Pitfalls of Conducting and Interpreting Estimates of Energy Availability in Free-Living Athletes

in International Journal of Sport Nutrition and Exercise Metabolism
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  • 1 Australian Institute of Sport
  • 2 Australian Catholic University
  • 3 University of Copenhagen

The human body requires energy for numerous functions including, growth, thermogenesis, reproduction, cellular maintenance, and movement. In sports nutrition, energy availability (EA) is defined as the energy available to support these basic physiological functions and good health once the energy cost of exercise is deducted from energy intake (EI), relative to an athlete’s fat-free mass (FFM). Low EA provides a unifying theory to link numerous disorders seen in both female and male athletes, described by the syndrome Relative Energy Deficiency in Sport, and related to restricted energy intake, excessive exercise or a combination of both. These outcomes are incurred in different dose–response patterns relative to the reduction in EA below a “healthy” level of ∼45 kcal·kg FFM−1·day−1. Although EA estimates are being used to guide and monitor athletic practices, as well as support a diagnosis of Relative Energy Deficiency in Sport, problems associated with the measurement and interpretation of EA in the field should be explored. These include the lack of a universal protocol for the calculation of EA, the resources needed to achieve estimates of each of the components of the equation, and the residual errors in these estimates. The lack of a clear definition of the value for EA that is considered “low” reflects problems around its measurement, as well as differences between individuals and individual components of “normal”/“healthy” function. Finally, further investigation of nutrition and exercise behavior including within- and between-day energy spread and dietary characteristics is warranted since it may directly contribute to low EA or its secondary problems.

The human body requires energy to support a range of functions, including growth, thermogenesis, reproduction, cellular maintenance, and movement. Elegantly evolved systems of energy conservation and management mean that energy expended in one function is no longer available for other purposes. In bioenergetics, energy availability (EA) is defined as energy that can be devoted to individual body systems. In sports nutrition, EA is more specifically defined as the residual energy available to support an athlete’s body functions, once the energy expenditure of exercise (EEE) is deducted from energy intake (EI; Loucks, 2004). Since fat-free mass (FFM) has a greater capacity for energy expenditure than fat mass, it is intuitive to express EA relative to FFM (or lean body mass [LBM]) rather than total body mass, yielding the equation (Loucks, 2004):

EA=(EIEEE)/FFM.
Ideally, an athlete’s EI, relative to the energy cost of his or her exercise program, would provide sufficient EA to support the cost of all body functions that underpin health, adaptation to training, and optimal performance. However, a large body of work, commencing in the 1990s with observations of the clustering of eating disorders, amenorrhea, and osteoporosis in female athletes (Nattiv et al., 1994; Otis et al., 1997), evolved to recognize low EA (LEA) as the cornerstone of impaired menstrual function and suboptimal bone health in these athletes (Nattiv et al., 2007). More recently, LEA was identified as a unifying theme linking numerous health problems in both female and male athletes, and described by the syndrome Relative Energy Deficiency in Sport (RED-S; Mountjoy et al., 2014, 2018).

Evidence for the importance of EA on athlete health was derived from the controlled laboratory investigations of Professor Anne Loucks. However, its main use in sports nutrition now belongs to situations of research and the support of free-living athletes, including dietary prescription and counseling, health assessments, or diagnosis of the existence or cause of impaired health/performance. Unfortunately, although the scientific basis for the effects of LEA on various body systems is robust, whether LEA can be adequately assessed and diagnosed in these situations is a matter of some importance. This paper explores the challenges and pitfalls of the estimation and interpretation of EA in free-living athletes.

Background to the Development of EA and Current LEA Concepts

The concept and mathematical definition of EA are derived from elegantly conducted laboratory investigations and the eloquent insights of Professor Anne Loucks. The seminal investigation of EA involved a laboratory-based study of healthy sedentary females who were eumenorrheic and weight stable. Manipulations of EI and/or EEE were undertaken to expose these subjects to 4-day periods of EA that was adequate (∼189 kJ [45 kcal]·kg LBM−1·day−1) or low (42 kJ [10 kcal]·kg LBM−1·day−1) (Figure 1). Substantial impairments of hormonal pulsatility were found, regardless of the origin of the LEA (Loucks et al., 1998). Contrary to the contemporary belief, exercise per se did not perturb hormonal health outside its potential contribution to suboptimal EA; indeed, LEA derived from a high training load appeared to cause less disturbance of the hormonal profiles than dietary restriction alone (Loucks et al., 1998). Other laboratory studies in similar female populations investigated the effects of different levels of LEA on other body systems; in this series, the average EA associated with health and weigh maintenance was set at ∼189 ± 25 kJ (45 kcal)·kg FFM−1·day−1 (Hilton & Loucks, 2000; Ihle & Loucks, 2004; Loucks & Heath, 1994; Loucks et al., 1998; Loucks & Thuma, 2003). Meanwhile, 5 days of LEA (∼125 kJ [30 kcal]·kg LBM−1·day−1) was found to reduce blood glucose levels; elevate blood cortisol; suppress normal blood concentrations or pulsatility (the amplitude and frequency of the oscillations in concentrations) of metabolic hormones (e.g., insulin, insulin-like growth factor 1, leptin, triiodothyronine; and reproductive hormones (e.g., estradiol, gonadotropin-releasing hormone, luteinizing hormone) and interfere with normal markers of bone turnover (e.g., reduce osteocalcin and Type I procollagen carboxy-terminal propeptide, increase urinary N-telopeptide) (Ihle & Loucks, 2004; Loucks & Thuma, 2003). If maintained chronically, LEA can lead to oligomenorrhea or functional hypothalamic amenorrhea (Cumming & Cumming, 2001; Loucks & Thuma, 2003). Thus, a scale of healthy, subclinical and low EA was developed (see Table 1; Loucks et al., 2011).

Figure 1
Figure 1

—Overview of study design where EA was controlled for 4 days at a healthy level of ∼189 (45 kcal)·kg LBM−1·day−1 or at a low level of ∼42 kJ (10 kcal)·kg LBM−1·day−1 either by severe dietary restriction or by high volume training. Both treatments of low EA resulted in changes in hormone concentrations and the pulsatility of LH. EA = energy availability; LBM = lean body mass; LH = luteinizing hormone. (Redrawn from Loucks et al., 1998).

Citation: International Journal of Sport Nutrition and Exercise Metabolism 28, 4; 10.1123/ijsnem.2018-0142

Table 1

Classification of Zones of EA Derived From Loucks et al. (2011)

Energy availability rangeZone of EA and commentsExample
>189 kJ (>45 kcal) kg FFM−1·day−1Supporting body mass gain

High EA for growth or gain of body mass (Loucks et al., 2011)
Athlete A: 65 kg and 20% body fat

FFM = 80% × 65 kg = 52 kg

Weekly training = 23.5 MJ (5,600 kcal) above that of sedentary energy expenditure = 3.4 MJ/day

Daily EI = 14.7 MJ (3,520 kcal)

EA = (14.7–3.4)/52 = 217 kJ (52 kcal)·kg FFM−1·day−1
∼189 kJ (∼ 45 kcal) kg FFM−1·day−1Optimal

Healthy EA for EB/weight maintenance providing adequate energy for all physiological functions (De Souza et al., 2014)
Athlete B: 65 kg and 15% body fat

FFM = 85% × 65 kg = 55 kg

Weekly training = 23.5 MJ (5,600 kcal) above that of sedentary energy expenditure = 3.4 MJ/day

Daily EI = 13.8 MJ (3,285 kcal)

EA = (13.8–3.4)/55 = 189 kJ (45 kcal)·kg FFM−1·day−1
125–189 kJ (30–45 kcal) kg FFM−1·day−1Subclinical or reduced

May be tolerated for short periods such as a well-constructed weight loss program (Loucks et al., 2011)
Athlete C: 55 kg and 20% body fat

FFM = 80% × 55 kg = 44 kg

Weekly training = 23.5 MJ (5,600 kcal) above that of sedentary energy expenditure = 3.4 MJ/day

Daily EI = 9.8 MJ (2,340 kcal)

EA = (9.8–3.4)/44 = 145 kJ (35 kcal)·kg FFM−1·day−1
<125 kJ (<30 kcal) kg FFM−1·day−1Low

Health implications with impairment of many body systems including training adaptation and performance (De Souza et al. 2014; Mountjoy et al. 2014, 2018)
Athlete D: 55 kg and 25% body fat

FFM = 75% × 55 kg = 41 kg

Weekly training = 23.5 MJ (5,600 kcal) above that of sedentary energy expenditure = 3.4 MJ/day

Daily EI = 8.3 MJ (1,980 kcal)

EA = (8.3–3.4)/41 = 120 kJ (29 kcal)·kg FFM−1·day−1

Note. EA = energy availability; EB = energy balance; EI = energy intake; FFM = fat-free mass.

Although LEA has been promoted for well over a decade as a key factor in the Female Athlete Triad (De Souza et al., 2014; Nattiv et al., 2007) and underpins RED-S (Mountjoy et al., 2014, 2018), the concept is still not fully appreciated by sports science/medicine professionals. Indeed, it is usually necessary to point out that EA is not synonymous with energy balance (EB), nor is LEA always associated with loss of body mass. When EB is disturbed because dietary energy is less than total energy expenditure (TEE), either the body’s energy reserves (e.g., adipose tissue, body proteins) can contribute to fuel needs and/or there is conservation of energy expenditure involved with other body functions as an evolutionary adaptation favoring survival. Long-term negative EB (EI < TEE) will eventually cause adaptations to reduce energy expenditure on various physiological function to prevent further weight loss and to promote survival, and the body may return to a new situation of EB (EI = TEE). Therefore, an athlete may be weight stable and not excessively low in body mass/body fat levels yet may nevertheless be suffering from the penalties of impaired physiological function secondary to LEA.

In theory and practice, LEA can be produced by various manipulations of EI and/or the energy cost of exercise. Disordered eating underpins a large proportion of cases of LEA and typically occurs with increased prevalence among female athletes in “lean build” sports such as endurance, aesthetic, or weight category activities (Gibbs et al., 2013a; Sundgot-Borgen & Torstveit, 2004; Torstveit & Sundgot-Borgen, 2005), but also among male athletes from such sports (Chatterton & Petrie, 2013; Filaire et al., 2007; Sundgot-Borgen & Torstveit, 2004). Eating behaviors and attitudes exhibited by athletes with disordered eating span the spectrum from abnormal to the pathology associated with diagnosed eating disorders but share a general characteristic of being compulsive. However, a mismatch between EI and EEE may occur without such a psychological overlay. In some cases, it represents a well intentioned, and even well justified, program to reduce body mass or body fat, whereby the athletes engineer a large negative EB to achieve their goals quickly. The athlete may not be fully aware of the performance impairing consequences of such endeavors and should be advised on how to achieve the same physique goals at a slower but more sustainable pace (Garthe et al., 2013; Stellingwerff, 2018). A variation is the injured athlete who drastically reduces their food intake to avoid weight gain when faced with a period of inactivity and immobility; in such cases, the athlete may feel that they have behaved responsibly although, in fact, EB is critical during rehabilitation (Tipton, 2015). The athlete who needs to maintain a low competition weight over a prolonged season may also face challenges due to prolonged energy restriction. In all scenarios, behaviors are intentional and even rational but may be misinformed or mismanaged.

A final scenario of inadvertent LEA involves the athlete with extreme exercise commitments who is either unaware of the energy cost or unable to consume sufficient food to match it. Appetite does not always track energy expenditure at high or unaccustomed levels of exercise/activity (Larson-Meyer et al., 2012); for example, a controlled laboratory study found that ad libitum intake of energy did not increase sufficiently over 7 days to fully compensate for the introduction of a high volume cycling program (Stubbs et al., 2004). Further systematic studies in athletic populations of the acute and chronic effects of exercise on appetite are required, since general responses appear to be variable and individual (King et al., 1997). Additional factors that may exacerbate the athlete’s inability to meet their energy requirements include the inhibitory effect of fatigue on the motivation and effort required to prepare food, reduced opportunities for food-related activities on days in which a substantial number of hours is devoted to exercise (Burke et al., 2001), and the difficulty of consuming adequate amounts of bulky fiber rich and low energy-dense foods (Melin et al., 2016; Reed et al., 2013). The attainment of high EI may require dietary and behavioral adjustments rather than reliance on appetite or intrinsic responses; this may be harder to achieve when athlete’s training or competition schedule is variable and there is insufficient nutritional knowledge and opportunity for healthy and performance enhancing dietary habits to develop.

Before tackling the current challenges to the practical applications of EA in the sports nutrition field, several other positive attributes about its fundamental principles should be highlighted. First, the mathematical derivation of EA is theoretically simple in comparison to EB:

EB=EITEE,
where TEE = REE + TEF + EEE + NEAT + growth.

Specifically, the estimation of EA does not require a calculation of TEE; this removes the requirement for information based on expensive and specialized techniques such as doubly labeled water (DLW; Capling et al., 2017) or the separate calculation of the individual components of resting energy expenditure (REE), thermic effect of food (TEF), nonexercise activity thermogenesis (NEAT), and other contributors to TEE. Therefore, once the concept can be fully grasped, and notwithstanding the difficulties that will be explored, it provides a more intuitive and less complicated metric to assess. Furthermore, the main components of the EA concept—EI and EEE—are under the behavioral control of the athlete, unlike REE, TEF, and NEAT which may be adjusted by physiological control (Loucks et al., 2011). Therefore, the construct is practical and empowering, because the athlete can make changes, should they be required to improve health and performance. Indeed, in working with an athlete who has a confirmed diagnosis of LEA, the practitioner and the athlete would be able to focus on strategies to manipulate EEE and EI, with these metrics being better visualized and tracked than all the components of EB.

Caveats to the Use of EA to Assess Athlete Practice

LEA has been extremely useful in conceptualizing the development of the impairments of health and function and frequently reported in female, and more recently, male athletes (Burke et al., 2018). Furthermore, it has provided a framework to allow the systematic study of the effects of energy deficiency on a number of body functions and physiological systems. Although such advice has helped to guide and monitor athletic practices, there are some limitations to the science and application of EA to free-living athletes which should be explored. Indeed, studies undertaken in field settings using self-reported nutritional data have failed to find clear thresholds or associations between EA and objective measures of energy conservation or health impairment such as disruption to metabolic hormones (Heikura et al., 2018; Koehler et al., 2013) and menstrual disturbances (Liebermann et al., 2018; Melin et al., 2015; Reed et al., 2013; Williams et al., 2015). Some primary difficulties in measuring EA in free-living athletes and explanations for the discrepancy between laboratory and “real life” associations between EA and athlete health are now covered.

Lack of a Single Protocol for the Assessment of EA in Free-Living Situations

There are no clear guidelines on field calculations of EA, including the period of assessment and the techniques used to measure each of the components of the EA equation. Typically, researchers/practitioners attempt to observe behavior over periods of 3–7 days; reflecting either a unit in the athlete’s life (e.g., a training microcycle or period of the social calendar) or the period during which acceptable compliance to recording is expected. However, these periods may neither reflect habitual practices; indeed, although the number of days of recorded intake likely to reflect true habitual EI in sedentary populations is typically 3–4 (Marr & Heady, 1986), this has not been established in athletic populations and may vary between different types of sports (Braakhuis et al., 2003). Importantly, the period of the food record may not cover the time span over which the athlete has incurred energy conservation. Table 2 summarizes the available studies in which EA has been estimated in athletic populations, identifying various assessment protocols. Differences in the prevalence and degree of LEA in different populations and of relationships between EA and measurements of impaired body functions are likely due to methodological differences in assessment protocols as well as actual differences in correlations between estimated EA and its outcomes.

Table 2

Studies Involving Measurement of EA in Athletes

Methods
ReferenceSubjectsEIEEEExercise definitionDEReproductive functionBMDRemarksEA (kJ·kg FFM−1·day−1)
Hoch et al. (2009)Mixed female athletes (n = 80)

Sedentary controls (n = 80); 13–18 years
3 days weighed dietary recordEstimated by activity logs and calculated using METsSport participationEAT-26Menstrual history and sex hormonesYesEEE not adjusted for SA

FFM determined using DXA
Athletes: 6% LEA, 30% reduced EA

Controls: 4% low, 35% reduced EA
Doyle-Lucas et al. (2010)Elite female ballet dancers (n = 15)

Controls (n = 15)

18–35 years
4 days dietary recordsEstimated by activity logs and calculated using METsAll physical activityTFEQ

EAT-26
Menstrual historyYesEEE not adjusted for SA

FFM determined using DXA

RMR measured
Dancers: 16

Control: 172
Hoch et al. (2011)Professional female ballet dancers (n = 22), 18–35 years3 days dietary recordSimultaneously estimated by accelerometers, individually calibrated at two self-selected intensities of exerciseAll physical activityEDE-QMenstrual history and sex hormonesYesEEE not adjusted for SA

EA not adjusted for FFM

Low/negative EA defined as a negative value. Endothelial function determined
77% had LEA
Schaal et al. (2011)Female runners and triathletes; MD (n = 6), 31 ± 4.3 years. EUM (n = 6), 30 ± 2.5 years7 days weighed dietary recordSimultaneously estimated by activity logs, HR, and intensity using the 10-point RPE scale. EEE estimated using RPE and HR during exercise to matched O2 consumption and RER during laboratory testingExercise verified by RPEEDE-QMenstrual history and diagnosis verified by physicianEEE not adjusted for SA

FFM determined using DXA

Catecholamines, glucose, and BP measured. POMS questionnaire
MD: 75

EUM: 121
Reed et al. (2013)Female soccer players (n = 19), 18–21 years3 × 3 days dietary records, estimation of portion sizes using scaled diagramsSimultaneously estimated using HR during team exercise sessions. HR and activity logs during individual exercise

METs for exercise sessions without HR monitoring
Team exercise sessions and purposeful exercise sessionsEDI-2Menstrual historyEEE not adjusted for SA

FFM determined using DXA

T3 measured
Preseason: 180 (26% LEA)

Midseason: 146 (33% LEA)

Postseason: 188 (12% LEA)
Koehler et al. (2013)Athletes from mixed sports; Females (n = 185) Males (n = 167), 11–25 years7 days dietary record with 193 foods listed with standard portion sizesEstimated using activity records, including a list of exercise-related activities with different intensity levels, and METsExercise-related activitiesEA corrected with age-specific factors for EE

FFM determined using BIA

Leptin, IGF-1, T3, and insulin measured
Females: 126, 51% had LEA Males: 125, 56% had LEA
Woodruff and Meloche (2013)Female volleyball players (n = 10), 19–23 years7 days dietary records based on household measuresSimultaneously assessed from accelerometers and METs, based on dates and times of practices, warm-ups, and gamesVolleyball practice, warm-ups, and gamesMenstrual historyEEE not adjusted for SA

FFM determined using air displacement plethysmography
178
Lagowska et al. (2014a)Female rowers, triathletes, and synchronized swimmers with MD (n = 31), 18.1 ± 2.6 years7 days dietary record using a photographic album of dishesHR monitors for 3 days. For each subject, the relationship between HR and VO2 was established for lying in supine position, sitting quietly, standing quietly, and continuous graded exercise on a cycle ergometerExerciseMenstrual history, sex hormones, and gynaecological ultrasound examinationEEE not adjusted for SA

RMR measured

FFM determined using BIA
118 (baseline)

150 (at 3 months)
Vanheest et al. (2014)Female swimmers:

MD (n = 5), EUM (n = 5), 15–17 years
7 × 3 days weighed dietary records and 24-hr recall7 days exercise logs and swimming specific EE tableSwimming practiceProspective menstrual cycle record during 12 weeks, sex hormones at 0 and 2 weeksEEE not adjusted for SA

FFM calculated by skinfold assessment. Performance assessed after 12-week training program. RMR, T3, and IGF-1 measured
MD: 42–50

EUM: 117–155
Melin et al. (2015)Female elite endurance athletes

MD (n = 24), EUM (n = 16), 18–38 years
7 days weighed dietary recordSimultaneously estimated by activity logs and HR. EEE was estimated on the basis of HR during exercise matched O2 consumption and RER during laboratory testingExerciseEDI-3 and EDE-16Menstrual history, sex hormones, and gynaecological ultrasound examinationYesFFM determined using DXA

RMR, glucose, insulin, leptin, IGF-1, cortisol, T3, and blood lipids measured

LEAF-Q completed
MD: 162 (67% had LEA or reduced EA)

EUM: 172 (56% had LEA or reduced EA)
Cialdella-Kam et al. (2014)Female endurance athletes

MD (n = 8) 22.6 ± 0.3 years, EUM (n = 9) 23.1 ± 4.3 years
2 × 7 days weighed dietary recordAssessed from accelerometers and exercise logs. Running EEE accessed using indirect calorimetryMET > 4EDI-2Menstrual history and ovulation status measured daily for ≥1 month + sex hormonesYesEEE not adjusted for SA

FFM determined using DXA

RMR, T3, vitamins, strength/power, bone, and protein metabolism markers measured, POMS
EUM: 160

MD: 154 (baseline), 190 (at 6 months)
Lagowska et al. (2014b)Female athletes; rowers, triathletes, synchronized swimmers with MD (n = 31) 18.1 ± 2.6 years and female ballet dancers with MD (n = 21) 17.1 ± 0.9 years7 days dietary record using a photographic album of dishesHR monitors for 3 days. For each subject, the relationship between HR and VO2 was established for lying in supine position, sitting quietly, standing quietly, and continuous graded exercise on a cycle ergometerExerciseMenstrual history, sex hormones, and gynecological ultrasound examinationEEE not adjusted for SA

RMR and leptin measured

FFM determined by bio impedance
Athletes: 150–221

Ballet dancers: 91–156
Silva and Paiva (2015)Female rhythmic gymnasts (n = 67) 16–26 years24-hr recordEstimated by a characterization questionnaire and calculated using METsExerciseMenstrual historyEEE not adjusted for SA

FFM determined by BIA
152

37% had reduced EA and 45% had LEA
Viner et al. (2015)Competitive endurance cyclist

Females (n = 5) Males (n = 6) 29–49 years
3 days dietary record/months through one cycling seasonEstimated by activity logs, including speed, perceived exertion and HR zones, and calculated using METsMET > 4TFEQYesFFM determined using DXA70%, 90%, 80% during preseason, competition, and off season, respectively

Females: 110, 107, 100. Males: 79, 82, 91
Łagowska and Kapchuc (2016)Female athletes with MD; rowers, triathletes, synchronized swimmers (n = 31) 18.1 ± 2.6 years and ballet dancers (n = 21) 17.1 ± 0.9 years7 days dietary record using a photographic album of dishesHR monitors for 3 days. For each subject, the relationship between HR and VO2 was established for lying in supine position, sitting quietly, standing quietly, and continuous graded exercise on a cycle ergometerExerciseMenstrual history, sex hormones, and gynecological ultrasound examinationEEE not adjusted for SA

FFM determined BIA
Athletes: 119

Ballet dancers: 91
Muia et al. (2016)Elite female Kenyan athletes (n = 56) and nonathletes (n = 45) 16–17 years5 days weighed dietary recordEstimated by activity logs and RPE according to the 20-point Borg scale and calculated using METsExerciseEDI-3 and TFEQMenstrual historyYes by ultrasoundEEE not adjusted for SA

FFM determined by skinfold measures
Athletes: 153 (18% had LEA)

Nonathletes: 165 (2% had LEA)
Schaal et al. (2016)Elite female synchronized swimmers (n = 9) 20.4 ± 0.4 years3 × 4 days dietary record using smart phone photographyActivity logs and HR. EEE (kcal/kg/min) = (5.95 HRaS × 0.23 age)  − 134/4,186.8ExerciseEEE not adjusted for SA

FFM determined by skinfold measures Leptin, ghrelin, and cortisol saliva measured
Baseline: 105

Midpoint: 93

End: 75
Silva and Silva (2017)Male rink-hockey players (n = 38 children, n = 34 adolescents) and controls (n = 43 children, n = 36 adolescents) 8–16 years3 days dietary record based on household measurementsEstimated by a characterization questionnaire and calculated using METsHockey training and physical education lessonsSelf-reported sexual maturation stageEEE not adjusted for SA

FFM determined by skinfold measures
Athletic children: 200, adolescents: 225. Control children: 208, adolescents: 231

10% of adolescent athletes reduced EA
Silva et al. (2017)Elite athletes (n = 57);

handball, volleyball, basketball, triathlon, and swimming (n = 39 males, n = 18 females) 18.7 ± 3.3 years
Derived as EB + EE, estimated using DXA and DLW during a 1 weeks intervalPhysical activity EE (kcal/day) = TEE − 0.1 TEE − REE,

Total EE assessed using 7 days DLW
All physical activityEEE not adjusted for SA

FFM determined using DXA

EA not adjusted for FFM, RMR
Females: 179

Males: 164
Brown et al. (2017)Female preprofessional contemporary

dancers (n = 25)

21 ± 2 years
7 days weighed dietary record +24 hr recallAccelerometers, diaries to register nonwear periods, and METsAll physical activityTFEQMenstrual historyEEE not adjusted for SA

FFM determined by skinfold measures
100
Heikura et al. (2018)Female and male elite endurance athletes. MD (n = 13),

EUM (n = 22), low TES (n = 10), normal TES (n = 4) 18–40 years
7 days household measures/weighed dietary recordTraining logs and calculated using METsExerciseMenstrual historyYesFFM determined using DXA

Sex hormones, T3, IGF-1, and insulin measured. LEAF-Q, RED-S, and Triad tool completed
EUM: 146, MD: 134

Normal TES: 146

Low TES: 130

31% females LEA, 25% males LEA
Torstveit et al. (2018)Male endurance athletes. Normal RMR (n = 11), suppressed RMR (n = 20) 18–50 years4 days weighed dietary recordTraining logs and HR. EEE (kcal/kg/min) = ([5.95 HRaS] + [0.23 age] + [84 × 1] − 134)/4,186.8ExerciseFFM determined using DXA

RMR, glucose, cortisol, testosterone, and T3 measured
Normal RMR: 172

Suppressed RMR: 155
Silva et al. (2018)Acrobatic gymnasts

Males (n = 21), Females (n = 61) 12.8 ± 3.1 years
3 days dietary record (household measures)Characterization questionnaire and calculated using METsGymnastics trainingMenstrual historyEEE not adjusted for SA

Sleep duration assessed

FFM determined by skinfold measures and bio impedance
Female children 192, adolescents 137

Male children: 223, adolescents: 189
Black et al. (2018)Recreational female athletes (n = 38). At risk for low EA (n = 24), not at risk for low EA (n = 14) 22.6 ± 5.6 years3 days weighed dietary recordTraining logs and calculated using METsExerciseMenstrual historyEEE not adjusted for SA

FFM determined using BIA

Blood lipids, cortisol, testosterone, progesterone, and T3, LEAF-Q completed
At risk for LEA: 152

Not at risk for LEA: 199
Braun et al. (2018)Elite female football players (n = 56) 14.8 ± 0.7 years7 days dietary record (standard portion sizes)Estimated by activity logs and calculated using METsSports-related activitiesEEE not adjusted for SA

FFM determined using BIA

Iron and vitamin D status
126

53% had LEA

Note. BIA = bioelectrical impedance analysis; BP = blood pressure; BMD = bone mineral density; DE = disordered eating behavior; DLW = double labeled water technique; DXA = dual-energy X-ray absorptiometry, EA = energy availability; EAT-26 = eating attitudes test-26; EB = energy balance; EDI = eating disorder inventory; EDE = eating disorder examination; EDE-Q = eating disorder examination questionnaire; EE = energy expenditure; EEE = exercise energy expenditure; EI = energy intake; EUM = eumenorrhea; FFM = fat-free mass; HR = heart rate; HRaS = HR above sleeping; HRIGF-1 = insulin growth factor-1; LEA = low energy availability; LEAF-Q = Low Energy Availability in Females Questionnaire; MD = menstrual dysfunction; MET = Metabolic Equivalent of Task; POMS = profile of mood states; RED-S = relative energy deficiency in sport; REE = resting energy expenditure; RER = respiratory exchange ratio; RMR = resting metabolic rate; RPE = ratings of perceived exertion; SA = sedentary activities; T3 = triiodothyronine; TEE = total energy expenditure; TFEQ = Three-Factor Eating Questionnaire, TES = testosterone.

Difficulties and Errors in Measuring Each of the Components of EA

By definition, an EA calculation requires information about an individual’s FFM, EI, and the energy cost of their exercise program; each aspect relies on having accurate measurement tools and a clear definition of what should be measured. Many athletes do not have the resources to gain a reliable and accurate measurement of body composition using techniques such as dual energy X-ray absorptiometry. Even when these resources are available, best practice protocols should be implemented to minimize the acute effects of factors such as hydration status or recent exercise or food/drink intake on estimates of FFM (Nana et al., 2016). However, residual errors and discrepancies between different techniques, and even different machines using the same technique, are still likely. Although surface anthropometry (e.g., breadths, girths, subcutaneous fat etc.) can be measured using standardized and accredited techniques (e.g., protocols developed by the International Society for the Advancement of Kinanthropometry), this is best used to track differences or changes in discrete physique characteristics in athletes rather than make doubly indirect estimates of LBM (Larsen-Myer et al., 2018). Nevertheless, errors of measurement of LBM or FFM contribute a relatively small discrepancy to estimates of EA compared with other inputs.

Estimates of EI rely on the notoriously difficult task of gaining valid and reliable information about an athlete’s habitual or period-specific dietary intake by either prospective recording or retrospective analysis. Most assessments of EI in EA studies have relied on the use of food records (Table 2), which are prone to errors of underreporting as well as failure to gain a true or typical picture of longer-term intake (Burke et al., 2001; Capling et al., 2017). Recording a food diary—by written record, electronic tools, and/or photo assessment—is known to change usual intake, as well as cause errors of omission, underrecording of portion sizes, underreporting of foods considered “unhealthy,” and overreporting of foods judged to be nutritious (Burke et al., 2001; Capling et al., 2017). Although various techniques have been used in the attempt to improve recording accuracy (e.g., cross-referencing with other assessment techniques, weighing food serves) or reliability (e.g., analyzing multiple time points), large residual errors persist in the estimation of EI in individual athletes and groups. The errors of self-reported intakes have been less well studied in specific populations such as athletes rather than the general community (Hill & Davies, 2001). However, a recent meta-analysis of studies, in which reported EI were validated against DLW-derived measurements of TEE, reported that the mean bias was a 19% underreporting of EI (0.4–36%), representing a daily intake of ∼2,500 kJ (∼600 kcal).

In addition to reporting errors, these dietary assessments place a great burden on subjects (affecting compliance) and the practitioner/researcher (requiring substantial time and resources to process the data). Differences between the tools and protocols used by the sports nutrition professional—for example, the coding of information about food intake into a food composition database (Braakhuis et al., 2003)—adds further variability and error to the process. Very few studies reporting EA summarized in Table 2 have assessed the validity of dietary intake data (Melin et al., 2016; Schaal et al., 2011; Woodruff & Meloche, 2013), even by using the simple standard Goldberg or Black cutoffs (Black, 2000; Goldberg et al., 1991) and none has employed more rigorous assessments using biomarkers or DLW measurements of TEE (Capling et al., 2017)

Finally, there is the measurement of EEE which also contributes significant error as well as causing substantial observer and participant burden. Although GPS units, heart rate monitors and power meters can provide individualized feedback on the energy cost of simple exercise tasks (e.g., running or cycling), there are few data on the more complex or field based exercise activities (e.g., resistance training, team sport, swimming). Protocols for estimating EEE in the existing literature vary (Table 2). While some studies have used training records and heart rate monitors to estimate EEE by relationships between heart rate and O2 consumption/respiratory exchange ratio determined during laboratory testing (Łagowska & Kapchuc, 2016; Lagowska et al., 2014a, 2014b; Melin et al., 2015; Schaal et al., 2011), others have used accelerometers monitoring bodily movements (Brown et al., 2017; Hoch et al., 2011; Woodruff & Meloche, 2013) or a combination of these methods (Cialdella-Kam et al., 2014; Reed et al., 2013). A recent validation study on the validity of wearable devices for measuring TEE (e.g., Jawbone, Fitbit, Actigraph monitors etc) found that all underestimated expenditure in free-living populations compared with DLW estimates (Murakami et al., 2016); the mean daily underestimation ranged from 400 to 2,500 kJ (100–600 kcal) between devices. The precision and accuracy of monitoring devices can be improved by simultaneously measuring EEE by indirect calorimetry during specific activities, since accelerometers may underestimate EEE at more vigorous exercise levels (Abel et al., 2008). As athletes often cross-train in addition to their primary sport, the same method and monitoring devices should be used across all types of exercise. Another frequently used method, although less precise, is the calculation of EEE from activity logs using Metabolic Equivalent of Task (Ainsworth et al., 2000) or other tables of the energy cost of exercise (Black et al., 2018; Braun et al., 2018; Doyle-Lucas et al., 2010; Heikura et al., 2018; Hoch et al., 2009; Koehler et al., 2013; Muia et al., 2016; Silva et al., 2017, 2018; Silva & Paiva, 2015; Silva & Silva, 2017; Vanheest et al., 2014; Viner et al., 2015).

What constitutes exercise for a free-living athlete is also unclear, with options including only purposeful training/competition sessions, adding leisure or transport activities or involving activities according to an arbitrary level of intensity. In the existing literature, different terminology and definitions have been used, ranging from all sport or exercise sessions (Hoch et al., 2009; Koehler et al., 2013; Melin et al., 2015; Schaal et al., 2011; Vanheest et al., 2014), any physical activity (Hoch et al., 2011), or all physical activity except daily living activities (e.g., cleaning the house or walking the dog; Reed et al., 2013; Table 2). A standardized guideline is needed, since the use of different parameters in the same population can lead to different calculations and interpretations of the adequacy of EA (Guebels et al., 2014). Another important discrepancy involves the subtraction of energy expenditure of sedentary activity from EEE during the exercise period as intended in the original definition of EA (Loucks et al., 1998; Loucks, 2014). This is otherwise likely to add a significant overestimation of EEE and underestimation of true EA for athletes who undertake prolonged periods of exercise with moderate energy cost. Such adjustments also include methodological considerations such as measurements of REE versus using standard equations of predicted basal metabolic rate—the latter would probably overestimate REE in metabolically adapted athletes and thereby underestimate EEE and overestimate EA. However, adjustment of EEE from sedentary activities have only been performed in a few studies (Heikura et al., 2018; Koehler et al., 2013; Melin et al., 2015; Torstveit et al., 2018; Viner et al., 2015).

In summary, the estimation of EA places a commitment and compliance burden on the athlete to collect data, while the calculation of EA from these data is a time-intensive activity requiring resources and professional expertise in nutrition and sports physiology. Significant errors of validity and reliability occur during the process. The mean magnitude of errors of estimation of EI, EEE, and TEE can be in the order of 1,200–2,500 kJ/day (300–600 kcal/day); this is equivalent to, or exceeds, the discrepancy in EA or within-day energy deficits that has been associated with physiological impairments (Deutz et al., 2000; Tortsveit et al., 2018).

Differences in Thresholds of EA for Individuals and Individual Issues

The development of the “zones of EA” concept (Table 1) created an opportunity to reframe earlier confusion about the contribution of body composition, training volume/intensity, and eating disorders to the etiology of the health impairments seen in (female) athletes. This tool suggests EA can be reduced by a substantial margin (i.e., by ∼33%) without negative consequences to health. However, these zones are best seen as an ideological concept rather than a precise diagnostic tool. Indeed, there is plentiful evidence that EA of 125 kJ (30 kcal)·kg FFM−1·day−1 does not provide a universal threshold between optimal and impaired function, either for individual athletes or for all issues of health, well-being, and performance. First, this threshold was identified in a sophisticated but imprecise methodology in relation to impairment of hormones related to the female reproductive cycle in eumenorrheic, weight stable, and sedentary women (Loucks & Thuma, 2003). Even though the group response showed an apparent “breakpoint” below 125 kJ (30 kcal)·kg LBM−1·day when comparing EA of 189 kJ (45 kcal)·kg LBM−1·day to randomly allocated EA scenarios of 189 kJ (30 kcal), 84 kJ (20 kcal), and 42 kJ (10 kcal)·kg LBM−1·day−1 for 5 days, interindividual differences were not considered. Indeed, a similar laboratory-based protocol showed differences in responses to an EA reduction from 189 kJ (45 kcal) to 42 kJ (10 kcal)·kg FFM−1 based on the gynecological age of subjects (Loucks, 2006).

Second, investigations of perturbations in other body systems associated with similar stepwise reductions in EA (Ihle & Loucks, 2004; Loucks & Thuma, 2003) have shown differences in the nature of the decline in function, and different “thresholds” below which a substantial quantitative reduction or clinical impairment occurs. Figure 2 shows a representation of the apparent perturbation to acute markers of bone metabolism and different aspects of metabolism based on such studies, suggesting that various body systems are affected differently. Systematic investigation of the effect of reductions in EA on other factors in body health and performance is desirable and should be extended to different populations, including males and well-trained athletes. A dose–response study of EA in male athletes, done under field conditions which allowed only fasting hormone concentrations to be measured rather than a serial investigation of fluctuations in daily hormones, found perturbations to some (leptin and insulin), but not all (triiodothyronine, testosterone, insulin-like growth factor 1, ghrelin), hormones when comparing 4 days exposure to EA of 168 kJ (40 kcal) versus 63 kJ (15 kcal)·kg FFM−1·day−1 (Koehler et al., 2016). Another study (Papageorgiou et al., 2017), comparing 5 days of EA of 189 kJ (45 kcal) and 63 kJ (15 kcal)·kg LBM−1·day−1 reported decreased bone formation and increased bone resorption in active women, but not in men. How much the variation in findings between studies reflect true sex differences or differences in their methodological designs is difficult to identify. In the meantime, a controlled study of resistance trained athletes found that males and females showed an equal decline in tracer determined rates of postabsorptive muscle protein synthesis after 5 days of exposure to EA of 189 kJ (45) versus 125 kJ (30 kcal)·kg FFM−1·day−1 (Areta et al., 2014).

Figure 2
Figure 2

—Summary of the effect of reduced EA on the activity of hormones representing different body functions in healthy females. The plots show differences in the dose–response impairments of LH (solid line) where normal pulse frequency is maintained until a threshold at around 125 kJ (30 kcal)·kg FFM−1·day−1; while concentrations of insulin, leptin, and PICP (short dashes) are immediately reduced in response to a decrease in EA. The responses of T3, IGF-1, and OC (long dashes) are nonlinear with the largest reductions in concentrations occurring between EA of 84–125 kJ (20–30 kcal)·kg FFM−1·day−1. EA = energy availability; FFM = fat-free mass; LH = luteinizing hormone; PICP = procollagen I C-terminal propeptide; T3 = triiodothyronine; IGF-1 = insulin-like growth factor 1; OC = osteocalcin. (Adapted from Loucks, 2015.)

Citation: International Journal of Sport Nutrition and Exercise Metabolism 28, 4; 10.1123/ijsnem.2018-0142

Third, LEA studies have been conducted using a linear scaling of EA relative to LBM/FFM. Indeed, the EA threshold for impaired reproductive function (125 kJ [30 kcal]·kg FFM−1) was later realized to approximate the value of resting metabolic rate in healthy athletes of average body size (Loucks et al., 2011). However, due to differential metabolic rates of vital organs and skeletal muscle, there are different intercepts for measured sleeping metabolic rate (Westerterp, 2003) and the linear scaling of EA to FFM (Figure 3). The practical implication of this finding is that a “threshold” of 125 kJ (30 kcal)·kg FFM−1 does not scale across different body sizes, overestimating, and particularly underestimating resting metabolic rate for large and small athletes, respectively.

Figure 3
Figure 3

—Sleeping metabolic rate plotted as a function of FFM from work of Westerterp (2003) whereby SMR (MJ) = 2.27 + 0.091 × FFM. The solid regression line has a significant nonzero intercept. The dashed line represents energy availability of 125 kJ (30 kcal)·kg FFM−1. • = females; ○ = males. FFM = fat-free mass; SMR = sleeping metabolic rate. (Adapted from “Energy availability in athletes,” by A.B. Loucks, B. Kiens, and H.H. Wright, 2011,  Journal of  Sports Sciences, 29, pp. S7–S15. Copyright 2011 by Taylor & Francis.)

Citation: International Journal of Sport Nutrition and Exercise Metabolism 28, 4; 10.1123/ijsnem.2018-0142

Differences Between Free-Living Athletes and Controlled Laboratory Situations

Potential artifacts arise from laboratory research on metabolic and hormonal consequences of LEA compared with real-life. The studies which established the EA concepts typically involved careful but artificial control of diet and exercise for short (4–7 days periods in which prescribed EI was spread evenly over the day and standardized from day to day (Ihle & Loucks, 2004; Loucks & Heath, 1994; Loucks et al., 1998). Moreover, monitored exercise loads were repeated each day. In contrast, over the periods of observation in field research, or the chronic periods which affect their health, free-living athletes undertake different exercise sessions each day, and consume energy-containing food and fluids in a variety of different patterns. The possibility that the timing of EI versus expenditure between and within days, or characteristics around dietary quality, could either attenuate or exaggerate the effects of LEA is both theoretically possible and supported by preliminary research.

The periodicity of EI, within and between days, and in relation to exercise expenditure, has been largely ignored in EA investigations. Yet, the calculation of mean EA over a period of days could hide a large number of variations that might hypothetically alter the degree of physiological stress on the body (Table 3). For example, the spacing of eating events between or within days may create long periods between high EEE and energy support. Preliminary data in athletes show that the magnitude of within-day energy deficits can alter physiological outcomes. Greater mismatches in the within-day spread of EI related to expenditure in runners and gymnasts were associated with higher body fat levels which may be attributed to an adaptive reduction in resting metabolic rate (Deutz et al., 2000). Meanwhile, greater within-day energy deficiency was associated with functional hypothalamic amenorrhea and clinical markers of metabolic disturbances in a group of female endurance athletes (Fahrenholtz et al., 2018).

Table 3

Variations in Dietary Characteristics and Spread of EI and Exercise Expenditure Over a Week That Might Lead to the Same Mean Calculation of EA But Impose a Different Level of Metabolic Stress

Laboratory conditions under which EA has been manipulated and studied over 4–7 daysExamples of different patterns of EI and expenditure in free-living individuals that might contribute to same mean EA over a week
Equal EI and expenditure from day to day, with intake spread evenly over the day5 days of very restrained eating and 2 days of binge eating
7 days of equal EI but large fluctuations in training load from day to day
Little EI over the early part of the day when energy expenditure is high, and most EI consumed in the evening when inactive
Dietary intake associated with laboratory studies of EAExamples of nutritional characteristics often associated with low EA in diets of free-living subjects
Macronutrient intake based on healthy eating guidelines with at least moderate carbohydrate availability and micronutrient intake meeting nutrient reference valuesLow carbohydrate intake and low carbohydrate availability in relation to training sessions
Inadequate protein intake and poor spread of protein over the day or in relation to training
Very high fiber intake and low energy density meaning that large volumes of food need to be consumed
High intake of water, artificially sweetened and other noncaloric beverages
High intake of caffeine (and possibly other stimulants)

Notes. EA = energy availability; EI = energy intake.

Finally, the composition of the diet and psychogenic stress associated with eating may differ between athletes and contribute to metabolic/hormonal outcomes. Athletes with menstrual disorders or disturbed metabolism are often found to have higher values for dietary restraint or drive for thinness on eating behavior questionnaires than their more regular counterparts (Gibbs et al., 2011, 2013b; Melin et al., 2016). It has been suggested that psychosocial stress acts synergistically to exacerbate the effect of metabolic stress on neuroendocrine health (Pauli & Berga, 2010). In addition, a variety of nutritional characteristics which may be absent or present within the athlete’s diet (Table 3) could directly contribute to the metabolic/hormonal readjustments of LEA or further impair the function associated with it. For example, low carbohydrate availability is a possible contributor to the stress of energy deficiency (Viner et al., 2015); exercise-induced LEA, which preserved carbohydrate status due to a sparing of glycogen use, was associated with an attenuation of the hormonal impairments seen with diet-induced LEA (Loucks et al., 1998). Furthermore, the threshold for disruptions of EA on reproductive hormones was seen to mirror that of alterations to blood glucose concentrations, suggesting a potential effect on brain metabolism (Loucks & Thuma, 2003). In terms of exacerbating the effects of LEA on body systems, inadequate protein or calcium intake may increase the impairment of lean mass or bone status, respectively. Other dietary patterns that have been observed anecdotally and in studies (Barron et al., 2016; Heikura et al., 2018; Melin et al., 2016; Reed et al., 2011) of female athletes with LEA or menstrual dysfunction include high intakes of noncaloric and caffeinated beverages, low dietary energy density, and high intakes of fiber, vegetables, and flavoring condiments. Whether these and other dietary factors directly contribute to LEA or its secondary problems is an area for future research. Despite the errors involved with the quantitative assessment of EA from food records, the process may add value in creating interactions between the practitioner and the athlete which allow a qualitative evaluation of food choices, unusual dietary behaviors, and undue stress about food.

Moving Forward With the Measurement and Diagnosis of LEA

We have established in this review that the estimation of EA in free-living athletes is difficult, burdensome, and prone to producing a result where the noise (errors of reliability and validity) is potentially equal or even greater than the signal (the undershoot of EA due to mismatch between actual EI and/or EEE that could be associated with impairment of health or performance). Clearly, this means that a randomly achieved calculation of an athlete’s EA should not be used in isolation to diagnose or manage LEA. The development of a Best Practice Protocol(s) to achieve a standardized measurement format would be useful in research and practice scenarios to move forward both areas; this should be considered a priority for the expert groups managing the bodies of work around the Female Athlete Triad and RED-S. However, even then it should be considered a “second line of attack,” used to help to confirm a diagnosis of LEA in athletes who have been found to exhibit signs of this syndrome. This could occur as follow-up in individuals who have been flagged by screening tools with known sensitivity and specificity in identifying athletes at high risk of LEA (e.g., the Low Energy Availability in Females Questionnaire, Melin et al., 2014; RED-S Clinical Assessment Tool, Mountjoy et al., 2015; or cumulative risk assessment tool for the Triad, Joy et al., 2014). Alternatively, it might be paired with biochemical, clinical, and hormonal tests to attribute the findings of abnormal results or impaired function to LEA. A benefit of the assessment process is that interaction with a sports dietitian might gain qualitative insights into nutrition beliefs, dietary practices, and food choices that contribute to an energy mismatch, as well as exacerbate the impairment of health or performance in the individual. Even if the quantitative analysis of EA is challenging, insights gained around these elements might help to inform a management plan to unravel the energy mismatch and associated problems.

Conclusion

EA is a key concept in sports nutrition that provides the practitioner with a framework to assess the underlying cause, and therefore management of athletes with conditions associated with RED-S. LEA impairs of many body functions with the reduction in metabolic and hormonal function following a range of different dose–response relationships. Importantly, LEA causes functional outcomes that are important to athletes: increasing illness and injury risks and impairing training adaptation and performance. While the concept has evolved from systematic study of the effects of restrained eating and/or unsupported EEE in controlled laboratory situations, its measurement under field conditions and utility in real-life appears more complicated. In the field, assessment or screening of EA is time consuming and includes a degree of error, even when using the best techniques available to the practitioner. Therefore, screening of athletes for risk factors or symptoms included in the RED-S syndrome, could be more cost effective by identifying those who would benefit from a more detailed EA assessment, using a standardized technique that is yet to be determined, as well as providing additional information to interpret the results.

Acknowledgments

No funding was received in the preparation of this paper. The authors declare no conflict of interest. L.M. Burke, B. Lundy, I.L. Fahrenholtz, and A.K. Melin all participated in the writing of this paper.

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Burke and Lundy are with Sports Nutrition, Australian Institute of Sport, Bruce, Australian Capital Territory, Australia; and the Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria, Australia. Fahrenholtz and Melin are with the Dept. of Nutrition, Exercise and Sports, University of Copenhagen, Frederiksberg, Denmark.

Address author correspondence to Louise M. Burke at louise.burke@ausport.gov.au.
  • View in gallery

    —Overview of study design where EA was controlled for 4 days at a healthy level of ∼189 (45 kcal)·kg LBM−1·day−1 or at a low level of ∼42 kJ (10 kcal)·kg LBM−1·day−1 either by severe dietary restriction or by high volume training. Both treatments of low EA resulted in changes in hormone concentrations and the pulsatility of LH. EA = energy availability; LBM = lean body mass; LH = luteinizing hormone. (Redrawn from Loucks et al., 1998).

  • View in gallery

    —Summary of the effect of reduced EA on the activity of hormones representing different body functions in healthy females. The plots show differences in the dose–response impairments of LH (solid line) where normal pulse frequency is maintained until a threshold at around 125 kJ (30 kcal)·kg FFM−1·day−1; while concentrations of insulin, leptin, and PICP (short dashes) are immediately reduced in response to a decrease in EA. The responses of T3, IGF-1, and OC (long dashes) are nonlinear with the largest reductions in concentrations occurring between EA of 84–125 kJ (20–30 kcal)·kg FFM−1·day−1. EA = energy availability; FFM = fat-free mass; LH = luteinizing hormone; PICP = procollagen I C-terminal propeptide; T3 = triiodothyronine; IGF-1 = insulin-like growth factor 1; OC = osteocalcin. (Adapted from Loucks, 2015.)

  • View in gallery

    —Sleeping metabolic rate plotted as a function of FFM from work of Westerterp (2003) whereby SMR (MJ) = 2.27 + 0.091 × FFM. The solid regression line has a significant nonzero intercept. The dashed line represents energy availability of 125 kJ (30 kcal)·kg FFM−1. • = females; ○ = males. FFM = fat-free mass; SMR = sleeping metabolic rate. (Adapted from “Energy availability in athletes,” by A.B. Loucks, B. Kiens, and H.H. Wright, 2011,  Journal of  Sports Sciences, 29, pp. S7–S15. Copyright 2011 by Taylor & Francis.)

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