Establishing energy requirements for body weight maintenance: validation of an intake-balance method

Criterion validity of wrist accelerometry for assessing energy intake via the intake-balance technique

BACKGROUND

Intake-balance assessments measure energy intake (EI) by summing energy expenditure (EE) with concurrent change in energy storage (ΔES). Prior work has not examined the validity of such calculations when EE is estimated via open-source techniques for research-grade accelerometry devices. The purpose of this study was to test the criterion validity of accelerometry-based intake-balance methods for a wrist-worn ActiGraph device.

METHODS

Healthy adults (n = 24) completed two 14-day measurement periods while wearing an ActiGraph accelerometer on the non-dominant wrist. During each period, criterion values of EI were determined based on ΔES measured by dual X-ray absorptiometry and EE measured by doubly labeled water. A total of 11 prediction methods were tested, 8 derived from the accelerometer and 3 from non-accelerometry methods (e.g., diet recall; included for comparison). Group-level validity was assessed through mean bias, while individual-level validity was assessed through mean absolute error, mean absolute percentage error, and Bland-Altman analysis.

RESULTS

Mean bias for the three best accelerometry-based methods ranged from -167 to 124 kcal/day, versus -104 to 134 kcal/day for the non-accelerometry-based methods. The same three accelerometry-based methods had mean absolute error of 323-362 kcal/day and mean absolute percentage error of 18.1-19.3%, versus 353-464 kcal/day and 19.5-24.4% for the non-accelerometry-based methods. All 11 methods demonstrated systematic bias in the Bland-Altman analysis.

CONCLUSIONS

Accelerometry-based intake-balance methods have promise for advancing EI assessment, but ongoing refinement is necessary. We provide an R package to facilitate implementation and refinement of accelerometry-based methods in future research (see paulhibbing.com/IntakeBalance).

Predicting energy intake with an accelerometer-based intake-balance method

Nutritional interventions often rely on subjective assessments of energy intake (EI), but these are susceptible to measurement error. To introduce an accelerometer-based intake-balance method for assessing EI using data from a time-restricted eating (TRE) trial. Nineteen participants with overweight/obesity (25-63 years old; 16 females) completed a 12-week intervention (NCT03129581) in a control group (unrestricted feeding; n 8) or TRE group (n 11). At the start and end of the intervention, body composition was assessed by dual-energy X-ray absorptiometry (DXA) and daily energy expenditure (EE) was assessed for 2 weeks via wrist-worn accelerometer. EI was back-calculated as the sum of net energy storage (from DXA) and EE (from accelerometer). Accelerometer-derived EI estimates were compared against estimates from the body weight planner of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Mean EI for the control group declined by 138 and 435 kJ/day for the accelerometer and NIDDK methods, respectively (both P ≥ 0·38), v. 1255 and 1469 kJ/day, respectively, for the TRE group (both P < 0·01). At follow-up, the accelerometer and NIDDK methods showed excellent group-level agreement (mean bias of -297 kJ/day across arms; standard error of estimate 1054 kJ/day) but high variability at the individual level (limits of agreement from -2414 to +1824 kJ/day). The accelerometer-based intake-balance method showed plausible sensitivity to change, and EI estimates were biologically and behaviourally plausible. The method may be a viable alternative to self-report EI measures. Future studies should assess criterion validity using doubly labelled water.

The Effects of an Acute Bout of Aerobic or Resistance Exercise on Nonexercise Physical Activity

Introduction/Purpose

A reduction in nonexercise physical activity (NEPA) after exercise may reduce the effectiveness of exercise interventions on weight loss in adults with overweight or obesity. Aerobic exercise (AEx) and resistance exercise (REx) may have different effects on NEPA. The purpose of this secondary analysis was to examine the effect of a single bout of AEx or REx on NEPA and sedentary behavior in inactive adults with overweight or obesity.

Methods

Adults with overweight or obesity (n = 24; 50% male; age, 34.5 ± 1.5 yr; body mass index, 28.5 ± 0.9 kg·m-2) not meeting current physical activity guidelines completed a single 45-min bout of AEx, REx, or a sedentary control on different days in random order. After each condition, participants' NEPA was recorded for 84 h by accelerometer. Time spent sedentary and in light, moderate, and vigorous physical activity; steps; metabolic equivalent of task (MET)-hours; and sit-to-stand transitions were calculated using activity count data.

Results

No differences were observed in the percent of waking time spent sedentary and in light, moderate, and vigorous activity between conditions (P > 0.05). No differences were observed in steps, MET-hours, or sit-to-stand transitions between conditions (P > 0.05). NEPA responses were variable among individuals, with approximately half of participants reducing and half increasing NEPA over the 84 h after each exercise condition.

Conclusion

NEPA was not reduced after an acute bout of AEx or REx in a sample of inactive adults with overweight or obesity.

Machine learning modeling practices to support the principles of AI and ethics in nutrition research

BACKGROUND

Nutrition research is relying more on artificial intelligence and machine learning models to understand, diagnose, predict, and explain data. While artificial intelligence and machine learning models provide powerful modeling tools, failure to use careful and well-thought-out modeling processes can lead to misleading conclusions and concerns surrounding ethics and bias.

METHODS

Based on our experience as reviewers and journal editors in nutrition and obesity, we identified the most frequently omitted best practices from statistical modeling and how these same practices extend to machine learning models. We next addressed areas required for implementation of machine learning that are not included in commercial software packages.

RESULTS

Here, we provide a tutorial on best artificial intelligence and machine learning modeling practices that can reduce potential ethical problems with a checklist and guiding principles to aid nutrition researchers in developing, evaluating, and implementing artificial intelligence and machine learning models in nutrition research.

CONCLUSION

The quality of AI/ML modeling in nutrition research requires iterative and tailored processes to mitigate against potential ethical problems or to predict conclusions that are free of bias.

Negative energy balance during military training: The role of contextual limitations

During multiday training exercises, soldiers almost systematically face a moderate-to-large energy deficit, affecting their body mass and composition and potentially their physical and cognitive performance. Such energy deficits are explained by their inability to increase their energy intake during these highly demanding periods. With the exception of certain scenarios in which rations are voluntarily undersized to maximize the constraints, the energy content of the rations are often sufficient to maintain a neutral energy balance, suggesting that other limitations are responsible for such voluntary and/or spontaneous underconsumption. In this review, the overall aim was to present an overview of the impact of military training on energy balance, a context that stands out by its summation of specific limitations that interfere with energy intake. We first explore the impact of military training on the various components of energy balance (intake and expenditure) and body mass loss. Then, the role of the dimensioning of the rations (total energy content above or below energy expenditure) on energy deficits are addressed. Finally, the potential limitations inherent to military training (training characteristics, food characteristics, timing and context of eating, and the soldiers' attitude) are discussed to identify potential strategies to spontaneously increase energy intake and thus limit the energy deficit.

An objective measure of energy intake using the principle of energy balance

BACKGROUND

The measurement of energy intake is central to the understanding of energy balance and predicting changes in body weight. Until recently, the most commonly used methods of assessing intake were self-reported diet recalls, diet diaries, or food-frequency questionnaires. These methods, however, are subject to systematic biases and are often inaccurate.

AIM

Review the validations and applications of an expenditure/balance method for measuring energy intake.

METHODS

Review the literature regarding the theory and practice of objectively measuring energy intake based on the principle of energy balance i.e., energy intake is calculated from the measured total energy expenditure plus the change in body energy stores (ES). The attainable precision is modeled and compared with the accuracy and precision of validations against known energy intake.

RESULTS

Measurement of energy intake, calculated in this way, is accurate to within 2% and has a precision of 4-37% depending on the expenditure and body composition methods used and the time interval between measures. Applications of this expenditure/balance (EB) method have provided novel data on the compliance to dietary restriction and its association with physical activity interventions, and the effects of bariatric surgery on energy intake and weight gain. Practical limitations to this method, however, include cost and limited access to the analyses required by the DLW method.

CONCLUSION

The EB method of objectively measuring energy intake is objective, accurate, and reasonably precise. It is practical for moderate-sized studies.

The biology of human overfeeding: A systematic review

This systematic review has examined more than 300 original papers dealing with the biology of overfeeding. Studies have varied from 1 day to 6 months. Overfeeding produced weight gain in adolescents, adult men and women and in older men. In longer term studies, there was a clear and highly significant relationship between energy ingested and weight gain and fat storage with limited individual differences. There is some evidence for a contribution of a genetic component to this response variability. The response to overfeeding was affected by the baseline state of the groups being compared: those with insulin resistance versus insulin sensitivity; those prone to obesity versus those resistant to obesity; and those with metabolically abnormal obesity versus those with metabolically normal obesity. Dietary components, such as total fat, polyunsaturated fat and carbohydrate influenced the patterns of adipose tissue distribution as did the history of low or normal birth weight. Overfeeding affected the endocrine system with increased circulating concentrations of insulin and triiodothyronine frequently present. Growth hormone, in contrast, was rapidly suppressed. Changes in plasma lipids were influenced by diet, exercise and the magnitude of weight gain. Adipose tissue and skeletal muscle morphology and metabolism are substantially altered by chronic overfeeding.

Best (but oft-forgotten) practices: identifying and accounting for regression to the mean in nutrition and obesity research

BACKGROUND

Regression to the mean (RTM) is a statistical phenomenon where initial measurements of a variable in a nonrandom sample at the extreme ends of a distribution tend to be closer to the mean upon a second measurement. Unfortunately, failing to account for the effects of RTM can lead to incorrect conclusions on the observed mean difference between the 2 repeated measurements in a nonrandom sample that is preferentially selected for deviating from the population mean of the measured variable in a particular direction. Study designs that are susceptible to misattributing RTM as intervention effects have been prevalent in nutrition and obesity research. This field often conducts secondary analyses of existing intervention data or evaluates intervention effects in those most at risk (i.e., those with observations at the extreme ends of a distribution).

OBJECTIVES

To provide best practices to avoid unsubstantiated conclusions as a result of ignoring RTM in nutrition and obesity research.

METHODS

We outlined best practices for identifying whether RTM is likely to be leading to biased inferences, using a flowchart that is available as a web-based app at https://dustyturner.shinyapps.io/DecisionTreeMeanRegression/. We also provided multiple methods to quantify the degree of RTM.

RESULTS

Investigators can adjust analyses to include the RTM effect, thereby plausibly removing its biasing influence on estimating the true intervention effect.

CONCLUSIONS

The identification of RTM and implementation of proper statistical practices will help advance the field by improving scientific rigor and the accuracy of conclusions. This trial was registered at clinicaltrials.gov as NCT00427193.

Human Brown Adipose Tissue Estimated With Magnetic Resonance Imaging Undergoes Changes in Composition After Cold Exposure: An in vivo MRI Study in Healthy Volunteers

Aim: Magnetic resonance imaging (MRI) is increasingly being used to evaluate brown adipose tissue (BAT) function. Reports on the extent and direction of cold-induced changes in MRI fat fraction and estimated BAT volume vary between studies. Here, we aimed to explore the effect of different fat fraction threshold ranges on outcomes measured by MRI. Moreover, we aimed to investigate the effect of cold exposure on estimated BAT mass and energy content. Methods: The effects of cold exposure at different fat fraction thresholding levels were analyzed in the supraclavicular adipose depot of nine adult males. MRI data were reconstructed, co-registered and analyzed in two ways. First, we analyzed cold-induced changes in fat fraction, T2* relaxation time, volume, mass, and energy of the entire supraclavicular adipose depot at different fat fraction threshold levels. As a control, we assessed fat fraction differences of deltoid subcutaneous adipose tissue (SAT). Second, a local analysis was performed to study changes in fat fraction and T2* on a voxel-level. Thermoneutral and post-cooling data were compared using paired-sample t-tests (p < 0.05). Results: Global analysis unveiled that the largest cold-induced change in fat fraction occurred within a thermoneutral fat fraction range of 30-100% (-3.5 ± 1.9%), without changing the estimated BAT volume. However, the largest cold-induced changes in estimated BAT volume were observed when applying a thermoneutral fat fraction range of 70-100% (-3.8 ± 2.6%). No changes were observed for the deltoid SAT fat fractions. Tissue energy content was reduced from 126 ± 33 to 121 ± 30 kcal, when using a 30-100% fat fraction range, and also depended on different fat fraction thresholds. Voxel-wise analysis showed that while cold exposure changed the fat fraction across nearly all thermoneutral fat fractions, decreases were most pronounced at high thermoneutral fat fractions. Conclusion: Cold-induced changes in fat fraction occurred over the entire range of thermoneutral fat fractions, and were especially found in lipid-rich regions of the supraclavicular adipose depot. Due to the variability in response between lipid-rich and lipid-poor regions, care should be taken when applying fat fraction thresholds for MRI BAT analysis.