-
PloS One 2024Previous literature suggests that mindfulness meditation can have positive effects on mental health, however, its mechanisms of action are still unclear. In this...
Previous literature suggests that mindfulness meditation can have positive effects on mental health, however, its mechanisms of action are still unclear. In this pre-registered study, we investigate the effects of mindfulness training on lapses of attention (and their associated neural correlates) during meditation practice. For this purpose, we recorded Electroencephalogram (EEG) during meditation practice before and after 8 weeks of mindfulness training (or waitlist) in 41 participants (21 treatment and 20 controls). In order to detect lapses of attention and characterize their EEG correlates, we interrupted participants during meditation to report their level of focus and drowsiness. First, we show that self-reported lapses of attention during meditation practice were associated to an increased occurrence of theta oscillations (3-6 Hz), which were slower in frequency and more spatially widespread than theta oscillations occurring during focused attention states. Then, we show that mindfulness training did not reduce the occurrence of lapses of attention nor their associated EEG correlate (i.e. theta oscillations) during meditation. Instead, we find that mindfulness training was associated with a significant slowing of alpha oscillations in frontal electrodes during meditation. Crucially, frontal alpha slowing during meditation practice has been reported in experienced meditators and is thought to reflect relative decreases in arousal levels. Together, our findings provide insights into the EEG correlates of mindfulness meditation, which could have important implications for the identification of its mechanisms of action and/or the development of neuromodulation protocols aimed at facilitating meditation practice.
Topics: Humans; Meditation; Mindfulness; Male; Female; Adult; Electroencephalography; Self Report; Attention; Middle Aged
PubMed: 38843236
DOI: 10.1371/journal.pone.0299275 -
Australian Journal of General Practice Jun 2024Shift work is characterised by displaced sleep opportunities and associated sleep disturbance. Shift workers often report sleepiness and other wake time symptoms...
BACKGROUND
Shift work is characterised by displaced sleep opportunities and associated sleep disturbance. Shift workers often report sleepiness and other wake time symptoms associated with poor sleep. However, clinical sleep disorders are also prevalent in shift workers. Although prevalence rates are similar or higher in shift workers compared with the general population, help seeking in shift workers with sleep disorders is low.
OBJECTIVE
This article aims to provide general practitioners with a contemporary overview of the prevalence rates for sleep disorders in shift workers, to clarify the existing evidence relating to mental and physical health consequences of sleep disorders in shift workers and to highlight the need to consider undiagnosed sleep disorders before attributing sleep-related symptoms solely to work schedules.
DISCUSSION
Symptoms of sleep loss associated with shift work overlap with symptoms experienced by individuals living with sleep disorders. Although >40% of middle-aged Australians live with a sleep disorder that requires investigation and management, symptoms in shift workers are often attributed to the work schedule and, as a result, might not be investigated for appropriate diagnosis and treatment. We argue that screening for sleep disorders in shift workers with sleep complaints should be a priority.
Topics: Humans; Sleep Wake Disorders; Australia; General Practice; Sleep Disorders, Circadian Rhythm; Prevalence; Shift Work Schedule; Work Schedule Tolerance
PubMed: 38840377
DOI: 10.31128/AJGP-12-22-6647 -
Australian Journal of General Practice Jun 2024Obstructive sleep apnoea (OSA) is a highly prevalent condition associated with significant adverse health consequences affecting multiple organ systems. As the first...
BACKGROUND
Obstructive sleep apnoea (OSA) is a highly prevalent condition associated with significant adverse health consequences affecting multiple organ systems. As the first point of contact for most patients with OSA, general practitioners (GPs) have an important role in the diagnosis of this common sleep disorder.
OBJECTIVE
The aim of this paper is to improve awareness of common risk factors for and clinical presentation of OSA in primary care to improve patient health outcomes. We seek to understand how screening tools, such as the OSA50 questionnaire and the Epworth Sleepiness Scale, can help GPs identify patients who are at high risk for OSA with significant daytime sleepiness.
DISCUSSION
Patients at high risk of symptomatic moderate-severe OSA should proceed to further investigation with sleep study testing. Referral to a sleep physician should be considered for patients with complex presentations or other suspected sleep disorders, commercial drivers, and those who fail to comply with or respond to initial OSA treatments.
Topics: Humans; Sleep Apnea, Obstructive; Primary Health Care; Risk Factors; Surveys and Questionnaires; Polysomnography
PubMed: 38840373
DOI: 10.31128/AJGP-03-23-6740 -
Australian Journal of General Practice Jun 2024Obstructive sleep apnoea (OSA) and insomnia are the two most common sleep disorders and are frequent reasons for presentation in Australian general practice.
BACKGROUND
Obstructive sleep apnoea (OSA) and insomnia are the two most common sleep disorders and are frequent reasons for presentation in Australian general practice.
OBJECTIVE
This article describes the development, content and suggested uses of the online sleep health primary care clinical resource, which provides general practitioners and other primary healthcare professionals with evidence-based information on the aetiology, assessment, management, referral and ongoing care for OSA and chronic insomnia.
DISCUSSION
The Royal Australian College of General Practitioners-accepted clinical resource for the management of OSA and chronic insomnia in primary care was developed by the Australian National Centre for Sleep Health Services Research. The resource is designed to be used during consultations (eg following the steps in assessment and management and the use of online questionnaires for the assessment of OSA [Epworth Sleepiness Scale/OSA50/STOP-Bang] and insomnia [Sleep Condition Indicator/and Insomnia Severity Index]) and as an education/training tool (eg evidence on the role of continuous positive airway pressure/mandibular advancement splints for management of OSA and brief behavioural therapy for insomnia/cognitive behavioural therapy for insomnia for the management of insomnia).
Topics: Humans; Primary Health Care; Sleep Apnea, Obstructive; Sleep Initiation and Maintenance Disorders; Surveys and Questionnaires; Australia; Continuous Positive Airway Pressure
PubMed: 38840372
DOI: 10.31128/AJGP-03-23-6779 -
Sleep Health Jun 2024To compare patients treated with cognitive behavioral therapy for insomnia (CBT-I) with healthy sleepers and individuals with past but not current insomnia on...
OBJECTIVES
To compare patients treated with cognitive behavioral therapy for insomnia (CBT-I) with healthy sleepers and individuals with past but not current insomnia on multidimensional sleep health.
METHODS
The study evaluates CBT-I on six dimensions of sleep health (regularity, satisfaction, alertness, timing, efficiency, duration) in a sample of individuals with insomnia compared to two other unique sleep samples. Participants were in one of three groups: insomnia (CUR, n = 299), healthy sleeper (HS, n = 122), or past insomnia (PAST, n = 35). Daily diaries and validated measures were employed to capture six dimensions of sleep health. The CUR group received four 60-minute sessions of CBT-I every 2weeks, and sleep health indices were measured at baseline and post-treatment. The HS and PAST groups were measured only at baseline.
RESULTS
Results of the pairwise t tests indicated improvements in sleep satisfaction, alertness (fatigue but not sleepiness), timing, efficiency, and duration (Cohen's d=0.22 to 1.55). ANCOVA models revealed significant differences in sleep health scores between treated insomnia patients and the other two sleep groups. Treated patients demonstrated less bedtime and risetime variability, in addition to lower napping duration. Overall, the study observed significant changes in various domains of sleep health after four sessions of cognitive behavioral therapy for insomnia; however, differences remain when compared to the other groups in the study.
CONCLUSION
There may be ongoing sleep vulnerability in patients treated with cognitive behavioral therapy for insomnia though future inclusion of a control group would increase internal validity. Borrowing from transdiagnostic sleep modules may be helpful to support remaining deficits after cognitive behavioral therapy for insomnia.
PubMed: 38839483
DOI: 10.1016/j.sleh.2024.03.005 -
Nature and Science of Sleep 2024Excessive daytime sleepiness (EDS) forms a prevalent symptom of obstructive sleep apnea (OSA) and narcolepsy type 1 (NT1), while the latter might always be overlooked....
BACKGROUND
Excessive daytime sleepiness (EDS) forms a prevalent symptom of obstructive sleep apnea (OSA) and narcolepsy type 1 (NT1), while the latter might always be overlooked. Machine learning (ML) models can enable the early detection of these conditions, which has never been applied for diagnosis of NT1.
OBJECTIVE
The study aimed to develop ML prediction models to help non-sleep specialist clinicians identify high probability of comorbid NT1 in patients with OSA early.
METHODS
Totally, clinical features of 246 patients with OSA in three sleep centers were collected and analyzed for the development of nine ML models. LASSO regression was used for feature selection. Various metrics such as the area under the receiver operating curve (AUC), calibration curve, and decision curve analysis (DCA) were employed to evaluate and compare the performance of these ML models. Model interpretability was demonstrated by Shapley Additive explanations (SHAP).
RESULTS
Based on the analysis of AUC, DCA, and calibration curves, the Gradient Boosting Machine (GBM) model demonstrated superior performance compared to other machine learning (ML) models. The top five features used in the GBM model, ranked by feature importance, were age of onset, total limb movements index, sleep latency, non-REM (Rapid Eye Movement) sleep stage 2 and severity of OSA.
CONCLUSION
The study yielded a simple and feasible screening ML-based model for the early identification of NT1 in patients with OSA, which warrants further verification in more extensive clinical practices.
PubMed: 38836216
DOI: 10.2147/NSS.S456903 -
Clinical Case Reports Jun 2024This study suggests that severe obstructive sleep apnea can present as sleep-related epileptic or non-epileptic seizures. A detailed history and physical examination,...
KEY CLINICAL MESSAGE
This study suggests that severe obstructive sleep apnea can present as sleep-related epileptic or non-epileptic seizures. A detailed history and physical examination, along with polysomnography and video electroencephalography findings can lead to the correct diagnosis.
ABSTRACT
Obstructive sleep apnea (OSA) is defined by recurrent episodes of the upper airway complete or partial collapse while sleeping. The obstructive episodes result in gradual suffocation that increases breathing attempts till the person is awakened. The main manifestations are excessive daytime sleepiness, snoring, observed episodes of stopped breathing, and abrupt awakenings accompanied by gasping or choking. Nevertheless, there are very few reports of patients with OSA, manifesting other symptoms such as seizure-like movements. Differentiating OSA with nocturnal seizures could be challenging due to their overlapping features. A 53-year-old man presented to the clinic, experiencing seizure-like involuntary movements during nocturnal sleep for the past 2 years with a frequency of 2-3 times per night. Neurologic examinations were normal. Further evaluation with polysomnography revealed impaired arousal followed by seizure-like movements during sleep. Video electroencephalography (EEG) did not show any epileptiform discharges, ruling out the nocturnal seizure diagnosis. The patient was diagnosed with OSA. Subsequently, continuous positive airway pressure (CPAP) treatment resolved all symptoms.
PubMed: 38836112
DOI: 10.1002/ccr3.9004 -
Psychiatry Research Aug 2024Sleep disturbances are well-known symptoms of major depressive disorder (MDD). However, the prospective risk of MDD in the presence of sleep disturbances in a general...
Sleep disturbances are well-known symptoms of major depressive disorder (MDD). However, the prospective risk of MDD in the presence of sleep disturbances in a general population-based cohort is not well known. This study investigated associations between both polysomnography (PSG)-based or subjective sleep features and incident MDD. Participants representative of the general population who had never had MDD completed sleep questionnaires (n = 2000) and/or underwent PSG (n = 717). Over 8 years' follow-up, participants completed psychiatric interviews enabling the diagnosis of MDD. Survival Cox models were used to analyze associations between sleep features and MDD incidence. A higher Epworth Sleepiness Scale and presence of insomnia symptoms were significantly associated with a higher incidence of MDD (hazard ratio [HR] [95 % confidence interval (CI)]: 1.062 [1.022-1.103], p = 0.002 and 1.437 [1.064-1.940], p = 0.018, respectively). Higher density of rapid eye movements in rapid eye movement (REM) sleep was associated with a higher incidence of MDD in men (HR 1.270 [95 % CI 1.064-1.516], p = 0.008). In women, higher delta power spectral density was associated with a lower MDD incidence (HR 0.674 [95 % CI 0.463-0.981], p = 0.039). This study confirmed the associations between subjective and objective sleep features and the incidence of MDD in a large community dwelling cohort.
Topics: Humans; Male; Depressive Disorder, Major; Female; Adult; Middle Aged; Incidence; Sleep Wake Disorders; Polysomnography; Cohort Studies; Sleep Initiation and Maintenance Disorders; Proportional Hazards Models; Surveys and Questionnaires; Risk Factors
PubMed: 38833937
DOI: 10.1016/j.psychres.2024.115934 -
Scientific Reports Jun 2024Evidence has shown that both sleep loss and daily caffeine intake can induce changes in grey matter (GM). Caffeine is frequently used to combat sleepiness and impaired... (Randomized Controlled Trial)
Randomized Controlled Trial
Repeated caffeine intake suppresses cerebral grey matter responses to chronic sleep restriction in an A adenosine receptor-dependent manner: a double-blind randomized controlled study with PET-MRI.
Evidence has shown that both sleep loss and daily caffeine intake can induce changes in grey matter (GM). Caffeine is frequently used to combat sleepiness and impaired performance caused by insufficient sleep. It is unclear (1) whether daily use of caffeine could prevent or exacerbate the GM alterations induced by 5-day sleep restriction (i.e. chronic sleep restriction, CSR), and (2) whether the potential impact on GM plasticity depends on individual differences in the availability of adenosine receptors, which are involved in mediating effects of caffeine on sleep and waking function. Thirty-six healthy adults participated in this double-blind, randomized, controlled study (age = 28.9 ± 5.2 y/; F:M = 15:21; habitual level of caffeine intake < 450 mg; 29 homozygous C/C allele carriers of rs5751876 of ADORA2A, an A adenosine receptor gene variant). Each participant underwent a 9-day laboratory visit consisting of one adaptation day, 2 baseline days (BL), 5-day sleep restriction (5 h time-in-bed), and a recovery day (REC) after an 8-h sleep opportunity. Nineteen participants received 300 mg caffeine in coffee through the 5 days of CSR (CAFF group), while 17 matched participants received decaffeinated coffee (DECAF group). We examined GM changes on the 2nd BL Day, 5th CSR Day, and REC Day using magnetic resonance imaging and voxel-based morphometry. Moreover, we used positron emission tomography with [F]-CPFPX to quantify the baseline availability of A adenosine receptors (AR) and its relation to the GM plasticity. The results from the voxel-wise multimodal whole-brain analysis on the Jacobian-modulated T1-weighted images controlled for variances of cerebral blood flow indicated a significant interaction effect between caffeine and CSR in four brain regions: (a) right temporal-occipital region, (b) right dorsomedial prefrontal cortex (DmPFC), (c) left dorsolateral prefrontal cortex (DLPFC), and (d) right thalamus. The post-hoc analyses on the signal intensity of these GM clusters indicated that, compared to BL, GM on the CSR day was increased in the DECAF group in all clusters but decreased in the thalamus, DmPFC, and DLPFC in the CAFF group. Furthermore, lower baseline subcortical AR availability predicted a larger GM reduction in the CAFF group after CSR of all brain regions except for the thalamus. In conclusion, our data suggest an adaptive GM upregulation after 5-day CSR, while concomitant use of caffeine instead leads to a GM reduction. The lack of consistent association with individual AR availability may suggest that CSR and caffeine affect thalamic GM plasticity predominantly by a different mechanism. Future studies on the role of adenosine A receptors in CSR-induced GM plasticity are warranted.
Topics: Humans; Caffeine; Male; Adult; Gray Matter; Receptor, Adenosine A1; Positron-Emission Tomography; Female; Magnetic Resonance Imaging; Double-Blind Method; Sleep Deprivation; Young Adult; Receptor, Adenosine A2A
PubMed: 38830861
DOI: 10.1038/s41598-024-61421-8 -
Journal of Family Medicine and Primary... Apr 2024In diabetes mellitus (DM) patients, obtaining a good night's sleep is crucial for maintaining body caloric intake, controlling insulin levels, and reducing the...
INTRODUCTION
In diabetes mellitus (DM) patients, obtaining a good night's sleep is crucial for maintaining body caloric intake, controlling insulin levels, and reducing the likelihood of engaging in unhealthy behavior. Patients with poor sleep quality may experience impaired glycemia, playing a significant role in the development of chronic complications. This study aims to explore the effects of DM complications on sleep quality among Saudi Arabian patients.
PATIENT AND METHODS
This is a cross-sectional study conducted among patients with diabetes. A self-administered, validated questionnaire translated into Arabic was distributed among diabetic patients using an online survey. The questionnaire includes sociodemographic characteristics (i.e. age, gender, marital status, etc.), the medical history of the patients, and a questionnaire about sleep quality.
RESULTS
Out of 4171 patients involved, 52.7% were females and 27.7% were aged between 40 and 60 years old. The prevalence of patients with perceived poor sleep quality was 24.1%. Significant predictors of poor sleep quality were the gender female, having social stressors, comorbid sleep disorders, associated comorbidities, increasing HbA1c levels, being overweight/obese, and diabetes complications. Furthermore, sleep disturbance, taking sleep medications, daytime sleepiness, and having bad dreams during sleep were also identified as prognostic factors for poor sleep quality.
CONCLUSION
The subjective poor sleep quality of patients with diabetes was 24.1%. Poor sleep quality was significantly demonstrated by females who were having social stressors, comorbid sleep disorders, comorbidities, uncontrolled HbA1c levels, elevated BMI levels, and complications of diabetes. However, regular physical activity and adequate sleep were estimated to be the protective factors against poor sleep quality. Further research is needed to establish the effect of sleep quality among patients with DM.
PubMed: 38827724
DOI: 10.4103/jfmpc.jfmpc_473_23