Sleep-wake parameters can be detected in chronic stroke patients using a multi-sensor accelerometer: a validation study.

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.