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Energy balance measurement: when something is not better than nothing

Abstract

Energy intake (EI) and physical activity energy expenditure (PAEE) are key modifiable determinants of energy balance, traditionally assessed by self-report despite its repeated demonstration of considerable inaccuracies. We argue here that it is time to move from the common view that self-reports of EI and PAEE are imperfect, but nevertheless deserving of use, to a view commensurate with the evidence that self-reports of EI and PAEE are so poor that they are wholly unacceptable for scientific research on EI and PAEE. While new strategies for objectively determining energy balance are in their infancy, it is unacceptable to use decidedly inaccurate instruments, which may misguide health-care policies, future research and clinical judgment. The scientific and medical communities should discontinue reliance on self-reported EI and PAEE. Researchers and sponsors should develop objective measures of energy balance.

Introduction

Almost weekly, a new study links self-reported subjective energy intake (EI) or physical activity energy expenditure (PAEE) to yet another lifestyle factor or disease. For instance, a recent study discovered that 42% of patients with acute hip fracture consume insufficient energy and urged clinicians to consider steps to ensure sufficient consumption among such patients.1 Another study concluded that after-school snacks contribute 13% of children’s total EI, representing a target for energy reduction2 to control obesity. The recent declaration of obesity as a disease by the American Medical Association underscores the need for a united multi-disciplinary effort to develop obesity prevention and treatment strategies. Considerable research is conducted to understand the contribution of EI and expenditure to global increases in obesity. Such studies have enormous potential to shape clinical, public health and policy conclusions or recommendations. Therefore, it is vitally important to examine whether self-reported EI (SREI) and expenditure data are sound. What if the evidential foundation of those conclusions and recommendations is a house of cards? Here, we alert readers to the significant limitations of self-report measures of EI and PAEE. We go beyond the commonly voiced view that self-report measures of EI and PAEE are imperfect, but nevertheless suitable for use and offer the contrary view that they are so poor as measures of actual EI and actual PAEE that they no longer have a justifiable place in scientific research aimed at understanding actual EI and actual PAEE.

A comprehensive understanding of energy balance is needed

Recent high-profile papers concluded that US adults and children have decreased EI in recent years,3, 4 prompting speculation of why corresponding decreases in obesity prevalence were not observed. These studies, however, like many before them, examined self-reported changes in only one of the components of energy balance to draw inferences about obesity prevalence. Fat accretion results from long-term-positive energy balance, which is collectively determined by EI, resting energy expenditure, diet-induced thermogenesis, energy absorption and PAEE. Hence, accurate determination of energy balance requires careful measures of all energy balance components,5 either in the confines of a metabolic ward or by frequent clinical measurements. Such resource-intense approaches are feasible in short-term studies with few participants at highly specialized research facilities. Studies conducted at the community level, therefore, often focus on only two (EI and PAEE) of the components of energy balance. In this case, it is imperative that the information obtained be accurate.

Are the subjective methods for determining EI or PAEE accurate?

Self-reported EI

Changing EI typically has a higher magnitude of impact on body weight than does changing activity.6 Therefore, the accuracy of EI data is critical. A large number of influential studies on US trends in aspects of diet (for example7) rely on EI data collected by the NHANES (National Health and Nutrition Examination survey), a highly valuable effort conducted by the Centers for Disease Control and Prevention to collect extensive health-related information from the US population. Although NHANES collects many rigorous measurements related to body weight, it obtains EI data through self-reported dietary recall, despite the fact that self-report questionnaires have been repeatedly shown to be seriously flawed. To emphasize this point, a top team of nutritional epidemiologists and biostatisticians recently pooled data from five large studies that had examined the validity of Food Frequency Questionnaires and 24-h dietary recalls against recovery biomarkers. Across this diverse sample of Americans, subjective estimates of EI explained <10% of the variance of true EI.8 Such large random error variance will not just lead to reduced power and underestimated associations or effects as is often claimed, but if EI is a confounder, if one controls only for SREI, then substantial residual confounding and spurious findings (type 1 errors; false positives) will occur. Because of that, the lead author of that pooling project wrote ‘I agree that self-reported EIs should not be used for investigating associations between EI and health-related outcomes,’ though he allowed that they could be used for other purposes.9 Moreover, the errors are decidedly not random. Compared with measures from the doubly labeled water technique, self-reported measures systematically underestimate EI by hundreds of kcal per day.10 Specifically, the NHANES surveys may underreport EI as much as 800 kcal per day.11 Despite repeated demonstrations of the serious shortcomings of self-reported measures of EI, many researchers continue to assert the validity of SREI solely on the basis of its reproducibility.12 This is like using a mismarked ruler. One can reproduce the result, but it is still wrong.

Several approaches have been suggested to adjust for underreporting. One such approach is to compare estimated basal metabolic rate with SREI. If the value falls below a threshold that is deemed physiologically implausible (<1.35 × basal metabolic rate), then those data are ignored.13 There are, however, several issues with this approach, including the exclusion of large amounts of data that fall below the cutoff and a large residual unexplained variation in basal metabolic rate determination.14 Also, the cutoff approach assumes that only a subset of the population underreports. However, dietary underreporting varies with the type of foods consumed, age, gender, smoking habits, education, social class, dietary restraint, the body mass index of respondent and other life-stage factors.15 Thus, a correction of self-reported intakes by a fixed factor cannot satisfactorily correct EI.

Self-reported PAEE

Another key component of energy balance is PAEE, which can be broadly divided into energy expenditure due to intentional and non-intentional activity. PAEE includes exercise; sports; occupational, leisure-time, and household activities; personal care; and transportation. Such multiple activities make it difficult to recall and then categorize PAEE.16 PAEE is often overreported and the percent of overreporting varies with race, degree of overweight and weight loss.17 This creates both systematic and random errors that are similar to those seen in SREI.

Technological advancements in portable sensing devices such as pedometers and accelerometers have made it possible to determine physical activity objectively in real time. However, for a given activity and duration, the expended energy may differ by body weight and efficiency of motion. Thus, approaches such as the combination of accelerometry with heart rate monitoring18 or additional movement measures19 have remarkably improved the accuracy of determining PAEE when compared against the doubly labeled water technique.

Bad data lead to bad conclusions

Creating negative energy balance by decreasing EI and increasing energy expenditure is a cornerstone of the management of obesity. Admittedly, recording habitual intake and activity is linked with better weight loss20 and may be encouraged for enhancing patient compliance and weight loss. For drawing scientific conclusions, however, reliance on the accuracy of reported EI or PAEE is problematic, even in clinically oriented research.

As noted, studies consistently demonstrate that a large number of subjects underreport EI and overreport PAEE. This makes it challenging to reconcile weight change (or lack thereof) with a patient’s reported effort and may influence clinical judgment. For instance, a recent study concluded that 45% of pregnant women underreported their daily EI.21 EI was reported to be as low as 1194 kcal for the underreporting subgroup compared with 2125 kcal per day for the group considered to be normal reporters.21

Subjective methods of consumption are not only used for studies related to energy balance and body weight, but also to draw conclusions that inform clinical practice and dietary recommendations for conditions such as chronic kidney disease, cancers, heart disease, diabetes and Alzheimer’s disease. The lack of validity of some messages intended for clinical application is highly disconcerting. Inaccurate dietary data collection can result in spurious associations and effects22, 23 that can directly impact patient care. Despite the published cautions and limitations of subjective measures of food intake for numerous health conditions,24, 25, 26, 27 studies using SREI often yield conclusions, such as, 'we identified a dietary pattern that was strongly protective against the development of Alzheimer’s Disease'.28 Yet, many such studies continue to reach varying and often conflicting conclusions about the associations of various food components with specific disease risks. This makes it difficult for clinicians and health-care professionals, who are often at the receiving end of questions from patients about the ability of a particular food or food product to help or harm, to respond confidently with evidence-based advice. By extension, avoiding inaccuracy is particularly vital for conclusions about national trends. An assertion such as 'energy intake has decreased in recent years,' if erroneous, could adversely impact obesity-related policies at the local and national levels.

Moving forward

For research, clinical practice and policy determination, a great need exists for accurately determining EI and expenditure. It is time to recognize that even if methods are cheap and convenient, inaccurate scientific methods will lead to inaccurate conclusions. Interpretations based on inadequate measurement tools may cause misguided national and local health-care policies, funding for and by major organizations, and health-care advice to individuals. Like other antiquated methods, self-reported PAEE and EI need to be recognized as inaccurate measurements by the scientific and funding communities.

In the future, we urge that resources be redirected to developing superior methods to objectively, precisely and accurately measure alterations in energy balance. Improvements have been made in the development of more objective tools for measuring PAEE. Such tools are not perfect but have been shown to better assess PAEE compared with self-report.29 Objective measures of EI are not as mature, and accuracy (especially at the individual level) is still only modest, but emerging alternatives include digital photography, chewing and swallowing monitors, and wrist motion detectors that count plate to mouth motion.30, 31, 32, 33 More research is needed to develop these and other objective tools. In addition, long-term funding should be made available for the measurement of EI where consumption can be measured for long periods of time. Meanwhile, the use of decidedly inaccurate instruments to accurately measure EI or PAEE needs to be discontinued. In this case, the adage ‘something is better than nothing’ must be changed to ‘something is worse than nothing.’

Human subjects research approval

Not applicable.

References

  1. 1

    Bell J, Bauer J, Capra S, Pulle CR . Barriers to nutritional intake in patients with acute hip fracture: time to treat malnutrition as a disease and food as a medicine? Can J Physiol Pharmacol 2013; 91: 489–495.

    CAS  Article  Google Scholar 

  2. 2

    Gilbert JA, Miller D, Olson S, St-Pierre S . After-school snack intake among Canadian children and adolescents. Can J Public Health 2012; 103: e448–e452.

    PubMed  Google Scholar 

  3. 3

    Ford ES, Dietz WH . Trends in energy intake among adults in the United States: findings from NHANES. Am J Clin Nutr 2013; 97: 848–853.

    CAS  Article  Google Scholar 

  4. 4

    Ervin RB, Ogden CL . Trends in intake of energy and macronutrients in children and adolescents from 1999–2000 through 2009–2010. National Center for Health Statistics: Hyattsville, MD, USA, 2013. NCHS 411 data brief no. 113..

  5. 5

    Hall KD, Heymsfield SB, Kemnitz JW, Klein S, Schoeller DA, Speakman JR . Energy balance and its components: implications for body weight regulation. Am J Clin Nutr 2012; 95: 989–994.

    Article  Google Scholar 

  6. 6

    Thomas DM, Bouchard C, Church T, Slentz C, Kraus WE, Redman LM et al. Why do individuals not lose more weight from an exercise intervention at a defined dose? An energy balance analysis. Obes Rev 2012; 13: 835–847.

    CAS  Article  Google Scholar 

  7. 7

    Austin GL, Ogden LG, Hill JO . Trends in carbohydrate, fat, and protein intakes and association with energy intake in normal-weight, overweight, and obese individuals: 1971-2006. Am J Clin Nutr 2011; 93: 836–843.

    CAS  Article  Google Scholar 

  8. 8

    Freedman LS CJ, Commins JM, Moler JE, Arab L, Baer D, Kipnis V et al. Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for energy and protein intake. Am J Epidemiol 2014; 180: 172–188.

    Article  Google Scholar 

  9. 9

    LS F. Recommendations, Dissents and Comments for [Freedman LS et al., Am J Epidemiol 2014, 180(2):172-88]. F1000Prime.com: F1000Prime; 2014 (cited 2014 05 Oct); available from: F1000Prime.com/718445976.

  10. 10

    Schoeller DA, Bandini LG, Dietz WH . Inaccuracies in self-reported intake identified by comparison with the doubly labelled water method. Can J Physiol Pharmacol 1990; 68: 941–949.

    CAS  Article  Google Scholar 

  11. 11

    Archer E, Hand GA, Blair SN . Validity of U.S. Nutritional surveillance: national health and nutrition examination survey caloric energy intake data, 1971-2010. PLoS One 2013; 8: e76632.

    CAS  Article  Google Scholar 

  12. 12

    Hu FB, Rimm E, Smith-Warner SA, Feskanich D, Stampfer MJ, Ascherio A et al. Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. Am J Clin Nutr 1999; 69: 243–249.

    CAS  Article  Google Scholar 

  13. 13

    Goldberg GR, Black AE, Jebb SA, Cole TJ, Murgatroyd PR, Coward WA et al. Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording. Eur J Clin Nutr 1991; 45: 569–581.

    CAS  Google Scholar 

  14. 14

    Johnstone AM, Murison SD, Duncan JS, Rance KA, Speakman JR . Factors influencing variation in basal metabolic rate include fat-free mass, fat mass, age, and circulating thyroxine but not sex, circulating leptin, or triiodothyronine. Am J Clin Nutr 2005; 82: 941–948.

    CAS  Article  Google Scholar 

  15. 15

    Heitmann BL, Lissner L . Dietary underreporting by obese individuals—is it specific or non-specific? BMJ 1995; 311: 986–989.

    CAS  Article  Google Scholar 

  16. 16

    Plasqui G, Westerterp KR . Physical activity assessment with accelerometers: an evaluation against doubly labeled water. Obesity (Silver Spring) 2007; 15: 2371–2379.

    Article  Google Scholar 

  17. 17

    Walsh MC, Hunter GR, Sirikul B, Gower BA . Comparison of self-reported with objectively assessed energy expenditure in black and white women before and after weight loss. Am J Clin Nutr 2004; 79: 1013–1019.

    CAS  Article  Google Scholar 

  18. 18

    Villars C, Bergouignan A, Dugas J, Antoun E, Schoeller DA, Roth H et al. Validity of combining heart rate and uniaxial acceleration to measure free-living physical activity energy expenditure in young men. J Appl Physiol 2012; 113: 1763–1771.

    CAS  Article  Google Scholar 

  19. 19

    Tudor-Locke C, Martin CK, Brashear MM, Rood JC, Katzmarzyk PT, Johnson WD . Predicting doubly labeled water energy expenditure from ambulatory activity. Appl Physiol Nutr Metab 2012; 37: 1091–1100.

    Article  Google Scholar 

  20. 20

    Hollis JF, Gullion CM, Stevens VJ, Brantley PJ, Appel LJ, Ard JD et al. Weight loss during the intensive intervention phase of the weight-loss maintenance trial. Am J Prev Med 2008; 35: 118–126.

    Article  Google Scholar 

  21. 21

    McGowan CA, McAuliffe FM . Maternal nutrient intakes and levels of energy underreporting during early pregnancy. Eur J Clin Nutr 2012; 66: 906–913.

    CAS  Article  Google Scholar 

  22. 22

    Heitmann BL, Frederiksen P . Imprecise methods may both obscure and aggravate a relation between fat and breast cancer. Eur J Clin Nutr 2007; 61: 925–927.

    CAS  Article  Google Scholar 

  23. 23

    Heitmann BL, Lissner L . Can adverse effects of dietary fat intake be overestimated as a consequence of dietary fat underreporting? Public Health Nutr 2005; 8: 1322–1327.

    Article  Google Scholar 

  24. 24

    Bross R, Noori N, Kovesdy CP, Murali SB, Benner D, Block G et al. Dietary assessment of individuals with chronic kidney disease. Semin Dial 2010; 23: 359–364.

    Article  Google Scholar 

  25. 25

    Schatzkin A, Kipnis V, Carroll RJ, Midthune D, Subar AF, Bingham S et al. A comparison of a food frequency questionnaire with a 24-hour recall for use in an epidemiological cohort study: results from the biomarker-based Observing Protein and Energy Nutrition (OPEN) study. Int J Epidemiol 2003; 32: 1054–1062.

    Article  Google Scholar 

  26. 26

    Aune D, Chan DS, Vieira AR, Navarro Rosenblatt DA, Vieira R, Greenwood DC et al. Dietary compared with blood concentrations of carotenoids and breast cancer risk: a systematic review and meta-analysis of prospective studies. Am J Clin Nutr 2012; 96: 356–373.

    CAS  Article  Google Scholar 

  27. 27

    Samuel-Hodge CD, Fernandez LM, Henriquez-Roldan CF, Johnston LF, Keyserling TC . A comparison of self-reported energy intake with total energy expenditure estimated by accelerometer and basal metabolic rate in African-American women with type 2 diabetes. Diabetes Care 2004; 27: 663–669.

    Article  Google Scholar 

  28. 28

    Gu Y, Nieves JW, Stern Y, Luchsinger JA, Scarmeas N . Food combination and Alzheimer disease risk: a protective diet. Arch Neurol 2010; 67: 699–706.

    Article  Google Scholar 

  29. 29

    Warren JM, Ekelund U, Besson H, Mezzani A, Geladas N, Vanhees L . Assessment of physical activity - a review of methodologies with reference to epidemiological research: a report of the exercise physiology section of the European Association of Cardiovascular Prevention and Rehabilitation. Eur J Cardiovasc Prev Rehabil 2010; 17: 127–139.

    Article  Google Scholar 

  30. 30

    Martin CK, Correa JB, Han H, Allen HR, Rood J, Champagne CM et al. Validity of the Remote Food Photography Method (RFPM) for estimating energy and nutrient intake in near real-time. Obesity (Silver Spring) 2012; 20: 891–899.

    CAS  Article  Google Scholar 

  31. 31

    Martin CK, Han H, Coulon SM, Allen HR, Champagne CM, Anton SD . A novel method to remotely measure food intake of free-living individuals in real time: the remote food photography method. Br J Nutr 2009; 101: 446–456.

    CAS  Article  Google Scholar 

  32. 32

    Dong Y, Hoover A, Scisco J, Muth E . A new method for measuring meal intake in humans via automated wrist motion tracking. Appl Psychophysiol Biofeedback 2012; 37: 205–215.

    Article  Google Scholar 

  33. 33

    Sazonov ES, Makeyev O, Schuckers S, Lopez-Meyer P, Melanson EL, Neuman MR . Automatic detection of swallowing events by acoustical means for applications of monitoring of ingestive behavior. IEEE Trans Biomed Eng 2010; 57: 626–633.

    Article  Google Scholar 

Download references

Acknowledgements

Dr Allison, Dr Brown and Ms Jeansonne are supported in part by grant P30DK056336 from the National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases. The opinions expressed are those of the authors and not necessarily those of the NIH or any other organization.

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Correspondence to D B Allison.

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Dr Schoeller reports a patent, Methods of determining energy balance using breath carbon isotope ratios, pending. The remaining authors declare no conflict of interest.

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Author contributions

Nikhil Dhurandhar and David Allison conceived this manuscript and drafted the majority of its content. All other authors read and edited the manuscript for important content. The corresponding author, David Allison, had the final responsibility for the decision to submit for publication.

The ‘Energy Balance Measurement Working Group’ members listed alphabetically

David B Allison, PhD, Distinguished Professor, Quetelet Endowed Professor of Public Health, Associate Dean for Science, Director of the Office of Energetics, Director of the Nutrition Obesity Research Center, University of Alabama at Birmingham, Birmingham, AL, USA. E-mail: Dallison@UAB.edu

Arne V Astrup, MD, DMSc, Head, Professor, and Faculty of Science, Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark. E-mail: ast@life.ku.dk

Richard L Atkinson, MD, Clinical Professor of Pathology, Virginia Commonwealth University; Director, Obetech Obesity Research Center; Richmond, VA, USA. E-mail: ratkinson2@vcu.edu

Martin Binks, PhD, FTOS, Associate Professor, Nutritional Sciences, Texas Tech University, Lubbock, TX, USA. E-mail: mbinks@binkshealth.com

George L Blackburn, MD, PhD, S. Daniel Abraham Chair in Nutrition Medicine Harvard Medical School and Director, Center for the Study of Nutrition Medicine Beth Israel Deaconess Medical Center, Boston, MA, USA. E-mail: gblackbu@bidmc.harvard.edu

John E Blundell, Professor and Chair, PsychoBiology, Institute of Psychological Sciences, Faculty of Medicine and Health, University of Leeds, Leeds, UK. E-mail: j.e.blundell@leeds.ac.uk

Michelle M Bohan Brown, PhD, Assistant Professor, Department of Food, Nutrition, and Packaging Sciences, Clemson University, Clemson, SC, USA. E-mail: mbohanb@clemson.edu

Andrew W Brown, PhD, Scientist, Nutrition Obesity Research Center and Office of Energetics, University of Alabama at Birmingham, Birmingham, AL, USA. E-mail: awbrown@uab.edu

Nuala M Byrne, BHMS, MAppSc, PhD, Program Head of Exercise and Sports Science, Director of CRN for Advancing Exercise and Sports Science, Faculty of Health Sciences & Medicine, Bond Institute of Health and Sport, Bond University, Gold Coast, QLD, Australia. E-mail: n.byrne@qut.edu.au

Lawrence J Cheskin, MD, FACP, Associate Professor, Health, Behavior & Society; Medicine (GI); Human Nutrition; Public Health Studies; Director, Johns Hopkins Weight Management Center; Director, Rapid Response Project Core; Co-Associate Director, Johns Hopkins Global Center on Childhood Obesity, Johns Hopkins School of Public Health, Baltimore, MD, USA. E-mail: lcheskin@jhsph.edu

Richard Cooper, MD, Professor and Chair, Public Health Sciences, Loyola University Chicago Stritch School of Medicine, Chicago, IL, USA. E-mail: rcooper@lumc.edu

Barbara E Corkey, PhD, Zoltan Kohn Professor of Medicine and Vice Chair for Research, Obesity Research Center, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA. E-mail: bcorkey@bu.edu

Nikhil V Dhurandhar, PhD, John Henry Hernandez Endowed Professor in Health Promotion, Infection and Obesity Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA. E-mail: Nikhil.Dhurandhar@pbrc.edu

John P Foreyt, PhD, Professor and Director of the Behavioral Medicine Research Center, Baylor College of Medicine, Houston, Texas, USA. E-mail: jforeyt@bcm.tmc.edu

Paul Selberg-Franks, PhD, Adjunct Professor of Nutrition, Department of Nutrition, Harvard School of Public Health, Boston, MA, USA. E-mail: paul.franks@med.lu.se

Yoni Freedhoff, MD, CCFP, Medical Director, Bariatric Medical Institute, Ottawa, ON, Canada. E-mail: drfreedhoff@bmimedical.ca

Michael I Goran, PhD, Dr Robert C and Veronica Atkins Endowed Chair in Childhood Obesity and Diabetes, Co-Director, USC Diabetes and Obesity Research Institute and Professor of Preventive Medicine, Director, Childhood Obesity Research Center (CORC), USC Keck School of Medicine, Los Angeles, California, USA. E-mail: goran@usc.edu

Barbara C Hansen, PhD, Professor of Internal Medicine and Director of Center for Preclinical Research, Department of Internal Medicine & Pediatrics, University of South Florida, Tampa, FL, USA. E-mail: bhansen@health.usf.edu

Johannes Hebebrand, MD, Professor, Department of Psychiatry, Psychosomatics and Psychotherapy for Children and Adolescents, Institute of the University of Duisburg-Essen, NW, Germany. E-mail: johannes.hebebrand@lvr.de

Berit Lilienthal Heitmann, PhD, Professor and Director for Research, Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospitals, Frederiksberg, Denmark. E-mail: Berit.Lilienthal.Heitmann@regionh.dk

Steven B Heymsfield, MD, George A. Bray, Jr. Endowed Super Chair in Nutrition and Executive Director of the Pennington Biomedical Research Center, Baton Rouge, LA, USA. E-mail: Steven.Heymsfield@pbrc.edu

James O Hill, PhD, Executive Director, Anschutz Health and Wellness Center, Anschutz Professor of Health and Wellness, Professor of Pediatrics, Medicine, and Physiology & Biophysics, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA. E-mail: james.hill@ucdenver.edu

Andrew P Hills, PhD, FSAMF, Professor of Allied Health Research—Maternity & Neonatology, Mater Mothers’ Hospital Director—Centre for Nutrition and Exercise, Mater Research—University of Queensland Conjoint Professorial appointment: Centre for Musculoskeletal Research, Griffith Health Institute, South Brisbane, QLD, Australia. E-mail: ahills@mmri.mater.org.au

Gary Hunter, PhD, Professor of Kinesiology, Department of Human Studies, School of Education, and the Nutrition Obesity Research Center, Birmingham, AL, USA. E-mail: ghunter@uab.edu

Ted Kyle, RPh, MBA, ConscienHealth, Pittsburgh, PA, USA. E-mail: Ted.Kyle@ConscienHealth.org

David A Levitsky, PhD, Weiss Presidential Fellow and Professor of Nutrition and Psychology, Cornell University, Ithaca, NY, USA. E-mail: dal4@cornell.edu

Amy Luke, PhD, Professor, Department of Public Health Sciences, Loyola University Chicago, Maywood, IL, USA. E-mail: aluke@lumc.edu

Ian Macdonald, PhD, Head of School of Life Sciences, Professor of Metabolic Physiology, Faculty of Medicine & Health Sciences, University of Nottingham Medical School, Nottingham, UK. E-mail: ian.macdonald@nottingham.ac.uk

Robert AJ Matthews, MA, CPhys, FRAS, Visiting Reader in Science, Aston University, Birmingham, UK. E-mail: rajm@physics.org

Corby K Martin, PhD, Associate Professor, Director of Behavioral Science and Epidemiology, Director of Ingestive Behavior Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA. E-mail: Corby.Martin@pbrc.edu

Ann Yelmokas McDermott, PhD, MS, LDN, Associate Scientist, Department of International Health and Assistant Director, Global Obesity Prevention Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. E-mail: amcderm4@jhu.edu

Timothy H Moran, PhD, Paul R McHugh Professor, Vice Chair, Director of Research, Director of the Behavioral Neuroscience Laboratory, Johns Hopkins University School of Medicine, Baltimore, MD, USA. E-mail: tmoran@jhmi.edu

Sir Stephen O’Rahilly, MD, FRS, FMSci, Professor of Clinical Biochemistry and Medicine, University of Cambridge and Director, MRC Metabolic Diseases Unit, Cambridge, UK. E-mail: so104@medschl.cam.ac.uk

Angelo Pietrobelli, MD, Associate Professor, Pediatric Section of the Department of Life and Reproduction Sciences, Verona University Medical School, Verona, Italy. E-mail: angpie@tin.it

Stephan Rössner, MD, Professor emeritus, Karolinska Institutet, Apple Bay Obesity Research Centre, Bromma, Sweden. E-mail: stephan@rossner.se

Wim HM Saris MD, PhD, Professor of Human Nutrition, NUTRIM School for Nutrition, Toxicology and Metabolism, Maastricht University Medical Centre, Maastricht, The Netherlands. E-mail: w.saris@maastrichtuniversity.nl

Dale A Schoeller, PhD, Professor of Nutritional Sciences, University of Wisconsin-Madison, Madison, WI, USA. E-mail: dschoell@nutrisci.wisc.edu

Arya M Sharma, MD, PhD, Professor of Medicine, Chair in Obesity Research & Management, University of Alberta, Edmonton, AB, Canada. E-mail: amsharm@ualberta.ca

Diana Thomas, PhD, Professor of Mathematical Sciences, Montclair State University, Montclair, NJ, USA. E-mail: thomasdia@mail.montclair.edu

Thorkild IA Sørensen, PhD, Professor, The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark and Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospitals – Part of Copenhagen University Hospital, The Capital Region, Denmark. E-mail: TSOE0005@regionh.dk

John R Speakman, DSc, Institute of Biological and Environmental Sciences, University of Aberdeen, Scotland, UK and 1000 talents Professor, State key laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China. E-mail: j.speakman@abdn.ac.uk

Judith S Stern, ScD, Distinguished Professor Emerita, Department of Nutrition, University of California, Davis, CA, USA. E-mail: jsstern@ucdavis.edu

Luc Van Gaal, MD, PhD, Associate Professor, Diabetes and Metabolic Disorders, Universiteit Antwerpen, Antwerp, Belgium. E-mail: luc.van.gaal@uza.be

S Stanley Young, PhD, Assistant Director of Bioinformatics, National Institute of Statistical Science, Research Triangle Park, NC, USA. E-mail: young@niss.org

Shankuan Zhu, MD, PhD, Qiushi Chair Professor, CMB Distinguished Professor, Obesity and Body Composition Research Center, Chronic Disease Research Institute Zhejiang University School of Public Health, School of Medicine, Hangzhou, China. E-mail: zsk@zju.edu.cn

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Dhurandhar, N., Schoeller, D., Brown, A. et al. Energy balance measurement: when something is not better than nothing. Int J Obes 39, 1109–1113 (2015). https://doi.org/10.1038/ijo.2014.199

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