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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
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Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
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Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

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Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...

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Related Experiment Video

Updated: Jun 6, 2026

The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time
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The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time

Published on: February 19, 2021

Informing Scalable Implementation Strategies for Returning Individual Research Results: A Mixed-Methods Study Across

Denise Kent1, Michelle Villegas-Downs1, Mary Pasquinelli1

  • 1University of Illinois at Chicago.

Research Square
|June 5, 2026
PubMed
Summary
This summary is machine-generated.

Developing effective strategies for returning individual research results (IRRs) in multi-site studies is crucial. This research identified key challenges and proposed theory-informed solutions for consistent and efficient communication.

Keywords:
Consolidated Framework for Implementation ResearchHealth communicationImplementation scienceImplementation strategiesIncidental findingsIndividual research results (IRRs)Mixed-methodsMultisite researchParticipant engagementReturn of results

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Area of Science:

  • Implementation Science
  • Clinical Research Management
  • Health Communication

Background:

  • National guidance supports returning individual research results (IRRs) to participants.
  • Scalable, theory-informed strategies for consistent IRR return in multi-site studies are underdeveloped.

Purpose of the Study:

  • Identify multilevel determinants influencing IRR return in multi-site research.
  • Inform the development of implementation strategies for effective IRR return.

Main Methods:

  • A mixed-methods study guided by the Consolidated Framework for Implementation Research (CFIR).
  • Conducted within the American Lung Association's Lung Health Cohort (LHC) study (26 sites).
  • Utilized CFIR-informed surveys and qualitative thematic analysis of focus group data.

Main Results:

  • Identified multilevel determinants of IRR return across all CFIR domains.
  • Key challenges included communication complexity, participant variability, workflow heterogeneity, team preparedness, and lack of standardized tools.
  • Specified implementation strategies: standardized templates, scripts, escalation pathways, navigation supports, and tracking systems.

Conclusions:

  • This study provides a foundation for developing scalable, theory-informed implementation strategies for returning IRRs in multi-site research.
  • Linking determinants to actionable strategies improves consistency, efficiency, and participant-centered communication.
  • Supports future intervention development and evaluation for research result dissemination.