AN ABSTRACT OF THE THESIS OF
Charlotte P. Guebels for the degree of Master of Science in Nutrition presented
on April 22, 2011.
Title: Active Women with and without Menstrual Disorders: Comparison of Resting
Metabolic Rate and Energy Availability.
Abstract approved:
______________________________________________________________________
Melinda M. Manore
The prevalence of exercise-induced menstrual dysfunction (ExMD) ranges between
6-79% in endurance-trained women and may result from a low energy availability (EA;
kcal/kgFFM/d). EA is the energy remaining after planned exercise, which is available for
basic physiological processes and daily living activities. One mechanism for energy
conservation may be a reduced resting metabolic rate (RMR). PURPOSE: To determine
if the restoration of menses in endurance-trained women with ExMD, using a daily
carbohydrate-protein (CHO-PRO) supplement, is associated with improvements in EA
and RMR. Eumenorrheic (Eumen) active controls were also compared to ExMD before
and after the 6-mo diet intervention. METHODS: Active women with ExMD (n=8; 7
amenorrheic, 1 oligomenorrheic, age=23±3y, VO2max=49±6 mL/kg/min, body
fat=22±5%) participated in a 6-mo intervention and consumed 325 mL/d of CHO-PRO
supplement (360 kcal/d). Menstrual status was confirmed by measuring reproductive
hormones. At baseline (0-mo) and 6-mo, two RMR measurements were made using
indirect calorimetry. Energy intake (EI) and expenditure were assessed using 7-d diet
and activity records, respectively. All ExMD participants wore an accelerometer for 7-d.
Exercise energy expenditure (EEE) was defined using 4 methods. ExMD participants
completed all measurements at 0-mo and 6-mo; Eumen controls (n=9, age 25±5y,
VO2max=50±5 mL/kg/min, body fat=23±5%) were measured at 0-mo only. Pre- to postintervention comparisons (ExMD only) of EI, EA, EB, and RMR were made using one-
sided paired t-tests; two-sided paired t-tests were used for all remaining comparisons.
Between-group comparisons (ExMD vs. Eumen) were made using one-sided unpaired ttests for the previously listed variables and two-sided unpaired t-tests for the remaining
variables. RESULTS: All ExMD participants resumed menses (2.6±2.2 mo to 1st
menses, 3.5±1.9 cycles) during the 6-mo intervention; mean weight gain was 1.6±2.0 kg
(p=0.029). No significant changes in EA (0-mo=36.7; 6-mo=45.4 kcal/kgFFM/d) or RMR
(0-mo=1515±142; 6-mo=1522±134 kcal/d) occurred due to the intervention; however,
mean EA improved 24-39% over the intervention. When comparing ExMD to Eumen,
there were no significant differences in EI and EA (Eumen=38.3 kcal/kgFFM/d);
however, EA for ExMD was 18.5% higher at 6-mo compared to Eumen. Mean EI for
ExMD was 2312 kcal/d and 2694 kcal/d at 0-mo and 6-mo, respectively, while mean EI
for Eumen was 2430 kcal/d. Training volume (min/wk) for ExMD was higher than Eumen
controls (p<0.04) when exercise was defined as all planned exercise (Method 1)
(ExMD=736±199; Eumen=473±168 min/wk) and all planned exercise+ bike commute+all
walking (Method 2) (ExMD=1215±305; Eumen=934±183 min/wk). At 0-mo, mean total
energy expenditure (TEE) was not different between groups (ExMD=2822±264 kcal/d;
Eumen=2601±273 kcal/d (p=0.122), yet EB was different (p=0.049) (ExMD=-10.3±6.9;
Eumen=-3.0±9.7 kcal/kgFFM/d). RMR was significantly lower in Eumen (29.1±1.9
kcal/kg FFM/d) vs. ExMD (0-mo=31.3±1.8; 6-mo=31.5±2.7 kcal/kg FFM/d) (p<0.02).
CONCLUSION: The addition of 360 kcal/d to improve EA was effective in resuming
menses in active women with ExMD, but did not alter RMR. EA was similar between
ExMD and Eumen at baseline. Conversely, when menses resumed at post-intervention,
the EA of ExMD was 18% higher than Eumen, suggesting varying susceptibility to low
EA. Differences in menstrual status may be more closely linked to higher TEE in those
with ExMD, rather than an absolute EA value.
©Copyright by Charlotte P. Guebels
April 22, 2011
All Rights Reserved
Active Women with and without Menstrual Disorders:
Comparison of Resting Metabolic Rate and Energy Availability
by
Charlotte P. Guebels
A THESIS
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Master of Science
Presented April 22, 2011
Commencement June 2011
Master of Science thesis of Charlotte P. Guebels
presented on April 22, 2011.
APPROVED:
_______________________________________________________
Major Professor, representing Nutrition
_______________________________________________________
Chair of the Department of Nutrition and Exercise Sciences
_______________________________________________________
Dean of the Graduate School
I understand that my thesis will become part of the permanent collection of Oregon State
University libraries. My signature below authorizes the release of my thesis to any reader
upon request.
_______________________________________________________
Charlotte P. Guebels, Author
ACKNOWLEDGEMENTS
I would like to express my sincere appreciation to all committee members, colleagues,
family, and friends. Specifically, I would like to thank Dr. Melinda Manore, for selecting
me as her graduate student and guiding me through graduate school with advice and
opportunities. I would also like to thank Dr. Stewart Trost, for lending the accelerometer
devices that were used in this project and dedicating time to my thesis. In addition, Ingrid
Skoog has been invaluable to my graduate learning experience, offering not only advice
but genuine support. I am also very grateful to the doctoral student on the project, Lynn
Cialdella-Kam, for allowing me to be a part of her dissertation, mentoring me throughout
the project, and sharing her technical expertise. I would like to acknowledge all of the
undergraduate students who assisted with this study: Rachelle, Jigna, ArieIa, Emily,
Dana, Jessica, Sarah, Aimee, Laura, Katie, Kim, Haily, and David. Ultimately, this
research project would not have been possible without our motivated research
participants, who I would like to thank for their time and cooperation with the numerous
assessments. I am extremely grateful to my family for their ongoing love,
encouragement, and confidence in my scholarly abilities. Special thanks to my father,
who has always found the time to help me figure out the answers to technical questions.
And finally, I would like to thank Jonas Hoffman for his love, patience, and continual
support, without which this thesis would not have been possible.
CONTRIBUTION OF AUTHORS
Dr. Lynn Cialdella-Kam was directly involved with the study design, participant
recruitment, data collection, and data analysis. Dr. Gianni Maddalozzo performed all
body composition assessments. Dr. Melinda M. Manore assisted with the study design,
finding funding, data analysis and interpretation, and writing.
TABLE OF CONTENTS
Page
GENERAL INTRODUCTION…………………………………………………………………...1
REVIEW OF LITERATURE…………………………………………………………….1
The normal menstrual cycle: eumenorrhea………………………………….1
Factors leading to exercise-induced menstrual dysfunction……………….2
Mechanism of exercise-induced menstrual dysfunction……………………3
Health consequences of exercise-induced menstrual dysfunction………..3
Energy balance and exercise-induced menstrual dysfunction…………….4
Assessment of energy intake and energy expenditure…………………….5
Suggested causes of negative energy balance……………………………..7
Cross-sectional research………………………………………………………9
Intervention studies…………………………………………………………...10
Research aims and hypotheses…………………………………………..…10
Aim 1…………………………………………………………………….....10
Aim 2…………………………………………………………………….....11
REVIEW OF LITERATURE TABLES………………………………………………..12
ACTIVE WOMEN WITH AND WITHOUT MENSTRUAL DISORDERS: COMPARISON
OF RESTING METABOLIC RATE AND ENERGY AVAILABILITY…………...………….16
INTRODUCTION………………………………………………………………………17
RESEARCH DESIGN AND METHODS…………………………………………….20
Subjects………………………………………………………………………..20
Menstrual status………………………………………………………………20
Experimental design………………………………………………………….20
Aerobic capacity test………………………………………………………….21
Body composition……………………………………………………………..21
Energy intake (EI)……………………………………………………………..22
Total energy expenditure (TEE)……………………………………………..22
Accelerometers………………………………………………………………..22
Running energy expenditure…………………………………………………23
Resting metabolic rate (RMR)……………………………………………….24
Exercise energy expenditure (EEE).........................................................24
Method 1: All planned exercise…………………………………………25
Method 2: All planned exercise plus bike commute and all walking..25
Method 3: All exercise ≥ 4 METs……………………………………….25
Method 4: All exercise >4 METs………………………………………..26
EEE calculation summary……………………………………………………26
Energy balance (EB)………………………………………………………….26
Energy availability (EA)………………………………………………………27
TABLE OF CONTENTS (Continued).
Page
Statistical analysis……………………………………………………….……27
RESULTS………………………………………………………………………………28
Subjects……………………………………………………………………..…28
Menstrual status…………………………………………………………….…28
Energy intake (EI)…………………………………………………………..…28
Total energy expenditure (TEE)…………………………………………..…28
Resting metabolic rate (RMR)…………………………………………….…29
Exercise energy expenditure (EEE)………………………………………...29
Training volume……………………………………………………………….29
Energy balance (EB)……………………………………………………….…30
Energy availability (EA)………………………………………………………30
DISCUSSION………………………………………………………………………..…31
Resting metabolic rate (RMR): ExMD vs. Eumen…………………………31
Measured vs. predicted RMR in active women with and without menstrual
dysfunction……………………………………………………………………..32
Measured energy availability (EA)...........................................................33
Calculated energy availability (EA)………………………………………….34
Defining and quantifying exercise…………………………………………...36
Limitations and strengths..…………………………………………………...37
Recommendations……………………………………………………………38
Conclusion……………………………………………………………………..38
FIGURES AND TABLES …………………….………….……………………………39
GENERAL CONCLUSION………………………………………………………………….…50
BIBLIOGRAPHY………………………………………………………………………………..51
APPENDICES………….…………………………....………………………………………….59
LIST OF FIGURES
Figure
Page
1. All planned exercise (EEE Method 1) performed by the ExMD group (n=8) before and
after the 6-mo diet intervention, and the Eumen group at 0-mo (n=9)……………………40
2. Comparison of Energy Intake (EI: kcal/d) vs. Total Energy Expenditure (TEE; kcal/d)
……………………………………………………………………………………………………41
3. Measured Resting Metabolic Rate (RMR) over the 6-mo diet intervention (ExMD) and
compared to Eumen ……..…………………………………………………………………….42
4. Comparison of Exercise Energy Expenditure (EEE) using 4 different methods…..….43
5. Comparison of Energy Availability (EA) calculations to Energy Intake (EI)..………….44
LIST OF TABLES
Table
Page
REVIEW OF LITERATURE
1. Studies that have measured resting metabolic rate (RMR) in active women with and
without menstrual dysfunction……………………………………………………………...…13
2. Research reporting energy availability (EA) in post-adolescent females……………...14
3. Calculated energy availability (EA) from studies reporting necessary data for active
women with and without menstrual disorders…..…………………………………………...15
ACTIVE WOMEN WITH AND WITHOUT MENSTRUAL DISORDERS: COMPARISON
OF RESTING METABOLIC RATE AND ENERGY AVAILABILITY
1. Different methods to quantify exercise energy expenditure (EEE)………………….…45
2. Characteristics of active women with exercise-induced menstrual dysfunction (ExMD)
and active eumenorrheic (Eumen) controls………………………………………………….46
3. Average daily Energy Intake (EI), Energy Balance (EB), and Energy Availability (EA)
……………………………………………………………………………………………………47
4. Components of Total Energy Expenditure (TEE); Resting Metabolic Rate (RMR) and
Exercise Energy Expenditure (EEE)………………………………………………………….48
5. Training volume (min/wk) of active women with exercise-induced menstrual
dysfunction (ExMD) and active eumenorheic (Eumen) controls…………………………..49
LIST OF APPENDICES
Page
Questionnaires………………………………………………………………………………….60
Health History ……………..………….……………………………………………….60
Physical Activity………………. ………………………………………………………65
Menstrual History ……………….…………………………………………………….68
Diet Record Template………………………………………………………………………….70
Activity Record Template……………………………………………………………………...73
Accelerometer Instructions…………………………………………………………………….78
Menstrual Diary…………………………………………………………………………………79
Resting Metabolic Rate Datasheet…………………………………………………………...80
Running Energy Expenditure Datasheet…………………………………………………….81
VO2max Datasheet……………………………………………………………………………..82
DEDICATION
This thesis is dedicated to my parents, who motivated me to pursue higher education.
Active Women with and without Menstrual Disorders:
Comparison of Resting Metabolic Rate and Energy Availability
GENERAL INTRODUCTION
REVIEW OF LITERATURE
It is well-known that participation in regular physical activity improves overall
health and promotes longevity. Still, too much of a good thing without some form of
compensation can lead to imbalance. Active premenopausal women who expend more
energy than they are willing or able to consume may increase their risk for one or more
of the female athlete triad components; the triad refers to the interrelated spectrums of
low energy availability (with or without an eating disorder), amenorrhea, and
osteoporosis. Currently, there is a critical need to better understand the minimal energy
requirements of active women (exercising 7+ h/wk) in order to prevent, diagnose, and
treat exercise-induced menstrual disorders through diet alone.
Menstrual dysfunction is prevalent in 6 to 79% of active women (16-35y) (Beals &
Hill, 2006; Cannavo, Curto, & Trimarchi, 2001; De Souza, et al., 1998; Manore, 2002; S.
H. Thompson, 2007), especially those participating in sports that emphasize a lean
physique or that gain a performance benefit from low bodyweight (e.g. endurance
runners). Comparatively, only 2 to 5% of the general population report menstrual
dysfunction (Highet, 1989; Wilmore, et al., 1992). Although some females experience
delayed menarche during their pubertal years (primary amenorrhea), others menstruate
normally until they significantly increase exercise training (secondary amenorrhea).
Exercise induced menstrual dysfunction (ExMD) exists on a continuum from
asymptomatic short luteal phase and anovulation (no ovulation), to the more serious
symptomatic oligomenorrhea (menstrual cycles at intervals longer than 35 days) and
amenorrhea (absence of a menstrual cycle for more than 90 consecutive days) (Nattiv,
et al., 2007).
The normal menstrual cycle: eumenorrhea. The normal menstrual cycle
encompasses two distinct phases and characteristic hormone fluctuations; it typically
lasts between 28 and 32 days, with menstruation marking the first day of the cycle. The
first phase of the menstrual cycle is termed the follicular phase, as it is the time during
2
which a single follicle grows and develops. Follicular stimulating hormone (FSH) allows
for the recruitment of the follicle, which in turn secretes estradiol and stimulates the
release of luteinizing hormone (LH). A mid-cycle surge in LH (around day 15-18) allows
for ovulation (release of the egg) and the possibility for fertilization. The second phase of
the menstrual cycle is termed the luteal or secretory phase and is characterized by an
increased production of estradiol and progesterone from the ovary. Once an egg is
released from the follicle, the remaining corpus luteum produces the necessary estradiol
and progesterone. If fertilization occurs, the uterus is now ready for implantation with the
help of these steroidal hormones. Without fertilization, the corpus luteum breaks down,
terminating the menstrual cycle. Under normal health conditions, the mature female
body will repeat this menstrual cycle from puberty to menopause, with possible
interruptions due to pregnancy and metabolic, and/or physiological stress (Dueck,
Manore, & Matt, 1996).
Factors leading to exercise-induced menstrual dysfunction. A number of factors can
contribute to ExMD. The primary contributing factor to ExMD is thought to be a low
energy availability (EA), sometimes termed „energy drain‟ (Loucks, 2007; Manore, Kam,
& Loucks, 2007). EA refers to the energy remaining after exercise that is available for
basic physiological processes such as digestion, respiration, cell regeneration, growth,
and reproduction (Manore, 2002); it is calculated as EA = dietary energy intake (EI) –
exercise energy expenditure (EEE), and typically reported in units of kcal/kg FFM/d. Low
EA can arise under different scenarios. Some athletes may consciously try to restrict
energy intake by watching what they eat to try and „make weight‟ or attain an optimal
figure for their specific sport. Conversely, many just do not consume enough kilocalories
to meet energy needs. Inadequate EI in women with ExMD does not necessarily suggest
the existence of a clinical eating disorder (Manore, 2002). For highly active individuals,
the drive for food (energy intake) may not match energy needs (Hubert, King, & Blundell,
1998), especially while consuming the high carbohydrate diets that are often
recommended for endurance athletes. These types of diets have previously been
associated with more severe energy deficits due to an often simultaneous reduction in
energy dense, fat-containing foods (Horvath, Eagen, Ryer-Calvin, & Pendergast, 2000;
3
Stubbs, et al., 2004). Furthermore, low EA can strictly be a result of higher EEE in active
women with ExMD compared to Eumen active controls (Reed, De Souza, Bowell, &
Williams, 2010; Scheid, Williams, West, VanHeest, & De Souza, 2009).
Further contributing factors to ExMD that have been proposed in the research
literature include: nutritional inadequacies, low bodyweight, low body fat, and
psychological stress (Anderson, 1999; Greydanus & Patel, 2002). Still, a low dietary
energy intake in contrast to energy expenditure continues to be the most widely
accepted contributing factor. Research continues to suggest high individual variation in
susceptibility to contributing factors (Manore, 2002).
Mechanism of exercise-induced menstrual dysfunction. Changes in the
hypothalamic-pituitary-gonadal axis (functional hypothalamic amenorrhea) are observed
with ExMD and impact the signaling pathways required for reproduction (Manore, 2002).
Reproduction depends critically on the pulsatile secretion of luteinizing hormone (LH)
from the pituitary gland into the blood, controlled by the release of gonadotropin
releasing hormone (GnRH) from the hypothalamus. Research by Loucks and colleagues
has shown a disrupted LH pulsatility in habitually sedentary individuals when EA fell
below 30 kcal/kg FFM/d (Loucks, 2006; Loucks & Heath, 1994; Loucks & Thuma, 2003;
Loucks, Verdun, & Heath, 1998). Their research suggests that menstrual dysfunction is
associated with a low energy intake in contrast to high energy expenditure.
Health consequences of exercise-induced menstrual dysfunction. Menstrual
dysfunction, regardless of form, can negatively influence fertility and overall health.
ExMD reduces estrogen levels necessary for bone building and bone maintenance.
Under normal conditions, estrogen helps maintain a balance between osteoclasts (bone
resorptive cells) and osteoblasts (bone-building cells). A drop in estrogen production
allows osteoclasts to predominate, leading to excess bone resorption and diminished
bone mineral density (BMD) (De Souza, et al., 2008; Turner, Riggs, & Spelsberg, 1994).
While weight-bearing exercise is promoted to improve bone health by mechanically
loading the skeleton, studies continue to observe significantly lower BMD values in
amenorrheic athletes than eumenorrheic athletes (Drinkwater, Bruemner, & Chesnut,
4
1990; Fischer, Nelson, Frontera, Turksoy, & Evans, 1986; Keen & Drinkwater, 1997).
Low BMD values are of concern because they increase the risk for stress fractures and
set the athlete up for premature osteoporosis at a time (age 20-35y) when peak bone
mass should be attained (Nattiv, et al., 2007). Hormone replacement therapy and oral
contraceptives prescribed to elevate estrogen levels have not shown to be effective at
reversing bone loss (Fredericson & Kent, 2005; Warren, et al., 2003); additionally, oral
contraceptives have side-effects of nausea, fatigue, negative mood, and weight gain
(Dueck, Matt, Manore, & Skinner, 1996; Oinonen, 2009). The current rise in use of
progesterone-only contraceptives, which are potent inhibitors of gonadotropin release,
may also induce further bone loss in young women (Sarfati & de Vernejoul, 2009). More
research exploring the mechanism of these hormone contraceptives is needed.
If dietary energy restriction is the cause of low EA and ExMD, then poor
nutritional status can result. For the female athlete consuming less than 1800 kcal/d, it
is nearly impossible to meet adequate energy and nutrient needs along with a high
volume training regimen (Manore, 2002). Inadequate energy intake increases the
athlete‟s risk for inadequate macronutrient intakes (carbohydrate, protein, and essential
fatty acids). In addition, active women with ExMD are typically low in Vitamin B-6,
riboflavin, folate, calcium, magnesium, iron, and zinc due primarily to low energy intakes
(Manore, 2002). For example, Thompson (2007) reported 51% of female athletes with
menstrual dysfunction consuming less than the recommended amounts of dietary
calcium (1,500 mg/d). Along with the nutritional side-effects, active women with ExMD
have also complained of diminished performance, fatigue, and irritable mood (Dueck,
Matt, et al., 1996).
Energy balance and exercise-induced menstrual dysfunction. Researchers
frequently report a negative energy balance in female athletes with ExMD (Beidleman,
Puhl, & De Souza, 1995; De Souza, et al., 1998; Dueck, Matt, et al., 1996; Edwards,
Lindeman, Mikesky, & Stager, 1993; Kopp-Woodroffe, Manore, Dueck, Skinner, & Matt,
1999; Mulligan & Butterfield, 1990). Energy balance (EB) refers to the difference
between dietary energy intake (EI) and total energy expenditure (TEE). To calculate
energy balance, the following formula is used: EB = EI – TEE. Whereas energy intake
5
comes exclusively from the diet, TEE is the sum of several parts: resting metabolic rate
(RMR), thermic effect of food (TEF), exercise energy expenditure (EEE), and activities of
daily living (ADL). By definition, an individual in energy balance has a stable body
weight, yet researchers continue to report a negative energy balance in weight-stable
female athletes with ExMD (Beidleman, et al., 1995; Edwards, et al., 1993; Loucks &
Heath, 1994; Mulligan & Butterfield, 1990; Myerson, et al., 1991). These studies do not
report the length of time participants have remained weight-stable, whether significant
weight-loss occurred prior to establishing weight-stability, or data on body composition
changes.
Accurate assessments of energy balance require the cooperation of research
participants recording or recalling their diet and physical activity, as well as researcher
availability of equipment and computer programs to quantify energy expenditure and
analyze energy intake.
Assessment of energy intake and energy expenditure. In free-living active women,
dietary energy intake (EI) is commonly assessed using diet records. Researchers
typically choose either 3-d (Tomten & Hostmark, 2006; Wilmore, et al., 1992) or 7-d
recording periods (Kopp-Woodroffe, et al., 1999; Lagowska, Jeszka, & Bajerska, 2010;
Thong, McLean, & Graham, 2000). When a 3-d diet record is used, participants are
asked to record all foods and beverages consumed for 2 weekdays and 1 weekend day;
conversely, 7-d diet records are kept on 7 consecutive days to represent an active
individual‟s typical training week. Diet records kept for 7-d have been shown to correlate
well with longer time periods ranging from 28d to several months (Acheson, Campbell,
Edholm, Miller, & Stock, 1980; St. Jeor, Guthrie, & Jones, 1983); however, recording
periods longer than 7-d may reduce participant compliance. Furthermore, dietary energy
intake may be quantified by means of a 24-hour diet recall alone or in combination with
diet records (Myerson, et al., 1991); this method requires recollection of foods and
beverages consumed in the last 24-hour period, as well as the ability to quantify serving
sizes from memory.
Advanced assessment techniques exist to quantify energy expenditure.
Respiratory chambers, although not commonly a part of research facilities, allow for
6
measurement of TEE via direct calorimetry. Participants analyzed using a respiratory
chamber are required to remain in a sealed room for a defined period of time, as the
room air is consistently drawn, sampled for oxygen and carbon dioxide, and circulated.
This confined method, however, does not allow for typical activities of daily living,
particularly in active individuals who would not be allowed to exercise in the chamber.
Conversely, the doubly labeled water technique (DLW) is currently a gold standard for
assessing total energy expenditure (TEE) in free-living individuals. This method can
objectively quantify TEE by measuring the excretion of stable isotopes (oxygen and
hydrogen) in body fluids. While safe and non-restrictive, the DLW technique requires
advanced laboratory equipment and does not allow for the analysis of each individual
TEE component (RMR, TEF, EEE, ADL).
Acknowledging the limitations and expenses of the previous methods,
researchers quantifying energy expenditure in free-living active women have derived
various other techniques. Lagowska et al. (2010) utilized physical activity questionnaires
to estimate TEE and EEE. In their study, BMR was estimated using a prediction
equation (Schofield, 1985) and physical activity ratios were utilized to estimate EEE and
TEE. Similarly, others have relied on the analysis of activity logs using a computerized
activity analysis program (Kopp-Woodroffe, et al., 1999) or energy expenditure tables
(Laughlin & Yen, 1996; Thong, et al., 2000). In an effort to improve estimates of energy
expenditure, some researchers (De Souza, et al., 2008; Myerson, et al., 1991; Wilmore,
et al., 1992) have directly measured one or multiple components of TEE (RMR, TEF,
EEE). Researchers (Myerson, et al., 1991; Schaal, Van Loan, & Casazza, 2010; Tomten
& Hostmark, 2006) have matched free-living reports of heart rate (HR), rate of perceived
exertion (RPE), and/or running speed to oxygen consumption from laboratory exercise
tests. While these measurements have assisted in improving energy expenditure
estimates, they continue to rely on the accuracy of self-reported activity.
In an effort to obtain more objective assessments, accelerometers have also
been utilized to estimate TEE and/or EEE. Reading et al. (2002) utilized a threedimensional portable accelerometer (Tritrac-R3D ®) to estimate EEE. Similarly, Caltrac
and Sensewear Pro 3 accelerometers have been utilized to estimate TEE in previous
studies (De Souza, et al., 1998; Loucks, et al., 1998; Schaal, et al., 2010). In these
7
studies, it remains unclear how accelerometer data (units: counts) were converted to
estimates of energy expenditure (units: kcals/d). In addition, the use of accelerometers
still requires participant compliance when asked to wear the monitor at all times during
waking hours except when participating in water activities. Thus, accelerometers are
less useful in active women that swim as part of their exercise training.
Suggested causes of negative energy balance. Underreporting of EI may be a
possible explanation for some of the reported energy imbalance in active women, along
with weight stability. Still, highly motivated individuals with a low body mass index (BMI)
have been shown to underreport EI less frequently than obese individuals and to provide
more reliable estimates of their dietary energy intake (Black, et al., 1993; Edwards, et al.,
1993). In addition, data show that individuals trained to record foods and beverages
provide EI data similar to DLW data (Champagne, et al., 2002). A common practice to
control for under-reporters is to identify the ratio of dietary energy intake to basal
metabolic rate (EI:BMR) (Poslusna, Ruprich, de Vries, Jakubikova, & van't Veer, 2009).
The Goldberg cut-off method tests whether reported EI can be representative of longterm habitual intake; a 1.35 EI:BMR ratio is suggested as the acceptable cut-off when
BMR is measured rather than predicted (Goldberg, et al., 1991).
A second plausible explanation for weight stability along with negative energy
balance is energy conservation. It has been suggested that when the intake of energy
falls significantly short of expenditure for a prolonged period of time, the body adapts by
becoming more efficient and compromising reproduction (Wilmore, et al., 1992). For
example, weight stability has been reported in developing countries where people
consume as low as 60% of their expended energy (Mulligan & Butterfield, 1990), which
would require some sort of a physiological adaptation assuming that the energy
measurements were accurate.
The presence of an energy deficit may not only compromise reproduction but
also decrease energy expended at rest (RMR) in order to meet daily energy needs. The
most extreme example of this type of adaptation has been observed in individuals
suffering from eating disorders or chronic underfeeding (Scalfi & DiBiase, 1993).
Research examining RMR in active women with ExMD in comparison to controls have
8
reported mixed results. For example, Wilmore et al. (1992) found no evidence in support
of energy conservation in elite female runners, whereas Myerson et al. (1991) reported
significantly lower RMR values in amenorrheic runners when compared to eumenorrheic
runners (see TABLE 1). Still, considering that RMR is highly dependent on fat-free mass
(FFM), athletes who typically have higher FFM are expected to have higher RMR
measurements than their sedentary counterparts. Elevated RMR values have been
observed in trained individuals with normal menstrual function when compared to less
active and untrained individuals (Beidleman, et al., 1995; Mulligan & Butterfield, 1990).
Lastly, trained female athletes have also shown diminished meal-induced thermogenesis
(thermic effect of food) when compared to moderately active and sedentary females
(LeBlanc, Mercier, & Samson, 1984), possibly adding to the proposed conservation
mechanism.
Several studies have measured RMR in active women with and without ExMD
(M. Lebenstedt, Platte, & Pirke, 1999; Myburgh, Berman, Novick, Noakes, & Lambert,
1999; Myerson, et al., 1991; Wilmore, et al., 1992), whereas others have relied on
prediction equations to estimate RMR in their participants (Lagowska, et al., 2010;
Sjodin, Andersson, Hogberg, & Westerterp, 1994; Tomten & Hostmark, 2006).
Suppressed metabolic rates have been reported in active women with ExMD when
compared to their active eumenorrheic counterparts (De Souza, Lee, et al., 2007; M.
Lebenstedt, et al., 1999; Myburgh, et al., 1999; Myerson, et al., 1991; Scheid, et al.,
2009), although two studies (Reading, McCargar, & Harber, 2002; Wilmore, et al., 1992)
have reported similar RMR values between groups (see TABLE 1). Thus, prediction
equations may not be appropriate for estimating RMR in this population, as conservation
of energy (in the form of a reduced RMR) remains uncertain. Sjodin et al. (1994)
reported energy expenditure in cross-country skiers using doubly labeled water and
expressed concern about calculating RMR with predictions equations in highly trained
subjects whom were not assessed for menstrual function. To date, no study has
measured changes in RMR as active females with ExMD resume menses. Further
studies that measure RMR in active women with and without menstrual dysfunction are
needed.
9
Cross-sectional research. Due to the existing challenges in quantifying energy
expenditure and the new EA terminology, few studies (De Souza, et al., 1998; Hoch, et
al., 2009; Schaal, et al., 2010) have directly reported values for EA in active women with
ExMD (see TABLE 2). The original EA research that defined an EA threshold of
30kcal/kg FFM/d for retention of LH pulsatility, was performed on habitually sedentary
individuals in an experimentally controlled environment (Ihle & Loucks, 2004; Loucks &
Thuma, 2003; Loucks, et al., 1998). Thus, the suggested EA threshold may not hold true
for free-living active women. Of those that have reported EA in active females, Hoch et
al. (2009) did not separate their participants according to menstrual function and only
reported on the prevalence of EA <30 kcal/kg FFM/d in high school athletes (5 out of 80
athletes: 6%). They estimated EI using 3-d diet records (2 weekdays, 1 weekend day)
and quantified EEE based on reported duration and intensity of organized sports using
the Ainsworth compendium of physical activity (Ainsworth, et al., 1993; Ainsworth, et al.,
2000). De Souza et al. (1998) calculated mean EA values for all exercising groups
(ovulatory, luteal phase deficient, anovulatory); however, they reported EA only in units
of kcal/d and kcal/kg/d. Without the necessary FFM data, it is difficult to compare their
results to the suggested energy threshold. In their study, EI was quantified using 7-d diet
records and EEE estimated by evaluating activity logs. The energy cost of specific
activities (running and other weight-bearing activities) was determined by multiplying the
minutes engaged in a particular activity by the estimated expenditure (kcal/min) of the
activity (McArdle, Katch, & Katch, 1996). Lastly, a recent study by Schaal, Van Loan,
and Casazza (2010) reported EA values for a group of 5 amenorrheic (EA: 18 kcal/kg
FFM/d) and 5 eumenorrheic endurance-trained athletes (EA: 29 kcal/kg FFM/d). Here,
EI was estimated from 7-d diet records and EEE was calculated on the basis of RPE and
HR during training, matched to oxygen consumption and RER in their laboratory. The
mean EA for the eumenorrheic participants in this study was close to the suggested
energy threshold of 30kcal/kg FFM/d to maintain menstrual status, however, betweengroup differences in EI, EEE, and EA were not significant (Schaal, et al., 2010).
Some researchers have reported the necessary data for calculating energy
availability (EI and EEE), without doing the calculation themselves (Kopp-Woodroffe, et
al., 1999; Lagowska, et al., 2010; Laughlin & Yen, 1996; Myerson, et al., 1991; Thong, et
10
al., 2000; Tomten & Hostmark, 2006; Wilmore, et al., 1992). TABLE 3 displays
calculated energy availability from data reported on active women with and without
menstrual disorders. Interestingly, when EA is calculated for these studies, four out of
the seven (57%) produce mean EA values ≥30 kcal/kg FFM/d for eumenorrheic
participants and EA <30 kcal/kg FFM/d for participants with menstrual dysfunction
(Kopp-Woodroffe, et al., 1999; Lagowska, et al., 2010; Myerson, et al., 1991; Thong, et
al., 2000). Still, methods for quantifying energy intake and expenditure vary widely
between studies.
Intervention studies. Despite growing evidence suggesting a state of low EA in active
women with ExMD, only two studies (Dueck, Matt, et al., 1996; Kopp-Woodroffe, et al.,
1999) have explored interventions different from hormonal therapy to resume menstrual
function. These studies were successful in resuming menstrual cycles using a diet and
exercise intervention, which improved energy balance and nutritional status (Dueck,
Matt, et al., 1996; Kopp-Woodroffe, et al., 1999). Both of these studies required
participants to include 1 day of rest/week and increase EI by consuming one 11-oz
nutrition supplement per day (360 kcal/d). Still, the intervention studies were short term
(time= 15-20 weeks), had small sample sizes (n=1-4 active women), and did not
measure EA or RMR changes over the course of the intervention (Dueck, Matt, et al.,
1996; Kopp-Woodroffe, et al., 1999). To date, no study has aimed to restore menstrual
function by providing solely a diet intervention without changing the athlete‟s training
regimen. This type of intervention may be more desirable for devoted athletes, whom are
reluctant to add 1 day rest/week to their strict training regimens.
Research aims and hypotheses.
Aim 1: To determine if a 6-mo diet intervention, which feeds a Carbohydrate-Protein
(CHO-PRO) supplement (360 kcal/d), improves energy availability (EA) for the
endurance-trained female with ExMD (0-mo to 6-mo). Also, to assess if differences in EA
exist between active women with ExMD (0-mo and 6-mo) and Eumen active controls (0mo).
11
Our hypothesis was that the supplement would improve EA (0-mo to 6-mo) and restore
normal menstrual function. We anticipated that weight would increase to a small degree
if EA improved. We predicted that active women with ExMD (0-mo) would have a lower
calculated EA than Eumen active controls. We also assessed energy balance for both
groups (ExMD 0-mo and 6-mo; Eumen 0-mo).
Aim 2: To assess changes in Resting Metabolic Rate (RMR) over the 6-mo diet
intervention (ExMD group). To compare measured RMR values for the ExMD group (0mo and 6-mo) to the Eumen group (0-mo).
Our hypothesis was that RMR would increase with increased energy intake and
resumption of menses (ExMD 0-mo to 6-mo). We presumed that RMR measurements
for the Eumen group (0-mo) would be higher than for the ExMD group (0-mo and 6-mo).
12
REVIEW OF LITERATURE TABLES
TABLE 1. Studies that have measured resting metabolic rate (RMR) in active women with and without menstrual
dysfunction.
13
TABLE 2. Research reporting energy availability (EA) in post-adolescent females.
14
TABLE 3. Calculated energy availability (EA) from studies reporting necessary data for active women with and
without menstrual disorders.
15
16
ACTIVE WOMEN WITH AND WITHOUT MENSTRUAL DISORDERS:
COMPARISON OF RESTING METABOLIC RATE AND ENERGY AVAILABILITY
Charlotte P. Guebels, Lynn Cialdella-Kam, Gianni Maddalozzo, Melinda M. Manore
17
INTRODUCTION
Exercise-induced menstrual dysfunction (ExMD) is prevalent among active
women, particularly those in sports that emphasize a lean physique or gain a
performance benefit from low body weight. The prevalence of ExMD (6-79%) (Beals &
Hill, 2006; Cannavo, et al., 2001; De Souza, et al., 1998; Manore, 2002; S. H.
Thompson, 2007) varies widely between sports and type of menstrual dysfunction, yet
occurrence in active individuals is much higher than in the general population (2-5%)
(Highet, 1989; Wilmore, et al., 1992). ExMD exists on a continuum from asymptomatic
anovulation (no ovulation) and luteal phase deficiency, to symptomatic oligomenorrhea
(cycles >35d) and amenorrhea (no menses for >3 mo) (Nattiv, et al., 2007). Although
physical activity is known to promote a wide variety of health benefits, ExMD is
associated with increased risk of stress fractures and early onset osteoporosis (Khan, et
al., 2002; Torstveit & Sundgot-Borgen, 2005; Waldrop, 2005), compromised immune
function (Bhaskaram, 2001; Montero, Lopez-Varela, Nova, & Marcos, 2002;
Venkatraman & Pendergast, 2002), decreased performance (Dueck, Matt, et al., 1996),
negative impacts on cardiovascular health (De Souza & Williams, 2004; Rickenlund,
Eriksson, Schenck-Gustafsson, & Hirschberg, 2005; Zeni Hoch, et al., 2003), and poor
nutritional status (Manore, 2002).
Based on the current research, a low energy availability (EA) is a primary
contributing factor to ExMD (Loucks, et al., 1998; Nattiv, et al., 2007; Williams,
Helmreich, Parfitt, Caston-Balderrama, & Cameron, 2001). EA refers to the energy
remaining after exercise, for basic physiological processes such as respiration,
digestion, cell regeneration, and reproduction. Reproduction depends on the pulsatile
secretion of luteinizing hormone (LH) from the pituitary gland, which results from the
hypothalamic pulsatile secretion of gonadotropin releasing hormone (GnRH) (Loucks,
Mortola, Girton, & Yen, 1989). Loucks and colleagues reported a disruption in LH
pulsatility when EA fell below 30 kcal/kg FFM/d in habitually sedentary individuals
exercising at 70% of their aerobic capacity (Loucks, 2006; Loucks & Heath, 1994;
Loucks & Thuma, 2003; Loucks, et al., 1998). Thus, when exercise training volume
18
increases abruptly without concurrent increases in energy intake (EI), the body may
adapt to the large metabolic demands via cessation of menstruation.
EA is calculated by subtracting exercise energy expenditure (EEE) from dietary
energy intake (EI) (EA=EI- EEE), and is expressed relative to fat-free mass (FFM)
(kcal/kg FFM/d). In active women, a low EA may result from inadequate EI, high energy
expenditure, or a combination of these factors. Some athletes may consciously restrict
EI to attain an optimal figure or weight for their specific sport, while others inadvertently
do not eat enough to meet energy requirements. For highly active individuals, the drive
for food may not match energy needs (Hubert, et al., 1998), especially when consuming
the high carbohydrate diets that are often recommended for endurance athletes.
Conversely, some researchers report similar EI in individuals with ExMD and
eumenorrheic (Eumen) active controls; this suggests that higher levels of EEE, not low
EI, are contributing to low EA. Participants with ExMD have been reported to exercise
more frequently than Eumen participants (Reed, et al., 2010; Tomten, Hostmark, &
Stromme, 1996); therefore, expending higher amounts of energy overall (Lebenstedt,
Bickenback, Pirke, & Platen, 2001; Scheid, et al., 2009).
Prior to the more recently determined EA, discrepancies between EI and total
energy expenditure (TEE) were reported in terms of energy balance (EB; EB= EI- TEE).
TEE refers to the sum of resting metabolic rate (RMR), thermic effect of food (TEF),
exercise energy expenditure (EEE), and all remaining activities of daily living (ADL).
Successful assessment of TEE in free-living individuals is challenging and requires
sophisticated research laboratory equipment. While doubly-labeled water (DLW) is
considered the gold standard when assessing TEE in free-living individuals, it does not
separate TEE into its individual components (RMR, TEF, EEE, ADL). Thus, researchers
often rely on self-reported diet and activity records to quantify energy status; the
accuracy of these methods relies heavily on the cooperation of the research participants.
Throughout the existing research literature, active women with ExMD are
reported to be in negative EB while reporting weight-stability (Beidleman, et al., 1995;
Edwards, et al., 1993; Loucks & Heath, 1994; Mulligan & Butterfield, 1990; Myerson, et
al., 1991); these reports are inconsistent with the concept of energy balance. Underreporting is one plausible explanation for the reported energy imbalance, however,
19
highly motivated individuals with low body mass index (BMI; kg/m2) have been shown to
underreport less frequently than obese individuals and to provide more reliable
estimates of their energy intake (Black, et al., 1993; Edwards, et al., 1993). Another
explanation for a negative EB concurrent with weight stability is the existence of energy
conservation with ExMD. Energy conservation, in the form of a reduced RMR, has been
reported in several studies measuring RMR in active women with and without ExMD (De
Souza, Lee, et al., 2007; M. Lebenstedt, et al., 1999; Myburgh, et al., 1999; Myerson, et
al., 1991; Scheid, et al., 2009). Conversely, others have reported similar RMR values
between groups (Reading, et al., 2002; Wilmore, et al., 1992).
Diet and exercise interventions have shown promising results in reversing ExMD.
In 1996, Dueck et al. re-established menstrual cyclicity in an amenorrheic endurance
athlete with the addition of 360 kcal/d and reduction of training by 1 session/wk. Their
15-wk experiment transitioned the athlete from a negative EB to a positive EB. KoppeWoodroffe et al. (1999) provided the same diet and exercise training intervention (20wks) and observed similar results in 4 athletes with ExMD. In their study, reported data
allowed for EA calculations; the average EA of their participants upon entry into the
study was 25 kcal/kg FFM/d, which was raised to 30kcal/kg FFM/d by the end of the
intervention when menses resumed. These results support the energy threshold of 30
kcal/kg FFM/d proposed by Loucks et al. (1998), yet little detail is provided on the EEE
and TEE calculations. Lastly, a non-human primate model (2001) successfully showed
that amenorrhea could be reversed simply by the addition of energy intake without
changes to exercise (Williams, et al., 2001).
Although previous studies document the possibility of reversing ExMD using a
diet and exercise intervention (Dueck, Manore, et al., 1996; Kopp-Woodroffe, et al.,
1999), the nonhuman primate ExMD model is the only one that has successfully
resumed menses with diet alone (Williams, et al., 2001). To our knowledge, no long-term
diet intervention study has examined changes in RMR and EA with the resumption of
menses in active women. The purpose of the current study was to determine if a 6-mo
diet intervention (CHO-PRO supplement: 360 kcal/d) would improve EA in endurancetrained active women with ExMD, resume menses, and influence RMR. We included
Eumen active controls for comparison to the ExMD.
20
RESEARCH DESIGN AND METHODS
Subjects. Endurance-trained women (n=22) between the ages of 18-35y were recruited
into one of two groups based on menstrual status: 1) menstrual dysfunction due to
exercise (n=12; ExMD) or 2) normal menstrual cycles (n=10; Eumen). Participation
criteria included exercising regularly (minimum 7 h/wk), no use of oral contraceptives or
hormone therapy for the last 6 months, and a score <14 on the Eating Disorder Inventory
(EDI-2) assessment (Garner & Olmstead, 1984). Questionnaires were used to assess
general health, exercise training, and dietary history. Four ExMD participants were
dropped from the study due to pregnancy, noncompliance with the intervention, and
failure to complete post-intervention assessments; one Eumen participant was identified
as an under-reporter and excluded from the results. Active women with ExMD (n=7
amenorrheic, n=1 oligomenorrheic) also participated in a 6-mo diet intervention where
they consumed 325mL of CHO-PRO supplement daily (360 kcal/d). The active Eumen
women were used as controls (n=9).
Menstrual status. Self-reported menstrual dysfunction was confirmed by measuring
reproductive hormones (estradiol, follicle stimulate hormone, luteinizing hormone,
progesterone, and prolactin) and no ovulation (ClearBlue ® Easy Fertility Monitor).
Subjects were classified as amenorrheic if they reported no menses >90d or as
oligomenorrheic if cycle intervals were >35d. Eumenorrhea (10-13 cycles/y or intervals
of ~28d) was confirmed if participants had normal reproductive hormones and tested
positive for ovulation (Nattiv, et al., 2007). Menstrual history and age at menarche were
collected via a questionnaire and reviewed with participants. Women with primary
amenorrhea or non-exercise induced menstrual dysfunction were not considered.
Experimental design. Assessment measurements (blood, energy expenditure and
intake, ovulation) were completed at baseline for both groups and repeated after 6-mo
for the ExMD group only. In addition, ExMD participant compliance was monitored
during weekly meetings and measurements at 3-mo (3-d diet/activity records, 2 Resting
Metabolic Rate measurements [RMR], and a Running Energy Expenditure [Running EE]
21
test). This study was approved by the Institutional Review Board at Oregon State
University (#4079).
Aerobic capacity test. A continuous graded exercise test was used to assess VO2max
using indirect calorimetry (TrueOne 2400; ParvoMedics Metabolic Cart, Sandy, UT); the
same treadmill was used for all tests (Trackmaster, TMX 22; Newton, KS). Flow
calibration was performed using a 3-L calibration syringe (Hans Rudolph Series 5530,
Kansas City, MO) and gas calibration using standardized gases of known concentration
(16% O2, 4% CO2). The protocol began with a 6-min warm-up: 2-min at a self-selected
slow pace to familiarize the subject with the equipment, and 4-min at a self-selected
training pace at a 0% grade. Following the warm-up, the grade was changed to 4% for 2min, and then increased by 2% every 2-min thereafter until volitional exhaustion. Gas
exchange was monitored throughout the testing period and reported in 15-sec
increments. A heart rate (HR) monitor (Polar Xtrainer Plus, Finland) was worn for the
entire testing period, and Rate of Perceived Exertion (RPE) was assessed at the end of
each stage using hand signals (20-point RPE scale)(Borg, 1982). VO2max was achieved
when three of the four criteria were met: 1) VO2 reached a plateau, 2) RPE was greater
than 17, 3) Respiratory Exchange Ratio (RER) >1.1, and 4) HR was within 10 bpm of
predicted maximum HR (220–age). Subjects were required to have a minimum VO2max
of 38 mL/kg/min to remain in the study.
Body composition. Body composition was determined by dual-energy x-ray
absorptiometry (DXA) (Hologic QDR-4500 Elite A Waltham, MA). Values were reported
for total body mass (kg), lean mass (LM) (kg), fat mass (FM) (kg), fat free mass (FFM)
(LM + bone mineral content [BMC]), and body fat percentage (FM/total mass). All scans
were performed and analyzed by a trained laboratory technician using Hologic software
version Oasis QDR for Windows® XP (Hologic, Inc., Waltham, MA). All follow-up scans
were analyzed using the compare mode. The coefficient of variation (CV) for repeated
DXA scans at the Oregon State University Bone Research Laboratory is 1.5% for whole
body.
22
Energy intake (EI). After receiving detailed instructions, subjects completed 7-d
consecutive weighed food records. Each subject was provided with a calibrated food
scale and asked to keep food labels and recipes. To avoid special eating regimens close
to competition or on vacation days, subjects were asked not to complete diet records at
those times. Upon return, diet records were reviewed by a researcher for adequacy;
subjects were contacted for further clarification as needed. EI was estimated from the 7d diet records using a computerized diet analysis program (Food Processor SQL version
9.91, 2006; ESHA Research). When consumed foods were not found in the database,
ingredients were entered manually using food labels and recipes provided by
participants. Three participants (n=1 ExMD, n=2 Eumen) were identified as underreporters using the Goldberg cut-off method (1.35 x Basal Metabolic Rate [BMR])
(Golberg, et al. 1991). The ExMD participant was included in the analysis, since
inadequate EI can be expected in this population. One of the Eumen subjects was also
retained (Goldberg cut-off: 1.24) because upon further review, her records appeared to
reflect her typical eating behaviors. All analyses reflect data from 8 ExMD and 9 Eumen
subjects.
Total energy expenditure (TEE). Subjects also completed 7-d physical activity logs that
were concurrent with the 7-d diet records. Activities were reported in 15-min increments
and included planned exercise and all activities of daily living (e.g. eating, showering,
working, sleeping). Completed activity logs were entered into the diet and activity
analysis program (Food Processor SQL version 9.91, 2006; ESHA Research) to
determine TEE. Activity codes and MET intensities in this program are based on the
American College of Sports Medicine‟s Resource Manual for Guidelines for Exercise
Testing and Prescription (ACSM, 5th Ed, Appendix A; 2006)(Ainsworth, et al., 2000).
Program data were exported and measured values for resting metabolic rate (RMR) and
running energy expenditure (running EE) were substituted for predicted values to
improve accuracy of TEE estimates.
Accelerometers. Accelerometer data were used to objectively assess any changes in
training volume over the course of the 6-mo diet intervention. At 0-mo and 6-mo, ExMD
23
subjects wore an accelerometer (ActiGraph LLC, Pensacola, FL) during the 7-d diet and
activity recording period. Subjects were instructed to wear the accelerometer on their
right hip at all times, except during sleep and water activities (e.g. bathing, swimming,
and rowing). Data were reported as average counts/min to control for individual
differences in wear time; days with <600 min of wear time were not considered valid. No
fewer than 3 valid days were accepted during each recording period; however, all
participants had 4-7 valid days at 0-mo and all were within 3-7d at 6-mo (n=2 with 3d,
n=4 with 6-7d). One ExMD subject did not follow the appropriate protocol at 6-mo and
another only provided 2 valid days at 0-mo; both participants were excluded from the
data. At 0-mo, average ExMD activity (n=6) was quantified as 516 ± 223 counts/min,
similar to 493 ± 191 counts/min at 6-mo (2-sided paired t-test; p=0.772). Additionally,
monitored minutes of moderate (≥ 3.0 METs) and vigorous (≥ 6.0 METs) activity were
not different at 0-mo vs. 6-mo (69 ± 32 min/d and 73 ± 41 min/d, respectively; 2-sided
paired t-test, p=0.832). Using these data, training volume was maintained over the
course of the 6-mo diet intervention.
Running energy expenditure. Assessments of running EE were used to improve the
accuracy of individual exercise energy expenditure (EEE) and TEE predictions.
Measurements were performed using indirect calorimetry (TrueOne 2400; ParvoMedics
Metabolic Cart, Sandy, UT), with gas and flow rate calibration prior to each
measurement. Menstruating subjects were measured during the first week of their
menstrual cycle (follicular phase). Upon arrival, participants reported 4 typical training
speeds: a warm-up pace (easy), a marathon/long-distance pace (easy-moderate), a 10k
pace (moderate-fast), and a 5k pace (fast). Participants ran at each pace for 5-min while
wearing a HR monitor (Polar Xtrainer Plus, Finland). HR was recorded at the end of
each 5-min segment, when steady-state exercise was achieved. After test completion,
subjects were instructed to cool-down until their heart rate dropped below 120 bpm. Only
average VO2 and VCO2 outputs collected between minutes 2:30 and 4:30 for each running
pace were used. These time points were selected to reflect steady-state exercise and
avoid transition into the next running pace. The Weir equation was used to convert
respiratory measurements to units of kcal/min (Weir, 1949).
24
Resting metabolic rate (RMR). RMR was measured to assess changes over the 6-mo
diet intervention, and to determine if there were differences between the ExMD and the
Eumen group; measured RMR was also used to improve estimates of TEE.
Measurements occurred on 2 separate days within a 7-d period for all subjects at 0-mo,
and the same protocol was followed at 6-mo for the ExMD subjects. The second RMR
measurement always occurred within a few days of the first, and if the two
measurements differed by more than 5%, the subject was asked to repeat the test again
until results fell within 5%. Menstruating subjects were measured during the first week of
their menstrual cycle (follicular phase) to control for any hormonal variations. All
measurements were performed using indirect calorimetry (TrueOne 2400; ParvoMetics
Metabolic Cart, Sandy, UT). Both gas and flow rate were calibrated prior to each
measurement; the standardized gas for this test consisted of 16% O2 and 1% CO2.
Subjects reported to the laboratory in the morning following an overnight fast (minimum
8h; on average 10-11h since last food intake) and no morning exercise. All subjects
drove or were driven to the test site, except for those living within 3 miles of the
laboratory and without access to a car. These individuals (n=2 ExMd, n=5 Eumen) were
allowed to walk or bike slowly to the test and then rest prior to testing. On average,
measurements occurred 19h since the last exercise work-out (range: 11 to >24h). Upon
arrival, subjects were familiarized with the procedures and asked to lie supine for 20-30
min under a ventilated hood. Subjects were instructed to rest and remain awake for the
duration of the measurement. Average VO2 and VCO2 outputs obtained from 8-10 min of
steady-state (≤10% variation) were used to calculate RMR (kcal/d) with the Weir
equation (Weir, 1949). The CV for repeated RMR measurements at the Oregon State
University Human Performance Laboratory is 2.2±1.4% (n=43 endurance-trained
women).
Exercise energy expenditure (EEE). Because types, intensity, and duration of physical
activity varied between participants, EEE was quantified using 4 different methods. All
data were obtained from 7-d activity logs entered into the diet and activity analysis
program (Food Processor SQL version 9.91, 2006) and adjusted for measured RMR and
running EE.
25
Method 1: All planned exercise. Planned exercise included any intentionally scheduled
physical activity, regardless of intensity. Low intensity physical activities considered as
part of EEE included: hiking, stretching, yoga, pilates, calisthenics (sit-up and push-ups),
yard work, skateboarding, dancing, and walking (lasting ≥30 consecutive minutes or
within an exercise work-out). Not included in planned exercise was any physical activity
that resulted from social games (billiards, ping-pong, twister, playing catch), hobbies
(camping, fishing), leisure pastimes (watering plants, picking berries), shopping/cleaning,
short durations of walking without purpose, or any commute biking or walking (<30
consecutive minutes) to school/work. TABLE 1 reports planned exercise (min/wk) for all
subjects (ExMD and Eumen). FIGURE 1 shows the percentage of total minutes spent
performing different planned activities for each group.
Method 2: All planned exercise plus bike commute and all walking. All but one
participant commuted to school/work by bike or foot; Method 2 considers these activities
as planned. In addition, some individuals were found to be constantly on the move
throughout their day. In an effort to consider these differences in movement, all walking
was included in this method, such as: walking around the house or at work, walking at
the farmer‟s market, and walking between classes. For consistency purposes, the
bicycle commute for all participants was entered into the diet and activity analysis
program as “general/leisure bicycling” (4.0 METs) and all walking was entered as
“moderate intensity walking” (3.3 METs). No other activities identified as being equal to
4.0 METs or 3.3 METs were performed by our subjects. Thus, all 4.0 MET and 3.3 MET
activities were added to Method 1 to calculate Method 2. We did not double-count the
walking (lasting ≥30 consecutive minutes or within an exercise work-out) already
considered as part of Method 1. One-way commute durations varied widely between
subjects (walking 5-30 min; bicycling 2-30 min). In summary, this method helped
determine additional amounts of biking or walking that may otherwise have been
considered as activities of daily living.
Method 3: All exercise ≥ 4 METs. This method quantifies EEE more objectively using
intensity of physical activity. The 4.0 MET cut-off was used to incorporate the more
26
strenuous bike commute (selected as 4.0 METs for all subjects) into the EEE
measurement, but left out any walking equivalent to 3.3 METs. No other activities
identified as being equal to 4.0 METS were performed by our subjects. Activities >4.0
METs included: badminton and dancing (both 4.5 METs), general health club exercises
(5.5 METs), cycling (conditioning) (5.5-7.0 METs), swimming (7.0-10.0 METs), water
jogging (8.0 METs), circuit training (8.0 METs), rowing (not for warm-up) (7.0-8.5 METs),
and running for which energy expenditure was directly measured. Not included in this
method were: stretching/yoga (2.5 METs), light weight-lifting (3.0 METs), recreational
volleyball and frisbee (both 3.0 METs), walking (3.3 METs), calisthenics (3.5 METs), and
warm-up rowing (3.5 METs).
Method 4: All exercise >4 METs. This method of measuring EEE included all of the
activities included in Method 3, except for the bike commute (4.0 METs). TABLE 1
reports types of exercise >4.0 METS (min/wk) for all subjects (ExMD and Eumen).
EEE calculation summary. Active women typically participate in more than one sport,
and individuals within a sport do not always perform the same activities. It is important to
consider all exercise when calculating EEE, not just an individual‟s primary sport. An
activity considered as “exercise” by one individual may be part of activities of daily living
for another; thus, planned exercise remains a subjective assessment. In addition, some
active women worked jobs that included a significant amount of physical activity or
overall movement throughout the day. We propose 4 EEE methods in an effort to
consider these differences in what may be considered as “exercise” and suggest
possible ways of objectively quantifying EEE in terms of MET values. The methods for
calculating EEE are summarized in TABLE 1.
Energy balance (EB). EB (kcal/d) was calculated as the difference between total EI
(kcal/d) and TEE (kcal/d): EB= EI-TEE. TEE estimated from the computerized diet and
analysis program (Food Processor SQL version 9.91, 2006) was adjusted for measured
RMR (kcal/d) and running EE (kcal/d), as previously described. Results represent the
average of 7-d and were reported in kcal/d and kcal/kg FFM/d.
27
Energy availability (EA). EA was calculated by subtracting EEE from total EI. Multiple
EA values were reported due to the fact that EEE was quantified using 4 different
methods (defined earlier). Results represent the average of 7-d and were reported in
kcal/d and kcal/kg FFM/d. EA (kcal/kg FFM/d) was calculated as follows:
EA = EI (kcal/d) – EEE (kcal/d).
Statistical analysis. Data are presented as mean ± standard deviation for each group.
For the ExMD group, one-sided paired t-tests were performed to determine changes
over time (0-mo vs 6-mo) in EI, EA, EB, RMR, and weight due to the intervention; we
hypothesized that the intervention would improve all of these variables. Two-sided
paired t-tests were performed on all of the remaining reported variables to determine
changes over time. Between-group comparisons at 0-mo (ExMD 0-mo vs. Eumen) were
made using one-sided unpaired t-tests for EI, EA, EB, and RMR; we hypothesized that
these variables would be higher in the Eumen group than the ExMD group. All remaining
between-group comparisons (ExMD 0-mo vs Eumen, ExMD 6-mo vs. Eumen) were
made using 2-sided t-tests. Statistical significance was set at p<0.05.
28
RESULTS
Subjects. Demographic data for participants are given in TABLE 2. For the ExMD
participants, the 6-mo intervention, which supplied an additional 360 kcal/d, contributed
to an average weight gain of 1.6 ± 2.0 kg (p=0.029). The composition of weight gain was
primarily fat mass (1.6 ± 1.1 kg) (p=0.004). FFM and VO2max remained constant for
ExMD subjects over the 6-mo intervention (p>0.05). When Eumen and ExMD groups
were compared at time 0-mo and again at 6-mo, they were similar in age, age at
menarche, height, weight, BMI (kg/m2), body composition, and VO2max.
Menstrual status. At baseline, seven ExMD subjects were classified as amenorrheic
and one as oligomenorrheic. All 8 resumed menses over the 6-mo diet intervention
(mean time to first menses = 2.63 mo, range 1-7 mo), and 7 out of the 8 reported
ovulating at 6-mo. Menses was verified with a positive ovulation test.
Energy intake (EI). Over the intervention, ExMD subjects increased mean total EI from
2,312±324 kcal/d at baseline to 2,694±541 kcal/d by 6 mo (p=0.039). This represented
an average increase of 380 ± 525 kcal/d or 16.5%; the CHO-PRO supplement provided
360kcal/d. There were no differences in average EI between the groups when compared
at 0-mo and 6-mo. See TABLE 3 (kcal/d) and FIGURE 2 (kcal/kg FFM/d) for
comparisons of EI between groups.
Total energy expenditure (TEE). As shown in TABLE 4 and FIGURE 2, the ExMD
subjects maintained TEE over the course of the 6-mo intervention. The average TEE
was 2,822 ± 264 kcal/d at 0-mo and 2,739 ± 414 kcal/d at 6-mo (p=0.531); results were
also similar when data were expressed as kcal/kg FFM/d (p=0.550). Conversely, the
Eumen group had a significantly lower mean TEE than the ExMD participants when
expressed as kcal/kg FFM/d at time 0-mo (p<0.02). Mean TEE for the Eumen was
50.6±2.4 kcal/kg FFM/d, while the mean TEE in the ExMD was 58.3±4.4 kcal/kg FFM/d
(0-mo) and 56.7±8.4 kcal/kg FFM/d (6-mo).
29
Resting metabolic rate (RMR). For the ExMD group, mean absolute RMR did not
change over the 6-month intervention (1,514±142 kcal/d at 0-mo; 1,522 ±134 kcal/d at 6mo) (p=0.332). Thus, we did not observe an increase in RMR with resumption of menses
(see TABLE 4 and FIGURE 3). Results remained similar when RMR was expressed as a
function of total mass (kg) and FFM (kg). For the Eumen group, mean RMR was
1,491±117 kcal/d and similar to that measured in the ExMD group. When RMR was
expressed as a function of FFM, the Eumen had a lower RMR compared to the ExMD at
0-mo and 6-mo (p<0.05).
Exercise energy expenditure (EEE). Estimates of EEE varied widely depending on the
method used (TABLE 4, FIGURE 4). For the ExMD group, mean EEE ranged between
517 kcal/d (Method 2) and 943 kcal/d (Method 2) during the intervention; however, when
comparing each method over time, EEE remained similar from 0-mo to 6-mo (p>0.05).
For the Eumen group, mean EEE ranged from 484 kcal/d (Method 4) to 763 kcal/d
(Method 2). Comparing the Eumen group to the ExMD group, no differences were found
in EEE within each method at 0-mo and 6-mo (p>0.05). Method 2, which included all
planned exercise plus the bike commute and all walking, produced the largest EEE
values, while Method 4 (all exercise >4.0 METs) produced the smallest values. EEE
values obtained using Methods 1 and 3 were comparable (~10-87 kcal/d difference).
Methods 3 and 4 are the most reproducible methods, since they define exercise more
objectively in term of MET values.
Training volume. For the ExMD group, minutes of exercise varied between methods,
but were not significantly different from 0-mo to 6-mo (p>0.05) (see TABLE 5). Using
Method 1, the ExMD group (0-mo and 6-mo) reported more minutes of planned exercise
per week (736 ± 199 min/wk and 610 ± 218 min/wk, respectively) than the Eumen group
(473 ± 168 min/wk), but this difference was only significant for between-group
comparisons at 0-mo (p=0.034). When EEE was defined as all exercise >4.0 METS
(Method 4), training volumes of the Eumen and ExMD groups were similar. Activities
included in planned exercise are shown in FIGURE 1 and MET values in TABLE 1.
30
Energy balance (EB). EB data are presented in TABLE 3. For the ExMD group,
calculated EB improved over the course of the intervention, although differences were
not statistically significant (p=0.07). At time 0-mo, the Eumen EB (-3.0 ± 9.7 kcal/kg
FFM/d) was significantly higher than ExMD at 0-mo (-10.3 ± 6.9 kcal/kg FFM/d)
(p=0.049); differences only approached significance when expressed in kcal/d
(p=0.064). When comparing Eumen to ExMD at 6-mo, there were no differences in EB.
Energy availability (EA). The mean EA (kcal/kg FFM/d) data for the ExMD group at
each time period and for each method of measuring EEE are presented in Table 3.
Depending on the method used to calculate EEE, mean EA ranged from 28.2 to 36.7
kcal/kg FFM/d at time 0-mo for the ExMD group. By the end of the intervention, mean
EA had increased and ranged from 39.2 to 45.4 kcal/kg FFM/d at 6-mo. Although EA
improved by 24% (Method 4) to 39% (Method 2) over the intervention, these changes
were not significantly different (p>0.05). Only when EA was calculated using Method 2
for determining EEE was the mean EA <30 kcal/kg FFM/d for the ExMD group (0mo)(see Figure 5); depending on EEE method used, 3 or 4 of the 8 ExMD subjects
experienced menstrual dysfunction with an EA <30kcal/kgFFM/d . For the ExMD
subjects, resumption of menses occurred at a mean EA of ~40 kcal/kg FFM/d or greater;
7 of the 8 ExMD subjects resumed menses with an EA>30 kcal/kg FFM/d (Method 1, 3,
4). For the Eumen group, mean EA ranged between 32.9 and 38.3 kcal/kg FFM/d. When
the Eumen group was compared to the ExMD group at time 0-mo and 6-mo, EA
calculations were not significantly different for any of the 4 methods, although the Eumen
were consistently higher at time 0-mo (p>0.05). At 6-mo, the ExMD group had higher EA
than the Eumen group, but differences were not significant.
31
DISCUSSION
Energy conservation has been suggested as a mechanism to maintain body
weight and energy balance in active women with menstrual dysfunction (Mulligan &
Butterfield, 1990; Myerson, et al., 1991). To date, no diet intervention study has
examined changes in RMR and EA with the resumption of menses in active women.
Contrary to our hypothesis, we did not observe improvements in RMR for the ExMD
group as they resumed menses by 6-mo. Since time to resumption of menses varied
widely between participants (1-6 mo), we presume that alterations in RMR may require a
longer time period or that maintenance of RMR is a result of other factors. We did,
however, see significant improvements in EI (p=0.039), but this change did not translate
into significant changes in EA (p~0.08).
Resting metabolic rate (RMR): ExMD vs. Eumen. Suppressed metabolic rates are
frequently reported in active women with menstrual dysfunction when compared to their
active eumenorrheic counterparts (De Souza, Lee, et al., 2007; M. Lebenstedt, et al.,
1999; Myburgh, et al., 1999; Myerson, et al., 1991; Scheid, et al., 2009). Only two
studies (Reading, et al., 2002; Wilmore, et al., 1992) have reported similar RMR values
between groups. Conversely, we found RMR values in endurance-trained active women
with ExMD to be significantly higher than in endurance-trained Eumen participants
(kcal/kg FFM/d) at 0-mo. Our findings are thus inconsistent with the current research
literature. Possible reasons for this difference are discussed below.
First, RMR assessment protocols differ between studies. Three studies (M.
Lebenstedt, et al., 1999; Myburgh, et al., 1999; Myerson, et al., 1991) did not perform
repeated measurements of RMR on separate days to consider day-to-day variations. In
addition, only two studies (De Souza, Hontscharuk, Olmsted, Kerr, & Williams, 2007; De
Souza, et al., 2008; Scheid, et al., 2009) and the present study report the analysis of
only steady-state data (≤10% variation) within any particular RMR measurement.
Second, RMR could be influenced by the last exercise bout prior to an RMR
measurement. In the current study, participants were not asked to abstain from exercise
on the day prior to each RMR measurement. They were only asked to refrain from
32
exercise on the mornings of each RMR measurement. The last exercise work-out
occurred between 11 and >24 hours (average 19 h) of the measurement depending on
the participant. Our participants typically exercised 7 d/wk, thus, skipping an exercise
work-out would not have been typical. Research by Bullough, Gillette, Harris, and Melby
(1995) has found that RMR is highly influenced by the total energy flux in the body. It is
therefore possible that participants who exercise more often have higher RMR values
arising from the residual effect of exercise on RMR. Our ExMD participants reported
more mean minutes per week of planned exercise (736 min/wk) than the Eumen group
(473 min/wk). Similarly, Scheid et al. (2009) reported amenorrheic active women
exercising significantly more (620 min/wk) than ovulatory active women (505 min/wk).
Furthermore, Tomten et al. (1996) found long distance runners with irregular menstrual
function reported more training at lower intensities compared to their eumenorrheic
counterparts, but equal amounts of high intensity training (Tomten, et al., 1996). This is
also in agreement with our findings. We found similar exercise volume between the
ExMD and Eumen groups when exercise was defined as exercise ≥ 4.0 METs or >
4.0METs (moderate-high intensity only).Third, low energy intakes are associated with
reduced RMRs. With the exception of reports by Lebenstedt et al. (1999), our research
participants were consuming more kcals/d (126-1212 kcal/d more) than active women
previously measured for RMR (Myburgh, et al., 1999; Myerson, et al., 1991; Reading, et
al., 2002; Scheid, et al., 2009; Wilmore, et al., 1992). These differences may have added
to the greater energy flux of our participants.
Measured vs. predicted RMR in active women with and without menstrual
dysfunction. De Souza et al. (2008) classified exercising women as energy deficient if
their ratio of Resting Energy Expenditure (REE) to predicted REE (pREE) (Harris &
Benedict, 1919) was ≤0.90, and energy replete if REE:pREE was >0.90. Using this
classification system, all of our participants would be considered energy replete (ExMD
0-mo: 1.04, ExMD 6-mo: 1.03, Eumen: 1.00). Similar to Thompson and Manore (1996),
we used the Root Mean Squared Prediction Error method (RMSPE) to compare various
predicted RMRs to our measured RMR; this method encompasses individual deviations
from prediction equations prior to evaluating group means. Thompson and Manore
33
(1996) found the Cunningham equation (1980) to predict measured RMR most
accurately (within 103 kcal/d) for endurance-trained women. Using this method, we
found the Cunningham (1980) and Harris-Benedict (1919) prediction equations to most
accurately estimate RMR in the ExMD group (within 98-102 kcal/d). Conversely, we
found the Harris-Benedict (1919), the Mifflin-St. Jeor (1990), the WHO.FAO.UNU (1985)
(using height and weight), and the Schofield (1985) equations to be appropriate (within
79-100 kcal/d) for Eumen participants. These findings suggest the Harris-Benedict
equation (1919) as an appropriate prediction equation in active women with and without
ExMD.
Measured energy availability (EA). Low EA appears to be the primary contributing
factor to menstrual dysfunction (Loucks, et al., 1998; Nattiv, et al., 2007; Williams, et al.,
2001). Previous laboratory research manipulating diet and exercise in habitually
sedentary individuals has suggested 30kcal/kgFFM/d as the energy threshold for
retaining luteinizing hormone pulsatility and therefore menstrual function (Loucks, et al.,
1998). To date, few studies (De Souza, et al., 1998; Hoch, et al., 2009; Schaal, et al.,
2010) have reported on EA in free-living active females. Hoch et al. (2009) did not
separate their participants according to menstrual function and only reported on the
prevalence of EA <30 kcal/kg FFM/d in high school athletes (5 out of 80 athletes: 6%).
They estimated EI using 3-d diet records (2 weekdays, 1 weekend day) and quantified
EEE based on reported duration and intensity of organized sports using the Ainsworth
compendium of physical activity (Ainsworth, et al., 1993; Ainsworth, et al., 2000). De
Souza et al. (1998) calculated mean EA values for all exercising groups (ovulatory, luteal
phase deficient, anovulatory); however, they reported EA only in units of kcal/d and
kcal/kg/d. Without the necessary FFM data, it is difficult to compare their results to the
suggested energy threshold. In their study, EI was quantified using 7-d diet records and
EEE estimated by evaluating activity logs. The energy cost of specific activities (running
and other weight-bearing activities) was determined by multiplying the minutes engaged
in a particular activity by the estimated expenditure (kcal/min) of the activity (McArdle, et
al., 1996). Lastly, a recent study by Schaal, Van Loan, and Casazza (2010) reported EA
values for a group of 5 amenorrheic (EA: 18 kcal/kg FFM/d) and 5 eumenorrheic
34
endurance-trained athletes (EA: 29 kcal/kg FFM/d). Here, EI was estimated from 7-d diet
records and EEE was calculated on the basis of RPE and HR during training, matched
to oxygen consumption and RER in their laboratory. The mean energy availability for the
eumenorrheic participants in this study was close to the suggested energy threshold of
30kcal/kg FFM/d to maintain menstrual status, however, between-group differences in
EI, EEE, and EA were not significant (Schaal, et al., 2010).
In the current study, the mean EA of 8 ExMD participants improved between 24%
and 39% (depending on method used) over the course of the 6-mo diet intervention;
however, mean improvements in EA were not statistically significant (p>0.05).
Comparing the ExMD at 0-mo to the Eumen participants, mean calculated EA was not
significantly different between groups. Only method 2 resulted in an EA <30
kcal/kgFFM/d for the ExMD, otherwise, all remaining EA values for both groups were >
30 kcal/kgFFM/d.
Our data does not support a set energy availability threshold of 30 kcal/kg FFM/d
to resume menstrual status. Since both ExMD and Eumen participants had similar EA
values, we presume that some women may be more susceptible to low EA than others.
This is in agreement with reports by Schaal et al. (2010), who calculated EA for one
participant with long-term amenorrhea at 36 kcal/kg FFM/d and EA for 2 eumenorrheic
participants <30kcal/kg FFM/d. These researchers suggest that EA may fluctuate daily
with changes in training volume and that EA likely only affects menstrual function if
chronically low (Schaal, et al., 2010). Still, our research provides evidence in support of
increasing EA in order to resume menstrual function in active women with exerciseinduced menstrual dysfunction.
Calculated energy availability (EA). A number of studies provide values for EI and
EEE in active women with and without menstrual dysfunction, which allow for the indirect
calculation of EA (Kopp-Woodroffe, et al., 1999; Lagowska, et al., 2010; Laughlin & Yen,
1996; Myerson, et al., 1991; Thong, et al., 2000; Tomten & Hostmark, 2006; Wilmore, et
al., 1992). Interestingly, when EA is calculated for these studies, four out of the seven
(57%) produce mean EA values ≥30 kcal/kg FFM/d for eumenorrheic participants and
EA <30 kcal/kg FFM/d for participants with menstrual dysfunction (Kopp-Woodroffe, et
35
al., 1999; Lagowska, et al., 2010; Myerson, et al., 1991; Thong, et al., 2000). These
studies quantify EI using 7-d diet records, however, they vary widely in methods to
quantify EEE. For example, Myerson et al. (1991) measured running EE directly using
indirect calorimetry and multiplied reported km/wk by running EE (kcal/min). In their
study, no other physical activities were considered as part of training energy
expenditure. Conversely, others have used energy expenditure tables, physical activity
questionnaires, and physical activity ratios to estimate EEE (Kopp-Woodroffe, et al.,
1999; Lagowska, et al., 2010; Thong, et al., 2000).
The calculated EA for 2 of the remaining 3 studies (Laughlin & Yen, 1996;
Wilmore, et al., 1992) is much lower than 30 kcal/kg FFM/d for both the eumenorrheic
and amenorrheic runners and triathletes. Low calculated EA for Wilmore et al. (1992)
may be a result of including walking in EEE calculation (EEE range: 880-955 kcal/d), in
addition to low EI reports (~1700 kcal/d). Finally, the EA calculated from data reported
by Tomten and Hostmark (2006) is much higher than 30 kcal/kg FFM/d for both the
regular menstrual function group (52.6 kcal/kg FFM/d) and irregular menstrual function
group (37.9 kcal/kg FFM/d) (Tomten & Hostmark, 2006). Here, the researchers
extrapolated EEE using individual HR-VO2 regression lines. Participants‟ HR was
monitored for 7 consecutive days and a laboratory running test allowed for
measurements of VO2 at different running speeds. In contrast to previous studies,
Tomten and Hostmark (2006) specifically defined EEE as training-related excess EE,
whereby sedentary EE (1.82 BMR) was removed from gross EE during training (EEE=
Gross EE – 1.82 BMR); BMR was predicted using the FAO/WHO/UNU prediction
equation (World Health Organization, 1985). This definition is in agreement with the
2007 ACSM Position Stand on the Female Athlete Triad, which defines EEE as “the
energy expended during exercise training in excess of the energy that would have been
expended in non-exercise activity during the same time interval” (Nattiv, et al., 2007).
Thus, it remains challenging to compare EA calculations from different research studies.
In the present study, if mean EA is re-calculated as training-related excess EE,
our mean values for EA increase by 1-2 kcal/kgFFM/d (RMR contributes ~66 kcal for a
60 min exercise work-out). This supports the idea that neglecting to adjust for nonexercise activity results in a slight under-reporting error (Nattiv, et al., 2007). Finally, we
36
identified one study that utilized doubly-labeled water (DLW) and a metabolic chamber to
quantify EEE in elite women distance runners (Schultz, Alger, Harper, Wilmore, &
Ravussin, 1992). In their study, researchers subtracted sedentary/confined TEE
(metabolic chamber) from free-living TEE (DLW). They additionally made adjustments in
TEF based on the assumption that individuals consume more food on days that they are
active (Schultz, et al., 1992). Although researchers did not separate their participants
based on menstrual status (n=9; 2 with irregular menses, 2 on oral contraceptives),
calculated EA ranged between 18.2 and 43.6 kcal/kg FFM/d. Since data are provided for
each individual participant, it is possible to separate the two lowest EA values (18.2 and
20.3 kcal/kg FFM/d) from the remaining seven values (range: 28.9 to 43.6 kcal/kg
FFM/d). It may be that the two individuals with an EA <30 kcal/kg FFM/d were those with
ExMD; however, this was not reported by the researchers.
Methodological differences between research experiments make EA
comparisons challenging, particularly when participants participate in different sports.
EEE should consist of more than just an individual‟s primary sport, as cross-training is a
common practice to prevent injury. Future research should develop guidelines assessing
EI and EEE, thereby allowing for the most accurate EA calculations in this population.
Defining and quantifying exercise. The difference between exercise and non-exercise
physical activity remains unclear. Our study is novel in that it suggests various ways in
which exercise can be defined for active individuals. Active individuals typically
participate in a wide variety of planned and unplanned physical activities beyond their
primary sport. Most of our participants were college students, walking or biking to and
from class, and participating in a variety of recreational activities that could be
considered exercise, but not their primary sport. We observed large differences in
training volume and EEE depending on how exercise was defined for our participants.
Few researchers that have considered training beyond one sport or clearly defined what
is considered to be exercise when quantifying training volume.
De Souza et al (2007) defines purposeful exercise as any physical activity that
elicits a HR >55% of predicted HR max (220-age) for 3 or more min, as documented on
activity logs. Participants in their study must therefore assess HR via manual palpation
37
upon performing any activity. These researchers define total activity as both purposeful
and non-purposeful activity lasting greater than 5 min (De Souza, Hontscharuk, et al.,
2007). Our participants did not measure HR while completing their activity logs nor did
we require a minimum duration of time spent in any activity. Instead, we determined
activities by MET level based on assigning the bike commute for all participants at 4.0
METs. All activities classified as 4.0 METs or greater likely result in heart rates >107-108
bpm (55% of predicted max for our participants). Our objective definitions for physical
activities considered as exercise (≥ 4 METs and > 4 METs) are consistent with the
activity definitions suggested by Fogelholm et al (1995). In their study, they defined
moderate intensity activities (e.g. cycling < 20 km/hr) at 4.0 METs, strenuous activities at
7.0 METs, and very strenuous activity at 10 METs (Fogelholm, et al., 1995).
Limitations and strengths. We acknowledge that energy status estimates from selfreports should be interpreted with caution, however, this is currently the most widely
used method to assess EI in free-living individuals. We carefully trained participants to
weigh and record food, and 7 consecutive days were selected to represent a typical
training week. Data show that individuals trained in this technique provide EI data similar
to DLW data (Champagne, et al., 2002). In addition, we screened for under-reporters
following diet analysis. Our estimates of energy expenditure were improved by
measuring RMR and running EE in our participants using indirect calorimetry. The ExMD
group additionally wore accelerometers to objectively monitor training volume before and
after the 6-mo intervention. Due to the long duration of the intervention, daily fluctuations
in diet and activity took place as participants trained for a variety of competitions and
some underwent injuries. To address this, we met weekly with the ExMD participants to
assess any factors that could have impacted the study and obtained 24 hr recalls.
A limitation of our RMR measurements is that not all participants drove to the
laboratory on their testing days. However, participants were asked to rest upon arrival
and prior to the measurement. We measured RMR on at least two separate days and
considered only 7-10 minutes of steady-state (≤10 % variation), although the total testing
period ranged from 35 to 40 minutes (25-30 min with ventilated hood).
38
To our knowledge, this is the first time EEE has been quantified using several
different methods in order to calculate EA. Although we did not directly measure energy
expenditure for all types of activity, we attempted to define activity both subjectively (selfreport) and more objectively (MET values). The use of MET values for quantifying the
energy cost of activities other than running is limited by the fact that some people
perform activities more vigorously than others (Ainsworth, et al., 1993). We realize that
the objective estimate of EEE still relied on participant self-reported daily activities.
Recommendations. Studies reporting on EA should clearly define what physical
activities they considered as programmed exercise, and specify whether gross EE
during exercise was used or excess EE above resting energy expenditure. We
recommend that future studies perform at least 2 RMR measurements on separate days
(<5% variation between measurements) and quantify RMR based on a minimum of 7-10
minutes of steady-state (≤10% variation within measurements). Participants should
drive or be driven to the test, be allotted a minimum of 10-20 minute resting time upon
arrival, and lay supine for the duration of the measurement. It is also important to control
for the time of the menstrual cycle (follicular phase: d1-7) for menstruating participants.
Lastly, future studies should define whether active participants should be instructed to
abstain from planned physical activity on the day prior to RMR measurements if this is
not part of their typical training routine. Quantifying the number of hours since the last
exercise would be helpful.
Conclusion. In the current study, a 6-mo diet intervention that provided an additional
360 kcal/d, resulted in a 24-39% improvement in EA and resumption of menstrual cycles
in all ExMD participants. RMR did not change with resumption of menses and was lower
in Eumen participants than ExMD participants at both time periods. Differences in
menstrual status may be more closely linked to higher TEE, rather than an absolute EA
value. Based on our findings, a dietary intervention that improves energy status (~1-2 kg
weight gain), may be an appropriate alternative to pharmacological approaches to treat
ExMD.
39
FIGURES AND TABLES
Eumen group *
*Activities are expressed as a percent of total exercise minutes for the group.
Bicycling: commute and training; Calisthenics: sit-ups and push-ups; Walking: ≥ 30 min or a part of an exercise work-out;
Water Activites: swimming and aquajogging; Other: badminton, basketball, frisbee, box jumping, rugby, dancing,
volleyball, soccer, baseball, yard work, rock climbing, healthclub exercises.
FIGURE 1. All planned exercise (EEE Method 1) performed by the ExMD group (n=8) before and after the 6-mo
diet intervention, and the Eumen group at 0-mo (n=9).
ExMD group *
40
FIGURE 2. Comparison of Energy Intake (EI: kcal/d) vs. Total Energy Expenditure (TEE: kcal/d).
* EI=ExMD 0-mo vs. ExMD 6-mo (p=0.039).
41
#
*Eumen (0-mo) significantly different from ExMD (0-mo); p<0.05.
#
Eumen (0-mo) significantly different from ExMD (6-mo); p<0.05
FIGURE 3. Measured Resting Metabolic Rate (RMR) over the 6-mo diet intervention (ExMD) and compared to
Eumen.
42
FIGURE 4. Comparison of Exercise Energy Expenditure (EEE) using 4 different methods.
43
FIGURE 5. Comparison of Energy Availability (EA) calculations to Energy Intake (EI).
EA calculated using 4 different methods of EEE. Units expressed in kcal/kg FFM/d.
44
45
TABLE 1. Different methods to quantify exercise energy expenditure (EEE).
METHOD
Activities Included
Method 1:
All planned exercise
Low Intensity: hiking, stretching, yoga, pilates, calisthenics (situp/ push-ups), yard work, skateboarding, dancing, and walking
(≥30 min or within a work-out session).
Moderate/High Intensity: running, bicycling (training), swimming,
soccer, rowing, rugby, weight-training, health club exercises,
waterjogging, frisbee, volleyball, rock-climbing, tennis, badminton
All planned exercise from Method 1
+ bike commute (general/leisure bicycling: 4.0 METS)
+ walking (moderate intensity walking: 3.3 METS)
Method 2:
All planned exercise
+ bike commute
+ all walking
Method 3:
All exercise ≥ 4.0 METS
Method 4:
All exercise > 4.0 METS
*walking included in Method 1 was only counted once.
**no other activities identified as being 3.3 METS or 4.0 METS
were performed by our subjects.
#
-general/leisure bicycling (4.0 METs)
-badminton and dancing (4.5-4.8 METs)
-skateboarding (5.0 METs)
-yardwork (building fence, mowing lawn) (5.0-6.0 METs)
-general health club exercises (5.5 METs)
-cycling (training) (5.5-7.0 METs)
-soccer and tennis (7.0 METs)
-swimming (7.0-10.0 METs)
-rowing (not for warm-up) (7.0-8.5 METs)
-water jogging (8.0 METs)
-circuit training (8.0 METs)
-rock-climbing (8.0-11.0 METs)
-rugby (10.0 METs)
-running (measured using indirect calorimetry)
-badminton and dancing (4.5-4.8 METs)
-skateboarding (5.0 METs)
-yardwork (building fence, mowing lawn) (5.0-6.0 METs)
-general health club exercises (5.5 METs)
-cycling (training) (5.5-7.0 METs)
-soccer and tennis (7.0 METs)
-swimming (7.0-10.0 METs)
-rowing (not for warm-up) (7.0-8.5 METs)
-water jogging (8.0 METs)
-circuit training (8.0 METs)
-rock-climbing (8.0-11.0 METs)
-rugby (10.0 METs)
-running (measured using indirect calorimetry)
7-d activity logs were analyzed with Food Processor SQL (version 9.91, 2006; ESHA Research);
activity codes and MET intensities in this program are based on the ACSM‟s Resource Manual for
th
Guidelines for Exercise Testing and Prescription ( ACSM, 5 Ed, Appendix A; 2006).
#
general/leisure bicycling (4.0 METs) is the only difference between Method 3 and Method 4.
TABLE 2. Characteristics of active women with exercise-induced mendstrual dysfunction (ExMD) and active
eumenorrheic (Eumen) controls.
46
TABLE 3. Average daily Energy Intake (EI), Energy Balance (EB), and Energy Availability (EA).
47
TABLE 4. Components of Total Energy Expenditure (TEE); Resting Metabolic Rate (RMR) and Exercise
Energy Expenditure (EEE).
48
TABLE 5. Training volume (min/wk) of active women with exercise-induced menstrual dysfunction (ExMD) and
active eumenorrheic (Eumen) controls.
49
50
GENERAL CONCLUSION
A 6-mo diet intervention, which provided an additional 360 kcal/d, was successful
in resuming menstrual cycles in endurance-trained women with ExMD; resumption of
menses was concurrent with a mean weight gain of ~2 kg (~4.5 lbs). RMR did not
change with the resumption of menses in the ExMD group, and the ExMD group had a
significantly higher RMR than the Eumen group. While EA improved by 24-39% over the
intervention, changes in EA were not statistically significant; EA was similar between
active women with and without menstrual disorders. Endurance-trained women with
normal menstrual cycles (ExMD at 6-mo; Eumen group) were found to be closer to EB
than those with ExMD.
51
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APPENDICES
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