To provide evidence-based recommendations and guidance to the public regarding indicators of good sleep quality across the life-span. The National Sleep Foundation assembled a panel of experts from the sleep community and representatives appointed by stakeholder organizations (Sleep Quality Consensus Panel). A systematic literature review identified 277 studies meeting inclusion criteria. Abstracts and full-text articles were provided to the panelists for review and discussion. A modified Delphi RAND/UCLA Appropriateness Method with 3 rounds of voting was used to determine agreement. For most of the sleep continuity variables (sleep latency, number of awakenings >5minutes, wake after sleep onset, and sleep efficiency), the panel members agreed that these measures were appropriate indicators of good sleep quality across the life-span. However, overall, there was less or no consensus regarding sleep architecture or nap-related variables as elements of good sleep quality. There is consensus among experts regarding some indicators of sleep quality among otherwise healthy individuals. Education and public health initiatives regarding good sleep quality will require sustained and collaborative efforts from multiple stakeholders. Future research should explore how sleep architecture and naps relate to sleep quality. Implications and limitations of the consensus recommendations are discussed.

Idiopathic REM sleep behaviour disorder (iRBD) is a powerful early sign of Parkinson’s disease, dementia with Lewy bodies, and multiple system atrophy. This provides an unprecedented opportunity to directly observe prodromal neurodegenerative states, and potentially intervene with neuroprotective therapy. For future neuroprotective trials, it is essential to accurately estimate phenoconversion rate and identify potential predictors of phenoconversion. This study assessed the neurodegenerative disease risk and predictors of neurodegeneration in a large multicentre cohort of iRBD. We combined prospective follow-up data from 24 centres of the International RBD Study Group. At baseline, patients with polysomnographically-confirmed iRBD without parkinsonism or dementia underwent sleep, motor, cognitive, autonomic and special sensory testing. Patients were then prospectively followed, during which risk of dementia and parkinsonsim were assessed. The risk of dementia and parkinsonism was estimated with Kaplan-Meier analysis. Predictors of phenoconversion were assessed with Cox proportional hazards analysis, adjusting for age, sex, and centre. Sample size estimates for disease-modifying trials were calculated using a time-to-event analysis. Overall, 1280 patients were recruited. The average age was 66.3 ± 8.4 and 82.5% were male. Average follow-up was 4.6 years (range = 1–19 years). The overall conversion rate from iRBD to an overt neurodegenerative syndrome was 6.3% per year, with 73.5% converting after 12-year follow-up. The rate of phenoconversion was significantly increased with abnormal quantitative motor testing [hazard ratio (HR) = 3.16], objective motor examination (HR = 3.03), olfactory deficit (HR = 2.62), mild cognitive impairment (HR = 1.91–2.37), erectile dysfunction (HR = 2.13), motor symptoms (HR = 2.11), an abnormal DAT scan (HR = 1.98), colour vision abnormalities (HR = 1.69), constipation (HR = 1.67), REM atonia loss (HR = 1.54), and age (HR = 1.54). There was no significant predictive value of sex, daytime somnolence, insomnia, restless legs syndrome, sleep apnoea, urinary dysfunction, orthostatic symptoms, depression, anxiety, or hyperechogenicity on substantia nigra ultrasound. Among predictive markers, only cognitive variables were different at baseline between those converting to primary dementia versus parkinsonism. Sample size estimates for definitive neuroprotective trials ranged from 142 to 366 patients per arm. This large multicentre study documents the high phenoconversion rate from iRBD to an overt neurodegenerative syndrome. Our findings provide estimates of the relative predictive value of prodromal markers, which can be used to stratify patients for neuroprotective trials.

Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph—a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 s instead of 30 s scoring epochs. A T1N marker based on unusual sleep stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies.