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Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

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Pharmacodynamic Models: Additive and Proportional Drug Effect Model

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Related Experiment Video

Updated: Jun 8, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

Doping Prevalence in Sport from Indirect Estimation Models: A Systematic Review and Meta-analysis.

Dominic Sagoe1,2, Maarten Cruyff3, Razieh Chegeni4,5

  • 1Department of Psychosocial Science, University of Bergen, Christiesgate 12, 5015, Bergen, Norway. dominic.sagoe@uib.no.

Sports Medicine - Open
|June 7, 2026
PubMed
Summary
This summary is machine-generated.

This study reveals that approximately one in five competitive athletes admit to doping, while one in six recreational athletes report past-year doping. Indirect Estimation Models (IEM) highlight varying prevalence rates across different athlete types.

Keywords:
DopingIndirect estimation modelsPrevalenceRandomised response techniquesSport

Related Experiment Videos

Last Updated: Jun 8, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

Area of Science:

  • Sports Science
  • Doping Control
  • Public Health

Background:

  • No prior systematic reviews or meta-analyses exist for doping prevalence using Indirect Estimation Models (IEM).
  • Understanding doping prevalence is crucial for effective anti-doping strategies.

Purpose of the Study:

  • To systematically review and meta-analyze empirical studies on admitted doping prevalence in sports using IEM.
  • To estimate lifetime and past-year doping prevalence rates.

Main Methods:

  • Conducted comprehensive electronic database and ad hoc searches up to March 2025.
  • Employed a cross-classified model to estimate prevalence based on lifetime/past-year, sample (competitive/recreational), and sports (multi-sport/single-sport) types.
  • Included 46 records (30 for meta-analysis, 34 independent studies), applying World Anti-Doping Agency definitions where possible.

Main Results:

  • Lifetime doping prevalence was highest in multi-sport competitive athletes (22.6%) and lowest in single-sport competitive athletes (12.7%).
  • Past-year doping prevalence was highest in single-sport recreational sportspersons (15.5%) and lowest in multi-sport recreational sportspersons (8.7%).
  • Most studies were conducted in Europe, assessed past-year prevalence, and used Unrelated Question or Forced Response with Cheater Detection models.

Conclusions:

  • Under IEM, approximately 20% of multi-sport competitive athletes and 16% of single-sport recreational athletes reported doping.
  • Multi-sport competitive athletes exhibit higher doping prevalences compared to single-sport athletes, while single-sport recreational athletes report higher prevalences than multi-sport recreational athletes.
  • Secondary data re-analysis presents a methodological challenge for future meta-analyses.