ArticleJournal of medical Internet research2017
Mobile Device-Based Electronic Data Capture System Used in a Clinical Randomized Controlled Trial: Advantages and Challenges.
Article in Journal of medical Internet research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- A Town-Level Comprehensive Intervention Study to Reduce Salt Intake in China: Cluster Randomized Controlled Trial.Nutrients · 2022Trial
- App based education programme to reduce salt intake (AppSalt) in schoolchildren and their families in China: parallel, cluster randomised controlled trial.BMJ (Clinical research ed.) · 2022Trial
- Evaluation of an mHealth-enabled hierarchical diabetes management intervention in primary care in China (ROADMAP): A cluster randomized trial.PLoS medicine · 2021Trial
- The Effectiveness and Safety of Utilizing Mobile Phone-Based Programs for Rehabilitation After Lumbar Spinal Surgery: Multicenter, Prospective Randomized Controlled Trial.JMIR mHealth and uHealth · 2019Trial
- A transparent and standardized performance measurement platform is needed for on-prescription digital health apps to enable ongoing performance monitoring.PLOS digital health · 2024Review
- Improving the management of type 2 diabetes in China using a multifaceted digital health intervention in primary health care: the SMARTDiabetes cluster randomised controlled trial.The Lancet regional health. Western Pacific · 2024Article
- Article
- General characteristics and reasons for the discontinuation of drug clinical trials in mainland China.BMC medical research methodology · 2021Observational
- Clinical researchers' lived experiences with data quality monitoring in clinical trials: a qualitative study.BMC medical research methodology · 2021Article
- Restaurant interventions for salt reduction in China: protocol for a randomised controlled trial.BMJ open · 2020Article
- An Electronic Data Capture Framework (ConnEDCt) for Global and Public Health Research: Design and Implementation.Journal of medical Internet research · 2020Article
- Reducing Salt Intake in China with "Action on Salt China" (ASC): Protocol for Campaigns and Randomized Controlled Trials.JMIR research protocols · 2020Article
- A town level comprehensive intervention study to reduce salt intake in China: protocol for a cluster randomised controlled trial.BMJ open · 2020Article
- An Application-based programme to reinforce and maintain lower salt intake (AppSalt) in schoolchildren and their families in China.BMJ open · 2019Article
- A Web-Based Application to Improve Data Collection in an Interventional Study Targeting Childhood Obesity: Pre-Post Analysis.Journal of medical Internet research · 2019Article
- Improving Information and Communications Technology (ICT) Knowledge and Skills to Develop Health Research Capacity in Kenya.Online journal of public health informatics · 2019Article
- The role of remote data capture, wearables, and digital biomarkers in decentralized clinical trials.Perspectives in clinical researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundElectronic data capture (EDC) systems have been widely used in clinical research, but mobile device-based electronic data capture (mEDC) system has not been well evaluated.
objectiveThe aim of our study was to evaluate the feasibility, advantages, and challenges of mEDC in data collection, project management, and telemonitoring in a randomized controlled trial (RCT).
methodsWe developed an mEDC to support an RCT called "Telmisartan and Hydrochlorothiazide Antihypertensive Treatment (THAT)" study, which was a multicenter, double-blinded, RCT, with the purpose of comparing the efficacy of telmisartan and hydrochlorothiazide (HCTZ) monotherapy in high-sodium-intake patients with mild to moderate hypertension during a 60 days follow-up. Semistructured interviews were conducted during and after the trial to evaluate the feasibility, advantage, and challenge of mEDC. Nvivo version 9.0 (QSR International) was used to analyze records of interviews, and a thematic framework method was used to obtain outcomes.
resultsThe mEDC was successfully used to support the data collection and project management in all the 14 study hospitals. A total of 1333 patients were recruited with support of mEDC, of whom 1037 successfully completed all 4 visits. Across all visits, the average time needed for 141 questions per patient was 53 min, which were acceptable to both doctors and patients. All the interviewees, including 24 doctors, 53 patients, 1 clinical research associate (CRA), 1 project manager (PM), and 1 data manager (DM), expressed their satisfaction to nearly all the functions of the innovative mEDC in randomization, data collection, project management, quality control, and remote monitoring in real time. The average satisfaction score was 9.2 (scale, 0-10). The biggest challenge came from the stability of the mobile or Wi-Fi signal although it was not a problem in THAT study.
conclusionsThe innovative mEDC has many merits and is well acceptable in supporting data collection and project management in a timely manner in clinical trial.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.