Clinical validation of at-home methods of sleep assessment in Rett Syndrome

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Project Summary Rett Syndrome (RTT) is a rare, X-linked neurogenetic disorder that affects 1 in 10,000 females and is caused by mutations in the methyl CpG-binding protein 2 gene (MECP2). The classic, or typical, form of RTT is characterized by a period of developmental regression, loss of purposeful hand use, loss of spoken language, gait abnormalities and repetitive, stereotyped hand movements. Also included in the RTT phenotype are sleep problems as well as a variety of symptoms suggestive of autonomic dysregulation including breathing irregularities (e.g. hyperventilation, apnea, breath holding), heart rate variability, temperature dysregulation. Disrupted sleep adversely impacts the quality of life of children with RTT and their parents/caregivers. Clinical trials are underway in RTT, and several include sleep-based outcome measures consisting of caregiver reports from questionnaires and sleep diaries. While these strategies are widely used for studying sleep, they are subject to multiple forms of bias including recall as well as observer bias. Objective, caregiver independent methods of assessing sleep quality are thus required. The gold-standard for objective assessment of sleep is polysomnography (PSG), however this is time-intensive, costly, and impractical for longitudinal assessments that are necessarily part of clinical trials in RTT. Wearable devices are increasingly utilized in sleep research studies because of their ease of use, and ability to capture data for extended periods of time within the home environment. Although our pilot data demonstrates the feasibility of a wearable device to assess sleep in RTT, the sensitivity or specificity of these measurements for sleep quality in RTT is unknown, and they have never been validated against PSG or measures of clinical severity. As such, this project will use state-of-the-art signal processing and pattern recognition methodologies to develop a quantifiable, remote/at-home measure of sleep for use within the context of a clinical trial. We will determine the reliability of a wearable device for sleep quality in RTT against gold-standard PSG. The device being utilized in this study also captures autonomic physiological measurements, so we will additionally determine the change in sensitivity and specificity for sleep quality in RTT when adding these measurements beyond actigraphy alone. Finally, we will determine the convergent validity between sleep quality measured by a wearable device and currently used outcome measures for clinical trials in RTT. Successful completion of these aims could have high impact for RTT because they could be utilized as biomarkers of clinical severity and for the purposes of patient stratification for clinical trials. Finally, these measures could assist in formulating more personalized, targeted interventions aimed at improving sleep quality and caregiver quality of life.