How Reliable Is Self-Reported Body Position during Sleep?

Russo, Kathryn; Bianchi, Matt T.

doi:10.5664/jcsm.5410

Cited by 20 publications

(13 citation statements)

References 5 publications

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“…This might suggest, instead of subjects' incompliance, that sleeping position is to some extent not under voluntary control. Somewhat inconsistent with McIntyre et al 's findings, Russo and Bianchi's (2016) …”

Section: Introductioncontrasting

confidence: 54%

Why is Self-Report of Sleep Position Sometimes Unreliable?

Yu¹

2017

Sleep Hypn

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Section: Introductioncontrasting

confidence: 54%

Why is Self-Report of Sleep Position Sometimes Unreliable?

Yu¹

2017

Sleep Hypn

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“…Device-assisted therapy is important, especially because patients’ self-report of body position during sleep carries substantial uncertainty. 68 By contrast, REM dominance does not as easily translate into clinical care recommendations for therapy, but REM-dominant OSA has been increasingly linked to hypertension, 69 and thus might impact treatment motivation. Insufficient evidence exists regarding REM-suppressing agents as pharmacological therapy for OSA.…”

Section: Resultsmentioning

confidence: 99%

Big data in sleep medicine: prospects and pitfalls in phenotyping

Bianchi

Russo

Gabbidon

et al. 2017

NSS

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Clinical polysomnography (PSG) databases are a rich resource in the era of “big data” analytics. We explore the uses and potential pitfalls of clinical data mining of PSG using statistical principles and analysis of clinical data from our sleep center. We performed retrospective analysis of self-reported and objective PSG data from adults who underwent overnight PSG (diagnostic tests, n=1835). Self-reported symptoms overlapped markedly between the two most common categories, insomnia and sleep apnea, with the majority reporting symptoms of both disorders. Standard clinical metrics routinely reported on objective data were analyzed for basic properties (missing values, distributions), pairwise correlations, and descriptive phenotyping. Of 41 continuous variables, including clinical and PSG derived, none passed testing for normality. Objective findings of sleep apnea and periodic limb movements were common, with 51% having an apnea–hypopnea index (AHI) >5 per hour and 25% having a leg movement index >15 per hour. Different visualization methods are shown for common variables to explore population distributions. Phenotyping methods based on clinical databases are discussed for sleep architecture, sleep apnea, and insomnia. Inferential pitfalls are discussed using the current dataset and case examples from the literature. The increasing availability of clinical databases for large-scale analytics holds important promise in sleep medicine, especially as it becomes increasingly important to demonstrate the utility of clinical testing methods in management of sleep disorders. Awareness of the strengths, as well as caution regarding the limitations, will maximize the productive use of big data analytics in sleep medicine.

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“…While some studies suggest there is high reliability among patients when asked to recall their most common sleep position, 21 other research suggests that self-reported body position may not match position during sleep. 40 Video research has shown people typically change body position an average of 13 times per night. 21 The influence of the "back-to-sleep" campaign on sleep position preference later in life is something this study was not capable of assessing and is a topic for future studies requiring collaboration with fields outside of plastic surgery.…”

Section: Discussionmentioning

confidence: 99%