Neurobiology of Sleep and Circadian Rhythms (May 2022)
Sleep disorders among Aboriginal Australians with Machado-Joseph Disease: Quantitative results from a multiple methods study to assess the experience of people living with the disease and their caregivers
Abstract
Background: Machado-Joseph Disease (MJD), or Spinocerebellar Ataxia Type 3 (SCA3), is a genetic disorder that causes progressive muscle weakness, loss of motor control, ataxia and permanent physical disability. Sleep disturbances are associated with MJD but remain poorly understood. Objective: To investigate frequency and characteristics of sleep disorders and their association with health-related quality of life and psychosocial wellbeing for Aboriginal Australians living with MJD. Methods: A convenience sample of MJD participants n = 24 participated in a semi-attended, ambulatory diagnostic sleep study to capture polysomnography, actigraphy and sleep diary data. Self-report measures collected were the Pittsburgh Sleep Quality Index (PSQI), STOP-BANG Questionnaire for Obstructive Sleep Apnoea (OSA), International Restless Legs Syndrome Study Group rating scale (IRLS), Kessler-5 (K5) and EuroQoL-5 Dimension (EQ5D). Caregivers (n = 22) reported EQ-5D, K5 and bed partners’ sleep behaviour (Mayo Sleep Questionnaire-Informant). Environmental factors were measured. Results: We observed Nocturia, Sleep Related Leg Cramps, OSA, REM Behaviour Disorder, and RLS, respectively in 100%, 71%, 47%, 43%, and 33% of participants with a significant positive correlation between Body mass index (BMI) and Apnoea hypopnea index (AHI). The majority of sleep was spent in non-rapid eye movement sleep (NREM)-N2 stage (77.8% (67.7, 81.6)). Overcrowding (92%) and overnight care needs (42%) interrupted sleep. MJD participants and caregivers reported high psychological distress (K5 median 12.5 IQR 7, 16.5 & 8 IQR 6, 12 respectively). Conclusion: Poor sleep quality and sleep disturbances are prevalent among this cohort. Disease manifestations and environmental factors are driving factors. Larger sample sizes are required to predict risk factors and confirm observed associations.