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070 Metabolic/endocrine disease, older females, longer race distance, slower race pace and higher WBGT are independent risk factors associated with medical encounters in 21.1 km and 56 km runners: a SAFER study in 76654 starters
  1. Martin Schwellnus1,2,3,
  2. Nicola Sewry1,
  3. Mats Borjesson4,5,6,
  4. Sonja Swanevelder7,
  5. Esme Jordaan7,8
  1. 1Sport, Exercise Medicine and Lifestyle Institute (SEMLI), Pretoria, South Africa
  2. 2IOC Research Centre, South Africa, Pretoria, South Africa
  3. 3Emeritus Professor of Sport and Exercise Medicine, Faculty of Health Sciences, University of Cape Town, South Africa, Cape Town, South Africa
  4. 4Institute of Neuroscience and Physiology, Sahlgrenska Academy, Göteborg University, Göteborg, Sweden
  5. 5Center for Health and Performance, Göteborg University, Göteborg, Sweden
  6. 6Sahlgrenska University Hospital/Östra, Göteborg, Göteborg, Sweden
  7. 7Biostatistics Unit, South African Medical Research Council, Cape Town, South Africa
  8. 8Statistics and Population Studies Department, University of the Western Cape, Cape Town, South Africa

Abstract

Background Recent data indicate that pre-race medical screening and education can reduce medical encounters (MEs) at an endurance running event. However, the relationship between the risk of MEs and specific risk factors from pre-race medical screening, together with race day factors, and has not been explored.

Objective To determine the independent risk factors that are associated with MEs during distance running events, using data from pre-race medical screening and race day.

Design Prospective study, with cross-sectional analyses.

Setting 2012–2015 Two Oceans marathon races (21.1km, 56km), South Africa.

Patients (or Participants) 76654 consenting race entrants.

Interventions (or Assessment of Risk Factors) All entrants completed a pre-race medical screening questionnaire upon entry, coupled with an educational intervention, as per their responses to questions. Race day data were collected from the race organisers and all MEs were recorded by medical staff on race day.

Main Outcome Measurements Risk factors associated with ME (both injury- and illness-related) were investigated using a multiple regression model with a Poisson distribution (reporting the prevalence ratio - PR: 95% CI) that included: demographics (age, sex), race day data [wet-bulb globe temperature (WBGT), race distance (21.1km or 56km), race pace], and individual pre-race medical screening data.

Results Independent risk factors associated with MEs were: history of metabolic disease (2.1: 1.3–3.3; p=0.0030), older females (>55years) (2.5: 1.6–4.1; p=0.0002), longer race distance (56km vs. 21.1km, 1.9: 1.5–2.4; p<0.0001), slower race pace (increase of 1min/km, 1.2: 1.1–1.3; p=0.0029), and higher WBGT (p=0.0264).

Conclusions Metabolic/endocrine disease, older females, longer race distance, slower race pace and higher WBGT were independent risk factors for MEs in distance running events. In addition to environmental factors, these data support initiatives to obtain pre-race medical screening, demographic, and running pace data in order to design and implement ME prevention programs at distance running events.

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