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|Title:||Sleep-wake parameters can be detected in chronic stroke patients using a multi-sensor accelerometer: a validation study.||Austin Authors:||Gottlieb, Elie;Churilov, Leonid ;Werden, Emilio ;Churchward, Thomas J ;Pase, Matthew P;Egorova, Natalia;Howard, Mark E ;Brodtmann, Amy||Affiliation:||University of Melbourne, Melbourne, Victoria, Australia
The Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia
Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts..
Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Victoria, Australia
Institute for Breathing and Sleep
|Issue Date:||25-Sep-2020||metadata.dc.date:||2020-09-25||Publication information:||Journal of Clinical Sleep Medicine : JCSM 2020; online first: 25 September||Abstract:||Sleep-wake dysfunction is bidirectionally associated with the pathogenesis and evolution of stroke. Longitudinal and prospective measurement of sleep after chronic stroke remains poorly characterised due to a lack of validated objective and ambulatory sleep measurement tools in neurological populations. This study aimed to validate a multi-sensor sleep monitor, the SenseWear Armband, in ischaemic stroke patients and controls using at-home polysomnography (home-PSG). Twenty-eight radiologically-confirmed ischaemic stroke patients (age: 69.61±7.35 years, mean: 4.1 years post-stroke) and 16 controls (73.75±7.10 years) underwent overnight home-PSG in-tandem with the SenseWear Armband (SWA). Lin's concordance coefficient and reduced major axis regressions were employed to assess concordance of SWA versus PSG-measured total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset. Subsequently, data were converted to 30-second epochs to match home-PSG. Epoch-by-epoch agreement between SWA and home-PSG was estimated using crude agreement, Cohen's kappa, sensitivity, and specificity. Total sleep time was the most robustly quantified sleep-wake variable (CCC=0.49). The SWA performed poorest for sleep measures requiring discrimination of wakefulness (sleep onset latency, CCC=0.16). Sensitivity of the SWA was high (95.90%) for stroke patients and controls (95.70%). Specificity of the SWA was fair-moderate for stroke patients (40.45%) and moderate (45.60%) for controls. Epoch-by-epoch agreement rate was fair (78%) in stroke patients. 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 used as a diagnostic tool or in populations with significant sleep-wake fragmentation.||URI:||https://ahro.austin.org.au/austinjspui/handle/1/24935||DOI:||10.5664/jcsm.8812||PubMed URL:||32975195||Type:||Journal Article||Subjects:||accelerometer
behavioural sleep medicine
|Appears in Collections:||Journal articles|
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