Lesson 2: How to Calculate Total Daily Energy Expenditure

Table of Contents

Introduction

In lesson 1 I discussed the concept of one’s TDEE (total daily energy expenditure) and its components. Here I will discuss how to estimate your TDEE. In the next lesson you will see how to use this information to guide the dieting process to help achieve a healthy body weight.

There are several different practical methods to estimate your TDEE, all of which are imprecise. However, this still serves as a useful starting point; in the next lesson it will become clear how to deal with the inaccuracy.

Calculating your TDEE with formulas

Several formulas have been derived to estimate one’s TDEE. These typically estimate your basal metabolic rate (“BMR”) and then multiply that by an activity factor based on your lifestyle. A few of these formulas include the Harris-Benedict equation, the Mifflin-St. Jeor equation, and the Katch-McArdle equation. Katch-McArdle depends on your lean body mass (“LBM”), not total weight, so you need to be able to estimate your body fat percentage to use this. Practically speaking it does not matter which you use as you are simply generating an estimate. If curious, one recent analysis of several different equations found that the Plucker equations may be the best to estimate the TDEE, though all of the evaluated equations may be off by ~500 calories daily.(Fernández-Verdejo, 2022) When just considering estimating one’s BMR while losing weight these equations can be off by several hundred calories.(de Lima Macena, 2023) Additionally, a recent analysis indicated that equations that incorporate measures of body composition to determine one’s BMR are less accurate, presumably due to inaccuracy in determining one’s body composition.(Macena, 2022) If you’d like to use one of these calculators, simply google “TDEE calculator” and choose any of the available links. At the time of me writing this there is a compilation of different formulas here.

              

Note: There is one calculator I want to particularly draw attention to, and that is the NIH body weight planner. This will estimate how many calories you need to eat daily to reach a goal weight by a goal date. The calculations are based on group averages; due to all of the variability discussed in Lesson 1 the specific numbers it proposes may not be fully accurate. However, as you only need to generate an estimate, this does the trick and can be quite informative.

Example: Using the NIH body weight planner, enter:

  • starting weight 225 pounds
  • height 5’6″
  • age 30
  • male
  • physical activity level of 1.6

Set a goal weight of 180 pounds with a goal of achieving this in 1 year. Click on the “Calculate” button when it asks how you will change your physical activity. Place two entries, one for walking at a medium pace 3 times a week for 20 minutes and one for running at a medium pace 3 times a week for 20 minutes.

Entering this it tells you that to maintain your current body weight without changing your physical activity level you need to consume 3,080 kcal/day. This is your estimated current TDEE. It tells you that if you eat 2,542 kcal/day then you will reach your goal weight of 180 pounds in 1 year. After reaching it you can increase to 2,860 kcal/day to maintain this new body weight. The increase is due to several of the concepts discussed in Lesson 1; when you are no longer losing weight the adaptive thermogenesis abates, non-exercise activity thermogenesis (“NEAT”) will likely increase to a small degree, and your thermic effect of feeding goes up slightly when you increase calories.

By switching to expert mode a data table shows the expected body weight every single day over a year. Notice that 1.5 pounds is lost in the first two days, this is presumably due to a decrease in sodium intake, glycogen stores in the body, and the associated water loss. This is common when starting a diet and is one of the reasons the Atkin’s diet became so popular; a low carbohydrate diet will cause a large loss in water weight the first few days. Without knowing better you may assume you are losing body fat when you are actually losing water weight.

Importantly, look at the graph (reproduced below). A final body weight of 180 pounds is expected, but the margin of error includes 161 to 198 pounds. This reflects the individual variability alluded to previously and again indicates this is dealing with estimates, not exact calculations. You can click on the “Body Fat %” tab to see how this will change over time but this calculator assumes no resistance training is done; if you incorporate resistance training (which I strongly recommend) this will likely decrease further as you will likely retain more muscle while losing additional fat. If you click on the “Intake & Expenditure” tab (reproduced below) you will see how your TDEE will decrease over time. This occurs due to loss of body fat, loss of LBM, adaptive thermogenesis, and a decrease in NEAT. You can click on the “Advanced Controls” button and “Lifestyle Change” tab to see the impact of additional changes if you would like.

Graphs using input data from the above example

Graph showing how body weight changes over time when remaining in a caloric deficit
Graph showing how energy expenditure decreases over time when remaining in a caloric deficit

This graph shows the expected body weight with range of uncertainty over time.

This graph shows how the TDEE (red line) changes over time. The blue line represents calories consumed daily, which is assumed constant.

Calculating your TDEE by counting calories

If you are maintaining your weight over an extended period of time (ie, weeks), then you are roughly consuming the same number of calories that you burn daily, and your average caloric intake is equivalent to your TDEE. You can count all of the calories you eat, and that will give you the answer. To do this, I recommend for 3 typical days of eating to write down every single thing you eat and drink, record the total number of calories, take the 3 day average, and use this as an approximation of your TDEE. If you normally eat differently on weekdays and weekends then make sure to include at least one weekend day.

 graphic showing list of foods consumedTip: If you decide to record items you eat & drink I suggest keeping a pencil and piece of paper with you to record these throughout the day rather than risk misremembering later. You can look up the nutrition content at the end of the day. If nutrition labels are available you can write down the nutrition facts while you eat the foods but it may be difficult to do this discretely in a public setting if this is a concern. Alternatively, you can take pictures with your phone or send text messages to yourself of everything you consume. Nutrition labels can be found with google or through https://nutritiondata.self.com/, and you can record things with Excel or by hand. Apps can be used for this purpose as well (see below), and they may have the advantage of helping you track the various macronutrients and micronutrients in addition to calories.

Potential pitfalls when counting calories

It can be easy to forget to record all items you consume. If you go to the kitchen and eat a cookie, you need to include it. Cooking oil used for food preparation should be included. If somebody brings cookies to work and you eat one that also counts. You need to include any beverage that has calories as well. For people who have a tendency to eat mindlessly and not even realize it, this can be especially challenging.

In addition to recording what you consume, you need to record the quantity. This can be surprisingly difficult. In general if a nutrition label is available you should trust the label. Meat should be weighed prior to cooking assuming the nutrition label does not specify cooked. Importantly, if you eat at a restaurant the listed calories can be significantly off. Restaurant calorie counts will likely be more accurate for food items that are weighed (ie, steak) or mass produced (ie, McDonald’s cheeseburgers). For an item such as chicken pasta alfredo the amount of alfredo can vary considerably and this can significantly impact the total calorie count. If you are eating a mixed dish prepared by others tracking this can be tricky. You can ask for the recipe and estimate the amount you consumed but do realize this can be prone to error.

Tip: Using food weight is generally more accurate than food volume for solids. A kitchen scale measuring in grams is very helpful and can generally be found for <$15. For example, a container of cottage cheese may state 1 serving is ½ cup and weighs 113 grams. However, if you measure out ½ cup and then weigh it this may weigh ~140 grams. This would lead to 24% more calories being consumed than expected.

Here's a quick visualization showing it can be difficult to estimate quantity by eye

This image shows the difficulty of estimating caloric content of peanut butter visually
This image shows the difficulty of estimating caloric content of peanut butter visually

The slice on the left has 10 grams of peanut butter for 55 calories, the slice on the right has 30 grams of peanut butter for 165 calories.

Even if you quantify everything accurately nutrition labels can legally be off by up to 20%. Occasionally they are off by more. A lot of this error will cancel itself out and thus this is not a huge concern but merits mention.

For all of these reasons, tracking calories will generate an estimate and may not be completely correct.

Note: There have been many studies showing that people as a whole do a poor job of estimating the number of calories they consume. This has been seen in registered dietitians(Champagne, 2002) as well as the general public in several large studies.(Freedman, 2014; Park, 2018; McKenzie 2021) Generally, people with excess body weight under-report their caloric consumption to a greater degree.(Wehling, 2019; González-Gil, 2021) One study has found that underestimation occurs to a larger degree acutely when people are hungry.(Tal, 2023) A 2025 study found that likely ~40% of adults and >55% of children underreport their calorie intake, and as other studies found this increases as the individual’s BMI increases.(Bajunaid, 2025) For a BMI of 40 the discrepancy was ~1,000 kcal/day for adults and ~2,000 kcal/day for children.

Anecdotally, many people think they are on a very low calorie diet and not losing weight; in reality many of these people are underestimating the number of calories they are consuming. Additionally, misreporting also occurs with the timing of food consumption.(Gioia, 2022)

While not the topic of this nutrition course, if curious, misreporting is also frequent with physical activity.(Sharifzadeh, 2020)

Tracking calories gets easier with time

So how much time does it take to track all of these calories? If you have never tracked calories, macronutrients (protein, carbohydrates, fats), or micronutrients (sodium, potassium, etc), this seems like a monumental task when first starting. Initially it can take time to find foods in the databases and figure out how to scan things properly. It can seem like a nuisance to do this when eating out and estimating what is in a certain meal. However, most people eat most of the same foods on a day-to-day or at least week-to-week basis. Thankfully, after recording foods or meals once you can simply pick that same selection when you eat the food again and that saves a lot of time. Once you do this for a few days to a week it becomes much easier and faster.

Tip: There are several apps to aid in tracking calories and other nutrients. One example is MyFitnessPal. icon for myfitnesspal If you are interested, I suggest installing it (or a different app of your choice) and experimenting; you can easily scan barcodes from packages to upload nutrition content once you get the hang of it. Another app I will mention is Cronometer, which is free and can do a more comprehensive job of compiling micronutrients (micronutrients are discussed in Lesson 9). icon for cronometer Feel free to try several apps and see which one you prefer. The food databases are another source of potential pitfalls; entries may not exactly match what you are eating and may be inaccurate if uploaded by casual users incorrectly.                                      

Do you need to estimate your TDEE and/or track calories?

So when should you even calculate your TDEE? Remember, all of the above methods provide an estimate. As shown in Lesson 1, when you change how much you eat and exercise this will alter your TDEE; the calculators cannot fully account for this on an individual level due to individual variability. My recommended approach is to estimate your TDEE one time when starting the dieting process for the purpose of losing or gaining weight. In the next lesson I will go over how to use this number. For people who are happy with their current body weight estimating your TDEE is less useful but may still be interesting.

Do you even need to track calories? Lots of people gain muscle, lose fat, and live healthy lives without doing so. If you are happy with your weight without ever having tracked calories there is no need to start now. For people who want to lose or gain weight you can first attempt to do so by making healthy changes (tips discussed in upcoming lessons) and see where this leads. If good progress is made then there is no need to start tracking. However, if at some point progress stalls and it is not clear what is going wrong, counting calories can be immensely helpful to get on track. Additionally, one of the biggest predictors of weight loss success is initial weight loss; if your initial efforts are not working within a few weeks it is important to re-evaluate your strategy sooner than later, and tracking calories for a short time can help jump-start progress.(Chopra, 2021)

Note: A happy medium between not tracking anything and tracking all calories can be to simply track “unhealthy” or “calorie-dense” foods. This has been shown to be almost or as effective as tracking all calories in a real-world setting.(Nezami, 2022) The idea would be to not limit and track the healthier food choices but to track and limit the “guilty pleasures” or “ultra-processed” foods that are easy to overeat, stimulate further appetite, and may not contribute significantly to satiation, and can be somewhat addicting.(Contreras-Rodriguez, 2022; Gearhardt, 2023; Juul, 2025) You could keep their intake at some threshold, such as 3 servings per day where each serving is <200 kcal, a total consumption at <500 kcal per day, a total consumption at <20% of your estimated TDEE, or any other strategy that makes sense and works for you.

Tip: One study has found that hyper-palatable foods fall into 3 general categories(Fazzino, 2019):

  • foods with >25% kcals from fat and >=0.30% sodium by weight (such as pizza, meats)
  • foods with >20% kcals from fat and >20% kcals from sugar (such as desserts)
  • foods with >40% kcals from carbohydrates and >=0.20% sodium by weight (breads, carbohydrate-based savory items)

Many of these foods will be ultra-processed foods. In recent decades, at least in the United States, minimally-processed food intake has decreased while ultra-processed food intake has increased.(Juul, 2021) A good first step if simply trying to eat healthier is to reverse this trend and prioritize minimally-processed food, while particularly eliminating sugar-sweetened beverages.(Wang, 2022) This naturally decreases the amount of calories people typically consume in a day (potentially due to a greater satiating effect from relatively higher fiber and protein content as well as the need for a greater amount of time spent chewing for harder-textured food items(Appleton, 2021; Teo, 2022; Lasschuijt, 2023) and is associated with better weight outcomes.(Hall, 2019; Askari, 2020) This also can lead to better health outcomes when looking at a variety of different health conditions, at least in adults.(Chen, 2020; Lane, 2021; Pagliai, 2021; Suksatan, 2022; Dai, 2024)

Of interest, one recent study compared different types of ultraprocessed food meals, finding that meals considered harder, dryer, and thicker led to slower eating and less energy consumption.(Heuven, 2024) A separate study found that a lunch meal of ultraprocessed foods manipulated to contain a lower energy density and yield a slower eating rate led to 570 kcal intake, while manipulating it to contain higher energy density and yield a faster eating rate led to 1,143 kcal intake, with no difference in appetite sensations in the subsequent 90 minutes.(Lasschuijt, 2025) Another study found that even when conforming an ultraprocessed food-based diet to dietary guidelines, intake was still higher than when following a minimally processed food-based diet to dietary guidelines (by an estimatd ~200 kcals), leading to a smaller amount of weight loss compared to a baseilne diet of less healthy ultraprocessed food intake.(Dicken, 2025) Another study found that when purposely eating high-protein ultraprocessed food meals this still lead to excess energy consumption relative to less-processed meals but not to the same degree as lower-protein ultraprocessed food meals.(Hägele, 2025) One study found an ultraprocessed food diet led to ~800 kcal/day greater intake, which associated with faster eating rate and fewer chews per bite.(Hamano, 2024) One study has found that people value foods with higher amounts of fat and carbohydrate more than foods with predominantly only fats or carbohydrates, while being unable to accurately assess their energy density, and generally more processed foods contain both.(DiFeliceantonio, 2018)

Thus, while avoiding ultraprocessed foods is likely best for weight management, if you are going to consume then preferentially choose higher-protein options that take longer to chew and consume.

Use caution if concerned for disordered eating

CAUTION: Some people may find that tracking calories triggers disordered eating or has other negative impacts on their mental health. If this is a concern then I recommend not tracking calories. Excellent progress can be made by sticking to general healthy eating principles, and I will discuss how to do this in upcoming lessons. If you begin tracking calories without problems but then realize you are developing concerning habits or behaviors (ie, becoming too restricted regarding food intake, fearing social gatherings due to available food, following binge-restrict cycles to “make up” for excess calories one day by restricting severely on the next, etc), then it is extremely important to stop tracking immediately and to consider consulting your healthcare provider to discuss how to proceed in a healthier manner.

Conclusion

Estimating your TDEE with the above formulas is relatively straightforward. Considerably more effort is required to estimate your TDEE with calorie counting. Anecdotally, many people struggle with weight loss until they begin tracking calories and developing this skill can be a worthwhile endeavor. For people who do not wish to make counting calories part of their regular lifestyle, tracking for short periods of time when attempting to troubleshoot the dieting process (when desired weight loss or gain is not happening) can be an effective compromise. Bottom line, whether or not to track calories is a personal choice but can be a very enlightening and effective tool when used appropriately.

In the next lesson I will discuss how to determine a healthy body weight goal (if desired), how to use your TDEE estimate to determine caloric intake (if desired) and how to track progress over time (regardless if you are tracking calories or not). This will then allow you to make adjustments as needed to continue moving closer to your goals.

Click here to proceed to Lesson 3

References

  1. Appleton KM, Newbury A, Almiron-Roig E, Yeomans MR, Brunstrom JM, de Graaf K, Geurts L, Kildegaard H, Vinoy S. Sensory and physical characteristics of foods that impact food intake without affecting acceptability: Systematic review and meta-analyses. Obes Rev. 2021 Aug;22(8):e13234. doi: 10.1111/obr.13234. Epub 2021 Mar 23. PMID: 33754456; PMCID: PMC8365638.
  2. Askari M, Heshmati J, Shahinfar H, Tripathi N, Daneshzad E. Ultra-processed food and the risk of overweight and obesity: a systematic review and meta-analysis of observational studies. Int J Obes (Lond). 2020 Oct;44(10):2080-2091. doi: 10.1038/s41366-020-00650-z. Epub 2020 Aug 14. PMID: 32796919
  3. Bajunaid R, Niu C, Hambly C, Liu Z, Yamada Y, Aleman-Mateo H, Anderson LJ, Arab L, Baddou I, Bandini L, Bedu-Addo K, Blaak EE, Bouten CVC, Brage S, Buchowski MS, Butte NF, Camps SGJA, Casper R, Close GL, Cooper JA, Cooper R, Das SK, Davies PSW, Dabare P, Dugas LR, Eaton S, Ekelund U, Entringer S, Forrester T, Fudge BW, Gillingham M, Goris AH, Gurven M, El Hamdouchi A, Haisma HH, Hoffman D, Hoos MB, Hu S, Joonas N, Joosen AM, Katzmarzyk P, Kimura M, Kraus WE, Kriengsinyos W, Kuriyan R, Kushner RF, Lambert EV, Lanerolle P, Larsson CL, Leonard WR, Lessan N, Löf M, Martin CK, Matsiko E, Medin AC, Morehen JC, Morton JP, Must A, Neuhouser ML, Nicklas TA, Nyström CD, Ojiambo RM, Pietiläinen KH, Pitsiladis YP, Plange-Rhule J, Plasqui G, Prentice RL, Racette SB, Raichlen DA, Ravussin E, Redman LM, Reilly JJ, Reynolds R, Roberts SB, Samaranayakem D, Sardinha LB, Silva AM, Sjödin AM, Stamatiou M, Stice E, Urlacher SS, Van Etten LM, van Mil EGAH, Wilson G, Yanovski JA, Yoshida T, Zhang X, Murphy-Alford AJ, Sinha S, Loechl CU, Luke AH, Pontzer H, Rood J, Sagayama H, Schoeller DA, Westerterp KR, Wong WW, Speakman JR. Predictive equation derived from 6,497 doubly labelled water measurements enables the detection of erroneous self-reported energy intake. Nat Food. 2025 Jan;6(1):58-71. doi: 10.1038/s43016-024-01089-5. Epub 2025 Jan 13. PMID: 39806218; PMCID: PMC11772230.
  4. Champagne CM, Bray GA, Kurtz AA, et al. Energy intake and energy expenditure: a controlled study comparing dietitians and non-dietitians. J Am Diet Assoc. 2002;102(10):1428‐1432. doi:10.1016/s0002-8223(02)90316-0
  5. Chen X, Zhang Z, Yang H, Qiu P, Wang H, Wang F, Zhao Q, Fang J, Nie J. Consumption of ultra-processed foods and health outcomes: a systematic review of epidemiological studies. Nutr J. 2020 Aug 20;19(1):86. doi: 10.1186/s12937-020-00604-1. PMID: 32819372; PMCID: PMC7441617.
  6. Chopra S, Malhotra A, Ranjan P, Vikram NK, Sarkar S, Siddhu A, Kumari A, Kaloiya GS, Kumar A. Predictors of successful weight loss outcomes amongst individuals with obesity undergoing lifestyle interventions: A systematic review. Obes Rev. 2021 Mar;22(3):e13148. doi: 10.1111/obr.13148. Epub 2020 Nov 16. PMID: 33200547.
  7. Contreras-Rodriguez O, Solanas M, Escorihuela RM. Dissecting ultra-processed foods and drinks: Do they have a potential to impact the brain? Rev Endocr Metab Disord. 2022 Feb 2. doi: 10.1007/s11154-022-09711-2. Epub ahead of print. PMID: 35107734.
  8. Dai S, Wellens J, Yang N, Li D, Wang J, Wang L, Yuan S, He Y, Song P, Munger R, Kent MP, MacFarlane AJ, Mullie P, Duthie S, Little J, Theodoratou E, Li X. Ultra-processed foods and human health: An umbrella review and updated meta-analyses of observational evidence. Clin Nutr. 2024 Jun;43(6):1386-1394. doi: 10.1016/j.clnu.2024.04.016. Epub 2024 Apr 18. PMID: 38688162.
  9. de Lima Macena M, Tenório da Costa Paula D, da Silva Júnior AE, Rodrigues Silva Praxedes D, Bueno NB. Longitudinal estimates of resting energy expenditure using predictive equations in individuals with excess weight after weight loss: A systematic review with meta-analysis. Clin Nutr ESPEN. 2023 Dec;58:263-269. doi: 10.1016/j.clnesp.2023.10.004. Epub 2023 Oct 16. PMID: 38057015.
  10. Dicken SJ, Jassil FC, Brown A, Kalis M, Stanley C, Ranson C, Ruwona T, Qamar S, Buck C, Mallik R, Hamid N, Bird JM, Brown A, Norton B, Gandini Wheeler-Kingshott CAM, Hamer M, van Tulleken C, Hall KD, Fisher A, Makaronidis J, Batterham RL. Ultraprocessed or minimally processed diets following healthy dietary guidelines on weight and cardiometabolic health: a randomized, crossover trial. Nat Med. 2025 Aug 4. doi: 10.1038/s41591-025-03842-0. Epub ahead of print. PMID: 40760353.
  11. DiFeliceantonio AG, Coppin G, Rigoux L, Edwin Thanarajah S, Dagher A, Tittgemeyer M, Small DM. Supra-Additive Effects of Combining Fat and Carbohydrate on Food Reward. Cell Metab. 2018 Jul 3;28(1):33-44.e3. doi: 10.1016/j.cmet.2018.05.018. Epub 2018 Jun 14. PMID: 29909968.
  12. Fazzino TL, Rohde K, Sullivan DK. Hyper-Palatable Foods: Development of a Quantitative Definition and Application to the US Food System Database. Obesity (Silver Spring). 2019 Nov;27(11):1761-1768. doi: 10.1002/oby.22639. PMID: 31689013.
  13. Fernández-Verdejo R, Galgani JE. Predictive equations for energy expenditure in adult humans: From resting to free-living conditions. Obesity (Silver Spring). 2022 Aug;30(8):1537-1548. doi: 10.1002/oby.23469. Epub 2022 Jul 19. PMID: 35854398.
  14. Freedman LS, Commins JM, Moler JE, 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(2):172‐188. doi:10.1093/aje/kwu116
  15. Gearhardt AN, DiFeliceantonio AG. Highly processed foods can be considered addictive substances based on established scientific criteria. Addiction. 2023 Apr;118(4):589-598. doi: 10.1111/add.16065. Epub 2022 Nov 9. PMID: 36349900.
  16. Gioia SC, Guirette M, Chen A, Tucker C, Gray BE, Vetter C, Garaulet M, Scheer FAJL, Saxena R, Dashti HS. How Accurately Can We Recall the Timing of Food Intake? A Comparison of Food Times from Recall-Based Survey Questions and Daily Food Records. Curr Dev Nutr. 2022 Jan 10;6(2):nzac002. doi: 10.1093/cdn/nzac002. PMID: 35198846; PMCID: PMC8856939.
  17. González-Gil EM, Huybrechts I, Aguilera CM, Béghin L, Breidenassel C, Gesteiro E, González-Gross M, de Henauw S, Kersting M, Donne CL, Manios Y, Marcos A, Meirhaeghe A, De Miguel-Etayo P, Molina-Hidalgo C, Molnár D, Papadaki A, Widhalm K, Moreno LA, Bel-Serrat S. Cardiometabolic Risk is Positively Associated with Underreporting and Inversely Associated with Overreporting of Energy Intake Among European Adolescents: The Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) Study. J Nutr. 2021 Mar 11;151(3):675-684. doi: 10.1093/jn/nxaa389. PMID: 33484148.
  18. Hägele FA, Herpich C, Koop J, Grübbel J, Dörner R, Fedde S, Götze O, Boirie Y, Müller MJ, Norman K, Bosy-Westphal A. Short-term effects of high-protein, lower-carbohydrate ultra-processed foods on human energy balance. Nat Metab. 2025 Apr;7(4):704-713. doi: 10.1038/s42255-025-01247-4. Epub 2025 Mar 13. PMID: 40082711; PMCID: PMC12021659.
  19. Hall KD, Ayuketah A, Brychta R, Cai H, Cassimatis T, Chen KY, Chung ST, Costa E, Courville A, Darcey V, Fletcher LA, Forde CG, Gharib AM, Guo J, Howard R, Joseph PV, McGehee S, Ouwerkerk R, Raisinger K, Rozga I, Stagliano M, Walter M, Walter PJ, Yang S, Zhou M. Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake. Cell Metab. 2019 Jul 2;30(1):67-77.e3. doi: 10.1016/j.cmet.2019.05.008. Epub 2019 May 16. Erratum in: Cell Metab. 2019 Jul 2;30(1):226. Erratum in: Cell Metab. 2020 Oct 6;32(4):690. PMID: 31105044; PMCID: PMC7946062.
  20. Hamano S, Sawada M, Aihara M, Sakurai Y, Sekine R, Usami S, Kubota N, Yamauchi T. Ultra-processed foods cause weight gain and increased energy intake associated with reduced chewing frequency: A randomized, open-label, crossover study. Diabetes Obes Metab. 2024 Nov;26(11):5431-5443. doi: 10.1111/dom.15922. Epub 2024 Sep 12. PMID: 39267249.
  21. Heuven LAJ, van Bruinessen M, Tang CS, Stieger M, Lasschuijt MP, Forde CG. Consistent effect of eating rate on food and energy intake across twenty-four ad libitum meals. Br J Nutr. 2024 Sep 16;132(4):1-12. doi: 10.1017/S0007114524001478. Epub ahead of print. PMID: 39279635; PMCID: PMC11499084.
  22. Juul F, Parekh N, Martinez-Steele E, Monteiro CA, Chang VW. Ultra-processed food consumption among US adults from 2001 to 2018. Am J Clin Nutr. 2022 Jan 11;115(1):211-221. doi: 10.1093/ajcn/nqab305. PMID: 34647997.
  23. Juul F, Martinez-Steele E, Parekh N, Monteiro CA. The role of ultra-processed food in obesity. Nat Rev Endocrinol. 2025 Jul 14. doi: 10.1038/s41574-025-01143-7. Epub ahead of print. PMID: 40659796.
  24. Lane MM, Davis JA, Beattie S, Gómez-Donoso C, Loughman A, O’Neil A, Jacka F, Berk M, Page R, Marx W, Rocks T. Ultraprocessed food and chronic noncommunicable diseases: A systematic review and meta-analysis of 43 observational studies. Obes Rev. 2021 Mar;22(3):e13146. doi: 10.1111/obr.13146. Epub 2020 Nov 9. PMID: 33167080.
  25. Lasschuijt M, Camps G, Mars M, Siebelink E, de Graaf K, Bolhuis D. Speed limits: the effects of industrial food processing and food texture on daily energy intake and eating behaviour in healthy adults. Eur J Nutr. 2023 Oct;62(7):2949-2962. doi: 10.1007/s00394-023-03202-z. Epub 2023 Jul 14. PMID: 37452167; PMCID: PMC10469122.
  26. Lasschuijt MP, Heuven LA, Gonzalez-Estanol K, Siebelink E, Chen Y, Forde CG. The Effect of Energy Density and Eating Rate on Ad Libitum Energy Intake in Healthy Adults-A Randomized Controlled Study. J Nutr. 2025 Aug;155(8):2602-2610. doi: 10.1016/j.tjnut.2025.06.006. Epub 2025 Jun 12. PMID: 40516651.
  27. Macena ML, Paula DTDC, da Silva Júnior AE, Praxedes DRS, Pureza IROM, de Melo ISV, Bueno NB. Estimates of resting energy expenditure and total energy expenditure using predictive equations in adults with overweight and obesity: a systematic review with meta-analysis. Nutr Rev. 2022 May 13:nuac031. doi: 10.1093/nutrit/nuac031. Epub ahead of print. PMID: 35551409.
  28. McKenzie BL, Coyle DH, Santos JA, Burrows T, Rosewarne E, Peters SAE, Carcel C, Jaacks LM, Norton R, Collins CE, Woodward M, Webster J. Investigating sex differences in the accuracy of dietary assessment methods to measure energy intake in adults: a systematic review and meta-analysis. Am J Clin Nutr. 2021 May 8;113(5):1241-1255. doi: 10.1093/ajcn/nqaa370. PMID: 33564834; PMCID: PMC8106762.
  29. Nezami BT, Hurley L, Power J, Valle CG, Tate DF. A pilot randomized trial of simplified versus standard calorie dietary self-monitoring in a mobile weight loss intervention. Obesity (Silver Spring). 2022 Mar;30(3):628-638. doi: 10.1002/oby.23377. Epub 2022 Feb 11. PMID: 35146942.
  30. Pagliai G, Dinu M, Madarena MP, Bonaccio M, Iacoviello L, Sofi F. Consumption of ultra-processed foods and health status: a systematic review and meta-analysis. Br J Nutr. 2021 Feb 14;125(3):308-318. doi: 10.1017/S0007114520002688. Epub 2020 Aug 14. PMID: 32792031; PMCID: PMC7844609.
  31. Park Y, Dodd KW, Kipnis V, et al. Comparison of self-reported dietary intakes from the Automated Self-Administered 24-h recall, 4-d food records, and food-frequency questionnaires against recovery biomarkers. Am J Clin Nutr. 2018;107(1):80‐93. doi:10.1093/ajcn/nqx002
  32. Sharifzadeh M, Bagheri M, Speakman JR, Djafarian K. Comparison of total and activity energy expenditure estimates from physical activity questionnaires and doubly labelled water: a systematic review and meta-analysis. Br J Nutr. 2021 May 14;125(9):983-997. doi: 10.1017/S0007114520003049. Epub 2020 Jul 28. PMID: 32718378.
  33. Suksatan W, Moradi S, Naeini F, Bagheri R, Mohammadi H, Talebi S, Mehrabani S, Hojjati Kermani MA, Suzuki K. Ultra-Processed Food Consumption and Adult Mortality Risk: A Systematic Review and Dose-Response Meta-Analysis of 207,291 Participants. Nutrients. 2021 Dec 30;14(1):174. doi: 10.3390/nu14010174. PMID: 35011048; PMCID: PMC8747520.
  34. Tal A. Wanting More, Seeing Less: Hunger Reduces Calorie Evaluations. Int J Environ Res Public Health. 2021 Nov 23;18(23):12270. doi: 10.3390/ijerph182312270. PMID: 34885996; PMCID: PMC8656885.
  35. Teo PS, Lim AJ, Goh AT, R J, Choy JYM, McCrickerd K, Forde CG. Texture-based differences in eating rate influence energy intake for minimally processed and ultra-processed meals. Am J Clin Nutr. 2022 Jul 6;116(1):244-254. doi: 10.1093/ajcn/nqac068. PMID: 35285882; PMCID: PMC9257473.
  36. Wang Y, Zhao R, Wang B, Zhao C, Zhu B, Tian X. The Dose-Response Associations of Sugar-Sweetened Beverage Intake with the Risk of Stroke, Depression, Cancer, and Cause-Specific Mortality: A Systematic Review and Meta-Analysis of Prospective Studies. Nutrients. 2022 Feb 12;14(4):777. doi: 10.3390/nu14040777. PMID: 35215425; PMCID: PMC8875574.
  37. Wehling H, Lusher J. People with a body mass index ⩾30 under-report their dietary intake: A systematic review. J Health Psychol. 2019 Dec;24(14):2042-2059. doi: 10.1177/1359105317714318. Epub 2017 Jul 21. PMID: 28810493.
Scroll to Top