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Objective: The Huawei Band 9 (HWB 9), a consumer sleep-monitoring device with a high market share and a large user base in China, can provide sleep staging parameters and has broad representativeness in sleep monitoring applications. This study aims to compare the accuracy bias of polysomnography (PSG) and consumer sleep-monitoring devices, specifically the HWB 9, in measuring total sleep time (TST) and explore the factors affecting accuracy bias.
Methods: This study employed a sequential explanatory mixed-methods design, with quantitative research comprising 108 samples and qualitative research comprising 18 samples. Select hospitalized patients who required polysomnographic monitoring due to their condition from November 2024 to March 2025 were chosen as the research subjects, and who used PSG and HWB 9 for synchronous sleep monitoring throughout the night. Quantitative data analysis was conducted using descriptive statistics, the Wilcoxon test, Bland-Altman plots, univariate analysis, and multiple linear regression analysis. Qualitative content data were analyzed using NVivo 14.0 software.
Results: The statistical analysis showed a significant difference (P < 0.05) between the HWB 9 and PSG in measuring TST. The Bland-Altman plot showed that the measured values deviated from the consistency interval, indicating systematic overestimation bias. The multiple linear regression analysis showed that turning frequency and sleep posture were significant factors affecting measurement bias. Two main themes were found in the qualitative research: sleep habits and environmental factors, and individual differences and psychological perceptions.
Conclusion: Considering the significant variations in individuals, data from such devices should be used with caution in clinical practice.
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http://dx.doi.org/10.2147/NSS.S537489 | DOI Listing |
BMC Public Health
September 2025
Department of Social and Health Sciences in Sport, Bayreuth Center of Sport Science, University of Bayreuth, Bayreuth, Germany.
Background: Sedentary behavior (SB) and the absence of physical activity (PA) have become increasingly prevalent in modern societies due to changes in physical and social-environmental conditions, particularly in university students. This cross-sectional study aimed to describe and identify the prevalence and correlates of self-reported and accelerometer-determined SB and PA of German university students.
Methods: A convenience sample of 532 students participated in a questionnaire survey during the lecture period in the summer term 2018.
Sleep Breath
September 2025
University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, 70000, Vietnam.
Purposes: Sleep apnea or hypopnea is a sleep-related breathing disorder characterized by insufficient ventilation during sleep. Sleep apnea is classified into two major forms: obstructive sleep apnea (OSA) and central sleep apnea (CSA). The conventional diagnosis with Polysomnography (PSG) is time-consuming, uncomfortable, and costly in the clinical setting.
View Article and Find Full Text PDFCurr Opin Pulm Med
August 2025
Department of Medicine & Pharmacology, Texas A&M University, College Station, Texas, USA.
Purpose Of Review: Artificial intelligence (AI) is in the era of rapid evolution. Like other healthcare fields, AI has significantly impacted sleep medicine. We aim to explain the evolving role of AI in sleep medicine and provide clinicians with key information related to its benefits and limitations.
View Article and Find Full Text PDFJ Sleep Res
August 2025
School of Health and Human Performance, Dublin City University, Dublin, Ireland.
Sleep monitoring is a tool widely used to support recovery and performance in endurance athletes. This study aimed to assess agreement between research-grade actigraphy (ActiGraph GT9X), consumer-grade smartwatches (Garmin), and self-reported sleep diaries in masters endurance athletes. Seventy athletes (43 males, 46.
View Article and Find Full Text PDFPhysiol Rep
August 2025
Human Performance Collaborative, Office of Research, The Ohio State University, Columbus, Ohio, USA.
Modern wearable devices report several heart rate-based nocturnal health metrics, including resting heart rate (RHR) and heart rate variability (HRV). The purpose of this study was to assess the validity of nocturnal RHR and HRV from five wearable devices (Garmin Fenix 6, Oura Generation 3, Oura Generation 4, Polar Grit X Pro, & Whoop 4.0) against an electrocardiogram (ECG) reference.
View Article and Find Full Text PDF