Behind the athletic body: a clinical interview study of identification of eating disorder symptoms and diagnoses in elite athletes.
Mia Beck LichtensteinKaren Krogh JohansenEik RungeMarina Bohn HansenTrine Theresa HolmbergKristine TarpPublished in: BMJ open sport & exercise medicine (2022)
Eating disorders are more prevalent in athletes than in the general population and may have severe consequences for sports performance and health. Identifying symptoms can be difficult in athletes because restrictive eating and slim body images are often idealised in a sports setting. The Eating Disorders Examination Questionnaire (EDE-Q) and the SCOFF (Sick, Control, One stone, Fat and Food) questionnaire (SCOFF) are widely used generic instruments to identify symptoms of eating disorders. This study aimed to investigate the instruments' validity and explore eating disorder symptoms in a sample of athletes. A sample of 28 athletes (25 females) competing at a national level was interviewed based on the diagnostic criteria for eating disorders. We interviewed 18 athletes with a high score on EDE-Q and 10 with a low score. All interviews were transcribed and analysed from a general inductive approach. We identified 20 athletes with an eating disorder diagnosis, while 8 had no diagnosis. EDE-Q found 90% of the cases, while SCOFF found 94%. EDE-Q found no false-positive cases, while SCOFF found one. The qualitative results showed that most athletes reported eating concerns, restrictive eating, eating control (counting calories), weight concerns, body dissatisfaction (feeling fat and non-athletic), excessive exercise and health problems (eg, pain, fatigue). In conclusion, EDE-Q and SCOFF seem valid instruments to screen athletes' samples but may fail to find 6%-10% cases with eating disorders. Despite athletic bodies and normal body mass index, many athletes report severe eating problems and dissatisfaction with weight and body appearance. Implementation of regular screening may identify these symptoms at an early stage.
Keyphrases
- human health
- risk assessment
- climate change
- physical activity
- high school
- body mass index
- weight loss
- early stage
- healthcare
- sleep quality
- mental health
- weight gain
- public health
- primary care
- systematic review
- deep learning
- squamous cell carcinoma
- body composition
- machine learning
- spinal cord
- social media
- high intensity
- health information
- drug induced
- lymph node
- resistance training
- body weight
- convolutional neural network