Search Results (1,227)

Objectives: To examine disordered eating behaviors, orthorexic tendencies, binge-eating episodes, attitudes toward exercise, perceived hormone-related symptoms and body image perception among competitive bodybuilders across different levels of competitive experience. Methods: In this cross-sectional study, 60 competitive bodybuilders (29 men, 31 women) completed an anonymous online questionnaire. The survey evaluated demographic characteristics, coaching and training management, phase-specific symptoms (such as libido, sleep, eating behaviors, and menstrual alterations), orthorexic tendencies, exercise addiction, and body-image perception. Results: Both sexes reported reduced libido, increased hunger, and sleep disturbances, along with frequent weight monitoring and common binge-eating episodes. Moreover, females frequently reported menstrual irregularities. ORTO-15 scores indicated a potential risk of orthorexia nervosa, while EAI-3 scores suggested a risk of exercise addiction in novice females and advanced males, with differences in mood regulation and guilt across sex and experience. Males showed higher perceived and ideal muscle mass, whereas females reported higher perceived body fat and a preference for leaner physiques. Conclusions: Competitive bodybuilders of both sexes exhibit post-competition binge eating, mood- and appearance-driven exercise behaviors, and pronounced body-image concerns. Screening, education on energy availability, structured post-competition support, and health-focused coaching are recommended to prevent the progression from sport-specific practices to clinical pathology. 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

Background: Korean registered nurses face substantial cumulative occupational stress. Telomere length, a biomarker of cellular aging, is increasingly used in occupational stress research, but evidence on early-career Korean nurses is scarce. This study examined the association between occupational characteristics and telomere length in female nurses aged 20–39 years. Methods: Sixty-eight female nurses from a tertiary hospital in South Korea completed the questionnaires. We assessed demographics, occupational factors, burnout (Maslach Burnout Inventory), and sleep quality (Pittsburgh Sleep Quality Index—Korean [PSQI-K]). Salivary telomere length was measured using quantitative polymerase chain reaction (qPCR). Data were analyzed using t-tests, ANOVA with a Bonferroni post hoc test, Pearson correlations, and multivariable linear regression. Results: Participants showed moderate to high burnout levels (Emotional Exhaustion [EE] = 24.78 ± 10.96), with 41.2% exceeding the high EE threshold. Sleep quality was poor (PSQI-K = 7.90 ± 3.07), with 82.4% exceeding the cut-off. Univariable analyses revealed that younger age, unmarried status, shorter work experience, and higher personal accomplishment were associated with longer telomeres (all p < 0.05); multivariable analysis identified only age group as a significant predictor (B = −2.055 kb for nurses aged ≥30 years compared to those <30 years, p < 0.001). The model explained 83% of the variance in telomere length. Shift work, burnout, and sleep quality were not significantly associated with telomere length after controlling for age. Conclusions: Age was the main factor associated with telomere length in young female nurses, suggesting that biological manifestation of occupational effects may require longer exposure. The high prevalence of burnout and sleep disturbances warrants immediate organizational intervention. Saliva-based qPCR demonstrated reliable precision as a non-invasive method for biological monitoring in occupational health research. These findings provide a basis for future longitudinal studies examining the cumulative effects of occupational stress and inform targeted wellness interventions for early-career nurses. Full article

The continuous monitoring of population health is a major focus in scientific literature, with numerous studies highlighting the critical role of sleep. However, to the best of the authors’ knowledge, the multi-modal data processing required to fully map the tripartite relationship between environmental stimuli, sleep, and health has not been achieved. This paper proposes a comprehensive data fusion strategy, integrating public databases to extract common features from historical sensor data. The present paper proposes a robust processing architecture by training four classes of algorithms (mathematical, machine learning, artificial intelligence, and ensemble models) to analyse how environmental inputs impact sleep quality and, consequently, physiological health. The resulting state-of-the-art model, a multi-modal architecture comprising 10 integrated models, was tested on a massive combined dataset of 139,950 rows and 8249 columns. The model achieved an R-squared of 0.958, demonstrating superior data processing and predictive accuracy. Alongside the integrated dataset, this research establishes the computational groundwork for human-centric Digital Twins, paving the way for closed-loop IoT environments where sensor-driven analytics inform automated actuator interventions to improve sleep and health. Full article

Background/Objectives: Nocturnal glycemic variability in pediatric type 1 diabetes (T1D) disrupts caregiver sleep and quality of life; advanced hybrid closed-loop (AHCL) systems may be associated with reduced caregiver burden by providing more stable overnight glucose control. We aimed to evaluate changes in caregiver-reported sleep quality and continuous glucose monitoring (CGM) targets three months after transition to an AHCL system. Methods: We conducted a prospective single-center real-world study in a tertiary pediatric diabetes unit that included children aged 6–17 years with T1D who switched from continuous subcutaneous insulin infusion (MiniMed) and intermittently scanned CGM (FreeStyle Libre 2) to an AHCL system (MiniMed 780G) with Guardian 4 sensor. Caregivers completed the Pittsburgh Sleep Quality Index (PSQI) at baseline and after 3 months; CGM metrics (TIR 70–180 mg/dL, TAR1 180–250 mg/dL, TAR2 > 250 mg/dL, TBR1 54–70 mg/dL, TBR2 < 54 mg/dL) were extracted at the same time points. Analyses used Shapiro–Wilk, Wilcoxon signed-rank, Spearman correlations, and McNemar tests (α = 0.05). Results: Twenty-two caregivers completed baseline PSQI; 16 provided PSQI data at three months. The proportion with PSQI > 5 decreased from 56.3% to 18.8% (p = 0.034), and 81.3% showed lower global PSQI at 3 months (p = 0.018). The largest mean improvements were observed in daytime dysfunction (−0.94), subjective sleep quality (−0.81), and sleep duration (−0.63), with slight increases in sleep disturbance (+0.13) and sleep-medication use (+0.13). The proportion of participants meeting international CGM consensus targets improved: the percentage achieving TIR > 70% increased from 26.7% to 80.0% (p = 0.008); those meeting TAR > 180 mg/dL < 30% increased from 26.7% to 80.0% (p = 0.008); and those meeting TAR2 > 250 mg/dL < 5% increased from 20.0% to 53.3% (p = 0.008). Hypoglycemia-related targets showed no significant change, and no episodes of symptomatic or level 3 hypoglycemia were reported. Exploratory analyses suggested that poorer PSQI at 3 months was associated with greater Δ TBR1, and increases in TAR2 with higher sleep disturbance and sleep-medication use. Conclusions: Transition to an AHCL system was associated with improvements in caregiver-reported sleep and attainment of CGM consensus targets within three months. Residual nocturnal hyperglycemia was associated with features of ongoing sleep disturbance, highlighting the potential relevance of individualized alert settings, sleep-focused education, and inclusion of objective sleep measures in future studies. Full article

Polysomnography monitors sleep health with non-linear physiological time-series data, consequently making interpretability a challenge. This study explores the feasibility of applying control charts, a statistical process control method, to cardio-respiratory signals derived from polysomnography studies to provide transparent and interpretable analysis of sleep-related physiological variability. Cardio-respiratory signals from a publicly available polysomnography dataset were preprocessed, transformed, and analyzed using univariate control charts. Sleep stage annotations were used as reference information to contextualize physiological variability across wake and non-REM sleep stages. Phase-level control chart rule violations were examined relative to annotated sleep-state transitions and summarized quantitatively. The results indicate that control chart rule violations occur more frequently during wakefulness and at wake–non-REM sleep transitions, while remaining relatively stable during sustained non-REM sleep. These findings indicate structural correspondence between SPC-based variability flags and annotated sleep stage dynamics. This exploratory, feasibility-focused study does not evaluate diagnostic performance or detection accuracy. Instead, it provides evidence that SPC control charts can serve as a transparent and interpretable analytical framework for exploring physiological variability in sleep data and for supporting future research on sleep-state analysis and explainable data-driven methods. Full article

Sleep deprivation (DS) is a reduction in sleep duration due to voluntary or external factors. The mechanisms underlying the psychological and cognitive consequences of DS are complex and incompletely understood; one proposed pathway involves alterations in the serotonin (5-HT) and melatonin (MLT) systems. This study aimed to assess the effects of a single night of DS on the tryptophan (TP)-5-HT-MLT system and to examine their associations with mood and cognitive performance. Eighty healthy adults underwent polysomnography (PSG) and actigraphy-monitored DS. Blood samples, mood assessments, and cognitive tests (BEHCT, TMT, Stroop) were performed before and after PSG and DS. Levels of serotonin transporter (SERT) mRNA, TP, 5-HT, and MLT were measured. Participants were classified as Responders (RE) or Non-Responders (NR) based on post-DS mood change. DS significantly decreased TP and MLT overall. In NR, 5-HT increased and MLT decreased, unlike in RE. ΔBEHCT correlated positively with ΔTP (RE), Δ5-HT (overall), and ΔMLT (overall and RE), and negatively with ΔSERT mRNA (NR). In RE, ΔSERT mRNA negatively correlated with ΔStroop performance and positively with ΔTMT. Acute DS disrupts the TP–5-HT–MLT axis, with effects differing by mood response. These changes may influence cognitive outcomes after sleep loss. Full article

Background and Objectives: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex disorder characterized by persistent or relapsing fatigue lasting at least six months, not alleviated by rest and not previously present. It is accompanied by post-exertional symptom exacerbation and non-restorative sleep. Fatigue is often disabling and reduces daily activity by more than 50%. This study aimed to evaluate the long-term frequency of somatic and psychiatric disorders in women previously diagnosed with ME/CFS and to describe the long-term clinical course, laboratory findings, and fatigue-related changes during a 16-year follow-up period. Materials and Methods: Sixteen years ago, 40 women diagnosed with ME/CFS according to then-current CDC criteria were enrolled at the Clinic for Infectious Diseases and the Center for Laboratory Medicine, University Clinical Center of Vojvodina. All participants provided informed consent. After 16 years, 20 women agreed to follow-up evaluation. At both time points, participants underwent structured questionnaires, clinical examination, psychological assessment, and comprehensive laboratory testing, including hematological, biochemical, endocrinological, and virological analyses. Fatigue severity was assessed using the FibroFatigue Scale (FFS) and the Multidimensional Assessment of Fatigue (MAF) scale. Results: During follow-up, 15% of participants were diagnosed with rheumatoid arthritis, 10% with cervical or breast cancer, 5% experienced premature myocardial infarction, 5% developed bronchial asthma, and 20% were diagnosed with clinical depression. Progression of ME/CFS was observed in 15%, while 5% reported infertility. Additionally, 15% developed arterial hypertension. Only 15% of participants did not report symptom worsening or new diagnoses. Conclusions: Over the 16-year follow-up, 85% of women with ME/CFS developed significant somatic or psychiatric conditions. These findings suggest that women diagnosed with ME/CFS may experience substantial long-term somatic and psychiatric disease burden, supporting the need for continued clinical monitoring and individualized follow-up. Full article

Background: Boswellia serrata has traditionally been used in Ayurvedic medicine for its anti-inflammatory and antioxidant properties. Although several studies support clinical analgesic efficacy, the underlying mechanisms have not been investigated in human experimental pain models. This randomized, double-blind, placebo-controlled, crossover pilot trial aimed to examine the mode of action of Boswellia serrata to differentiate between its peripheral and central effects. This exploratory pilot study was designed to generate preliminary effect size estimates and assess functional pain-processing outcomes, rather than to provide definitive evidence of clinical efficacy. Methods: Twelve healthy volunteers were recruited and received either 300 mg of Boswellia serrata extract or a visually identical placebo twice daily for 28 days, separated by a 4-week washout period. Pain and sensitization were induced using a topical capsaicin model. Outcomes included spontaneous pain intensity, mechanical allodynia, pinprick hyperalgesia, thermal thresholds, and conditioned pain modulation, alongside psychological assessments of mood, anxiety, sleep, and structured adverse-event monitoring. Results: Results showed no significant difference in the primary endpoint of spontaneous pain intensity between Boswellia and placebo (VAS 43 ± 21 vs. 47 ± 17; d = 0.18; p = 0.539). Conclusions: While Boswellia serrata did not significantly reduce acute peak pain in this model, the observed trends suggest a potential multi-level modulatory influence on nociceptive processing and endogenous pain inhibition. These findings warrant larger clinical trials to further elucidate its therapeutic potential, particularly in populations with impaired pain modulation. Full article

Background/Objectives: Circadian misalignment, including mistimed sleep or eating, is associated with altered glucose metabolism. The importance of eating window timing for time-restricted eating (TRE) is increasingly recognized. This secondary analysis examined associations between meal to sleep timing intervals and glycemic parameters in individuals with obesity across three dietary interventions [TRE, CR: caloric restriction, and UE: unrestricted eating]. Methods: Participants aged 18–65 years with obesity were randomized to a 12-week intervention: TRE (8 h eating window), CR (15% reduction in daily caloric intake), or UE (usual eating habits). CGM and actigraphy were assessed over two weeks at baseline and end-intervention. Mixed effects models examined associations between continuous glucose monitoring (CGM) outcomes and two intervals: last meal to sleep onset (PM meal-Sleep) and awakening to first meal (Awake-AM meal). Results: Each hour increase in the Awake-AM meal interval was associated with lower overnight (1 AM–5 AM) average glucose, lower glycemic variability, lower %time > 180 mg/dL, and greater %time < 70 mg/dL. Each hour increase in the PM meal-Sleep interval was associated with lower overnight (1 AM–5 AM) average glucose. Both associations persisted after adjustment for baseline sleep duration, HbA1c, and randomization assignment. Conclusions: In individuals with obesity, morning (Awake-AM meal interval) and evening (PM meal-Sleep interval) fasting relative to sleep were differentially associated with glycemic control. These findings highlight the relevance of eating and sleep timing to glycemic parameters and may inform eating window selection for individuals practicing TRE. Full article

Smartphones are now deeply embedded in daily life, but excessive dependence may increase the risk of nomophobia, which is associated with anxiety, sleep disruption, and reduced productivity. Existing screening methods mainly rely on self-reported questionnaires, which are subjective and difficult to scale for continuous monitoring. This study proposes a privacy-preserving federated deep learning framework for three-level nomophobia risk prediction (Normal, Mild, and Severe) using smartphone usage logs while keeping raw user data on local devices. The proposed pipeline uses a publicly available secondary dataset with 1000 original records and expands it to 100,000 records through constraint-aware synthetic augmentation. A continuous risk score is computed from standardized smartphone usage indicators and then converted into three classes using tertile-based thresholds. Several local architectures, including CNN, MLP, ResMLP, Wide & Deep, and a lightweight TabNet-style gated model, are evaluated under FedAvg. In the reported experiments, differential privacy is enabled through DP-SGD with gradient clipping and Gaussian noise. To protect update transmission, the framework applies protected update sharing through encrypted transport of masked updates. Each client masks its local update and encrypts the masked payload before transmission. This mechanism improves communication confidentiality and reduces the direct exposure of client updates. Under a fixed federated setup with five clients and 25 communication rounds, tabular models achieved near-ceiling performance on the constructed test set. The MLP achieved 99.12% accuracy, 99.12% F1-score, 0.9868 MCC, and 0.9997 AUC, while Wide & Deep achieved 98.95% accuracy, 98.95% F1-score, 0.9843 MCC, and 0.9997 AUC. In contrast, sequential models such as RNN and LSTM showed near-random performance, suggesting that the current aggregated feature representation is better suited to tabular learning than temporal modeling. These results indicate that the proposed federated pipeline can effectively learn the constructed nomophobia risk labels while preserving local data ownership. However, because the labels are derived from usage features rather than clinical or psychometric assessment, the findings should be interpreted as proof-of-concept results for constructed risk labels rather than evidence of clinical diagnostic validity. Full article

Considerable efforts have been devoted to accurately monitoring the depth of anesthesia to ensure patient safety during surgery. Traditional approaches typically rely on electroencephalogram (EEG)-based indices, such as the Bispectral Index (BIS), which require specialized equipment. In contrast, electrocardiogram (ECG) signals are widely available in clinical settings and can be conveniently acquired via wearable devices, while also exhibiting strong responsiveness to anesthetic agents. Inspired by biomimetic physiological regulation mechanisms, this study proposes a wearable-compatible ECG-based framework for depth-of-anesthesia detection that leverages autonomic nervous system characteristics and a knowledge graph-enhanced graph convolutional network (GCN). ECG recordings from 110 patients were preprocessed, and 20 anesthesia-related features were extracted, spanning morphological, statistical, spectral, heart rate variability (HRV), and entropy-based descriptors; feature selection methods identified 13 discriminative features. A patient-level knowledge graph was first constructed using the 88 training patients (1760 nodes), and test patient nodes were incorporated only after training was complete for inductive inference. Experimental results demonstrate that the proposed deep knowledge GCN achieves a test accuracy of 98.18% in distinguishing between awake and deep sleep anesthesia states, indicating that biomimetic, wearable-compatible ECG analysis combined with knowledge graph learning holds strong potential as a cost-effective alternative to traditional EEG-based anesthesia monitoring systems. Full article

Web-based repeated-measures cohorts enable remote, scalable, and temporally structured monitoring of health-related outcomes in naturalistic settings. This paper presents the DiCoBENE study, a web-based cohort of healthcare-track university students, and reports evidence-informed instrument selection together with protocol features and pilot baseline findings. A structured review was used to inform the web-based administration of patient-reported outcome measures (PROMs) covering sleep quality, perceived stress, anxiety symptoms, depressive symptoms, and quality of life. In the pilot baseline sample, 442 students constituted the analytic dataset and 370–372 completed the core PROM battery, depending on the instrument. Poor sleep quality, anxiety symptoms, depressive symptoms, and perceived stress were common. Internal consistency was good to excellent for the Generalized Anxiety Disorder 7-item scale (GAD-7), the Patient Health Questionnaire 9-item depression module (PHQ-9), and the 10-item Perceived Stress Scale (PSS-10), and moderate for the Pittsburgh Sleep Quality Index (PSQI). Exploratory multivariate analyses, including latent profile analysis, principal component analysis, and partial-correlation network analysis, suggested that baseline heterogeneity was more parsimoniously summarized as a graded multidimensional burden continuum than as sharply separated phenotypes. Taken together, these findings position DiCoBENE as a methodologically explicit framework for web-based repeated outcome assessment in student well-being research. Full article

Prefabricated myofunctional appliances (PMAs) are designed to improve airway function by advancing the mandible, enhancing tongue posture, and reducing airway resistance, thereby facilitating nasal breathing in children with sleep-disordered breathing (SDB). This retrospective study evaluated the effects of PMAs on airway dimensions in children with skeletal Class II division 1 malocclusion. Patients were selected from a departmental database (2017–2019). The treatment group included children with Class II division 1 malocclusion, an incisor overjet of ≥6 mm, cervical vertebral maturation (CVM) stage III or earlier, and documented myofunctional dysfunction (e.g., adenoid hypertrophy, allergic rhinitis, or mouth breathing), with complete pretreatment and one-year follow-up lateral cephalometric radiographs. Patients with prior orthodontic intervention or poor compliance were excluded. A matched observation group consisted of untreated patients undergoing growth monitoring. Airway dimensions of the nasopharynx, oropharynx, and hypopharynx were measured using cephalometric radiographs, along with McNamara Airway Analysis. The total nasal symptom score (TNSS) was used as a self-report measure. A total of 34 patients (mean age 9.4 years) were included in the PMA group and 29 patients (mean age 9.6 years) in the observation group. Compared with controls, the PMA group demonstrated significant increases in nasopharyngeal (p = 0.044) and oropharyngeal (p = 0.039) airway areas, while changes in the hypopharyngeal area were not significant (p = 0.121). McNamara Airway Analysis also showed a significant improvement in upper pharyngeal airway dimensions (p = 0.018). TNSS revealed significant changes following PMA therapy (p < 0.001). These findings indicate that PMA therapy is associated with enlargement of the nasopharyngeal and oropharyngeal airway in children with skeletal Class II division 1 malocclusion, suggesting functional airway adaptation beyond simple mandibular advancement. Full article

Background and Perspectives: Obstructive sleep apnea (OSA) is a prevalent condition associated with increased perioperative risks. Endobronchial ultrasound (EBUS), a diagnostic and staging procedure requiring deep sedation, may pose additional risks for patients at high risk of OSA. The Integrated Pulmonary Index (IPI), derived from capnography and vital signs, offers a single numerical value reflecting respiratory status. This study aimed to assess the association between high OSA risk and adverse events using the IPI during EBUS under sedation. Materials and Methods: This prospective cohort study included 65 patients undergoing EBUS with sedation between December 2024 and April 2025 at a tertiary referral center. STOP-Bang questionnaire scores were used to stratify patients into high- (≥3) and low-risk (<3) OSA groups. During the procedure, IPI, oxygen saturation, end-tidal carbon dioxide, respiratory rate, and hemodynamic parameters were recorded at multiple time points. Hypoxemia, hypoventilation, and apnea were defined using standard thresholds. Logistic regression and Generalized Linear Mixed Models (GLMM) were applied to examine associations between OSA risk and respiratory outcomes. Results: Forty-three patients (66.2%) were classified as high risk for OSA. Patients with high STOP-Bang scores were older and had higher BMI, comorbidity rates, and ASA scores (all p < 0.05). IPI values were lowest between 5 and 10 min, accompanied by more frequent interventions. Logistic regression showed no significant association between STOP-Bang scores and low IPI or hypoxemia. GLMM analysis also indicated no significant association between high OSA risk and low IPI (OR = 1.02; 95% CI = 0.36–2.86; p = 0.974). Hypoxemia was nearly threefold higher in high-risk patients, though not statistically significant (p = 0.080). Conclusions: Although no statistically significant association was identified between high OSA risk and adverse respiratory events, GLMM analyses revealed that patients with high STOP-Bang scores demonstrated approximately three times higher odds of developing hypoxemia (OR = 2.76; 95% CI = 0.99–7.66; p = 0.052), a result that approached statistical significance. The present findings do not support the routine use of IPI-based monitoring in this setting, and further adequately powered studies are warranted. The early procedural period (5–10 min) is critical for hypoxemia and respiratory compromise. Full article