Elie Gottlieb1,2, Leonid Churilov2, Emilio Werden1,2, Thomas Churchward3,4, Matthew P Pase5,6, Natalia Egorova1,7, Mark E Howard2,3,4,8, Amy Brodtmann1,2,8. 1. The Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia. 2. University of Melbourne, Melbourne, Victoria, Australia. 3. Institute for Breathing and Sleep, Melbourne, Victoria, Australia. 4. Austin Health, Heidelberg, Victoria, Australia. 5. Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Victoria, Australia. 6. Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts. 7. Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia. 8. Co-senior authors.
Abstract
STUDY OBJECTIVES: Sleep-wake dysfunction is bidirectionally associated with the pathogenesis and evolution of stroke. Longitudinal and prospective measurement of sleep after chronic stroke remains poorly characterized because of a lack of validated objective and ambulatory sleep measurement tools in neurological populations. This study aimed to validate a multisensor sleep monitor, the SenseWear Armband (SWA), in patients with ischemic stroke and control patients using at-home polysomnography. METHODS: Twenty-eight radiologically confirmed patients with ischemic stroke (aged 69.61 ± 7.35 years; mean = 4.1 years poststroke) and 16 control patients (aged 73.75 ± 7.10 years) underwent overnight at-home polysomnography in tandem with the SWA. Lin's concordance correlation coefficient and reduced major axis regressions were employed to assess concordance of SWA vs polysomnography-measured total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset. Subsequently, data were converted to 30-second epochs to match at-home polysomnography. Epoch-by-epoch agreement between SWA and at-home polysomnography was estimated using crude agreement, Cohen's kappa, sensitivity, and specificity. RESULTS: Total sleep time was the most robustly quantified sleep-wake variable (concordance correlation coefficient = 0.49). The SWA performed poorest for sleep measures requiring discrimination of wakefulness (sleep onset latency; concordance correlation coefficient = 0.16). The sensitivity of the SWA was high (95.90%) for patients with stroke and for control patients (95.70%). The specificity of the SWA was fair-moderate for patients with stroke (40.45%) and moderate for control patients (45.60%). Epoch-by-epoch agreement rate was fair (78%) in patients with stroke and fair (74%) in controls. CONCLUSIONS: The SWA shows promise as an ambulatory tool to estimate macro parameters of sleep-wake; however, agreement at an epoch level is only moderate-fair. Use of the SWA warrants caution when it is used as a diagnostic tool or in populations with significant sleep-wake fragmentation.
STUDY OBJECTIVES: Sleep-wake dysfunction is bidirectionally associated with the pathogenesis and evolution of stroke. Longitudinal and prospective measurement of sleep after chronic stroke remains poorly characterized because of a lack of validated objective and ambulatory sleep measurement tools in neurological populations. This study aimed to validate a multisensor sleep monitor, the SenseWear Armband (SWA), in patients with ischemic stroke and control patients using at-home polysomnography. METHODS: Twenty-eight radiologically confirmed patients with ischemic stroke (aged 69.61 ± 7.35 years; mean = 4.1 years poststroke) and 16 control patients (aged 73.75 ± 7.10 years) underwent overnight at-home polysomnography in tandem with the SWA. Lin's concordance correlation coefficient and reduced major axis regressions were employed to assess concordance of SWA vs polysomnography-measured total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset. Subsequently, data were converted to 30-second epochs to match at-home polysomnography. Epoch-by-epoch agreement between SWA and at-home polysomnography was estimated using crude agreement, Cohen's kappa, sensitivity, and specificity. RESULTS: Total sleep time was the most robustly quantified sleep-wake variable (concordance correlation coefficient = 0.49). The SWA performed poorest for sleep measures requiring discrimination of wakefulness (sleep onset latency; concordance correlation coefficient = 0.16). The sensitivity of the SWA was high (95.90%) for patients with stroke and for control patients (95.70%). The specificity of the SWA was fair-moderate for patients with stroke (40.45%) and moderate for control patients (45.60%). Epoch-by-epoch agreement rate was fair (78%) in patients with stroke and fair (74%) in controls. CONCLUSIONS: The SWA shows promise as an ambulatory tool to estimate macro parameters of sleep-wake; however, agreement at an epoch level is only moderate-fair. Use of the SWA warrants caution when it is used as a diagnostic tool or in populations with significant sleep-wake fragmentation.
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