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New Parkrunners Are Slower and the Attendance Gender Gap Narrowing Making Parkrun More Inclusive

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  • Andre S. Gilburn

    (Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA, UK)

Abstract

Parkrun is a weekly mass-participation event. Finishes are recorded, with the resulting database potentially containing important public health information. The aim of this study was to identify characteristics of events that overcome barriers to participation, and to identify changing patterns in the demographics of participants. GLMMs were generated of age-graded performance, gender ratio and age of participants at Scottish parkrun events. Predictor variables included age, gender, participant, runs, date, elevation gain, surface and travelling time to the next nearest venue. There was a decline in the mean performance of participants at events, yet individual performances improved. The gender ratio showed higher male participation with a narrowing gender gap. Events in the most remote parts of Scotland had lower performance and a higher proportion of female participants. Events on slower surfaces had more female participants. Parkrun events are becoming more inclusive, with more females and participants exhibiting low performance. In more remote parts of Scotland, more females participated in parkrun than males, suggesting parkrun has overcome traditional barriers to female participation in sport. Prioritising the creation of events at remote locations and on slower surfaces could increase inclusivity further. General practitioners prescribing parkrun might want to prescribe attendance at slower events for female patients.

Suggested Citation

  • Andre S. Gilburn, 2023. "New Parkrunners Are Slower and the Attendance Gender Gap Narrowing Making Parkrun More Inclusive," IJERPH, MDPI, vol. 20(4), pages 1-14, February.
  • Handle: RePEc:gam:jijerp:v:20:y:2023:i:4:p:3602-:d:1072279
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    References listed on IDEAS

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    1. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
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    Cited by:

    1. Ashley D. Tegart & Nadine Schuurman & Stella R. Harden, 2025. "Runnability: A Scoping Review," IJERPH, MDPI, vol. 22(1), pages 1-34, January.

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