Effects of a Time-Restricted Eating Intervention on Sleep and Body Composition in Adults with Obstructive Sleep Apnea: A Randomized Cross-over Clinical Trial.

Obstructive sleep apnea increases recurrent cardiovascular event risk in younger but not older patients with acute coronary syndrome: a prospective cohort study.

ABSTRACT Taxi drivers face elevated risks of fatigue-related aberrant driving behaviours (ADBs), exacerbated by sleep-related factors, yet predictive models incorporating these factors remain underexplored. This study employed a multi-modal approach to assess the predictive value of heart rate variability (HRV) metrics for fatigue-related ADBs and to assess the added value of integrating demographic and sleep data. A four-day naturalistic driving study involving 38 taxi drivers combined survey-based data with physiological, trajectory, and sleep monitoring. Participants with severe obstructive sleep apnea (OSA) exhibited significantly more hard accelerations, abrupt braking, and sharp steering compared to those without severe OSA. Three predictive models were developed: a long short-term memory (LSTM) model using HRV metrics; hybrid model A that combined HRV metrics, self-reported data, a hospital-based apnea-hypopnea index (AHI), and prior-night sleep disorder indices; and hybrid model B that extended hybrid model A by incorporating sleep disorder indices from the two preceding nights. The hybrid models outperformed the unidimensional LSTM model, with hybrid model B achieving the best performance (accuracy: 94.26%, specificity: 96.08%, sensitivity: 82.07%). The oxygen desaturation index (ODI-3%) emerged as the strongest predictor of ADB risk. These findings underscored the value of cumulative sleep data for fatigue-related ADB prediction and fatigue management.

Goueta etal. (Journal of Child Psychology and Psychiatry, 2025, 66, 1209) utilized a Random Intercept Cross-Lagged Panel Model (RI-CLPM) to investigate reciprocal links between ADHD symptoms and adolescent risky behavior. Their finding that stable between-person differences, rather than within-person fluctuations, primarily drive this association challenges common clinical assumptions. While commending their methodological rigor, this commentary proposes five refinements to better capture the dynamic ADHD-risk nexus. First, aggregating diverse risk behaviors may mask distinct symptom-coupled fluctuations, requiring multivariate models to separate impulsivity from social deviance. Second, relying exclusively on parent reports introduces bias; future studies should incorporate multi-informant designs, ecological momentary assessment (EMA), and passive sensing. Third, standard time metrics overlook critical developmental milestones; event-contingent sampling around salient transitions can address process non-stationarity. Fourth, integrating time-varying mediators, such as sleep and parental monitoring, can reveal precise windows of heightened risk for targeted interventions. Finally, dimensional scoring might obscure non-linear threshold effects and pharmacological impacts. By addressing behavioral heterogeneity, reporter variance, developmental contexts, and non-linearities, future research can clarify exactly when, for whom, and under what conditions ADHD symptom fluctuations forecast adolescent risk.

To describe sleep behaviors of male rugby league athletes and examine the association with self-reported sleep behaviors, attitudes, and knowledge. Twenty-two rugby league athletes completed 7 consecutive nights of sleep monitoring using wrist-worn ActiGraphy, with the Sleep Practices and Attitudes Questionnaire completed at the commencement of the monitoring period. ActiGraphy measures included time in-bed (hh:mm), time out-of-bed (hh:mm), duration in bed (minutes), sleep duration (minutes), sleep efficiency (percentages), sleep onset latency (minutes), wake after sleep onset (minutes), and number of awakenings. The Sleep Practices and Attitudes Questionnaire assessed self-reported sleep behaviors, sleep hygiene practices, and sleep knowledge. Linear regressions determined associations between objective sleep measures and subjective self-reported sleep behaviors, sleep knowledge, and sleep attitudes. Sleep behaviors showed mean bedtime at 10:25 PM (45) minutes and wake time 6:30 AM (25)minutes, with 484 (41)minutes in bed. Sleep efficiency was 73% (5%), resulting in a sleep duration of 355 (42)minutes. In contrast, subjective data reported sleep duration as 7.5 (1.0) hours. A small association was observed between lower objective sleep efficiency and longer subjective sleep duration (P < .05, r2 = .21). Earlier bedtimes related to better coping with chronic insomnia (r2 = .23, P = .02) but showed small associations with greater in-bed activities (r2 = .25, P < .05). Longer time in bed and increased sleep duration had small associations with poorer coping with chronic insomnia strategies (r2 = .25 and r2 = .24, respectively; P < .05). No associations existed between sleep behaviors and sleep knowledge (P < .05). Suboptimal sleep duration (<8h) and efficiency (<85%) exist in this cohort of players, despite high sleep knowledge. Therefore, sleep education needs to consider influencing factors on sleep behaviors when designing sleep education programs.

IntroductionIn China, women of childbearing age, who constitute a major demographic of online consumers and professionals in emerging industries are increasingly experiencing significant sleep disturbances. Existing studies have predominantly focused on the association between sleep disorders and polycystic ovary syndrome, often overlooking the independent impact of sleep patterns on ovulatory function in the general population of women attempting conception. Specifically, the synergistic effect of blue light exposure from electronic devices and delayed sleep onset remains underexplored, and evidence regarding the relationship between sleep duration, sleep quality and ovulation rates is insufficient. The primary objective of this study is to investigate the association between sleep habits and ovulation rates among Chinese women with fertility intentions. Second, it aims to determine the correlation between these sleep habits and factors such as screen time, night shift work and regional differences. Third, using a user-friendly sleep monitoring wristband, the study seeks to clarify the quantitative relationship between objective sleep parameters (eg, sleep onset time, sleep efficiency) and ovulation rates in this population.Methods and analysisThis is a 1.5-year multicentre prospective cohort study. Participants will be recruited from at least five reproductive medicine centres in mainland China between May 2025 and December 2026. The study will enrol women aged 20–50 years with fertility intentions. Baseline demographic information and habitual sleep patterns will be assessed through standardised questionnaires, with participants self-reporting their sleep conditions. Prepregnancy biochemical indicators and anthropometric data will be recorded. The primary outcome will be the ovulation rate, with secondary outcomes including pregnancy rate and miscarriage rate.Ethics and disseminationThis research project has been approved by the Clinical Research Ethics Committee of Guangdong Provincial Maternal and Child Health Hospital (Approval No.20250072), and has also received approval from the Primary and Specialty Nursing Committee for implementation and development. All participants will be informed verbally and in writing about the study procedures and objectives prior to signing the consent form. Data confidentiality will be ensured, and participants may withdraw their consent at any stage of the research. The findings will be presented at international conferences and published in peer-reviewed scientific journals.

Sudden cardiac death and health risks associated with abnormal sleep patterns highlight the urgent need for comprehensive, interference-resistant sleep monitoring technologies. Traditional polysomnography is constrained by complexity and discomfort, while existing flexible sensors often suffer from single-function limitations, signal coupling, and mechanical interference. Herein, we propose a multifunctional sensing patch inspired by the island-bridge structure to address these challenges. The patch integrates temperature sensors and electrocardiogram (ECG) electrodes on low-water hydrogel (islands) and strain sensors for respiratory monitoring on high-water hydrogel (bridges), all based on poly(acrylic acid) hydrogel to eliminate mechanical mismatch and prevent delamination. Low-water and high-water hydrogels are fabricated by regulating glycerol (Gly) and ethylene glycol (EG) ratios to tune water content, achieving strain insensitivity for temperature/ECG regions and strain sensitivity for respiratory detection. The unique material combinations endow the temperature sensor with high sensitivity (4.02 °C-1); the ECG electrode reduces noise interference by over 30% compared to commercial electrodes; the strain sensor achieves a gauge factor (GF, 23.31). When applied to human sleep monitoring, the patch enables precise, synchronous capture of body temperature, ECG signals, and respiratory patterns under various physiological conditions. This work provides a strategy for developing high-reliability, multifunctional sleep monitoring devices.

BackgroundPregnant and postpartum women encounter various health challenges, including physiological stress and mental health issues, which necessitate ongoing health monitoring. Smart bracelets present a promising solution; however, there is limited research on the preferences and willingness to pay (WTP) for such devices among this demographic.ObjectiveThis study aimed to investigate the preferences and WTP for smart bracelet attributes among pregnant and postpartum women in China and to explore how these preferences vary by sociodemographic factors, pregnancy stage, parity, and complications.MethodsA cross-sectional discrete choice experiment (DCE) was conducted involving 464 pregnant and postpartum women recruited from a maternal and child health hospital in Inner Mongolia. Six key attributes were evaluated: cost, hospital backend monitoring, primary function, privacy protection, ease of use, and monitoring report frequency. A mixed logit model was used to estimate preference weights and WTP for each attribute, with subgroup analyses based on income, employment, gestational stage, parity, and other factors.ResultsAmong the 464 pregnant and postpartum women included in the final analysis (valid data rate: 96.67%), the mean age was 31.06 (SD 4.05) years. The majority of participants were of Han ethnicity (n=385, 82.97%), had a high level of education (n=422, 90.95%), resided in urban areas (n=446, 96.12%), and were employed (n=353, 76.08%). In the DCE, cost negatively impacted smart wristband preferences (β=−0.000257; P=.01). Participants exhibited a strong preference for wristbands with fetal heart monitoring (β=1.275; P<.001), high-level privacy protection (β=.541; P<.001), and ease of use (β=.973; P<.001). They were willing to pay ¥4967.45 (based on an exchange rate of US $1=CN ¥6.93) for fetal heart monitoring, ¥2975.17 for sleep monitoring, ¥2109.29 for high-level privacy protection, and ¥3437.09 for daily monitoring. Subgroup analyses indicated that preferences varied according to income, employment, pregnancy stage, parity, complications, and age.ConclusionsThe design of smart bracelets should be tailored to meet the diverse needs of pregnant and postpartum users. Key considerations include the integration of fetal heart and vital sign monitoring, the assurance of data privacy, the enhancement of usability, and the provision of cost-effective options. Understanding the specific preferences of different subgroups can guide the development of inclusive and responsive wearable health technologies for maternal care.

Sleep quality declines with age and is a known contributor to multiple chronic health conditions, including Alzheimer disease. Emerging evidence suggests that certain electroencephalography (EEG) neural signatures measured during sleep may be predictive of cognitive decline in older adults. Sleep EEG signals are traditionally measured using bulky, rigid, and uncomfortable equipment in an unfamiliar laboratory setting, which can negatively impact sleep signals. Due to these limitations, sleep EEG data acquisition is typically limited to a single night. This study aimed to validate our recently developed portable, skin-like EEG monitoring patch for 7 nights in the home environment in a pilot sample of young and older adults by evaluating usability and acceptance, and replicating age-related differences in sleep architecture observed in the polysomnography literature. Eighteen young adults and 18 cognitively unimpaired older adults without sleep disorders were enrolled (data from 11 young adults and 12 older adults were included in the analyses) in a 7-night study during which they wore novel, gel-free, wireless, ultrathin, skin-conforming, sleep monitoring, fabric-based patches. These patches were self-applied to the forehead and face for optimal usability and comfort. The patches incorporate laser-cut mesh electrodes with low-profile electronics (including a rechargeable battery and amplifier) and transmit EEG signals to a participant-controlled, Bluetooth-enabled, tablet-based data acquisition app. An automated algorithm was used to stage sleep and assess microarchitecture features from the EEG commonly impacted for each participant. Averages across nights were computed for these sleep features for each participant. Young and older adults reported that the sleep patch was easy to use and comfortable to wear. There was no loss of signal power over 7 nights of wear across participants (retained-data signal-to-noise ratio over the 7-d period: young adult, mean 20.69, SD 12.78, maximum 52.13, minimum 5.19; older adult, mean 22.10, SD 9.39, maximum 49.96, minimum 13.79). Most datasets not retained were lost due to poor reference electrode adhesion on the nose (75/101, 74% of lost datasets in young adults and 57/88, 65% in older adults). Trained sleep technologists verified that the retained datasets were of sufficient quality to be scored without difficulty. Expected age-group differences in sleep features were observed, including age-related reductions in stage N3 sleep (young adult, mean 18.55, SD 6.70; older adult, mean 10.40, SD 6.43; Mann-Whitney U=42.0; P=.01) and reduced sleep spindle density (young adult, mean 2.92, SD 2.24; older adult, mean 0.94, SD 1.33; Mann-Whitney U=45.0; P=.006). This study demonstrates that our novel, comfortable, wearable patch can reliably measure physiological sleep data over multiple nights at home in adults across the lifespan, thereby making multinight sleep assessment in cognitive aging studies and clinical research more accessible than traditional polysomnography. In future studies, the small, lightweight system, which is highly scalable, can be shipped inexpensively to participants' homes, making this technology and research accessible to individuals who may have difficulty traveling or who are hesitant to travel to a laboratory or clinic.

Feasibility of continuous sleep and environmental monitoring in residential substance use recovery: Associations with mental health outcomes.

Remote zero-burden sleep monitoring in veterans with PTSD and suicidal ideation: A longitudinal investigation of risk.

Oxygen desaturation index and fibrinogen: An independent association in sleep-disordered breathing - A retrospective cross-sectional study.

Background: Continuous positive airway pressure (CPAP) therapy is the standard treatment for obstructive sleep apnea-hypopnea syndrome, yet its use as a passive sleep dynamics monitoring remains limited. CPAP devices record only airflow signals, called CPAP-flow, which constantly interact with the pressure delivered by the device. This interaction renders the signal vulnerable to device-related artifacts and inter-/intra-patient variability, posing significant challenges to its repurposing for monitoring sleep dynamics.Methods: Motivated by neural network-based studies of sleep-wake transition from CPAP-flow, we leverage existing physiological knowledge and introduce adual fusion multi-period convolutional neural network (DFMP-CNN)model. This deep learning architecture leverages multiple period-specific convolutional kernels and a dual-fusion mechanism to jointly encode known short- and long-range temporal dependencies in CPAP-flow, overcoming limitations of traditional fixed-scale models.Results: Extensive experiments demonstrate that DFMP-CNN achieves state-of-the-art performance in CPAP-based sleep staging. On the Yale dataset, it achieves 78.5% accuracy (Cohen'sκ = 0.605), with a best case ofκ = 0.886; on the Duke dataset, it reaches 73.6% accuracy (κ = 0.524), with a best case ofκ = 0.805. Cross-dataset evaluations confirm the model's transferability across clinical centers and device types, while feature and fusion ablation studies highlight its robustness.Conclusions: DFMP-CNN provides an unobtrusive approach for sleep monitoring using CPAP devices, providing a dual-purpose platform for therapy and longitudinal assessment. Significance: the robust performance of DFMP-CNN across datasets and device types underscores its potential to improve clinical assessment and optimize therapy management.

Accurate sleep stage classification is essential for evaluating sleep quality, yet clinical polysomnography is impractical for continuous home-based monitoring. Ballistocardiography (BCG) enables unobtrusive sleep monitoring through sensors embedded in sleep furniture; however, existing BCG-based approaches either rely on complex physiological feature extraction or employ fixed-parameter signal-to-image transformations that cannot adapt to inter-subject variability. This study proposes a learnable recurrence plot (RP) framework for three-stage sleep classification (Wake, NREM, REM) from single-channel BCG signals. The Learnable RP introduces three innovations: multi-scale phase-space reconstruction at physiologically motivated time delays (τ = 5, 10, 20), differentiable per-scale thresholds optimized end-to-end, and attention-based spatial fusion of multi-scale recurrence maps. The framework was evaluated through 10-fold stratified cross-validation across six backbone architectures using 50 overnight recordings. The Learnable RP consistently outperformed four baseline transformation methods (GAF, MTF, Classical RP, Modified RP), achieving an aggregate mean accuracy of 73.60%, with EfficientNet-B5 reaching 78.91%. and 78.91%. Statistical validation across all 24 pairwise comparisons (4 baselines × 6 backbones) confirmed consistent superiority (all p &lt; 0.001). The proposed framework achieves competitive performance without explicit physiological feature engineering, offering a viable path toward end-to-end unobtrusive sleep monitoring.

Many adolescents experience sleep that is too short and mis-timed for their circadian clock, which can adversely impact psychological and physical health. Feasible, targeted interventions to modify sleep behaviors and circadian timing could improve adolescent sleep and ultimately, health, and functioning. To determine whether a novel intervention integrating chronotherapeutic approaches (sleep scheduling, morning bright light glasses, and evening blue-light blocking glasses) would increase weeknight sleep duration and shift circadian timing earlier in adolescents with late sleep. This randomized clinical trial was conducted in a research setting in an academic medical center during school months (late August to mid-June) between 2018 and 2024. All analyses were intention to treat. Adolescents aged 16 to 19 years enrolled in a traditional high school who reported habitual weekend sleep onset later than 1 am. Participants were randomized to a Sleeping Late Teens Program or sleep monitoring control. The Sleeping Late Teens Program included 1 collaborative, problem-solving session (<1 hour) followed by 2 weeks of a personalized sleep schedule that shifted bedtimes and wake times earlier. Participants wore morning bright-light glasses for 30 to 60 minutes on waking and amber-tinted blue light-blocking glasses for 2 hours before bed. Primary outcome measures included weeknight circadian timing indexed by salivary dim-light melatonin onset (DLMO), weeknight sleep duration measured with actigraphy, and circadian alignment operationalized as the interval between DLMO and midsleep (middle of the nocturnal sleep period). Among 86 participants, 44 were randomly assigned to the intervention group and 42 to the control group. Of these, 80 completed baseline procedures (40 in each group): mean (SD) age, 17.5 (0.7) years; 48 female (60%) and 32 male (40%). The intervention group showed statistically significant and clinically relevant changes in sleep and circadian metrics compared with the sleep monitoring control. After 2 weeks, participants randomized to the active intervention, compared with the control, had earlier circadian timing (45 minutes; β = -0.55; 95% CI, -0.79 to -0.31; P = .003) and longer weeknight sleep duration (47 minutes; β = 0.74; 95% CI, 0.30-1.18; P = .003). DLMO-midsleep alignment shortened by 18 minutes in the intervention group compared with an 8-minute lengthening in controls; however, this difference was not statistically significant (β = -0.35; 95% CI, -0.72 to 0.02; P = .20). In this randomized clinical trial, results show that a short-term intervention that combined sleep scheduling, morning bright-light glasses, and evening blue light-blocking glasses shifted circadian timing earlier and extended weeknight sleep duration in adolescents. Larger trials are needed to confirm the effectiveness of this intervention. ClinicalTrials.gov Identifier: NCT03806296.

This evaluation examined sleep–wake patterns in 26 inpatients with progressive neurological conditions, including dementia and Huntington’s disease. One month of retrospective data from routine enhanced observations was analysed across four daily timeframes. Average sleep duration was 9.6 hours per 24 hours, with substantial individual variation. The findings show that grouplevel averages can obscure important individual differences. Sleep disturbance is common in specialist inpatient populations, and improved monitoring is needed to support personalised, evidencebased care.

To investigate whether gas cooking stove exposure and elevated indoor nitrogen dioxide (NO2) concentration were associated with adverse sleep outcomes in a pediatric sample. Children from urban neighborhoods in Boston, Massachusetts underwent in-home sleep assessments. Indoor NO2 concentrations were measured continuously over 7days by devices placed in the participants' living areas. Primary outcomes were short sleep duration (average 7-day sleep duration <8h by wrist actigraphy), and sleep-disordered breathing (SDB; ≥5 events/hour with ≥3% desaturation by a home sleep monitor). Associations between gas cooking stove exposure and elevated NO2 (≥ 69.48ppb) with each sleep outcome were assessed through logistic regression models, adjusting for demographic and socioeconomic factors and season. Sensitivity analyses further adjusted for health conditions, kitchen ventilation, and various sources of indoor NO2. Of the 242 children, 74% (n = 178) were exposed to gas cooking stoves. The median (interquartile range) of the average daily 95th percentile indoor NO2 was 41.1 (38.4) ppb. Children exposed to elevated indoor NO2 level were at a 2.88 increased adjusted odds (95% CI: 1.27, 6.55, p= .012) of short sleep duration compared to children exposed to lower levels. A positive but insignificant relationship between indoor NO2 exposure and SDB was found (odds ratios = 1.23, 0.61, 2.47). Gas cooking stove exposure was unassociated with any sleep outcome. Exposure to elevated indoor NO2 was associated with higher odds of short sleep duration in children. Interventions targeting indoor air quality may provide a novel approach for improving sleep health and reducing pediatric sleep disparities.

Insufficient or disturbed sleep impairs nocturnal physiological recovery and may negatively affect autonomic nervous system (ANS) regulation. Astronauts are a particularly vulnerable group due to sustained workloads, isolation, and exposure to extreme operational conditions. The present study investigated the interaction between sleep structure and ANS state under different sleep conditions during a prolonged space analogue mission (SAM) conducted as part of the Scientific International Research in a Unique Terrestrial Station (SIRIUS-19) project. Six healthy participants (three men and three women; age: 34.3 ± 5.7 years) were studied over eight nights during the four-month SIRIUS-19 mission: one night of undisturbed sleep pre-isolation, one night of undisturbed sleep post-isolation, and six nights during the isolation phase comprising three undisturbed nights, one night of complete sleep deprivation, and two nights of experimentally induced sleep fragmentation (repeated short awakenings vs. one prolonged awakening). Sleep structure and ANS state were assessed using a portable, self-applicable, medical-grade sleep recording system that captured electroencephalography (EEG), electrooculography (EOG), electrocardiogram (ECG), and plethysmography signals. Significant differences in total sleep time were observed across different nights (p = 0.003). On nights of undisturbed isolation, participants achieved more than 7 h of sleep, while nights with sleep fragmentation was associated with reduced sleep efficiency (<80%). ANS state parameters differed significantly across conditions, including the pulse rate (PR) (p < 0.0001) and the heart rate variability (HRV) LF/HF ratio (p < 0.005), with the most pronounced autonomic activation occurring during the night of complete sleep deprivation. Using a portable monitoring approach, this study demonstrates that nocturnal ANS regulation during prolonged isolation is relatively resilient to moderate sleep fragmentation, but is markedly affected by sustained sleep loss. These findings highlight the importance of preserving restorative sleep continuity when planning operationally demanding space missions and support the feasibility of portable sleep and ANS monitoring in extreme environments.

ABSTRACT Objectives Poor adolescent sleep is associated with negative outcomes in physical health, mood, behavior, academic performance, attention, and other domains. Parental involvement, including parent-adolescent communication and parental monitoring, is associated with positive sleep outcomes in non-clinical adolescent populations. However, there is a paucity of research examining the impact of parental involvement on sleep quality and hygiene among adolescent clinical sleep samples. This study analyzed the associations between parent involvement and adolescent sleep in a sample of adolescent patients with clinical sleep disturbances. Method Adolescents ages 12–18 (N = 139; 52.5% male, 67% Hispanic/Latinx) completed questionnaires measuring parent-adolescent communication, insomnia, sleep quality, and sleep hygiene, while their caregivers completed measures of parent monitoring (i.e. oversight of their child’s activities) and parent-adolescent communication. Multiple linear regression analyses examined associations between aspects of parental involvement and adolescent sleep. Results Better parent-adolescent communication, as reported by adolescents, was associated with fewer behavioral sleep problems (i.e. better sleep hygiene and sleep quality; ps < .05). Unexpectedly, greater parent monitoring was associated with worse physiological and cognitive and emotional aspects of sleep hygiene. Conclusions Findings indicate that in youth presenting with sleep disturbances, more open communication with caregivers as reported by adolescents is associated with better sleep outcomes, while parental monitoring showed inverse associations with specific aspects of sleep. As these findings emerged in a sample experiencing clinical sleep problems, family communication and monitoring should continue to be explored in clinical samples to determine their relevance in clinical interventions.

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. 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. 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. 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.