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OBJECTIVES: FRAX\u00ae uses clinical risk factors, with or without bone mineral density (BMD), to calculate 10-year fracture risk. Rheumatoid arthritis (RA) is a risk factor for osteoporotic fracture and a FRAX input variable. FRAX predates the current era of RA treatment. We examined how well FRAX predicts fracture in contemporary RA patients. METHODS: Administrative data from patients receiving BMD testing were linked to the Manitoba Population Health Research Data Repository. Observed cumulative 10-year Major Osteoporotic Fracture (MOF) probability was compared with FRAX-predicted 10-year MOF probability with BMD for assessing calibration. MOF risk stratification was assessed using Cox regression. RESULTS: RA patients (N\u2009=\u20092,099, 208 with incident MOF) and non-RA patients (N\u2009=\u20092,099, with 165 incident MOF) were identified. For RA patients, FRAX predicted 10-year risk was 13.2% and observed 10-year MOF risk was 13.2% (95% CI 11.6% to 15.1%). The slope of the calibration plot was 0.67 (95% CI 0.53-0. 81) in those with RA vs 0.98 (95% CI 0.61-1.34) in non-RA patients. Risk was overestimated in RA patients with high FRAX scores (>20%), but FRAX was well-calibrated in other groups. FRAX stratified risk in those with and without RA (hazard ratios 1.52, 95% 1.25-1.72 vs 2.00, 95% 1.73-2.31), with slightly better performance in the latter (p-interaction\u2009=\u20090.004). CONCLUSIONS: FRAX predicts fracture risk in contemporary RA patients but may slightly overestimate risk in those already at high predicted risk. Thus, the current FRAX tool continues to be appropriate for fracture risk assessment in RA patients.
\n \n\n \n \nBackground: Tobacco smoking is estimated to account for more than 1 million annual deaths in China, and the epidemic continues to increase in men. Large nationwide prospective studies linked to different health records can help to periodically assess disease burden attributed to smoking. We aimed to examine associations of smoking with incidence of and mortality from an extensive range of diseases in China. Methods: We analysed data from the prospective China Kadoorie Biobank, which recruited 512 726 adults aged 30\u201379 years, of whom 210 201 were men and 302 525 were women. Participants who had no major disabilities were identified through local residential records in 100\u2013150 administrative units, which were randomly selected by use of multistage cluster sampling, from each of the ten diverse study areas of China. They were invited and recruited between June 25, 2004, and July 15, 2008. Upon study entry, trained health workers administered a questionnaire assessing detailed smoking behaviours and other key characteristics (eg, sociodemographics, lifestyle, and medical history). Participants were followed up via electronic record linkages to death and disease registries and health insurance databases, from baseline to Jan 1, 2018. During a median 11-year follow-up (IQR 10\u201312), 285 542 (55\u00b77%) participants were ever hospitalised, 48 869 (9\u00b75%) died, and 5252 (1\u00b70%) were lost to follow-up during the age-at-risk of 35\u201384 years. Cox regression yielded hazard ratios (HRs) associating smoking with disease incidence and mortality, adjusting for multiple testing. Findings: At baseline, 74\u00b73% of men and 3\u00b72% of women (overall 32\u00b74%) ever smoked regularly. During follow-up, 1 137 603 International Classification of Diseases, 10th revision (ICD-10)-coded incident events occurred, involving 476 distinct conditions and 85 causes of death, each with at least 100 cases. Compared with never-regular smokers, ever-regular smokers had significantly higher risks for nine of 18 ICD-10 chapters examined at age-at-risk of 35\u201384 years. For individual conditions, smokers had significantly higher risks of 56 diseases (50 for men and 24 for women) and 22 causes of death (17 for men and nine for women). Among men, ever-regular smokers had an HR of 1\u00b709 (95% CI 1\u00b708\u20131\u00b711) for any disease incidence when compared with never-regular smokers, and significantly more episodes and longer duration of hospitalisation, particularly those due to cancer and respiratory diseases. For overall mortality, the HRs were greater in men from urban areas than in men from rural areas (1\u00b750 [1\u00b742\u20131\u00b758] vs 1\u00b725 [1\u00b720\u20131\u00b730]). Among men from urban areas who began smoking at younger than 18 years, the HRs were 2\u00b706 (1\u00b789\u20132\u00b724) for overall mortality and 1\u00b732 (1\u00b727\u20131\u00b737) for any disease incidence. In this population, 19\u00b76% of male (24\u00b73% of men residing in urban settings and 16\u00b72% of men residing in rural settings) and 2\u00b78% of female deaths were attributed to ever-regular smoking. Interpretation: Among Chinese adults, smoking was associated with higher risks of morbidity and mortality from a wide range of diseases. Among men, the future smoking-attributed disease burden will increase further, highlighting a pressing need for reducing consumption through widespread cessation and uptake prevention. Funding: British Heart Foundation, Cancer Research UK, Chinese Ministry of Science and Technology, Kadoorie Charitable Foundation, UK Medical Research Council, National Natural Science Foundation of China, Wellcome Trust.
\n \n\n \n \nAbsolute risks of stroke are typically estimated using measurements of cardiovascular disease risk factors recorded at a single visit. However, the comparative utility of single versus sequential risk factor measurements for stroke prediction is unclear. Risk factors were recorded on three separate visits on 13,753 individuals in the prospective China Kadoorie Biobank. All participants were stroke-free at baseline (2004\u20132008), first resurvey (2008), and second resurvey (2013\u20132014), and were followed-up for incident cases of first stroke in the 3\u00a0years following the second resurvey. To reflect the models currently used in clinical practice, sex-specific Cox models were developed to estimate 3-year risks of stroke using single measurements recorded at second resurvey and were retrospectively applied to risk factor data from previous visits. Temporal trends in the Cox-generated risk estimates from 2004 to 2014 were analyzed using linear mixed effects models. To assess the value of more flexible machine learning approaches and the incorporation of longitudinal data, we developed gradient boosted tree (GBT) models for 3-year prediction of stroke using both single measurements and sequential measurements of risk factor inputs. Overall, Cox-generated estimates for 3-year stroke risk increased by 0.3% per annum in men and 0.2% per annum in women, but varied substantially between individuals. The risk estimates at second resurvey were highly correlated with the annual increase of risk for each individual (men: r = 0.91, women: r = 0.89), and performance of the longitudinal GBT models was comparable with both Cox and GBT models that considered measurements from only a single visit (AUCs: 0.779\u20130.811 in men, 0.724\u20130.756 in women). These results provide support for current clinical guidelines, which recommend using risk factor measurements recorded at a single visit for stroke prediction.
\n \n\n \n \nObjective To validate the World Health Organization (WHO) non-laboratory-based cardiovascular disease risk prediction model in regions of China. Methods We performed an external validation of the WHO model for East Asia using the data set of China Kadoorie Biobank, an ongoing cohort study with 512 725 participants recruited from 10 regions of China from 2004\u20132008. We also recalculated the recalibration parameters for the WHO model in each region and evaluated the predictive performance of the model before and after recalibration. We assessed discrimination performance by Harrell\u2019s C index. Findings We included 412 225 participants aged 40\u201379 years. During a median follow-up of 11 years, 58 035 and 41 262 incident cardiovascular disease cases were recorded in women and men, respectively. Harrell's C of the WHO model was 0.682 in women and 0.700 in men but varied among regions. The WHO model underestimated the 10-year cardiovascular disease risk in most regions. After recalibration in each region, discrimination and calibration were both improved in the overall population. Harrell\u2019s C increased from 0.674 to 0.749 in women and from 0.698 to 0.753 in men. The ratios of predicted to observed cases before and after recalibration were 0.189 and 1.027 in women and 0.543 and 1.089 in men. Conclusion The WHO model for East Asia yielded moderate discrimination for cardiovascular disease in the Chinese population and had limited prediction for cardiovascular disease risk in different regions in China. Recalibration for diverse regions greatly improved discrimination and calibration in the overall population.
\n \n\n \n \nObjective: To evaluate the diagnostic accuracy of point-of-care (POC) tests for detecting proteinuria in pregnant women. Design: Systematic review and meta-analysis. Data Sources: MEDLINE and EMBASE databases were searched from inception to 13 November 2020. Eligibility Criteria and Data Analysis: Included studies measured the sensitivity and specificity of POC proteinuria testing compared to laboratory reference standards (protein-creatinine ratio (PCR), 24-hour urine collection). Bivariate meta-analyses determined pooled sensitivity and specificity. Random-effects inverse-variance model determined heterogeneity. Main Outcome Measures: The primary outcome was overall sensitivity and specificity, stratified by method of POC testing and reference standard. Secondary outcomes were sensitivity and specificity within the subgroups test brand, reference standard, and hypertension status. Results: 1078 studies were identified, 17 studies comprising 23 comparisons were included. The meta-analysis included 13 studies with 19 comparisons. Pooled sensitivity and specificity of visual dipsticks against PCR was 72 % (95 % CI: 56 % to 84 %) and 92 % (95 % CI: 76 % to 98 %), respectively. Pooled sensitivity and specificity of visual dipsticks against 24-hour collection was 69 % (55 % to 80 %) and 70 % (51 % to 84 %), respectively. Pooled sensitivity and specificity for automated readers against PCR was 73 % (53 % to 86 %) and 91 % (83 % to 95 %), respectively. Pooled sensitivity and specificity of automated readers against 24-hour collection was 65 % (42 % to 83 %) and 82 % (46 % to 96 %), respectively. Conclusion: Visual dipsticks have comparable accuracy to automated readers, yet are not adequate as a rule-out test for proteinuria. Proteinuria POC testing may be beneficial in antenatal care when repeat follow-up tests are performed. PROSPERO Registration Number: CRD42021231914.
\n \n\n \n \nBackground: This study aimed to: 1) assess the associations of biological age acceleration based on Klemera and Doubal's method (KDM-AA) with long-term risk of all-cause mortality; and 2) compare the association of KDM-AA with all-cause mortality among participants potentially at different stages of the cardiovascular disease (CVD) continuum. Methods: The present study was based on a subpopulation of the China Kadoorie Biobank, with baseline survey during 2004\u201308. A total of 12,377 participants free of ischemic heart disease, stroke, or cancer at baseline were included, in which 8180 participants were identified to develop major coronary event (MCE), ischemic stroke (IS), intracerebral hemorrhage (ICH) or subarachnoid hemorrhage (SAH), and 4197 remained free of these cardiovascular diseases before 1 January 2014. These participants were followed up until 1 Jan 2018. KDM-AA was calculated by regressing biological age measurement, which was constructed based on baseline 16 physical and 9 biochemical markers using Klemera and Doubal's method, on chronological age. We estimated the associations of KDM-AA with the mortality risk using the hazard ratio (HR) and 95% confidence interval (CI) from Cox proportional hazard models. We assessed discrimination performance by Harrell's C-index and net reclassification index (NRI). Findings: The participants who developed MCE (mean KDM-AA = 0.1 year, standard deviation [SD] = 1.6 years) or ICH/SAH (0.3 \u00b1 1.5 years) during subsequent follow-up showed accelerated aging at baseline compared to those of IS (0.0 \u00b1 1.2 years) and control (\u22120.3 \u00b1 1.3 years) groups. The KDM-AA was positively associated with long-term risk of all-cause mortality (HR = 1.20; 95% CI: 1.17, 1.23), and the association was robust for participants potentially at different stages of the CVD continuum. Adding KDM-AA improved mortality prediction compared to the model only with sociodemographic and lifestyle factors in whole participants, with the Harrell's C-index increasing from 0.813 (0.807, 0.819) to 0.821 (0.815, 0.826) (NRI = 0.011; 95% CI: 0.003, 0.019). Interpretation: In this middle-aged and elderly Chinese population, the KDM-AA is a promising measurement for biological age, and can capture the difference in cardiovascular health and predict the risk of all-cause mortality over a decade. Funding: This work was supported by National Natural Science Foundation of China ( 82192904, 82192901, 82192900, 81941018). The CKB baseline survey and the first re-survey were supported by a grant from the Kadoorie Charitable Foundation Hong Kong. The long-term follow-up is supported by grants from the UK Wellcome Trust ( 212946/Z/18/Z, 202922/Z/16/Z, 104085/Z/14/Z, 088158/Z/09/Z), grants ( 2016YFC0900500) from the National Key R&D Program of China, National Natural Science Foundation of China ( 81390540, 91846303), and Chinese Ministry of Science and Technology ( 2011BAI09B01).
\n \n\n \n \nBackground: The impact of solid fuel use on life expectancy (LE) in less-developed countries remains unclear. We aimed to evaluate the potential impact of household solid fuel use on LE in the rural and urban Chinese population, with the effect of smoking as a reference. Methods: We used data from China Kadoorie Biobank (CKB) of 484,915 participants aged 30\u201379 free of coronary heart disease, stroke, or cancer at baseline. Analyses were performed separately for solid fuel use for cooking, solid fuel use for heating, and smoking, with participants exposed to the other two sources excluded. Solid fuels refer to coal and wood, and clean fuels refer to electricity, gas, and central heating. We used a flexible parametric Royston-Parmar model to estimate hazard ratios of all-cause mortality and predict LE at age 30. Findings: Totally, 185,077, 95,228, and 230,995 participants were included in cooking-, heating-, and smoking-related analyses, respectively. During a median follow-up of approximately 12.1 years, 12,725, 7,531, and 18,878 deaths were recorded in the respective analysis. Compared with clean fuel users who reported cooking with ventilation, participants who used solid fuels with ventilation and without ventilation had a difference in LE (95% confidence interval [CI]) at age 30 of \u22121.72 (\u22122.88, \u22120.57) and \u22122.62 (\u22124.16, \u22121.05) years for men and \u22121.33 (\u22121.85, \u22120.81) and \u22121.35 (\u22122.02, \u22120.67) years for women, respectively. The difference in LE (95% CI) for heating was \u22122.23 (\u22123.51, \u22120.95) years for men and \u22121.28 (\u22122.08, \u22120.48) years for women. In rural men, the LE reduction (95% CI) related to solid fuel use for cooking (\u22122.55; \u22124.51, \u22120.58) or heating (\u22123.26; \u22126.09, 0.44) was more than that related to smoking (\u22121.71; \u22122.54, \u22120.89). Conversely, in urban men, the LE reduction (95% CI) related to smoking (\u22123.06; \u22123.56, \u22122.56) was more than that related to solid fuel use for cooking (\u22121.28; \u22122.61, 0.05) and heating (\u22121.90; \u22123.16, \u22120.65). Similar results were observed in women but with a smaller magnitude. Interpretation: In this Chinese population, the harm to LE from household use of solid fuels was greater than that from smoking in rural residents. Conversely, the negative impact of smoking was greater than solid fuel use in urban residents. Our findings highlight the complexity and diversity of the factors affecting LE in less-developed populations. Funding: National Natural Science Foundation of China, National Key R&D Program of China, Kadoorie Charitable Foundation, UK Wellcome Trust.
\n \n\n \n \nObjective: To develop and externally validate the STRAtifying Treatments In the multi-morbid Frail elderlY (STRATIFY)-Falls clinical prediction model to identify the risk of hospital admission or death from a fall in patients with an indication for antihypertensive treatment. Design: Retrospective cohort study. Setting: Primary care data from electronic health records contained within the UK Clinical Practice Research Datalink (CPRD). Participants: Patients aged 40 years or older with at least one blood pressure measurement between 130 mm Hg and 179 mm Hg. Main outcome measure: First serious fall, defined as hospital admission or death with a primary diagnosis of a fall within 10 years of the index date (12 months after cohort entry). Model development was conducted using a Fine-Gray approach in data from CPRD GOLD, accounting for the competing risk of death from other causes, with subsequent recalibration at one, five, and 10 years using pseudo values. External validation was conducted using data from CPRD Aurum, with performance assessed through calibration curves and the observed to expected ratio, C statistic, and D statistic, pooled across general practices, and clinical utility using decision curve analysis at thresholds around 10%. Results: Analysis included 1 772 600 patients (experiencing 62 691 serious falls) from CPRD GOLD used in model development, and 3 805 366 (experiencing 206 956 serious falls) from CPRD Aurum in the external validation. The final model consisted of 24 predictors, including age, sex, ethnicity, alcohol consumption, living in an area of high social deprivation, a history of falls, multiple sclerosis, and prescriptions of antihypertensives, antidepressants, hypnotics, and anxiolytics. Upon external validation, the recalibrated model showed good discrimination, with pooled C statistics of 0.833 (95% confidence interval 0.831 to 0.835) and 0.843 (0.841 to 0.844) at five and 10 years, respectively. Original model calibration was poor on visual inspection and although this was improved with recalibration, under-prediction of risk remained (observed to expected ratio at 10 years 1.839, 95% confidence interval 1.811 to 1.865). Nevertheless, decision curve analysis suggests potential clinical utility, with net benefit larger than other strategies. Conclusions: This prediction model uses commonly recorded clinical characteristics and distinguishes well between patients at high and low risk of falls in the next 1-10 years. Although miscalibration was evident on external validation, the model still had potential clinical utility around risk thresholds of 10% and so could be useful in routine clinical practice to help identify those at high risk of falls who might benefit from closer monitoring or early intervention to prevent future falls. Further studies are needed to explore the appropriate thresholds that maximise the model's clinical utility and cost effectiveness.
\n \n\n \n \nBackground Antihypertensives reduce the risk of cardiovascular disease but are also associated with harms including acute kidney injury (AKI). Few data exist to guide clinical decision making regarding these risks. Aim To develop a prediction model estimating the risk of AKI in people potentially indicated for antihypertensive treatment. Design and setting Observational cohort study using routine primary care data from the Clinical Practice Research Datalink (CPRD) in England. Method People aged \u226540 years, with at least one blood pressure measurement between 130 mmHg and 179 mmHg were included. Outcomes were admission to hospital or death with AKI within 1, 5, and 10 years. The model was derived with data from CPRD GOLD (n = 1 772 618), using a Fine\u2013Gray competing risks approach, with subsequent recalibration using pseudo-values. External validation used data from CPRD Aurum (n = 3 805 322). Results The mean age of participants was 59.4 years and 52% were female. The final model consisted of 27 predictors and showed good discrimination at 1, 5, and 10 years (C-statistic for 10-year risk 0.821, 95% confidence interval [CI] = 0.818 to 0.823). There was some overprediction at the highest predicted probabilities (ratio of observed to expected event probability for 10-year risk 0.633, 95% CI = 0.621 to 0.645), affecting patients with the highest risk. Most patients (>95%) had a low 1- to 5-year risk of AKI, and at 10 years only 0.1% of the population had a high AKI and low CVD risk. Conclusion This clinical prediction model enables GPs to accurately identify patients at high risk of AKI, which will aid treatment decisions. As the vast majority of patients were at low risk, such a model may provide useful reassurance that most antihypertensive treatment is safe and appropriate while flagging the few for whom this is not the case.
\n \n\n \n \nBACKGROUND: Patients diagnosed with psychosis often spend less time than others engaged in exercise and more time sitting down, which likely contributes to poorer physical and mental health. OBJECTIVE: The aim of this study was to develop a comprehensive framework from the perspective of patients, carers, and staff for understanding what promotes movement and physical activity. METHODS: A critical realist approach was taken to design the study. Interviews (n=23) and focus groups (n=12) were conducted with (1) outpatients aged 16 years or older diagnosed with psychosis, and under the care of a mental health team, (2) carers and (3) mental health staff working in the community. Purposive sampling was used to maximise variation in participant characteristics. Data were analysed using reflexive thematic analysis. FINDINGS: 19 patients (9 women and 10 men, mean age=45\u00b70 (SD=12\u00b72) years, 15 White British, 2 Black African, 1 Pakistani and 1 other ethnic group), 14 carers (11 women and 3 men, mean age=59\u00b79 (SD=12\u00b77) years, 13 White British and 1 Asian) and 18 staff (14 women and 4 men, mean age=38\u00b77 (SD=12\u00b73) years, 15 White British, 1 White other, 1 Asian Bangladeshi and 1 other Asian) participated in the study. Five factors were found to promote movement and physical activity. Patients must be able to find a purpose to moving which is meaningful to them (Factor 1: Purpose). Patients need to have an expectation of the positive consequences of movement and physical activity, which can be influenced by others' expectations (Factor 2: Predictions). A patient's current physical (eg, pain) and emotional state (eg, distress about voices) needs to be addressed to allow movement and physical activity (Factor 3: Present state). Movement and physical activity can also be encouraged by the availability of effective and tailored support, provided by engaged and supported people (Factor 4: Provision). Finally, through the identification and interruption of vicious cycles (eg, between inactivity and mood states) more positive cycles can be put in place (Factor 5: Process). CONCLUSIONS AND CLINICAL IMPLICATIONS: The 5 P (Purpose, Predictions, Present state, Provision and Process Physical Activity Framework) for understanding movement and physical activity for people diagnosed with psychosis has the potential to inform future research and guide interventions. A checklist is provided for clinicians to help foster change in activity levels.
\n \n\n \n \nAims: Adherence to pharmacological interventions in clinical trials is crucial for accurate identification of beneficial and adverse outcomes. The ways in which adherence to interventions should be reported in trial publications are described in the Template for Intervention Description and Replication (TIDieR), a 12-item extension of the Consolidated Standards of Reporting Trials reporting guidelines. The objective of this study was to assess compliance with TIDieR Items 11 and 12 of randomized controlled trials (RCTs) of interventions in SARS-CoV-2 infection published in 5 selected journals during 2021. Methods: We assessed pharmacological interventions for SARS-CoV-2 infection reported in RCTs published in 2021 in the Annals of Internal Medicine, The BMJ, JAMA, The Lancet and The New England Journal for Medicine for compliance with TIDieR items addressing intervention adherence (Items 11 and 12). We calculated proportional adherence for pharmacological and comparator interventions where available. Results: We found 75 eligible RCTs. Twenty-eight (37%) reported results of SARS-CoV-2 vaccinations. Compliance with Items 11 and 12 could be assessed in 71 of these 75. Of the 71 RCTs, 37 (52%) reported how adherence was assessed (Item 11), and 70 reported adherence rates (Item 12). Only 1 of the 71 RCTs (1.4%, 0\u20137.6%) fully complied with TIDieR Items 11 and 12. Conclusion: Half of RCTs of SARS-CoV-2 pharmacological interventions published in leading medical journals in 2021 complied with reporting of how adherence assessments were made and almost none complied with both TIDieR Items 11 and 12. The implications for interpretation, application and replication of findings based on these publications warrant consideration.
\n \n\n \n \nIntroduction: Loneliness can have a negative impact on people's physical and psychological well-being; building social capital is a potential means of addressing this connection. Community initiatives (e.g. groups, clubs, neighbourhood activities) may be a route that enables people to build social capital to tackle loneliness. Understanding what is known, and where gaps in knowledge exist, is important for advancing research on this topic. Methods: A scoping review was undertaken to explore the question \u2013 What community initiatives, with a focus on well-being, have been evaluated in the United Kingdom that include information about social capital and loneliness? Four databases (Medline, CINAHL, ASSIA and Embase) were searched for relevant research papers. References were screened by two researchers to identify if they met the review's inclusion criteria. Data were summarised as a narrative and in tables. Results: Five papers met the review's inclusion criteria. They all used qualitative methods. Findings suggested that social capital could be developed through creating a sense of trust, group cohesion and reciprocity among participants in the community initiatives. This connection enabled people to experience a sense of belonging and to feel they had a meaningful relationship with others, which appeared to alleviate feelings of loneliness. Conclusion: More research is warranted on the review topic, including studies that have employed quantitative or mixed methods. Clarity around definitions of social capital and loneliness in future research is required. Engagement with community initiatives can provide a formalised route to help people develop connections and counteract limitations in their social networks. However, individuals may be wary about attending community initiatives, needing support and encouragement to do so. Social prescribing link workers are one means of motivating people to access groups, events or organisations that could improve their well-being.
\n \n\n \n \nBackground: The purpose of this 6-month intervention pilot feasibility randomised trial was to test sending brief messages using mobile phones to promote self-management through taking medication as prescribed to people with type 2 diabetes. This was to inform the design and conduct of a future large-scale United Kingdom-based clinical trial and establish the feasibility of recruitment, the technology used, follow-up, and data collection. Methods: A multicentre individually randomised, controlled parallel group trial in primary care, recruiting adults (\u2265 35\u00a0years) with type 2 diabetes in England. Consenting participants were randomly allocated to receive short message system text messages up to four times a week, or usual care, for a period of 6\u00a0months; messages contained behavioural change techniques targeting medication use. The primary outcome was the rate of recruitment to randomisation of participants to the trial with a planned rate of 22 participants randomised per month. The study also aimed to establish the feasibility of follow-up at 6\u00a0months, with an aim of retaining more than 80% of participants. Data, including patient-reported measures, were collected at baseline and the end of the 6-month follow-up period, and a notes review was completed at 24\u00a0months. Results: The trial took place between 26 November 2018 and 30 September 2019. In total 209 participants were randomly allocated to intervention (n = 103) or usual care (n = 106). The maximum rate of monthly recruitment to the trial was 60\u201380 participants per month. In total, 12,734 messages were sent to participants. Of these messages, 47 were identified as having failed to be sent by the service provider. Participants sent 2,864 messages to the automated messaging system. Baseline data from medical records were available for > 90% of participants with the exception of cholesterol (78.9%). At 6\u00a0months, a further HbA1c measurement was reported for 67% of participants. In total medical record data were available at 6\u00a0months for 207 (99.0%) of participants and completed self-report data were available for 177 (84.7%) of participants. Conclusion: The feasibility of a large-scale randomised evaluation of brief message intervention for people with type 2 diabetes appears to be high using this efficient design. Failure rate of sending messages is low, rapid recruitment was achieved among people with type 2 diabetes, clinical data is available on participants from routine medical records and self-report of economic measures was acceptable. Trial registration: ISCTRN ISRCTN13404264. Registered on 10 October 2018.
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