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Biological and behavioral predictors of compensatory energy intake after exercise

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Abstract

Energy intake in the post-exercise state is highly variable and compensatory eating – i.e., (over‑) compensation of the expended energy via increased post-exercise energy intake – occurs in some individuals but not others. We aimed to identify predictors of post-exercise energy intake and compensation. In a randomized crossover design, 57 healthy participants (21.7 [SD=2.5] years; 23.7 [SD=2.3] kg/m², 75% White, 54% female) completed two laboratory-based test-meals following (1) 45-min exercise and (2) 45-min rest (control). We assessed associations between biological (sex, body composition, appetite hormones) and behavioral (habitual exercise via prospective exercise log, appetitive traits) characteristics at baseline and total energy intake, compensatory energy intake (intake – exercise expenditure), and the difference between post-exercise and post-rest intake. We found a differential impact of biological and behavioral characteristics on total post-exercise energy intake in men and women. In men, only fasting (baseline) concentrations of appetite-regulating hormones (peptide YY [PYY, β=0.88, P<0.001] and adiponectin [β=0.66, P=0.005] predicted total post-exercise energy intake, while in women, only habitual exercise (β=−0.44, P=0.017) predicted total post-exercise energy intake. Predictors of compensatory intake (intake – exercise expenditure) were almost identical to those of total intake. The difference in energy intake between exercise and rest was associated with VO2peak (β=−0.45, P=0.020), fasting PYY (β=0.53, P=0.036), and fasting adiponectin (β=0.57, P=0.021) in men but not women (all P>0.51). Our results show that biological and behavioral characteristics differentially affect total and compensatory post-exercise energy intake in men and women. This may help identify individuals who are more likely to compensate for the energy expended in exercise. Targeted countermeasures to prevent compensatory energy intake after exercise should take the demonstrated sex differences into account.
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Biological and behavioral predictors of compensatory energy intake after
exercise
Christoph Höchsmann
Technical University of Munich https://orcid.org/0000-0003-2007-3007
Saya E Beckford
University of Nebraska-Lincoln https://orcid.org/0000-0003-1886-9348
Jeffrey A French
University of Nebraska Omaha https://orcid.org/0000-0001-5304-1592
Julie B Boron
University of Nebraska Omaha https://orcid.org/0000-0003-1121-8120
Jeffrey R Stevens
University of Nebraska-Lincoln https://orcid.org/0000-0003-2375-1360
Karsten Koehler ( karsten.koehler@tum.de )
Technical University of Munich https://orcid.org/0000-0002-9618-2069
Research Article
Keywords: compensatory eating, test meal, aerobic exercise, habitual exercise, appetite hormones
Posted Date: March 7th, 2022
DOI: https://doi.org/10.21203/rs.3.rs-1414640/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License.Read Full License
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Abstract
Energy intake in the post-exercise state is highly variable and compensatory eating – i.e., (over) compensation of the expended energy via increased post-
exercise energy intake – occurs in some individuals but not others. We aimed to identify predictors of post-exercise energy intake and compensation. In a
randomized crossover design, 57 healthy participants (21.7 [SD=2.5] years; 23.7 [SD=2.3] kg/m2, 75% White, 54% female) completed two laboratory-based
test-meals following (1) 45-min exercise and (2) 45-min rest (control). We assessed associations between biological (sex, body composition, appetite
hormones) and behavioral (habitual exercise via prospective exercise log, appetitive traits) characteristics at baseline and total energy intake,
compensatory energy intake (intake–exercise expenditure), and the difference between post-exercise and post-rest intake. We found a differential impact
of biological and behavioral characteristics on total post-exercise energy intake in men and women. In men, only fasting (baseline) concentrations of
appetite-regulating hormones (peptide YY [PYY, β=0.88,
P
<0.001] and adiponectin [β=0.66,
P
=0.005] predicted total post-exercise energy intake, while in
women, only habitual exercise (β=−0.44,
P
=0.017) predictedtotal post-exercise energy intake. Predictors of compensatory intake (intake – exercise
expenditure) were almost identical to those of totalintake. The difference in energy intake between exercise and rest was associated with
VO2peak(β=−0.45,
P
=0.020), fasting PYY (β=0.53,
P
=0.036), and fasting adiponectin (β=0.57,
P
=0.021) in men but not women (all
P
>0.51). Our results show
that biological and behavioral characteristics differentially affect total and compensatory post-exercise energy intake in men and women. This may help
identify individuals who are more likely to compensate for the energy expended in exercise. Targeted countermeasures to prevent compensatory energy
intake after exercise should take the demonstrated sex differences into account.
1. Introduction
Regular physical activity (PA) and exercise are recommended as methods of weight control; however, there is substantial variability in post-exercise energy
intake. While some individuals show reduced energy intake post-exercise, allowing for an exercise-induced energy decit, others show a compensatory
increase in energy intake which negates the potential for exercise to promote negative energy balance and subsequent weight loss.1,2 Consequently, the
effectiveness of exercise as a method of weight management is highly variable.3,4 Biological and behavioral participant characteristics may play a role in
the energy intake response to exercise. Therefore, the objective of the present exploratory analyses was to assess predictors of
ad libitum
energy intake
during a laboratory-based test-meal in healthy participants following a single 45-minute aerobic exercise bout. Specically, we aimed to assess predictors
of (1) total test-meal energy intake (kcal) and (2) compensatory energy intake (kcal), i.e., test-meal energy intake relative to the energy expended during the
exercise bout. Additionally, as control, we aimed to identify predictors of the difference in energy intake between the post-exercise test-meal and an
ad
libitum
test-meal after 45 minutes of rest.
We investigated the contributions of sex, anthropometrics, behavioral characteristics, and physiological/endocrine factors at baseline to compensatory
ad
libitum
post-exercise energy intake. As energy balance is better regulated in individuals with an active lifestyle compared to those with a sedentary lifestyle,
5–7 we hypothesized that greater levels of habitual activity and exercise would be associated with lower compensatory post-exercise energy intake. Further,
appetitive traits such as cognitive restraint, uncontrolled eating, and emotional eating are associated with unintentional overeating in the presence of
food,8,9 and we aimed to explore whether these general appetitive traits would also predict post-exercise energy intake. Finally, studies in lean individuals
and those with overweight/obesity have shown that there is considerable variability in the appetite-regulating hormone responses to exercise,10–12 and we
aimed to assess whether fasting concentrations of appetite-regulating hormones at baseline would be predictive of post-exercise energy intake.
Identication of such biological and behavioral baseline predictors would allow identifying individuals who are more likely to show exercise-induced energy
compensation before these individuals engage in exercise and thereby pave the way for targeted countermeasures (e.g., cognitive-behavioral strategies,
preparation of post-exercise meal ahead of time, or consumption of small meal before exercise) ahead of time.
We specically sought to analyze the impact of fasting baseline concentrations of appetite-regulating hormones on post-exercise energy intake, as this
would allow prediction of compensatory eating at the earliest time possible to maximize the weight loss potential of exercise.
2. Materials And Methods
2.1. Study design and participants
Following two preliminary assessment visits, this randomized crossover study involved two study conditions in random order that were completed on two
separate days: (1) one 45-minute exercise bout and (2) one rest period of identical duration. Volunteers for this study were recruited from the University of
Nebraska and its surrounding communities via iers and word-of-mouth. Participants were eligible if they were 19–29 years old, had a body mass index
(BMI) of 18.5–29.9 kg/m2, were weight stable ( 2.5 kg weight change during the past six months), and exercised regularly ( 1 bout/week). The complete
list of inclusion and exclusion criteria has been published previously.13 All study procedures were approved by the University of Nebraska-Lincoln’s
Institutional Review Board (project number 17239) and written informed consent was obtained from all participants before participation in the study.
2.2. Measures
Anthropometric data, PA behavior, cardiorespiratory tness, and appetitive traits were assessed during the preliminary assessment visits that occurred
before participation in the study conditions, as published in detail previously.13 Briey:
2.2.1. Anthropometric data
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Weight and height were measured using a digital scale and stadiometer (Seca, Hamburg, Germany). Total body fat (%) and fat-free mass (FFM, kg) were
estimated via a 7-site skinfold assessment.
2.2.2. Physical activity behavior
PA behavior, and specically moderate-to-vigorous PA (MVPA, min/week), was assessed over seven days using accelerometry (hip-worn GT3X+, Actigraph,
Pensacola, FL). Participants were instructed to wear the GT3X + continuously throughout the 7-day monitoring period and to only remove it for swimming or
taking a shower. Additionally, as it has been reported before that Actigraph devices are inaccurate at recording activities such as strength training and
cycling,14–16 we instructed participants to prospectively record their habitual exercise (min/week and days/week) over the same period using an exercise
log.
2.2.3. Cardiorespiratory tness
Peak oxygen uptake (VO2peak) was measured using an incremental all-out exercise test on a bicycle ergometer (LC6, Monark, Vansbro, Sweden).
Participants began cycling at a resistance of 60 W for 3 minutes, and the work rate was increased by 35 W every 3 minutes until exhaustion.17 Maximal
exhaustion was accepted when at least two of the following were met: (1) Heart rate of  90% of age-predicted maximal heart rate, (2) a respiratory
exchange ratio  1.1, (3) rating of perceived exertion  19,18 (4) a plateau in oxygen uptake despite the increasing workload. Throughout the test,
respiratory gas parameters were analyzed breath by breath (Quark CPET, COSMED, Rome, Italy) and heart rate was monitored through telemetry (Polar,
Kempele, Finland).
2.2.4. Appetitive traits
Cognitive restraint, uncontrolled eating, and emotional eating were assessed with the revised 18-item Three-Factor Eating Questionnaire (TFEQ-R18v2). The
TFEQ-R18v2 is a shortened version of the original well-validated 51-item TFEQ by Stunkard and Messick,19 which has demonstrated improved
psychometric properties, minimized oor and ceiling effects in the emotional eating domain, and improved internal consistency in the cognitive restraint
domain compared to the earlier shortened versions of the TFEQ (TFEQ-R18 and TFEQ-R21), with an overall robust factor structure and good reliability in
two large North American samples.20,21
2.2.5. Appetitive-regulating hormones
Fasting plasma concentrations of glucagon-like peptide 1 (GLP-1), ghrelin, peptide YY (PYY), and adiponectin were measured at each study condition visit
immediately after arrival to the laboratory before participants received a standardized breakfast and continued with the initial 30-minute rest period. Whole-
blood samples were collected into ethylenediaminetetraacetic acid (EDTA) tubes from participants in a seated position. A protease inhibitor (aprotinin;
Sigma Aldrich, St. Louis, MO) was added to PYY and ghrelin samples. Immediately after collection, the EDTA tubes were placed on ice for 15 minutes and
then centrifuged at 1800 x
g
for 10 minutes at + 4°C. Subsequently, plasma fractions were aliquoted and stored at − 80°C until analysis. Enzyme-linked
immunosorbent assays (ELISAs) were used to measure concentrations of PYY (Millipore Sigma, Burlington, MA) and GLP-1, ghrelin, and adiponectin (all
Invitrogen™, Carlsbad, CA).
2.3. Study conditions
On the day of each study condition, participants arrived at the lab between 06:30 and 10:00, following an overnight fast and abstinence from alcohol for at
least 24 hours. Participants further refrained from exercise and strenuous physical activity the day before and the morning of their visits, with compliance
monitored via accelerometry (GT3X+, Actigraph, Pensacola, FL). During their rst study condition visit, participants completed a 24-hour diet recall using an
Automated Self-Administered 24-hour Dietary Assessment Tool (ASA24, National Cancer Institute, Bethesda, MD, USA). Participants received a copy of their
recall after the visit and they were instructed to replicate the diet as closely as possible on the day before their second study condition visit. At each study
condition visit, participants were provided with a small standardized breakfast (commercially available cereal bar [240 kcal] and 8 ounces of bottled water)
upon arrival and instructed to rest for 30 minutes in a seated position.
2.3.1. Exercise condition
Following the initial 30-minute rest, participants exercised on a bicycle ergometer (LC6, Monark, Vansbro, Sweden) for 45 minutes at an intensity of 60% of
their VO2peak. Heart rate and ratings of perceived exertion18 were monitored at regular intervals throughout the exercise bout. After completion of the
exercise bout, participants rested for another 30 minutes before being offered the test meal.
2.3.2. Rest condition
For the resting condition, participants were instructed to sit quietly in a chair for 45 minutes, following the initial 30-minute rest period. To ensure an overall
identical timing and sequence of the two study condition visits, participants rested for an additional 30 minutes after the 45-minute rest condition before
being offered the test meal. Throughout both visits, participants were allowed to listen to music or watch pre-approved TV programs that did not contain
any images of or references to food.
2.3.3. Ad libitum test-meal
Thirty minutes after each study condition (exercise or rest), participants were offered an identical single-item
ad libitum
test meal. The test meal (frozen
family-size cheese pizza, HyVee, West Des Moines, IA) was prepared by study staff, and participants were offered the entire pizza (~ 3,200 kcal, above
energy needs) at once. The test meal was consumed in a separate room and under supervision, and cell phone use was restricted. Participants were
Page 4/13
instructed to eat as much or as little of the test meal as they would like and to make sure to eat the pre-cut pizza slices evenly (i.e., not leave/discard the
crust or take the cheese off, etc.). Pre- and post-meal weights (grams) were recorded, with the difference in weight representing food intake. Gram weights
were converted to energy intake (kcal) using the pizza’s nutrition label.
2.4. Statistical Analyses
The distribution of variables was veried by visual inspection of histograms and quantile-quantile plots of the residuals. Exclusion of outliers ( 2 for all
models) did not change the results meaningfully; therefore, the models including outliers are reported. Descriptive data are reported as mean and standard
deviation (SD). We used simple linear regression models to estimate the effect of anthropometric characteristics (weight, BMI, FFM, percent body fat),
physiological characteristics (VO2peak, maximal power, and fasting concentrations of appetite-regulating hormones such as GLP-1, ghrelin, PYY, and
adiponectin), and behavioral characteristics (habitual exercise behavior, MVPA, and appetitive traits) on energy intake during an
ad libitum
single-item test
meal. Specically, we used the following four variables as dependent variables in our models: (1) post-exercise energy intake (kcal), (2) compensatory
energy intake, which was dened as post-exercise energy intake [kcal] – energy expenditure during exercise session [kcal], (3) energy intake following the
rest condition (kcal), and (4) the difference in energy intake between the post-exercise and the post-rest test meal (post-exercise energy intake [kcal] – post-
rest energy intake [kcal]). Because it has been demonstrated in several studies that FFM is a predictor of meal size and single meal food intake,22–24 we
included FFM as a covariate in sensitivity analyses; however, results did not differ meaningfully and we consequently report the results without FFM as a
covariate. Further, results for appetitive traits (cognitive restraint, uncontrolled eating, and emotional eating) based on the TFEQ-R18 and TFEQ-R21 did not
differ meaningfully from the results of the TFEQ-R18v2 presented herein. Because the TFEQ-R18v2 has been validated in North American samples and
improved internal consistency has been reported, as described above, only these results are reported. We used SPSS Statistics for Windows, version 27
(IBM Corp., Armonk, NY) for our analyses, and results were considered signicant at
P
 < 0.05.
3. Results
3.1. Participant characteristics
Sixty-ve participants were enrolled in the study. Eight participants were excluded (intensity of exercise session not at 60% VO2peak [n = 6], no exercise data
[n = 2]); hence 57 participants were included in our analyses. Baseline characteristics of all included participants (mean age 21.7 [SD = 2.5] years, mean BMI
23.7 [SD = 2.3], 75% White, 54% female) are presented in Table 1. Weight, percent body fat, FFM, VO2peak, and maximal power differed by sex (all
P
 0.001),
all other characteristics did not differ between men and women (all
P
 0.26). On average, participants expended 343 (SD = 85) kcal during the 45-minute
exercise session and consumed 867 (SD = 411) kcal during the post-exercise
ad libitum
test meal. Relative to the energy expended during the exercise
sessions, participants consumed 526 (SD = 406) kcal during the post-exercise test meal (compensatory intake). After the rest condition, energy intake
during the test meal was 821 (SD = 383) kcal with an average difference in intake between the two test meals of 46 (SD = 303) kcal (
P
 = 0.26). Exercise-
related energy expenditure and energy intake during the test meals by sex are presented in Table 1.
Page 5/13
Table 1
Participant characteristics.
All (N=57) Men (n=26) Women (n=31)
Race/Ethinicity, n (%)
White 43 (75.4) 18 (69.3) 25 (80.6)
African American 9 (15.8) 6 (23.1) 3 (9.7)
Asian 4 (7.0) 1 (3.8) 3 (9.7)
Other 1 (1.8) 1 (3.8) 0 (0.0)
Mean (SD) Mean (SD) Mean (SD)
Age, years 21.7 (2.5) 21.4 (2.4) 21.9 (2.6)
Weight, kg 68.7 (10.2) 73.6 (11.3) 64.6 (7.0)
BMI, kg/m223.7 (2.3) 23.8 (2.7) 23.5 (2.1)
Fat-free mass, kg 59.6 (9.0) 66.0 (8.9) 54.2 (4.6)
Total body fat, % 13.2 (6.0) 9.9 (5.4) 15.9 (5.1)
Physical activity behavior and cardiorespiratory tness
Total habitual exercise, min/week a245.9 (181.2) 236.0 (137.7) 254.4 (213.9)
Habitual exercise days, days/week 3.4 (1.9) 3.7 (1.9) 3.3 (2.0)
MVPA, min/week 332.4 (145.7) 350.4 (159.9) 317.3 (133.4)
Relative VO2peak, mL/kg/min 37.4 (6.2) 40.6 (5.8) 34.7 (5.2)
Absolute VO2peak, L/min 2.6 (0.6) 2.3 (0.6) 2.2 (0.3)
Maximal power, W b220.6 (48.5) 248.5 (47.6) 196.5 (34.8)
Appetitive traits
Cognitive Restraint via TFEQ-R18v2 5.9 (2.2) 5.7 (2.6) 6.0 (1.8)
Uncontrolled Eating via TFEQ-R18v2 17.3 (4.7) 17.6 (4.9) 17.0 (4.5)
Emotional Eating via TFEQ-R18v2 9.8 (3.3) 9.2 (2.7) 10.2 (3.7)
Appetite-regulating hormones c
GLP-1, pg/mL d10.9 (5.2) 10.0 (4.4) 11.4 (5.8)
PYY, pg/mL d110.8 (47.0) 110.4 (46.5) 111.1 (48.4)
Ghrelin, pg/mL e865.2 (393.0) 807.3 (435.0) 906.5 (365.3)
Adiponectin, ng/mL f11.7 (7.1) 8.9 (6.4) 13.8 (6.9)
Exercise session
Energy expenditure, kcal 343 (85) 400 (85) 296 (46)
Test meal
Energy intake (post exercise), kcal 867 (411) 1010 (478) 748 (304)
Energy Intake (post rest), kcal 821 (383) 999 (376) 672 (326)
Difference in energy intake, kcal g46 (303) 11 (398) 75 (193)
Compensatory energy intake, kcal h526 (406) 609 (482) 452 (320)
Data are mean (standard deviation) unless stated otherwise. Weight, percent body fat, FFM, VO2peak, and maximal power differed by sex (all P0.001), all
other characteristics did not differ between men and women (all P0.26).
a Data available for 54/57 participants (25/26 men and 29/31 women).
b Data available for 56/57 participants (26/26 men and 30/31 women).
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c Hormone concentrations are reported as means between pre-exercise and pre-rest. Fasting concentrations before the two study conditions did not differ
(all
P
0.08).
d Data available for 39/57 participants (16/26 men and 23/31 women).
e Data available for 36/57 participants (15/26 men and 21/31 women).
f Data available for 38/57 participants (16/26 men and 22/31 women).
g Post-exercise
ad libitum
energy intake (kcal) – energy intake following the rest condition (kcal).
h Energy intake during test meal (kcal) – energy expenditure during exercise session (kcal)
Abbreviations: BMI, body mass index; GLP-1, Glucagon-like Peptide 1; MVPA, moderate-to-vigorous physical activity; PYY, peptide YY; SD, standard
deviation; TFEQ-R18v2, revised 18-item Three-Factor Eating Questionnaire.
3.2 Predictors of total post-exercise energy intake
Total post-exercise energy intake was inversely associated with habitual exercise behavior (β=−0.29,
P
=0.032; Table2, Figure1A) and positively associated
with FFM (β=0.30,
P
=0.025; Table2) and fasting concentrations of PYY (β=0.39,
P
=0.015; Table2, Figure1D). We also found a sex effect, as men
consumed on average 261.9 kcal more than women (
P
=0.015). After stratifying by sex, PYY (β=0.88,
P
<0.001) and additionally adiponectin (β=0.66,
P
=0.005, Table2, Figure1G) were signicant predictors of total post-exercise energy intake only in men, while habitual exercise (β=−0.44,
P
=0.017) was a
signicant predictor of total post-exercise energy intake only in women.
Page 7/13
Table 2
Linear regression analysis for the association between anthropometrics, physiological and behavioral baseline characteristics and total energy intake
during the post-exercise
ad libitum
test meal.
All participants Men Women
Energy intake (kcal) Energy intake (kcal) Energy intake (kcal)
R2B SE βPR2B SE βPR2B SE βP
Sex a0.103 261.9 104.5 0.32 0.015  
Age, years 0.000 −2.3 22.3 −0.01 0.920 0.002 8.2 40.0 −0.04 0.840 0.008 10.7 22.0 0.090 0.632
Weight, kg 0.052 9.2 5.3 0.23 0.088 0.022 6.2 8.5 0.15 0.471 0.000 0.2 8.1 0.00 0.979
BMI, kg/m20.009 16.8 23.5 0.10 0.477 0.000 2.9 36.4 0.02 0.937 0.034 27.0 26.9 0.18 0.323
Fat-free
mass, kg 0.088 13.5 5.9 0.30 0.025 0.059 13.1 10.7 0.24 0.233 0.031 −11.6 12.0 −0.18 0.340
Total body
fat, % 0.018 −9.4 9.2 −0.14 0.315 0.016 −11.2 18.1 −0.13 0.542 0.060 14.7 10.8 0.25 0.183
Physical activity behavior and cardiorespiratory tness
Habitual
exercise,
min/week
0.085 −0.7 0.3 −0.29 0.032 0.032 −0.6 0.7 −0.18 0.395 0.194 −0.6 0.3 −0.44 0.017
Habitual
exercise
days/week
0.024 −33.0 28.1 −0.16 0.246 0.013 −28.9 50.6 −0.12 0.572 0.103 −49.3 27.0 −0.32 0.078
MVPA,
min/week 0.012 0.3 0.4 0.11 0.418 0.019 0.4 0.6 0.14 0.498 0.000 0.0 0.4 −0.02 0.924
Relative
VO2peak,
mL/kg/min
0.000 1.2 9.0 0.02 0.896 0.009 −7.8 16.8 −0.09 0.648 0.074 −16.0 10.5 −0.27 0.138
Absolute
VO2peak,
L/min
0.021 97.5 90.2 0.14 0.284 0.000 9.4 158.1 0.01 0.953 0.083 −270.1 166.2 0.29 0.115
Maximal
power, W 0.049 1.9 1.1 0.22 0.101 0.015 1.2 2.0 0.12 0.558 0.003 −0.5 1.7 −0.05 0.781
Appetitive
traits  
Cognitive
Restraint 0.013 −21.4 25.3 −0.11 0.401 0.058 −44.1 36.3 −0.24 0.237 0.040 34.5 31.5 0.20 0.282
Uncontrolled
Eating 0.010 8.9 11.9 0.10 0.455 0.004 −6.2 19.7 −0.06 0.757 0.091 20.6 12.1 0.30 0.098
Emotional
Eating 0.002 5.8 16.9 0.05 0.732 0.011 −18.0 35.5 −0.10 0.618 0.097 26.0 14.7 0.31 0.087
Appetite-regulating hormones
GLP-1, pg/mL 0.000 0.7 12.8 0.09 0.957 0.108 38.4 29.5 0.33 0.214 0.036 −8.4 9.5 −0.19 0.388
PYY, pg/mL 0.149 3.3 1.3 0.39 0.015 0.775 9.6 1.4 0.88 <0.001 0.013 −0.6 1.1 −0.12 0.600
Ghrelin, pg/mL 0.005 0.1 0.2 0.07 0.681 0.087 0.3 0.3 0.30 0.285 0.021 −0.1 0.2 −0.14 0.535
Adiponectin, ng/mL 0.043 12.0 9.5 0.21 0.213 0.438 52.9 16.0 0.66 0.005 0.010 3.8 8.4 0.10 0.659
Bold font indicates statistical signicance (
P
 < 0.05). Dependent variable in all models: Total energy intake during the post-exercise
ad libitum
test meal
(kcal).
a Female = 0, male = 1.
Abbreviations: B, unstandardized regression coecient; β, standardized regression coecient; BMI, body mass index; GLP-1, Glucagon-like Peptide 1;
MVPA, moderate-to-vigorous physical activity; PYY, peptide YY; SE, standard error.
3.3 Predictors of compensatory energy intake
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Similar to total post-exercise energy intake, compensatory energy intake was inversely associated with habitual exercise behavior (β=−0.31,
P
=0.024;
Table3, Figure1B) and positively associated with fasting concentrations of PYY (β=0.37,
P
=0.021; Table3,Figure 1E). Similar to total post-exercise energy
intake, PYY (β=0.85,
P
<0.001) and additionally adiponectin (β=0.69,
P
=0.003; Table3, Figure1H) were signicant predictors of compensatory energy intake
only in men, while habitual exercise (min/week: β=−0.44,
P
=0.016; days/week: β=−0.39,
P
=0.032) and additionally VO2peak (relative: β=−0,36,
P
=0.044;
absolute: β=−0.42;
P
=0.020; Table3) were signicant predictors of compensatory energy intake only in women.
Table 3
Linear regression analysis for the association between anthropometrics, physiological and behavioral baseline characteristics and compensatory post-
exercise
ad libitum
energy intake (energy intake [kcal] – energy expenditure [kcal]).
All participants Men Women
Compensatory energy intake (kcal) Compensatory energy intake (kcal) Compensatory energy intake (kcal)
R2B SE βPR2B SE βPR2B SE βP
Sex a0.038 157.4 106.9 0.19 0.147
Age, years 0.000 −2.0 22.0 −0.01 0.929 0.007 −16.1 40.3 −0.08 0.693 0.012 13.8 23.1 0.11 0.554
Weight, kg 0.007 3.4 5.4 0.09 0.528 0.000 0.9 8.7 0.02 0.923 0.002 −2.0 8.5 −0.04 0.816
BMI, kg/m20.000 3.0 23.3 0.02 0.899 0.007 −14.5 36.6 −0.08 0.695 0.020 21.8 28.5 0.14 0.449
Fat-free
mass, kg 0.019 6.1 6.0 0.14 0.312 0.013 6.1 11.0 0.11 0.583 0.054 −16.0 12.5 −0.23 0.209
Total body
fat, %   
Physical activity behavior and cardiorespiratory tness
Habitual
exercise,
min/week
0.094 −0.7 0.3 −0.31 0.024 0.035 −0.6 0.7 −0.19 0.372 0.197 −0.7 0.3 −0.44 0.016
Habitual
exercise
days/week
0.054 −48.7 27.4 −0.23 0.081 0.028 −41.9 50.6 −0.17 0.416 0.149 −62.4 27.6 −0.39 0.032
MVPA,
min/week 0.012 0.3 0.4 0.11 0.419 0.027 0.5 0.6 0.16 0.421 0.001 −0.1 0.4 −0.02 0.900
Relative
VO2peak,
mL/kg/min
0.019 −9.1 8.8 −0.14 0.306 0.043 −17.3 16.7 −0.21 0.312 0.133 −22.6 10.7 −0.36 0.044
Absolute
VO2peak,
L/min
0.004 −40.1 89.9 −0.06 0.657 0.026 −127.0 157.4 −0.16 0.428 0.173 −408.6 166.0 −0.42 0.020
Maximal
power, W 0.001 0.3 1.1 0.04 0.781 0.001 −0.4 2.1 −0.04 0.853 0.027 −1.5 1.7 −0.16 0.386
Appetitive
traits   
Cognitive
Restraint 0.012 −20.4 25.0 −0.11 0.420 0.050 −41.2 36.8 −0.22 0.274 0.021 26.4 33.4 0.15 0.436
Uncontrolled
Eating 0.008 7.7 11.7 0.09 0.517 0.008 −8.5 19.9 −0.09 0.671 0.094 22.0 12.7 0.31 0.093
Emotional
Eating 0.004 7.4 16.7 0.06 0.660 0.016 −22.6 35.7 −0.13 0.532 0.095 27.0 15.5 0.31 0.092
Page 9/13
Appetite-regulating hormones
GLP-1, pg/mL 0.002 3.0 12.5 0.04 0.811 0.108 38.8 29.8 0.33 0.214 0.024 −7.5 10.3 −0.16 0.477
PYY, pg/mL 0.136 3.1 1.3 0.37 0.021 0.724 9.4 1.6 0.85 <0.001 0.022 −0.8 1.2 −0.15 0.501
Ghrelin, pg/mL 0.018 0.1 0.2 0.14 0.431 0.090 0.4 0.3 0.30 0.278 0.001 −0.0 0.2 −0.03 0.911
Adiponectin, ng/mL 0.066 14.6 9.2 0.26 0.120 0.476 55.8 15.6 0.69 0.003 0.001 1.3 9.0 0.03 0.888
Bold font indicates statistical signicance (
P
 < 0.05). Dependent variable in all models: Compensatory post-exercise
ad libitum
energy intake (energy
intake during test meal [kcal] – energy expenditure during exercise session [kcal]).
a Female = 0, male = 1.
Abbreviations: B, unstandardized regression coecient; β, standardized regression coecient; BMI, body mass index; GLP-1, Glucagon-like Peptide 1;
MVPA, moderate-to-vigorous physical activity; PYY, peptide YY; SE, standard error.
3.4 Predictors of post-rest energy intake
Energy intake after the rest condition was positively associated with weight (β=0.35,
P
=0.008), FFM (β=0.38,
P
=0.004) and aerobic tness as measured by
absolute VO2peak (β=0.43,
P
<0.001) and maximal power during the exercise test (β=0.43,
P
<0.001; SupplementalTable1). Similar to post-exercise, post-rest
energy intake differed by sex (
P
<0.001). AbsoluteVO2peak (β=0.53,
P
=0.006) and maximal power (β=0.54,
P
=0.005) were only associated with
ad
libitum
energy intake in men, while in women, only habitual exercise minutes per week was a signicant predictor of
ad
libitum
energy intake (β=−0.37,
P=
0.048).
3.5 Predictors of the difference between post-exercise and post-rest energy intake
The difference in total energy intake between exercise and rest was inversely associated with aerobic tness as measured by relative (β=−0.31,
P
=0.020)
and absolute (β=−0.35,
P
=0.008) VO2peak. The difference between exercise and rest was also positively associated with fasting PYY concentrations
(β=0.33,
P
=0.038; Supplemental Table2,Figure 1F). Notably, signicant associations were driven by men, and they were not signicant for women
(SupplementalTable2).In men, above a VO2peak cut point of 40.9 mL/kg/min (3.0 L/min), post-rest energy intake was greater than post-exercise energy
intake, while below the cut point, post-exercise energy intake was greater than post-rest energy intake. For PYY in men, post-exercise energy intake was
greater than post-rest energy intake above the cut point of 118.6 pg/mL, while below the cut point, post-rest energy intake was greater than post-exercise
energy intake.
4. Discussion
The present study aimed to identify predictors of post-exercise energy intake and compensation in healthy adults following a single 45-minute aerobic
exercise bout. Our results show that individuals with lower habitual exercise and/or higher fasting concentrations of PYY eat more after an acute exercise
bout, even after accounting for the energy expended during the exercise bout. Notably, these biological and behavioral characteristics differentially affected
post-exercise energy intake in men and women;habitual exercise behavior was only predictive of post-exercise energy intake in women whereas fasting
PYY concentrations were only a signicant predictor of post-exercise intake in men. For habitual exercise in women, every 30 min/week increase was
associated with a decrease in post-exercise energy intake and compensation of ~20 kcal. In men, albeit not siginicant, the trend in the association
between habitual exercise and post-exercise energy intake was similar to that in women. Generally, the association between greater habitual exercise and
lower post-exercise energy compensation is in line with our hypothesis and previous research. It has been shown that energy balance is better regulated at
higher levels of PA-related energy expenditure due to better satiety signaling and the fact that exercise-induced food rewards and cravings play a less
important role.6,7,25,26 It is noteworthy that accelerometer-measured MVPA did not conrm these results, as MVPA was not a signicant predictor of post-
exercise energy intake. Overall, our sample showed relatively high average MVPA levels (~330 min/week) at baseline, with 89% of participants above the
established weekly recommendations of 150 min of MVPA and 54% even above 300 min/week.27 It can be speculated whether self-reported exercise
behavior (prospectively recorded) included certain types of (even high-intensity) exercise such as strength training, cycling, or swimming that were not
(accurately) captured by the hip-worn Actigraph devices, as demonstrated before,14–16 and whether this contributed to exercise behavior (min/week) being
a better predictor of post-exercise energy intake and compensation than overall MVPA. The generally high MVPA level suggests that most participants
would be in the regulated zone of Mayer’s curve, in which energy expenditure and energy intake are in balance.6,7 Nevertheless, despite the overall high
MVPA levels, there was substantial variability in the post-exercise energy intake, demonstrating that the single exercise session evoked a greater
compensatory energy intake response in some participants than others.
The associations between fasting concentrations of appetite-regulating hormones and post-exercise energy intake are striking. Particularly the sex
differences with a strong predictive value of PYY and adiponectin in men, explaining 78% and 44% of the variance in post-exercise energy intake,
respectively (72% and 48% for energy compensation) but no signicant associations with post-exercise energy intake in women were unexpected. In men,
every ten pg/mL increase in fasting PYY concentrations and every one ng/mL increase in fasting adiponectin concentrations was associated with an
increase in post-exercise energy intake of 96 kcal and 53 kcal, respectively (energy compensation: 94 kcal and 56 kcal, respectively). We are not aware of
previous ndings of similar sex differences in the association between fasting appetite-regulating hormones and (post-exercise) energy intake. In our study,
Page 10/13
PYY concentrations did not differ by sex at baseline, which is in line with the literature.28 In the fasted state, circulating concentrations of PYY are usually
low with rapid increases upon nutrient ingestion.28,29 During aerobic exercise, PYY has been shown to increase, with the appetite-suppressing effects
lasting up to 5 hours post-exercise.30 It has been reported that sex differences exist in the PYY response to moderate-intensity exercise (bike ergometer at
65% VO2max, similar to our study), with greater increases and a greater subsequent post-exercise appetite suppression in men compared to women.31
Importantly, however, the exercise-induced increases in PYY and associated appetite suppression do not always translate into de-facto decreases in post-
exercise energy intake. In fact, the majority of exercise studies show no change in energy intake after acute bouts of exercise,30 and it has been found in a
review that 19% of such studies even report increases in energy intake while 16% show a decrease (65% no change).32 Nevertheless, the strong
associations between greater fasting concentrations of PYY (and adiponectin) and greater post-exercise energy intake and compensation as shown in the
present study are still somewhat unexpected. Further research is needed to conrm our ndings and examine why fasting PYY may affect energy intake
and compensation after exercise but not after a no-exercise rest condition; also of interest is why PYY concentrations affect post-exercise energy intake and
compensation differently in men than women, despite similar fasting concentrations.
Contrary to previous ndings showing a general association between appetitive traits, and particularly disinhibition or uncontrolled eating, with overeating
in the presence of food,33–35 appetitive traits (cognitive restraint, uncontrolled eating, and emotional eating) were not signicant predictors of energy intake.
This is similar to recent ndings in adolescents in whom the three trait measures as assessed via the TFEQ-R18v2 were also not associated with
ad libitum
post-exercise energy intake.36 Further, physiological processes may override these appetitive traits in the post-exercise state; however, in our study, cognitive
restraint, uncontrolled eating, or emotional eating were also not associated with
ad libitum
energy intake following the control condition involving rest.
Energy intake after rest was associated with weight and FFM as well as cardiorespiratory tness (VO2peak and maximal power). Of note, only absolute but
not relative VO2peak were signicant predictors of post-rest energy intake, suggesting that this association was driven by the signicant predictors of weight,
FFM, and sex, which have repeatedly been shown to predict meal size and single meal food intake.22–24 When assessing predictors of the difference in
energy intake between after exercise and after rest, we found signicant associations with cardiorespiratory tness, in this case, both relative and
absoluteVO2peak, for men but not women. Further, the associations revealed a turning point at 40.9 mL/kg/min or 3.0 L/min, respectively. Individuals with
average-to-above-average tness seem to have reduced total energy intake post-exercise compared to post-rest while those with below-average tness
seem to have increased total energy intake post-exercise compared to post-rest. The nding of greater post-exercise energy intake in individuals with lower
tness compared to those with higher tness is supported by previous ndings showing that individuals with higher compared to lower tness (mean
VO2peakof 51.6 vs. 37.0) compensated after an acute exercise session compared to after rest.37 Additionally, this relationship may be inuenced by higher
body fat accumulation, which often co-occurs with lower tness and has been shown to predict greater post-exercise energy compensation due to a
progressively increasing impairment of energy balance.38
Overall, our results may help identify individuals who are likely to show post-exercise energy compensation and help explain why this adverse response to
exercise occurs in some individuals but not others. To prevent increased post-exercise energy intake, countermeasures such as the selection and
preparation of the post-exercise meal ahead of the exercise session may be benecial, as demonstrated previously.13,39 Strengths of the present study
include the crossover design, the relatively balanced sample of men and women, and the comprehensive analysis of the contribution of anthropometric,
behavioral, cognitive, and endocrine factors at baseline to post-exercise energy intake. Limitations include the relatively small sample, particularly for the
appetite-regulating hormone data, and the relative lack of racial and age-related diversity. However, we specically chose to recruit a convenience sample of
young adults to minimize the impact of age, as it has been shown that exercise-induced consequences of hunger, satiety, and compensation differentially
affect adults aged 60+ years.40
Nevertheless, future studies with larger and more diverse samples should examine potential differences in predictors of post-exercise energy intake by
race/ethnicity and age. Further, it would be interesting to assess how different exercise modalities (type, intensity, duration) with similar energy expenditure
affect post-exercise food intake and compensation and whether the found associations hold true for repeated exercise bouts.
5. Conclusions
Biological and behavioral characteristics differentially affect post-exercise energy intake in men and women. In women, habitual exercise behavior predicts
post-exercise energy intake, with more exercise protecting against compensatory eating. In men, appetite-regulating hormones, and specically PYY and
adiponectin, play a role in the energy intake response to acute exercise, even when measured before exercise and after controlling for post-rest energy
intake. Our results can help identify individuals who are more likely to (over-) compensate for the energy expended in exercise via increased post-exercise
energy intake allowing to deploy targeted countermeasures ahead of time.
Declarations
Conicts of Interest
The authors have no conicts of interest to declare.
Financial support
Page 11/13
This research was funded by the University of Nebraska Food for Health Collaboration Initiative.The funding source had no role in the design and conduct
of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript.
Ethical statement
The study was approved by the University of Nebraska-Lincoln’s Institutional Review Board (project number 17239) and written informed consent was
obtained from all participants before participation in the study. All study procedures were conducted in accordance with the Declaration of Helsinki.
Author contributions
K.K., J.R.S., J.B.B., and J.A.F. acquired funding. K.K. and J.R.S. designed the study and S.E.B., J.A.F., J.B.B., and K.K. collected data. C.H. conducted
statistical analyses, drafted the manuscript, and created tables and gures. K.K., S.E.B., J.A.F., J.R.S., and J.B.B. provided critical revision of the manuscript
for important intellectual content. All authors have read and agreed to the published version of the manuscript.
Data availability
The data that support the ndings of this study are available from the corresponding author on reasonable request
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Figures
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Figure 1
Associations between habitual exercise, fasting PYY concentrations, and fasting adiponectin concentrations and post-exercise energy intake,
compensatory energy intake (post-exercise energy intake − exercise energy expenditure), and the difference between post-exercise and postrest energy
intake. Regression lines are displayed for the entire sample (solid line), for men (dotted line), and for women (dashed line).
Supplementary Files
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SupplementalTable1.docx
SupplementalTable2.docx
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Objectives To describe new WHO 2020 guidelines on physical activity and sedentary behaviour. Methods The guidelines were developed in accordance with WHO protocols. An expert Guideline Development Group reviewed evidence to assess associations between physical activity and sedentary behaviour for an agreed set of health outcomes and population groups. The assessment used and systematically updated recent relevant systematic reviews; new primary reviews addressed additional health outcomes or subpopulations. Results The new guidelines address children, adolescents, adults, older adults and include new specific recommendations for pregnant and postpartum women and people living with chronic conditions or disability. All adults should undertake 150–300 min of moderate-intensity, or 75–150 min of vigorous-intensity physical activity, or some equivalent combination of moderate-intensity and vigorous-intensity aerobic physical activity, per week. Among children and adolescents, an average of 60 min/day of moderate-to-vigorous intensity aerobic physical activity across the week provides health benefits. The guidelines recommend regular muscle-strengthening activity for all age groups. Additionally, reducing sedentary behaviours is recommended across all age groups and abilities, although evidence was insufficient to quantify a sedentary behaviour threshold. Conclusion These 2020 WHO guidelines update previous WHO recommendations released in 2010. They reaffirm messages that some physical activity is better than none, that more physical activity is better for optimal health outcomes and provide a new recommendation on reducing sedentary behaviours. These guidelines highlight the importance of regularly undertaking both aerobic and muscle strengthening activities and for the first time, there are specific recommendations for specific populations including for pregnant and postpartum women and people living with chronic conditions or disability. These guidelines should be used to inform national health policies aligned with the WHO Global Action Plan on Physical Activity 2018–2030 and to strengthen surveillance systems that track progress towards national and global targets.
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Purpose of review: This review brings together current evidence from observational, acute, and chronic exercise training studies to inform public debate on the impact of physical activity and exercise on food reward. Recent findings: Low levels of physical activity are associated with higher liking and wanting for high-energy food. Acute bouts of exercise tend to reduce behavioral indices of reward for high-energy food in inactive individuals. A dissociation in liking (increase) and wanting (decrease) may occur during chronic exercise training associated with loss of body fat. Habitual moderate-to-vigorous physical activity is associated with lower liking and wanting for high-fat food, and higher liking for low-fat food. Food reward does not counteract the benefit of increasing physical activity levels for obesity management. Exercise training appears to be accompanied by positive changes in food preferences in line with an overall improvement in appetite control.
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Exercise facilitates weight control, partly through effects on appetite regulation. Single bouts of exercise induce a short-term energy deficit without stimulating compensatory effects on appetite, whilst limited evidence suggests that exercise training may modify subjective and homeostatic mediators of appetite in directions associated with enhanced meal-induced satiety. However, a large variability in responses exists between individuals. This article reviews the evidence relating to how adiposity, sex, and habitual physical activity modulate exercise-induced appetite, energy intake, and appetite-related hormone responses. The balance of evidence suggests that adiposity and sex do not modify appetite or energy intake responses to acute or chronic exercise interventions, but individuals with higher habitual physical activity levels may better adjust energy intake in response to energy balance perturbations. The effect of these individual characteristics and behaviours on appetite-related hormone responses to exercise remains equivocal. These findings support the continued promotion of exercise as a strategy for inducing short-term energy deficits irrespective of adiposity and sex, as well as the ability of exercise to positively influence energy balance over the longer term. Future well-controlled studies are required to further ascertain the potential mediators of appetite responses to exercise.
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OBJECTIVE: This study assessed the validity of a consumer activity wristband, a smartphone, and a research-grade accelerometer to measure steps in a free-living setting. APPROACH: Thirty healthy adults were equipped with two Garmin Vivofit (non-dominant wrist), one iPhone SE (right pants pocket), three ActiGraph wGT3X+ (two on the hip, one on the non-dominant wrist), and one StepWatch (right ankle) and instructed to wear the devices continuously during a 3-day monitoring period. All activities of daily living were recorded in 15-minute intervals in a diary. The StepWatch served as the criterion method and validity was evaluated by comparing each device with the criterion measure using mean absolute percentage errors (MAPE). MAIN RESULTS: The MAPE for the total step count during the 3-day monitoring period was high with a general underestimation of steps by all devices of >20% compared to the criterion measure. The wrist-worn ActiGraph markedly overestimated steps during predominantly low active (public transport or driving, and standing) or even inactive (sitting and lying) activities of daily living. SIGNIFICANCE: The overall high MAPE of the devices compared to the StepWatch during step-based activities, likely caused by inaccuracies during short and intermittent bouts of activity, may limit their validity in a free-living setting.
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Background: Exercise is recommended for weight management, yet exercise produces less weight loss than expected, which is called weight compensation. The mechanisms for weight compensation are unclear. Objective: The aim of this study was to identify the mechanisms responsible for compensation. Methods: In a randomized controlled trial conducted at an academic research center, adults (n = 198) with overweight or obesity were randomized for 24 wk to a no-exercise control group or 1 of 2 supervised exercise groups: 8 kcal/kg of body weight/wk (KKW) or 20 KKW. Outcome assessment occurred at weeks 0 and 24. Energy intake, activity, and resting metabolic rate (RMR) were measured with doubly labeled water (DLW; with and without adjustments for change in RMR), armband accelerometers, and indirect calorimetry, respectively. Appetite and compensatory health beliefs were measured by self-report. Results: A per-protocol analysis included 171 participants (72.5% women; mean ± SD baseline body mass index: 31.5 ± 4.7 kg/m2). Significant (P < 0.01) compensation occurred in the 8 KKW (mean: 1.5 kg; 95% CI: 0.9, 2.2 kg) and 20 KKW (mean: 2.7 kg; 95% CI: 2.0, 3.5 kg) groups, and compensation differed significantly between the exercise groups (P = 0.01). Energy intake by adjusted DLW increased significantly (P < 0.05) in the 8 KKW (mean: 90.7 kcal/d; 95% CI: 35.1, 146.4 kcal/d) and 20 KKW (mean: 123.6 kcal/d; 95% CI: 64.5, 182.7 kcal/d) groups compared with control (mean: -2.3 kcal/d; 95% CI: -58.0, 53.5 kcal/d). Results were similar without DLW adjustment. RMR and physical activity (excluding structured exercise) did not differentially change among the 3 groups. Participants with higher compared with lower compensation reported increased appetite ratings and beliefs that healthy behaviors can compensate for unhealthy behaviors. Furthermore, they increased craving for sweet foods, increased sleep disturbance, and had worsening bodily pain. Conclusions: Compensation resulted from increased energy intake and concomitant increases in appetite, which can be treated with dietary or pharmacological interventions. Compensation was not due to activity or metabolic changes. This trial was registered at clinicaltrials.gov as NCT01264406.