Sources of Nutrition Information in Recreational Ultra-marathon Runners: A Mixed Methods Analysis

Abstract

Ultra-marathon events (i.e., .42.2-km) continue to grow in popularity; however, little is known regarding the sources of nutrition information which inform their beliefs and habits. The objective of this study was to characterize the acquisition of sport-specific nutrition information among ultra-endurance athletes using a mixed methods design. Qualitative data were collected through focus groups and analyzed using thematic analysis. Three primary higher order themes were identified: Optimal Diet for Ultra-Endurance Athletes, Common Sources of Information, and Barriers to Scientific Information. Then, a self-report inventory (Sources of Nutrition Information-SONI questionnaire) was developed to assess common sources of nutrition information and characterize their beliefs about those sources. Likert-type questions were used, and primary sources were scored out of 3, sub-questions out of 5. Differences between sources were assessed using RM-ANOVA. Participants (N 5 224) accessed, responded to, and submitted the survey via a secure, study-specific web-based link. Peer reviewed literature was reported as the most frequently used (mean score 5 1.64, p , 0.001), credible (3.02, p , 0.001), and interesting (2.62, p 5 0.002). Social media was the most accessible (2.81, p , 0.001), but the least credible (1.87, p , 0.001). While social media was perceived less credible than other sources, its accessibility could make it a promising tool to provide evidence-based nutrition information to this population.

Examining dietary behaviours, diet quality, motives and supplementation use in physically active individuals following vegetarian‐based eating patterns

“…It is difficult to interpret the reported protein intake in the present study as there were a range of sporting types, intensities and abilities, although with a median intake of 1.18 grams of protein consumed per kilogram per day, it is likely that the majority of respondents consumed sufficient protein for moderate activity. The values were consistent with other studies including other athletes following vegan diets with 1.11 g/day/kg bodyweight in healthy physically active women (Boutros et al, 2020 ), 1.61 g/day of bodyweight in ultra‐runners (Mahoney et al, 2016 ; vegan and LOV combined) and 1.25 g/kg bodyweight in recreational runners (Nebl et al, 2019c ) and athletes following LOV diets with 1.34 g/kg (Nebl, Schuchardt, Wasserfurth, et al, 2019 ).…”

Dietary Intake of Recreational Endurance Runners Associated with Race Distance—Results from the NURMI Study (Step 2)

“…In line with the present study in which a considerable portion of runners were vegans and vegetarians, data indicate that the popularity of vegan and vegetarian diets in endurance runners is higher than general populations [23,66,73]. Research shows that 10% of distance runners follow a vegan/vegetarian diet, with a higher prevalence in runners competing over longer distances such as ultramarathon [25,53].…”

“…Another study showed that distance runners had a lower consumption of carbohydrates compared to other endurance athletes [65]. Differentiated findings from an investigation on ultra-marathoners indicate that there is a wide variation in their dietary intake, but it appears that they generally follow low-carbohydrate (high-protein and high-fat) diets compared to the dietary recommendations for endurance athletes [66]. While it has been well-established that training/racing behaviors of endurance runners are associated with their carbohydrate intake [23,61], dietary recommendations emphasize that endurance runners should generally consume higher amounts of carbohydrates compared to general populations [67,68].…”

Total fermentable oligo‐, di‐, monosaccharides and polyols intake, carbohydrate malabsorption and gastrointestinal symptoms during a 56 km trail ultramarathon event

“…(G*Power software, version 3.1.9.7, Franz Faul, Christian‐Albrechts‐Universität Kiel, Kiel, Germany). The sample size in this study is similar to other hypothesis‐generating studies (e.g., n = 33–47) evaluating the relationship between habitual dietary intake with gastrointestinal symptoms in ultra‐marathoners 27,28 …”

“…The sample size in this study is similar to other hypothesis-generating studies (e.g., n = 33-47) evaluating the relationship between habitual dietary intake with gastrointestinal symptoms in ultra-marathoners. 27,28…”