Dataset Information

A Computer-Assisted Personal Interview App in Research Electronic Data Capture for Administering Time Trade-off Surveys (REDCap): Development and Pretest.

ABSTRACT: BACKGROUND:The time trade-off (TTO) task is a method of eliciting health utility scores, which range from 0 (equivalent to death) to 1 (equivalent to perfect health). These scores numerically represent a person's health-related quality of life. Software apps exist to administer the TTO task; however, most of these apps are poorly documented and unavailable to researchers. OBJECTIVE:To fill the void, we developed an online app to administer the TTO task for a research study that is examining general public proxy health-related quality of life estimates for persons with Alzheimer's disease. This manuscript describes the development and pretest of the app. METHODS:We used Research Electronic Data Capture (REDCap) to build the TTO app. The app's modular structure and REDCap's object-oriented environment facilitated development. After the TTO app was built, we recruited a purposive sample of 11 members of the general public to pretest its functionality and ease of use. RESULTS:Feedback from the pretest group was positive. Minor modifications included clarity enhancements, such as rearranging some paragraph text into bullet points, labeling the app to delineate different question sections, and revising or deleting text. We also added a research question to enable the identification of respondents who know someone with Alzheimer's disease. CONCLUSIONS:We developed an online app to administer the TTO task. Other researchers may access and customize the app for their own research purposes.

SUBMITTER: Oremus M

PROVIDER: S-EPMC6334703 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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A Computer-Assisted Personal Interview App in Research Electronic Data Capture for Administering Time Trade-off Surveys (REDCap): Development and Pretest.

Oremus Mark M Sharafoddini Anis A Morgano Gian Paolo GP Jin Xuejing X Xie Feng F

JMIR formative research 20180123 1

<h4>Background</h4>The time trade-off (TTO) task is a method of eliciting health utility scores, which range from 0 (equivalent to death) to 1 (equivalent to perfect health). These scores numerically represent a person's health-related quality of life. Software apps exist to administer the TTO task; however, most of these apps are poorly documented and unavailable to researchers.<h4>Objective</h4>To fill the void, we developed an online app to administer the TTO task for a research study that is ...[more]

PMID: 30684429

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Project description:BackgroundRandomized controlled trials (RCT) are considered the ideal design for evaluating the efficacy of interventions. However, conducting a successful RCT has technological and logistical challenges. Defects in randomization processes (e.g., allocation sequence concealment) and flawed masking could bias an RCT's findings. Moreover, investigators need to address other logistics common to all study designs, such as study invitations, eligibility screening, consenting procedure, and data confidentiality protocols. Research Electronic Data Capture (REDCap) is a secure, browser-based web application widely used by researchers for survey data collection. REDCap offers unique features that can be used to conduct rigorous RCTs.MethodsIn September and November 2020, we conducted a parallel group RCT among Indiana University Bloomington (IUB) undergraduate students to understand if receiving the results of a SARS-CoV-2 antibody test changed the students' self-reported protective behavior against coronavirus disease 2019 (COVID-19). In the current report, we discuss how we used REDCap to conduct the different components of this RCT. We further share our REDCap project XML file and instructional videos that investigators can use when designing and conducting their RCTs.ResultsWe reported on the different features that REDCap offers to complete various parts of a large RCT, including sending study invitations and recruitment, eligibility screening, consenting procedures, lab visit appointment and reminders, data collection and confidentiality, randomization, blinding of treatment arm assignment, returning test results, and follow-up surveys.ConclusionsREDCap offers powerful tools for longitudinal data collection and conduct of rigorous and successful RCTs. Investigators can make use of this electronic data capturing system to successfully complete their RCTs.Trial registrationThe RCT was prospectively (before completing data collection) registered at ClinicalTrials.gov; registration number: NCT04620798 , date of registration: November 9, 2020.

Project description:BackgroundeCohort studies offer an efficient approach for data collection. However, eCohort studies are challenged by volunteer bias and low adherence. We designed an eCohort embedded in the Framingham Heart Study (eFHS) to address these challenges and to compare the digital data to traditional data collection.ObjectiveThe aim of this study was to evaluate adherence of the eFHS app-based surveys deployed at baseline (time of enrollment in the eCohort) and every 3 months up to 1 year, and to compare baseline digital surveys with surveys collected at the research center.MethodsWe defined adherence rates as the proportion of participants who completed at least one survey at a given 3-month period and computed adherence rates for each 3-month period. To evaluate agreement, we compared several baseline measures obtained in the eFHS app survey to those obtained at the in-person research center exam using the concordance correlation coefficient (CCC).ResultsAmong the 1948 eFHS participants (mean age 53, SD 9 years; 57% women), we found high adherence to baseline surveys (89%) and a decrease in adherence over time (58% at 3 months, 52% at 6 months, 41% at 9 months, and 40% at 12 months). eFHS participants who returned surveys were more likely to be women (adjusted odds ratio [aOR] 1.58, 95% CI 1.18-2.11) and less likely to be smokers (aOR 0.53, 95% CI 0.32-0.90). Compared to in-person exam data, we observed moderate agreement for baseline app-based surveys of the Physical Activity Index (mean difference 2.27, CCC=0.56), and high agreement for average drinks per week (mean difference 0.54, CCC=0.82) and depressive symptoms scores (mean difference 0.03, CCC=0.77).ConclusionsWe observed that eFHS participants had a high survey return at baseline and each 3-month survey period over the 12 months of follow up. We observed moderate to high agreement between digital and research center measures for several types of surveys, including physical activity, depressive symptoms, and alcohol use. Thus, this digital data collection mechanism is a promising tool to collect data related to cardiovascular disease and its risk factors.

Project description:BackgroundNext Generation Sequencing (NGS) is a commonly used technology for studying the genetic basis of biological processes and it underpins the aspirations of precision medicine. However, there are significant challenges when dealing with NGS data. Firstly, a huge number of bioinformatics tools for a wide range of uses exist, therefore it is challenging to design an analysis pipeline. Secondly, NGS analysis is computationally intensive, requiring expensive infrastructure, and many medical and research centres do not have adequate high performance computing facilities and cloud computing is not always an option due to privacy and ownership issues. Finally, the interpretation of the results is not trivial and most available pipelines lack the utilities to favour this crucial step.ResultsWe have therefore developed a fast and efficient bioinformatics pipeline that allows for the analysis of DNA sequencing data, while requiring little computational effort and memory usage. DNAscan can analyse a whole exome sequencing sample in 1 h and a 40x whole genome sequencing sample in 13 h, on a midrange computer. The pipeline can look for single nucleotide variants, small indels, structural variants, repeat expansions and viral genetic material (or any other organism). Its results are annotated using a customisable variety of databases and are available for an on-the-fly visualisation with a local deployment of the gene.iobio platform. DNAscan is implemented in Python. Its code and documentation are available on GitHub: https://github.com/KHP-Informatics/DNAscan . Instructions for an easy and fast deployment with Docker and Singularity are also provided on GitHub.ConclusionsDNAscan is an extremely fast and computationally efficient pipeline for analysis, visualization and interpretation of NGS data. It is designed to provide a powerful and easy-to-use tool for applications in biomedical research and diagnostic medicine, at minimal computational cost. Its comprehensive approach will maximise the potential audience of users, bringing such analyses within the reach of non-specialist laboratories, and those from centres with limited funding available.

Dataset Information

A Computer-Assisted Personal Interview App in Research Electronic Data Capture for Administering Time Trade-off Surveys (REDCap): Development and Pretest.

Publications

A Computer-Assisted Personal Interview App in Research Electronic Data Capture for Administering Time Trade-off Surveys (REDCap): Development and Pretest.

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