Search Results (706)

With the increasing demand for sleep health monitoring, automatic sleep staging using single-channel electroencephalogram (EEG) signals has become increasingly prominent due to its clinical practicality. Existing methods have achieved notable progress, but they often fail to adequately capture long-term temporal dependencies and struggle to characterize transition phases. We propose SleepLT, an automated sleep staging framework that integrates multi-scale wavelet decomposition (MWD) and multi-head latent Fourier attention (MLFA). The MLFA module incorporates Fourier analysis into self-attention mechanisms and employs a partially weight-sharing bottleneck to optimize Key/Value generation, effectively capturing sleep rhythms. Extensive experiments on SleepEDF-78 and SHHS datasets demonstrate strong and consistent performance, with Macro F1 improvements of 2.1–3.2% over the compared baselines. Visualizations confirm that SleepLT enhances inter-class discriminability between sleep stages, robustly detects salient waveforms, and effectively captures transitions through long-sequence modeling. These results indicate that SleepLT is effective for automatic sleep staging from single-channel EEG, particularly in improving the recognition of ambiguous transitional stages such as N1 and REM. Full article

Background: We previously demonstrated recapitulation of the relative hypogonadotropic hypogonadism of obesity, the Reprometabolic Syndrome (RMS), in women of normal BMI with a one-month high-fat, eucaloric diet (HFD). Objective: Assess effects of HFD on sleep, body composition and lifestyle and metabolic secondary outcomes and correlate insulin sensitivity changes with the RMS. Methods: A total of 18 normally cycling women aged 18–38 with BMI 18–24 kg/m² were enrolled for a four-month study including a eucaloric HFD (48% calories from fat) for one menstrual cycle. Activity, sleep, body composition, and the lipidome were measured in all participants. Fecal microbiome was analyzed in the last nine participants, and insulin sensitivity by two-stage hyperinsulinemic euglycemic clamp was measured before and after HFD in 15 participants. Results: Relative to the pre-diet period, BMI, activity and sleep measures did not change, except for waking after sleep onset (WASO), which appeared to decrease during and post HFD. DXA revealed statistically significant decreases in total percent fat, total fat mass, visceral fat volume, and trunk fat volume. Whole-body insulin sensitivity decreased with the HFD while adipocyte insulin sensitivity was unaffected. Insulin sensitivity changes did not correlate with change in gonadotropins or response to gonadotropin releasing hormone (GnRH). Multiple significant changes in plasma lipids were observed, including increased ceramides and glucosylceramides. Microbiome analysis revealed increased microbial diversity. Conclusions: A one-month eucaloric HFD that induced RMS in normal-weight, reproductive-aged women also induced whole-body insulin resistance (IR) and multiple lipidomics changes potentially associated with IR. These changes in IR occurred despite overall stable activity, BMI and sleep, but did not correlate with the HPO axis defects. The unexpected decrease in body fat and increase in microbial diversity may be related to specific dietary elements of the HFD. Full article

Background/Objectives: To evaluate the short-term effects of structured physical activity (PA), alone or combined with dietary counselling, in early-stage chronic kidney disease (CKD) patients managed in primary healthcare (PHC). Methods: This non-randomised controlled study was conducted in Croatia from 1 September to 30 November 2025. Ninety adults aged 40–75 years with early-stage CKD were allocated to three groups: structured PA, combined PA and dietary counselling, or control. Interventions included kinesiologist-led PA and, in the combined group, dietitian-led Mediterranean/plant-based counselling. Outcomes included estimated glomerular filtration rate (eGFR), urinary albumin-to-creatinine ratio (ACR), cardiometabolic risk factors, behavioural measures, quality of life, and sleep quality. Statistical significance was set at p < 0.01. Results: Seventy-eight participants completed follow-up. Changes in eGFR did not differ between groups (p = 0.310). Mean ± standard deviation changes in ACR were −1.10 ± 6.37, −0.86 ± 2.88, and +1.18 ± 3.13 in the PA, combined, and control groups, respectively (p = 0.017, not meeting the prespecified significance threshold). Significant between-group differences were observed for selected patient-reported and PA outcomes, including emotional well-being, energy/fatigue, role limitations due to emotional problems, sedentary time, and total PA (all p ≤ 0.006). Conclusions: Structured PA, with or without dietary counselling, improved PA behaviour and selected patient-reported outcomes in early-stage CKD managed in PHC but did not demonstrate significant short-term effects on kidney-related outcomes. These findings support the feasibility of integrating lifestyle-oriented interventions into PHC as part of integrated CKD care, while larger, longer-term studies are needed. Full article

Background: Gastroesophageal reflux disease (GERD) is a common digestive disorder with a substantial impact on quality of life. Emerging evidence suggests that dietary patterns and lifestyle behaviors are associated with the occurrence and severity of GERD symptoms; however, integrated data from Romania remain limited. Objective: The aim of this study was to evaluate associations between pro- and anti-inflammatory dietary patterns, lifestyle-related behavioral factors, and the presence and severity of gastroesophageal reflux symptoms in an adult Romanian population. Methods: A national cross-sectional observational study was conducted using a self-administered online questionnaire. All participants included in the study reported a prior diagnosis of gastroesophageal reflux disease (GERD), and participant classification was based exclusively on current symptomatology assessed using the GERD-Q score. Therefore, comparisons were not performed between patients and a healthy population, but rather between individuals at different stages of clinical expression of the same condition, characterized by a fluctuating course. The instrument included standardized GERD-Q items for symptom assessment, together with questions regarding dietary intake and lifestyle behaviors. Pro-inflammatory (PRO), anti-inflammatory (ANTI), and combined (PRO–ANTI) dietary scores were established. Statistical analyses included comparative and correlational tests as well as multivariable logistic regression models. Results: Among the 340 participants included in the study, 72.4% reported symptoms consistent with GERD according to the GERD-Q score. A higher pro-inflammatory dietary score was significantly associated with GERD, with participants in the highest PRO category showing more than a fourfold higher likelihood of GERD in multivariable analyses. Consumption of spicy foods and carbonated beverages was associated with an increased risk of GERD in univariate analyses; however, these associations did not remain significant in multivariable models. Late meals (defined as consumption of one’s last meal of the day less than two hours before bedtime) were independently associated with GERD. Combined analyses indicated a higher risk among participants who reported eating late meals, particularly when combined with large evening meals. Most foods considered protective, along with classical lifestyle factors (smoking, alcohol consumption, and sleeping position), were not independently associated with GERD. Conclusions: These findings suggest that overall dietary patterns with pro-inflammatory potential and meal timing in relation to the sleep–wake cycle may be more consistently associated with GERD symptoms in this sample than isolated food items or traditional lifestyle risk factors. Nutritional and behavioral interventions focused on improving overall dietary patterns and avoiding late meals may represent potential strategies for GERD management. Full article

Dementia caregiving represents a major public health challenge, with spousal caregivers assuming the greatest burden. Spouses, themselves typically older adults, provide high intensity, long-term, and largely unpaid care across all stages of cognitive decline. Despite their central role in dementia care, the health consequences experienced by spousal caregivers remain insufficiently characterized in the literature and inadequately addressed in clinical and public health practice. This structured narrative review synthesizes current evidence on the multidimensional impact of dementia caregiving on the physical, psychological, cognitive, social, and financial health of spousal caregivers. It further contextualizes these consequences within the trajectory of dementia progression, and identifies interventions, support systems, and policy considerations necessary to mitigate caregiver burden. Spousal caregivers experience disproportionate burden due to continuous, escalating responsibilities that often mirror the progressive deterioration of their partners. Emotional burdens, including uncertainty during pre-diagnostic stages, role strain, conflict, loss of intimacy, and anticipatory grief. Physically, spouses endure musculoskeletal strain, sleep disruption, poor nutrition, and heightened frailty risk. Psychologically, spousal caregivers exhibit elevated rates of depression, anxiety, loneliness, and stress-related disorders. Socially, caregivers experience substantial isolation, stigma, and erosion of social networks. Financial hardship, including early retirement, reduced employment, and uncompensated care hours, further exacerbate stress. Evidence suggests that chronic caregiving stress contributes to biological changes such as immune dysregulation, inflammation, acceleration, aging, and potential cognitive decline in caregivers themselves. Caregiver burden influences patient outcomes as evidenced by increased emergency department use, falls, and earlier institutionalization in persons with dementia whose caregiver is subjected to a high burden. Current care models rarely include routine, caregiver assessment or structured guidance following diagnosis, resulting in substantial unmet needs. Effective mitigation requires integrated, stage-sensitive interventions, including psychosocial support, caregiver education, respite services, culturally tailored programs, and digital health tools, alongside broader policy reforms to reduce financial and structural barriers. Full article

This study aims to provide insights into how physical activity is associated with sleep patterns in youth populations, in particular, Slovak adolescents, and how gender (boys vs. girls) and age (≤16 vs. ≥18) moderate this relationship, using an extreme-group comparison approach that excludes 17-year-olds to enhance contrast between developmental stages. Using a cross-sectional design, self-reported data were collected from 2504 (100%) high school students (aged 15–19; 45.6% boys, 54.4% girls) using the International Physical Activity Questionnaire-Short Form (IPAQ-SF) and the Pittsburgh Sleep Quality Index (PSQI). Participants aged 17 years were excluded from age-stratified analysis to create clearer separation between early/mid and late adolescence. The primary outcome was global sleep quality (PSQI > 5). Secondary outcomes included sleep duration and PSQI component scores. All other analyses (age- and gender-stratified comparisons and interaction models) were predefined as exploratory and hypothesis-generating to examine potential effect modification. Age-stratified analyses among girls showed that, within the low PA group, good sleep was reported by 37.7% of younger girls (≤16) and 28.6% of older girls (≥18). Among older girls, the proportion reporting good sleep increased to 49.8% in the high PA group (χ² = 29.16, p < 0.001). No consistent associations between PA and sleep quality were observed among boys; however, significant association was identified among younger boys (≤16 years), which was not observed in older boys. Logistic regression revealed a modest interaction between age and PA level in predicting sleep quality among girls (β = 0.346, p = 0.049), suggesting small age-dependent variation in the association. This effect should be interpreted cautiously given its borderline statistical significance. Component-level PSQI analyses showed that girls experienced higher rates of sleep disturbances (χ² = 91.40, p < 0.001), longer sleep latency (χ² = 26.71, p < 0.001), and greater daytime dysfunction (χ² = 79.90, p < 0.001). These findings provide region-specific evidence from Central and Eastern Europe and underscore the need for age- and gender-sensitive public health strategies targeting both physical activity promotion and better sleep outcomes, given their observed associations. Full article

Background/Objectives: Sufficient sleep quantity/quality in infancy is crucial for healthy development, so it is important to identify early associated predictive factors. Research findings highlight salient endogenous (infant temperament) and exogenous (breastfeeding) influences, though no known studies have examined nuanced and interactive relations among these variables from early to late infancy/toddlerhood. Thus, the current study examined the main and interaction effects of these variables on infant sleep at 6 and 14 months while controlling for prenatal cortisol exposure. Methods: Data from a subsample (n = 79) of the Infant Development and Healthy Outcomes in Mothers Study were used, including prenatal maternal saliva samples assayed for cortisol and maternal questionnaires that included retrospective reporting of infant temperament, sleep quality and quantity, and breastfeeding frequency. Results: Multiple linear regression results include a statistically significant negative relation between prenatal maternal cortisol area under the curve and 6-month infant sleep quantity. A greater breastfeeding frequency at 6 months was associated with decreased 6-month sleep quality via conditional but not interaction effects. Greater 6-month infant Surgency was associated with better sleep quality at 14 months. There were no statistically significant interaction effects. Conclusions: The findings suggest that maternal psychophysiological stress has a significant influence on infant sleep duration, while research should further investigate the role of infant temperament and breastfeeding in shaping infant sleep quality. Significant conditional effects highlight patterns that should be re-examined with a larger sample to determine whether infant temperament may buffer against negative associations between breastfeeding frequency and infant sleep quality in early and late infancy in a developmental stage-consistent manner. Future replication studies should include a multi-method, longitudinal assessment of all key study variables, as well as a larger, more sociodemographically diverse sample of maternal–infant dyads. Full article

Unauthorized occupation of parking spaces designated for individuals with disabilities remains a persistent challenge in urban environments, limiting accessibility and inclusive mobility. This paper proposes an integrated UAV-assisted enforcement framework that combines drone-based imaging, onboard license plate recognition (LPR), IoT connectivity, and a staged optimization strategy for energy-aware surveillance. The framework employs a two-phase approach: first, it derives energy-efficient UAV activation patterns via sleep–active scheduling, followed by coverage maximization under energy constraints. The inherently multi-objective problem—balancing energy consumption, coverage, and redundancy—is addressed via a weighted-aggregation formulation, enabling efficient optimization with classical metaheuristic algorithms. Seven algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO), Differential Evolution (DE), Artificial Bee Colony (ABC), and a Greedy baseline—are implemented in both conventional and staged variants to enable comprehensive evaluation. Experimental results demonstrate 32–45% reductions in energy consumption, over 95% coverage effectiveness, and 50–60% faster convergence compared to single-phase approaches, with all improvements statistically significant (p < 0.001). The proposed framework provides a scalable, practically deployable solution for intelligent enforcement of disability parking regulations while also enabling energy-efficient UAV coordination in smart urban monitoring systems. Full article

Background: Polysomnography (PSG) is the gold standard for diagnosing sleep disorders. However, the subjectivity of manual scoring can lead to inter-scorer variability, undermining diagnostic accuracy and subsequent clinical decisions. This study aims to quantitatively assess scoring concordance among multiple scorers across various clinical subgroups to identify the factors that contribute to interpretive discrepancies. Methods: We conducted a retrospective analysis of overnight diagnostic PSG data from adult patients at a tertiary university hospital sleep center. Interrater reliability was evaluated by three independent expert scorers for 30 subjects selected through stratified random sampling. The polysomnographic data were independently and blindly scored according to the American Academy of Sleep Medicine criteria, focusing on sleep stages, arousals, respiratory events, and leg movements, all scored in 30 s epochs. Interrater agreement was measured using Fleiss’ κ, along with 95% confidence intervals, and included subgroup analyses by diagnostic category. Results: The analysis included a total of 28,291 epochs from 30 adults across normal, insomnia, obstructive sleep apnea (OSA) [mild–severe], and periodic limb movement (PLM) disorder subgroups. The overall interrater agreement for sleep staging among the three scorers was nearly perfect (Fleiss’ κ = 0.932), with the highest concordance observed in stages W, N2, and R, and excellent agreement in stages N1 and N3. Respiratory events showed particularly high reliability, with near-perfect agreement for apnea (κ = 0.955) and substantial agreement for hypopnea, arousals, and PLMs. Pairwise analyses indicated the highest concordance between scorer 1 and scorer 3, while the agreement between scorer 1 and scorer 2 was lower, particularly for detecting arousals and limb movements. Subgroup analyses showed the highest and most stable agreement in moderate OSA, whereas severe OSA exhibited reduced reliability for sleep staging and arousal scoring, indicating increased scoring complexity with greater sleep fragmentation. Conclusions: Although expert PSG scoring demonstrates high overall reliability, significant variability persists in complex cases like severe OSA. These findings underscore the necessity for structured quality assurance and automated tools to improve diagnostic consistency in clinical practice. Full article

Introduction: Caffeine is the most widely consumed psychoactive stimulant worldwide and acts primarily through antagonism of adenosine A1 and A2A receptors, thereby reducing sleep pressure and promoting wakefulness. Although its alerting and performance-enhancing effects are well established, its influence on sleep-related electroencephalography (EEG) has been investigated across diverse paradigms with substantial methodological heterogeneity. This systematic and mechanistic review aimed to synthesize human evidence on how caffeine affects sleep architecture, quantitative sleep EEG, and neurophysiological markers of sleep homeostasis, and to interpret these findings within current models of adenosine-mediated sleep–wake regulation. Materials and methods: A systematic search of PubMed/MEDLINE, Web of Science, Scopus, Embase, PsycINFO, ResearchGate, and Google Scholar was conducted for studies published between January 1980 and January 2026, with the final search performed on 10 January 2026. Eligible studies were original human investigations examining caffeine exposure or administration and reporting sleep-related EEG outcomes, including polysomnographic sleep staging, spectral EEG analyses, or other EEG-derived sleep metrics. Two reviewers independently screened records and assessed eligibility, with disagreements resolved by consensus. Data on study design, participant characteristics, caffeine interventions, EEG methodology, and outcomes were extracted using a predefined form. Risk of bias was evaluated using the RoB 2 and ROBINS-I tools. Owing to marked heterogeneity across studies, findings were synthesized narratively within a mechanistic interpretive framework. Results: Thirty-two studies were included. Across highly heterogeneous paradigms—including acute bedtime or evening dosing, daytime or repeated caffeine use before nocturnal sleep, administration during prolonged wakefulness followed by recovery sleep, withdrawal protocols, and ambulatory/home EEG monitoring—the most consistent finding was suppression of low-frequency NREM EEG activity, particularly slow-wave activity and the lowest delta frequencies. Caffeine frequently increased faster EEG activity, including sigma/spindle and beta ranges, producing a lighter, more aroused, and more wake-like sleep EEG profile. These effects were especially prominent during early-night NREM sleep and in recovery sleep after sleep deprivation, where caffeine attenuated the expected homeostatic rebound in low-frequency power. REM-related effects were less consistent, but some studies reported delayed REM timing and subtler alterations in REM EEG. Emerging evidence further suggests that caffeine increases EEG complexity and shifts sleep dynamics toward a more excitation-dominant state. Several studies indicated that quantitative EEG measures were more sensitive than conventional sleep-stage variables in detecting caffeine-related sleep disruption. Dose, timing, habitual caffeine use, withdrawal state, age, circadian context, and adenosinergic genetic variation, particularly involving ADORA2A, moderated the magnitude of effects. We also highlighted the connection between current results and sports and sports science. Conclusions: Caffeine reliably alters the neurophysiological architecture of human sleep in a direction consistent with reduced sleep depth and weakened homeostatic recovery. The overall evidence supports a mechanistic model centered on adenosine receptor antagonism, attenuation of sleep-pressure build-up and expression, and a shift toward greater cortical arousal during sleep. Sleep EEG appears to be a sensitive marker of these effects, often revealing physiological disruption even when conventional sleep architecture changes are modest. Future research should prioritize larger and more diverse samples, pharmacokinetic and pharmacogenetic characterization, and ecologically valid high-resolution sleep monitoring to clarify the real-world and functional consequences of caffeine-induced EEG changes. Full article

Compared to polysomnography (PSG) and actigraphy, contactless consumer sleep-tracking devices (CCSTDs) are low-cost, user-friendly, and non-disruptive to sleep. This study evaluated the performance of two inexpensive, representative first-generation Chinese-made CCSTDs (the iSleep S200G and Sleep Dot B501) against PSG and actigraphy, using standardized validation protocols. The objective was to assess their feasibility as alternatives for large-scale, long-term preliminary research that does not rely on single-day high-precision sleep data. Eleven healthy young adults (mean age = 26.5 ± 4.8 years) participated in a two-night sleep laboratory study using four devices in parallel. Compared with PSG, the iSleep S200G exhibited no significant differences in TST and SE, while the Sleep Dot B501 showed no significant differences in TST, SE, SOL, and WASO. The intraclass correlation coefficient values and epoch-by-epoch agreement of the iSleep S200G and Sleep Dot B501 were as good as or better than those of actigraphy. Notably, the epoch-by-epoch agreement metric of both devices was not inferior to other consumer sleep-tracking devices already used for long-term, large-scale sleep monitoring. Therefore, even within budget constraints, first-generation CCSTDs can effectively meet the requirements for long-term, large-scale sleep monitoring without sleep stage detection. The results also provided data references for researchers using iteratively upgraded CCSTDs. Full article

Sleep quality is closely associated with cardiovascular, metabolic, and mental health outcomes, yet the clinical gold standard, polysomnography (PSG), is costly and intrusive for long-term home monitoring. Ballistocardiography (BCG) enables unobtrusive in-bed sensing and is therefore attractive for low-burden sleep assessment in natural environments. However, most existing BCG studies are PSG-referenced and mainly focus on sleep staging, while movement and out-of-bed episodes are often treated as artifacts rather than modeled jointly. In this study, we propose an interpretable unsupervised proxy-state modeling framework for three-state in-bed monitoring from BCG signals under an unlabeled setting. BCG recordings were segmented into 30 s windows with 50% overlap, and multi-domain features were extracted from waveform morphology, spectral power, heart rate-related dynamics, and wavelet energy distribution. K-means clustering (K = 3) was used to construct cluster-derived proxy labels, TreeSHAP-based feature ranking together with inner-CV-guided Top-N subset selection was used for training-only feature screening, and multiple classifiers were compared under a strict leave-one-subject-out protocol, with an ROA-optimized RBF-SVM achieving the best overall performance. Using data from 32 volunteers, the framework achieved an accuracy of 0.9932 ± 0.0047 (mean ± SD), together with consistently strong Macro-F1 and MCC scores. Overall, it outperformed the alternative methods compared in this study. Full article

Sleep plays a fundamental role in human health and cognitive functioning, motivating the development of reliable and scalable methodologies for sleep stage classification (SSC). Recent advances in non-invasive and economically sustainable sensing technologies enable continuous sleep monitoring beyond laboratory settings. However, SSC remains a challenging data analytics task due to the intrinsic class imbalance among sleep stages. This study investigates the effectiveness of different imbalanced data management strategies within a machine learning framework for non-invasive SSC. The proposed approach relies exclusively on heart rate and motion signals, which can be acquired through wearable devices or contactless under-mattress sensors, making it suitable for longitudinal monitoring scenarios. Using the PhysioNet DREAMT dataset, 32 experimental scenarios are defined by combining data-level techniques (ADASYN oversampling with different balancing weights), algorithm-level strategies (cost-sensitive learning), and hybrid solutions. Four model families are evaluated—Decision Tree, k-Nearest Neighbors, Ensemble Classifiers, and Artificial Neural Networks—across classification tasks involving 2, 3, 4, and 5 sleep stages. The experimental results show that ensemble-based models provide robust and consistent performance under severe class imbalance, achieving macro accuracies of 82% for sleep–wake detection, 73% for 3-stage classification, 72% for 4-stage classification, and 64% for 5-stage classification. These findings confirm the relevance of imbalance-aware analytics and demonstrate the feasibility of accurate, minimally invasive SSC within big data and cognitive computing paradigms. Full article

Background: Restless legs syndrome (RLS) is a prevalent neurological sleep disorder that often impairs sleep maintenance. This single-arm, open-label study evaluated the efficacy, safety, and tolerability of extended-duration tonic motor activation (XD-TOMAC) in adults with RLS who experience frequent awakenings with symptoms. Methods: The study comprised three stages: Stage 1 (2 weeks of no intervention), Stage 2 (8 weeks XD-TOMAC), and Stage 3 (2 weeks of no intervention). XD-TOMAC consisted of bilateral high-frequency peroneal nerve stimulation programmed to 180 min duration and administered nightly at bedtime. Nineteen adults with moderate–severe RLS were enrolled, each reporting at least three nights per week of RLS symptoms causing increased awakenings or interfering with returning to sleep after waking. Results: The intent-to-treat analysis population included all patients who began Stage 2 (n = 15). After 8 weeks of XD-TOMAC, the mean change in International RLS Study Group Rating Scale (IRLS) score was −10.6 points (p < 0.001), and the mean change in Medical Outcomes Study Sleep Problems Index II (MOS-II) was −29.5 points (p < 0.001). The mean change in the number of nocturnal awakenings was −1.1 per night (p = 0.009), and the mean change in sleep efficiency was +8.5% (p = 0.001). The mean change in time awake with RLS symptoms after sleep onset was −28.1 min (p = 0.009). Each of these improvements was sustained at the end of Stage 3 (p < 0.01). There were no serious or severe device-related adverse events. Conclusions: Compared with prior 30 min TOMAC studies, XD-TOMAC demonstrated greater efficacy and similar tolerability, supporting its potential as a nonpharmacological therapy for RLS patients whose symptoms frequently disrupt sleep. Full article

Background/Objectives: Patients with poor sleep are at high risk of developing type II diabetes mellitus (T2DM). Since T2DM is linked to increased risk of obstructive sleep apnea (OSA), and Metformin is commonly used to treat T2DM, we examined how Metformin affects sleep stages in patients with concurrent T2DM and OSA-related symptoms of snoring and fatigue. Patients with T2DM on Metformin progressively develop increased insulin resistance associated with sleep disturbances and poor glycemic control. We therefore explored sleep pattern changes in patients with OSA symptoms and T2DM on Metformin, with a special focus on whether Metformin affects sleep architecture. Methods: Polysomnogram (PSG) data from patients with T2DM on Metformin was evaluated along with data on age, body-mass index (BMI), and biological sex. Data analysis included mean ± standard deviation, t-test with p < 0.05 taken as significant, and linear regression. Results: Patients with a BMI of less than 30 (non-obese) and taking Metformin exhibited a significantly shorter rapid eye movement sleep stage (REM) duration than patients on alternative therapies (p = 0.036). No such difference in REM was found for patients with a BMI of 30 or greater (obese) taking Metformin. While there was also no significant difference in slow-wave sleep stage (N3) duration with Metformin use, linear regression identified a moderate negative correlation between N3 and age in patients taking non-Metformin therapies (R² = 0.4555). No significant correlations between sleep stage duration and patient sex, smoking status, or BMI greater than 30 were identified. Conclusions: Overall, patients with OSA and T2DM on Metformin had lower mean quantities of N3, and REM sleep compared to those not on Metformin. Non-obese patients with T2DM and OSA being treated with Metformin were observed to have less REM sleep, regardless of sex or smoking history. N3 and REM sleep are needed for the timely secretion of growth hormone and memory consolidation. Since Metformin is correlated with differences in N3 and REM sleep, it may contribute to the development of insulin resistance. Future studies are needed to explore potential causes for this relationship and how it may affect the treatment of T2DM. Full article