The effect of older-person centered and integrated health management model on multiple lifestyle behaviors: A randomized controlled trial from China

UNSTRUCTURED Excess weight is a major risk factor for chronic diseases. In Australia, over 60% of adults are overweight or obese. The overconsumption of energy-dense nutrient-poor (EDNP) foods and low physical activity (PA) levels are key factors contributing to population obesity. New cost-effective approaches to improve population diet and PA behaviors are needed. This 1-year randomized controlled trial (6-month intervention and 6-month follow-up) aims to investigate whether a tailored intervention using mobile technology can improve diet and PA behaviors leading to weight loss in adults (aged 18-65 years) who are overweight or obese and recruited through a social marketing campaign (LiveLighter). All eligible participants will provide data on demographics and lifestyle behaviors online at baseline, 6 months, and 12 months. Using two-stage randomization, participants will be allocated into one of three conditions (n=200 per group): tailored feedback delivered via email at seven time points, informed by objective dietary (mobile food record app) and activity (wearable activity monitor) assessment; active control receiving no tailored feedback, but undergoing the same objective assessments as tailored feedback; and online control receiving no tailored feedback or objective assessments. Primary outcome measures at 6 and 12 months are changes in body mass, EDNP food and beverage consumption, and daily moderate-to-vigorous PA (measured via accelerometry). Secondary outcomes include change in fruit and vegetable consumption, daily sedentary behaviors, and cost effectiveness. Enrolment commenced in August 2017. Primary outcomes at 12 months will be available for analysis from September 2019. Tailored email feedback provided to individuals may deliver a cost-effective strategy to overcome existing barriers to improving diet and PA. If found to be successful and cost effective, upscaling this intervention for inclusion in larger-scale interventions is highly feasible. Australian New Zealand Clinical Trials Registry ACTRN12617000554369; https://www.anzctr.org.au /Trial/Registration/TrialReview.aspx?id=371325&isReview=true.

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Road to Hierarchical Diabetes Management at Primary Care (ROADMAP) Study in China: Protocol for the Statistical Analysis of a Cluster Randomized Controlled Trial (Preprint)

10.2196/preprints.18333 ◽

2020 ◽

Author(s):

Xian Li ◽

Nadila Duolikun ◽

Fengzhuo Cheng ◽

Laurent Billot ◽

Weiping Jia ◽

...

Keyword(s):

Primary Care ◽

Randomized Controlled Trial ◽

Statistical Analysis ◽

Diabetes Management ◽

Controlled Trial ◽

Management Model ◽

Potential Bias ◽

Randomized Controlled ◽

Principle Investigator ◽

Cluster Randomized

BACKGROUND As the management of type 2 diabetes remains suboptimal in primary care, the Road to Hierarchical Diabetes Management at Primary Care (ROADMAP) study was designed and conducted in diverse primary care settings to test the effectiveness of a three-tiered diabetes management model of care in China. OBJECTIVE This paper aims to predetermine the detailed analytical methods for the ROADMAP study before the database lock to reduce potential bias and facilitate transparent analyses. METHODS The ROADMAP study adopts a community-based, cluster randomized controlled trial design that compares the effectiveness of a tiered diabetes management model on diabetes control with usual care among patients with diabetes over a 1-year study period. The primary outcome is the control rate of glycated hemoglobin (HbA1c) <7% at 1 year. Secondary outcomes include the control rates of ABC (HbA1c, blood pressure, and low-density lipoprotein cholesterol [LDL-C], individual and combined) and fasting blood glucose, and the change in each outcome. The primary analysis will be the log-binomial regression with generalized estimating equation (GEE), which accounts for the clustering within communities, for binary outcomes and linear regression with GEE for continuous outcomes. For both, the baseline value of the analyzed outcome will be the covariate. The other covariate further adjusted models and the repetitive models after multiple imputation (when more than 10% of observations in HbA1c after 1 year are missing) will be used for sensitivity analysis. Five prespecified subgroup analyses have also been planned to explore the heterogeneity of the intervention effects by adding the subgroup variable and its interaction with the intervention to the primary model. RESULTS This plan has been finalized, approved, and signed off by the principle investigator, co-principle investigator, and lead statisticians as of November 22, 2019, and made public on the institutional website without any knowledge of intervention allocation. Templates for the main figure and tables are presented. CONCLUSIONS This statistical analysis protocol was developed for the main results of the ROADMAP study by authors blinded to group allocation and with no access to study data, which will guarantee the transparency and reduce potential bias during statistical analysis. CLINICALTRIAL Chinese Clinical Trial Registry ChiCTR-IOC-17011325; https://tinyurl.com/ybpr9xrq INTERNATIONAL REGISTERED REPORT DERR1-10.2196/18333

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