Search Results (162)

Background: The financial burden of diagnostic testing for obstructive sleep apnea (OSA) represents a substantial barrier to treatment initiation, with cost-related access disparities disproportionately affecting the low-income and underinsured population. Home sleep testing (HST) offers a cost-effective diagnostic alternative, yet economic patterns across provider types remain unclear. This study assessed whether board-certified sleep medicine provider (BCSMP) status is associated with differences in provider-billed HST charges and evaluated how organizational and payment contexts influence these charges. Methods: A retrospective cross-sectional analysis was conducted using 2019 data from Optum’s de-identified Clinformatics^® Data Mart Database (N = 61,531 adult HST claims). The main exposure was provider status (BCSMP vs. non-BCSMP). The outcome was total provider-requested charge per HST procedure. Generalized Linear Models with a gamma distribution estimated adjusted charge differences, controlling for organizational context, place of service, and payer type. Results: BCSMP encounters had significantly lower adjusted mean HST charges than non-BCSMPs (mean difference: −$78.04; 95% CI: −$89.06 to −$67.02; p < 0.001). Individual practitioners charged $168.48 less than hospital-affiliated providers, while group practices and other facilities charged more (all p < 0.001). Fee-for-service arrangements were associated with lower charges than commercial and Medicare Advantage plans (p < 0.001). Conclusions: Board-certified sleep medicine providers and individual practice settings were associated with lower billed charges for home sleep testing; however, these findings do not necessarily reflect actual cost reduction. To translate these baseline charge differences into equitable clinical protocols and healthcare policies, future research must analyze negotiated reimbursement rates, billing structures, and practice environments to determine how these cost parameters impact the overall cost of an OSA diagnosis. Full article

Background: Gorlin–Goltz syndrome is a rare autosomal dominant condition with characteristic craniofacial and odontogenic anomalies. Orofacial alterations in childhood may precede dermatological findings, highlighting the relevance of early orthodontic and functional evaluation. Objective: This case describes a multidisciplinary orthodontic and Home Sleep Apnea Testing (HSAT)-based approach for the assessment of craniofacial morphology and sleep-disordered breathing (SDB) risk in a pediatric patient with Gorlin–Goltz syndrome. Methods: A 12-year-old male with a genetically confirmed PTCH1 mutation underwent digital intraoral scanning, orthodontic evaluation, and SDB screening using the Pediatric Sleep Questionnaire (PSQ). Following a positive screening score, HSAT with the Philips Alice NightOne^® system was performed under specialist supervision. Results: The patient showed recurrent odontogenic cysts, a lateral open bite, and unilateral Class II canine relationship. The PSQ score was 0.579, exceeding the validated cut-off of 0.33 and indicating an elevated SDB risk. HSAT findings were suggestive of mild obstructive sleep apnea based on Respiratory Event Index (REI) values (REI 4.7/h), with an isolated SpO₂ nadir of 77% and a maximum recorded apnea duration of 425 s, warranting cautious specialist interpretation and follow-up assessment. Conclusions: Integrating orthodontic assessment, digital documentation, validated screening tools, and objective HSAT-based evaluation may support the early recognition of functional compromise in syndromic pediatric patients. Positive screening results should prompt specialist referral and objective sleep assessment, while attended polysomnography remains indicated when comprehensive sleep architecture evaluation or definitive characterization is required. Full article

Background: Continuous Positive Airway Pressure (CPAP) therapy is the standard treatment for obstructive sleep apnea–hypopnea syndrome (OSAHS), and photoplethysmography (PPG) sensors are commonly used in wearable devices for home sleep apnea testing. The recorded airflow and PPG signals from both sensors capture rich physiological patterns. We hypothesize that by combining information from these signals, we can efficiently estimate sleep dynamics of patients receiving CPAP treatment. Methods: The airflow signals were obtained from CPAP titration devices, denoted as CPAP-airflow, while the PPG signals were collected using the PranaQ TipTraQ (TTQ001), a fingertip-worn wearable device. We separately trained one-dimensional convolutional neural networks for CPAP-airflow and PPG signals and fused their outputs through probabilistic ensembling to predict sleep stages. The ensemble method is a late-fusion soft-voting scheme that computes a linearly weighted combination of synchronized softmax probability vectors from the modality-specific models. Results: For three-stage classification (Wake, REM, NREM), the PPG-based and CPAP-airflow-based models achieved overall Cohen’s kappa scores of 0.511 and 0.452, respectively, while the ensembled model improved the overall kappa to 0.587. The F1-score for the REM stage improved to 0.706 using the ensemble method, compared to 0.685 and 0.532 achieved by the individual models, respectively. In the four-stage classification (Wake, REM, Light, Deep) task, a deep sleep sensitivity of 0.596 was attained through the application of probabilistic ensembling. Conclusions: A fusion scheme of complementary information from the CPAP and PPG enhances the accuracy of sleep stage detection and hence enables more precise sleep monitoring, especially with an improved REM identification. Clinical implications include applying the proposed algorithm to improve in-home auto-CPAP titration by capturing REM-related respiratory instability and avoiding under-titration in REM-dominant OSAHS, better reflecting the patient’s true nocturnal respiratory needs. Full article

Introduction: Obstructive sleep apnea (OSA) is highly prevalent, affecting up to 50% of individuals over 65 years. Elderly patients often present with atypical, fewer and less severe symptoms, suggesting age-specific phenotypes. However, comprehensive clinical phenotyping that incorporates cognitive outcomes remains limited. This study aimed to characterize OSA phenotypes through cluster analysis and evaluate their association with cognitive impairment, independently of age. Materials and Methods: Between 2020 and 2024, 409 adults with moderate-to-severe OSA were enrolled and stratified into three age groups (<65, 65–74, ≥75 years). All underwent home sleep apnea testing (HSAT), comprehensive symptom assessment, Epworth Sleepiness Scale (ESS), and Montreal Cognitive Assessment (MoCA, pathological ≤ 25 pts). Hierarchical cluster analysis (Ward’s method) used AHI, T90, BMI, and ESS. Logistic regression identified independent predictors of cognitive impairment. Results: Older groups showed lower BMI, higher comorbidity burden, fewer symptoms, and greater cognitive impairment prevalence (4.5% vs. 9.7% vs. 45.9%; p < 0.001), despite comparable polysomnographic severity across age groups. Cluster analysis identified three phenotypes: Cluster 1 (classical OSA: high AHI, BMI, T90, ESS); Cluster 2 (geriatric phenotype: low AHI, BMI, T90, ESS, highest cognitive impairment rate: 27.7%); Cluster 3 (hypersymptomatic: low AHI and T90, high sleepiness and asthenia, prevalent depression). On multivariate regression, age (OR 1.155; p < 0.001), male sex (OR 2.223; p = 0.034), and Cluster 2 (OR 3.131; p < 0.001) were independent predictors of cognitive impairment. Conclusions: Three clinically distinct OSA phenotypes were identified regardless of age and severity. The geriatric phenotype was associated with three-fold increased risk of cognitive impairment, supporting routine cognitive screening and age-adapted diagnostic strategies in elderly OSA patients. Full article

Firefighters face high-stress occupational demands and irregular shift work that negatively impact sleep quality, which is intrinsically linked to long-term physical and psychological health. This cross-sectional study examines how the physical sleep environment (home vs. work) and station sleeping arrangements (bunk-style vs. individual dorm-style quarters) influence subjective sleep quality in this population. Sixty-six career firefighters (Age = 40.89 ± 11.05 years), completed the Pittsburgh Sleep Quality Index (PSQI) to assess their sleep in both home and fire station environments, with data analyzed using Wilcoxon Signed Ranks and Mann–Whitney U tests. The results reveal significant differences (p < 0.001), with sleep duration, efficiency, subjective quality, and global PSQI scores all performing significantly better at home than at work. Notably, no significant differences were found between bunk-style and dorm-style sleeping quarters at the station. These findings suggest that firefighters experience poorer sleep while on duty regardless of room design, indicating that operational stressors like call volume and nocturnal arousal may be more influential on sleep quality than the physical arrangement of sleeping quarters, and could inform organizational policies and wellness programs aimed at reducing occupational fatigue. Full article

Background and Objectives: Adherence to positive airway pressure (PAP) is often suboptimal, and current monitoring relies on device logs that, by design, cannot detect respiratory events outside the therapy window. This creates a physiological blind spot during periods of non-usage. This study aimed to demonstrate the clinical necessity of independent, continuous monitoring using a smartphone-based home sleep apnea test (S-HSAT) by validating treatment effectiveness on adherent nights and quantifying the untreated apnea burden caused by partial adherence. Methods: We prospectively monitored 63 obstructive sleep apnea (OSA) patients commencing PAP therapy. Nightly apnea–hypopnea index (AHI) and usage time were recorded simultaneously by an S-HSAT (ApnoTrack) and the PAP device over a 30-day period. Nights were categorized by the duration discrepancy between S-HSAT and PAP (full-use, ≤5 min; intermediate-use, 5–30 min; partial-use, >30 min) using physiologically and operationally derived thresholds. Results: Final analysis included 39 participants contributing 667 nights (24 participants excluded due to non-use of one or both devices). Full-use nights (46.2%) showed close agreement between S-HSAT and PAP mean AHI (2.8 ± 4.3 vs. 2.5 ± 2.0 events/h; p = 0.13). On intermediate-use and partial-use nights (20.7% and 33.1%, respectively), substantial AHI discrepancies emerged (7.3 ± 5.5 vs. 3.8 ± 3.3 and 11.0 ± 7.4 vs. 2.8 ± 2.5 events/h, respectively; both p < 0.001). Conclusions: Independent S-HSAT monitoring quantified an untreated apnea burden that is invisible to PAP logs alone, while confirming therapeutic efficacy on well-adherent nights. These findings suggest that continuous independent monitoring may help bridge the gap between prescribed therapy and actual physiological outcomes in OSA care. Full article

Background and Objectives: Obstructive sleep apnea (OSA) is a heterogeneous disease with diverse clinical presentations and high global prevalence. Obesity is a common comorbidity in OSA, but its relationship with symptom subtypes remains unclear. This study aimed to evaluate the association between OSA symptom subtypes and obesity in a clinical cohort. Methods: This observational study analyzed data from the Santiago Obstructive Sleep Apnea (SantOSA) prospective clinical cohort, including adults with OSA confirmed by home sleep apnea testing. Symptom subtypes were identified using latent class analysis. Associations between obesity and symptom subtypes were evaluated using multivariable regression models adjusted for age, sex, tobacco use, RDI, T90%, TST, hypertension, and diabetes. Statistical significance was set at p < 0.05. Results: A total of 1167 patients were included (943 men). Latent class analysis identified three symptom subtypes: non-sleepy, disturbed sleep, and excessive daytime sleepiness. Among obese patients, 30.7%, 50.0%, and 19.3% were classified into these subtypes, respectively. Obesity prevalence was 50.2%, and compared with non-obese OSA patients, obese patients showed a higher prevalence of severe OSA (46.1% vs. 26.9%), hypertension (54.4% vs. 34.9%), and diabetes (37.2% vs. 19.4%), as well as higher ESS scores and higher RDI and T90% values (all p < 0.01). In adjusted analyses, obesity remained independently associated with the excessive daytime sleepiness subtype after controlling for age, sex, tobacco use, RDI, T90%, TST, and comorbidities. Conclusions: Obesity is highly prevalent in OSA and is associated with specific symptom-defined phenotypes, particularly excessive daytime sleepiness and disturbed sleep. These findings support the relevance of considering symptom profiles alongside traditional severity metrics. Full article

Background/Objectives: Two diagnostic approaches for sleep studies are commonly used worldwide: in-laboratory polysomnography [PSG] and home sleep apnea testing [HSAT]. Although HSAT has gained increasing acceptance due to its convenience and lower cost, clinical criteria for HSAT use remain complex and cannot be inferred directly from AHI/ODI severity indices alone. The aim of the present exploratory study was to examine associations between routinely collected demographic, clinical, and symptom-related variables and objective indices of disease severity, namely the apnea–hypopnea index [AHI] and oxygen desaturation index [ODI] as an initial, hypothesis-generating step toward future patient-level model development and validation. Methods: A retrospective observational analysis was conducted in 1100 individuals who previously underwent in lab-polysomnography [PSG] at the University Hospital of Thessaly, Greece, between 2006 and 2023. Specific demographic, clinical and symptom-related variables were included in this study [six continuous and fifteen categorical], which were analyzed in relation to AHI and ODI values. A three-step process was carried out: variable selection followed a screening and backward elimination process. Multivariable linear regression models were subsequently estimated within a Bayesian framework using Hamiltonian Monte Carlo methods. Results: Out of 1100 individuals, the mean age was 51.9 years with the predominant gender being male [76%]. Obesity [65.6%] and hypertension [40.5%] were the most common comorbidities. For AHI, male gender, body mass index [BMI], Epworth Sleepiness Scale [ESS] score, reported breathing interruptions during sleep, and chronic obstructive pulmonary disease [COPD] were significant predictors. For ODI, significant predictors included male gender, BMI, ESS score, breathing interruptions during sleep, daytime sleepiness, obesity, and COPD. COPD showed an inverse association with both indices. Conclusions: These findings support the feasibility of integrating routinely available clinical variables within a Bayesian probabilistic framework to estimate disease severity pre-test probability. The current analysis may not constitute a validated tool for HSAT versus PSG selection; however, it is an initial, hypothesis-generating step toward future model development. Full article

Sleep apnea is a prevalent sleep-related breathing disorder characterized by recurrent cessations or reductions in airflow during sleep. It significantly impacts the quality of life, yet current diagnostic methods like polysomnography (PSG) are expensive and uncomfortable, limiting accessibility and ease of use. We developed a novel non-contact biosensing system using secondary laser speckle pattern analysis and dedicated image processing algorithms for apnea-like breathing cessations. The proposed method was tested on $14$ healthy subjects with diverse body characteristics, aged 22–50 years (mean $33.1 \pm 9.3$ years) and body mass index (BMI) ranging from $19.6$ to $28.7$ kg/m² (mean $24.6 \pm 3.0$ kg/m²) at different ‘simulated’ sleeping positions (back-lying, stomach-lying and side-lying), using voluntary breath-holding protocols to simulate apnea-like cessations lasting 10–20 s (short duration) and 20–30 s (long duration). To evaluate the performance of the system without selection bias, two complementary five-fold cross-validation procedures were applied: a participant-level and a class-level stratification. Using class-wise stratification, the system achieved an overall accuracy of $87.0 \pm 3.0\%$ (95% CI: [85.3%, 88.7%]), long-cessation sensitivity of $91 \pm 12.4\%$$(95\% \mathrm{C}\mathrm{I}:[83.8\%,98.2\%\left]\right)$ and a short-cessation sensitivity of $88.0 \pm 11\%$$\left(95\% \mathrm{C}\mathrm{I}:[81.6\%,94.4\%]\right)$. The two-class classification strategy confirm the robustness of the approach, supporting the potential of secondary laser speckle pattern analysis as a low-cost, non-contact alternative for home-based sleep apnea screening. Full article

Traditional sleep staging studies typically rely on signals collected using contact-based sensors, which may interfere with the natural sleep state of subjects and thus affect the authenticity and reliability of the recorded data. To address this limitation, this study proposes an automatic sleep staging method based on non-contact single-channel ballistocardiogram (BCG) signals. First, band-pass filtering is applied to the raw BCG signals to separate the heart rate and respiratory components. Heart rate variability (HRV) and respiratory rate variability (RRV) features are then extracted, and mutual information is used to select key feature subsets that exhibit strong correlations with different sleep stages. Considering the complexity and prominent temporal characteristics of real-world sleep data, a temporal modeling network named TLA-SleepNet is constructed to enhance the model’s capability in capturing complex sequential features and improving robustness. Experiments conducted on 10 independent sleep recordings containing a total of 10,614 sleep epochs demonstrate that, under subject non-independent testing conditions with five-fold cross-validation, the proposed method achieves an accuracy of 87.1% in the sleep staging task, with precision, kappa coefficient, and F1-score reaching 92.4%, 81.9%, and 88.7%, respectively. The results indicate that the proposed method can achieve a reliable sleep staging performance without direct contact between sensors and the human body, providing a feasible solution for non-contact sleep monitoring in home-based and mobile healthcare applications. Full article

The clinical diagnosis of sleep apnea conventionally necessitates resource-intensive Polysomnography (PSG). We propose a weakly supervised framework to detect apnea using non-invasive Ballistocardiography (BCG), thereby addressing the critical scarcity of labeled BCG data. Instead of manual annotation, our pipeline transfers knowledge from a synchronized ECG signal, using it as a “teacher” to generate pseudo-labels for the BCG model. We formulated a User-Defined Function (UDF) that combines Heart Rate Variability and ECG-Derived Respiration to autonomously label the BCG windows. These pseudo-labels were subsequently employed to train a 1D Convolutional Neural Network. Testing on a public dataset, the CNN model achieved 71.8% accuracy against the pseudo-labels. When projected against the clinical ground truth, we estimate a true accuracy of 77.7%. These results validate that ECG-based supervision can effectively train low-cost home sensors without the bottleneck of manual medical annotation. Full article

Background: Joubert syndrome (JS) is a rare ciliopathy characterized by cerebellar and brainstem malformations and the molar tooth sign on magnetic resonance imaging. Motor impairment is primarily driven by axial hypotonia, impaired postural control, and disrupted respiratory-postural integration. Longitudinal reports describing structured neurorehabilitation with standardized functional outcomes remain limited. Case presentation: We report a female child with prenatally suspected vermian hypoplasia and postnatally MRI-confirmed Joubert syndrome. Subsequent molecular testing performed at the age of 3 years and 11 months identified heterozygous variants in the B9D2 gene associated with Joubert syndrome. Early development was marked by axial hypotonia, global motor delay, impaired trunk stabilization, sleep-disordered breathing, and early hip migration. At 2.5 years of age, following motor plateau under conventional therapy, a structured 12-month rehabilitation programme was introduced, combining Vojta-based reflex locomotion, respiratory therapy targeting thoraco-diaphragmatic synchronization, daily home-based practice, and supported standing. Results: After 12 months, gross motor function improved substantially, with GMFM-88 increasing from 12% to 52% (+40 percentage points). PEDI scaled scores improved across all domains, with mobility increasing from 8 to 40, self-care from 15 to 45, and social function from 25 to 50. Ataxia severity decreased from 22 to 15 on the modified Brief Ataxia Rating Scale, consistent with improved trunk stability and coordination. Postural and respiratory organization improved, reflected by a reduction in the subcostal angle from 137° to 90°, an increase in sacral slope from 5° to 10°, and increased expiratory pressure from 10 to 25 mmHg. Caregiver-reported assessment combined with structured clinical observation indicated improved functional visual performance, including enhanced visual attention, visuomotor coordination, and environmental visual interaction. Conclusions: Structured neurorehabilitation was associated with substantial functional improvement across motor, postural, and respiratory domains. These findings support the clinical relevance of mechanism-oriented neurorehabilitation and standardized longitudinal outcome assessment in Joubert syndrome. Full article

Background: Mild traumatic brain injury (mTBI) is frequently associated with persistent cognitive and psychosocial symptoms, yet biological correlates of recovery remain poorly understood, particularly among Asian and Pacific Islander (API) populations. Pre-injury psychosocial adversity may further shape post-injury recovery trajectories. This pilot study examined associations between participation in a 2-week, home-based, dual-task cognitive–walking intervention (Daily Brain Exercise; DBE) and changes in cognitive, psychological, and salivary microRNA (miRNAs) measures among APIs with and without a self-reported history of mTBI. Methods: API participants completed remote cognitive testing (CNS Vital Signs), psychosocial assessments (Neuro-QoL), and saliva collection before and after DBE participation. Salivary RNA was purified, and miRNA expression was profiled using nCounter^® Human v3 miRNA Expression Panels (NanoString). Differential expression analyses were conducted using ROSALIND^® platform (OnRamp Bioinformatics, San Diego, CA, USA), a cloud-based bioinformatics analysis system, to calculate fold changes and p-values. Pre-injury psychosocial adversity was assessed via the Trauma History Screen and examined descriptively as a contextual modifier of functional outcomes. Results: Twenty-one APIs (mean age 22.9 years; 76.7% female) were enrolled, including 14 individuals with a self-reported history of mTBI (mean 4.64 years post-injury; 50% with multiple injuries). Following DBE participation, increases in cognitive flexibility and executive function scores were observed in both mTBI and control groups. Additional increases in psychomotor speed, processing speed, sleep disturbance, and depressive symptoms were observed descriptively within the mTBI group. Subgroup analyses suggested variability in pre–post patterns across combinations of mTBI history and pre-injury psychosocial adversity. Exploratory miRNA analyses identified seven miRNAs that were differentially expressed in the mTBI group following DBE (unadjusted p < 0.005), including hsa-miR-7-5p, previously reported in association with neurodevelopmental and neurological pathways. Conclusions: In this pilot, feasibility-focused study, participation in a brief, home-based, dual-task intervention was associated with descriptive changes in selected cognitive and psychosocial measures among APIs, particularly those with a history of mTBI and pre-injury adversity. The observed subgroup patterns warrant confirmation in adequately powered, controlled studies. Exploratory changes in salivary miRNAs co-occurred with functional improvements, thus generating a hypothesis for a future investigation. Full article

Long-term home-based sleep monitoring requires wearable sensing devices that strictly balance signal precision with power constraints. This study presents the design and implementation of a low-noise, low-power wearable single-channel electroencephalography (EEG) system for automatic sleep staging. The hardware architecture integrates a TI ADS1298 analog front-end with an STM32F4 microcontroller, utilizing differential sampling and hardware-based filtering to effectively suppress power-line interference and baseline drift. System-level testing demonstrates an average power consumption of approximately 150.85 mW, enabling over 24.6 h of continuous operation on a 1000 mAh battery, which meets the requirements for overnight monitoring. To achieve accurate staging without draining the wearable’s battery, we adopted and deployed a lightweight deep learning model, SleePyCo, on the cloud backend. This architecture was specifically optimized for our edge–cloud collaborative execution, which combines contrastive representation learning with temporal dependency modeling. Validation on the ISRUC dataset yielded an overall accuracy of 79.3% ± 3.0%, with a notable F1-score of 88.3% for Deep Sleep (N3). Furthermore, practical field trials involving 10 healthy subjects verified the system’s engineering stability, achieving a valid data rate exceeding 97% and a Bluetooth packet loss rate of only 0.8%. These results confirm that the proposed hardware–software co-designed system provides a robust, energy-efficient IoMT sensing solution for daily sleep health management. Full article

Background: Obstructive sleep apnea (OSA) is a major modifiable risk factor for atrial fibrillation (AF), promoting arrhythmogenesis through intermittent hypoxia, autonomic activation, and atrial remodeling. Although continuous positive airway pressure (CPAP) effectively treats OSA, real-world evidence linking objectively measured CPAP exposure to clinically relevant AF recurrence remains limited. Aims: We aimed to evaluate the association between CPAP adherence and risk of recurrent paroxysmal AF, and to compare time to first recurrence between patients with mean nightly CPAP use ≥4 h/night versus <4 h/night. Materials and Methods: In this prospective observational cohort (2017–2024), consecutive hospitalized and outpatient adults with severe obstructive sleep apnea (OSA; apnea–hypopnea index > 30 events/h) and documented paroxysmal atrial fibrillation (AF) were enrolled. Persistent and long-standing persistent AF were excluded to ensure a homogeneous population with respect to atrial substrate. OSA was assessed using home sleep apnea testing (ResMed ApneaLink), and all patients initiated continuous positive airway pressure (CPAP) therapy (ResMed AirSense 10). Objective adherence data were obtained via the ResMed AirView telemonitoring platform. Exclusion criteria included permanent AF, prior pulmonary vein isolation, central sleep apnea, left ventricular ejection fraction < 50%, end-stage chronic kidney disease (eGFR < 15 mL/min/1.73 m² or dialysis), or inability to initiate or maintain CPAP therapy. Patients were followed for 12 months. The primary endpoint was time to first documented recurrence of paroxysmal AF (≥30 s on 12-lead electrocardiography or 24-h Holter monitoring). Progression to permanent AF, defined after unsuccessful rhythm control attempts and subsequent transition to a rate control strategy, was assessed as a secondary endpoint. Time-to-event analyses used Kaplan–Meier estimates with log-rank testing, and Cox proportional hazards regression adjusted for age, body mass index, apnea–hypopnea index, heart failure, left atrial volume index, and antiarrhythmic drug therapy. Results: The final analysis included 91 patients (mean age 62.15 ± 8.29 years; 68.13% men). Mean nightly CPAP use was ≥4 h/night in 49 patients and <4 h/night in 42 patients. During follow-up, paroxysmal AF recurrence occurred in 12/49 (24.5%) patients in the ≥4 h/night group and 16/42 (38.1%) in the <4 h/night group. Mean arrhythmia-free survival at 12 months was numerically higher in the ≥4 h/night group (11.25 vs. 10.51 months), without a statistically significant difference in Kaplan–Meier curves (log-rank p = 0.11). In multivariable Cox regression, binary adherence (≥4 h/night) was not independently associated with recurrence (HR 0.52, p = 0.13), whereas mean nightly CPAP use analyzed as a continuous variable remained independently associated with delayed recurrence (per 1-h increase: HR 0.66, 95% CI 0.48–0.91, p = 0.01). Progression to permanent AF occurred in 4/49 (10.0%) versus 9/42 (17.6%) patients, respectively (p = 0.29). Conclusions: In this real-world cohort of patients with severe OSA and paroxysmal AF, higher objectively measured CPAP exposure was independently associated with delayed AF recurrence when analyzed as a continuous variable, suggesting a graded association between objectively measured CPAP exposure and AF recurrence. Larger studies with extended follow-up and continuous rhythm monitoring are warranted to confirm long-term rhythm benefits and effects on AF progression. Full article