Objectives Although both physical inactivity and poor sleep are deleteriously associated with mortality, the joint effects of these two behaviours remain unknown. This study aimed to investigate the joint association of physical activity (PA) and sleep with all-cause and cause-specific mortality risks. Methods 380 055 participants aged 55.9 (8.1) years (55% women) from the UK Biobank were included. Baseline PA levels were categorised as high, medium, low and no moderate-to-vigorous PA (MVPA) based on current public health guidelines. We categorised sleep into healthy, intermediate and poor with an established composited sleep score of chronotype, sleep duration, insomnia, snoring and daytime sleepiness. We derived 12 PA–sleep combinations, accordingly. Mortality risks were ascertained to May 2020 for all-cause, total cardiovascular disease (CVD), CVD subtypes (coronary heart disease, haemorrhagic stroke, ischaemic stroke), as well as total cancer and lung cancer. Results After an average follow-up of 11.1 years, sleep scores showed dose-response associations with all-cause, total CVD and ischaemic stroke mortality. Compared with high PA-healthy sleep group (reference), the no MVPA-poor sleep group had the highest mortality risks for all-cause (HR (95% CIs), (1.57 (1.35 to 1.82)), total CVD (1.67 (1.27 to 2.19)), total cancer (1.45 (1.18 to 1.77)) and lung cancer (1.91 (1.30 to 2.81))). The deleterious associations of poor sleep with all outcomes, except for stroke, was amplified with lower PA. Conclusion The detrimental associations of poor sleep with all-cause and cause-specific mortality risks are exacerbated by low PA, suggesting likely synergistic effects. Our study supports the need to target both behaviours in research and clinical practice.

Introduction: Many intensive care patients experience sleep disruption potentially related tonoise, light and treatment interventions. The purpose of this study was tocharacterise, in terms of quantity and quality, the sleep of intensive carepatients, taking into account the impact of environmental factors. Methods: This observational study was conducted in the adult ICU of a tertiary referralhospital in Australia, enrolling 57 patients. Polysomnography (PSG) was performedover a 24-hour period to assess the quantity (total sleep time: hh:mm) and quality(percentage per stage, duration of sleep episode) of patients' sleep while in ICU.Rechtschaffen and Kales criteria were used to categorise sleep. Interrater checkswere performed. Sound pressure and illuminance levels and care events weresimultaneously recorded. Patients reported on their sleep quality in ICU using theRichards Campbell Sleep Questionnaire and the Sleep in Intensive CareQuestionnaire. Data were summarised using frequencies and proportions or measuresof central tendency and dispersion as appropriate and Cohen's Kappa statistic wasused for interrater reliability of the sleep data analysis. Results: Patients' median total sleep time was 05:00 (IQR: 02:52 to 07:14). The majority ofsleep was stage 1 and 2 (medians: 19 and 73%) with scant slow wave and REM sleep.The median duration of sleep without waking was 00:03. Sound levels were high(mean Leq 53.95 dB(A) during the day and 50.20 dB(A) at night) and illuminancelevels were appropriate at night (median <2 lux) but low during the day(median: 74.20 lux). There was a median 1.7 care events/h. Patients' meanself-reported sleep quality was poor. Interrater reliability of sleep staging washighest for slow wave sleep and lowest for stage 1 sleep. Conclusions: The quantity and quality of sleep in intensive care patients are poor and may berelated to noise, critical illness itself and treatment events that disturb sleep.The study highlights the challenge of quantifying sleep in the critical caresetting and the need for alternative methods of measuring sleep. The resultssuggest that a sound reduction program is required and other interventions toimprove clinical practices to promote sleep in intensive care patients. Trial registration: Australian New Zealand clinical trial registry(http://www.anzctr.org.au/): ACTRN12610000688088.

Objective: This paper aims to present and describe the Standardised Tool for the Assessment of Bruxism (STAB), an instrument that was developed to provide a multidimensional evaluation of bruxism status, comorbid conditions, aetiology and consequences.Methods: The rationale for creating the tool and the road map that led to the selection of items included in the STAB has been discussed in previous publications.Results: The tool consists of two axes, specifically dedicated to the evaluation of bruxism status and consequences (Axis A) and of bruxism risk and etiological factors and comorbid conditions (Axis B). The tool includes 14 domains, accounting for a total of 66 items. Axis A includes the self‐reported information on bruxism status and possible consequences (subject‐based report) together with the clinical (examiner report) and instrumental (technology report) assessment. The Subject‐Based Assessment (SBA) includes domains on Sleep Bruxism (A1), Awake Bruxism (A2) and Patient's Complaints (A3), with information based on patients' self‐report. The Clinically Based Assessment (CBA) includes domains on Joints and Muscles (A4), Intra‐ and Extra‐Oral Tissues (A5) and Teeth and Restorations (A6), based on information collected by an examiner. The Instrumentally Based Assessment (IBA) includes domains on Sleep Bruxism (A7), Awake Bruxism (A8) and the use of Additional Instruments (A9), based on the information gathered with the use of technological devices. Axis B includes the self‐reported information (subject‐based report) on factors and conditions that may have an etiological or comorbid association with bruxism. It includes domains on Psychosocial Assessment (B1), Concurrent Sleep‐related Conditions Assessment (B2), Concurrent Non‐Sleep Conditions Assessment (B3), Prescribed Medications and Use of Substances Assessment (B4) and Additional Factors Assessment (B5). As a rule, whenever possible, existing instruments, either in full or partial form (i.e. specific subscales), are included. A user's guide for scoring the different items is also provided to ease administration.Conclusions: The instrument is now ready for on‐field testing and further refinement. It can be anticipated that it will help in collecting data on bruxism in such a comprehensive way to have an impact on several clinical and research fields.

OSA is the result of structural and functional abnormalities that promote the repetitive collapse of the upper airway during sleep. This common disorder is estimated to occur in approximately 4% of men and 2% of women, with prevalence studies from North America, Australia, Europe and Asia indicating that occurrence is relatively similar across the globe. Anatomical factors, such as obesity and craniofacial morphology, are key determinants of the predisposition to airway collapse; however, their relative importance for OSA risk likely varies between ethnicities. Direct inter‐ethnic studies comparing craniofacial phenotypes in OSA are limited. However, available data suggest that Asian OSA populations primarily display features of craniofacial skeletal restriction, African Americans display more obesity and enlarged upper airway soft tissues, while Caucasians show evidence of both bony and soft tissue abnormalities. Our recent comparison of Chinese and Caucasian OSA patients found for the same degree of OSA severity. Caucasians were more obese, and Chinese had more skeletal restriction. However, the ratio of obesity to craniofacial bony size (or anatomical balance, an important determinant of upper airway volume and OSA risk) was similar between Caucasians and Chinese OSA patients. Ethnicity appears to influence OSA craniofacial phenotype but furthermore the relative contribution of the anatomical factors underlying OSA risk. The skeletal restriction craniofacial phenotype may be particularly vulnerable to increasing obesity rates. Better understanding of craniofacial phenotypes encompassing ethnicity may help improve OSA recognition and treatment; however, further studies are needed to elucidate ethnic differences in OSA anatomical risk factors.

A recent study of patients with moderate–severe obstructive sleep apnea (OSA) in Iceland identified three clinical clusters based on symptoms and comorbidities. We sought to verify this finding in a new cohort in Iceland and examine the generalizability of OSA clusters in an international ethnically diverse cohort. Using data on 972 patients with moderate–severe OSA (apnea–hypopnea index [AHI] ≥ 15 events per hour) recruited from the Sleep Apnea Global Interdisciplinary Consortium (SAGIC), we performed a latent class analysis of 18 self-reported symptom variables, hypertension, cardiovascular disease, and diabetes. The original OSA clusters of disturbed sleep, minimally symptomatic, and excessively sleepy replicated among 215 SAGIC patients from Iceland. These clusters also generalized to 757 patients from five other countries. The three clusters had similar average AHI values in both Iceland and the international samples, suggesting clusters are not driven by OSA severity; differences in age, gender, and body mass index were also generally small. Within the international sample, the three original clusters were expanded to five optimal clusters: three were similar to those in Iceland (labeled disturbed sleep, minimal symptoms, and upper airway symptoms with sleepiness) and two were new, less symptomatic clusters (labeled upper airway symptoms dominant and sleepiness dominant). The five clusters showed differences in demographics and AHI, although all were middle-aged (44.6–54.5 years), obese (30.6–35.9 kg/m²), and had severe OSA (42.0–51.4 events per hour) on average. Results confirm and extend previously identified clinical clusters in OSA. These clusters provide an opportunity for a more personalized approach to the management of OSA.