Allison Martin

Interdisciplinary lifestyle intervention for weight management in a community population (HealthTrack study): Study design and baseline sample characteristics

BACKGROUNDnIntegrating professional expertise in diet, exercise and behavioural support may provide more effective preventive health services but this needs testing. We describe the design and baseline results of a trial in the Illawarra region of New South Wales, Australia.nnnMETHODSnThe HealthTrack study is a 12 month randomised controlled trial testing effects of a novel interdisciplinary lifestyle intervention versus usual care. The study recruited overweight and obese adults 25-54 years resident in the Illawarra. Primary outcomes were weight, and secondary outcomes were disease risk factors (lipids, glucose, blood pressure), and behaviour (diet, activity, and psychological factors). Protocols, recruitment and baseline characteristics are reported.nnnRESULTSnBetween May 2014 and April 2015, 377 participants were recruited and randomised. The median age (IQR) of the mostly female sample (74%) was 45 (37-51) years. The sample comprised obese (BMI 32 (29-35) kg/m(2)) well educated (79% post school qualifications) non-smokers (96%). A high proportion reported suffering from anxiety (26.8%) and depression (33.7%). Metabolic syndrome was identified in 34.9% of the sample.nnnCONCLUSIONSnThe HealthTrack study sample was recruited to test the effectiveness of an interdisciplinary approach to preventive healthcare in self-identified overweight adults in the Illawarra region. The profile of participants gives some indication of those likely to use services similar to the trial design.

Effect of interdisciplinary care on weight loss: A randomised controlled trial

Objective To determine the effectiveness of a novel interdisciplinary treatment compared with usual care on weight loss in overweight and obese adult volunteers. Design Single blinded controlled trial. Participants randomly assigned to usual care (C, general guideline-based diet and exercise advice), intervention (I, interdisciplinary protocol) or intervention + a healthy food supplement (30 g walnuts/day) (IW). Setting Community based study, Illawarra region, south of Sydney, Australia. Participants Generally well volunteer adult residents, 25-54 years, body mass index (BMI) 25-40kg/m2 were eligible. At baseline 439 were assessed, 377 were randomised, 298 completed the 3-month intensive phase and 178 completed the 12-month follow-up. Interventions Treatment was provided at clinic visits intensively (0 months, 1 month, 2 months, 3 months) then quarterly to 12 months. Support phone calls were quarterly. All participants underwent blinded assessments for diet, exercise and psychological status. Primary and secondary measures The primary outcome was difference in weight loss between baseline and 12 months (clinically relevant target 5% loss). Secondary outcomes were changes in blood pressure, fasting blood glucose and lipids, and changes in diet, exercise and psychological parameters. Results At 12 months, differences in weight loss were identified (p<0.001). The I group lost more than controls at 3 months (91.11 (92.23,90.00), p<0.05) and the IW more than controls at 3 months (91.25 (92.35,90.15), p<0.05) and 6 months (92.20 (93.90,90.49), p<0.01). The proportion achieving 5% weight loss was significantly different at 3 months, 6 months and 9 months (p=0.04, p=0.03, p=0.03), due to fewer controls on target at 3 months, 6 months and 9 months and more IW participants at 6 months. Reductions in secondary outcomes (systolic blood pressure, blood glucose/lipid parameters and lifestyle measures) followed the pattern of weight loss. Conclusions An interdisciplinary intervention produced greater and more clinically significant and sustained weight loss compared with usual care. The intensive phase was sufficient to reach clinically relevant targets, but long-term management plans may be required. Trial registration number ANZCTRN 12614000581662; Post-results.

Impact of providing walnut samples in a lifestyle intervention for weight loss: a secondary analysis of the HealthTrack trial

ABSTRACT Background: Being more specific about individual food choices may be advantageous for weight loss. Including a healthy food (e.g. walnuts) may help to expose effects. Objective: To examine the impact of including walnuts in diets for weight loss. Design: Secondary analysis of the HealthTrack lifestyle intervention trial. Overweight and obese participants were randomized to: usual care (C), interdisciplinary intervention including individualized dietary advice (I), or interdisciplinary intervention including 30 g walnuts/day (IW). Changes in body weight, energy intake, intake of key foods, physical activity, and mental health over three and 12 months were explored. Results: A total of 293 participants completed the intensive three-month study period, and 175 had data available at 12 months. The IW group achieved the greatest weight loss at three months. IW reported significant improvements in healthy food choices, and decreased intakes of discretionary foods/beverages, compared to C. Weight loss remained greatest in IW at 12 months. Discussion: Significant effects were seen after three months, with the IW group achieving greater weight loss and more favorable changes in food choices. Conclusions: Including 30 grams walnuts/day in an individualized diet produced weight loss and positive changes in food choice.

Data mining: potential applications in research on nutrition and health

AIMnData mining enables further insights from nutrition-related research, but caution is required. The aim of this analysis was to demonstrate and compare the utility of data mining methods in classifying a categorical outcome derived from a nutrition-related intervention.nnnMETHODSnBaseline data (23 variables, 8 categorical) on participants (n = 295) in an intervention trial were used to classify participants in terms of meeting the criteria of achieving 10 000 steps per day. Results from classification and regression trees (CARTs), random forests, adaptive boosting, logistic regression, support vector machines and neural networks were compared using area under the curve (AUC) and error assessments.nnnRESULTSnThe CART produced the best model when considering the AUC (0.703), overall error (18%) and within class error (28%). Logistic regression also performed reasonably well compared to the other models (AUC 0.675, overall error 23%, within class error 36%). All the methods gave different rankings of variables importance. CART found that body fat, quality of life using the SF-12 Physical Component Summary (PCS) and the cholesterol: HDL ratio were the most important predictors of meeting the 10 000 steps criteria, while logistic regression showed the SF-12PCS, glucose levels and level of education to be the most significant predictors (P ≤ 0.01).nnnCONCLUSIONSnDiffering outcomes suggest caution is required with a single data mining method, particularly in a dataset with nonlinear relationships and outliers and when exploring relationships that were not the primary outcomes of the research.

Development of an at-risk assessment approach to dietary data quality in a food-based clinical trial

Accurate and valid dietary data is the basis to investigate diet-disease relationships. Potential data discrepancies may be introduced when collecting and analysing data, despite rigorous quality assurance protocols. The aim of this study was to identify at-risk areas of dietary data in a food-based clinical trial. Source data verification was performed on a 10% random sample (n=38) of paper-based baseline diet history interview records in a registered clinical trial. All items listed in the source data underwent 100% manual verification based on the food input data from FoodWorks nutrient analysis software. Food item discrepancies were explored using food categories and summarised based on meals. The differences in identified discrepancies for energy and macronutrient output generated from FoodWorks software between previously entered data and re-entered data were compared. An overall discrepancy rate of 4.88% was identified. It was found that dinner intake data were more prone to discrepancy incidences than breakfast, lunch and snacks. Furthermore, assessing intake based on reported quantity and frequency may be more effective to correct discrepancies for quality improvement. Therefore, the dinner meal appeared to be an at risk area of dietary data. The method implemented in this study offers a systematic approach to evaluating dietary data in a research setting.

Identifying metabolic syndrome in a clinical cohort: Implications for prevention of chronic disease

In the clinical setting, calculating cardiovascular disease (CVD) risk is commonplace but the utility of the harmonised equation for metabolic syndrome (MetS) (Alberti et al., 2009) is less well established. The aims of this study were to apply this equation to an overweight clinical cohort to identify risk factors for being metabolically unhealthy and explore associations with chronic disease. Baseline data were analysed from a lifestyle intervention trial of Illawarra residents recruited in 2014/2015. Participants were aged 25–54 years with a BMI 25–40 kg/m2. Data included MetS, CVD risk, insulin sensitivity, weight, body fat, diet, peripheral artery disease (PAD), physical activity, socio-economic position and psychological profile. Backward stepwise regression tested the association of covariates with MetS status and linear or logistic regression tested associations between MetS and risk of CVD, coronary heart disease, PAD and insulin resistance. 374 participants were included in the analysis with 127 (34.0%) categorised with MetS. Covariates significantly and positively associated with MetS were higher BMI (odds 1.26, p < 0.01) and older age (odds 1.08, p < 0.01). MetS participants (n = 351) had a 4.50% increase in CVD risk and were 8.1 and 12.7 times (respectively) more likely to be at risk of CHD and insulin resistance, compared to participants without MetS. The utility of the harmonised equation in the clinical setting was confirmed in this overweight clinical cohort. Those classified as having MetS were more likely to be older, overweight/obese individuals and they had a substantially higher risk of developing CVD and insulin resistance than those without MetS.

Feasibility of a community‐based interdisciplinary lifestyle intervention trial on weight loss (the HealthTrack study)

Aim n nThe aim of this study was to test the feasibility and acceptability of a novel interdisciplinary intervention on weight loss. n n n nMethods n nA 3-month parallel, blinded, randomised controlled trial compared the effects of an interdisciplinary model of care (individualised interdisciplinary advice delivered through dietitians) with control (general advice on diet and physical activity delivered by primary care nurses). The primary outcome was assessing feasibility and acceptability of the protocol, with secondary outcomes including body weight, clinical, dietary, physical activity and psychological variables. n n n nResults n nTwenty-four participants were randomised and 21 included in the final analysis. The recruitment rate was 42% (24/57) and the eligibility rate 83% (24/29). The withdrawal rate was low (13% overall) compared with similar trials. Attendance at study visits was higher in the intervention arm compared with control (100 vs 83%), which may be an artefact of the greater individualised treatment provided in the integrated model. n n n nConclusions n nThis study confirmed the feasibility and acceptability of the novel interdisciplinary lifestyle intervention within the region.

Assessing data quality and the variability of source data verification auditing methods in clinical research settings

INTRODUCTIONnData audits within clinical settings are extensively used as a major strategy to identify errors, monitor study operations and ensure high-quality data. However, clinical trial guidelines are non-specific in regards to recommended frequency, timing and nature of data audits. The absence of a well-defined data quality definition and method to measure error undermines the reliability of data quality assessment. This review aimed to assess the variability of source data verification (SDV) auditing methods to monitor data quality in a clinical research setting.nnnMATERIAL AND METHODSnThe scientific databases MEDLINE, Scopus and Science Direct were searched for English language publications, with no date limits applied. Studies were considered if they included data from a clinical trial or clinical research setting and measured and/or reported data quality using a SDV auditing method.nnnRESULTSnIn total 15 publications were included. The nature and extent of SDV audit methods in the articles varied widely, depending upon the complexity of the source document, type of study, variables measured (primary or secondary), data audit proportion (3-100%) and collection frequency (6-24u202fmonths). Methods for coding, classifying and calculating error were also inconsistent. Transcription errors and inexperienced personnel were the main source of reported error. Repeated SDV audits using the same dataset demonstratedu202f∼u202f40% improvement in data accuracy and completeness over time. No description was given in regards to what determines poor data quality in clinical trials.nnnCONCLUSIONSnA wide range of SDV auditing methods are reported in the published literature though no uniform SDV auditing method could be determined for best practice in clinical trials. Published audit methodology articles are warranted for the development of a standardised SDV auditing method to monitor data quality in clinical research settings.

Exploring Data Quality Management within Clinical Trials

BACKGROUNDnClinical trials are an important research method for improving medical knowledge and patient care. Multiple international and national guidelines stipulate the need for data quality and assurance. Many strategies and interventions are developed to reduce error in trials, including standard operating procedures, personnel training, data monitoring, and design of case report forms. However, guidelines are nonspecific in the nature and extent of necessary methods.nnnOBJECTIVEnThis article gathers information about current data quality tools and procedures used within Australian clinical trial sites, with the aim to develop standard data quality monitoring procedures to ensure data integrity.nnnMETHODSnRelevant information about data quality management methods and procedures, error levels, data monitoring, staff training, and development were collected. Staff members from 142 clinical trials listed on the National Health and Medical Research Council (NHMRC) clinical trials Web site were invited to complete a short self-reported semiquantitative anonymous online survey.nnnRESULTSnTwenty (14%) clinical trials completed the survey. Results from the survey indicate that procedures to ensure data quality varies among clinical trial sites. Centralized monitoring (65%) was the most common procedure to ensure high-quality data. Ten (50%) trials reported having a data management plan in place and two sites utilized an error acceptance level to minimize discrepancy, set at <5% and 5 to 10%, respectively. The quantity of data variables checked (10-100%), the frequency of visits (once-a-month to annually), and types of variables (100%, critical data or critical and noncritical data audits) for data monitoring varied among respondents. The average time spent on staff training per person was 11.58 hours over a 12-month period and the type of training was diverse.nnnCONCLUSIONnClinical trial sites are implementing ad hoc methods pragmatically to ensure data quality. Findings highlight the necessity for further research into standard practice focusing on developing and implementing publically available data quality monitoring procedures.