Study Objectives: Gentle rocking movements provided by a moving bed have been proposed as a promising non-pharmacological way to promote sleep. In rodents the sleep promoting effect of rocking movements depended on the peak acceleration (named "stimulation intensity") perceived by the vestibular system. We set out to verify previous reports on the sleep promoting effect of rocking movements and to investigate the importance of stimulation intensity in this process. Methods: Side-to-side rocking movements along a pendulum trajectory with different peak accelerations (control: 0 m/s 2 , low intensity: 0.15 m/s 2 , medium intensity: 0.25 m/s 2 , high intensity: 0.35 m/s 2) were provided for 45 min during an afternoon nap opportunity. Participants were assigned to a low intensity group (n = 10) experiencing control, low and medium intensity stimulation or a high intensity group (n = 12) experiencing control, medium and high intensity stimulation. Sleep and sleep-related memory performance were assessed using polysomnography and a word-pair memory task, respectively. Results: Participants transitioned faster into deep sleep under the influence of medium intensity rocking as was evident by a faster buildup of delta power compared to the control condition (n = 22). The faster buildup did not affect sleep architecture, since e.g., the proportion of the nap spent in deep sleep or latencies did not change. Previously reported effects like a shorter latency to stage N2 and a higher density of sleep spindles were not observed. Sleep quality during control naps of the low intensity group was worse than in the high intensity group. In the low intensity group, we also observed a significant increase in delta power throughout the nap, as well as a higher density of slow oscillations both under the influence of low and medium intensity vestibular stimulation. No such effects were observed in the high intensity group. Conclusion: Rocking movements may promote nap sleep in young adults. Due to a difference in sleep quality during control naps between the low and high intensity group no conclusion regarding the influence of stimulation intensity were possible. Thus, optimal stimulation settings in humans need further investigation.

Childhood sleep-related rhythmic movement disorder (RMD)-sleep-related repetitive movements involving large muscle groups-can impair sleep quality, cause local injury and disturb household members. Previous parental reports indicate prevalence rates in children under 3 years of age between 5.5 and 67%. We studied the prevalence of RMD with objective home videosomnography. Methods Parents of 707 children having their one-year routine health check (357 male), 740 children having their two-year health check (395 male), and 17 children of unknown age (9 male), were asked if their child showed sleep-related rhythmic movements. If telephone interview confirmed likely RMD, parents completed a standardised clinical questionnaire and three nights of home videosomnography. Results At the one-year health check, 31/707 possible cases of RMD were identified (maximal prevalence: 4.38%; 95% CI [2.81, 5.89]) compared to 11/740 at the two-year check (maximal prevalence: 1.49%, 95% CI [0.61, 2.36]). Of 42 possible cases, 9 had resolved; 14 were uncontactable, or did not wish to participate, and 4 did not complete the study protocol. In four of ten remaining one-year olds and four of five remaining two-year olds parental report was objectively confirmed by videosomnography. Minimal prevalence based on objective observation was therefore 0.28% (95% CI [0.08, 1.30]) at one-year check and 0.41% (95% CI [0.08, 1.24]) at two-year check. Conclusions Prevalence of RMD in a large population of infants and toddlers was lower than previously reported (maximum prevalence 2.87%, minimum prevalence 0.34%). It is important to confirm parental report using objective measures. Highlights • Sleep-related rhythmic movements may have severe clinical consequences. • Prevalence was assessed in a large cohort (N=1464) of infants and toddlers. • Parental report of symptoms could be objectively confirmed in 53.3% of cases. • The prevalence in our sample is much lower (maximal 2.87%) than previously reported.

Background: Unlike other episodic sleep disorders in childhood, there are no agreed severity indices for rhythmic movement disorder. While movements can be characterized in detail by polysomnography, in our experience most children inhibit rhythmic movement during polysomnography. Actigraphy and home video allow assessment in the child’s own environment, but both have limitations. Standard actigraphy analysis algorithms fail to differentiate rhythmic movements from other movements. Manual annotation of 2D video is time consuming. We aimed to develop a sensitive, reliable method to detect and quantify rhythmic movements using marker free and automatic 3D video analysis. Method: Patients with rhythmic movement disorder (n = 6, 4 male) between age 5 and 14 years (M: 9.0 years, SD: 4.2 years) spent three nights in the sleep laboratory as part of a feasibility study (). 2D and 3D video data recorded during the adaptation and baseline nights were analyzed. One ceiling-mounted camera captured 3D depth images, while another recorded 2D video. We developed algorithms to analyze the characteristics of rhythmic movements and built a classifier to distinguish between rhythmic and non-rhythmic movements based on 3D video data alone. Data from 3D automated analysis were compared to manual 2D video annotations to assess algorithm performance. Novel indices were developed, specifically the rhythmic movement index, frequency index, and duration index, to better characterize severity of rhythmic movement disorder in children. Result: Automatic 3D video analysis demonstrated high levels of agreement with the manual approach indicated by a Cohen’s kappa >0.9 and F1-score >0.9. We also demonstrated how rhythmic movement assessment can be improved using newly introduced indices illustrated with plots for ease of visualization. Conclusion: 3D video technology is widely available and can be readily integrated into sleep laboratory settings. Our automatic 3D video analysis algorithm yields reliable quantitative information about rhythmic movements, reducing the burden of manual scoring. Furthermore, we propose novel rhythmic movement disorder severity indices that offer a means to standardize measurement of this disorder in both clinical and research practice. The significance of the results is limited due to the nature of a feasibility study and its small number of samples. A larger follow up study is needed to confirm presented results.