A LATENT CLASS ANALYSIS PREDICTIVE MODELING APPROACH TO PROFILE DIVISION I COLLEGIATE ATHLETES FOR NUTRITION AND RELATIVE ENERGY DEFICIENCY IN SPORT (RED-S) CONCERN
Screening collegiate athletes for nutrition-related concerns and low energy syndromes such as Relative Energy Deficiency in Sport (RED-S) provides insight for nutrition care and can lead to necessary referrals in the sports medicine team. Screening may be a part of an athletic department’s protocol, but there is a lack of consensus on a validated tool for this population. The goal of this cross-sectional research was to use a Latent Class Analysis (LCA) predictive modeling approach to determine classes of collegiate athletes who present with nutrition and RED-S concern. LCA is a person-centered approach, intending to uncover subgroups of a population with common characteristics. A total of 216 athletes (144 female, 72 male) at a Division I university competing in various team sports completed a pre-participation nutrition screening survey prior to participation in athletic sports. Measures such as menstrual function, bone health, disordered eating, restrictive diets, food insecurity, body image, and nutrition knowledge were collected. For female athletes, the LCA model provided some clinical relevance that female athletes can be profiled into a two-class solution, providing practitioners and sports dietitians insight into profiling athletes who may be at risk for low energy syndromes. For male athletes, there was not enough evidence that a two-class solution was superior to a one-class solution, highlighting the need for high-quality low energy syndrome tools to be developed in the male collegiate athlete population. Future research should consider large sample sizes of athletes to conduct predictive modeling techniques along with high quality, validated measurement tools
