Validation of an inexpensive and accurate mathematical method to measure long-term changes in free-living energy intake

Sanghvi, Arjun; Redman, Leanne M.; Martin, Corby K.; Ravussin, Éric; Hall, Kevin D.

doi:10.3945/ajcn.115.111070

Cited by 65 publications

(72 citation statements)

References 31 publications

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“…28,29 Using data obtained from the CALERIE study, Sanghvi et al describe a method of estimating long-term kilocalorie intake using only demographic variables and changes in weight that is accurate to within 132 kilocalories per day for most participants. 30 These new methods offer promising avenues for individuals to track their kilocalorie intake.…”

Section: Discussionmentioning

confidence: 99%

Comparison between Human and Bite-Based Methods of Estimating Caloric Intake

Salley¹,

Hoover²,

Wilson³

et al. 2016

Journal of the Academy of Nutrition and Dietetics

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Background Current methods of self-monitoring kilocalorie intake outside of laboratory/clinical settings suffer from a systematic underreporting bias. Recent efforts to make kilocalorie information available have improved these methods somewhat, but it may be possible to derive an objective and more accurate measure of kilocalorie intake from bite count. Objective This study sought to develop and examine the accuracy of an individualized bite-based measure of kilocalorie intake and to compare that measure to participant estimates of kilocalorie intake. It was hypothesized that kilocalorie information would improve human estimates of kilocalorie intake over those with no information, but a bite-based estimate of kilocalorie intake would still outperform human estimates. Method Two-hundred eighty participants were allowed to eat ad-libitum in a cafeteria setting. Their bite count and kilocalorie intake were measured. After completion of the meal, participants estimated how many kilocalories they consumed, some with the aid of a menu containing kilocalorie information and some without. Using a train and test method for predictive model development, participants were randomly divided into one of two groups: one for model development (training group) and one for model validation (test group). Multiple regression was used to determine if height, weight, age, gender, and waist-to-hip ratio could predict an individual’s average kilocalories-per-bite for the training sample. The model was then validated with the test group, and the model-predicted kilocalorie intake was compared to human estimated kilocalorie intake. Results Only age and gender significantly predicted average kilocalories-per-bite, but all variables were retained for the test group. The bite-based measure of kilocalorie intake outperformed human estimates with and without kilocalorie information. Conclusions Bite count may serve as an easily measured, objective proxy for kilocalorie intake. A tool that can monitor bite count may be a powerful assistant to self-monitoring.

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Section: Discussionmentioning

confidence: 99%

Comparison between Human and Bite-Based Methods of Estimating Caloric Intake

Salley¹,

Hoover²,

Wilson³

et al. 2016

Journal of the Academy of Nutrition and Dietetics

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“…We used measured body weight, BW, and baseline patient characteristics in the previously published placebo‐controlled trail of canagliflozin . We calculated the changes in energy intake, ΔEI, for each subject using a validated mathematical method using the following equation:

Δ {EI}_{i} = ρ \frac{d {BW}_{i}}{d t} + ε ({BW}_{i} - {BW}_{0}) + \frac{Δ δ}{true(1 - β true)} {BW}_{0} + UGE

…”

Section: Methodsmentioning

confidence: 99%

How Strongly Does Appetite Counter Weight Loss? Quantification of the Feedback Control of Human Energy Intake

Polidori

Sanghvi

Seeley

et al. 2016

Obesity

Self Cite

164

189

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Objective To quantify the feedback control of energy intake in response to long-term covert manipulation of energy balance in free-living humans. Methods We used a validated mathematical method to calculate energy intake changes during a 52 week placebo-controlled trial in 153 patients treated with canagliflozin, a sodium glucose co-transporter inhibitor that increases urinary glucose excretion thereby resulting in weight loss without patients being directly aware of the energy deficit. We analyzed the relationship between the body weight time course and the calculated energy intake changes using principles from engineering control theory. Results We discovered that weight loss leads to a proportional increase in appetite resulting in eating above baseline by ~100 kcal/day per kg of lost weight – an amount more than 3-fold larger than the corresponding energy expenditure adaptations. Conclusions While energy expenditure adaptations are often thought to be the main reason for slowing of weight loss and subsequent regain, feedback control of energy intake plays an even larger role and helps explain why long-term maintenance of a reduced body weight is so difficult.

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“…A validated mathematical method 89 using repeated body weight measurements calculated the long-term changes in energy intake in patients with type 2 diabetes who were treated for 1 year with an inhibitor of the renal sodium glucose transporter type-2, causing a substantial increase in the amount of glucose excreted in the urine. 88 The loss of glucose calories occurred without the subjects being directly aware of the calorie deficit and resulted in gradual weight loss.…”

Section: Feedback Control Of Energy Intakementioning

confidence: 99%

Obesity Energetics: Body Weight Regulation and the Effects of Diet Composition

2017

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Weight changes are accompanied by imbalances between calorie intake and expenditure. This fact is often misinterpreted to suggest that obesity is caused by gluttony and sloth and can be treated by simply advising people to eat less and move more. However, various components of energy balance are dynamically interrelated and weight loss is resisted by counterbalancing physiological processes. While low-carbohydrate diets have been suggested to partially subvert these processes by increasing energy expenditure and promoting fat loss, our meta-analysis of 32 controlled feeding studies with isocaloric substitution of carbohydrate for fat found that both energy expenditure (26 kcal/d; P <.0001) and fat loss (16 g/d; P <.0001) were greater with lower fat diets. We review the components of energy balance and the mechanisms acting to resist weight loss in the context of static, settling point, and set-point models of body weight regulation, with the set-point model being most commensurate with current data.

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Validation of an inexpensive and accurate mathematical method to measure long-term changes in free-living energy intake

Abstract: Accurate and inexpensive estimates of ΔEI that are comparable to the DLW/DXA method can be obtained by using a mathematical model and repeated body weight measurements.

Cited by 65 publications

References 31 publications

Comparison between Human and Bite-Based Methods of Estimating Caloric Intake

Comparison between Human and Bite-Based Methods of Estimating Caloric Intake

How Strongly Does Appetite Counter Weight Loss? Quantification of the Feedback Control of Human Energy Intake

Obesity Energetics: Body Weight Regulation and the Effects of Diet Composition

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