Sleep Medicine

BackgroundEmerging research indicates that light exposure may influence sleep quality. Identifying key light-exposure behaviours associated with poor sleep quality in athletes may allow practitioners to efficiently screen for sleep difficulties and prioritise athletes for further assessment. Translating these findings into a practical screening tool could enhance willingness of high-performance professionals to monitor sleep and light exposure in athletes. HypothesisKey predictor variables identified by feature reduction techniques will lead to higher predictive accuracy in determining which light behaviours are associated with poor sleep quality in athletes. Study DesignCross-sectional study. Level of EvidenceLevel 3. Methods121 athletes from varying competitive levels completed questionnaires, including the Light Exposure Behaviour Assessment (LEBA) and Pittsburgh Sleep Quality Index (PSQI). Poor sleep quality was defined using the PSQI cut-off >5. Least absolute shrinkage and selection operator (LASSO) regression identified light exposure variables from the LEBA questionnaire most strongly associated with good and poor sleep quality in athletes. Three models were compared: a full-variable model (23 items), a factor-specific model (Factor 3: screen/device use), and a feature-reduced model (LASSO-selected items). ResultsPhone use before bed, checking phone/watch during the night, were identified as variables of greatest association with poor sleep quality and used for reduced feature set modelling. On an independent test set, the feature-reduced model achieved area under the curve (AUC) 0.83, sensitivity 0.70, and specificity 0.92. ConclusionsOur findings report that phone-related behaviours before and in bed are associated with a higher likelihood of poor sleep quality. These behaviours, combined with the developed nomogram, provide a preliminary, low-burden screening tool to identify athletes who may be experiencing sleep difficulties. The high specificity indicates that athletes flagged by the tool are likely to have genuine poor sleep quality, warranting further assessment to identify underlying causes and appropriate interventions. Clinical RelevanceEducation and interventions focused on light exposure factors were identified as most influencing sleep quality in a multifaceted athletic population and could be prioritised to optimise sleep quality. The developed sleep quality nomogram may be useful as a decision-making tool to improve sleep monitoring practice among practitioners.

This study compared the effects of a 25-min nap opportunity and a 10-min non-sleep deep rest (NSDR) condition on perceptual, cognitive, and physical performance in physically active young adults. Sixty participants (26 female, 34 male; 22 {+/-} 4 years) were randomly assigned to one of three groups (nap, NSDR, control; n = 20 each). All groups completed identical assessments immediately, 20 min, and 40 min post-intervention. Mixed-effects models, adjusted for sex, prior-night sleep, and weekly physical activity, revealed a significant Group x Time interaction for sleepiness, fatigue, readiness to perform, and handgrip strength (p < 0.05). At 40 min post-intervention, the nap group reported lower fatigue than control and higher readiness to perform than both control and NSDR (p < 0.05). No significant effects were observed for the NSDR condition on perceptual, cognitive, or physical outcomes (p > 0.05). These findings indicate that a short nap can enhance perceived readiness and reduce fatigue after a brief latency period, whereas NSDR did not elicit significant effects under the present conditions.

Background: Nursing interns are at high risk of psychological distress due to academic and clinical stressors. While poor sleep quality is linked to anxiety and depression, the buffering role of social support remains underexplored in this population. Aims: To explore the role of social support in regulating the relationship between sleep and mental health among nursing interns. Methods: A total of 396 nursing interns completed self-administered questionnaires including the Pittsburgh Sleep Quality Index (PSQI), Social Support Rate Scale (SSRS), Generalized Anxiety Disorder-7 (GAD-7), and Patient Health Questionnaire-9 (PHQ-9). Hierarchical regression and simple slope analyses were used to test moderation effects. Results: Poor sleep quality was significantly associated with higher anxiety ({beta}=0.449, P<0.001) and depression ({beta}=0.535, P<0.001). Social support significantly moderated these relationships. Under low social support, the effects of sleep quality on anxiety ({beta} = 0.602) and depression ({beta} = 0.779) were stronger than under high support (anxiety: {beta} = 0.396; depression: {beta} = 0.515). Conclusions: Social support buffers the adverse psychological effects of poor sleep among nursing interns. Interventions should integrate sleep hygiene education with strategies to enhance social support.

Sleep architecture is essential for metabolic and cardiovascular health, yet the impact of day-to-day dietary variation on objective sleep physiology remains unclear. Using 4.8 thousand person-nights with real-time dietary logs and multi-stage wearable sleep recordings, we examined how prior-day nutrition relates to next-night sleep under free-living conditions. Higher fiber density was associated with increased restorative sleep, including +0.59 pp deep sleep, +0.76 pp REM sleep, -1.35 pp light sleep, and -1.14 bpm lower mean nocturnal heart rate. Greater plant diversity and higher whole-plant food intake were similarly associated with lower nocturnal heart rate (-0.72 to -0.94 bpm). Meal-timing behaviors primarily influenced sleep duration, sleep-onset latency, and autonomic tone: heavier evening meals were associated with +7.7 min longer total sleep time and +0.73 bpm higher nocturnal heart rate. In contrast, short-term variation in macronutrient energy distribution and micronutrient consumption showed no robust associations with sleep outcomes. When analyses were restricted to more extreme dietary contrasts, effect magnitudes increased while remaining directionally consistent. These findings indicate that routine daily dietary choices, particularly plant-forward composition and meal timing, have immediate and measurable effects on objective sleep architecture.

BackgroundThe cognitive demands of medical education require optimal learning environments. While the influence of background music on cognition has been widely studied, existing research exhibits a significant Eurocentric bias, predominantly focusing on Western classical music like the "Mozart Effect." This leaves a critical gap in understanding the impact of culturally salient, non-Western musical traditions on learning within their native contexts. MethodsA single-blind, randomized controlled trial was conducted with 147 clinical (4th, 5th and 6th) year medical students stratified by ethnicity at the Faculty of Medicine, Nnamdi Azikiwe University, Nnewi Campus, Anambra State, Nigeria between March and September 2025. Participants were randomly assigned to one of three background music conditions: Igbo Highlife (instrumental), Western classical (Mozarts Sonata K.448), or silence (control with pink noise masking). Cognitive performance was assessed through a short-term memory recall test of 20 medical terms and a timed clinical problem-solving task comprising 20 multiple-choice questions. Baseline mood was controlled for using the Positive and Negative Affect Schedule (PANAS). Data were analyzed using ANOVA and post-hoc Tukey HSD tests. ResultsMusic condition had a highly significant effect on student performance (p < 0.001). The Igbo Highlife group demonstrated superior outcomes, achieving the highest scores in memory recall (mean = 16.7) and problem-solving accuracy (mean = 15.7), alongside the fastest completion time (23.4 seconds/question), significantly outperforming both the classical and silence groups. A significant correlation was found between cultural familiarity with Highlife and enhanced cognitive performance (accuracy: {rho} = 0.268, p = 0.001). ConclusionsIncorporating music that holds cultural significance and familiarity to learners, specifically Igbo Highlife, is a highly effective auditory stimulus for enhancing learning efficiency in medical education. Students and educational institutions should consider integrating culturally familiar instrumental music into study environments to optimize cognitive performance and learning outcomes. Competing Interest StatementThe authors have declared no competing interest.

Sleep arousals trigger rapid autonomic shifts, yet their specific sympathetic signatures remain poorly characterized due to the mixed sympathetic-parasympathetic nature of traditional cardiovascular markers. Electrodermal activity (EDA), driven exclusively by sympathetic sudomotor pathways, offers a more direct opportunity to characterize arousal-related autonomic responses during sleep. This study quantifies the evolution of EDA-based features associated with arousal events in 100 adults using polysomnography and high-resolution EDA recordings. We implemented a time-varying frequency decomposition framework to isolate sleep-specific sympathetic components, extracting statistical and peak-based features from arousal segments and matched stable-sleep controls. Compared to controls, arousal segments exhibited robust sympathetic modulation in EDA persisting 40 seconds post-arousal. While long arousals produced robust responses, short arousals showed negligible sudomotor responses. REM and NREM sleep showed consistent feature trajectories, with greater variability during REM. The observed activation is primarily driven by clustered sympathetic bursts and amplitude enhancement rather than shifts in peak frequency. These findings establish EDA as a highly sensitive marker of sleep-related autonomic activation and provide a quantitative baseline for characterizing sympathetic responses to sleep arousals.

Background/ObjectivesElectromyography (EMG), video-polysomnography (vPSG), and wrist actigraphy are each used to develop diagnostic algorithms for Rapid eye movement sleep behavior disorder (RBD). However, the extent to which they capture overlapping versus distinct motor phenomena remains unknown. We evaluated the respective contributions of actigraphy, EMG and vPSG to the measurement of REM-sleep motor activity. MethodsSeventeen adults with RBD (Mount Sinai n = 9; Stanford n = 8) and eight control participants from an open Newcastle dataset underwent vPSG and concomitant wrist actigraphy. Flexor digitorum superficialis EMG activity and video-detected movements were manually scored in 3-second mini epochs. Actigraphy was quantified using an acceleration-magnitude-based activity count model. Statistical and agreement analyses were performed to assess the motor events captured by all three, any two, or by each modality independently during REM sleep. ResultsIn participants with RBD, actigraphy-derived movement load was significantly higher during REM sleep than during non-REM stages, a pattern not observed in control participants. Across 12,941 3-second mini epochs, EMG, actigraphy, and video detected 1,703, 1,613, and 811 motor events, of which 413 were detected concurrently by all three modalities. Pairwise agreement was moderate and increased from EMG-actigraphy ({kappa} = 0.27 {+/-} 0.10) to actigraphy-video ({kappa} = 0.41 {+/-} 0.12) and EMG-video ({kappa} = 0.45 {+/-} 0.15). Of EMG-detected events, 49.0% were also detected by actigraphy; of actigraphy-detected events, 37.2% were detected by EMG and 34.9% by video. Actigraphy activity counts were highest for events detected by all three modalities and lowest for actigraphy-only events. ConclusionActigraphy-measured REM-related motor activity was elevated in RBD but not in controls. EMG, actigraphy, and video captured partially overlapping motor events in RBD patient, with actigraphy showing the highest sensitivity and manually scored video the lowest.

BackgroundAuditory steady-state responses (ASSRs) provide an objective method for estimating hearing thresholds in individuals unable to provide behavioural responses. Bone conduction (BC) testing is required to differentiate conductive from sensorineural hearing loss. Accurate BC ASSR threshold estimation relies on "correction" factors, which are not yet well established. This meta-analysis evaluated the reliability of BC ASSR thresholds to estimate hearing thresholds at 500, 1000, 2000 and 4000 Hz. MethodsA systematic search of PubMed, the Cochrane Library, and Embase was conducted to identify studies involving normal-hearing (NH) and hearing-impaired (HI) participants of all ages. Outcomes were (1) the difference between ASSR behavioural and ASSR thresholds, and (2) ASSR thresholds. The risk of bias was evaluated using the Newcastle-Ottawa Scale. The mean and 95% confidence intervals (CI) were calculated for the thresholds at the four frequencies. The certainty of the evidence was assessed using GRADE approach. ResultsOf records identified, 11 records met the inclusion criteria, yielding a total of 27 studies. Sample sizes ranged from 60 to 249 participants across frequencies and age groups. The quality of records ranged from low to high. Data were synthesised using random-effects models due to heterogeneity. In NH adults, the mean differences ({+/-}95% CI) between BC ASSR thresholds and behavioural thresholds were 17.0 ({+/-}4.8), 15.5 ({+/-}6.0), 13.4 ({+/-}3.3), and 12.1 ({+/-}4.1) dB at 500, 1000, 2000, and 4000 Hz, respectively. In NH infants, mean ({+/-}95% CI) BC ASSR thresholds were 17.2 ({+/-}2.2), 10.5 ({+/-}3.6), 26.4 ({+/-}2.7), and 19.9 ({+/-}4.0) dB HL at the same frequencies. The certainty of the evidence was very low. ConclusionsBC ASSR can be a reliable method for estimating BC thresholds. However, age and frequency significantly impact BC ASSR thresholds, highlighting the need to develop of "correction" factors to accurately predict BC behavioural thresholds. RegistrationPROSPERO CRD42023422150.

Actigraphy is a popular behavioral sleep assessment tool in research and clinical practice. Hierarchical hand-scoring approaches remain the standard for actigraphy rest interval estimation, but can be impractical for large cohort studies and suffer from reproducibility problems. We developed a semi-automated pipeline (actiSleep) to set rest intervals consistent with best-practice hand-scoring algorithms incorporating event marker, diary, light, and activity data. To evaluate actiSleep performance, we used data from an observational study of 51 adolescents (14-19yr), with and without family history of bipolar disorder. Participants completed 2 weeks of wrist actigraphy and daily sleep diary. We first hand-scored records using a standardized hierarchical algorithm incorporating event marker, diary, light, and activity data. We then compared the hand-scored rest intervals to those from actiSleep and two automated activity-based algorithms (Activity-Merged, Activity-Only). Activity-Only used activity-based sleep estimation and Activity-Merged joined closely adjacent rest intervals. For rest onset, rest offset, and rest duration, all algorithms had strong mean agreement with hand-scoring: actiSleep estimates were within 1-3 minutes, Activity-Merged within 2-4 minutes, and Activity-Only within 7-14 minutes. However, actiSleep had notably better (narrower) margins of agreement with hand-scoring, as evidenced by Bland-Altman plots, and greater positive predictive value and true positive rates for rest detection, especially in the 60 minutes surrounding the onset and offset of the rest interval. The actiSleep algorithm successfully estimates actigraphy rest intervals comparable to hand-scoring while avoiding pitfalls of activity-only algorithms. actiSleep has potential to replace hand-scoring for research in adolescents but requires further testing and validation in other samples.

Sleep regularity is an important but under-measured dimension of sleep health. Objective indices from actigraphy or wearables are robust but resource-intensive. The Sleep Regularity Questionnaire (SRQ) offers a brief subjective tool, but its validity against objective and diary-based indices in healthy adults is unclear. In Part 1, 31 adults wore a smart ring continuously for 21 nights. Device-derived regularity metrics included the Sleep Regularity Index (SRI), interdaily stability (IS), social jetlag (SJL), composite phase deviation (CPD), and the standard deviation of sleep onset and wake time. In Part 2, 52 adults completed a one-week sleep diary, from which variability in sleep timing, total sleep time (TST), SJL and nightly perceived sleep quality were derived. All participants completed the SRQ and Brief Pittsburgh Sleep Quality Index (B-PSQI). In Part 1, associations between SRQ scores and device-derived SRI, IS, SJL, CPD and timing variability were small (absolute r [&le;] 0.36). Higher SRQ Global and Sleep Continuity scores were moderately associated with better B-PSQI global scores (r -0.37 to -0.44). In Part 2, SRQ Global and Circadian Regularity showed small-to-moderate associations with higher diary-rated sleep quality and lower bedtime variability (r {approx} 0.40 and -0.32 to -0.34), while correlations with other diary metrics and B-PSQI were weak (absolute r [&le;] 0.25). The SRQ shows modest convergent validity with diary-based timing variability and perceived sleep quality, but only weak correspondence with smart ring-based sleep regularity indices. It is likely to complement, rather than replace, objective monitoring in healthy adults with relatively regular sleep-wake patterns.

Type 1 narcolepsy (NT1), a disorder caused by the loss of hypocretin/orexin transmission, is characterized by daytime sleepiness and symptoms where Rapid Eye Movement (REM) sleep, a state normally occurring from middle to late in the night, can intermingle with wakefulness. This results in cataplexy and sleep paralysis, episodes of muscle paralysis when awake, or in the generation of dream-like hallucinations and vivid dreaming, periods of visual imagery or sensory experiences that occur while awake, notably when falling asleep (hypnagogic hallucinations) or lingering dreams with over-realistic recall. Using deep learning of nocturnal sleep polysomnography (PSG) signals (EEG, EMG and EOG) applied to sleep stage scoring, we found that NT1 shows abnormally short wake to REM sleep transitions and occurrences of abnormal sleep stages probabilities of wake, REM sleep and N1 (very light NREM) sleep abnormally co-occurs (sleep stage mixing). Interestingly, although presence of these during sleep enables NT1 diagnosis with performances similar to gold standard diagnostic procedure, the multiple sleep latency test (MSLT), the cortical localization of these dissociations remains unclear. In this work, we used electrode specific predictions of sleep stages to explore if these are global or observed at the local cortical level. Surprisingly, although sleep stage mixing was preeminent between REM sleep, N1 and wake across all electrodes, it was found to fluctuate across locations, with stronger fluctuations found in frontal and central locations, notably in the dominant (left) hemisphere. The strongest single discriminator for NT1 was N1-REM stage mixing across central electrodes (C3-C4), showing 4.3-fold higher dissociation in NT1 patients (Cohens d = 0.61). Analysis of sleep stage dissociations across varying time scales revealed that windows lasting several minutes were most predictive of NT1 status, aligning with the duration of clinically reported symptoms of dissociated REM sleep in narcolepsy. Local N1-W-REM sleep dissociations correlated with CSF orexin/hypocretin levels and severity as measured using MSLT. The predominance of stage mixing in frontal and central regions, areas typically associated with executive and motor control, may contribute to the partial preservation of awareness during dissociated REM phenomena. Further, self-reports of hypnagogic hallucinations correlated best with dissociations involving occipital locations, in agreement with its usual visual content. Coherence analysis was also conducted but did not reveal additional insight. These results suggest that orexin deficiency destabilizes REM sleep organization across cortical projection area contributing both to REM sleep dissociation and to abnormal state transitions observed in NT1.

BackgroundLight plays a critical role in mental health, as the primary input to the circadian system, which regulates mood, energy, and the sleep-wake cycle. Altered light sensitivity is a potential mechanism in circadian-associated mental disorders. MethodsActigraphy-derived sleep, physical activity, and circadian rhythm correlates of the pupillary light reflex were explored in young people with emerging mental disorders. Participants were 27 healthy controls (Mean age=25.67 {+/-} 2.83, 52% female) and 155 young people from the Neurobiology Youth Follow-up Study (Mean age=25.48 {+/-} 5.65; 60% female), recruited from an early intervention mental health service. 32% of the latter group were re-assessed over 12 months. Pupil constriction, average and maximal constriction velocity, and constriction latency were recorded by the PLR-3000 monocular pupillometer in response to dim ([~]10 lux) and bright ([~]1500 lux) pulses. ResultsCompared to healthy controls, young people with emerging mental disorders had a smaller change in pupil diameter (p=0.037) and a slower maximal constriction velocity (p=0.018) in response to dim light. In the full sample, decreased dim light sensitivity was correlated with later timing of actigraphy-derived sleep midpoint. Within the clinical cases, increased genetic risk for bipolar disorder was correlated with increased dim light sensitivity, and higher insomnia clinical scores were correlated with decreased dim light sensitivity. Pupillometry measures were stable across time and seasons. ConclusionAltered light sensitivity may be associated with the emergence of mood disorder in young people and with altered sleep-wake timing.

Study ObjectivesNarcolepsy type 1 (NT1) is characterized by excessive daytime sleepiness and cataplexy. Previous studies have implicated the amygdala, thalamus, brainstem and hippocampus in the pathophysiology of NT1. We here aimed to examine more detailed subregional case-control differences in MRI-based segmentations of these brain regions to gain deeper insights. MethodsWe obtained 3T MRI brain scans from 54 NT1 patients (39 females, mean age 21.8 {+/-} 11.0 years, 51 with confirmed hypocretin-deficiency and three patients that had not performed this measure) and 114 healthy controls (77 females, mean age 23.2 {+/-} 9.0 years). Automated segmentation of the hippocampus, amygdala, thalamus, and brainstem was performed on T1-weighted MRI data using FreeSurfer. Case-control volume differences were tested using general linear models and permutation testing. The false discovery rate was controlled at 5% with the Benjamini-Hochberg procedure. ResultsThe analysis revealed no significant case-control differences for any of the subregions in the hippocampus, thalamus, amygdala and brainstem after correction for multiple testing. ConclusionsBased on a detailed automated MRI-based segmentation analyses in a relatively large national sample, NT1 patients had no significant changes in any amygdala, thalamus, brainstem or hippocampus subregions compared to controls. In the future large multi-site studies could be performed to achieve sufficient power to detect more subtle group differences.

BackgroundSleep disturbance is a core feature of bipolar disorder (BD) and often precedes mood recurrence, particularly in youth. Although actigraphy provides objective sleep measurement, it is limited by cost and monitoring duration. Passive smartphone-based mobile sensing offers a scalable alternative, but its validity in youths with BD is unclear. MethodsAnalyses included adolescents and young adults (ages 14-25) with BD-I/II from the PROMPT-BD study with at least four days of concurrent actigraphy and mobile sensing. Actigraphy-derived sleep metrics (total sleep time [TST], sleep onset, sleep offset, midsleep, wake after sleep onset [WASO]) were compared with smartphone-derived proxies (total offline time [TOT], onset, offset, midsleep, phone use after sleep onset [PASO]). Agreement was evaluated using root mean squared error (RMSE) and mixed-effects models. Zero-inflated negative binomial models examined associations between WASO and PASO. Sensitivity analyses tested robustness to missing data, smartphone use patterns, sleep window definitions, operating system, presence vs. absence of mood symptoms and anxiety, and weekend effects. ResultsMobile sensing showed strong convergence with actigraphy for sleep timing and duration (standardized {beta} = 0.54-0.75, all p < .0001). RMSEs were <21 minutes for onset, offset, midsleep, and TST, with strongest agreement for midsleep (RMSE = 14.8 minutes). Mobile sensing slightly overestimated sleep duration and estimated earlier timing. PASO underestimated WASO (RMSE = 48.8 minutes), but greater WASO significantly increased the odds of detecting any PASO (OR per 15 minutes = 1.35, p < .0001). Findings were robust across sensitivity analyses. ConclusionsPassive smartphone-derived sleep metrics approximated actigraphy-based estimates of sleep timing and duration in youth with BD. Given the widespread availability of smartphones in this population, this supports their potential as scalable tools for monitoring circadian disruption and informing early intervention.

PurposeTo develop and validate a prediction model for sleep apnea syndrome (SAS) treated with continuous positive airway pressure (CPAP) in the general population. MethodsUsing claims and health checkup data held by JMDC Inc., linked to personal health records (Pep Up), we developed and internally validated a prediction model for SAS treated with CPAP, defined as a diagnosis of SAS and reimbursement records of CPAP. Every three months from January 1, 2022 to July 1, 2024 (i.e., 11 timepoints), we identified eligible individuals with available data both 1 year before and 1 year after that timepoint to define the presence/absence of SAS treated with CPAP, as well as 279 predictor variables. We developed a LightGBM model for the training and tuning datasets and evaluated its performance on the validation dataset. ResultsAmong 18,692,873 observations (mean age 44.8{+/-}11.3 years, women 37.5%) obtained from 1,858,566 people, 300,868 (1.6%) had SAS treated with CPAP. The area under the receiver operating characteristic curve was 0.898 (95% confidence interval 0.895-0.901). The positive predictive values among people with the top 1% and 10% prediction scores were 28.3% and 10.3%, respectively. According to the SHapley Additive exPlanations plot, male sex was the most important predictor, followed by age, body mass index, and waist circumference. We also demonstrated that personal health records significantly improved the predictive performance. ConclusionWe developed a prediction model to identify people at high risk of SAS and encourage them to undergo polysomnography or related tests.

BackgroundProlonged wakefulness, restricted sleep, and circadian factors can impact driving performance and road safety. Currently, there are no effective objective roadside tests to detect the state of drivers sleepiness during or prior to driving, or predict future driving impairment risk. This paper reports on an extended wakefulness protocol used to determine if a portable virtual reality device to administer vestibular-ocular motor function (VOM) tests can effectively detect 1) drivers state of sleepiness during or just prior to driving, and 2) predict trait sleepiness and future driving risk. MethodsFifty healthy adults with regular sleep within 9pm to 8am were recruited for an experimental laboratory procedure which involved two phases: an initial overnight sleep study, and a subsequent period of extended wakefulness lasting ~29 hours. During the wakefulness phase, participants undertook neurobehavioural testing, a simulated driving test, and repeat assessments of VOM to establish if ocular markers can predict sleepiness state and sleepiness-related performance impairments (Trial registry ACTRN12621001610820). DiscussionThis protocol outlined a study that aimed to establish the sensitivity of VOM test the effects of extended wakefulness and circadian phase on driver state and trait sleepiness and subsequent sleepiness-related driving impairment. Furthermore, the protocol aims to define the best VOM predictors to identify driver sleepiness state (road side testing and pre-drive assessments) and sleepiness trait (predicting future driving risk) to establish proof of concept for its potential application as a roadside, pre-drive and general sleepiness related fitness to drive test.

IntroductionTherapeutic communication is the basis of nursing care yet it has been considered so stressful by student nurses with only 3.5% of nursing students in Kampala Uganda exhibiting optimum therapeutic communication competence. This has been attributed to inadequate training. Faculty must explore means to impart these skills in nursing students. This study implemented and evaluated an educational intervention module on therapeutic communication skills for nurses for incorporation into their teaching learning activities. MethodsA one-group pre-post quasi-experimental study was conducted with 41 diploma extension nursing students, selected via census sampling. Data were collected using self-administered structured questionnaires (content validity = 0.98; Cronbachs = 0.96), on students knowledge and perceived confidence in performing therapeutic communication. Observation checklists were used to evaluate students ability to establish nurse-patient relationships and deliver bad news in the skills lab, both before and after the intervention. ResultsThere was a significant improvement of knowledge scores from 4 (IQR: 3, 5) to 8.0 (IQR: 7.0, 9.0), (P value <0.001); perceived confidence in practicing therapeutic communication scores from 144.0 (IQR: 136.0, 153.0) to 164.0 (IQR: 155.0, 174.0) (P value <0.001); ability to initiate a nurse-patient relationship from 12.0 (IQR: 10.0, 14.0) to 17.0 (15.0, 18.0) (P value <0.001); and the ability to break bad news to the patient/caretaker from 9.0 (IQR: 7.0, 12.0) to 16.0 (14.0, 18.0) (P value <0.001) after the intervention. All scores improved in all categories of sex, program and semester of study for all participants apart from participants in the first semester of study. ConclusionThis study offers preliminary evidence that the educational intervention improves nursing students therapeutic communication skills. Further longitudinal research is needed to assess the sustained effectiveness of the module, the teaching methods used, and patients perspectives on students TC competence.

IntroductionSleep spindles are electroencephalographic elements characteristic of non-rapid eye movement sleep generated by thalamo-cortical interactions. Spindles have been linked to some of the cognitive benefits afforded by sleep and high spindle activity is associated with increased arousal threshold (deeper sleep). Here, we demonstrate that targeting the thalamus with Transcranial Electrical Stimulation with Temporal Interference (TES-TI) can enhance spindle activity. Methods24 participants (25.5 {+/-} 9.5 years; 69.6% F) underwent thalamic TES-TI stimulation during daytime naps. Three stimulation protocols were tested during stage 2 of non-rapid eye movement sleep (N2): fixed difference frequency of 10 Hz (TES15kHz-TI10Hz), difference frequency matched to individual spindle peak (TES15kHz-TIPeak), and carrier frequency only (TES15kHz). Spectral power in the spindle (sigma) band and integrated spindle activity (ISA) were compared before and during the stimulation, and across stimulation protocols. ResultsTES15kHz-TI10Hz stimulation was associated with a significant increase in sigma band power ({Delta}[x]STIM-PRE = 0.49 log10{micro}V2, p = 0.021) and ISA ({Delta}[x]STIM-PRE = 7.48 {micro}V/s, p = 0.042). Cluster-based analysis localized the increase in sigma power over the frontal and centro-parietal areas (p = 0.022). Linear mixed effects models showed that both sigma band power and ISA during stimulation increased significantly in TES15kHz-TI10Hz compared to the TES15kHz protocol ({beta} = 0.67 log10{micro}V2, p = 0.018; {beta} = 14.70 {micro}V/s, p = 0.0077), while the TES15kHz-TIPeak did not show the same effect. ConclusionsThis study provides evidence supporting the successful use of TES-TI targeting the thalamus to enhance sleep spindle activity. Stimulation at a fixed difference frequency of 10 Hz increased sigma band power and ISA, whereas neither stimulation matched to individual sigma band peak nor TES alone produced comparable effects. These promising results warrant further investigations into the cognitive and clinical impact of TES-TI, a non-invasive neuromodulation tool that can reach deep brain regions. Statement of significanceThis study provides evidence that thalamo-cortical networks, which are central to many physiological and pathological brain activities, can be modulated non-invasively in humans. More specifically, the findings show that transcranial electrical stimulation with temporal interference targeting the thalamus can selectively enhance sleep spindle activity. This work introduces a new strategy for precisely targeting sleep-generating mechanisms regulated by deep brain circuits without surgery or medication. Key next steps include determining how this increase in spindle activity can positively impact cognition and assessing the translational potential of this approach for clinical populations.

BackgroundAdvanced Practice Providers (APPs) in emergency and urgent care settings experience high burnout rates, yet limited research examines cognitive factors influencing professional fulfillment. The Empowerment Dynamic framework suggests outcome-oriented thinking may protect against burnout compared to problem-oriented patterns. ObjectiveTo examine relationships between cognitive mindset orientation, professional fulfillment, and burnout among APPs while providing preliminary validation of a novel cognitive assessment instrument. MethodsCross-sectional survey of licensed APPs working in emergency departments and urgent care facilities across two health systems (July-October 2025). Professional fulfillment and burnout were measured using the Stanford Professional Fulfillment Index; cognitive orientation was assessed using a newly developed 22-item instrument. ResultsAmong 98 respondents (19.5% response rate), mean professional fulfillment was 5.8 and mean burnout was 4.5; 40.8% met burnout criteria. Professional fulfillment and burnout were inversely correlated (r = -0.62; P < .001). Problem orientation correlated positively with burnout (r = 0.56) and negatively with fulfillment (r = -0.36), while outcome orientation showed opposite patterns (burnout: r = -0.57; fulfillment: r = 0.44). In multivariable models, outcome orientation remained independently associated with lower burnout ({beta} = -1.51; P = .003) and higher fulfillment ({beta} = 1.73; P = .002). ConclusionsCognitive mindset orientation is associated with burnout and professional fulfillment among APPs. The novel assessment instrument demonstrates acceptable psychometric properties. Future longitudinal studies are needed to establish causality and evaluate cognitive interventions for burnout prevention.

Nocturnal glucose regulation is modulated by autonomic and circadian mechanisms, yet their dynamic interplay in apparently healthy, free-living populations remains poorly studied. Here, we assessed 227,860 nights of concurrent sleep data from Ultrahuman AIR ring and M1 continuous glucose monitoring (CGM) system across 5849 adults globally to examine nocturnal cardio-metabolic coupling. We found that higher sleep consistency was inversely associated with glucose variability, and vice versa. Unsupervised clustering of metrics characterizing nightly sleep quality and demographic factors revealed phenotypes corresponding to better vs poorer metabolic management. Clustering on aggregated sleep scores differentiated users on metabolic metrics with larger effect sizes, rather than on base sleep metrics. A subgroup analysis of sleep sessions in the upper and lower quartiles of the sleep-metabolic spectrum, revealed an asymmetric coupling between metabolic and sleep factors in determining phenotype. Nights corresponding to poorer sleep-metabolic management displayed greater shape similarity between nightly heart rate (HR) and glucose curves, compared to sleep sessions with better sleep-metabolic management. These findings demonstrate that multi-sensor digital phenotyping can improve the profiling of sleep and metabolic alignment in largely healthy adults, with simple sleep/wake regularity emerging as a behaviorally tractable determinant of cardio-metabolic homeostasis.