Objective: To identify sleep disturbances amongst individuals diagnosed with AOIFCD using wrist-worn accelerometers and subjective electronic questionnaires.
Background: Up to 70% of individuals diagnosed with AOIFCD report difficulties with sleep. Larger cohort actigraphy studies have emerged as an alternative to smaller studies using polysomnography in order to evaluate sleep architecture. To date, no such studies have been used to capture sleep quality amongst the AOIFCD cohort.
Method: To measure activity during the sleep/wake cycle, individuals wore a wrist device (Garmin vivosmart 4) continuously over seven days on their non-dominant wrist. They also completed a daily sleep diary, as well as completing standardised sleep and non-motor questionnaires (Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS), Dystonia Non-Motor Symptoms Questionnaire (DNMSQuest)) via a linked app, Oxygen by Aparito. Sleep measures were derived from the raw triaxial acceleration and heart rate values captured from the wrist-worn device using previously published validated algorithms.
Results: Data was collected from 20 individuals diagnosed with AOIFCD and 20 age- and sex-matched controls. PSQI and ESS scores were comparable between the cohorts (p=0.45, p=0.27). Those diagnosed with AOIFCD reported a higher number of non-motor symptoms than controls (p<0.001), including symptoms of fatigue (p=0.001). Mann-Whitney U tests revelated total minutes in non-rapid eye movement (REM) sleep and sleep efficiency were increased in those with AOIFCD compared to controls (p=0.02, p=0.05). In both cohorts none of the accelerometer sleep variables were related to any self-reported measures of sleep. However, in the AOIFCD cohort, DNMS scores were associated with increased REM sleep (p=0.03), increased sleep onset latency (p=0.02) and increased wake after sleep onset (p=0.04).
Conclusion: We found no significant differences in self-reported sleep variables amongst the AOIFCD cohort when compared to controls, however accelerometer-derived sleep efficiency and NREM sleep duration were increased in the AOIFCD cohort. There was also evidence to suggest that non-motor symptoms were associated with altered sleep architecture. Increased sample sizes are needed to determine if sleep disturbances can be identified using accelerometers.
References: Walch, O., Huang, Y., Forger, D., Goldstein, C. Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device, Sleep, Volume 42, Issue 12, December 2019, zsz180, https://doi.org/10.1093/sleep/zsz180
To cite this abstract in AMA style:G. Bailey, M. Wadon, S. Komarzynski, K. Szewczyk-Krolikowski, A. Moore, C. Matthews, EH. Davies, K. Peall. Sleep in adult-onset idiopathic focal cervical dystonia (AOIFCD): an evaluation using self-reported and accelerometer derived measures [abstract]. Mov Disord. 2022; 37 (suppl 2). https://www.mdsabstracts.org/abstract/sleep-in-adult-onset-idiopathic-focal-cervical-dystonia-aoifcd-an-evaluation-using-self-reported-and-accelerometer-derived-measures/. Accessed December 7, 2023.
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