Modeling transitions in body composition: the approach to steady state for anthropometric measures and physiological functions in the Minnesota human starvation study

Hargrove, James L.; Heinz, Grete; Heinz, O.

doi:10.1186/1476-5918-7-16

Cited by 5 publications

(6 citation statements)

References 38 publications

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“…To best estimate the model parameters, ISCEM algorithm was adopted because this algorithm can not only estimate parameters in complex functions but also conduct global optimization [18]. Energy intake and body weight were recorded for humans in an earlier study [19]. If the experimental data and model-derived data show a good fit, we can conclude that the model is suitable to describe the relationship between energy intake and body weight.…”

Section: Resultsmentioning

confidence: 99%

“…This research was based on experimental data from literature [19]. As such, no consent statement for participation is required.…”

Section: Methodsmentioning

confidence: 99%

“…Weight loss was induced by reducing food intake to two daily meals with 51 g of protein, 286 g of carbohydrates, and 30 g of fat, with 3 basic menus consisting of cereal, whole-wheat bread, potatoes, turnips, and cabbage, supplemented by scant amounts of meat and dairy products. During the entire starvation period, walking 22 miles a week and custodial work remained mandatory [19]. …”

Section: Methodsmentioning

confidence: 99%

“…Net energy intake is the difference between food intake and TEE. Although TEE was not measured in the Minnesota starvation study, TEE can be obtained through calculating REE and AEE [19,21]. …”

Section: Methodsmentioning

confidence: 99%

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Modeling the relationship between body weight and energy intake: A molecular diffusion-based approach

Gong

2012

Biol Direct

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BackgroundBody weight is at least partly controlled by the choices made by a human in response to external stimuli. Changes in body weight are mainly caused by energy intake. By analyzing the mechanisms involved in food intake, we considered that molecular diffusion plays an important role in body weight changes. We propose a model based on Fick's second law of diffusion to simulate the relationship between energy intake and body weight.ResultsThis model was applied to food intake and body weight data recorded in humans; the model showed a good fit to the experimental data. This model was also effective in predicting future body weight.ConclusionsIn conclusion, this model based on molecular diffusion provides a new insight into the body weight mechanisms.ReviewersThis article was reviewed by Dr. Cabral Balreira (nominated by Dr. Peter Olofsson), Prof. Yang Kuang and Dr. Chao Chen.

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Section: Resultsmentioning

confidence: 99%

“…This research was based on experimental data from literature [19]. As such, no consent statement for participation is required.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Modeling the relationship between body weight and energy intake: A molecular diffusion-based approach

Gong

2012

Biol Direct

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“…Decades ago, multiple dynamic models describing changes in bodyweight based on changes in energy expenditure and energy intake were already proposed to account for this [24][25][26][27][28]. Even models applied during starvation were proposed with the aim of characterizing how the body stores and metabolizes energy in the absence of food [29,30]. Besides, more complex models attempting to refine the description of the adaptation process of the human body during weight loss, as perturbations to the macronutrients metabolization and energy flux balances, have been proposed [2,[31][32][33].…”

mentioning

confidence: 99%

Real-Time Model Predictive Control of Human Bodyweight Based on Energy Intake

et al. 2019

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Featured Application: This work sets the basis for developing a control system that allows managing, in an automated and individualized manner, human bodyweight in terms of energy intake, by taking advantage of the real-time monitoring capabilities of the ever-growing wearable technology sector.Abstract: The number of overweight people reached 1.9 billion in 2016. Lifespan decrease and many diseases have been linked to obesity. Efficient ways to monitor and control body weight are needed. The objective of this work is to explore the use of a model predictive control approach to manage bodyweight in response to energy intake. The analysis is performed based on data obtained during the Minnesota starvation experiment, with weekly measurements on body weight and energy intake for 32 male participants over the course of 27 weeks. A first order dynamic auto-regression with exogenous variables model exhibits the best prediction, with an average mean relative prediction error value of 1.01 ± 0.02% for 1 week-ahead predictions. Then, the performance of a model predictive control algorithm, following a predefined bodyweight trajectory, is tested. Root mean square errors of 0.30 ± 0.06 kg and 9 ± 3 kcal day −1 are found between the desired target and simulated bodyweights, and between the measured energy intake and advised by the controller energy intake, respectively. The model predictive control approach for bodyweight allows calculating the needed energy intake in order to follow a predefined target bodyweight reference trajectory. This study shows a first possible step towards real-time active control of human bodyweight. the total number of obese men and women amounted up to 34 million and 71 million, respectively. These numbers rose up to 266 million men and 375 million women in 2014 [4], reaching more than 1.9 billion overweight people in 2016 [9]. Furthermore, the most worrisome trend is the rising rates of obesity in children and adolescents, the prevalence of which tripled between 1971 and 2002 [10]. By contrast, a negative energy balance leads to being underweight. Its prevalence in developed nations is increasing due to voluntary and involuntary caloric restriction [3]. Therefore, a prolonged imbalance in energy intake is a risk factor for several diseases (e.g., diabetes, anorexia of ageing) and affects a large number of people. Thus, it is important to find new and more efficient ways to monitor and control body weight [11].Health professionals (e.g., dietitians, physical trainers) are qualified to assist individuals in their quest to obtain an optimized energy balance. Based on our own research, we have proven that digital applications (e.g., use of smartphone, wearables . . . ) can assist in optimizing the energy balance and the health professionals are considered as buddies [12]. Nowadays, smartphones and wearables are suitable tools to collect and process information. The built-in digital cameras, sensors, storage capabilities and wireless connectivity of smartphones can be beneficial for automatic dietar...

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Nonlinear least-squares data fitting in Excel spreadsheets

2010

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We describe an intuitive and rapid procedure for analyzing experimental data by nonlinear least-squares fitting (NLSF) in the most widely used spreadsheet program. Experimental data in x/y form and data calculated from a regression equation are inputted and plotted in a Microsoft Excel worksheet, and the sum of squared residuals is computed and minimized using the Solver add-in to obtain the set of parameter values that best describes the experimental data. The confidence of best-fit values is then visualized and assessed in a generally applicable and easily comprehensible way. Every user familiar with the most basic functions of Excel will be able to implement this protocol, without previous experience in data fitting or programming and without additional costs for specialist software. The application of this tool is exemplified using the well-known Michaelis-Menten equation characterizing simple enzyme kinetics. Only slight modifications are required to adapt the protocol to virtually any other kind of dataset or regression equation. The entire protocol takes approximately 1 h.

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Modeling transitions in body composition: the approach to steady state for anthropometric measures and physiological functions in the Minnesota human starvation study

Cited by 5 publications

References 38 publications

Modeling the relationship between body weight and energy intake: A molecular diffusion-based approach

Modeling the relationship between body weight and energy intake: A molecular diffusion-based approach

Real-Time Model Predictive Control of Human Bodyweight Based on Energy Intake

Nonlinear least-squares data fitting in Excel spreadsheets

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