Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Modeling Relationships among Pain and Function in Individuals with Knee Osteoarthritis in the A2CPS Cohort.

The Clinical journal of pain·2026
Same author

Intensity-dependent topographical expansion of sensory representations.

bioRxiv : the preprint server for biology·2026
Same author

Performance of Age-Adjusted Whole Genome Sequencing Telomere Length in Idiopathic Pulmonary Fibrosis.

American journal of respiratory and critical care medicine·2026
Same author

A Beta-Binomial Model for Estimating Zero- or One-inflated Pain Trajectories.

bioRxiv : the preprint server for biology·2026
Same author

Detection of multiple influential observations on model selection.

Biometrics·2026
Same author

A mouse model to study persistent fatigue.

Research square·2026

Related Experiment Video

Updated: Jan 18, 2026

Author Spotlight: Quantifying Pain Experience – An Illustrative Approach Using the Pain Body Diagram
09:00

Author Spotlight: Quantifying Pain Experience – An Illustrative Approach Using the Pain Body Diagram

Published on: July 7, 2023

4.4K

Data Quality Assurance Tool for the Acute to Chronic Pain Signatures Study (A2CPS): An Interactive R Shiny

Briha Ansari1, Patrick Sadil1, James Ford2

  • 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.

Medrxiv : the Preprint Server for Health Sciences
|January 16, 2026
PubMed
Summary
This summary is machine-generated.

The Acute to Chronic Pain Signatures (A2CPS) Data Monitoring Web App reduced data errors by 50% in a large observational study. This tool enhances data quality for clinical trials and observational research.

Keywords:
Acute to Chronic Pain Signatures Study (A2CPS)Clinical TrialsData CurationData ManagementData MonitoringData Quality AssuranceR Shiny Web Application

More Related Videos

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli
09:16

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Published on: April 5, 2019

11.4K
Spontaneous and Evoked Measures of Pain in Murine Models of Monoarticular Knee Pain
08:03

Spontaneous and Evoked Measures of Pain in Murine Models of Monoarticular Knee Pain

Published on: February 22, 2019

8.9K

Related Experiment Videos

Last Updated: Jan 18, 2026

Author Spotlight: Quantifying Pain Experience – An Illustrative Approach Using the Pain Body Diagram
09:00

Author Spotlight: Quantifying Pain Experience – An Illustrative Approach Using the Pain Body Diagram

Published on: July 7, 2023

4.4K
Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli
09:16

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Published on: April 5, 2019

11.4K
Spontaneous and Evoked Measures of Pain in Murine Models of Monoarticular Knee Pain
08:03

Spontaneous and Evoked Measures of Pain in Murine Models of Monoarticular Knee Pain

Published on: February 22, 2019

8.9K

Area of Science:

  • Biomedical Informatics
  • Clinical Research Informatics
  • Data Science

Background:

  • The Acute to Chronic Pain Signatures (A2CPS) program is a large-scale, multi-site observational study focused on chronic post-surgical pain and opioid dependence.
  • High-quality data are crucial for identifying biomarkers predictive of pain progression.
  • Existing quality assurance measures needed enhancement for complex, multi-domain data collection.

Purpose of the Study:

  • To develop and implement an interactive web application for real-time data quality monitoring within the A2CPS study.
  • To streamline the identification, reporting, and remediation of data quality errors across multiple research sites.
  • To support the accuracy of predictive biomarker discovery in a large observational study.

Main Methods:

  • Developed a secure R Shiny web application, the A2CPS Data Monitoring Web App, accessible to authorized personnel.
  • Integrated data from REDCap, preprocessing it for analysis within the R Shiny framework.
  • Designed a user-friendly interface with distinct subpanels for specific quality assurance tasks, enabling generation of site-specific error reports.

Main Results:

  • Achieved a 50% reduction in data quality errors in case report form data over one year.
  • Demonstrated consistent error reduction across all participating sites, irrespective of enrollment rates.
  • Facilitated targeted feedback and training for research personnel, mitigating recurring errors.

Conclusions:

  • The A2CPS Data Monitoring Web App significantly improved data quality assurance in a complex observational study.
  • This open-source solution effectively reduces data entry errors and enhances communication with data collection sites.
  • The findings highlight the utility of open-source computational frameworks for data monitoring in both clinical trials and observational studies.