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Related Concept Videos

Factors Influencing Heart Rate01:30

Factors Influencing Heart Rate

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The heart rate, or pulse rate, is a vital indicator of cardiovascular health. It reflects the number of times the heart beats per minute. Various physiological and environmental factors influence heart rate, increasing or decreasing cardiac output. Understanding these factors is crucial for assessing heart function and identifying potential health issues.
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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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Glucose Homeostasis: Regulation of Blood Glucose01:02

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Carbohydrates consumed through foods are converted into glucose, a crucial energy source for the body. In the prandial state, high blood glucose levels stimulate the secretion of insulin from the pancreas. Insulin inhibits hepatic glucose production and stimulates glucose uptake and metabolism by muscle and adipose tissue. The excess glucose is converted into glycogen and stored in the liver and muscles.
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Regulation of Heart Rates01:31

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The regulation of heart rate is a complex process controlled by the autonomic nervous system (ANS), hormonal influences, and intrinsic cardiac mechanisms. The ANS has two main components: the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS).
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Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
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Pulse oximetry, or SpO2, is a non-invasive method for continuously monitoring arterial oxygen saturation (SaO2). This procedure involves attaching a probe or sensor to the patient's fingertip, forehead, earlobe, or nose bridge. The sensor works by detecting changes in oxygen saturation levels through light signals generated by the oximeter and reflected by the pulsing blood under the probe.
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Updated: Feb 28, 2026

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
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Dynamic Sleep-Derived Heart Rate and Heart Rate Variability Features Associated with Glucose Metabolism Status: An

Li Li1,2, Syarifah Nabilah Syed Taha1, Yoshiyuki Nishinaka2

  • 1Graduate School of Engineering Science, The University of Osaka, Osaka 560-8531, Japan.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

Consumer wearables can track heart rate variability (HRV) during sleep, revealing autonomic nervous system patterns linked to glucose metabolism. Dynamic HRV trends, not just averages, show promise for assessing cardiometabolic health and pre-diabetes risk.

Keywords:
autonomic functionelastic net regressionglucose metabolismheart rate variability (HRV)sleep physiologywearable sensors

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Area of Science:

  • Cardiovascular Physiology
  • Metabolic Health
  • Wearable Technology

Background:

  • Impaired glucose metabolism, a precursor to type 2 diabetes, is linked to autonomic nervous system dysregulation.
  • Consumer wearable devices offer continuous, non-invasive physiological monitoring for assessing autonomic states.
  • Dynamic heart rate (HR) and heart rate variability (HRV) features may reflect metabolic status.

Purpose of the Study:

  • To explore associations between dynamic sleep-derived HR/HRV features from a consumer wearable and glucose metabolism status in adults.
  • To identify specific HR/HRV metrics that correlate with glycemic risk.
  • To compare autonomic patterns between individuals with lower and higher glycemic risk.

Main Methods:

  • Analysis of 189 nights of sleep data from 18 participants using a wrist-worn device (Fitbit).
  • Application of Elastic Net regression to identify HR/HRV features associated with nocturnal mean glucose.
  • Between-group comparisons of dynamic HRV features between lower (estimated HbA1c < 5.5%) and higher (estimated HbA1c ≥ 5.5%) glycemic-risk groups.

Main Results:

  • Fourteen dynamic HR/HRV features, particularly overnight trends and variability patterns, showed significant associations with glucose metabolism.
  • Two dynamic HRV features significantly differed between glycemic-risk groups (p<0.05, Cohen's |d|>1.1).
  • Lower-risk group showed decreasing HRV variability trends (autonomic stabilization); higher-risk group showed increasing trends (autonomic instability).

Conclusions:

  • Dynamic sleep-derived HR/HRV features, especially overnight trends, are potential correlates of glycemic status.
  • Wearable devices show promise for continuous, real-world cardiometabolic health monitoring beyond activity tracking.
  • Findings are hypothesis-generating and warrant validation in larger cohorts with laboratory-measured HbA1c.