Sleep
http://hdl.handle.net/20.500.12904/191
2024-03-28T12:51:55ZTwenty-four-hour physical behaviour profiles across type 2 diabetes mellitus subtypes
http://hdl.handle.net/20.500.12904/18102
Twenty-four-hour physical behaviour profiles across type 2 diabetes mellitus subtypes
Davies, Melanie J; Hall, Andrew P; Henson, Joseph
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.
2024-01-08T00:00:00ZValidation of an automated sleep detection algorithm using data from multiple accelerometer brands
http://hdl.handle.net/20.500.12904/16869
Validation of an automated sleep detection algorithm using data from multiple accelerometer brands
Hall, Andrew P
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.
2022-10-31T00:00:00ZRelative protein intake and associations with markers of physical function in those with type 2 diabetes
http://hdl.handle.net/20.500.12904/16711
Relative protein intake and associations with markers of physical function in those with type 2 diabetes
Henson, Joseph; Arsenyadis, Franciskos; Redman, Emma; Brady, Emer M; Coull, Nicole; Hall, Andrew P; Khunti, Kamlesh; Davies, Melanie
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.
2022-04-15T00:00:00ZSleep extension and metabolic health in male overweight/obese short sleepers: A randomised controlled trial
http://hdl.handle.net/20.500.12904/16647
Sleep extension and metabolic health in male overweight/obese short sleepers: A randomised controlled trial
Hall, Andrew P
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. Trial registration: ClinicalTrials.gov NCT04467268.
2021-08-29T00:00:00Z