Article Text

Download PDFPDF

073 Promotion of para athlete well-being in South Africa (the PROPEL studies), part II: identification of sleep-associated risk factors
  1. Wayne Derman1,2,
  2. Phoebe Runciman1,2,3,
  3. James Craig Brown1,2,
  4. Marelise Badenhorst1,2
  1. 1Institute of Sport and Exercise Medicine (ISEM), Division of Orthopaedic Surgery, Department of Surgical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
  2. 2International Olympic Committee (IOC) Research Centre, Cape Town, South Africa
  3. 3Department of Sport Science, Faculty of Education, Stellenbosch University, Cape Town, South Africa


Background Good sleeping habits are necessary for optimal practice and performance, as well as for athlete health. Although the sleep characteristics of elite athletes are well described, research is limited on the sleep profile of similar-level para athletes.

Objective To evaluate sleep quality, sleepiness and chronotype of para athletes in South Africa.

Design Descriptive, cross-sectional survey.

Setting National to international level para athletes competing in the 2019 National Championships.

Patients (or Participants) A total of 124 athletes (93 males; 31 females) with a mean age 26.7 (±9.2).

Interventions (or Assessment of Risk Factors) Chi-square, with Fisher’s exact tests were used to evaluate differences in sleep latency, efficiency, daytime dysfunction, sleep duration, chronotype and sleepiness between ‘good’ and ‘poor’ quality sleep groups.

Main Outcome Measurements Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness scale and Morningness-Eveningness Questionnaire (MEQ-SA).

Results Fifty-eight percent (58%) of athletes identified as morning types, while 38% identified as intermediate types. Forty-eight percent (48%) were classified as having ‘good’ and the remainder as having ‘poor’ sleep quality. Moderate to severe daytime sleepiness was present in 30% of athletes. Thirty percent (30%) reported sleep duration of 5–6 hours per night, while 5% slept less than 5 hours. Morning types were significantly associated with the ‘good’ quality sleep group (p<0.001, V=0.35) and the ‘sleepiness’ group were associated with the ‘poor’ quality sleep group (p=0.04, V=0.19). Additionally, athletes with ‘poor’ sleep quality were associated with shorter sleep duration (p<0.001, V=.63), greater sleep latency (p<0.001, V=.62), lower sleep efficiency (p=0.001, V=.45), greater daytime dysfunction (p<0.001, V=.40) and greater sleep disturbances (p<0.001, V.34).

Conclusions The majority of athletes in this study presented with poor sleep quality. These findings demonstrate a need to identify, address and prevent possible mechanisms affecting poor sleep quality in this population.

Statistics from

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.