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.

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.

Long-term ambient air pollution exposure and prospective change in sedentary behaviour and physical activity in individuals at risk of type 2 diabetes in the UK.

BACKGROUND: Air pollution may be a risk factor for physical inactivity and sedentary behaviour (SED) through discouraging active lifestyles, impairing fitness and contributing to chronic diseases with potentially important consequences for population health. METHODS: Using generalized estimating equations, we examined the associations between long-term particulate matter with diameter ≤2.5 µm (PM2.5), ≤10 µm (PM10) and nitrogen dioxide (NO2) and annual change in accelerometer-measured SED, moderate-to-vigorous physical activity (MVPA) and steps in adults at risk of type 2 diabetes within the Walking Away from Type 2 Diabetes trial. We adjusted for important confounders including social deprivation and measures of the built environment. RESULTS: From 808 participants, 644 had complete data (1605 observations; 64.7% men; mean age 63.86 years). PM2.5, NO2 and PM10 were not associated with change in MVPA/steps but were associated with change in SED, with a 1 ugm-3 increase associated with 6.38 (95% confidence interval: 0.77, 12.00), 1.52 (0.49, 2.54) and 4.48 (0.63, 8.34) adjusted annual change in daily minutes, respectively. CONCLUSIONS: Long-term PM2.5, NO2 and PM10 exposures were associated with an annual increase in SED: ~11-22 min/day per year across the sample range of exposure (three standard deviations). Future research should investigate whether interventions to reduce pollution may influence SED.

Capturing How the Accelerometer Measured Physical Activity Profile Differs in People with Diabetic Foot Ulceration.

Diabetic Foot Ulcers (DFUs) are a major complication of diabetes, with treatment requiring offloading. This study aimed to capture how the accelerometer-assessed physical activity profile differs in those with DFUs compared to those with diabetes but without ulceration (non-DFU). Participants were requested to wear an accelerometer on their non-dominant wrist for up to 8days. Physical activity outcomes included average acceleration (volume), intensity gradient (intensity distribution), the intensity of the most active sustained (continuous) 5-120 min of activity (MXCONT), and accumulated 5-120 min of activity (MXACC). A total of 595 participants (non-DFU = 561, DFU = 34) were included in the analysis. Average acceleration was lower in DFU participants compared to non-DFU participants (21.9 mg [95%CI:21.2, 22.7] vs. 16.9 mg [15.3, 18.8], p < 0.001). DFU participants also had a lower intensity gradient, indicating proportionally less time spent in higher-intensity activities. The relative difference between DFU and non-DFU participants was greater for sustained activity (MXCONT) than for accumulated (MXACC) activity. In conclusion, physical activity, particularly the intensity of sustained activity, is lower in those with DFUs compared to non-DFUs. This highlights the need for safe, offloaded modes of activity that contribute to an active lifestyle for people with DFUs.

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.

Cost-effectiveness analysis of two interventions to promote physical activity in a multiethnic population at high risk of diabetes: an economic evaluation of the 48-month PROPELS randomized controlled trial.

INTRODUCTION: Physical activity (PA) is protective against type 2 diabetes (T2D). However, data on pragmatic long-term interventions to reduce the risk of developing T2D via increased PA are lacking. This study investigated the cost-effectiveness of a pragmatic PA intervention in a multiethnic population at high risk of T2D. MATERIALS AND METHODS: We adapted the School for Public Health Research diabetes prevention model, using the PROPELS trial data and analyses of the NAVIGATOR trial. Lifetime costs, lifetime quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs) were calculated for each intervention (Walking Away (WA) and Walking Away Plus (WA+)) versus usual care and compared with National Institute for Health and Care Excellence's willingness-to-pay of £20 000-£30 000 per QALY gained. We conducted scenario analyses on the outcomes of the PROPELS trial data and a threshold analysis to determine the change in step count that would be needed for the interventions to be cost-effective. RESULTS: Estimated lifetime costs for usual care, WA, and WA+ were £22 598, £23 018, and £22 945, respectively. Estimated QALYs were 9.323, 9.312, and 9.330, respectively. WA+ was estimated to be more effective and cheaper than WA. WA+ had an ICER of £49 273 per QALY gained versus usual care. In none of our scenario analyses did either WA or WA+ have an ICER below £20 000 per QALY gained. Our threshold analysis suggested that a PA intervention costing the same as WA+ would have an ICER below £20 000/QALY if it were to achieve an increase in step count of 500 steps per day which was 100% maintained at 4 years. CONCLUSIONS: We found that neither WA nor WA+ was cost-effective at a limit of £20 000 per QALY gained. Our threshold analysis showed that interventions to increase step count can be cost-effective at this limit if they achieve greater long-term maintenance of effect. TRIAL REGISTRATION NUMBER: ISRCTN registration: ISRCTN83465245: The PRomotion Of Physical activity through structuredEducation with differing Levels of ongoing Support for those with pre-diabetes (PROPELS)https://doi.org/10.1186/ISRCTN83465245.

The association of smoking with different eating and dietary behaviours: A cross-sectional analysis of 80 296 United Kingdom adults.

BACKGROUND AND AIMS: Smokers typically have a lower body mass index (BMI) than non-smokers, while smoking cessation is associated with weight gain. In pre-clinical research, nicotine in tobacco smoking suppresses appetite and influences subsequent eating behaviour; however, this relationship is unclear in humans. This study measured the associations of smoking with different eating and dietary behaviours. DESIGN: A cross-sectional analysis of data from health assessments conducted between 2004 and 2022. SETTING: An independent healthcare-based charity within the United Kingdom. PARTICIPANTS: A total of 80 296 men and women (mean ± standard deviation [SD]: age, 43.0 ± 10.4 years; BMI, 25.7 ± 4.2 kg/m2; 62.5% male) stratified into two groups based on their status as a smoker (n = 6042; 7.5%) or non-smoker (n = 74 254; 92.5%). MEASUREMENTS: Smoking status (self-report) was the main exposure, while the primary outcomes were selected eating and dietary behaviours. Age, sex and socioeconomic status (index of multiple deprivation [IMD]) were included as covariates and interaction terms, while moderate-to-vigorous exercise and sleep quality were included as covariates only. FINDINGS: Smokers had lower odds of snacking between meals and eating food as a reward or out of boredom versus non-smokers (all odds ratio [OR] ≤ 0.82; P < 0.001). Furthermore, smokers had higher odds of skipping meals, going more than 3 h without food, adding salt and sugar to their food, overeating and finding it hard to leave something on their plate versus non-smokers (all OR ≥ 1.06; P ≤ 0.030). Additionally, compared with non-smokers, smoking was associated with eating fried food more times per week (rate ratio [RR] = 1.08; P < 0.001), eating fewer meals per day, eating sweet foods between meals and eating dessert on fewer days per week (all RR ≤ 0.93; P < 0.001). Several of these relationships were modified by age, sex and IMD. CONCLUSIONS: Smoking appears to be associated with eating and dietary behaviours consistent with inhibited food intake, low diet quality and altered food preference. Several of these relationships are moderated by age, sex and socioeconomic status.