Sarcopenia prevalence using handgrip strength or chair stand performance in adults living with type 2 diabetes mellitus.

BACKGROUND: The updated European Working Group on Sarcopenia in Older People (EWGSOP2) recommends handgrip strength (HGS) and the chair stand test (CST) to assess muscle strength, with the CST being a convenient proxy for lower limb strength. However, adiposity may differentially influence these strength criteria and produce discrepant sarcopenia prevalence. OBJECTIVE: To determine the prevalence of sarcopenia using HGS or the CST, and to investigate the associations between these strength criteria and adiposity in adults with type 2 diabetes mellitus. METHODS: The EWGSOP2 definition was used to assess the prevalence of probable (low muscle strength), confirmed (plus low muscle mass) and severe (plus poor physical performance) sarcopenia. Linear regression models were used to study the association between different measures of muscle strength and adiposity. RESULTS: We used data from 732 adults with type 2 diabetes mellitus (35.7% female, aged 64 ± 8 years, body mass index 30.7 ± 5.0 kg/m2). Using the CST compared with HGS produced a higher prevalence of probable (31.7% vs. 7.1%), confirmed (5.6% vs. 1.6%) and severe (1.0% vs. 0.3%) sarcopenia, with poor agreement between strength criteria to identify probable sarcopenia. CST performance, but not HGS, was significantly associated with all measures of adiposity in unadjusted and adjusted models. CONCLUSIONS: Higher levels of adiposity may impact CST performance, but not HGS, resulting in a higher prevalence of sarcopenia in adults with type 2 diabetes mellitus. Consideration should be paid to the most appropriate measure of muscle function in this population.

Waking Up to the Importance of Sleep in Type 2 Diabetes Management: A Narrative Review.

For the first time, the latest American Diabetes Association/European Association for the Study of Diabetes (ADA/EASD) consensus guidelines have incorporated a growing body of evidence linking health outcomes associated with type 2 diabetes to the movement behavior composition over the whole 24-h day. Of particular note, the importance of sleep as a key lifestyle component in the management of type 2 diabetes is promulgated and presented using three key constructs: quantity, quality, and timing (i.e., chronotype). In this narrative review we highlight some of the key evidence justifying the inclusion of sleep in the latest consensus guidelines by examining the associations of quantity, quality, and timing of sleep with measures of glycemia, cardiovascular disease risk, and mortality. We also consider potential mechanisms implicated in the association between sleep and type 2 diabetes and provide practical advice for health care professionals about initiating conversations pertaining to sleep in clinical care. In particular, we emphasize the importance of measuring sleep in a free-living environment and provide a summary of the different methodologies and targets. In summary, although the latest ADA/EASD consensus report highlights sleep as a central component in the management of type 2 diabetes, placing it, for the first time, on a level playing field with other lifestyle behaviors (e.g., physical activity and diet), the evidence base for improving sleep (beyond sleep disorders) in those living with type 2 diabetes is limited. This review should act as a timely reminder to incorporate sleep into clinical consultations, ongoing diabetes education, and future interventions.

Twenty-four-hour physical behaviour profiles across type 2 diabetes mellitus subtypes.

AIM: To investigate how 24-h physical behaviours differ across type 2 diabetes (T2DM) subtypes. MATERIALS AND METHODS: We included participants living with T2DM, enrolled as part of an ongoing observational study. Participants wore an accelerometer for 7 days to quantify physical behaviours across 24 h. We used routinely collected clinical data (age at onset of diabetes, glycated haemoglobin level, homeostatic model assessment index of beta-cell function, homeostatic model assessment index of insulin resistance, body mass index) to replicate four previously identified subtypes (insulin-deficient diabetes [INS-D], insulin-resistant diabetes [INS-R], obesity-related diabetes [OB] and age-related diabetes [AGE]), via k-means clustering. Differences in physical behaviours across the diabetes subtypes were assessed using generalized linear models, with the AGE cluster as the reference. RESULTS: A total of 564 participants were included in this analysis (mean age 63.6 ± 8.4 years, 37.6% female, mean age at diagnosis 53.1 ± 10.0 years). The proportions in each cluster were as follows: INS-D: n = 35, 6.2%; INS-R: n = 88, 15.6%; OB: n = 166, 29.4%; and AGE: n = 275, 48.8%. Compared to the AGE cluster, the OB cluster had a shorter sleep duration (-0.3 h; 95% confidence interval [CI] -0.5, -0.1), lower sleep efficiency (-2%; 95% CI -3, -1), lower total physical activity (-2.9 mg; 95% CI -4.3, -1.6) and less time in moderate-to-vigorous physical activity (-6.6 min; 95% CI -11.4, -1.7), alongside greater sleep variability (17.9 min; 95% CI 8.2, 27.7) and longer sedentary time (31.9 min; 95% CI 10.5, 53.2). Movement intensity during the most active continuous 10 and 30 min of the day was also lower in the OB cluster. CONCLUSIONS: In individuals living with T2DM, the OB subtype had the lowest levels of physical activity and least favourable sleep profiles. Such behaviours may be suitable targets for personalized therapeutic lifestyle interventions.

The potential blunting effect of metformin and/or statin therapy on physical activity-induced associations with HbA1c in type 2 diabetes.

Highlights Our analysis indicates a potential blunting effect of metformin and/or statin therapy on physical activity-induced associations with HbA1c. The benefit of daily physical activity on glycemic control in people with type 2 diabetes is potentially more apparent in those prescribed neither metformin nor statin therapy. As physical activity is rarely prescribed in isolation of other background medications used to manage type 2 diabetes, the results of this analysis may help to maximize interventions delivered through routine clinical care, while allowing for personalization in prescribed physical activity and pharmacotherapy.

Differences in Dietary Intake, Eating Occasion Timings and Eating Windows between Chronotypes in Adults Living with Type 2 Diabetes Mellitus.

Chronotype studies investigating dietary intake, eating occasions (EO) and eating windows (EW) are sparse in people with type 2 Diabetes mellitus (T2DM). This analysis reports data from the CODEC study. The Morningness-Eveningness questionnaire (MEQ) assessed chronotype preference. Diet diaries assessed dietary intake and temporal distribution. Regression analysis assessed whether dietary intake, EW, or EO differed by chronotype. 411 participants were included in this analysis. There were no differences in energy, macronutrient intake or EW between chronotypes. Compared to evening chronotypes, morning and intermediate chronotypes consumed 36.8 (95% CI: 11.1, 62.5) and 20.9 (95% CI: -2.1, 44.1) fewer milligrams of caffeine per day, respectively. Evening chronotypes woke up over an hour and a half later than morning (01:36 95% CI: 01:09, 02:03) and over half an hour later than intermediate chronotypes (00:45 95% CI: 00:21; 01:09. Evening chronotypes went to sleep over an hour and a half later than morning (01:48 95% CI: 01:23; 02:13) and an hour later than intermediate chronotypes (01:07 95% CI: 00:45; 01:30). Evening chronotypes' EOs and last caffeine intake occurred later but relative to their sleep timings. Future research should investigate the impact of chronotype and dietary temporal distribution on glucose control to optimise T2DM interventions.

Accelerometer-Assessed Physical Activity in People with Type 2 Diabetes: Accounting for Sleep when Determining Associations with Markers of Health.

High physical activity levels during wake are beneficial for health, while high movement levels during sleep are detrimental to health. Our aim was to compare the associations of accelerometer-assessed physical activity and sleep disruption with adiposity and fitness using standardized and individualized wake and sleep windows. People (N = 609) with type 2 diabetes wore an accelerometer for up to 8 days. Waist circumference, body fat percentage, Short Physical Performance Battery (SPPB) test score, sit-to-stands, and resting heart rate were assessed. Physical activity was assessed via the average acceleration and intensity distribution (intensity gradient) over standardized (most active 16 continuous hours (M16h)) and individualized wake windows. Sleep disruption was assessed via the average acceleration over standardized (least active 8 continuous hours (L8h)) and individualized sleep windows. Average acceleration and intensity distribution during the wake window were beneficially associated with adiposity and fitness, while average acceleration during the sleep window was detrimentally associated with adiposity and fitness. Point estimates for the associations were slightly stronger for the standardized than for individualized wake/sleep windows. In conclusion, standardized wake and sleep windows may have stronger associations with health due to capturing variations in sleep durations across individuals, while individualized windows represent a purer measure of wake/sleep behaviors.

Age at Diagnosis of Type 2 Diabetes and Depressive Symptoms, Diabetes-Specific Distress, and Self-Compassion.

OBJECTIVE: To investigate the association between age at diagnosis of type 2 diabetes and depressive symptoms, diabetes-specific distress, and self-compassion among adults with type 2 diabetes. RESEARCH DESIGN AND METHODS: This analysis used data from the Chronotype of Patients with Type 2 Diabetes and Effect on Glycemic Control (CODEC) cross-sectional study. Information was collected on depressive symptoms, diabetes-specific distress, and self-compassion, measured using validated self-report questionnaires, in addition to sociodemographic and clinical data. Multivariable regression models, adjusted for diabetes duration, sex, ethnicity, deprivation status, prescription of antidepressants (selective serotonin reuptake inhibitors), and BMI were used to investigate the association between age at diagnosis of type 2 diabetes and each of the three psychological outcomes. RESULTS: A total of 706 participants were included; 64 (9.1%) were diagnosed with type 2 diabetes at <40 years, 422 (59.8%) between 40 and 59 years, and 220 (31.2%) at ≥60 years of age. After adjustment for key confounders, including diabetes duration, younger age at diagnosis was significantly associated with higher levels of depressive symptoms (ßadj: -0.18 [95% CI -0.25 to -0.10]; P < 0.01) and diabetes-specific distress (ßadj: -0.03 [95% CI -0.04 to -0.02]; P < 0.01) and lower levels of self-compassion (ßadj: 0.01 [95% CI 0.00 to 0.02]; P < 0.01). CONCLUSIONS: Diagnosis of type 2 diabetes at a younger age is associated with lower psychological well-being, suggesting the need for clinical vigilance and the availability of age-appropriate psychosocial support.

Validation of an automated sleep detection algorithm using data from multiple accelerometer brands.

To evaluate the criterion validity of an automated sleep detection algorithm applied to data from three research-grade accelerometers worn on each wrist with concurrent laboratory-based polysomnography (PSG). A total of 30 healthy volunteers (mean [SD] age 31.5 [7.2] years, body mass index 25.5 [3.7] kg/m2 ) wore an Axivity, GENEActiv and ActiGraph accelerometer on each wrist during a 1-night PSG assessment. Sleep estimates (sleep period time window [SPT-window], sleep duration, sleep onset and waking time, sleep efficiency, and wake after sleep onset [WASO]) were generated using the automated sleep detection algorithm within the open-source GGIR package. Agreement of sleep estimates from accelerometer data with PSG was determined using pairwise 95% equivalence tests (±10% equivalence zone), intraclass correlation coefficients (ICCs) with 95% confidence intervals and limits of agreement (LoA). Accelerometer-derived sleep estimates except for WASO were within the 10% equivalence zone of the PSG. Reliability between data from the accelerometers worn on either wrist and PSG was moderate for SPT-window duration (ICCs ≥ 0.65), sleep duration (ICCs ≥ 0.54), and sleep onset (ICCs ≥ 0.61), mostly good for waking time (ICCs ≥ 0.80), but poor for sleep efficiency (ICCs ≥ 0.08) and WASO (ICCs ≥ 0.08). The mean bias between all accelerometer-derived sleep estimates worn on either wrist and PSG were low; however, wide 95% LoA were observed for all sleep estimates, apart from waking time. The automated sleep detection algorithm applied to data from Axivity, GENEActiv and ActiGraph accelerometers, worn on either wrist, provides comparable measures to PSG for SPT-window and sleep duration, sleep onset and waking time, but a poor measure of wake during the sleep period.

Self-compassion, sleep quality and psychological well-being in type 2 diabetes: a cross-sectional study.

INTRODUCTION: Low self-compassion and poor sleep quality have been identified as potential key predictors of distress in type 2 diabetes (T2D). This study investigated relationships between sleep behaviors (sleep duration, social jetlag and daytime sleepiness), diabetes-related distress (DRD) and self-compassion in people with T2D. RESEARCH DESIGN AND METHODS: This cross-sectional study used data from 467 people with T2D derived from self-report questionnaires, accelerometer-assessed sleep measures and demographic information (clinicaltrials.gov registration: NCT02973412). All participants had a diagnosis of T2D and no comorbid sleep disorder (excluding obstructive sleep apnea). Hierarchical multiple regression and mediation analysis were used to quantify relationships between self-compassion, sleep variables and DRD. RESULTS: Significant predictors of DRD included two negative subscales of the Self-Compassion Scale (SCS), and daytime sleepiness. The 'overidentified' and 'isolation' SCS subscales were particularly important in predicting distress. Daytime sleepiness also partially mediated the influence of self-compassion on DRD, potentially through self-care around sleep. CONCLUSIONS: Daytime sleepiness and negative self-compassion have clear associations with DRD for people with T2D. The specific negative subscale outcomes suggest that strengthening individuals' ability to mindfully notice thoughts and experiences without becoming enmeshed in them, and reducing a sense of separateness and difference, might be key therapeutic targets for improving well-being in T2D. Psychological interventions should include approaches focused on reducing negative self-compassion and improving sleep behavior. Equally, reducing DRD may carry beneficial outcomes for sleep and self-compassion. Further work is however crucial to establish causation and long-term impact, and for development of relevant clinical resources.

Device-assessed sleep and physical activity in individuals recovering from a hospital admission for COVID-19: a multicentre study.

BACKGROUND: The number of individuals recovering from severe COVID-19 is increasing rapidly. However, little is known about physical behaviours that make up the 24-h cycle within these individuals. This study aimed to describe physical behaviours following hospital admission for COVID-19 at eight months post-discharge including associations with acute illness severity and ongoing symptoms. METHODS: One thousand seventy-seven patients with COVID-19 discharged from hospital between March and November 2020 were recruited. Using a 14-day wear protocol, wrist-worn accelerometers were sent to participants after a five-month follow-up assessment. Acute illness severity was assessed by the WHO clinical progression scale, and the severity of ongoing symptoms was assessed using four previously reported data-driven clinical recovery clusters. Two existing control populations of office workers and individuals with type 2 diabetes were comparators. RESULTS: Valid accelerometer data from 253 women and 462 men were included. Women engaged in a mean ± SD of 14.9 ± 14.7 min/day of moderate-to-vigorous physical activity (MVPA), with 12.1 ± 1.7 h/day spent inactive and 7.2 ± 1.1 h/day asleep. The values for men were 21.0 ± 22.3 and 12.6 ± 1.7 h /day and 6.9 ± 1.1 h/day, respectively. Over 60% of women and men did not have any days containing a 30-min bout of MVPA. Variability in sleep timing was approximately 2 h in men and women. More severe acute illness was associated with lower total activity and MVPA in recovery. The very severe recovery cluster was associated with fewer days/week containing continuous bouts of MVPA, longer total sleep time, and higher variability in sleep timing. Patients post-hospitalisation with COVID-19 had lower levels of physical activity, greater sleep variability, and lower sleep efficiency than a similarly aged cohort of office workers or those with type 2 diabetes. CONCLUSIONS: Those recovering from a hospital admission for COVID-19 have low levels of physical activity and disrupted patterns of sleep several months after discharge. Our comparative cohorts indicate that the long-term impact of COVID-19 on physical behaviours is significant.

Importance of Overall Activity and Intensity of Activity for Cardiometabolic Risk in Those with and Without a Chronic Disease.

INTRODUCTION: Higher levels of physical activity are associated with lower cardiometabolic risk. However, the relative contribution of overall activity and the intensity of activity are unclear. Our aim was to determine the relative contribution of overall activity and intensity distribution of activity to cardiometabolic risk in a cross-sectional analysis of apparently healthy office workers and in people with one or more chronic disease. METHODS: Clustered cardiometabolic risk score was calculated from mean arterial pressure, high-density lipoprotein cholesterol, triglycerides and HbA1c. Open-source software (GGIR) was used to generate average acceleration and intensity gradient from wrist-worn accelerometer data for two data sets: office-workers who did not have a self-reported medical condition ( n = 399, 70% women) and adults with one or more chronic disease ( n = 1137, 34% women). Multiple linear regression analyses were used to assess the relative contribution of overall activity and intensity of activity to cardiometabolic risk. RESULTS: When mutually adjusted, both overall activity and intensity of activity were independently associated with cardiometabolic risk in the healthy group ( P < 0.05). However, for the CD group, although mutually adjusted associations for average acceleration were significantly associated with cardiometabolic risk ( P < 0.001), intensity was not. In healthy individuals, cardiometabolic risk was lower in those with high overall activity and/or intensity of activity, and who also undertook at least 10 min brisk walking. In those with a chronic disease, risk was lower in those who undertook at least 60 min slow walking. CONCLUSIONS: These findings suggest interventions aiming to optimize cardiometabolic health in healthy adults could focus on increasing both intensity and amount of physical activity. However, in those with chronic disease, increasing the amount of activity undertaken, regardless of intensity, may be more appropriate.

Age at diagnosis of type 2 diabetes and cardiovascular risk factor profile: A pooled analysis.

BACKGROUND: The diagnosis of type 2 diabetes (T2D) in younger adults, an increasingly common public health issue, is associated with a higher risk of cardiovascular complications and mortality, which may be due to a more adverse cardiovascular risk profile in individuals diagnosed at a younger age. AIM: To investigate the association between age at diagnosis and the cardiovascular risk profile in adults with T2D. METHODS: A pooled dataset was used, comprised of data from five previous studies of adults with T2D, including 1409 participants of whom 196 were diagnosed with T2D under the age of 40 years. Anthropometric and blood biomarker measurements included body weight, body mass index (BMI), waist circumference, body fat percentage, glycaemic control (HbA1c), lipid profile and blood pressure. Univariable and multivariable linear regression models, adjusted for diabetes duration, sex, ethnicity and smoking status, were used to investigate the association between age at diagnosis and each cardiovascular risk factor. RESULTS: A higher proportion of participants diagnosed with T2D under the age of 40 were female, current smokers and treated with glucose-lowering medications, compared to participants diagnosed later in life. Participants diagnosed with T2D under the age of 40 also had higher body weight, BMI, waist circumference and body fat percentage, in addition to a more adverse lipid profile, compared to participants diagnosed at an older age. Modelling results showed that each one year reduction in age at diagnosis was significantly associated with 0.67 kg higher body weight [95% confidence interval (CI): 0.52-0.82 kg], 0.18 kg/m2 higher BMI (95%CI: 0.10-0.25) and 0.32 cm higher waist circumference (95%CI: 0.14-0.49), after adjustment for duration of diabetes and other confounders. Younger age at diagnosis was also significantly associated with higher HbA1c, total cholesterol, low-density lipoprotein cholesterol and triglycerides. CONCLUSION: The diagnosis of T2D earlier in life is associated with a worse cardiovascular risk factor profile, compared to those diagnosed later in life.

Relative protein intake and associations with markers of physical function in those with type 2 diabetes.

AIMS: To examine the independent associations between relative protein intake (g kg-1  day 1 ) and markers of physical function in those with type 2 diabetes, while also comparing with current guidelines for protein intake. METHODS: This analysis reports data from the ongoing Chronotype of Patients with Type 2 Diabetes and Effect on Glycaemic Control (CODEC) study. Functional assessments included: Short Physical Performance Battery (SPPB), 60 s sit-to-stand (STS-60), 4-m gait speed, time to rise from a chair (×5) and handgrip strength. Participants also completed a self-reported 4 day diet diary. Regression analyses assessed whether relative protein intake was associated with markers of physical function. Interaction terms assessed whether the associations were modified by sex, age, HbA1c or body mass index (BMI). RESULTS: 413 participants were included (mean ± SD:age = 65.0 ± 7.7 years, 33% female, BMI = 30.6 ± 5.1 kg/m2 ). The average total protein intake was 0.88 ± 0.31 g kg-1  day-1 . 33% of individuals failed to meet the reference nutrient intake for the United Kingdom (≥0.75 g kg-1  day-1 ), and 87% for European recommendations (≥1.2 g kg-1  day-1 ). After adjustment, each 0.5 g/kg of protein intake was associated with an 18.9% (95% CI: 2.3, 35.5) higher SPPB score, 22.7% (1.1, 44.3) more repetitions in STS-60, 21.1% (4.5, 37.7) faster gait speed and 33.2% (16.9, 49.5) lower chair rise time. There were no associations with handgrip strength or any interactions. CONCLUSIONS: Relative protein intake was positively associated with physical function outcomes, even after consideration of total energy intake. As a number of individuals were below the current guidelines, protein intake may be a modifiable factor of importance for people with type 2 diabetes.

Chronotype and well-being in adults with established type 2 diabetes: A cross-sectional study.

AIMS: 'Chronotype' describes an individual's sleep-wake schedule, and can be classified into morning, intermediate or evening types. Evening chronotype has been widely associated with increased cardiometabolic risk and mortality in people with type 2 diabetes. We explored associations between chronotype and markers of well-being in people with type 2 diabetes. METHODS: Participants of the 'Chronotype of Patients with Type 2 Diabetes and Effect on Glycaemic Control' (CODEC) observational study completed questionnaires to determine chronotype (Morningness-Eveningness Questionnaire, MEQ) and concurrent measures of well-being (Diabetes-related Distress scale, Patient Health Questionnaire-9 to measure depression, and Self-Compassion Scale), as a secondary endpoint of the study. Adjusted generalised linear models were used to compare well-being between chronotype subgroups in this cohort. RESULTS: Of the 808 individuals included in the CODEC study, from convenience sampling, 476 individuals completed the psychosocial questionnaire substudy. Of these, 67% (n = 321) were male, and 86% (n = 408) were white European. From the MEQ, 24% (n = 114) were morning chronotype, 24% (n = 113) were evening and 52% (n = 249) were intermediate chronotype. Diabetes-related distress was significantly higher in evening chronotypes (exponentiated adjusted coefficient = 1.18 (CI: 1.05-1.32)), compared to morning (padjusted  = 0.005) and intermediate chronotypes (padjusted  = 0.039). Similarly, depression was significantly higher in evening chronotypes (exponentiated adjusted coefficient = 1.84 (CI: 1.28-2.65)) compared to morning (padjusted  = 0.001) and intermediate chronotypes (padjusted  = 0.016). DISCUSSION: Evening chronotype in people with type 2 diabetes may be associated with higher levels of diabetes-related distress and depression. These findings warrant further investigation to establish causality and evidence-based interventions that negate the effects of evening chronotype in people with type 2 diabetes.

Sleep extension and metabolic health in male overweight/obese short sleepers: A randomised controlled trial.

While limited evidence suggests that longer sleep durations can improve metabolic health in habitual short sleepers, there is no consensus on how sustained sleep extension can be achieved. A total of 18 men (mean [SD] age 41 [ 9] years), who were overweight/obese (mean [SD] body mass index 30 [3] kg/m2 ) and short sleepers at increased risk of type 2 diabetes were randomised to a 6-week sleep-extension programme based on cognitive behavioural principles (n = 10) or a control (n = 8) group. The primary outcome was 6-week change in actigraphic total sleep time (TST). Fasting plasma insulin, insulin resistance (Homeostatic Model Assessment for Insulin Resistance [HOMA-IR]), blood pressure, appetite-related hormones from a mixed-meal tolerance test, and continuous glucose levels were also measured. Baseline to 6-week change in TST was greater in the sleep-extension group, at 79 (95% confidence interval [CI] 68.90, 88.05) versus 6 (95% CI -4.43, 16.99) min. Change in the sleep-extension and control groups respectively also showed: lower fasting insulin (-11.03 [95% CI -22.70, 0.65] versus 7.07 [95% CI -4.60, 18.74] pmol/L); lower systolic (-11.09 [95% CI -17.49, -4.69] versus 0.76 [95% CI -5.64, 7.15] mmHg) and diastolic blood pressure (-12.16 [95% CI -17.74, -6.59] versus 1.38 [95% CI -4.19, 6.96] mmHg); lower mean amplitude of glucose excursions (0.34 [95% CI -0.57, -0.12] versus 0.05 [95% CI -0.20, 0.30] mmol/L); lower fasting peptide YY levels (-18.25 [95%CI -41.90, 5.41] versus 21.88 [95% CI -1.78, 45.53] pg/ml), and improved HOMA-IR (-0.51 [95% CI -0.98, -0.03] versus 0.28 [95% CI -0.20, 0.76]). Our protocol increased TST and improved markers of metabolic health in male overweight/obese short sleepers.

Physical behaviors and chronotype in people with type 2 diabetes.

INTRODUCTION: Previous investigations have suggested that evening chronotypes may be more susceptible to obesity-related metabolic alterations. However, whether device-measured physical behaviors differ by chronotype in those with type 2 diabetes (T2DM) remains unknown. RESEARCH DESIGN AND METHODS: This analysis reports data from the ongoing Chronotype of Patients with Type 2 Diabetes and Effect on Glycaemic Control (CODEC) observational study. Eligible participants were recruited from both primary and secondary care settings in the Midlands area, UK. Participants were asked to wear an accelerometer (GENEActiv, ActivInsights, Kimbolton, UK) on their non-dominant wrist for 7 days to quantify different physical behaviors (sleep, sedentary, light, moderate-to-vigorous physical activity (MVPA), intensity gradient, average acceleration and the acceleration above which the most active continuous 2, 10, 30 and 60 min are accumulated). Chronotype preference (morning, intermediate or evening) was assessed using the Morningness-Eveningness Questionnaire. Multiple linear regression analyses assessed whether chronotype preference was associated with physical behaviors and their timing. Evening chronotypes were considered as the reference group. RESULTS: 635 participants were included (age=63.8±8.4 years, 34.6% female, body mass index=30.9±5.1 kg/m2). 25% (n=159) of the cohort were morning chronotypes, 52% (n=330) intermediate and 23% (n=146) evening chronotypes. Evening chronotypes had higher sedentary time (28.7 min/day, 95% CI 8.6 to 48.3) and lower MVPA levels (-9.7 min/day, -14.9 to -4.6) compared to morning chronotypes. The intensity of the most active continuous 2-60 min of the day, average acceleration and intensity gradient were lower in evening chronotypes. The timing of physical behaviors also differed across chronotypes, with evening chronotypes displaying a later sleep onset and consistently later physical activity time. CONCLUSIONS: People with T2DM lead a lifestyle characterized by sedentary behaviors and insufficient MVPA. This may be exacerbated in those with a preference for 'eveningness' (ie, go to bed late and get up late).

Rationale and design of a cross-sectional study to investigate and describe the chronotype of patients with type 2 diabetes and the effect on glycaemic control: the CODEC study.

INTRODUCTION: A person's chronotype is their entrained preference for sleep time within the 24 hours clock. It is described by the well-known concept of the 'lark' (early riser) and 'owl' (late sleeper). Evidence suggests that the 'owl' is metabolically disadvantaged due to the standard organisation of our society which favours the 'lark' and places physiological stresses on this chronotype. The aim of this study is to explore cardiometabolic health between the lark and owl in a population with an established metabolic condition - type 2 diabetes. METHODS: This cross-sectional, multisite study aims to recruit 2247 participants from both secondary and primary care settings. The primary objective is to compare glycaemic control between late and early chronotypes. Secondary objectives include determining if late-chronotype is associated with poorer cardiometabolic health and other lifestyle factors, including well-being, compared with early-chronotype; describing the prevalence of the five different chronotypes in this cohort and examining the trends in glycaemic control, cardiometabolic health, well-being and lifestyle factors across chronotype. ANALYSIS: The primary outcome (glycated haemoglobin (HbA1c)), linear regression analysis will compare HbA1c between early and late chronotypes, with and without adjustment for confounding variables. Chronotype will be modelled as a categorical variable with all five levels (from extreme-morning to extreme-late type), and as a continuous variable to calculate p for trend across the five categories. A number of models will be created; unadjusted through to adjusted with age, sex, ethnicity, body mass index, duration of diabetes, family history of diabetes, current medication and dietary habits. All secondary outcomes will be analysed using the same method. ETHICS: Ethical approval from the West Midlands - Black Country Research Ethics Committee (16/WM/0457). DISSEMINATION: The results will be disseminated through publication in peer-reviewed medical journal, relevant medical/health conferences and a summary report sent to patients. TRIAL REGISTRATION NUMBER: NCT02973412 (Pre-Results).

Sleep, sleep studies and sleep-disordered breathing: basic knowledge for the anesthesiologist.

PURPOSE OF REVIEW: To provide a basic understanding of sleep physiology, the pathophysiology of sleep-disordered breathing and the processes applied in undertaking and assessing sleep studies. RECENT FINDINGS: It has become increasingly apparent that obstructive sleep apnoea is associated with heightened perioperative risk. Furthermore, the condition still remains under-diagnosed in patients presenting for surgery. SUMMARY: This review describes the physiology of sleep including sleep stages, sleep monitoring, the normal hypnogram and investigation from simple overnight pulse oximetry to full polysomnography. The pathophysiology of sleep-disordered breathing is discussed; from simple snoring through obstructive sleep apnoea to obesity hypoventilation syndrome. The relationship to metabolic syndrome is explored.Salient points in the interpretation of sleep study reports are presented.

The impact of diet and lifestyle management strategies for obstructive sleep apnoea in adults: a systematic review and meta-analysis of randomised controlled trials.

PURPOSE: To systematically evaluate the impact of diet, exercise and lifestyle modification programmes on indices of obesity, Obstructive Sleep Apnoea (OSA) parameters and quality of life (QoL) in adults with OSA. METHODS: Electronic databases were searched to identify randomised controlled trials published in English with an intervention based on dietary weight loss, exercise and/or lifestyle programme in adults with OSA. Meta-analyses were conducted using random-effects models. RESULTS: Twelve studies met the inclusion criteria with nine comparing similar interventions. Diet and diet plus continuous positive airway pressure (CPAP) therapy were compared in three studies (n = 261), and intensive lifestyle programmes and routine care were compared in six studies (n = 483). Diet with CPAP therapy reduced weight by -2.64 kg (95 % Confidence Interval (CI) -3.98, -1.30, I (2) = 0 %) compared with diet alone. No differences were observed for QoL or Epworth Sleepiness Scale. A significant reduction in weight was seen in participants receiving an intensive lifestyle intervention of -5.65 kg (95 % CI -10.91, -0.40, I(2) = 95.7 %) compared with controls. Reductions were also observed for waist circumference (-5.80 cm, 95 % CI -8.64, -2.96, I(2) = 77.7 %), body mass index (BMI) (-2.33 kg/m(2), 95 % CI -3.41, -1.24, I(2) = 78.8 %) and the Apnoea Hypopnoea Index (AHI) (-4.55 events/h, 95 % CI -7.12, -1.98, I(2) = 54.4 %) but with high levels of heterogeneity. CONCLUSIONS: Intensive lifestyle management can significantly reduce obesity indices and improve AHI. Future research is required to investigate this effect due to a limited number of studies identified.