This review discusses the potential value of tracking interstitial glucose with continuous glucose monitors (CGMs) in athletes, highlighting possible applications and important considerations in the collection and interpretation of interstitial glucose data. CGMs are sensors that provide real time, longitudinal tracking of interstitial glucose with a range of commercial monitors currently available. Recent advancements in CGM technology have led to the development of athlete-specific devices targeting glucose monitoring in sport. Although largely untested, the capacity of CGMs to capture the duration, magnitude, and frequency of interstitial glucose fluctuations every 1–15 min may present a unique opportunity to monitor fueling adequacy around competitive events and training sessions, with applications for applied research and sports nutrition practice. Indeed, manufacturers of athlete-specific devices market these products as a “fueling gauge,” enabling athletes to “push their limits longer and get bigger gains.” However, as glucose homeostasis is a complex phenomenon, extensive research is required to ascertain whether systemic glucose availability (estimated by CGM-derived interstitial glucose) has any meaning in relation to the intended purposes in sport. Whether CGMs will provide reliable and accurate information and enhance sports nutrition knowledge and practice is currently untested. Caveats around the use of CGMs include technical issues (dislodging of sensors during periods of surveillance, loss of data due to synchronization issues), practical issues (potential bans on their use in some sporting scenarios, expense), and challenges to the underpinning principles of data interpretation, which highlight the role of sports nutrition professionals to provide context and interpretation.

Triathlon is a physically demanding sport, requiring athletes to make informed decisions regarding their daily food and fluid intake to align with daily training. With an increase in uptake for online learning, remotely delivered education programs offer an opportunity to improve nutritional knowledge and subsequent dietary intake in athletes. This single-arm observational study aimed to evaluate the effectiveness of a remotely delivered nutrition education program on sports nutrition knowledge and the dietary intake of junior elite triathletes (n = 21; female n = 9; male n = 12; 18.9 ± 1.6 y). A total of 18 participants completed dietary intake assessments (4-day food diary via Easy Diet Diary^TM) and 14 participants completed an 83-question sports nutrition knowledge assessment (Sports Nutrition Knowledge Questionnaire (SNKQ)) before and after the 8-week program. Sports nutrition knowledge scores improved by 15% (p < 0.001, ES = 0.9) following the program. Male participants reported higher energy intakes before (3348 kJ, 95% CI: 117–6579; p = 0.043) and after (3644 kJ, 95% CI: 451–6836; p = 0.028) the program compared to females. Carbohydrate intake at breakfast (p = 0.022), daily intakes of fruit (p = 0.033), dairy (p = 0.01) and calcium (p = 0.029) increased following nutrition education. Irrespective of gender, participants had higher intakes of energy (p < 0.001), carbohydrate (p = 0.001), protein (p = 0.007), and fat (p = 0.007) on heavy training days compared to lighter training days before and after the program with total nutrition knowledge scores negatively correlated with discretionary food intake (r = −0.695, p = 0.001). A remotely delivered nutrition education program by an accredited sports nutrition professional improved sports nutrition knowledge and subsequent dietary intake of junior elite triathletes, suggesting remote delivery of nutrition education may prove effective when social distancing requirements prevent face-to-face opportunities.

Background: Recently, a focus has been placed on investigating the potential benefits of adherence to a ketogenic diet in enhancing body composition, physical health, psychological well-being, and performance of athletes from various sporting disciplines. As the available research is yet to be collated and analyzed in a single review, this scoping review aims to analyze and draw conclusions from the available literature that exists on the efficacy of a ketogenic diet among athletic populations. Methods: Several primary research databases and any relevant citation lists were searched to locate appropriate studies for inclusion in this scoping review. Studies that investigated the effects of adherence to a ketogenic diet (KD), defined by a carbohydrate intake of less than 5% of total energy intake, on body composition, physical health, psychological well-being, and performance among an athletic population were included in the review. From 814 articles screened, 12 were identified as meeting the inclusion criteria and were included in the final scoping review. Results: Adherence to a KD has beneficial effects on body weight and fat mass. Varying effects were identified on physical health with the diet, eliciting positive effects on fat oxidation but potentially deleterious effects on stool microbiota and iron metabolism. Conflicting results were reported regarding the effects of a KD on sporting performance. Benefits were reported regarding athlete well-being following commencement of a KD, but only after week two. Conclusions: The results of this scoping review demonstrate that there are both beneficial and detrimental effects associated with adherence to a KD among athletic populations. It is understood that further research is required to make any concrete recommendations regarding a KD to athletes.

Objectives: The application of continuous glucose monitors (CGMs) to measure interstitial glucose in athletic populations is limited by the lack of accepted athlete-specific reference values. The aim of this study was to develop athlete-specific reference ranges for glycemic variability under standardized diet and exercise conditions. Methods: A total of 12 elite racewalkers (n = 7 men, 22.4 ± 3.5 years, VO _2max 61.6 ± 7.3 mL kg ⁻¹ min ⁻¹ ) completed two 4-d trials separated by 4-d. Athletes were provided a high-energy, high-carbohydrate diet (225 ± 1.6 kJ kg ⁻¹ day ⁻¹ , 8.4 ± 0.3 g kg ⁻¹ day ⁻¹ carbohydrate) and completed standardized daily exercise. The timing of food consumed and exercise undertaken were matched each day across the 4-d trials. Interstitial glucose data were collected via Freestyle Libre 2 CGMs. Glycemic variability was calculated as the mean amplitude of glycemic excursions (MAGEs), mean of daily differences (MODD), and standard deviation (SD). Results: Twenty-four hour MODD, MAGE, and SD for interstitial glucose were 12.6 ± 1.8 mg/dL (0.7 ± 0.1 mmol/L), 36.0 ± 5.4 mg/dL (2.0 ± 0.3 mmol/L), and 16.2 ± 1.8 mg/dL (0.9 ± 0.1 mmol/L), respectively. Twenty-four hour mean glucose (MG; 102.6 ± 5.4 mg/dL [5.7 ± 0.3 mmol/L]) was higher than overnight (91.8 ± 5.4 mg/dL [5.1 ± 0.3 mmol/L]; P < .0001) and was lower in women than men (99.0 ± 3.6 mg/dL [5.5 ± 0.2 mmol/L] vs 104.4 ± 3.6 mg/dL [5.8 ± 0.2 mmol/L]; P = .059, d = 1.4). Conclusions: This study provides reference indices under standardized diet and exercise conditions for glycemic variability derived from CGMs in endurance athletes which are similar than previously reported for healthy individuals, despite strenuous daily training and a high daily energy and carbohydrate diet.

This study aimed to determine energy availability (EA) and within-day energy balance (WDEB) in female soccer players during preseason and also explored eating disorder risk and athlete food choice. We hypothesized commonly used indicators of low energy availability (LEA) risk would correlate with measured EA and WDEB variables, and that food choice determinants would differ according to EA. Eleven National Premier League female soccer players participated in this observational cross-sectional study over 3 weeks. Assessment of resting metabolic rate and physique traits, including bone mineral density, was conducted during Weeks 1 or 3. During Week 2, dietary intake, energy expenditure, and continuous monitor-derived glucose were measured for 5 days. EA was calculated daily and WDEB calculated hourly with deficits/surpluses carried continuously. Questionnaires were administered throughout the 3 weeks, including the Athlete Food Choice Questionnaire, the Eating Disorders Screen for Athletes, and the Low Energy Availability in Females Questionnaire. Resting metabolic rate ratio, bone mineral density, Low Energy Availability in Females Questionnaire, and Eating Disorders Screen for Athletes scores were used as indicators of LEA risk. EA averaged 30.7 ± 7.5 kcals·kg fat-free mass⁻¹·day⁻¹. Approximately one-third (36%) of athletes were at risk of an eating disorder, while approximately half (45%) were identified at risk of the female athlete triad via Low Energy Availability in Females Questionnaire, compared with approximately one-third (36%) of athletes identified with EA < 30 kcal·kg fat-free mass⁻¹·day⁻¹. No athlete achieved EA >45 kcal·kg fat-free mass⁻¹·day⁻¹, and no indicator of LEA risk was associated with calculated EA or WDEB. However, overnight glycemic variability was positively correlated with measured EA (r = .722, p = .012).

This exploratory study examined the effect of carbohydrate and subsequent energy availability (EA) on training adaptations, health, and sleep metrics across a 7-week preseason in semiprofessional female rugby union athletes. Full food provision on training days (four/week) manipulated carbohydrate intake: 6 versus 3 g·kg ⁻¹ ·day ⁻¹ for intervention ( n = 7) and CON ( n = 6), respectively while standardizing protein intake (>1.2 g·kg ⁻¹ ·day ⁻¹ ). Nontraining days involved ad libitum intake. Quantification of exercise energy expenditure via an individually calibrated wearable device allowed estimation of EA. Strength (three-repetition max squat, bench pull, and bench press), Bronco shuttle time; countermovement jump; biomarkers including iron studies, thyroid function, and cholesterol; sleep metrics; and physique (four-compartment model) were assessed before and after preseason. Training day carbohydrate intake was higher in intervention than CON (6.3 ± 0.2 vs. 3.1 ± 0.2 g·kg ⁻¹ ·day ⁻¹ , p < .001), resulting in higher EA across the preseason (45.0 ± 5.8 vs. 29.9 ± 5.8 kcal·kg ⁻¹ fat-free mass ⁻¹ ·day ⁻¹ , p < .001). Significant time effects were observed for bench pull (+3.6 kg, p = .024) and bench press (+4.6 kg, p = .011), fat mass (−1.1 kg, p < .001), fat-free mass (+1.2 kg, p = .025), and ferritin (−29 μg/L, p = .009). No significant interaction was observed for training adaptations, physique, or biochemical markers. Greater sleep duration was observed in CON on training nights, F (1, 156.072) = 5.821, p = .017, although other sleep metrics showed limited between-group differences. While increased carbohydrate intake improved EA, it had limited effect on training adaptations, health, or sleep metrics in this small, exploratory cohort.