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Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

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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Multi-Modal Home Sleep Monitoring in Older Adults
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Are Wearable Sleep-Tracking Devices Reliable Alternatives to Polysomnography? A Systematic Review and Meta-Analysis.

Margarida Agostinho1,2, Maria Borges1, Telmo Pereira1,3

  • 1Polytechnic University of Coimbra, Coimbra, Portugal.

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Wearable sleep trackers offer general sleep insights but vary in accuracy compared to polysomnography (PSG). While useful for large groups, they aren't yet reliable for precise sleep stage diagnosis.

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Area of Science:

  • Sleep Science
  • Biomedical Engineering
  • Wearable Technology

Background:

  • Polysomnography (PSG) is the gold standard for sleep studies but is costly and difficult to scale.
  • Wearable devices are increasingly used for naturalistic sleep monitoring, prompting evaluation against PSG.
  • This study systematically reviews and analyzes wearable sleep trackers' performance in healthy adults.

Purpose of the Study:

  • To compare wearable sleep-tracking devices with laboratory polysomnography (PSG) in healthy adults.
  • To evaluate device accuracy across standard sleep metrics and sleep stage durations.
  • To determine the suitability of wearables for large-scale sleep assessments.

Main Methods:

  • Systematic review and meta-analysis of studies comparing wearable devices and PSG.
  • Searched PubMed, Scopus, Scielo, Web of Science, and Cochrane Library following PRISMA guidelines.
  • Synthesized mean differences for sleep metrics (TST, SL, SE, WASO) and sleep stages (N1+N2, N3, REM) with significance at p<0.01.

Main Results:

  • Sixteen studies were included; ten contributed to the meta-analysis.
  • Wearables generally overestimated total sleep time and sleep efficiency, and underestimated wake after sleep onset.
  • No single device showed consistently superior performance; Oura Ring and Fitbit models showed closer agreement in specific metrics in some studies.

Conclusions:

  • Wearable devices can provide reasonable estimates of global sleep metrics for population monitoring and self-tracking.
  • Variable performance, methodological differences, and risk of bias limit current wearables as standalone diagnostic tools.
  • Further research is needed to improve accuracy and reliability for detailed sleep stage characterization.