Dietary Determinants Associated with Low Energy Availability among Athletes: A Scoping Review

All published articles of this journal are available on ScienceDirect.

SCOPING REVIEW

Dietary Determinants Associated with Low Energy Availability among Athletes: A Scoping Review

Shrishti Gupta1 , * Open Modal iD Priti Rishi Lal1
Authors Info & Affiliations
The Open Sports Sciences Journal • 10 Apr 2026 • SCOPING REVIEW • DOI: 10.2174/011875399X449583260407061445
Previous Article

Abstract

Introduction

Globally, a high prevalence of Low Energy Availability (LEA) (< 30 kcal·kg−1 FFM) is observed among competitive athletes. A comprehensive review of various factors associated with dietary intake will provide a better perception of management strategies for improving food intake and population-specific areas of future research. Thus, this study is aimed at identifying the diverse factors that influence dietary energy intake in relation to Energy Availability (EA) among male and female athletes who engage in training and competition across various sports.

Methods

Five search engines were searched using nine keywords, and original papers were extracted spanning over two decades (2003-2023). A predetermined participant-concept-context criterion was used for the inclusion of studies on competitive athletes.

Results

A total of 1462 studies were identified, of which 53 were deemed suitable for inclusion in this review. LEA was prevalent among 24% of male and 58% of female athletes globally and in 87.5% of adolescents in India.

Discussion

LEA was influenced by factors classified in six themes: training/performance demands, psychosocial/cultural influences, dietary practices/nutritional beliefs, environmental/logistical/educational factors, physiological considerations, and methodological issues, requiring culturally tailored biomarkers.

Conclusion

The significant prevalence of LEA globally necessitates culturally tailored causal research and biomarker-informed, standardised Randomized Controlled Trial (RCT) interventions that address multifaceted determinants to effectively prevent LEA.

Keywords: Low energy availability, Dietary intake, Energy intake, Determinants, Athletes.

1. INTRODUCTION

The theoretical framework for physiological dysregulation and negative health and performance outcomes caused by low energy intake relative to energy expenditure in exercising humans is given in the models of Female Athlete Triad (FAT) and Relative Energy Deficiency in Sports (REDs) [1, 2]. Energy Availability (EA), a core component of REDs in exercising populations, as defined by Loucks in 2020, is the daily amount of energy available to sustain all physiological functions outside of exercise, given as the difference between Energy Intake (EI) and Exercise Energy Expenditure (EEE), expressed relative to an individual’s Lean Body Mass (LBM) [1, 3]. Low Energy Availability (LEA) (EA < 30 kcal/kg FFM) during periods of high energy expenditure results from concomitant low EI, which may or may not be accompanied by eating disorders in athletes [4, 5].

Research across various countries has identified several factors contributing to low EI among athletes. These factors include time constraints, financial limitations, inadequate cooking skills, the pressure to maintain a sport-appropriate physique among Australians [6], and training and sport-specific requirements as reviewed in research from the US and Europe [7], although their association with LEA is yet to be established. Current evidence provides country-specific and setting-specific determinants for low EI leading to LEA, reporting several factors in the domains of social and environment, among which “food availability”, “socio-economic status”, “pressure from coaches”, “body shaming”, “competitiveness between teammates”, and “inadequate dietary intake” emerge as key factors associated with LEA [8, 9]. However, no comprehensive review covering the global aspects of diet associated with LEA among competitive athletes is yet available.

Hence, this scoping review has been planned to identify the various determinants of dietary energy intake associated with EA in male and female athletes training for and competing in different sports. This review will provide an overview of the numerous factors linked with inadequate food intake among athletes with LEA around the world. This data synthesis will offer up-to-date, concise information on the dietary determinants linked to LEA. The findings will be valuable for future global research and in developing dietary strategies to prevent REDs in elite athletes.

2. METHODS

This review followed the scoping review protocol proposed by Arksey & O’Malley in 2005 for data selection [10]. The guidelines for the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) were used for reporting of the study [11].

2.1. Eligibility Criteria

Participants: Studies included ensured that the athletic populations were professional, elite, or competing. All competitive athletes, irrespective of their age, gender, and sport, were included, while recreational and injured athletes were excluded.

Concept: Studies that measured EA among competitive athletes and reported any “dietary determinant”, operationally defined as “any factor that influenced EI”, were included. Other factors reported in these studies were also summarized.

Context: All studies reporting determinants of LEA conducted during any phase of the athlete’s career: training, competition, and/or recovery were included. Athletic phases were not restricted to gaining insight into myriad dietary determinants of LEA among competitive athletes.

Original research, reported in English with a primary, quantitative, qualitative, observational, case-study, or interventional study design, was considered for inclusion. Those not meeting the described Participant— Concept—Context (PCC) criteria were excluded.

2.2. Search Strategy

Available and relevant databases and a grey literature source were searched to identify the full published articles. The research was undertaken on five databases in the following order: Web of Science, SCOPUS, PubMed, ProQuest Dissertations and Theses Global, and Google Scholar. All articles published from January 2003 to July 2023 were included. The search strategy used keywords to download all available research on the topic. The keywords used were “Energy Availability”, “Athlete”, “Energy”, “Diet”, “Food”, “Intake”, “Choice”, “Habit”, and “Practice”. The search terms were combined with Boolean operators (AND and OR), and truncation and wildcard symbols were also used (Appendix 1). Search strings were saved for reference.

2.3. Selection of Relevant Studies

The studies from the databases were downloaded into Microsoft Excel 2023 and collated into one spreadsheet. Later, the duplicates were removed, and studies were screened and selected based on inclusion criteria by two independent reviewers, SG and PRL. The disagreements were resolved by consensus following discussion between reviewers. Inaccessible studies were removed after contacting authors by email, with no response. As a consensus amongst reviewers, no article was rejected due to quality because it captured the maximum number of potential determinants for future research.

2.4. Data Extraction and Reporting

The studies selected were organized in a tabular form with information about the author(s) and year of publication, study design, sample size, gender, age, performance level, sport, origin of study, competitive phase, energy availability methodology, energy availability assessment method, energy availability cutoff, energy intake assessment method, energy expenditure assessment method, resting metabolism assessment method, body composition assessment method, reporting of energy availability, and concluded dietary determinants.

3. RESULTS

The identification of studies yielded 1462 studies, out of which 53 were found eligible for inclusion in this review. The reasons for exclusion are presented in Fig. (1). The final studies given in Table 1 include 45 primary research studies and 8 theses. The study design as given by the authors was cross-sectional (n = 22) [12-33], observational (n = 9) [13, 34-41], interventional (n = 4) [23, 25, 31, 42], pilot (n = 1) [43], case study (n = 1) [35], and descriptive (n = 2) [44, 45].

Fig. (1).

PRISMA flow diagram of the study selection process.

Table 1.
Data extraction.
Author(s) and Year (Melin et al., 2016) (Cherian et al., 2018) (Silva & Paiva, 2015) (Hertig-Godeschalk et al., 2023) (Ong & Brownlee, 2017) (Coelho et al., 2013) (Logue et al., 2019)
Reference [12] [13] [56] [43] [60] [14] [15]
Study design Cross-sectional Cross-sectional and observational NA Pilot NA Cross-sectional controlled Cross-sectional
POPULATION
Sample size n=25 n=40 n=67 n=14 n=11 n=24 n=833
Gender Female Male (n=21)
Female (n=19)		 Female Male (n=6)
Female (n=8)		 Female Female Female
Age (years) 18 to 38 Under-12 and Under-16 18.7 ± 2.9 34 ± 9 21 to 26 12 to 19 18 and above
Performance level (as described by authors) National National International Elite Elite Competitive and sedentary control group International (n=162), National (n=155), Competitive (n=281), Recreational (n=235)
Sport(s) Weight-sensitive endurance Soccer Rhythmic Gymnastics Multiple wheelchair Dragon boat race Tennis 38 different sports
CONTEXT
Origin Denmark India Portugal Switzerland Singapore Brazil Ireland
Competitive phase During training Pre competition Pre competition Pre competition and Competition During training Training NA
CONCEPT
Energy Availability assessment method Calculated using EI, EEE and FFM EI – EEE
Represented in terms of kcal·kg−1 fat-free mass		 EI - EEE
Normalised to FFM		 EI - EEE
Relative to the FFM		 EI (kJ) - EEE (kJ))/FFM (kg) EI - EEE
Normalized to FFM		 LEAF-Q
Energy Availability cutoff (in study) LEA = <30 kcal (125 kJ)/kg FFM/day;
Reduced EA = <45 kcal (188 kJ)/kg FFM/day		 LEA = <30 kcal·kg -1 FFM;
Adequate EA = ≥30 kcal·kg−1 FFM		 LEA = <45 kcal/kg FFM/day LEA = ≤30 kcal/kg FFM/day EA detrimental to health = <125.5 kJ/kg body fat-free mass/day;
EA maintain normal physiological functions = ≥188 kJ/kg/day		 LEA <45 kcal/kg FFM/day At risk of LEA = score ≥8
Energy Intake assessment method 7-day weighed food records 3-days 24-hour dietary recall 24 hr dietary recall 3-days weighed food intake diaries 3-day food dairy 3-day food records NA
Exercise Energy Expenditure assessment method MET values of physical activities (Ainsworth compendium 2011) Portable metabolic analyzer MET values of physical activities (Ainsworth compendium 2011) Recommendations of Conger and Basset SensewearTM armband Using the compendium of energy expenditures for youth (Ridley, Ainsworth, & Olds, 2008) NA
Author(s) and Year (Melin et al., 2016) (Cherian et al., 2018) (Silva & Paiva, 2015) (Hertig-Godeschalk et al., 2023) (Ong & Brownlee, 2017) (Coelho et al., 2013) (Logue et al., 2019)
Resting Metabolic Rate assessment method NA NA Cunningham equation NA Wong, J. E. et al., for Southeast Asian athletes NA NA
Body Composition assessment method DXA Skinfold measurements and Body plethysmography Bio-impedance analysis DXA Portable Bioelectrical impedance monitor DXA NA
OUTCOME
Energy Availability reported LEA = 3 subjects
Reduced EA = 11 subjects		 LEA = 5 boys; U16=4
LEA = 11 girls; U16=7		 LEA below 45 kcal/kg FFM/day = 37.3% gymnasts
EA below 30 kcal/kg FFM/day = 44.8% of gymnasts		 All of the athletes experienced LEA for at least one day Energy availability <188 kJ/kg FFM/day = 8
Energy availability <125.5 kJ/kg FFM/day = 6		 Low energy availability = 91.7% of athletes
Low energy availability = 71.4% of controls		 At risk = Almost 40% (39.7%, n = 331)
Dietary determinants concluded Training load, Energy Density, Weight cutting, Appetite, Meal composition Energy Density, Age, Meal timing, Ad libitum intake, Food familiarity Competitive Phase Refuelling resources, Training load, Meal timing, Meal composition, Body image Weight cutting, Competitive phase (preparation) Weight cutting, Disordered Eating, Training load, Meal composition Restrictive diets, Meal composition
Abbreviations: NA= Not Available.
EI= Energy Intake.
EEE= Exercise Energy Expenditure.
FFM= Fat Free Mass.
LEAF-Q= Low Energy Availability-Questionnaire.
EA= Energy Availability.
MET= Metabolic Equivalent of Task.
DXA= Dual-Energy X-ray Absorptiometry.
Author(s) and Year (Magee et al., 2020) (Matt et al., 2022) (Traversa et al., 2022) (Vescovi & Van Heest, 2016) (Macuh et al., 2023) (Taylor et al., 2022) (Otte et al., 2023)
Reference [34] [16] [46] [35] [61] [36] [17]
Study design Observational Cross-sectional NA Observational case Study NA Observational Cross-sectional observational
POPULATION
Sample size n=18 n= 41+31 n=15 n=1 n=23 n=10 n=19
Gender Female Female (n=41+19) and Male (n=12) Female Female NA Male Female
Age (years) 19.2 ± 1.1 14 to 17 20.5 ± 0.4 19 18 to 31 22 ± 8 24 ± 5
Performance level (as described by authors) National Collegiate High School Competitive University Union Junior Elite Professional Elite Competitive
Sport(s) Soccer Endurance running Rugby Triathlon Football Road-cycling Football
CONTEXT
Origin US US Canada Canada Slovenia UK Australia
Competitive phase Season midpoint Beginning of season Practice Day, Recovery Day and Game Day Training Preparation Pre-season training Pre-season
CONCEPT
Energy Availability assessment method EI - EEE
Expressed as kcals per kilogram of FFM; LEAF-Q		 NA (EI - EEE)/kg FFM EI - EEE
expressed as kcal/kg FFM/day		 EI (kcal) - EEE (kcal) / FFM (kg) EI – Net EEE / FFM LEAF- Q
Energy Availability cutoff (in study) LEA = <30 kcal/kg of FFM;
At LEA risk = Score ≥8 on LEAF-Q		 LEA = <30 kcal/kgFFM/day;
Suboptimal EA (male) = 30-45 kcal/kgFFM/day;
EA = >45 kcal/kgFFM/day		 Optimal EA = >45 kcal/kg FFM/day;
Moderate EA = 30–45 kcal/kg FFM/day;
Poor EA = <30 kcal/kg FFM/day		 LEA = <30 kcal/kgFFM/day Clinically LEA = <30 kcal/kg FFM
Subclinical LEA = 30 - 40 kcal/kg FFM
Optimal or high EA = >40 kcal /kg FFM		 NA At LEA risk = Score ≥8 on LEAF-Q
Energy Intake assessment method 4-day diet logs 2005 FFQ and 2014 FFQs 7-day dietary record 3–5 days diet logs 3-day food diaries 7 days remote food photography method (RFPM) 3-day weighed food records
Exercise Energy Expenditure assessment method Wearable monitoring devices Actiheart heart rate monitors Based on lab study performed by Nyman & Spriet in 2021 Heart rate monitor MET values of physical activities (Ainsworth compendium 2011) MET values of physical activities (Ainsworth compendium 2011) Global Positioning System (GPS) monitors
Resting Metabolic Rate assessment method NA NA Harris-Benedict equation Harris-Benedict equation NA Harris-Benedict equation NA
Author(s) and Year (Magee et al., 2020) (Matt et al., 2022) (Traversa et al., 2022) (Vescovi & Van Heest, 2016) (Macuh et al., 2023) (Taylor et al., 2022) (Otte et al., 2023)
Body Composition assessment method Air displacement plethysmography DXA and BIA Bioelectrical impedance analysis (BIA) DXA BIA Assumed 13% body fat Portable BIA
OUTCOME
Energy Availability reported LEA in 66.7% athletes (23.0 ± 5.7 kcals/kg FFM);
Non-LEA in 6 athletes (36.4 ± 7.3 kcals/kg FFM);
At LEA risk = 56.3% of athletes		 EA (<30) = 36 athletes (female=33; male=3)
EA (30-44.99) = 18 athletes
EA (>45 kcal/kgFFM/day) = 16.4% of the total sample		 Mean EA over the 7-day period was:
Forwards = 29.0 ± 3.7 (kcal/kg/FFM/day);
Backs = 33.3 ± 3.5 (kcal/kg/FFM/day);
86% of players did not achieve optimal EA		 Energy availability observed = <30 kcal/kg FFM/d Energy availability = 29 kcal/kg FFM EA = (−)21.9 to 76.0 kcal · kg FFM−1 · day−1 At risk of LEA = 42% athletes
Dietary determinants concluded Refuelling resources, Nutritional knowledge, Performance level Gender, Disordered Eating Playing position, Competitive phase, Appetite, Training load, External stress Inadvertent undereating, Training load, Time constraints, Convenience, Appetite Food beliefs, Training load, Supplement use Appetite, Weight cutting, Training load, Nutritional knowledge, Time constraints, Intake assessment, Food choices, Competitive phase (training day) Competitive phase, Nutritional knowledge, Time constraints, Training load, Appetite, Gut health, Employment status (time), Convenience
Abbreviation: BIA= Dual-Energy X-ray Absorptiometry.
Author(s) and Year (L. C. de Souza et al., 2023) (Silva et al., 2018) (Dasa et al., 2023) (Jesus et al., 2022) (Condo et al., 2019) (Zabriskie et al., 2019) (Robbeson et al., 2013)
Reference [18] [19] [37] [38] [20] [47] [21]
Study design Cross-sectional Cross-sectional Prospective observational Observational Cross-sectional NA Cross-sectional descriptive
POPULATION
Sample size n=24 n=82 n=51 n=88 n=30 n=20 n=16
Gender Female Female (n=61) and Male (n=21) Female Male (n=64) and Female (n=24) Female Female Female
Age (years) 19.5 ± 1.3 12.8 ± 3.1 22 ± 4 16 to 35 18 to 35 20.4 ± 1.8 18 to 30
Performance level (as described by authors) National Collegiate Competitive Elite National and International Elite National Collegiate University and higher
Sport(s) Acrobatics & Tumbling (A&T) Acrobatic gymnastics (ACRO) Football Basketball (n = 29), Handball (n = 7), Volleyball (n = 9), Swimming (n = 18), and Triathlon (n = 25) Australian Rules Football Lacrosse Track and field
CONTEXT
Origin US Portugal Norway Portugal Australia US South Africa
Competitive phase Training Competition Training and Match Preparation and Competition Pre-season Entire season In season
CONCEPT
Energy Availability assessment method TEI – EEE / FFM EI - EEE Normalised to FFM [EI – EEE] / FFM EI (in kcal) − EEE (in kcal)/ FFM (in kg) LEAF-Q (EI – AEE)/kg FFM Mean daily EI (kcal) - Mean daily estimated EEE (kcal)/ FFM (kg)
Energy Availability cutoff (in study) LEA = <30 kcals·FFM−1 Low energy availability
LEA = <45 kcal/kg FFM/day		 LEA = <30 kcal/kg FFM/day Clinical LEA = <30 kcal/kg FFM for both sexes;
Subclinical LEA = 30–40 kcal/kg FFM for males or 30–45 kcal FFM for females;
Optimal EA = ≥40 kcal/kg FFM for males and ≥45 kcal/kg FFM for females		 At risk LEA = score ≥8;
Not at-risk LEA = score ≤7		 Clinical LEA = <30 kcal/kg FFM Low estimated EA = <30 kcal/kg FFM/day;
Healthy estimated EA = ≥45 kcal/kg FFM/day
Energy Intake assessment method 3-day dietary recall 3-day food records 3 (24-hour) diet recalls NA 3 (24 h) dietary recalls 4-day dietary record 4 (24-hour) dietary recalls
Exercise Energy Expenditure assessment method Proposed by Beermann, 2020 MET values of physical activities (Ainsworth compendium 2011) DLW and GPS data DLW and MET values of physical activities (Ainsworth compendium 2011) NA Physical activity monitor MET values of physical activities (Ainsworth compendium 2011)
Author(s) and Year (L. C. de Souza et al., 2023) (Silva et al., 2018) (Dasa et al., 2023) (Jesus et al., 2022) (Condo et al., 2019) (Zabriskie et al., 2019) (Robbeson et al., 2013)
Resting Metabolic Rate assessment method Cunningham equation Cunningham equation Cunningham equation;
Harris-Benedict equation		 Indirect calorimetry NA Cunningham equation;
Schofield equation		 NA
Body Composition assessment method DEXA Skinfold thicknesses and BIA DEXA Air displacement plethysmograph NA DEXA scan DEXA
OUTCOME
Energy Availability reported LEA = 58.3%, n = 14 student-athletes Female adolescent gymnasts EA = 32.8 ± 9.4 kcal/kg FFM/day;
Female children EA = 45.8 ± 8.7 kcal/ kg FFM/day;
Male gymnasts EA = 45.1 ± 14.7 kcal/kg FFM/day;
Male children EA = 53.8 ± 9.1 kcal/kg FFM/day		 LEA on training days = 23% of the players;
LEA on match days = 36% of the players		 Clinical low EA at the preparatory phase = Eleven athletes (12.5%) At risk of LEA = 30% of players EA (phase 1) = 30.4 ± 11.0 kcal/kg FFM;
EA (phase 2) = 26.2 ± 10.5 kcal/kg FFM;
EA (phase 3) = 22.9 ± 8.5 kcal/kg FFM;
EA (phase 4) = 28.7 ± 9.5 kcal/kg FFM;
EA (phase 5) = 28.9 ± 9.2 kcal/kg FFM		 Low estimated EA = Eleven athletes (73.3%)
Dietary determinants concluded Training load, Meal composition (low CHO) Training load Competition phase, Food beliefs, Training load Type of sport, Weight cutting, Competition phase Restrictive diet, Food beliefs, Nutritional knowledge, Estimated energy requirements, Training load, Food intolerances Availability, Travel, Competition phase, Training load, Time constraints, Convenience, Education level Disordered eating, Weight cutting, Body image
Abbreviations: TEI= Total Energy Intake.
DLW= Doubly Labeled Water.
DEXA= Dual-Energy X-ray Absorptiometry.

Author(s) and Year (Reed et al., 2014) (Jurov et al., 2021) (Reed et al., 2013) (Stenqvist et al., 2020) (Joaquim et al., 2018) (Schaal et al., 2017) (Cook & Dobbin, 2022)
Reference [48] [22] [49] [42] [57] [53] [39]
Study design NA Cross-sectional controlled laboratory NA Prospective intervention NA NA Observational cohort
POPULATION
Sample size Pre-season n=19;
Mid-season n=15;
Post season n=17		 n=12 n=19 n=20 n=17 n=9 n=36
Gender Female Male Female Male Male (n=9) and Female (n=8) Female Male
Age (years) 18 to 21 18 to 35 18 to 21 18 to 50 26 (6.17) 20.4 ± 0.4 23.1 ± 3.9
Performance level (as described by authors) National Collegiate Trained (n =3), Well-trained (n = 4) and Professional athletes (n = 5) National Collegiate Regional and National Elite National Elite (National and International)
Sport(s) Soccer Endurance Soccer Cycling Paralympic track and field Synchronized swimming Cycling
CONTEXT
Origin US Slovenia US Norway Brasil France UK
Competitive phase Pre-season, Mid-season, and Post season Training Pre-season, Mid-season, and Post season Training Pre-competitive and Competitive training Pre competition Preparatory
CONCEPT
Energy Availability assessment method EA = EI - EEE relative to kilograms of LBM (kcal. kg–1 LBM) EA = (EI-EEE)/ FFM EA = EI - EEE relative to kilograms of LBM (kcal. kg–1 LBM) EA = (EI [kcal] - EEE [kcal])/(FFM [kg])/day EA = EI (kcal) - EEex(kcal)/ FFM (kg) EA = EI - ExEE normalizing the resulting value to lean body mass EAy = (EI − EEE) ∕ eLBM
Energy Availability cutoff (in study) LEA = <30 kcal. kg–1 LBM;
Higher EA = ≥30 kcal. kg–1 LBM,		 Optimal EA = ≥40 kcal/kg FFM/day LEA = <30 kcal. kg–1 LBM;
Higher EA = ≥30 kcal. kg–1 LBM,		 LEA = <30 kcal·kg−1 FFM·day−1 Adequate EA = ≥45kcal/kgFFM/day;
Reduced EA = 30 to 45kcal/kgFFM/day;
LEA = ≤30kcal/kgFFM/day		 Energy balanced state EA = 45 kcal.kg/LBM/day;
LEA = <30 kcal.kg/LBM/day		 EA (healthy physiological functioning) = ~45 kcal·kg LBM−1·day−1;
EA (impaired physiological functioning) = <30 kcal·kg LBM− 1·day−1
Energy Intake assessment method 3-day diet logs Dietary diaries for 7 consecutive days 3-day diet logs 4 four consecutive days weighed and registered dietary inatkes 4 consecutive days digital worksheet and a photographic record 4 consecutive days food photographs 3 consecutive days food records
Author(s) and Year (Reed et al., 2014) (Jurov et al., 2021) (Reed et al., 2013) (Stenqvist et al., 2020) (Joaquim et al., 2018) (Schaal et al., 2017) (Cook & Dobbin, 2022)
Exercise Energy Expenditure assessment method Polar Team2 software, Heart rate monitors, and purposeful exercise logs Wearable heart rate monitors Polar Team2 software, Heart rate monitors, and purposeful exercise logs MET values of physical activities (Ainsworth compendium 2011) Multidirectional accelerometer Heart rate monitor MET values of physical activities (Ainsworth compendium 2011)
Resting Metabolic Rate assessment method World Health Organization equation Indirect calorimetry:
Harris-Benedict equation was used		 World Health Organization equation Indirect calorimetry;
Cunningham equation		 NA NA Harris-Benedict equation
Body Composition assessment method DXA Bioelectrical impedance device DXA DXA Skinfold measures Skinfold measurement Boer formula for men
OUTCOME
Energy Availability reported LEA (preseason) = 5 of 19 (26%);
LEA (midseason) = 5 of 15 (33%);
LEA (postseason) = 2 of 17 (12%)		 EA = 29.5 kcal/kg FFM/ day LEA (preseason) = 5 of 19 (26%);
LEA (midseason) = 5 of 15 (33%);
LEA (postseason) = 2 of 17 (12%)		 Markers associated with LEA found low T3, lowered RMR, and increased cortisol LEA (day 1) = 17.6% of athletes;
LEA (day 2) = 33.3% of athletes;
LEA (day 3) = 33.3% of athletes;
LEA (day 4) = 8.3% of athletes		 Baseline EA = 25.0 ± 3.2 kcal.kg/LBM/day;
ITWK2 EA = 22.3 ± 1.9 kcal.kg/LBM/day;
ITWK4 EA = 18.0 ± 2.8 kcal.kg/LBM/day		 EA (rest day) = 44 ± 14 kcal·kg LBM−1·day−1;
EA (training days) = 16 ± 18 kcal·kg LBM−1·day−1;
Mean EA = 25 ± 13 kcal·kg LBM−1·day−1
Dietary determinants concluded Energy density, Meal composition, Weight cutting Weight cutting, Training load Body image, Meal composition, Training load, Inadvertent undereating, Meal preparation (Food availability), Appetite Training load, Inadvertent undereating, Meal composition Taste, Athlete-guide, Disability Appetite, Hormone, Gut health, External stress (social and environmental), Weight cutting, Type of sport, Competition phase, Training load Training load, Eating disorder
Abbreviation: LBM= Lean Body Mass.
Author(s) and Year (Egger & Flueck, 2020) (Viner et al., 2015) (Jurov et al., 2021) (Kinoshita et al., 2021) (Torstveit et al., 2019) (Jurdana et al., 2022) (Kettunen et al., 2021)
Reference [54] [58] [23] [24] [25] [59] [55]
Study design NA NA Intervention cross-sectional controlled laboratory Cross-sectional Cross-sectional intervention Prospective NA
POPULATION
Sample size n=14 n=10 n=12 n=18 n=53 n=10 n=19
Gender Male (n=8) and Female (n=6) Male (n=6) and Female (n=4) Male Female Male Male Female
Age (years) 18 to 60 29 to 49 NA 15 to 19 18 to 50 15 to 30 Under-18
Performance level (as described by authors) Elite (National) Competitive National and Professional Competitive high school Regional competitive Competitive Elite National
Sport(s) Wheelchair Endurance cycling Cycling, Triathlon, Endurance Middle- or Long- distance running Endurance (Cycling, Triathlon, Long-distance running) Cycling XC Skiers
CONTEXT
Origin Switzerland US Slovenia Japan Norway Slovenia Finland
Competitive phase Pre-season Training and Competition NA Training Training Competitive training Preparation
CONCEPT
Energy Availability assessment method (EI - EEE)/kg FFM {EI – [EEE – (RMR/min × exercise min)]} ·FFM (kg)–1·day–1 (EI-EEE)/FFM EI - EEE
Adjusted for fat free mass (FFM)		 EI - EEE
Relative to fat free mass (FFM)		 EI - EEE
Relative to FFM		 EI - EEE
Expressed in kcal/kg fat-free mass (FFM)-1/day (d)-1
Energy Availability cutoff (in study) Optimal EA = ≥45 kcal kg-1 FFM day-1;
Suboptimal EA = 30 kcal kg-1FFM day-1 to 45 kcal kg-1FFM day-1;
LEA = ≤30 kcal kg-1FFM day-1 		LEA = <30 kcal·kg FFM–1·day–1 LEA being = <30 kcal/kg FFM/day LEA = <30 kcal⋅kg−1 FFM⋅d−1;
Optimal EA = 45 kcal⋅kg−1 FFM⋅d−1 		LEA = <30 kcal/kg FFM/day LEA = 30 kcal/kg FFM/day LEA = <30 kcal/kgFFM-1/d-1;
Optimal EA = >45 kcal/kgFFM-1/d-1
Energy Intake assessment method Weighed 7-consecutive day food diary 3 days·month–1 7 consecutive days food diaries 7-day dietary records 3 or 4 consecutive days food logs 3-day food diaries and photographic records 48-hour food logs
Exercise Energy Expenditure assessment method Recommendations by Conger & Bassett, 2011 MET values of physical activities (Ainsworth compendium 2011) Wearable heart rate monitors Wearable HR monitor Heart rate monitor MET values of physical activities (Ainsworth compendium 2011) Equations by Charlot, 2014
Author(s) and Year (Egger & Flueck, 2020) (Viner et al., 2015) (Jurov et al., 2021) (Kinoshita et al., 2021) (Torstveit et al., 2019) (Jurdana et al., 2022) (Kettunen et al., 2021)
Resting Metabolic Rate assessment method Metabolic cart;
Equation for SCI by Pelly, 2017		 Cunningham equation Indirect calorimetry Whole room calorimeter;
Cunningham’s equation		 Ventilated hood;
Cunningham equation		 Handheld indirect calorimeter Cunningham equation
Body Composition assessment method DXA DXA Bioelectrical impedance DXA DEXA BIA Bioimpedance measurement
OUTCOME
Energy Availability reported LEA = 73% of the days in female athletes
LEA = 30% of the days in male athletes		 LEA (Pre-season) = 70% athlete;
LEA (Competition) = 90% athlete;
LEA (Off-season) = 80% athlete		 EA reduced by 50% LEA = 6 of the 18 participants EA (Lower EXDS) = 41.0 ± 11.0 kcal/kg FFM/day;
EA (Higher EXDS) = 35.1 ± 10.3 kcal/kg FFM/day		 EA = 35 kcal/kg FFM Suboptimal EA at HOME = 89% of athletes;
Suboptimal EA at CAMP = 58% of athletes;
LEA at HOME = 5 (26%) athletes;
LEA at CAMP = 7 (37%) athletes
Dietary determinants concluded Weight cutting, Competition phase, Training load, Gender Food beliefs, Restrictive diets, Weight cutting (Body composition management), Competition phase, Energy Density, Meal composition Training load Body image, Ad libitum intake Eating disorder, Body image Type of sport, Competitive phase, Training load, Residence, Meal composition (Macronutrient intake) Nutritional knowledge, Meal composition (macronutrient intake), Resource availability (prepared meal), Training load, Residence
Author(s) and Year (Kuikman et al., 2021) (Wright et al., 2014) (Hoch et al., 2009) (Torres-McGehee et al., 2021) (Simič et al., 2022) (McGuire et al., 2023)
Reference [50] [26] [27] [28] [29] [62]
Study design Survey Cross-sectional descriptive Prospective cross-sectional Cross-sectional Cross-sectional NA
POPULATION
Sample size n=642+257 n=22 n=80+80 n=121 n=27 n=20
Gender Female (n = 642) and Male (n = 257) Female Female Female Male (n=13) and Female (n=14) Male
Age (years) 18 above 18 to 30 13 to 18 19.8 ± 2.0 13 to 18 18 to 40
Performance level (as described by authors) Recreational (n = 148/62), Collegiate (n = 217/38), National (n = 156/90), and International athletes (n = 119/66) University (provincial or national level) Varsity athletes (n=80) and sedentary students/controls (n=80) Collegiate athletes and performing artists Competitive Elite inter-county
Sport(s) Cycling, long-distance running = 84% (n = 565/188), Soccer, rugby = 8% (n = 32/38), Sprinting, shot-putting = 5% (n = 30/16) ; and Archery, equestrian = 1% (n = 11/0) Field hockey (n = 9) and netball (n = 13) Track (n=24), cross-country (n=25), volleyball (n=13), basketball (n=14), soccer (n=24), tennis (n=7), swimming (n=17), golf (n=3) and softball (n=6) Equestrian (n=28), soccer (n=20), beach volleyball (n=18), softball (n=17), volleyball (n=12), and ballet (n=26). Climbing Gaelic football
CONTEXT
Origin Canada Australia US US Slovenia Ireland
Competitive phase Training and Competition Training NA Training Training (selection) Pre-season and In-season
CONCEPT
Energy Availability assessment method LEA (females) = LEAF-Q
LEA (males) = Non validated questionnaire		 mean EI (kcal) – mean estEEE (kcal)/ FFM (kg) DI - EEE [EI - EEE]/kg/FFM EI - EEE / FFM EI - EEE
Relative to kilograms of lean body mass per day
Energy Availability cutoff (in study) At risk LEA = score ≥8 Low estEA = <30 kcal/kg FFM/day;
Optimal estEA = ≥45 kcal/kg FFM/day		 LEA = <45 kcal/kg/LBM. LEA = ≤30 kcal/kg of fat-free fat mass Optimal EA = 45 kcal/kg FFM/day;
Reduced EA = 30–45 kcal/kg FFM/day		 High EA = >40 kcal.kg LBM−1.d−1;
Optimal EA = ≥40 kcal.kg LBM−1.d−1;
Subclinical EA = 30 - 40 kcal.kg LBM−1.d−1;
Clinical EA = <30 kcal.kg LBM−1.d−1
Author(s) and Year (Kuikman et al., 2021) (Wright et al., 2014) (Hoch et al., 2009) (Torres-McGehee et al., 2021) (Simič et al., 2022) (McGuire et al., 2023)
Energy Intake assessment method NA 3-day diet record 3-day food diary 7 consecutive days 3-day food records 3 consecutive days photographed diary
Exercise Energy Expenditure assessment method NA MET values of physical activities (Ainsworth compendium 2011) Ainsworth compendium of physical activity (1993, 2000, 2008) SenseWear Armband with an accelerometer MET values of physical activities (Ainsworth compendium 2000) MET values of physical activities (Ainsworth compendium 2011)
Resting Metabolic Rate assessment method NA Cunningham equation NA Indirect calorimetry An integrated software Cunningham equation
Body Composition assessment method NA DEXA DXA DEXA BIA Skinfold measurements
OUTCOME
Energy Availability reported Likelihood of being at risk of LEA was 2.5 times for female athletes compared to controls Low estEA (24± 12 kcal/kg fat-free mass/day) = 59% of the athletes LEA in athletes = 36%
LEA in sedentary/ control subjects = 39%		 LEA = 81% (n=98) participants Average EA = 27.5 ± 9.8 kcal/kg FFM/day LEA (at pre-season) = 65%
LEA (at in season) = 70%
Dietary determinants concluded Performance level, Disordered eating, Athlete support Weight cutting (dieting), Training load, Body image, Environment familiarity, External stress (media, peer) Inadvertent undereating, External stress (culture and media) Type of sport (clothing), External stress, Competitive phase, Time availability, Team support, Weight cutting, Training load (unaccounted increased EEE) Inadvertent undereating, Food group avoidance, Restrictive diets Restrictive diets, Competitive phase
Author(s) and Year (Imandel et al., 2021) (Halfacre Katharine L., 2020) (Peterson et al., 2018) (Day, 2016) (Brown et al., 2013) (Nalder, 2012)
Reference [51] [30] [44] [52] [31] [45]
Study design NA Cross-sectional Descriptive NA Cross-sectional Intervention Descriptive
POPULATION
Sample size n=92 n=9 n=81 n=25 n=29 Athlete (n=22) and non-athlete control (n=22)
Gender Male Male Male (n=38) and Female (n=43) Female Female Male
Age (years) 19.8 ± 1.4 24 to 35 18 to 25 19.5 ± 1.8 13 to 18 18 to 45
Performance level (as described by authors) Collegiate Professional (n=6) and amateur (n=3) Collegiate Collegiate High School Elite
Sport(s) Cross-country and track and field MMA fighters Track and field, Football, Volleyball, Soccer, Golf, Basketball, and Tennis Distance runners, sprinters, hurdlers, and jumpers Track Cycling
CONTEXT
Origin US US US US US US
Competitive phase Training Competition (RWL) NA NA Training Training
CONCEPT
Energy Availability methodology (described by) Collegiate Professional Sports Dietitians Association, 2018 NA Loucks & Thuma, 2003 Kopp-Woodroffe, 1999 Nattiv, 2007 Nattiv, 2007
Energy Availability assessment method Food Energy Intake – Exercise Energy Expenditure = Energy Availability ALEA = Defined by a negative value for TEA, which is the difference between caloric intake and TEE DI – ExEE kcal/kg of LBM/day average daily EI - daily EEE;
Converted to an index of EA by dividing EA by kg of FFM		 DI - EEE mean EI – mean EEE normalized for FFM
Energy Availability cutoff (in study) LEA = <30 kcal/kg fat-free mass per day;
Energy balance = 45 kcal/kg fat-free mass per day		 0 = Adequate Energy, TEA ≥ 0;
1 = ALEA, TEA < 0		 LEA = <30 kcal/kg of LBM/day;
Reduced EA = 30-45 kcal/kg of LBM/day;
Adequate EA = ≥45 kcal/kg of LBM/day		 EB = ≥45 kcals/kg of FFM/d;
Below EB = <45 kcal/kg of FFM/d;
Restricted EI = ≤30 kcal/kg of FFM/d		 Optimal EA = ≥45 kcal/kg LBM;
EA (bone turnover) = <45 kcal/kg LBM;
EA (menstrual dysfunction) = <30 kcal/kg LBM;
Moderate LEA = <45 but >30 kcal/kg LBM		 Energy balance = >45 kcal-kg-1FFM-d-1;
suppressed reproductive function and bone formation = <30 kcal-kg-1FFM-d-1
Author(s) and Year (Imandel et al., 2021) (Halfacre Katharine L., 2020) (Peterson et al., 2018) (Day, 2016) (Brown et al., 2013) (Nalder, 2012)
Energy Intake assessment method 24-hour dietary recall 7-day food journals 3-days 24 h recall 3-day diet record 3 consecutive 24-hour diet recall 3-day food record
Exercise Energy Expenditure assessment method MET values of physical activities (Ainsworth compendium 2011) MET values of physical activities (Ainsworth compendium 2011) Accelerometers and PA logs Actigraph GTX3 triaxial accelerometer MET values of physical activities (Ainsworth compendium 2011) MET values of physical activities (Ainsworth compendium 2011)
Resting Metabolic Rate assessment method NA Cunningham equation NA NA World Health Organization, 1985 Indirect calorimetry
Body Composition assessment method Air displacement plethysmography or DXA Skinfold measurements DXA Skinfold measurements Bod Pod Bioelectrical impedance
OUTCOME
Energy Availability reported EA <30.0 kcal/kgFFM/day = 35% (n=7) Negative EA (fight week) = (n=8) 88.9% participants LEA (males) = n=9; 23.7%;
LEA (females) = n=5; 11.6%		 Mean EA = 30.8 kcal/kg of FFM/d;
EA < 45 kcal/kg of FFM/d = 92% (23 participants);
EA < 30 kcal/kg of FFM/d = 52% (13 participants)		 Moderate LEA = 50% (n = 11);
EA > 45 kcal/kg LBM = 27.3% (n=6);
EA < 30 kcal/kg LBM = 22% (n=5)		 EA (cyclists) = 17.7 ± 8.9 kcal-kg-1 FFM-d-1;
EA (controls) = 33.86 ± 9.8 kcal-kg-1 FFM-d-1;
LEA (cyclists) = 91% (n=20);
LEA (controls) = 41% (n=9)
Dietary determinants concluded Dietary restraint Weight cutting (RWL) Inadvertent undereating Time constraints, Body image (Attitude about body fat and its relation to performance) Body image, External stress (media, society) Restrictive diets, Weight cutting, Performance enhancement
Abbreviations: ALEA= Acute Low Energy Availability.
TEA= Total Energy Availability.
TEE= Total Energy Expenditure.
Author(s) and Year (Reed, 2012) (Moss et al., 2020) (Villa et al., 2021) (Muia et al., 2016) (Kalpana et al., 2023) (Fenton, 2022)
Reference [32] [63] [40] [9] [41] [33]
Study design Cross-sectional NA Observational NA Observational Cross-sectional
POPULATION
Sample size n=19 n=13 Pre-teen (n=17) and Teen (n=13) n=61+49 n=52 n=24
Gender Female Female NA Female Male Male (n =10) and Female (n =14)
Age (years) 18 to 21 23.7 ± 3.4 9 to 12; 13 to 18 16 to 17 16 to 31 16 to 35
Performance level (as described by authors) Collegiate Professional Elite Competitive athlete (n=61) and non-athlete (n=49) National Amateur elite
Sport(s) Soccer Soccer Gymnasts Runners Kho-Kho Track
CONTEXT
Origin US UK Spain East Africa India New Zealand
Competitive phase Pre, Mid, and Post season In season (Training and competition) Training Training Training NA
CONCEPT
Energy Availability assessment method EI - EEE
Relative to kilograms of lean body mass (kcal/kg LBM)		 (EI − EEE)/ FFM NA Mean daily EI (kcal ∙ d−1) – Mean daily EEE (kcal ∙ d−1) / Fat-free mass (kg)−1 EI - EEE
Expressed as kcal per kg FFM		 Female = LEAF-Q;
Male = Gastrointestinal questions from the LEAF-Q, sexual desire inventory (SDI) and the androgen deficiency in aging males (ADAM)
Energy Availability cutoff (in study) Low EA = <30 kcal/kg lean body mass Optimal EA = >45 kcal·kg FFM−1·day−1;
Reduced EA = 30-45 kcal·kg FFM−1·day−1;
Low EA = <30 kcal·kg FFM−1·day−1 		Optimal EA =r >45 kcal/kg FFM/day;
Sub-clinical EA = 30–45 kcal/kg FFM/day;
Extreme LEA = <10 kcal/kg FFM		 Clinical LEA= <30 kcal ∙ kg FFM−1 ∙ d−1;
Subclinical LEA = 30–45 kcal ∙ kg FFM−1 ∙ d−1;
Optimal EA = ≥45 kcal ∙ kg FFM−1 ∙ d−1 		LEA = ≤25 kcal per kg FFM At LEA risk (female) = score ≥8;
At risk (male) = score ≥20
Energy Intake assessment method 3-consecutive days diet logs 5-day weighed food diary 7-day food diary 5-day diet record 1-day direct weighment plus recall method NA
Exercise Energy Expenditure assessment method Polar Team2 software, Heart rate monitors, and purposeful exercise logs MET values of physical activities (Ainsworth compendium 2011) ActiGraph accelerometer MET values of physical activities (Ainsworth compendium 2000) MET values of physical activities (Ainsworth compendium 2000) NA
Author(s) and Year (Reed, 2012) (Moss et al., 2020) (Villa et al., 2021) (Muia et al., 2016) (Kalpana et al., 2023) (Fenton, 2022)
Resting Metabolic Rate assessment method Indirect calorimetry;
Harris-Benedict equation		 Indirect calorimetry;
Cunningham equation		 World Health Organization (WHO) (2006) Schofield equation Cunningham equation NA
Body Composition assessment method DXA DXA Bioimpedance analysis Skinfold measurements Skinfold measurements NA
OUTCOME
Energy Availability reported Low EA (pre-season) = 9 of 19 (47%);
Low EA (mid-season) = 6 of 15 (40%);
Low EA (post-season) = 2 of 17 (12%)		 Optimal EA = 15% of players;
Reduced EA = 62% of players;
Low EA = 23% of players		 Subclinical EA (Pre-Teen) = 12 (70.6%);
LEA (Pre-Teen) = 2 (29.4%);
LEA (Teen) = 13 (100%)		 Clinical LEA = 17.9% (n = 10/56) of athletes;
Clinical LEA = 2.2% (n = 1/45) of non-athletes;
Subclinical LEA = 76% participants		 LEA = 44% of players At risk (female) = 8
At risk (male) = 5
Dietary determinants concluded Competitive phase, Body image, Resource availability (Food availability, affordability), Appetite Training load, Meal composition (Lack of periodization), Inadvertent undereating Age, Athlete support Meal composition, Appetite, Socio-economic status (financial affordability), Planned food plate, Attitude (care-free), Body image, Training load Training load, Food environment, Inadvertent undereating Body image, Weight cutting, Performance (under fuelling), Inadvertent undereating

3.1. Sample

The athletes in the studies were competing at high school (n = 3) [16, 24, 31], university (n = 16) [9, 18, 21, 26-28, 32, 34, 44, 46-52], national (n = 12) [12, 13, 15, 23, 38, 39, 41, 42, 50, 53-55], and international (n = 5) levels [15, 38, 39, 50, 56]. The sports played by athletes in the studies can be categorized into endurance (n = 25) [9,, 12, 16, 21-25, 27, 31, 33, 35, 36, 38, 39, 42, 44, 45, 50-52, 55, 57-59], team (n = 22) [13, 17, 20, 26-28, 32, 34, 37, 38, 41, 44, 46-50, 53, 60-63], skill games (n = 8) [18,, 19, 27, 28, 40, 44, 50, 56], and power (n = 2) [30, 50]. The majority of the studies were undertaken in the US (n = 16) [16, 18, 27, 28, 30-32, 34, 44, 45, 47-49, 51, 52, 58] and Slovenia (n = 5) [22, 23, 29, 59, 61]. Out of the 53 studies, eleven had an age group less than 18 years [9,, 13, 14, 16, 19, 24, 27, 29, 31, 40, 55], four studies belonged to the age group 16-30 years, while 37 had an age group greater than 18 years [12, 15, 17, 18, 20-22, 25, 26, 28, 30, 32-39, 41-54, 56-63].

3.2. EA Methodology

The three main methods described for assessing EA used FFM in the majority (n = 45) of studies [9, 12-14, 18, 19, 21-29, 31, 32, 34-49, 52-63], while a few used LBM (n = 7) and LEAF-Q (n = 6) [15, 17, 20, 23, 32-34, 44, 48, 49, 50, 53, 62]. The cut-off used to classify athletes in the LEA category was < 30 kcal ∙ kg FFM−1 ∙ d−1 or ≤ 30 kcal ∙ kg FFM−1 ∙ d−1 in most studies (n = 35), while in some (n = 4) it was < 45 kcal ∙ kg FFM−1 ∙ d−1 [9, 12, 13, 14, 16, 18, 19, 21, 23-28, 32, 34, 35, 37-39, 42-44, 46-49, 51, 53-63]. The majority of studies (n = 26) utilized 3-day dietary recalls for energy intake assessment [13, 14, 17-20, 25-27, 29, 31, 32, 35, 37, 39, 43-45, 48, 49, 52, 58-62]. The exercise energy expenditure was assessed using MET values (n = 23) [9, 12, 14, 19, 21, 26, 27, 29-31, 36, 38, 39, 41, 42, 45, 51, 56, 58, 59, 61-63], wearable monitors (n = 21) [13, 16, 17, 22-25, 28, 32, 34, 35, 37, 40, 44, 47-49, 52, 53, 57, 60], DLW (n = 2) [37, 38], recommendations of Conger and Basset (n = 2) [43, 54], a lab study performed by Nyman & Spriet in 2021 (n = 1) [46], and equation by Charlot et al. 2014 (n = 1) [55, 64]. The predictive equations used for assessing resting metabolic rate were the Cunningham equation (n = 15) [18, 19, 24-26, 30, 37, 41, 42, 47, 55, 56, 58, 62, 63], the Harris and Benedict equation (n = 7) [22, 32, 35-37, 39, 46], World Health Organization equation (n = 4) [31, 40, 48, 49], the Schofield equation (n = 2) [9, 47], the equation for Southeast Asian athletes (n = 1) [60], equation for SCI (n =1) [54]. Indirect calorimetry (n = 11) was also used in studies to assess the resting metabolic rate [22-25, 28, 32, 38, 42, 45, 59, 63]. The body composition of the study participants was measured using Dual X-ray Absorptiometry (DXA) scans (n = 23) [12, 14, 16, 18, 21, 24-28, 32, 35, 37, 42-44, 47-49, 51, 54, 58, 63], Bioelectrical Impedance Analyser (BIA) (n=14) [16, 17, 19, 22, 23, 29, 40, 45, 46, 55, 56, 59-61], skinfold measurements (n = 9) [9, 13, 19, 30, 41, 52, 53, 57, 62], air plethysmography (n = 5) [13, 31, 34, 38, 51], and Boer’s formula (n = 1) [39]. 40 out of 53 studies reported participants had EA < 30 kcal ∙ kg FFM−1 ∙ d−1 while four suggested athletes being at risk of LEA [9, 12, 13, 15-18, 20, 21, 22, 24, 26-29, 31-41, 43-49, 51-55, 57, 58, 60-63].

3.3. Dietary Determinants

Various determinants emerged in the review, which were categorized into themes as given in Table 2.

Table 2.
Categorisation of determinants into themes.
Theme Key Determinants
1. Training and Performance Demands - Increased training loads
- Athletic season/periodization
- Sport-specific requirements (e.g., weight-class, aesthetics)
2. Psychosocial and Cultural Influences - Body image concerns
- Media and sporting culture pressures
- Gender-related vulnerabilities (especially in female athletes)
- Psychological stress
3. Dietary Practices and Nutritional Beliefs - Restrictive diets (e.g., paleo, gluten-free)
- Carbohydrate and macronutrient misconceptions
- Meal timing constraints
- Voluntary intake restriction
4. Environmental, Logistical, and Educational Factors - Time constraints (academic/workload)
- Food availability and affordability
- Nutrition knowledge
- Parental or coach influence
- Access to support staff
5. Physiological considerations - Appetite suppression from training/intensity
- Hormonal influences (e.g., ghrelin)
6. Methodological Considerations - Underreporting or altered behaviour during assessments

3.3.1. Training and Performance Demands

Elevated training loads without a proportional increase in EI resulted in LEA in 27 studies [9, 12, 14, 17-20, 22, 23, 26, 28, 35-37, 39, 41-43, 46, 47, 49, 53-55, 59, 61, 63], with a 42% frequency among Australian AFLW females during preseason [17]; 44% Indian Kho-Kho males due to inadvertent undereating [41]. Phases of training affected EA, which decreased to 4.2 kcal·kg−1 FFM·day−1 in multi-sport populations [47], interfacing with weight-cutting practices during particular seasons (n = 16) via dietary restriction for performance or body image considerations [12, 14, 21, 22, 26, 28, 30, 33, 36, 38, 45, 48, 53, 54, 58, 60]. The phases of the athletic season (n = 15) affected dietary intake by elevating energy expenditure [17, 28, 32, 36-38, 46, 47, 53, 54, 56, 58-60, 62], resulting in food shortages due to travel, and imposing time limits (90% competition LEA). Conversely, inadequate nutritional periodisation during load variations and the miscalculation of requirements resulted in inadvertent undereating (n = 9) [27, 29, 33, 35, 41, 42, 44, 49, 63]. Sport-specific requirements, such as weighing in or conforming to clothing in weight-class/aesthetic disciplines (n = 4) [28, 38, 53, 59], led to dietary restriction, with Indian junior males displaying 67.5% LEA from unadjusted demands [13].

3.3.2. Psychosocial and Cultural Influences

Body image concerns resulted in calorie restriction and exercise reliance (n = 11; 40% LEAF-Q at-risk Irish women) [9, 15, 21, 24-26, 31-33, 43, 49, 52], exacerbated by media and sports culture influences, as well as psychological stress leading to undereating (n = 6) [26-28, 31, 46, 53]. Females were more susceptible to disordered eating and had lower EI than males (55% daily LEA vs. 35%; 2.5-fold risk) [16, 50], particularly in US high school endurance runners and Canadian aesthetic groups, where cultural stressors converged with training requirements to maintain restraint.

3.3.3. Dietary Practices and Nutritional Beliefs

The macronutrient composition (n = 12) affected energy density and satiety (n = 4) [9, 12, 13, 15, 18, 42, 43, 48, 49, 55, 58, 59, 63], while misconceptions regarding low-carbohydrate diets for weight management (n = 4) and restrictive diets (n=6; e.g., gluten-free/paleo, food group avoidance) limited intake (30% suboptimal EA among Australian rugby females) [15, 20, 29, 37, 45, 58, 61, 62]. Meal timing constraints associated with training schedules (n = 2) reduced EI even in ad libitum conditions [13, 43], indicating a correlation between nutritional attitudes and performance-driven under-eating across genders, with female-dominant samples showing the highest reduction.

3.3.4. Environmental, Logistical, and Educational Factors

Time limitations due to academic or professional commitments (n = 6) resulted in convenience eating, adversely affecting nutritional adequacy (81% of suboptimal EI among collegiate athletes) [17, 28, 35, 36, 47, 52], further compounded by inadequate knowledge (n = 5); 66.7% of youth in the US/ Finland) [17, 20, 34, 36, 55]. The influence of parents and coaches impacted dietary intake among youth (29.4% pre-teen LEA) [40], whereas food availability and affordability varied according to socioeconomic status, travel conditions, and performance level—elite athletes accessed superior nutrition in contrast to amateurs [28, 40, 50, 57]. Familiarity with the food environment, taste, and buffet/low-calorie planning all had an impact on EI [9, 26, 36, 41, 57], but collegiate class schedules limited eating windows.

3.3.5. Physiological Considerations

Increased training, meal composition, and gastrointestinal health resulted in appetite suppression and lower EI (n = 9) [9, 12, 17, 32, 35, 36, 46, 49, 53]. Ghrelin levels increased as a result of compensatory intake attempts among females [53]. Rest-training energy availability discrepancies (44 vs. 17.7 kcal·kg−1FFM·day−1) highlighted the relationship between training and physiology, particularly in mixed and male cohorts [39, 45].

3.3.6. Methodological Considerations

Underreporting or assessment-induced intake alterations biased results (70-90%; 3-7 day recalls/diaries), inflating LEA through heterogeneous cutoffs (< 30-45 kcal·kg−1 FFM·day−1), EEE records inaccuracies, and cross-sectional predominance confound gender/country-specific interpretations without validation with gold methods [36].

4. DISCUSSION

LEA, defined as < 30 kcal·kg−1 FFM·day−1 [65], manifests globally at 24% among male athletes and 58% among females. This trend is reinforced by recent data indicating 63% RED-S risk and associated performance impairments such as diminished endurance and agility [66]. Despite high rates of LEA, few studies have comprehensively identified all evidence-based dietary factors that promote LEA. Future research using multi-method cohort designs that include biomarkers (e.g., reduced fT3, oestradiol) is recommended to examine dietary components that are specific to culture, gender, and age, allowing for customized interventions, as also demonstrated in recent cohorts.

Training and performance demands significantly hinder EI, with heightened EEE showing strong correlations with LEA, attributed to inadequate periodisation, travel disruptions, appetite suppression, and socioeconomic barriers [36, 37]. Prevalence reported in recent studies indicates LEA among 11–67% of athletes during off-season and preseason/peak periods in team sports [67]. Although mechanistic insights remain limited, emerging data indicate that high training loads may reduce hunger [68]. The future scope of research in this area includes longitudinal Randomized Controlled Trials (RCTs) evaluating phase-specific EI-EEE synchronization, including carbohydrate periodisation during matches, training, and rest days, which is crucial to avert catabolic conditions, while practice tips include energy-dense food consumption during such periods.

Concerns regarding body image, in conjunction with other psychosocial factors such as gender norms, cultural pressures, media exposure, and stress, are significant drivers of low EI. Reduced caloric intake among athletes may be inadvertently promoted by coaches and the media, and female athletes may be more prone to adopt restrictive eating practices [16, 54]. This finding aligns with the current evidence of 2025, which identifies body image dissatisfaction (the pursuit of muscularity/thinness) as a significant pathway to RED-S [69]. Our findings reveal the scope of future population-stratified studies on Eating Disorders (ED) and Disordered Eating (DE) with RCTs focused on gender-specific nutritional counselling, emphasising body image acceptance and coping strategies through comprehensive psychological evaluation.

Inadequate dietary practices directly reduce EI due to macronutrient deficits, restricted diets (such as low-carbohydrate), prevalent myths and beliefs, and time constraints. These diminish energy density and satiety [13]. Currently, educational programmes that improve macro-awareness and efficacy have been reported as a solution [70]. Our data support the need for prospective evaluation of culture-specific attitudes and intake motivators and consistent athlete counselling, supported by various institutional policies and programmes to improve EI.

Lack of nutritional literacy and socioeconomic status are critical factors of LEA, intensified by environmental obstacles including insufficient team support, unfamiliarity with food, and challenges related to availability and pricing [55, 57]. Data from recent publications demonstrate a significant correlation between poor socioeconomic status and limited access to facilities and diminished motivation [71]. Contextual heterogeneity highlights nation-specific processes. Future scope includes country-specific environmental studies underpinning strategic dietary guidelines, emphasising policy-driven access to nourishment for vulnerable populations.

Physiological adaptations resulting from training disrupt appetite regulation and hormonal equilibrium (such as elevated ghrelin levels and dysregulation of PYY/CCK), a domain that remains insufficiently explored, as indicated by neural profiling of postprandial suppression in 2025 [53, 72]. This necessitates, in the future, thorough hormonal phenotyping, particularly in fasting and training conditions, essential for establishing precision nutrition frameworks that improve performance and metabolic health.

Methodological limitations decrease the integrity of LEA data, with insufficient incorporation of Indian biomarkers (e.g., T3, ferritin) for cutoff validation, as critiqued in recent reviews of tool heterogeneity [36, 73]. Standardized, strong designs that use population-specific biomarkers are needed to obtain accurate prevalence metrics.Heterogeneous evaluation modalities for EA produce varying estimates due to the lack of standardised protocols, a shortcoming highlighted by recent studies that call for cohesive multi-method frameworks [74]. Establishing global and population-adjusted standards using gold-standard methods and biomarkers can improve study comparability and clinical applicability.

In summation, the review reveals that LEA is widespread globally and is primarily influenced by factors including training, psychosocial, dietary, socioeconomic, physiological, and methodological aspects. It therefore advocates for RCT-based interventions that are culturally appropriate, biomarker-informed, and standardized.

5. LIMITATIONS

Due to a scarcity of literature, the review included all study designs, using single-reviewer database searches and no quality exclusions to capture all reported determining factors.

CONCLUSION

The high prevalence of LEA among competitive athletes globally suggests the need to conduct causal research to build a deeper understanding of the various factors leading to inadequate dietary intake among athletes, especially phase-specific, population-specific factors across cultures. This needs to be in conjunction with concomitant investigation of biomarkers and standardized RCTs to examine interventions for the identified factors influencing LEA. This will enable the development of strategic eating plans, counselling, and motivators for effective LEA prevention.

AUTHORS’ CONTRIBUTIONS

The authors confirm contribution to the paper as follows: S.G., P.R.L.: Study conception and design; S.G.: Data collection; S.G., P.R.L.: Analysis and interpretation of results; S.G.: Draft manuscript. All authors reviewed the results and approved the final version of the manuscript.

LIST OF ABBREVIATIONS

FAT = Female Athlete Triad
REDs = Relative Energy Deficiency in Sports
EA = Energy Availability
EI = Energy Intake
EEE = Exercise Energy Expenditure
LBM = Lean Body Mass
LEA = Low Energy Availability
RCTs = Controlled Trials
ED = Eating Disorders
DE = Disordered Eating

CONSENT FOR PUBLICATION

Not applicable.

STANDARDS OF REPORTING

PRISMA guidelines were followed.

AVAILABILITY OF DATA AND MATERIALS

All the data and supporting information are provided within the article.

FUNDING

None.

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

ACKNOWLEDGEMENTS

Declared none.

SUPPLEMENTARY MATERIAL

PRISMA checklist is available as supplementary material on the publisher’s website along with the published article.

Appendix 1.
Search strategy.
DATABASE/DATE FILTERS COMMAND LINE
WOS
(14-07-2023)		 Search fields: WOS Core
Collection All editions, All fields Date: 01-01-2003 to 14-07-2023		 “Energy Availability” AND Athlet* AND “Energy Intake”
“Energy Availability” AND Athlet* AND (“Die* Intake*” OR “Die* Choic*” OR
“Die* Habi*” OR “Die* Practic*”)
“Energy Availability” AND Athlet* AND (“Food Intake*” OR “Food Choic*”
OR “Food Habi*” OR “Food Practic*”)
1 OR 2 OR 3
SCOPUS
(14-07-2023)		 Search fields:
Article Title, Abstract, Keywords Year: 2003-2023		 {Energy Availability} AND Athlet* AND “Energy Intake”
(“Die* Intake*” OR “Die* Choic*” OR “Die* Habi*” OR “Die* Practic*”) AND
{Energy Availability} AND Athlet*
(“Food Intake*” OR “Food Choic*” OR “Food Habi*” OR “Food Practic*”)
AND {Energy Availability} AND Athlet*
1 OR 2 OR 3
PUBMED
(14-07-2023)		 Search fields:
All fields
Date: 01-01-2003 to 14-07-2023		 “Energy Availability” AND Athlet* AND “Energy Intake”
“Energy Availability” AND Athlet* AND (“Dietary Intake*” OR “Dietary
Choic*” OR “Dietary Habi*” OR “Dietary Practic*”)
“Energy Availability” AND Athlet* AND (“Food Intake*” OR “Food Choic*”
OR “Food Habi*” OR “Food Practic*”)
1 OR 2 OR 3
PROQUEST (14-07-2023) Search fields:
Full Text(Document Text) Year: 2003-2023		 “Energy Availability” AND Athlet* AND “Energy Intake”
“Energy Availability” AND Athlet* AND (“Die* Intake*” OR “Die* Choic*” OR
“Die* Habi*” OR “Die* Practic*”)
“Energy Availability” AND Athlet* AND (“Food Intake*” OR “Food Choic*”
OR “Food Habi*” OR “Food Practic*”)
1 OR 2 OR 3
GOOGLE SCHOLAR
(16-07-2023)		 Year: 2003-2023
Sort by Relevance		 “Energy Availability” Athlete “Energy Intake”
“Energy Availability” AND Athlet* AND (“Diet Intake” OR “Diet Choice” OR
“Diet Habit” OR “Diet Practice”)
“Energy Availability” AND Athlet* AND (“Dietary Intake” OR “Dietary
Choice” OR “Dietary Habit” OR “Dietary Practice”)
“Energy Availability” AND Athlete AND (“Food Intake” OR “Food Choice”
OR “Food Habit” OR “Food Practice”)
1 + 2 + 3 + 4

REFERENCES

1
Mountjoy M, Sundgot-Borgen JK, Burke LM, et al. IOC consensus statement on relative energy deficiency in sport (RED-S): 2018 update. Br J Sports Med 2018; 52(11): 687-97.
2
De Souza MJ, Nattiv A, Joy E, et al. 2014 Female Athlete Triad Coalition consensus statement on treatment and return to play of the female athlete triad: 1st International Conference held in San Francisco, CA, May 2012, and 2nd International Conference held in Indianapolis, IN, May 2013. Clin J Sport Med 2014; 24(2): 96-119.
3
Areta JL, Taylor HL, Koehler K. Low energy availability: History, definition and evidence of its endocrine, metabolic and physiological effects in prospective studies in females and males. Eur J Appl Physiol 2021; 121(1): 1-21.
4
Mountjoy M, Sundgot-Borgen J, Burke L, et al. The IOC consensus statement: Beyond the Female Athlete Triad—Relative Energy Deficiency in Sport (RED-S). Br J Sports Med 2014; 48(7): 491-7.
5
Shetty PS. Adaptation to low energy intakes: The responses and limits to low intakes in infants, children and adults. Eur J Clin Nutr 1999; 53(S1): s14-33.
6
Heaney S, O’Connor H, Naughton G, Gifford J. Towards an understanding of the barriers to good nutrition for elite athletes. Int J Sports Sci Coaching 2008; 3(3): 391-401.
7
Noll M, de Mendonça CR, de Souza Rosa LP, Silveira EA. Determinants of eating patterns and nutrient intake among adolescent athletes: A systematic review. Nutr J 2017; 16(1): 46.
8
Wasserfurth P, Palmowski J, Hahn A, Krüger K. Reasons for and consequences of low energy availability in female and male athletes: Social environment, adaptations, and prevention. Sports Med Open 2020; 6(1): 44.
9
Muia EN, Wright HH, Onywera VO, Kuria EN. Adolescent elite Kenyan runners are at risk for energy deficiency, menstrual dysfunction and disordered eating. J Sports Sci 2016; 34(7): 598-606.
10
Arksey H, O’Malley L. Scoping studies: Towards a methodological framework. Int J Soc Res Methodol 2005; 8(1): 19-32.
11
Tricco AC, Lillie E, Zarin W, et al. PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Ann Intern Med 2018; 169(7): 467-73.
12
Melin A, Tornberg ÅB, Skouby S, et al. Low‐energy density and high fiber intake are dietary concerns in female endurance athletes. Scand J Med Sci Sports 2016; 26(9): 1060-71.
13
Cherian KS, Sainoji A, Nagalla B, Yagnambhatt VR. Energy balance coexists with disproportionate macronutrient consumption across pretraining, during training, and posttraining among Indian junior soccer players. Pediatr Exerc Sci 2018; 30(4): 506-15.
14
Coelho GMO, Farias MLF, Mendonça LMC, et al. The prevalence of disordered eating and possible health consequences in adolescent female tennis players from Rio de Janeiro, Brazil. Appetite 2013; 64: 39-47.
15
Logue DM, Madigan SM, Heinen M, McDonnell SJ, Delahunt E, Corish CA. Screening for risk of low energy availability in athletic and recreationally active females in Ireland. Eur J Sport Sci 2019; 19(1): 112-22.
16
Matt SA, Barrack MT, Gray VB, et al. Adolescent endurance runners exhibit suboptimal energy availability and intakes of key nutrients. J American Nutrit Associat 2022; 41(6): 551-8.
17
Otte CJ, Mantzioris E, Salagaras BS, Hill AM. Seasonal health tracking of Australian Football League Women’s athletes. Sci Med Football 2024; 8(2): 103-11.
18
de Souza LC, Moris JM, Lee KM, Fant KH, Gallucci A, Funderburk LK. Dietary intake and menstrual health among acrobatics and tumbling ncaa division i student-athletes. J American Nutrit Associat 2024; 43(1): 101-9.
19
Silva MRG, Silva HH, Paiva T. Sleep duration, body composition, dietary profile and eating behaviours among children and adolescents: A comparison between Portuguese acrobatic gymnasts. Eur J Pediatr 2018; 177(6): 815-25.
20
Condo D, Lohman R, Kelly M, Carr A. Nutritional intake, sports nutrition knowledge and energy availability in female Australian rules football players. Nutrients. Nutrients 2019; 11(5): 971.
21
Robbeson J, Havemann-Nel L, Wright H. The female athlete triad in student track and field athletes. South Afr J Clin Nutr 2013; 26(2): 19-24.
22
Jurov I, Keay N, Hadžić V, Spudić D, Rauter S. Relationship between energy availability, energy conservation and cognitive restraint with performance measures in male endurance athletes. J Int Soc Sports Nutr 2021; 18(1): 24.
23
Jurov I, Keay N, Rauter S. Severe reduction of energy availability in controlled conditions causes poor endurance performance, impairs explosive power and affects hormonal status in trained male endurance athletes. Applied Sciences (Switzerland). Appl Sci 2021; 11(18): 8618.
24
Kinoshita N, Uchiyama E, Ishikawa-Takata K, Yamada Y, Okuyama K. Association of energy availability with resting metabolic rates in competitive female teenage runners: A cross-sectional study. J Int Soc Sports Nutr 2021; 18(1): 70.
25
Torstveit MK, Fahrenholtz IL, Lichtenstein MB, Stenqvist TB, Melin AK. Exercise dependence, eating disorder symptoms and biomarkers of Relative Energy Deficiency in Sports (RED-S) among male endurance athletes. BMJ Open Sport Exerc Med 2019; 5(1): 000439.
26
Wright H, Ford R, Botha C. A desire for weight loss in season increases disordered eating behaviour risk and energy deficiency in athletes. South Afr J Clin Nutr 2014; 27(3): 120-6.
27
Hoch AZ, Pajewski NM, Moraski L, et al. Prevalence of the female athlete triad in high school athletes and sedentary students. Clin J Sport Med 2009; 19(5): 421-8.
28
Torres-McGehee TM, Emerson DM, Pritchett K, Moore EM, Smith AB, Uriegas NA. Energy availability with or without eating disorder risk in collegiate female athletes and performing artists. J Athl Train 2021; 56(9): 993-1002.
29
Simič V, Jevšnik Š, Mohorko N. Low energy availability and carbohydrate intake in competitive adolescent climbers. Kinesiology 2022; 54(2): 268-77.
30
Halfacre KL. Making the cut nutrition, hydration, & performance in combat sports. Thesis, University of Mississippi 2020.
31
Brown NK. Nutrition education to minimize health risk: Approaches for teaching college students and female high school athletes. Thesis, Utah State University 2013.
32
Reed JL. The role of low energy availability in predicting an energy deficiency and menstrual disturbances in recreational and competitive female athletes. Thesis, The Pennsylvania State University 2012.
33
Fenton ER. The prevalence of low energy availability risk in New Zealand track athletes. Thesis, Massey University 2022.
34
Magee MK, Lockard BL, Zabriskie HA, et al. Prevalence of low energy availability in collegiate women soccer athletes. J Funct Morphol Kinesiol 2020; 5(4): 96.
35
Vescovi JD, VanHeest JL. Case study: Impact of inter- and intra-day energy parameters on bone health, menstrual function, and hormones in an elite junior female triathlete. Int J Sport Nutr Exerc Metab 2016; 26(4): 363-9.
36
Taylor HL, Garabello G, Pugh J, et al. Patterns of energy availability of free-living athletes display day-to-day variability that is not reflected in laboratory-based protocols: Insights from elite male road cyclists. J Sports Sci 2022; 40(16): 1849-56.
37
Dasa MS, Friborg O, Kristoffersen M, et al. Energy expenditure, dietary intake and energy availability in female professional football players. BMJ Open Sport Exerc Med 2023; 9(1): 001553.
38
Jesus F, Sousa M, Nunes CL, et al. Energy availability over one athletic season: An observational study among athletes from different sports. Int J Sport Nutr Exerc Metab 2022; 32(6): 479-90.
39
Cook O, Dobbin N. The association between sport nutrition knowledge, nutritional intake, energy availability, and training characteristics with the risk of an eating disorder amongst highly trained competitive road cyclists. Sport Sci Health 2022; 18(4): 1243-51.
40
Villa M, Villa-Vicente JG, Seco-Calvo J, Mielgo-Ayuso J, Collado PS. Body composition, dietary intake and the risk of low energy availability in elite-level competitive rhythmic gymnasts. Nutrients 2021; 13(6): 2083.
41
Kalpana K, Cherian KS, Khanna GL. Energy availability and RED-S risk assessment among Kho-Kho players in India. Sport Sci Health 2023; 19(4): 1101-8.
42
Stenqvist TB, Torstveit MK, Faber J, Melin AK. Impact of a 4-week intensified endurance training intervention on markers of relative energy deficiency in sport (RED-S) and performance among well-trained male cyclists. Front Endocrinol 2020; 11: 512365.
43
Hertig-Godeschalk A, Ruettimann B, Valido E, Glisic M, Stoyanov J, Flueck JL. Energy availability and nutritional intake during different training phases of wheelchair athletes. Nutrients 2023; 15(11): 2578.
44
Peterson K. Energy availability and relative energy deficiency in sport (RED-S) in division I athletes. Thesis, University of Idaho 2018.
45
Nalder B. Estimating energy availability and examining its association with bone mineral density in male cyclists. Thesis, The University of Utah 2012.
46
Traversa C, Nyman DLE, Spriet LL. Dietary intake over a 7-day training and game period in female varsity rugby union players. Nutrients 2022; 14(11): 2281.
47
Zabriskie HA, Currier BS, Harty PS, Stecker RA, Jagim AR, Kerksick CM. Energy status and body composition across a collegiatewomen’s lacrosse season. Nutrients 2019; 11(2): 470.
48
Reed JL, De Souza MJ, Kindler JM, Williams NI. Nutritional practices associated with low energy availability in Division I female soccer players. J Sports Sci 2014; 32(16): 1499-509.
49
Reed JL, De Souza MJ, Williams NI. Changes in energy availability across the season in Division I female soccer players. J Sports Sci 2013; 31(3): 314-24.
50
Kuikman MA, Mountjoy M, Burr JF. Examining the relationship between exercise dependence, disordered eating, and low energy availability. Nutrients. Nutrients 2021; 13(8): 2601.
51
Imandel E. Dietary intake behavior and bone mass in male collegiate runners according to disordered eating status. Thesis, California State University 2021.
52
Day J. Identifying and reducing risk of the female athlete triad in Division I athletes. Thesis, Utah State University 2016.
53
Schaal K, Tiollier E, Le Meur Y, Casazza G, Hausswirth C. Elite synchronized swimmers display decreased energy availability during intensified training. Scand J Med Sci Sports 2017; 27(9): 925-34.
54
Egger T, Flueck JL. Energy availability in male and female elite wheelchair athletes over seven consecutive training days. Nutrients. Nutrients 2020; 12(11): 3262.
55
Kettunen O, Heikkilä M, Linnamo V, Ihalainen JK. Nutrition knowledge is associated with energy availability and carbohydrate intake in young female cross-country skiers. Nutrients. Nutrients 2021; 13(6): 1769.
56
Silva MRG, Paiva T. Low energy availability and low body fat of female gymnasts before an international competition. Eur J Sport Sci 2015; 15(7): 591-9.
57
Joaquim DP, Juzwiak CR, Winckler C. Do paralympic track and field athletes have low levels of energy available?. Brazilian J Kinanthropomet Human Perf 2018; 20(1): 71-82.
58
Viner RT, Harris M, Berning JR, Meyer NL. Energy availability and dietary patterns of adult male and female competitive cyclists with lower than expected bone mineral density. Int J Sport Nutr Exerc Metab 2015; 25(6): 594-602.
59
Jurdana M, Pražnikar ZJ, Jakus T. Macronutrient Intake and Energy Availability in Young Male Elite Cyclists:The Importance of Adequate Cho Intake. Progress in Nutrition. Mattioli 1885; 2022: 24.
60
Ong J, Brownlee I. Energy expenditure, availability, and dietary intake assessment in competitive female dragon boat athletes. Sports. Sports 2017; 5(2): 45.
61
Macuh M, Levec J, Kojić N, Knap B. Dietary intake, body composition and performance of professional football athletes in slovenia. Nutrients 2022; 15(1): 82.
62
McGuire A, Warrington G, Doyle L. Energy availability and macronutrient intake in elite male Gaelic football players. Science and Medicine in Football 2023; 7(1): 1-7.
63
Moss SL, Randell RK, Burgess D, Ridley S, ÓCairealláin C, Allison R. Assessment of energy availability and associated risk factors in professional female soccer players. Eur J Sport Sci 2020; •••: 1-27.
64
Charlot K, Cornolo J, Borne R, et al. Improvement of energy expenditure prediction from heart rate during running. Physiol Meas 2014; 35(2): 253-66.
65
Mountjoy M, Ackerman KE, Bailey DM, et al. 2023 International Olympic Committee’s (IOC) consensus statement on Relative Energy Deficiency in Sport (REDs). Br J Sports Med 2023; 57(17): 1073-98.
66
Gallant TL, Ong LF, Wong L, et al. Low energy availability and relative energy deficiency in sport: A systematic review and meta-analysis. Sports Med 2025; 55(2): 325-39.
67
Jagim AR, Fields J, Magee MK, Kerksick CM, Jones MT. Contributing factors to low energy availability in female athletes: A narrative review of energy availability, training demands, nutrition barriers, body image, and disordered eating. Nutrients 2022; 14(5): 986.
68
Holliday A, Blannin A. Appetite, food intake and gut hormone responses to intense aerobic exercise of different duration. J Endocrinol 2017; 235(3): 193-205.
69
Ruscheck T, Kopp C, Nieß AM, Haigis D. The Athlete’s body image in the context of relative energy deficiency in sport—a scoping review. J Funct Morphol Kinesiol 2025; 10(4): 413.
70
Fuller A, Bird SP. A strengths-based approach to increasing nutrition knowledge in student-athletes: The ‘Eat 2 Win’ pilot program. Nutrients 2025; 17(2): 361.
71
Weerasinghe K, Jayawardena R, Palangasinghe R, Hills AP. Navigating sports nutrition: Athlete and coach experiences from a 16-week personalized intervention. Nutr Metab 2025; 22(1): 119.
72
Tanwar E, Kalpana K. Interactions between exercise, environmental factors, and diet in modulating appetite-regulating hormones: Implications for athletes and physically active individuals. Korean J Fam Med 2025; 46(3): 137-51.
73
Dvořáková K, Paludo AC, Wagner A, Puda D, Gimunová M, Kumstát M. A literature review of biomarkers used for diagnosis of relative energy deficiency in sport. Front Sports Act Living 2024; 6: 1375740.
74
Jeppesen JS, Hellsten Y, Melin AK, Hansen M. Short‐term severe low energy availability in athletes: Molecular mechanisms, endocrine responses, and performance outcomes—A narrative review. Scand J Med Sci Sports 2025; 35(6): 70089.