Brendon J. Yee

We are writing in response to the paper “Effects of aerobic exercise and resistance training on obstructive sleep apnea: a systematic review and meta-analysis” by Lin et al recently published in the Journal of Clinical Sleep Medicine.1 We wish to commend the authors on their endeavors in this systematic review and meta-analysis, while noting some constructive concerns with their analysis and subsequent conclusions. An important distinction made in Lin et al relates to studies that included continuous positive airway pressure (CPAP) treatment with an exercise intervention compared to those that treated obstructive sleep apnea with exercise alone. They report that only two studies (Ackel-D’Elia et al and Servantes et al)2,3 of their twelve inclusions had CPAP treatment during the exercise protocol, and these cohorts were analyzed separately from exercise-only studies. However, Lin et al also included data from Karlsen et al4 (who recorded a CPAP-treatment majority during their exercise intervention) but failed to identify or analyze this data in the same fashion as the other two CPAP studies included in the review. Instead, the Karlsen et al cohort is included in subgroup analysis with studies that were specifically segregated based on their non-CPAP inclusion (see published supplemental data). Karlsen et al confirmed that CPAP usage was indeed maintained during the study intervention and follow-up as reported in their original article. Lin et al report in their conclusions that their key finding from the subgroup analysis, related to CPAP and its effect on the apnea-hypopnea index (AHI) and Epworth Sleepiness Scale (ESS), is based on assessment which contains this misidentified data. These conclusions thus should be appraised with caution. It is not our intention to criticize Lin et al but rather to advise that their subgroup analysis has a mixed-CPAP cohort contrary to their claims and objective. It is hoped this input can assist in clarifying their conclusions regarding differences in responsiveness to exercise between CPAP-adherent and CPAP-naïve patients.

Excessive daytime sleepiness has traditionally been regarded as the hallmark symptom of obstructive sleep apnea (OSA), yet nearly half of individuals with OSA do not report significant sleepiness. While treatments are well established for sleepy patients, their role in non-sleepy individuals remains relatively underexplored. This review discusses the limitations of current tools used to measure sleepiness, evaluates the evidence for various treatment options for OSA in non-sleepy populations, and outlines key considerations for shared decision-making. We examine noninvasive therapies including positive airway pressure (PAP), oral appliance therapy, and weight loss interventions only. Randomized controlled trials have not demonstrated cardiometabolic benefits of PAP therapy in non-sleepy individuals with OSA, though these studies are limited by poor PAP adherence and imprecise tools for identifying high-risk patients. As such, a pragmatic trial of PAP may be a reasonable strategy in non-sleepy people with moderate-to-severe OSA, cardiovascular comorbidities, or other OSA-related complications; provided patients are counseled about the challenges of adherence and the uncertain benefits in this population. Looking ahead, management of non-sleepy OSA will likely be guided by individualized, risk-based approaches incorporating physiological endotyping, objective biomarkers of cardiovascular risk, and patient preferences.

Background: Medical care for sleep-disordered breathing (SDB) in severe mental illness (SMI) is often ignored or poorly delivered. Here we describe an oximetry screening and management pathway for obstructive sleep apnoea (OSA) and assess the night-to-night reliability in a specialist cardiometabolic disease management clinic for patients with SMI. Objective: The implementation and evaluation of a sleep service for patients living with SMI. Methods: Prospective evaluation of a translational programme. Methods: A multidisciplinary outpatient clinic for patients with SMI. Methods: The clinic was prospectively evaluated between May 2019 and December 2020. We used questionnaires and three nights of oximetry to screen patients for OSA. A project coordinator managed the testing-treatment pathway while liaising with health care providers. We also evaluated the agreement between two nights of oximetry. Results: It is feasible to integrate sleep service into a cardiometabolic clinic for patients with SMI. Oximetry data were collected from 140/153 patients and 129/140 had at least adequate oximetry data for one night, and 107 (82%) had two nights. Oximetry indicated likely moderate-to-severe OSA in 33 patients and severe OSA in 22 patients. A total of 96/140 patients were referred to the SMI sleep clinic, and 40 (42%) recommended polysomnography (PSG) and 31 (78%) completed PSG. Of the 44 patients recommended continuous positive airway pressure (CPAP) therapy, 38 initiated CPAP and 20 (51.3%) demonstrated adherence (>4 hours 70% of nights over 30 days). Bland-Altman analysis of two nights of oxygen desaturation events greater than 4% per hour found a mean difference of -0.2 (95% CI -14.0 to 14.0). Misclassification of OSA severity was seen in 12 patients (18.7%). Conclusions: Our recount shows the feasibility and effectiveness of implementing a sleep service in a cardiometabolic clinic for patients with SMI, and using oximetry is an effective diagnostic test of SDB. Having a dedicated project coordinator to oversee the clinical pathway avoids fragmentation of clinical services.

Objective: Nocturnal pulse oximetry (NPO) is a simple and inexpensive assessment tool that has previously been shown to correlate with prognosis and timing of non-invasive ventilation (NIV) initiation in people living with motor neuron disease (plwMND). However, the optimal number of nights for measuring NPO has not been defined for this population, with other respiratory conditions exhibiting both low and high night-to-night variability in NPO parameters. This study aims to determine the inter-night variability in NPO data over three nights in plwMND. Methods: We conducted a retrospective analysis of 132 studies in which plwMND underwent three consecutive nights of NPO. Intraclass correlation coefficients (ICC) were used to assess the reliability of key NPO parameters, including mean percentage of total recording time with oxygen saturation (SpO2) < 90% (T90), oxygen desaturation index (ODI), basal SpO2 and nadir SpO2. The proportion of plwMND meeting NIV criteria based on single-night versus multi-night assessments was also compared. Results: Excellent reliability was observed for T90 (ICC(1) = 0.940) and ODI (ICC(1) = 0.901), while basal SpO2 (ICC(1) = 0.845) and nadir SpO2 (ICC(1) = 0.768) demonstrated good reliability. However, relying on a single-night NPO assessment failed to identify 12% of plwMND who met NIV criteria when evaluated over three nights. Conclusions: Despite good to excellent inter-night variability of NPO data in plwMND, multi-night NPO monitoring improves the accuracy of identifying plwMND requiring NIV. These findings support the need for multi-night assessments to enhance clinical decision-making in MND management.

Cannabinoids, particularly Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD), have gained popularity as alternative sleep aids; however, their effects on sleep architecture and next-day function remain poorly understood. Here, in a pilot trial, we examined the effects of a single oral dose containing 10 mg THC and 200 mg CBD (THC/CBD) on objective sleep outcomes and next-day alertness using 256-channel high-density EEG in 20 patients with DSM-5 diagnosed insomnia disorder (16 female; mean (SD) age, 46.1 (8.6) years). We showed that THC/CBD decreased total sleep time (-24.5 min, p = 0.05, d = -0.5) with no change in wake after sleep onset (+10.7 min, p > 0.05) compared to placebo. THC/CBD also significantly decreased time spent in REM sleep (-33.9 min, p < 0.001, d = -1.5) and increased latency to REM sleep (+65.6 min, p = 0.008, d = 0.7). High-density EEG analysis revealed regional decreases in gamma activity during N2 sleep, and in delta activity during N3 sleep, and a regional increase in beta and alpha activity during REM sleep. While there was no observed change in next-day objective alertness, a small but significant increase in self-reported sleepiness was noted with THC/CBD (+0.42 points, p = 0.02, d = 0.22). No changes in subjective sleep quality, cognitive performance, or simulated driving performance were observed. These findings suggest that a single dose of cannabinoids, particularly THC, may acutely influence sleep, primarily by suppressing REM sleep, without noticeable next-day impairment (≥ 9 h post-treatment). Australian New Zealand Clinical Trial Registry (ACTRN12619000714189) https://www.anzctr.org.au/.