Purpose: The cardiorespiratory polysomnography (PSG) is an expensive and limited resource. The Sleepiz One + is a novel radar-based contactless monitoring device that can be used e.g. for longitudinal detection of nocturnal respiratory events. The present study aimed to compare the performance of the Sleepiz One + device to the PSG regarding the accuracy of apnea–hypopnea index (AHI). Methods: From January to December 2021, a total of 141 adult volunteers who were either suspected of having sleep apnea or who were healthy sleepers took part in a sleep study. This examination served to validate the Sleepiz One + device in the presence and absence of additional SpO2 information. The AHI determined by the Sleepiz One + monitor was estimated automatically and compared with the AHI derived from manual PSG scoring. Results: The correlation between the Sleepiz-AHI and the PSG-AHI with and without additional SpO2 measurement was r_p = 0.94 and r_p = 0,87, respectively. In general, the Bland–Altman plots showed good agreement between the two methods of AHI measurement, though their deviations became larger with increasing sleep-disordered breathing. Sensitivity and specificity for recordings without additional SpO2 was 85% and 88%, respectively. Adding a SpO2 sensor increased the sensitivity to 88% and the specificity to 98%. Conclusion: The Sleepiz One + device is a valid diagnostic tool for patients with moderate to severe OSA. It can also be easily used in the home environment and is therefore beneficial for e.g. immobile and infectious patients. Trial registration number and date of registration for prospectively registered trials: This study was registered on clinicaltrials.gov (NCT04670848) on 2020–12-09.

Purpose: In this proof of principle study, we evaluated the diagnostic accuracy of the novel Nox BodySleep^TM 1.0 algorithm (Nox Medical, Iceland) for the estimation of disease severity and sleep stages based on features extracted from actigraphy and respiratory inductance plethysmography (RIP) belts. Validation was performed against in-lab polysomnography (PSG) in patients with sleep-disordered breathing (SDB). Methods: Patients received PSG according to AASM. Sleep stages were manually scored using the AASM criteria and the recording was evaluated by the novel algorithm. The results were analyzed by descriptive statistics methods (IBM SPSS Statistics 25.0). Results: We found a strong Pearson correlation (r=0.91) with a bias of 0.2/h for AHI estimation as well as a good correlation (r=0.81) and an overestimation of 14 min for total sleep time (TST). Sleep efficiency (SE) was also valued with a good Pearson correlation (r=0.73) and an overestimation of 2.1%. Wake epochs were estimated with a sensitivity of 0.65 and a specificity of 0.59 while REM and non-REM (NREM) phases were evaluated a sensitivity of 0.72 and 0.74, respectively. Specificity was 0.74 for NREM and 0.68 for REM. Additionally, a Cohen’s kappa of 0.62 was found for this 3-class classification problem. Conclusion: The algorithm shows a moderate diagnostic accuracy for the estimation of sleep. In addition, the algorithm determines the AHI with good agreement with the manual scoring and it shows good diagnostic accuracy in estimating wake-sleep transition. The presented algorithm seems to be an appropriate tool to increase the diagnostic accuracy of portable monitoring. The validated diagnostic algorithm promises a more appropriate and cost-effective method if integrated in out-of-center (OOC) testing of patients with suspicion for SDB.

Vital signs, such as body temperature, heart rate, respiratory rate, and blood pressure are standard indicators of an individual’s physiological functions in most medical settings.¹ Monitoring these vital parameters is crucial for early diagnosis, medical treatment, risk assessment, and patient recovery monitoring.^2,3 With the advancement of medical measurement technology, oxygen saturation has increasingly become recognized as an indispensable fifth vital sign.⁴ Oxygen saturation indicates the percentage of oxygenated hemoglobin (${\mathrm{HbO}}_2$) and hemoglobin (Hb) in the blood, in which the artery should be in the range of 95% to 100% in healthy individuals.⁵ Many pulmonary diseases cause abnormalities in oxygen saturation values, such as acute pneumonia, chronic obstructive pulmonary disease (COPD), and sleep apnea syndrome (SAS). Furthermore, the outbreak of coronavirus (COVID-19) has further underscored the critical importance of oxygen saturation measurement.

Background/Objectives: Recent studies indicate that sleep and sleep disorders differ between men and women, but corresponding data in people with chronic lung diseases are lacking. This study aims to answer the question of what the sex-specific differences in sleep profiles and responses to elexacaftor/tezacaftor/ivacaftor (ETI) therapy in people with cystic fibrosis (pwCF) are. Methods: Adult pwCF and a matched control group (adults with suspected sleep-disordered breathing undergoing in-laboratory polysomnography (PSG)) were included. PSG data at baseline and after 6 months’ ETI therapy were compared between men (mwCF) and women (wwCF) with cystic fibrosis. PSG data at baseline and 6-month follow-up for mwCF/wwCF were compared with baseline PSG data for men/women in the control group. Daytime sleepiness was evaluated using the Epworth Sleepiness Scale (ESS). Correlations between change in percentage predicted forced expiratory volume in 1 s from baseline to 6 months were correlated with corresponding changes in key sleep parameters. Changes in transferrin during ETI therapy were also documented. Results: Twenty-eight pwCF (12 wwCF, 16 mwCF) and 28 matched controls were included. Both mwCF (4 ± 5 vs. 9 ± 20 events/h, p = 0.028) and wwCF (3 ± 3 vs. 8 ± 9 events/h, p = 0.004) had fewer respiratory events during sleep versus male and female controls, but worse sleep efficiency (75 ± 11% vs. 84 ± 11%; p = 0.004 and 76 ± 10% vs. 83 ± 11%; p = 0.011. The baseline ESS score was significantly higher in wwCF versus female controls (8 ± 4 vs. 14 ± 8; p = 0.040). Although some sleep parameters normalized during ETI therapy in pwCF, sleep quality remained poor. The transferrin levels at baseline (2.7 ± 0.4 vs. 2.2 ± 0.5; p = 0.049) and 6 months (3.8 ± 0.4 vs. 2.6 ± 0.5; p < 0.001) were significantly higher in the wwCF versus the mwCF, and the change from baseline during ETI therapy was significantly greater in women versus men (1.1 ± 0.6 vs. 0.4 ± 0.4; p < 0.001). Conclusions: These data suggest that wwCF and mwCF should be managed differently with respect to their sleep.