Mark S Aloia’s research while affiliated with National Jewish Health and other places

Inadequate information exists regarding physiological changes post-COVID-19 infection. We used smart beds to record biometric data following COVID-19 infection in nonhospitalized patients. Recordings of daily biometric signals over 14 weeks in 59 COVID-positive participants’ homes in 2020 were compared with the same participants’ data from 2019. Participants completed a survey of demographic information, health conditions, COVID exposure and testing, and symptom prevalence/subjective severity. Mean age was 47.5 years (standard deviation [SD] 9.5), mean body mass index was 30.1 kg/m² (SD 7.1), and 46% were men. During acute infection, 64% exhibited 5–6 h increased sleep duration, 51% had increased movement, and 64% had increased breathing rate (BR). Nearly 34% had paradoxical bradycardia (decreased heart rate by ~ 10 BPM concomitant with elevated BR and/or fever), with more-severe symptoms. Smart beds can detect physiological changes during COVID-19. A subtype of acute response (paradoxical bradycardia) may predict delay recovery from COVID-19.

The transition from wakefulness to sleep occurs when the core body temperature decreases. The latter is facilitated by an increase in the cutaneous blood flow, which dissipates internal heat into the micro-environment surrounding the sleeper’s body. The rise in cutaneous blood flow near sleep onset causes the distal (hands and feet) and proximal (abdomen) temperatures to increase by about 1 °C and 0.5 °C, respectively. Characterizing the dynamics of skin temperature changes throughout sleep phases and understanding its relationship with sleep quality requires a means to unobtrusively and longitudinally estimate the skin temperature. Leveraging the data from a temperature sensor strip (TSS) with five individual temperature sensors embedded near the surface of a smart bed’s mattress, we have developed an algorithm to estimate the distal skin temperature with a minute-long temporal resolution. The data from 18 participants who recorded TSS and ground-truth temperature data from sleep during 14 nights at home and 2 nights in a lab were used to develop an algorithm that uses a two-stage regression model (gradient boosted tree followed by a random forest) to estimate the distal skin temperature. A five-fold cross-validation procedure was applied to train and validate the model such that the data from a participant could only be either in the training or validation set but not in both. The algorithm verification was performed with the in-lab data. The algorithm presented in this research can estimate the distal skin temperature at a minute-level resolution, with accuracy characterized by the mean limits of agreement [−0.79 to +0.79 °C] and mean coefficient of determination R2=0.87. This method may enable the unobtrusive, longitudinal and ecologically valid collection of distal skin temperature values during sleep. Therelatively small sample size motivates the need for further validation efforts.

Study objectives: Cannabis is a common sleep aid, however the effects of its use prior to sleep are poorly understood. This study aims to determine the impact of non-medical whole plant cannabis use three hours prior to sleep and measured cannabis metabolites on polysomnogram measures. Methods: This is a cross-sectional study of 177 healthy adults who provided detailed cannabis use history, underwent a one-night home sleep test (HST) and had measurement of eleven plasma and urinary cannabinoids, quantified using mass spectroscopy, the morning after the HST. Multivariable models were used to assess the relationship between cannabis use proximal to sleep, which was defined as use three hours before sleep, and individual HST measurements. Correlation between metabolite concentrations and polysomnogram measures were assessed. Results: In adjusted models, cannabis use proximal to sleep was associated with increased wake after sleep onset (median 60.5 versus 45.8 minutes), rate ratio 1.59 (1.22, 2.05), and increased proportion of stage one sleep (median 15.2% versus 12.3%), effect estimate 0.16 (0.06, 0.25). Compared to non-users, frequent cannabis users (>20 days per month) also had increased wake after sleep onset and stage one sleep, in addition to increased REM latency and decreased percent sleep efficiency. Delta-9 tetrahydrocannabinol metabolites correlated with these HST measures. Conclusions: Cannabis use proximal to sleep was associated with minimal changes in sleep architecture. Its use wasn't associated with measures of improved sleep including increased sleep time or efficiency and may be associated with poor quality sleep through increase wake onset and stage 1 sleep.

Introduction Excessive daytime sleepiness is characterized by the difficulty of staying awake during daytime. The goal of this research is to examine the role of regularity, sleep duration, and chronotype on the association between sleep disorders and daytime sleepiness. To do this, we leveraged the Sleep Number platform to acquire longitudinal objective sleep with survey data from a single point in time. Methods An IRB-approved survey, which included questions about diagnosed sleep disorders and daytime sleepiness, was presented to a cohort of Sleep Number customers in the June 12 to 26, 2023 period. Objective data including sleep duration, bedtime, restful sleep duration, mean breathing rate, and heart rate collected between May 1 and June 30, 2023 were used along with survey responses. Individual sleep regularity and chronotype were quantified using objective data. Regularity was categorized into regular or irregular if bedtime variability was lower or greater than 60 minutes. Chronotype was categorized into early, intermediate, or late if mean sleep onset time was before 10 pm, between 10 and 11:59 pm, or after 12 am. Odd-ratios (OR) were used to quantify the influence of regularity, chronotype, and sleep duration on daytime sleepiness moderated by sleep disorders. Results The responder count was 22,082 (9530M/12479F). Men were 56.6 (SD 13.9) and Women 54.9 (SD 13.8) years-old on average. Daytime sleepiness was significantly associated with the presence of untreated sleep disorders (insomnia, apnea, and RLS). Individuals being treated for apnea and insomnia demonstrated significantly less daytime sleepiness. Healthy individuals showed a significant association between early chronotype and longer sleep duration with reduced daytime sleepiness. Individuals with apnea who receive treatment showed a significant association between longer sleep and reduced daytime sleepiness. In the case of insomnia, regularity, chronotype or sleep duration do not moderate any significant relationship with daytime sleepiness. For RLS longer sleep duration leads to lower daytime sleepiness. Conclusion Daytime sleepiness is significantly associated with any untreated sleep disorders. Regularity, chronotype and sleep duration had limited influence on daytime sleepiness caused by sleep disorders. Healthy individuals had lower daytime sleepiness if they had an earlier chronotype and longer sleep duration. Support (if any)

Introduction Snoring and obstructive sleep apnea (OSA) are bidirectionally associated. The majority of OSA patients snore and a substantial percentage of those who snore have OSA. The intensity and the acoustic properties of snoring in apnea patients have been shown to correlate with OSA presence. The goal of this research is to use objective sleep data collected by the Sleep Number platform and survey responses to examine the relationship between snoring, sleep metrics, demographics, and OSA. Methods An IRB-approved survey, which included demographics and questions about sleep and its associated disorders, was presented to a cohort of Sleep Number (SN) customers during the period between June 12 to 26, 2023. Objective data collected by the SN platform included sleep duration, restful sleep, sleep latency, sleep quality, mean respiration rate, mean heart rate, and mean heart rate variability. Data were collected May 1st - June 30-2023, and were augmented by survey responses to examine associations between apnea presence, snoring properties, and objective data. Results Out of 22048 (12476F/9537M) respondents with mean age 55.8 (SD: 14.1) years, 3163 reported a diagnosis of apnea and 10558 reported no sleep disorder. Snoring was reported by 89.9% and 65.2% of the apnea and healthy sleepers, respectively. Among the respondents who snored, 29.2% had an apnea diagnosis (apnea and snoring are associated with R2=0.02). A mixed model considering age, gender, reported breathing interruption, snore intensity and frequency as predictors and apnea as the dependent variable resulted in a higher R2 = 0.19. The addition of objective data collected by the smart bed increased the R2 = 0.53. A logistic regression model using demographics, snoring properties, and objective metrics as independent and apnea presence as dependent variables, was trained and tested with the data from 80% and 20% of the individuals respectively. This model resulted in 0.91 area under the receiving operator curve, and 84% sensitivity and 90% specificity to detect apnea. Conclusion Objective data from a smart bed combined with demographic and snore properties can be used to screen apnea risk. This research is limited by the absence of information about apnea severity. Support (if any)

Introduction Snoring, as a common manifestation of sleep apnea, has often been used as a surrogate parameter for apnea. Significant associations were found between snoring and daytime sleepiness and cardiovascular conditions. The goal of this research is to use objective sleep data collected by the Sleep Number platform and survey responses to examine the relationship between snoring and sleep quality depending on the presence of treated or untreated apnea. Methods An IRB-approved survey, which included demographics and questions about sleep and its associated disorders, was presented to a cohort of Sleep Number (SN) customers during the period between June 12 to 26, 2023. Objective data collected by the SN platform included sleep duration, restful sleep, sleep latency, mean respiration rate, mean heart rate, mean heart rate variability, and sleep quality. The latter considers a demographic dependent aggregation of sleep duration, restful sleep and cardiorespiratory activity. Data were collected May 1st - June 30-2023, and were augmented by survey responses. Four groups were defined for analysis HNS: non-snorers reporting no sleep disorder, HES: snorers reporting no sleep disorders, TAS: snorers with treated apnea, and UAS: snorers with untreated apnea. Analysis of variance (ANOVA) was performed to identify any significant objective data differences between the groups. A second analysis focused on identifying the specific metric showing significant differences. Results The demographic responder composition per group was HNS 2454 (1717F/734M) individuals aged 53.9 (SD: 15.0) years-old, HES: 4702 (2626F/2069M) individuals aged 55.1 (SD: 13.5) years-old, TAS: 1784 (679F/1103M) individuals aged 59.7 (SD: 12.0) years-old, and UAS: 134 (59F/75M) individuals aged 59.4 (SD: 10.9) years-old. The ANOVA revealed significant objective data differences between groups. Only sleep quality showed significant group differences. Quality decreased significantly with respect to the HNS reference. Healthy snorers had a 0.96% lower quality, snorers with treated apnea had 1.8% lower quality, and snorers with untreated apnea had 3.9% lower quality. Conclusion Compared to healthy non-snorers, sleep quality quantification by a smart bed suggests a gradual decrease for healthy snorers, snorers with treated apnea, and snorers with untreated apnea. Support (if any)

Introduction Insomnia causes serious adverse health effects and is estimated to affect 10–30% of the worldwide population. This study leverages personalized fine-tuned machine learning algorithms to detect insomnia risk based on questionnaire and longitudinal objective sleep data collected by a smart bed platform. Methods Users of the Sleep Number smart bed were invited to participate in an IRB approved study which required them to respond to four questionnaires (which included the Insomnia Severity Index; ISI) administered 6 weeks apart from each other in the period from November 2021 to March 2022. For 1,489 participants who completed at least 3 questionnaires, objective data (which includes sleep/wake and cardio-respiratory metrics) collected by the platform were queried for analysis. An incremental, passive-aggressive machine learning model was used to detect insomnia risk which was defined by the ISI exceeding a given threshold. Three ISI thresholds (8, 10, and 15) were considered. The incremental model is advantageous because it allows personalized fine-tuning by adding individual training data to a generic model. Results The generic model, without personalizing, resulted in an area under the receiving-operating curve (AUC) of about 0.5 for each ISI threshold. The personalized fine-tuning with the data of just five sleep sessions from the individual for whom the model is being personalized resulted in AUCs exceeding 0.8 for all ISI thresholds. Interestingly, no further AUC enhancements resulted by adding personalized data exceeding ten sessions. Discussion These are encouraging results motivating further investigation into the application of personalized fine tuning machine learning to detect insomnia risk based on longitudinal sleep data and the extension of this paradigm to sleep medicine.

Sleep regularity and chronotype can affect health, performance, and overall well-being. This observational study examines how sleep regularity and chronotype affect sleep quality and cardiorespiratory metrics. Data was collected from 1 January 2019 through 30 December 2019 from over 330 000 Sleep Number smart bed users across the United States who opted into this at-home study. A pressure signal from the smart bed reflected bed presence, movements, heart rate (HR), and breathing rate (BR). Participants (mean age: 55.69 years [SD: 14.0]; 51.2% female) were categorized by chronotype (16.8% early; 62.2% intermediate, 20.9% late) and regularity of sleep timing. Participants who were regular sleepers (66.1%) experienced higher percent restful sleep and lower mean HR and BR compared to the 4.8% categorized as irregular sleepers. Regular early-chronotype participants displayed better sleep and cardiorespiratory parameters compared to those with regular late-chronotypes. Significant variations were noted in sleep duration (Cohen's d = 1.54 and 0.88, respectively) and restful sleep (Cohen's d = 1.46 and 0.82, respectively) between early and late chronotypes, particularly within regular and irregular sleep patterns. This study highlights how sleep regularity and chronotype influence sleep quality and cardiorespiratory metrics. Irrespective of chronotype, sleep regularity demonstrated a substantial effect. Further research is necessary to confirm these findings.