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Is it time to embrace a multinight sleep study?
Sleep Medicine Network
Respiratory-Related Sleep Disorders Section
Since the 1960s, sleep researchers have been intrigued by the first-night effect (FNE) in polysomnography (PSG) studies. A meta-analysis by Ding and colleagues revealed FNE’s impact on sleep metrics, like total sleep time and REM sleep, without affecting the apnea-hypopnea index, highlighting PSG’s limitations in simulating natural sleep patterns.1
Lechat and colleagues conducted a study using a home-based sleep analyzer on more than 67,000 individuals, averaging 170 nights each.2 This study found that single-night studies could lead to a 20% misdiagnosis rate in OSA, attributed to overlooking real sleep factors such as body posture, environmental effects, alcohol, and medication. Despite this, the wider use of multinight studies for accurate diagnosis is limited by insurance coverage issues.3
The last decade has seen substantial advances in health technology, particularly in consumer wearables capable of detecting various medical conditions. Devices employing techniques like actigraphy and accelerometry have reached a level of performance comparable with US Food and Drug Administration-approved clinical tools. However, these technologies are still in development for the diagnosis and classification of sleep-disordered breathing.
Tech companies are actively innovating sleep sensing technologies, smartwatches, bed sensors, wireless EEG, radiofrequency, and ultrasound sensors. With significant investments in this sector, these technologies could be ready for widespread use in the next 5 to 10 years. Health care professionals should consider data from sleep-tracking wearables when there are inconsistencies between a patient’s sleep study results and symptoms. The insights from these devices could provide crucial diagnostic information, enhancing the accuracy of sleep disorder diagnoses.
References
1. Ding L, Chen B, Dai Y, Li Y. A meta-analysis of the first-night effect in healthy individuals for the full age spectrum. Sleep Med. 2022;89:159-165. Preprint. Posted online December 17, 2021. PMID: 34998093. doi: 10.1016/j.sleep.2021.12.007
2. Lechat B, Naik G, Reynolds A, et al. Multinight prevalence, variability, and diagnostic misclassification of obstructive sleep apnea. Am J Respir Crit Care Med. 2022;205(5):563-569. PMID: 34904935; PMCID: PMC8906484. doi: 10.1164/rccm.202107-1761OC
3. Abreu A, Punjabi NM. How many nights are really needed to diagnose obstructive sleep apnea? Am J Respir Crit Care Med. 2022;206(1):125-126. PMID: 35476613; PMCID: PMC9954337. doi: 10.1164/rccm.202112-2837LE
Sleep Medicine Network
Respiratory-Related Sleep Disorders Section
Since the 1960s, sleep researchers have been intrigued by the first-night effect (FNE) in polysomnography (PSG) studies. A meta-analysis by Ding and colleagues revealed FNE’s impact on sleep metrics, like total sleep time and REM sleep, without affecting the apnea-hypopnea index, highlighting PSG’s limitations in simulating natural sleep patterns.1
Lechat and colleagues conducted a study using a home-based sleep analyzer on more than 67,000 individuals, averaging 170 nights each.2 This study found that single-night studies could lead to a 20% misdiagnosis rate in OSA, attributed to overlooking real sleep factors such as body posture, environmental effects, alcohol, and medication. Despite this, the wider use of multinight studies for accurate diagnosis is limited by insurance coverage issues.3
The last decade has seen substantial advances in health technology, particularly in consumer wearables capable of detecting various medical conditions. Devices employing techniques like actigraphy and accelerometry have reached a level of performance comparable with US Food and Drug Administration-approved clinical tools. However, these technologies are still in development for the diagnosis and classification of sleep-disordered breathing.
Tech companies are actively innovating sleep sensing technologies, smartwatches, bed sensors, wireless EEG, radiofrequency, and ultrasound sensors. With significant investments in this sector, these technologies could be ready for widespread use in the next 5 to 10 years. Health care professionals should consider data from sleep-tracking wearables when there are inconsistencies between a patient’s sleep study results and symptoms. The insights from these devices could provide crucial diagnostic information, enhancing the accuracy of sleep disorder diagnoses.
References
1. Ding L, Chen B, Dai Y, Li Y. A meta-analysis of the first-night effect in healthy individuals for the full age spectrum. Sleep Med. 2022;89:159-165. Preprint. Posted online December 17, 2021. PMID: 34998093. doi: 10.1016/j.sleep.2021.12.007
2. Lechat B, Naik G, Reynolds A, et al. Multinight prevalence, variability, and diagnostic misclassification of obstructive sleep apnea. Am J Respir Crit Care Med. 2022;205(5):563-569. PMID: 34904935; PMCID: PMC8906484. doi: 10.1164/rccm.202107-1761OC
3. Abreu A, Punjabi NM. How many nights are really needed to diagnose obstructive sleep apnea? Am J Respir Crit Care Med. 2022;206(1):125-126. PMID: 35476613; PMCID: PMC9954337. doi: 10.1164/rccm.202112-2837LE
Sleep Medicine Network
Respiratory-Related Sleep Disorders Section
Since the 1960s, sleep researchers have been intrigued by the first-night effect (FNE) in polysomnography (PSG) studies. A meta-analysis by Ding and colleagues revealed FNE’s impact on sleep metrics, like total sleep time and REM sleep, without affecting the apnea-hypopnea index, highlighting PSG’s limitations in simulating natural sleep patterns.1
Lechat and colleagues conducted a study using a home-based sleep analyzer on more than 67,000 individuals, averaging 170 nights each.2 This study found that single-night studies could lead to a 20% misdiagnosis rate in OSA, attributed to overlooking real sleep factors such as body posture, environmental effects, alcohol, and medication. Despite this, the wider use of multinight studies for accurate diagnosis is limited by insurance coverage issues.3
The last decade has seen substantial advances in health technology, particularly in consumer wearables capable of detecting various medical conditions. Devices employing techniques like actigraphy and accelerometry have reached a level of performance comparable with US Food and Drug Administration-approved clinical tools. However, these technologies are still in development for the diagnosis and classification of sleep-disordered breathing.
Tech companies are actively innovating sleep sensing technologies, smartwatches, bed sensors, wireless EEG, radiofrequency, and ultrasound sensors. With significant investments in this sector, these technologies could be ready for widespread use in the next 5 to 10 years. Health care professionals should consider data from sleep-tracking wearables when there are inconsistencies between a patient’s sleep study results and symptoms. The insights from these devices could provide crucial diagnostic information, enhancing the accuracy of sleep disorder diagnoses.
References
1. Ding L, Chen B, Dai Y, Li Y. A meta-analysis of the first-night effect in healthy individuals for the full age spectrum. Sleep Med. 2022;89:159-165. Preprint. Posted online December 17, 2021. PMID: 34998093. doi: 10.1016/j.sleep.2021.12.007
2. Lechat B, Naik G, Reynolds A, et al. Multinight prevalence, variability, and diagnostic misclassification of obstructive sleep apnea. Am J Respir Crit Care Med. 2022;205(5):563-569. PMID: 34904935; PMCID: PMC8906484. doi: 10.1164/rccm.202107-1761OC
3. Abreu A, Punjabi NM. How many nights are really needed to diagnose obstructive sleep apnea? Am J Respir Crit Care Med. 2022;206(1):125-126. PMID: 35476613; PMCID: PMC9954337. doi: 10.1164/rccm.202112-2837LE