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

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.</p

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

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.</p

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

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.</p

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.</p

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

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 cardio-metabolic risk. However, the relative contribution of overall activity and the intensity of activity is unclear. Our aim was to determine the relative contribution of overall activity and intensity distribution of activity to cardio-metabolic risk in in a cross-sectional analysis of apparently healthy office workers and in people with one or more chronic disease. Methods  Clustered cardio-metabolic risk score was calculated from mean arterial pressure, HDL cholesterol, triglycerides and HbA1c. Open-source software (GGIR) was used to generate average acceleration and intensity gradient from wrist-worn accelerometer data for two datasets: office-workers who did not have a self-reported medical condition (N = 399, 70% women) and adults with ≥1 chronic disease (N = 1,137, 34% women). Multiple linear regression analyses were used to assess the relative contribution of overall activity and intensity of activity to cardio-metabolic risk. Results  When mutually adjusted, both overall activity and intensity of activity were independently associated with cardio-metabolic risk in the healthy group (p Conclusions  These findings suggest interventions aiming to optimise cardio-metabolic 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.</p

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