Recent Submissions

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

    Davies, Melanie J; Hall, Andrew P; Henson, Joseph (2024-01-08)
    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.
  • Validation of an automated sleep detection algorithm using data from multiple accelerometer brands

    Hall, Andrew P (2022-10-31)
    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.
  • 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 (2022-04-15)
    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.
  • Sleep extension and metabolic health in male overweight/obese short sleepers: A randomised controlled trial

    Hall, Andrew P (2021-08-29)
    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: NCT04467268.
  • Age at diagnosis of type 2 diabetes and cardiovascular risk factor profile: A pooled analysis

    Zaccardi, Francesco; Brady, Emer M; Gulsin, Gaurav S; Hall, Andrew P; Henson, Joseph; Khunti, Kamlesh; McCann, Gerry; Redman, Emma; Webb, David; Yeo, Jian; et al.
    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.
  • Device-measured physical activity and its association with physical function in adults with type 2 diabetes mellitus

    Mickute, Monika; Henson, Joseph; Sargeant, Jack; Webb, David; Hall, Andrew P; Brady, Emer M; Khunti, Kamlesh; Davies, Melanie (2020-08-26)
    Aim: To quantify how differences in metrics characterizing physical activity and sedentary behaviour in type 2 diabetes are associated with physical function. Methods: This analysis included participants' data from the Chronotype of Patients with Type 2 Diabetes and Effect on Glycaemic Control (CODEC) cross-sectional study. Data were stratified into two groups according to their short physical performance battery (SPPB) score (impaired physical function = SPPB < 10 and normal physical function = SPPB ≥ 10). Hand-grip strength, sit-to-stand 60 (STS-60) and the Duke Activity Status Index (DASI) score were used to assess functional capacity, while physical activity metrics were measured with a wrist-worn accelerometer. The associations between physical activity metrics and measures of functional capacity were analysed using generalized linear modelling. Results: Some 635 adults (median age 66 years, 34% female) were included in this analysis. Overall, 29% of the cohort scored < 10 in the SPPB test indicating impaired physical function. This group spent more time in prolonged sedentary behaviour (600.7 vs. 572.5 min) and undertook less-intense physical activity. Each sd increase in physical activity volume and intensity gradients for those with impaired physical function was associated with 17% more repetitions for STS-60 with similar associations seen for DASI score. Each sd in sedentary time was associated with 15% fewer repetitions in STS-60 and 16% lower DASI score in those with impaired physical function, whereas in normal physical function group it was 2% and 1%, respectively. Conclusions: The strength of the associations for physical activity measures and functional capacity were modified by physical function status, with the strongest association seen in those with impaired physical function.