Dr Hana Hassanin

Study Objectives To compare the 24-hour sleep assessment capabilities of two contactless sleep technologies (CSTs) to actigraphy in community-dwelling older adults. Methods We collected 7–14 days of data at home from 35 older adults (age: 65–83), some with medical conditions, using Withings Sleep Analyser (WSA, n = 29), Emfit QS (Emfit, n = 17), a standard actigraphy device (Actiwatch Spectrum [AWS, n = 34]), and a sleep diary (n = 35). We compared nocturnal and daytime sleep measures estimated by the CSTs and actigraphy without sleep diary information (AWS-A) against sleep-diary-assisted actigraphy (AWS|SD). Results Compared to sleep diary, both CSTs accurately determined the timing of nocturnal sleep (intraclass correlation [ICC]: going to bed, getting out of bed, time in bed >0.75), whereas the accuracy of AWS-A was much lower. Compared to AWS|SD, the CSTs overestimated nocturnal total sleep time (WSA: +92.71 ± 81.16 minutes; Emfit: +101.47 ± 75.95 minutes) as did AWS-A (+46.95 ± 67.26 minutes). The CSTs overestimated sleep efficiency (WSA: +9.19% ± 14.26%; Emfit: +9.41% ± 11.05%), whereas AWS-A estimate (−2.38% ± 10.06%) was accurate. About 65% (n = 23) of participants reported daytime naps either in bed or elsewhere. About 90% in-bed nap periods were accurately determined by WSA while Emfit was less accurate. All three devices estimated 24-hour sleep duration with an error of ≈10% compared to the sleep diary. Conclusions CSTs accurately capture the timing of in-bed nocturnal sleep periods without the need for sleep diary information. However, improvements are needed in assessing parameters such as total sleep time, sleep efficiency, and naps before these CSTs can be fully utilized in field settings.

Lipid nanoparticle (LNP) encapsulated self-amplifying RNA (saRNA) is well tolerated and immunogenic in SARS-CoV-2 seronegative and seropositive individuals aged 18–75. A phase 2a expanded safety and immunogenicity study of a saRNA SARS-CoV-2 vaccine candidate LNP-nCoVsaRNA, was conducted at participating centres in the UK between 10th August 2020 and 30th July 2021. Participants received 1 μg then 10 μg of LNP-nCoVsaRNA, ∼14 weeks apart. Solicited adverse events (AEs) were collected for one week post-each vaccine, and unsolicited AEs throughout. Binding and neutralisating anti-SARS-CoV-2 antibody raised in participant sera was measured by means of an anti-Spike (S) IgG ELISA, and SARS-CoV-2 pseudoneutralisation assay. (The trial is registered: ISRCTN17072692, EudraCT 2020-001646-20). 216 healthy individuals (median age 51 years) received 1.0 μg followed by 10.0 μg of the vaccine. 28/216 participants were either known to have previous SARS-CoV2 infection and/or were positive for anti-Spike (S) IgG at baseline. Reactogenicity was as expected based on the reactions following licensed COVID-19 vaccines, and there were no serious AEs related to vaccination. 80% of baseline SARS-CoV-2 naïve individuals (147/183) seroconverted two weeks post second immunization, irrespective of age (18–75); 56% (102/183) had detectable neutralising antibodies. Almost all (28/31) SARS-CoV-2 positive individuals had increased S IgG binding antibodies following their first 1.0 μg dose with a ≥0.5log10 increase in 71% (22/31). Encapsulated saRNA was well tolerated and immunogenic in adults aged 18–75 years. Seroconversion rates in antigen naïve were higher than those reported in our dose-ranging study. Further work is required to determine if this difference is related to a longer dosing interval (14 vs. 4 weeks) or dosing with 1.0 μg followed by 10.0 μg. Boosting of S IgG antibodies was observed with a single 1.0 μg injection in those with pre-existing immune responses. Grants and gifts from the Medical Research Council UKRI (MC_PC_19076), the National Institute for Health Research/Vaccine Task Force, Partners of Citadel and Citadel Securities, Sir Joseph Hotung Charitable Settlement, Jon Moulton Charity Trust, Pierre Andurand, and Restore the Earth.

Background: Contactless sleep technologies (CSTs) hold promise for longitudinal, unobtrusive sleep monitoring in health and disease at scale, particularly in older people where the increased incidence of sleep abnormalities with aging is considered a risk factor for several neurodegenerative disorders. However, few CST have been evaluated in older people. Objective: To evaluate the performance of three contactless sleep technologies (a bedside radar [Somnofy] and two under-mattress devices [Withings Sleep Analyser and Emfit-QS]) compared to polysomnography (PSG) and actigraphy [Actiwatch Spectrum] recorded during a first night in a sleep laboratory, 10-hour time in bed protocol, which induced mild sleep disturbance. Methods: Thirty-five older men and women (70.8±4.9 years; 14 women) several of whom had comorbidities and/or sleep apnoea, participated in the study. Devices were evaluated by estimating a range of performance metrics for classification of sleep vs wake, and NREM and REM sleep stages (sleep summary and epoch by epoch concordance) and comparing to PSG metrics. Results: All three CSTs overestimated total sleep time (bias [mean]: > 90 min) and sleep efficiency (bias: > 13 %) with an associated underestimation of wake after sleep onset (bias: > 50 min). Sleep onset latency was accurately detected by the bedside radar (bias: 16 mins). CSTs did not perform as well as actigraphy in estimating the all-night sleep summary measures. The bedside radar performed better in discriminating sleep vs wake (MCC [mean and 95% CI]: 0.63 [0.57 0.69]) than the under-mattress devices (MCC: =0.41 [0.36 0.46]; Emfit-QS =0.35 [0.26 0.43]). Accuracy of identifying REM and Light sleep was poor across all CSTs while deep sleep was predicted with moderate accuracy (MCC: >0.45) by both Somnofy and Withings Sleep Analyser. The deep sleep duration estimates of Somnofy was found to be significantly correlated (r2=0.6, p

The effects of orexinergic peptides are diverse and are mediated by orexin-1 and orexin-2 receptors. Antagonists that target both receptors have been shown to promote sleep initiation and maintenance. Here, we investigated the role of the orexin-2 receptor in sleep regulation in a randomised, double-blind, placebo-controlled, three-period crossover clinical trial using two doses (20 and 50 mg) of a highly selective orexin-2 receptor antagonist (2-SORA) (JNJ-48816274). We used a phase advance model of sleep disruption where sleep initiation is scheduled in the circadian wake maintenance zone. We assessed objective and subjective sleep parameters, pharmacokinetic profiles and residual effects on cognitive performance in 18 healthy male participants without sleep disorders. The phase advance model alone (placebo condition) resulted in disruption of sleep at the beginning of the sleep period compared to baseline sleep (scheduled at habitual time). Compared to placebo, both doses of JNJ-48816274 significantly increased total sleep time, REM sleep duration and sleep efficiency, and reduced latency to persistent sleep, sleep onset latency, and REM latency. All night EEG spectral power density for both NREM and REM sleep were unaffected by either dose. Participants reported significantly better quality of sleep and feeling more refreshed upon awakening following JNJ-48816274 compared to placebo. No significant residual effects on objective performance measures were observed and the compound was well tolerated. In conclusion, the selective orexin-2 receptor antagonist JNJ-48816274 rapidly induced sleep when sleep was scheduled earlier in the circadian cycle and improved self-reported sleep quality without impact on waking performance.

Study Objective: To compare the 24-hour sleep assessment capabilities of two contactless sleep technologies (CSTs) to actigraphy in community-dwelling older adults. Methods: We collected 7 to 14 days of data at home from 35 older adults (age: 65-83), some with medical conditions, using Withings Sleep Analyser (WSA, n=29), Emfit-QS (Emfit, n=17), a standard actigraphy device (Actiwatch Spectrum [AWS, n=34]) and a sleep diary. We compared nocturnal and daytime sleep measures estimated by the CSTs and actigraphy without sleep diary information (AWS-A) against sleep diary assisted actigraphy (AWS|SD). Results: Compared to sleep diary, both CSTs accurately determined the timing of nocturnal sleep (ICC: going to bed, getting out of bed, time in bed > 0.75) whereas the accuracy of AWSA was much lower. Compared to AWS|SD, the CSTs overestimated nocturnal total sleep time (WSA: +92.71±81.16 min; Emfit: +101.47±75.95 min) as did AWS-A (+46.95±67.26 min). The CSTs overestimated sleep efficiency (WSA: +9.19±14.26 %; Emfit: +9.41±11.05 %) whereas AWS-A estimate (-2.38±10.06 %) was accurate. About 65% (n=23) of participants reported daytime naps either in-bed or elsewhere. About 90% in-bed nap periods were accurately determined by WSA while Emfit was less accurate. All three devices estimated 24-h sleep duration with an error of ≈10% compared to the sleep diary. Conclusions: CSTs accurately capture the timing of in-bed nocturnal sleep periods without the need for sleep diary information. However, improvements are needed in assessing parameters such as total sleep time, sleep efficiency and naps before these CSTs can be fully utilized in field settings. Statement of Significance: Contactless sleep technologies that do not pose a burden on participants are promising tools for longitudinal monitoring of sleep in the community. In a comparison with sleep diary assisted actigraphy, we show that two under-mattress devices used without sleep diary information, provide accurate information on nocturnal sleep timing and 24-hr bed presence. The study population comprised community-dwelling older adults, several of whom had medical conditions such as sleep apnea, arthritis, and type-2 diabetes, which adds to the relevance of these data. With further improvements in their performance, these unobtrusive sleep technologies have significant potential for at scale and longitudinal monitoring of 24-h sleep-wake patterns in older adults without the burden of completing sleep diaries.

Circadian rhythms, metabolism, and nutrition are closely linked.1 Timing of a 3-meal daily feeding pattern synchronises some human circadian rhythms.2 Despite animal data showing anticipation of food availability, linked to a Food Entrainable Oscillator3, it is unknown whether human physiology predicts mealtimes and restricted food availability. In a controlled laboratory protocol, we tested the hypothesis that the human circadian system anticipates large meals. Twenty-four male participants undertook an 8-day laboratory study, with strict sleep-wake schedules, light-dark schedules, and food intake. For six days, participants consumed either hourly small meals throughout the waking period, or two large daily meals (7.5 and 14.5-h after wake-up). All participants then undertook a 37-hour constant routine. Interstitial glucose was measured every 15 minutes throughout the protocol. Hunger was assessed hourly during waking periods. Saliva melatonin was measured in the constant routine. During the 6-day feeding pattern, both groups exhibited increasing glucose concentration early each morning. In the small meal group, glucose concentrations continued to increase across the day. However, in the large meal group, glucose concentrations decreased from 2-h after waking until the first meal. Average 24-h glucose concentration did not differ between groups. In the constant routine, there was no difference in melatonin onset between groups, but antiphasic glucose rhythms were observed, with low glucose at the time of previous meals in the large meal group. Moreover, in the large meal group, constant routine hunger scores increased before the predicted meal times. These data support the existence of human food anticipation.