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Supplemental material, sj-pdf-1-jrs-10.1177_01410768211073923 for Association between household size and COVID-19: A UK Biobank observational study by Clare L Gillies, Alex V Rowlands, Cameron Razieh, Vah�� Nafilyan, Yogini Chudasama, Nazrul Islam, Francesco Zaccardi, Daniel Ayoubkhani, Claire Lawson, Melanie J Davies, Tom Yates and Kamlesh Khunti in Journal of the Royal Society of Medicine

Additional file 1. This file includes additional tables and graphs that further the results of this study. Medcodes and ICD-10 code lists that were used to define variables, outcomes, and exposures in this study have also been included in this file.

Additional file 2: Figure S1. Continuum of CV risk in T2D. T2D is a risk factor for CVD, and several other risk factors are also often present in patients with T2D, as recognised by guidelines such as those of the ESC [38]. Glucose levels alone can be independently linked to progression of CAD [38]. While progression of cardiac disease is thus a feature of T2D, it may in some cases go undetected due to atypical symptom presentation or so-called ‘silent’ manifestations [152, 153], in the proposed ‘unrecognised diabetic cardiac impairment’ phenomenon [48]. Ultimately, overt CVD or heart failure may develop, both of which are prevalent among people living with T2D [4, 48, 152]. CAD, coronary artery disease; CVD, cardiovascular disease; ESC, European Society of Cardiology; MACE, major adverse cardiovascular events; T2D, type 2 diabetes. Figure S2. What to expect next from CVOT-related research. The results of CVOTs have raised several questions that are now being addressed in clinical and scientific studies, chief among which is: how do glucose-lowering drugs produce glucose-independent beneficial effects on cardiorenal outcomes? CVOT, cardiovascular outcomes trial; GLP-1, glucagon like peptide-1; GLP-1 RA, GLP-1 receptor agonist; SGLT2, sodium–glucose transporter 2.

Supplemental material, sj-pdf-1-jrs-10.1177_01410768211073923 for Association between household size and COVID-19: A UK Biobank observational study by Clare L Gillies, Alex V Rowlands, Cameron Razieh, Vah�� Nafilyan, Yogini Chudasama, Nazrul Islam, Francesco Zaccardi, Daniel Ayoubkhani, Claire Lawson, Melanie J Davies, Tom Yates and Kamlesh Khunti in Journal of the Royal Society of Medicine

Additional file 1: Table S1. Percentages of successful ambulatory blood pressure recordings. Table S2. Intra- and interobserver variability for E/A and E/e'. Table S3. Univariable correlation of blood pressure with MRI and echocardiographic parameters in T2D.

Additional file 2. Supplementary material.

Additional file 1: Figure S1. Flow chart of participants included in the UK Biobank Study. Table S1. Literature search on the most common pre-existing comorbidities in patients with severe SARS-CoV-2 infection. Table S2. Association between multimorbidity index using 3 or more conditions and risk of severe SARS-CoV-2 infection. Table S3. Sensitivity analyses using vitamin D levels at follow-up and last recorded air pollution levels. Table S4. Sensitivity analyses considering time in the study and removing cardiorespiratory fitness and C-reactive protein from the model. Checklist S1. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE).

Additional file 1: Supplementary Table S1. Rates of obesity and chronic disease by age group. Supplementary Table S2. Hazard ratio estimates used to draw Fig. 3. Supplementary Figure S1. Hazard ratios for each strata when those discharged into palliative care were included with in-hospital mortality as a composite outcome. Supplementary Figure S2. Hazard ratios for each disease and obesity analysed individually.

Additional file 4:. Per-protocol and multiple imputations results for the primary outcome.

Additional file 7:. Serious and non-serious adverse events.

Additional file 6:. Baseline value with 12- and 48-month intervention effect for secondary outcomes.

Additional file 1:. Characteristics of those with complete and missing primary outcome data by group.

Additional file 5:. Sub-group analysis testing whether intervention effect at 48-months for primary outcome is modified by key characteristics.

Additional file 3:. Self-efficacy and illness perception scores at baseline and follow-up by group.

Additional file 1. EDI Rapid Review Paper Author Responses

Additional file 2:. Use of behaviour change techniques at follow-up.

Additional file 3:Table S2. Adherence to hypertensives at 12 months.

Additional file 7:Table S6. Baseline characteristics of completers versus non-completers at 12 months.

Additional file 1. Focus group guides.

Additional file 5:Table S4. Process outcomes.

Additional file 2:Table S1. Baseline questionnaire data.

Additional file 4:Table S3. Questionnaires outcomes.

Additional file 1:Fig. S1. CONSORT checklist.

Additional file 6:Table S5. Baseline characteristics of participants between those with and without a urine measurement at 12 months.

Background Traditionally Type 2 Diabetes Mellitus (T2DM) was associated with older age, but is now being increasingly diagnosed in younger populations due to the increasing prevalence of obesity and inactivity. We aimed to evaluate whether a tool developed for community use to identify adolescents at high lifetime risk of developing T2DM agreed with a risk assessment conducted by a clinician using data collected from five European countries. We also assessed whether the tool could be simplified. Methods To evaluate the tool we collected data from 636 adolescents aged 12–14 years from five European countries. Each participant’s data were then assessed by two clinicians independently, who judged each participant to be at either low or high risk of developing T2DM in their lifetime. This was used as the gold standard to which the tool was evaluated and refined. Results The refined tool categorised adolescents at high risk if they were overweight/obese and had at least one other risk factor (High waist circumference, family history of diabetes, parental obesity, not breast fed, high sugar intake, high screen time, low physical activity and low fruit and vegetable intake). Of those found to be at high risk by the clinicians, 93% were also deemed high risk by the tool. The specificity shows that 67% of those deemed at low risk by the clinicians were also found to be a low risk by the tool. Conclusions We have evaluated a tool for identifying adolescents with risk factors associated with the development of T2DM in the future. Future work to externally validate the tool using prospective data including T2DM incidence is required. Electronic supplementary material The online version of this article (10.1186/s12902-019-0410-3) contains supplementary material, which is available to authorized users.

Supplemental material, Online_Only_Supplemental_Material_TAE for Differential left ventricular and left atrial remodelling in heart failure with preserved ejection fraction patients with and without diabetes by Gaurav S. Gulsin, Prathap Kanagala, Daniel C. S. Chan, Adrian S. H. Cheng, Lavanya Athithan, Matthew P. M. Graham-Brown, Anvesha Singh, Jing Yang, Zhuyin Li, Kamlesh Khunti, Melanie J. Davies, Jayanth R. Arnold, Iain B. Squire, Leong L. Ng and Gerry P. McCann in Therapeutic Advances in Endocrinology and Metabolism

Background: Identifying clusters of physical activity (PA) from accelerometer data is important to identify levels of sedentary behaviour and physical activity associated with risks of serious health conditions and time spent engaging in healthy PA. Unsupervised machine learning models can capture PA in everyday free-living activity without the need for labelled data. However, there is scant research addressing the selection of features from accelerometer data. The aim of this systematic review is to summarise feature selection techniques applied in studies concerned with unsupervised machine learning of accelerometer-based device obtained physical activity, and to identify commonly used features identified through these techniques. Feature selection methods can reduce the complexity and computational burden of these models by removing less important features and assist in understanding the relative importance of feature sets and individual features in clustering. Method: We conducted a systematic search of Pubmed, Medline, Google Scholar, Scopus, Arxiv and Web of Science databases to identify studies published before January 2021 which used feature selection methods to derive PA clusters using unsupervised machine learning models. Results: A total of 13 studies were eligible for inclusion within the review. The most popular feature selection techniques were Principal Component Analysis (PCA) and correlation-based methods, with k-means frequently used in clustering accelerometer data. Cluster quality evaluation methods were diverse, including both external(e.g. cluster purity) or internal evaluation measures (silhouette score most frequently). Only four of the 13studies had more than 25 participants and only four studies included two or more datasets. Conclusion: There is a need to assess multiple feature selection methods upon large cohort data consisting of multiple (3 or more) PA datasets. The cut-off criteria e.g. number of components, pairwise correlation value, explained variance ratio for PCA, etc. should be expressly stated along with any hyperparameters used in clustering. Refereed/Peer-reviewed

This study aimed to a) determine whether wrist acceleration varies by accelerometer brand, wear location, and age for self-paced “slow”, “normal” and “brisk” walking; b) develop normative acceleration values for self-paced walking and running for adults. One-hundred-and-three adults (40–79 years) completed self-paced “slow”, “normal” and “brisk” walks, while wearing three accelerometers (GENEActiv, Axivity, ActiGraph) on each wrist. A sub-sample (n = 22) completed a self-paced run. Generalized estimating equations established differences by accelerometer brand, wrist, and age-group (walking only, 40–49, 50–59, 60–69, 70–79 years) for self-paced walking and running. Brand*wrist interactions showed ActiGraph dominant wrist values were ~10% lower than GENEActiv/Axivity values for walking and running, and non-dominant ActiGraph values were ~5% lower for running only (p < 0.001). Acceleration during brisk walking was lower in those aged 70–79 (p < 0.05). Normative acceleration values (non-dominant wrist, all brands; dominant wrist GENEActiv/Axivity) for slow and normal walking were 140 mg and 210 mg. Brisk walking, values were 350 mg in those aged 40–69 years, but 270 mg in those aged 70–79 years. Accelerations >600 mg approximated running. These values facilitate user-friendly interpretation of accelerometer-determined physical activity in large cohort and epidemiological datasets. Refereed/Peer-reviewed

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). Results413 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. Refereed/Peer-reviewed

IntroductionLow 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 methodsThis 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.ResultsSignificant 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.ConclusionsDaytime 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.

Purpose This study aimed to determine the cross-sectional and cumulative compliance of adolescent girls to accelerometer wear at three deployment points and to identify variables associated with compliance. Methods Girls from 20 secondary schools were recruited: 10 schools were participating in the "Girls Active" intervention and 10 were control schools. Physical activity was measured using the GENEActiv accelerometer worn on the nondominant wrist 24 h·d⁻¹ for up to 7 d at baseline, 7 months, and 14 months. Demographic and anthropometric characteristics were recorded. Results Seven valid days (≥16 h) of accelerometer wear was obtained from 83%, 77%, and 68% of girls at baseline (n = 1734), 7 months (n = 1381), and 14 months (n = 1326), respectively. Sixty-eight percent provided 7 valid days for both baseline and 7 months, 59% for baseline and 14 months, and 52% for all three deployment points. Estimates of physical activity level from 3 d of measurement could be considered equivalent to a 7-d measure (i.e., they fell within a ±5% equivalence zone). Cross sectionally, 3 valid days was obtained from at least 91% of girls; cumulatively, this was obtained from ≥88% of girls across any two deployment points and 84% of girls across all three deployment points. When controlling for clustering at school level and other potential predictors, physical activity level, being South Asian, being in the intervention group, and prior compliance were positively associated with monitor wear. Conclusions Compliance reduced across deployment points, with the reduction increasing as the deployment points got further apart. High prior compliance and high physical activity level were associated with the most additional wear time. Refereed/Peer-reviewed

BACKGROUND: Objectively measured physical activity (PA) intensity has traditionally been categorised as light, moderate and vigorous using laboratory calibrated cut-points. The relative contribution of time spent across a spectrum of accelerometer-determined intensities on health outcomes is less clear. PURPOSE: To assess the relationship between objectively measured PA intensity on a continuous scale and markers of insulin sensitivity. METHODS: Participants at high risk of type 2 diabetes were recruited from primary care (Leicestershire, UK). PA was measured using an ActiGraph accelerometer. Fasting and post-challenge glucose and insulin levels were assessed using an oral glucose tolerance test. Insulin sensitivity (IS) was calculated using the Matsuda-IS and HOMA-IS indices. Log-linear regression modelling was used to assess the relationship between PA intensity, in 500 count per min (cpm) increments, with markers of IS. Models were controlled for known confounders. RESULTS: Complete data were available for 569 participants. PA intensity was favourably associated with fasting and 2 hour insulin and IS, with the association increasing in magnitude with each 500 cpm increment. Differences in HOMA-IS per 10 min of PA ranged from 12.4% (95% confidence intervals 3.7, 21.8%) to 26.8% (11.0, 44.7%) within the moderate-intensity PA category (from 2000-2499 cpm to 3500-3999 cpm). For Matsuda IS, these differences were 22.0% (10.3, 34.9%) and 34.7% (13.9, 59.3%) respectively. Significant associations for fasting insulin were no longer observed after controlling for BMI, whereas differences associated with 2 hour insulin and IS were attenuated but still significant. CONCLUSION: PA of any intensity may positively influence glucose regulation and insulin sensitivity in individuals at high risk of T2DM in a dose-response manner. Further research is required to identify the intensity thresholds at which clinically relevant benefits occur in this population. Refereed/Peer-reviewed

Background Approximately 425 million people globally have diabetes, with ~ 90% of these having Type 2 Diabetes Mellitus (T2DM). This is a condition that leads to a poor quality of life and increased risk of serious health complications. Structured self-management education (SSME) has been shown to be effective in improving glycaemic control and patient related outcome measures and to be cost-effective. However, despite the demonstrated benefits, attendance at SSME remains low. An intervention has been developed to embed SSME called the ‘Embedding Package’. The intervention aims to address barriers and enhance enablers to uptake of SSME at patient, healthcare professional and organisational levels. It comprises a marketing strategy, user friendly and effective referral pathways, new roles to champion SSME and a toolkit of resources. Methods A mixed methods study incorporating a wait-list cluster randomised trial and ethnographic study, including 66 UK general practices, will be conducted with two intervention start times (at 0 and 9 months), each followed by an active delivery phase. At 18 months, the intervention will cease to be actively delivered and a 12 month observational follow-up phase will begin. The intervention, the Embedding Package, aims to increase SSME uptake and subsequent improvements in health outcomes, through a clear marketing strategy, user friendly and effective referral pathways, a local clinical champion and an ‘Embedder’ and a toolkit of resources for patients, healthcare professionals and other key stakeholders. The primary aim is, through increasing uptake to and attendance at SSME, to reduce HbA1c in people with T2DM compared with usual care. Secondary objectives include: assessing whether there is an increase in referral to and uptake of SSME and improvements in biomedical and psychosocial outcomes; an assessment of the sustainability of the Embedding Package; contextualising the process of implementation, sustainability of change and the ‘fit’ of the Embedding Package; and an assessment of the cost-effectiveness of the Embedding Package. Discussion This study will assess the effectiveness, cost-effectiveness and sustainability of the Embedding Package, an intervention which aims to improve biomedical and psychosocial outcomes of people with T2DM, through increased referral to and uptake of SSME. Trial registration International Standard Randomised Controlled Trials Number ISRCTN23474120. Assigned 05/04/2018. The study was prospectively registered. On submission of this manuscript practice recruitment is complete, participant recruitment is ongoing and expected to be completed by the end of 2019.

Background Multimorbidity is an emerging public health priority. Physical activity (PA) is recommended as one of the main lifestyle behaviours, yet the benefits of PA for people with multimorbidity are unclear. We assessed the benefits of PA on mortality and life expectancy in people with and without multimorbidity. Methods Using the UK Biobank dataset, we extracted data on 36 chronic conditions and defined multimorbidity as (a) 2 or more conditions, (b) 2 or more conditions combined with self-reported overall health, and (c) 2 or more top-10 most common comorbidities. Leisure-time PA (LTPA) and total PA (TPA) were measured by questionnaire and categorised as low (