performed quantitative somatosensory testing (QST) in the acute/subacute phase (within 10 days) and on follow-up visits for 12 months. Based on previous QST studies, we hypothesized that QST values of cold detection threshold (CDT) and dynamic mechanical allodynia (DMA) would differ between CPSP and NPSS patients before the onset of pain.
Front Med (Lausanne)
December 2024
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
Objectives: To implement state-of-the-art deep learning architectures such as Deep-Residual-U-Net and DeepLabV3+ for precise segmentation of hippocampus and ventricles, in functional magnetic resonance imaging (fMRI). Integrate VGG-16 with Random Forest (VGG-16-RF) and VGG-16 with Support Vector Machine (VGG-16-SVM) to enhance the binary classification accuracy of Alzheimer's disease, comparing their performance against traditional classifiers.
Method: OpenNeuro and Harvard's Data verse provides Alzheimer's coronal functional MRI data.
Front Public Health
December 2024
Department of Anesthesiology, Chengdu Fifth People's Hospital (The Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, China.
Background: Postoperative pneumonia, a prevalent form of hospital-acquired pneumonia, poses significant risks to patients' prognosis and even their lives. This study aimed to develop and validate a predictive model for postoperative pneumonia in surgical patients using nine machine learning methods.
Objective: Our study aims to develop and validate a predictive model for POP in surgical patients using nine machine learning algorithms.
Interspeech
September 2024
Pattern Recognition Lab. Friedrich-Alexander University, Erlangen, Germany.
Magnetic Resonance Imaging (MRI) allows analyzing speech production by capturing high-resolution images of the dynamic processes in the vocal tract. In clinical applications, combining MRI with synchronized speech recordings leads to improved patient outcomes, especially if a phonological-based approach is used for assessment. However, when audio signals are unavailable, the recognition accuracy of sounds is decreased when using only MRI data.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!