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Related Experiment Video

Updated: Sep 3, 2025

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time
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Methods for capturing and analyzing adaptations: implications for implementation research.

Jodi Summers Holtrop1,2, Dennis Gurfinkel3, Andrea Nederveld4

  • 1Department of Family Medicine, University of Colorado, Aurora, CO, 80045, USA. Jodi.Holtrop@CUAnschutz.edu.

Implementation Science : IS
|July 29, 2022
PubMed
Summary
This summary is machine-generated.

Understanding intervention adaptations is key. This study developed methods to capture and analyze adaptations, revealing distinct "types" that may predict better health outcomes. Improved characterization enhances intervention effectiveness.

Keywords:
AdaptationImplementation fidelityQualitative methodsShared medical appointments

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Area of Science:

  • Implementation Science
  • Health Services Research
  • Behavioral Science

Background:

  • Intervention adaptations are common, but their impact on outcomes varies.
  • Understanding the goals and characteristics of adaptations is crucial for optimizing effectiveness.
  • Current methods for capturing and characterizing intervention adaptations are insufficient.

Purpose of the Study:

  • To characterize adaptations made to a complex diabetes intervention during a hybrid effectiveness-implementation study.
  • To evaluate multiple data collection and analytic methods for capturing and understanding intervention adaptations.
  • To identify patterns and types of adaptations that may be associated with favorable outcomes.

Main Methods:

  • Employed a hybrid effectiveness-implementation study design.
  • Utilized interviews, observations, and facilitator sessions for data collection on adaptations.
  • Applied the Framework for Reporting Adaptations and Modifications to Evidence-Based Interventions (FRAME) for cataloging.
  • Used co-occurrence tables, k-means clustering, and taxonomic analysis to characterize adaptations.

Main Results:

  • Multiple data collection methods identified a higher number of adaptations.
  • Diverse methods provided comprehensive understanding of adaptation components and rationale.
  • Analytic methods revealed distinct adaptation 'types' based on clustered components.

Conclusions:

  • Adaptations are prevalent and can be effectively identified using varied methods.
  • Characterizing adaptation types offers insights into tailoring interventions for better contextual fit.
  • Improved methods for describing adaptations can illuminate which strategies yield superior intervention outcomes.