Effectiveness of a brief behavioural intervention to prevent weight gain over the Christmas holiday period: randomised controlled trial

OBJECTIVE To test the effectiveness of a brief behavioural intervention to prevent weight gain over the Christmas holiday period. DESIGN Two group, double blinded randomised controlled trial. SETTING Recruitment from workplaces, social media platforms, and schools pre-Christmas 2016 and 2017 in Birmingham, UK. PARTICIPANTS 272 adults aged 18 years or more with a body mass index of 20 or more: 136 were randomised to a brief behavioural intervention and 136 to a leaflet on healthy living (comparator). Baseline assessments were conducted in November and December with follow-up assessments in January and February (4-8 weeks after baseline). INTERVENTIONS The intervention aimed to increase restraint of eating and drinking through regular self weighing and recording of weight and reflection on weight trajectory; providing information on good weight management strategies over the Christmas period; and pictorial information on the physical activity calorie equivalent (PACE) of regularly consumed festive foods and drinks. The goal was to gain no more than 0.5 kg of baseline weight. The comparator group received a leaflet on healthy living. MAIN OUTCOME MEASURES The primary outcome was weight at follow-up. The primary analysis compared weight at follow-up between the intervention and comparator arms, adjusting for baseline weight and the stratification variable of attendance at a commercial weight loss programme. Secondary outcomes (recorded at followup) were: weight gain of 0.5 kg or less, self reported frequency of self weighing (at least twice weekly versus less than twice weekly), percentage body fat, and cognitive restraint of eating, emotional eating, and uncontrolled eating. RESULTS Mean weight change was −0.13 kg (95% confidence interval −0.4 to 0.15) in the intervention group and 0.37 kg (0.12 to 0.62) in the comparator group. The adjusted mean difference in weight (intervention− comparator) was −0.49 kg (95% confidence interval −0.85 to −0.13, P=0.008). The odds ratio for gaining no more than 0.5 kg was non-significant (1.22, 95% confidence interval 0.74 to 2.00, P=0.44). CONCLUSION A brief behavioural intervention involving regular self weighing, weight management advice, and information about the amount of physical activity required to expend the calories in festive foods and drinks prevented weight gain over the Christmas holiday period

Test-treatment RCTs are susceptible to bias:a review of the methodological quality of randomized trials that evaluate diagnostic tests

Abstract Background There is a growing recognition for the need to expand our evidence base for the clinical effectiveness of diagnostic tests. Many international bodies are calling for diagnostic randomized controlled trials to provide the most rigorous evidence of impact to patient health. Although these so-called test-treatment RCTs are very challenging to undertake due to their methodological complexity, they have not been subjected to a systematic appraisal of their methodological quality. The extent to which these trials may be producing biased results therefore remains unknown. We set out to address this issue by conducting a methodological review of published test-treatment trials to determine how often they implement adequate methods to limit bias and safeguard the validity of results. Methods We ascertained all test-treatment RCTs published 2004–2007, indexed in CENTRAL, including RCTs which randomized patients to diagnostic tests and measured patient outcomes after treatment. Tests used for screening, monitoring or prognosis were excluded. We assessed adequacy of sequence generation, allocation concealment and intention-to-treat, appropriateness of primary analyses, blinding and reporting of power calculations, and extracted study characteristics including the primary outcome. Results One hundred three trials compared 105 control with 119 experimental interventions, and reported 150 primary outcomes. Randomization and allocation concealment were adequate in 57 and 37% of trials. Blinding was uncommon (patients 5%, clinicians 4%, outcome assessors 21%), as was an adequate intention-to-treat analysis (29%). Overall 101 of 103 trials (98%) were at risk of bias, as judged using standard Cochrane criteria. Conclusion Test-treatment trials are particularly susceptible to attrition and inadequate primary analyses, lack of blinding and under-powering. These weaknesses pose much greater methodological and practical challenges to conducting reliable RCT evaluations of test-treatment strategies than standard treatment interventions. We suggest a cautious approach that first examines whether a test-treatment intervention can accommodate the methodological safeguards necessary to minimize bias, and highlight that test-treatment RCTs require different methods to ensure reliability than standard treatment trials. Please see the companion paper to this article: http://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-016-0286-0

Sample size calculations for cluster randomised controlled trials with a fixed number of clusters

Background\ud Cluster randomised controlled trials (CRCTs) are frequently used in health service evaluation. Assuming an average cluster size, required sample sizes are readily computed for both binary and continuous outcomes, by estimating a design effect or inflation factor. However, where the number of clusters are fixed in advance, but where it is possible to increase the number of individuals within each cluster, as is frequently the case in health service evaluation, sample size formulae have been less well studied. \ud \ud Methods\ud We systematically outline sample size formulae (including required number of randomisation units, detectable difference and power) for CRCTs with a fixed number of clusters, to provide a concise summary for both binary and continuous outcomes. Extensions to the case of unequal cluster sizes are provided. \ud \ud Results\ud For trials with a fixed number of equal sized clusters (k), the trial will be feasible provided the number of clusters is greater than the product of the number of individuals required under individual randomisation ($n_i$) and the estimated intra-cluster correlation ($\rho$). So, a simple rule is that the number of clusters ($\kappa$) will be sufficient provided: \ud \ud $\kappa$ > $n_i$ x $\rho$\ud \ud Where this is not the case, investigators can determine the maximum available power to detect the pre-specified difference, or the minimum detectable difference under the pre-specified value for power. \ud \ud Conclusions\ud Designing a CRCT with a fixed number of clusters might mean that the study will not be feasible, leading to the notion of a minimum detectable difference (or a maximum achievable power), irrespective of how many individuals are included within each cluster. \ud \u

The acute kidney outreach to prevent deterioration and death – a large pilot study for a cluster randomised trial

Background and objectives: The Acute Kidney Outreach to Reduce Deterioration and Death (AKORDD) trial was a large pilot study for a cluster randomised trial of AKI Outreach. Design, Setting, Participants, and Measurements: An observational Control (Before) phase was conducted in two teaching hospitals (9 miles apart) and their respective catchment areas. In the Intervention (After) phase, a working hours AKI outreach service operated for the intervention hospital/area for 20 weeks, with the other site acting as a control. All AKI alerts in both hospital and community patients were screened for inclusion. Major exclusion criteria were patients who were end of life, or unlikely to benefit from Outreach, or lacking mental capacity, or already referred to the Renal team. The intervention arm included a model of escalation of renal care to AKI patients, depending on AKI stage. The 30-day primary outcome was a combination of death, or deterioration, as shown by any need for dialysis or progression in AKI stage. 1762 adult patients were recruited; 744 at the Intervention site during the After phase. Results: A median of 3.0 non-medication recommendations and 0.5 medication related recommendations per patient were made by the Outreach team, a median of 15.7 hours after the AKI alert. Relatively low rates of the primary outcomes of death within 30 days (11-15%), or requirement for dialysis (0.4 – 3.7%) were seen across all four groups. In an exploratory analysis, at the Intervention hospital during the After phase the was an odds ratio for the combined primary outcome of 0.73 (95% CI 0.42, 1.26, p = 0.26). Conclusions: An AKI outreach service can provide standardised specialist care to those with AKI across a healthcare economy. Trials assessing AKI outreach may benefit from focusing on those patients with "mid-range" prognosis, where nephrological intervention could have the most impact

Differences in axial segment reorientation during standing turns predict multiple falls in older adults

Author's version of an article in the journal: Gait and Posture. Also available from the publisher at: http://dx.doi.org/10.1016/j.gaitpost.2012.05.013Background: The assessment of standing turning performance is proposed to predict fall risk in older adults. This study investigated differences in segmental coordination during a 360° standing turn task between older community-dwelling fallers and non-fallers. Methods: Thirty-five older adults age mean (SD) of 71 (5.4) years performed 360° standing turns. Head, trunk and pelvis position relative to the laboratory and each other were recorded using a Vicon motion analysis system. Fall incidence was monitored by monthly questionnaire over the following 12 months and used to identify non-faller, single faller and multiple faller groups. Results: Multiple fallers were found to have significantly different values, when compared to non-fallers, for pelvis onset (p=. 0.002); mean angular separation in the transverse plane between the head and trunk (p=. 0.018); peak angular separation in the transverse plane between the trunk and pelvis (p=. 0.013); and mean angular separation between the trunk and pelvis (p<. 0.001). Conclusions: Older adults who subsequently experience multiple falls show a simplified turning pattern to assist in balance control. This may be a predictor for those at increased risk of falling

Urine steroid metabolomics as a diagnostic tool in primary aldosteronism

Primary aldosteronism (PA) causes 5-10% of hypertension cases, but only a minority of patients are currently diagnosed and treated because of a complex, stepwise, and partly invasive workup. We tested the performance of urine steroid metabolomics, the computational analysis of 24-hour urine steroid metabolome data by machine learning, for the identification and subtyping of PA. Mass spectrometry-based multi-steroid profiling was used to quantify the excretion of 34 steroid metabolites in 24-hour urine samples from 158 adults with PA (88 with unilateral PA [UPA] due to aldosterone-producing adenomas [APAs]; 70 with bilateral PA [BPA]) and 65 sex- and age-matched healthy controls. All APAs were resected and underwent targeted gene sequencing to detect somatic mutations associated with UPA. Patients with PA had increased urinary metabolite excretion of mineralocorticoids, glucocorticoids, and glucocorticoid precursors. Urine steroid metabolomics identified patients with PA with high accuracy, both when applied to all 34 or only the three most discriminative steroid metabolites (average areas under the receiver-operating characteristics curve [AUCs-ROC] 0.95-0.97). Whilst machine learning was suboptimal in differentiating UPA from BPA (average AUCs-ROC 0.65-0.73), it readily identified APA cases harbouring somatic KCNJ5 mutations (average AUCs-ROC 0.79-85). These patients showed a distinctly increased urine excretion of the hybrid steroid 18-hydroxycortisol and its metabolite 18-oxo-tetrahydrocortisol, the latter identified by machine learning as by far the most discriminative steroid. In conclusion, urine steroid metabolomics is a non-invasive candidate test for the accurate identification of PA cases and KCNJ5-mutated APAs.</p

Development and external validation of predictive models for prevalent and recurrent atrial fibrillation: a protocol for the analysis of the CATCH ME combined dataset

Background: Atrial fibrillation (AF) is caused by different mechanisms but current treatment strategies do not target these mechanisms. Stratified therapy based on mechanistic drivers and biomarkers of AF have the potential to improve AF prevention and management outcomes. We will integrate mechanistic insights with known pathophysiological drivers of AF in models predicting recurrent AF and prevalent AF to test hypotheses related to AF mechanisms and response to rhythm control therapy. Methods: We will harmonise and combine baseline and outcome data from 12 studies collected by six centres from the United Kingdom, Germany, France, Spain, and the Netherlands which assess prevalent AF or recurrent AF. A Delphi process and statistical selection will be used to identify candidate clinical predictors. Prediction models will be developed in patients with AF for AF recurrence and AF-related outcomes, and in patients with or without AF at baseline for prevalent AF. Models will be used to test mechanistic hypotheses and investigate the predictive value of plasma biomarkers. Discussion: This retrospective, harmonised, individual patient data analysis will use information from 12 datasets collected in five European countries. It is envisioned that the outcome of this analysis would provide a greater understanding of the factors associated with recurrent and prevalent AF, potentially allowing development of stratified approaches to prevention and therapy management