Improving energy expenditure estimates from wearable devices: A machine learning approach

Preliminary field validity of ActiGraph-based energy expenditure estimation in wheelchair users with spinal cord injury

STUDY DESIGN

Cross-sectional validation study.

OBJECTIVES

To develop a raw acceleration signal-based random forest (RF) model for predicting total energy expenditure (TEE) in manual wheelchair users (MWUs) and evaluate the preliminary field validity of this new model, along with four existing models published in prior literature, using the Doubly Labeled Water (DLW) method.

SETTING

General community and research institution in Pittsburgh, USA.

METHODS

A total of 78 participants' data from two previous studies were used to develop the new RF model. A seven-day cross-sectional study was conducted to collect participants' free-living physical activity and TEE data, resting metabolic rate, demographics, and anthropometrics. Ten MWUs with spinal cord injury (SCI) completed the study, with seven participants having valid data for evaluating the preliminary field validity of the five models.

RESULTS

The RF model achieved a mean absolute error (MAE) of 0.59 ± 0.60 kcal/min and a mean absolute percentage error (MAPE) of 23.6% ± 24.3% on the validation set. For preliminary field validation, the five assessed models yielded MAE from 136 kcal/day to 1141 kcal/day and MAPE from 6.1% to 50.2%. The model developed by Nightingale et al. in 2015 achieved the best performance (MAE: 136 ± 96 kcal/day, MAPE: 6.1% ± 4.7%), while the RF model achieved comparable performance (MAE: 167 ± 99 kcal/day, MAPE: 7.4% ± 5.1%).

CONCLUSIONS

Two existing models and our newly developed RF model showed good preliminary field validity for assessing TEE in MWUs with SCI and the potential to detect lifestyle change in this population. Future large-scale field validation studies and model iteration are recommended.

Machine learning prediction of pulmonary oxygen uptake from muscle oxygen in cycling

The purpose of this study was to test whether a machine learning model can accurately predict VO2 across different exercise intensities by combining muscle oxygen (MO2) with heart rate (HR). Twenty young highly trained athletes performed the following tests: a ramp incremental exercise, three submaximal constant intensity exercises, and three severe intensity exhaustive exercises. A Machine Learning model was trained to predict VO2, with model inputs including heart rate, MO2 in the left (LM) and right legs (RM). All models demonstrated equivalent results, with the accuracy of predicting VO2 at different exercise intensities varying among different models. The LM+RM+HR model performed the best across all intensities, with low bias in predicted VO2 for all intensity exercises (0.08 ml/kg/min, 95% limits of agreement: -5.64 to 5.81), and a very strong correlation (r = 0.94, p < 0.001) with measured VO2. Furthermore, the accuracy of predicting VO2 using LM+HR or RM+HR was higher than using LM+RM, and higher than the accuracy of predicting VO2 using LM, RM, or HR alone. This study demonstrates the potential of a machine learning model combining MO2 and HR to predict VO2 with minimal bias, achieving accurate predictions of VO2 for different intensity levels of exercise.

Future development trend of food-borne delivery systems of functional substances for precision nutrition

BACKGROUND

Precision nutrition, a personalized nutritional supplementation model, is widely acknowledged for its significant impact on human health. Nevertheless, challenges persist in the advancement of precision nutrition, including consumer dietary behaviors, nutrient absorption, and utilization. Thus, the exploration of effective strategies to enhance the efficacy of precision nutrition and maximize its potential benefits in dietary interventions and disease management is imperative.

SCOPE AND APPROACH

The primary objective of this comprehensive review is to synthesize and assess the latest technical approaches and future prospects for achieving precision nutrition, while also addressing the existing constraints in this field. The role of delivery systems is pivotal in the realization of precision nutrition goals. This paper outlines the potential applications of delivery systems in precision nutrition and highlights key considerations for their design and implementation. Additionally, the review offers insights into the evolving trends in delivery systems for precision nutrition, particularly in the realms of nutritional fortification, specialized diets, and disease prevention.

KEY FINDINGS AND CONCLUSIONS

By leveraging computer data collection, omics, and metabolomics analyses, this review scrutinizes the lifestyles, dietary patterns, and health statuses of diverse organisms. Subsequently, tailored nutrient supplementation programs are devised based on individual organism profiles. The utilization of delivery systems enhances the bioavailability of functional compounds and enables targeted delivery to specific body regions, thereby catering to the distinct nutritional requirements and disease prevention needs of consumers, with a particular emphasis on special populations and dietary preferences.

A contactless monitoring system for accurately predicting energy expenditure during treadmill walking based on an ensemble neural network

The monitoring of treadmill walking energy expenditure (EE) plays an important role in health evaluations and management, particularly in older individuals and those with chronic diseases. However, universal and highly accurate prediction methods for walking EE are still lacking. In this paper, we propose an ensemble neural network (ENN) model that predicts the treadmill walking EE of younger and older adults and stroke survivors with high precision based on easy-to-obtain features. Compared with previous studies, the proposed model reduced the estimation error by 13.95% and 66.20% for stroke survivors and younger adults, respectively. Furthermore, a contactless monitoring system was developed based on Kinect, mm-wave radar, and ENN algorithms, and the treadmill walking EE was monitored in real time. This ENN model and monitoring system can be combined with smart devices and treadmill, making them suitable for evaluating, monitoring, and tracking changes in health during exercise and in rehabilitation environments.

Comparing self-reported energy intake using an online dietary tool with energy expenditure by an activity tracker

OBJECTIVE

The aim of this study was to compare self-reported total energy intake (TEI) collected using an online multiple-pass 24-h dietary recall tool (Intake24) with total energy expenditure (TEE) estimated from Fitbit Charge 2-improved algorithms in adults from the NoHoW trial (12-mo weight maintenance after free-living weight loss).

METHODS

Bland-Altman plots were used to assess the level of agreement between TEI and TEE at baseline and after 12 mo. The ratio of TEI to TEE was also calculated.

RESULTS

Data from 1323 participants (71% female) was included in the analysis (mean ± SD: age 45 ± 12 y, body mass index 29.7 ± 5.4 kg/m2, initial weight loss 11.5 ± 6.5 kg). The TEI was lower than TEE on average by 33%, with limits of agreement ranging from -91% to +25%. Men, younger individuals, those with higher body mass index, those with the greater weight loss before enrollment, and those who gained weight during the study underestimated to a greater extent.

CONCLUSIONS

These findings contribute to the ongoing research examining the validity of technology-based dietary assessment tools.

Physical Activity Pattern of Adults With Metabolic Syndrome Risk Factors: Time-Series Cluster Analysis

Background

Physical activity plays a crucial role in maintaining a healthy lifestyle, and wrist-worn wearables, such as smartwatches and smart bands, have become popular tools for measuring activity levels in daily life. However, studies on physical activity using wearable devices have limitations; for example, these studies often rely on a single device model or use improper clustering methods to analyze the wearable data that are extracted from wearable devices.

Objective

This study aimed to identify methods suitable for analyzing wearable data and determining daily physical activity patterns. This study also explored the association between these physical activity patterns and health risk factors.

Methods

People aged >30 years who had metabolic syndrome risk factors and were using their own wrist-worn devices were included in this study. We collected personal health data through a web-based survey and measured physical activity levels using wrist-worn wearables over the course of 1 week. The Time-Series Anytime Density Peak (TADPole) clustering method, which is a novel time-series method proposed recently, was used to identify the physical activity patterns of study participants. Additionally, we defined physical activity pattern groups based on the similarity of physical activity patterns between weekdays and weekends. We used the χ2 or Fisher exact test for categorical variables and the 2-tailed t test for numerical variables to find significant differences between physical activity pattern groups. Logistic regression models were used to analyze the relationship between activity patterns and health risk factors.

Results

A total of 47 participants were included in the analysis, generating a total of 329 person-days of data. We identified 2 different types of physical activity patterns (early bird pattern and night owl pattern) for weekdays and weekends. The physical activity levels of early birds were less than that of night owls on both weekdays and weekends. Additionally, participants were categorized into stable and shifting groups based on the similarity of physical activity patterns between weekdays and weekends. The physical activity pattern groups showed significant differences depending on age (P=.004) and daily energy expenditure (P<.001 for weekdays; P=.003 for weekends). Logistic regression analysis revealed a significant association between older age (≥40 y) and shifting physical activity patterns (odds ratio 8.68, 95% CI 1.95-48.85; P=.007).

Conclusions

This study overcomes the limitations of previous studies by using various models of wrist-worn wearables and a novel time-series clustering method. Our findings suggested that age significantly influenced physical activity patterns. It also suggests a potential role of the TADPole clustering method in the analysis of large and multidimensional data, such as wearable data.

Diet composition and energy intake in humans

Absolute energy from fats and carbohydrates and the proportion of carbohydrates in the food supply have increased over 50 years. Dietary energy density (ED) is primarily decreased by the water and increased by the fat content of foods. Protein, carbohydrates and fat exert different effects on satiety or energy intake (EI) in the order protein > carbohydrates > fat. When the ED of different foods is equalized the differences between fat and carbohydrates are modest. Covertly increasing dietary ED with fat, carbohydrate or mixed macronutrients elevates EI, producing weight gain and vice versa. In more naturalistic situations where learning cues are intact, there appears to be greater compensation for the different ED of foods. There is considerable individual variability in response. Macronutrient-specific negative feedback models of EI regulation have limited capacity to explain how availability of cheap, highly palatable, readily assimilated, energy-dense foods lead to obesity in modern environments. Neuropsychological constructs including food reward (liking, wanting and learning), reactive and reflective decision making, in the context of asymmetric energy balance regulation, give more comprehensive explanations of how environmental superabundance of foods containing mixtures of readily assimilated fats and carbohydrates and caloric beverages elevate EI through combined hedonic, affective, cognitive and physiological mechanisms. This article is part of a discussion meeting issue 'Causes of obesity: theories, conjectures and evidence (Part II)'.

Do eating behavior traits predict energy intake and body mass index? A systematic review and meta-analysis

At present, it is unclear whether eating behavior traits (EBT) predict objectively measured short-term energy intake (EI) and longer-term energy balance as estimated by body mass index (BMI). This systematic review examined the impact of EBT on BMI and laboratory-based measures of EI in adults ( ≥ 18 years) in any BMI category, excluding self-report measures of EI. Articles were searched up until 28th October 2021 using MEDLINE, PsycINFO, EMBASE and Web of Science. Sixteen EBT were identified and the association between 10 EBT, EI and BMI were assessed using a random-effects meta-analysis. Other EBT outcomes were synthesized qualitatively. Risk of bias was assessed with the mixed methods appraisal tool. A total of 83 studies were included (mean BMI = 25.20 kg/m² , mean age = 27 years and mean sample size = 70). Study quality was rated moderately high overall, with some concerns in sampling strategy and statistical analyses. Susceptibility to hunger (n = 6) and binge eating (n = 7) were the strongest predictors of EI. Disinhibition (n = 8) was the strongest predictor of BMI. Overall, EBT may be useful as phenotypic markers of susceptibility to overconsume or develop obesity (PROSPERO: CRD42021288694).

Machine Learning in Nutrition Research

Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and thus lend itself as an alternative tool to deal with data of this nature. ML has already been applied in important problem areas in nutrition, such as obesity, metabolic health, and malnutrition. Despite this, experts in nutrition are often without an understanding of ML, which limits its application and therefore potential to solve currently open questions. The current article aims to bridge this knowledge gap by supplying nutrition researchers with a resource to facilitate the use of ML in their research. ML is first explained and distinguished from existing solutions, with key examples of applications in the nutrition literature provided. Two case studies of domains in which ML is particularly applicable, precision nutrition and metabolomics, are then presented. Finally, a framework is outlined to guide interested researchers in integrating ML into their work. By acting as a resource to which researchers can refer, we hope to support the integration of ML in the field of nutrition to facilitate modern research.

Can We Deliver Person-Centred Obesity Care Across the Globe?

PURPOSE OF REVIEW

This article discusses what person-centred care is; why it is critically important in providing effective care of a chronic, complex disease like obesity; and what can be learnt from international best practice to inform global implementation.

RECENT FINDINGS

There are four key principles to providing person-centred obesity care: providing care that is coordinated, personalised, enabling and delivered with dignity, compassion and respect. The Canadian 5AsT framework provides a co-developed person-centred obesity care approach that addresses complexity and is being tested internationally. Embedding person-centred obesity care across the globe will require a complex system approach to provide a framework for healthcare system redesign, advances in people-driven discovery and advocacy for policy change. Additional training, tools and resources are required to support local implementation, delivery and evaluation. Delivering high-quality, effective person-centred care across the globe will be critical in addressing the current obesity epidemic.

Personalized Energy Expenditure Estimation: Visual Sensing Approach With Deep Learning

Background

Calorimetry is both expensive and obtrusive but provides the only way to accurately measure energy expenditure in daily living activities of any specific person, as different people can use different amounts of energy despite performing the same actions in the same manner. Deep learning video analysis techniques have traditionally required a lot of data to train; however, recent advances in few-shot learning, where only a few training examples are necessary, have made developing personalized models without a calorimeter a possibility.

Objective

The primary aim of this study is to determine which activities are most well suited to calibrate a vision-based personalized deep learning calorie estimation system for daily living activities.

Methods

The SPHERE (Sensor Platform for Healthcare in a Residential Environment) Calorie data set is used, which features 10 participants performing 11 daily living activities totaling 4.5 hours of footage. Calorimeter and video data are available for all recordings. A deep learning method is used to regress calorie predictions from video.

Results

Models are personalized with 32 seconds from all 11 actions in the data set, and mean square error (MSE) is taken against a calorimeter ground truth. The best single action for calibration is wipe (1.40 MSE). The best pair of actions are sweep and sit (1.09 MSE). This compares favorably to using a whole 30-minute sequence containing 11 actions to calibrate (1.06 MSE).

Conclusions

A vision-based deep learning energy expenditure estimation system for a wide range of daily living activities can be calibrated to a specific person with footage and calorimeter data from 32 seconds of sweeping and 32 seconds of sitting.

The Validity of the Energy Expenditure Criteria Based on Open Source Code through two Inertial Sensors

Through this study, we developed and validated a system for energy expenditure calculation, which only requires low-cost inertial sensors and open source R software. Five healthy subjects ran at ten different speeds while their kinematic variables were recorded on the thigh and wrist. Two ActiGraph wireless inertial sensors and a low-cost Bluetooth-based inertial sensor (Lis2DH12), assembled by SensorID, were used. Ten energy expenditure equations were automatically calculated in a developed open source R software (our own creation). A correlation analysis was used to compare the results of the energy expenditure equations. A high interclass correlation coefficient of estimated energy expenditure on the thigh and wrist was observed with an Actigraph and Sensor ID accelerometer; the corrected Freedson equation showed the highest values, and the Santos-Lozano vector magnitude equation and Sasaki equation demonstrated the lowest one. Energy expenditure was compared between the wrist and thigh and showed low correlation values. Despite the positive results obtained, it was necessary to design specific equations for the estimation of energy expenditure measured with inertial sensors on the thigh. The use of the same formula equation in two different placements did not report a positive interclass correlation coefficient.

Energy Expenditure Estimation of Tabata by Combining Acceleration and Heart Rate

Tabata training plays an important role in health promotion. Effective monitoring of exercise energy expenditure is an important basis for exercisers to adjust their physical activities to achieve exercise goals. The input of acceleration combined with heart rate data and the application of machine learning algorithm are expected to improve the accuracy of EE prediction. This study is based on acceleration and heart rate to build linear regression and back propagate neural network prediction model of Tabata energy expenditure, and compare the accuracy of the two models. Participants (n = 45; Mean age: 21.04 ± 2.39 years) were randomly assigned to the modeling and validation data set in a 3:1 ratio. Each participant simultaneously wore four accelerometers (dominant hand, non-dominant hand, right hip, right ankle), a heart rate band and a metabolic measurement system to complete Tabata exercise test. After obtaining the test data, the correlation of the variables is calculated and passed to linear regression and back propagate neural network algorithms to predict energy expenditure during exercise and interval period. The validation group was entered into the model to obtain the predicted value and the prediction effect was tested. Bland-Alterman test showed two models fell within the consistency interval. The mean absolute percentage error of back propagate neural network was 12.6%, and linear regression was 14.7%. Using both acceleration and heart rate for estimation of Tabata energy expenditure is effective, and the prediction effect of back propagate neural network algorithm is better than linear regression, which is more suitable for Tabata energy expenditure monitoring.

A recurrent neural network architecture to model physical activity energy expenditure in older people

Through the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older people and linking these to personal health gains. To be able to measure PAEE in a health care perspective, methods from wearable accelerometers have been developed, however, mainly targeted towards younger people. Since elderly subjects differ in energy requirements and range of physical activities, the current models may not be suitable for estimating PAEE among the elderly. Furthermore, currently available methods seem to be either simple but non-generalizable or require elaborate (manual) feature construction steps. Because past activities influence present PAEE, we propose a modeling approach known for its ability to model sequential data, the recurrent neural network (RNN). To train the RNN for an elderly population, we used the growing old together validation (GOTOV) dataset with 34 healthy participants of 60 years and older (mean 65 years old), performing 16 different activities. We used accelerometers placed on wrist and ankle, and measurements of energy counts by means of indirect calorimetry. After optimization, we propose an architecture consisting of an RNN with 3 GRU layers and a feedforward network combining both accelerometer and participant-level data. Our efforts included switching mean to standard deviation for down-sampling the input data and combining temporal and static data (person-specific details such as age, weight, BMI). The resulting architecture produces accurate PAEE estimations while decreasing training input and time by a factor of 10. Subsequently, compared to the state-of-the-art, it is capable to integrate longer activity data which lead to more accurate estimations of low intensity activities EE. It can thus be employed to investigate associations of PAEE with vitality parameters of older people related to metabolic and cognitive health and mental well-being.

Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study

Background

Accurate solutions for the estimation of physical activity and energy expenditure at scale are needed for a range of medical and health research fields. Machine learning techniques show promise in research-grade accelerometers, and some evidence indicates that these techniques can be applied to more scalable commercial devices.

Objective

This study aims to test the validity and out-of-sample generalizability of algorithms for the prediction of energy expenditure in several wearables (ie, Fitbit Charge 2, ActiGraph GT3-x, SenseWear Armband Mini, and Polar H7) using two laboratory data sets comprising different activities.

Methods

Two laboratory studies (study 1: n=59, age 44.4 years, weight 75.7 kg; study 2: n=30, age=31.9 years, weight=70.6 kg), in which adult participants performed a sequential lab-based activity protocol consisting of resting, household, ambulatory, and nonambulatory tasks, were combined in this study. In both studies, accelerometer and physiological data were collected from the wearables alongside energy expenditure using indirect calorimetry. Three regression algorithms were used to predict metabolic equivalents (METs; ie, random forest, gradient boosting, and neural networks), and five classification algorithms (ie, k-nearest neighbor, support vector machine, random forest, gradient boosting, and neural networks) were used for physical activity intensity classification as sedentary, light, or moderate to vigorous. Algorithms were evaluated using leave-one-subject-out cross-validations and out-of-sample validations.

Results

The root mean square error (RMSE) was lowest for gradient boosting applied to SenseWear and Polar H7 data (0.91 METs), and in the classification task, gradient boost applied to SenseWear and Polar H7 was the most accurate (85.5%). Fitbit models achieved an RMSE of 1.36 METs and 78.2% accuracy for classification. Errors tended to increase in out-of-sample validations with the SenseWear neural network achieving RMSE values of 1.22 METs in the regression tasks and the SenseWear gradient boost and random forest achieving an accuracy of 80% in classification tasks.

Conclusions

Algorithms trained on combined data sets demonstrated high predictive accuracy, with a tendency for superior performance of random forests and gradient boosting for most but not all wearable devices. Predictions were poorer in the between-study validations, which creates uncertainty regarding the generalizability of the tested algorithms.

Evidence-Based Digital Tools for Weight Loss Maintenance: The NoHoW Project

BACKGROUND

Effective interventions and commercial programmes for weight loss (WL) are widely available, but most people regain weight. Few effective WL maintenance (WLM) solutions exist. The most promising evidence-based behaviour change techniques for WLM are self-monitoring, goal setting, action planning and control, building self-efficacy, and techniques that promote autonomous motivation (e.g., provide choice). Stress management and emotion regulation techniques show potential for prevention of relapse and weight regain. Digital technologies (including networked-wireless tracking technologies, online tools and smartphone apps, multimedia resources, and internet-based support) offer attractive tools for teaching and supporting long-term behaviour change techniques. However, many digital offerings for weight management tend not to include evidence-based content and the evidence base is still limited. The Project: First, the project examined why, when, and how many European citizens make WL and WLM attempts and how successful they are. Second, the project employed the most up-to-date behavioural science research to develop a digital toolkit for WLM based on 2 key conditions, i.e., self-management (self-regulation and motivation) of behaviour and self-management of emotional responses for WLM. Then, the NoHoW trial tested the efficacy of this digital toolkit in adults who achieved clinically significant (≥5%) WL in the previous 12 months (initial BMI ≥25). The primary outcome was change in weight (kg) at 12 months from baseline. Secondary outcomes included biological, psychological, and behavioural moderators and mediators of long-term energy balance (EB) behaviours, and user experience, acceptability, and cost-effectiveness.

IMPACT

The project will directly feed results from studies on European consumer behaviour, design and evaluation of digital toolkits self-management of EB behaviours into development of new products and services for WLM and digital health. The project has developed a framework and digital architecture for interventions in the context of EB tracking and will generate results that will help inform the next generation of personalised interventions for effective self-management of weight and health.

Voice Assistant Application for Avoiding Sedentarism in Elderly People Based on IoT Technologies

The rise in the use of virtual assistants such as Siri, Google Assistant, or Alexa among different sectors of society is facilitating access to information and services that were previously inconceivable due to the existing digital divide due to age. This situation allows especially the elderly to perform tasks much more easily and to access applications and services that could be a challenge for them with other digital user interfaces. With this in mind, the EMERITI project aims to improve the lives of the elderly through the use of virtual assistants in different case studies. In this sense, virtual voice assistants along with the use of Internet of Things (IoT) technologies can contribute to avoid sedentarism in the elderly; however, it is necessary to address the problem of proactivity presented by the virtual assistants available in the market. This article presents a solution that, through the use of activity monitoring smart bracelets, IoT devices and virtual voice assistants allow the elderly to monitor their daily physical activity simply by using their voice and therefore prevent them from sedentary patterns. Finally, this study presents the technical results obtained after the deployment of the proposed system and discusses the main advantages and the current challenges of the use of virtual assistants in applications to prevent sedentary lifestyles in the elderly.

Precision nutrition: A systematic literature review

Precision Nutrition research aims to use personal information about individuals or groups of individuals to deliver nutritional advice that, theoretically, would be more suitable than generic advice. Machine learning, a subbranch of Artificial Intelligence, has promise to aid in the development of predictive models that are suitable for Precision Nutrition. As such, recent research has applied machine learning algorithms, tools, and techniques in precision nutrition for different purposes. However, a systematic overview of the state-of-the-art on the use of machine learning in Precision Nutrition is lacking. Therefore, we carried out a Systematic Literature Review (SLR) to provide an overview of where and how machine learning has been used in Precision Nutrition from various aspects, what such machine learning models use as input features, what the availability status of the data used in the literature is, and how the models are evaluated. Nine research questions were defined in this study. We retrieved 4930 papers from electronic databases and 60 primary studies were selected to respond to the research questions. All of the selected primary studies were also briefly discussed in this article. Our results show that fifteen problems spread across seven domains of nutrition and health are present. Four machine learning tasks are seen in the form of regression, classification, recommendation and clustering, with most of these utilizing a supervised approach. In total, 30 algorithms were used, with 19 appearing more than once. Models were through the use of four groups of approaches and 23 evaluation metrics. Personalized approaches are promising to reduce the burden of these current problems in nutrition research, and the current review shows Machine Learning can be incorporated into Precision Nutrition research with high performance. Precision Nutrition researchers should consider incorporating Machine Learning into their methods to facilitate the integration of many complex features, allowing for the development of high-performance Precision Nutrition approaches.

From famine to therapeutic weight loss: Hunger, psychological responses, and energy balance-related behaviors

Understanding physiological and behavioral responses to energy imbalances is important for the management of overweight/obesity and undernutrition. Changes in body composition and physiological functions associated with energy imbalances provide the structural and functional context in which to consider psychological and behavioral responses. Compensatory changes in physiology and behavior are more pronounced in response to negative than positive energy balances. The physiological and psychological impact of weight loss (WL) occur on a continuum determined by (i) the degree of energy deficit (ED), (ii) its duration, (iii) body composition at the onset of the energy deficit, and (iv) the psychosocial environment in which it occurs. Therapeutic WL and famine/semistarvation both involve prolonged EDs, which are sometimes similar in magnitude. The key differences are that (i) the body mass index (BMI) of most famine victims is lower at the onset of the ED, (ii) therapeutic WL is intentional and (iii) famines are typically longer in duration (partly due to the voluntary nature of therapeutic WL and disengagement with WL interventions). The changes in psychological outcomes, motivation to eat, and energy intake in therapeutic WL are often modest (bearing in mind the nature of the measures used) and can be difficult to detect but are quantitatively significant over time. As WL progresses, these changes become more marked. It appears that extensive WL beyond 10%-20% in lean individuals has profound effects on body composition and physiological function. At this level of WL, there is a marked erosion of psychological functioning, which appears to run in parallel to WL. Psychological resources dwindle and become increasingly focused on alleviating escalating hunger and food seeking behavior. Functional changes in fat-free mass, characterized by catabolism of skeletal muscle and organs may be involved in the drive to eat associated with semistarvation. Higher levels of body fat mass may act as a buffer to protect fat-free mass, functional integrity and limit compensatory changes in energy balance behaviors. The increase in appetite that accompanies therapeutic WL appears to be very different to the intense and all-consuming drive to eat that occurs during prolonged semistarvation. The mechanisms may also differ but are not well understood, and longitudinal comparisons of the relationship between body structure, function, and behavior in response to differing EDs in those with higher and lower BMIs are currently lacking.

Assessment of energy expenditure: are calories measured differently for different diets?

PURPOSE OF REVIEW

The prevalence and burden of obesity has reached alarming levels. The assessment of human energy expenditure enables the identification of obesity-prone and obesity-resistant individuals and helps to explain the short and long-term success of weight loss treatments. In this review, we describe the state-of-the-art methods used in the assessment of human energy expenditure and the impact of dietary intake on the interpretation of the data.

RECENT FINDINGS

The reference techniques to assess energy expenditure in humans have not significantly changed during the last century. Today, indirect calorimetry, either using a metabolic chamber or a metabolic cart, is the favored method to assess human energy expenditure and is the only method enabling the assessment of macronutrient oxidation. The doubly labeled water method however provides accurate assessment of human energy expenditure under free living conditions.

SUMMARY

Although energy expenditure and macronutrient oxidation can be assessed by simple calculations from oxygen consumption and carbon dioxide production, these calculations can provide erroneous results or require corrections and/or more complex interpretation when several biochemical pathways are simultaneously engaged. Such physiological mechanisms are often elicited by dietary interventions including, among other, gluconeogenesis, lipogenesis, ketogenesis, alcohol oxidation and under or overfeeding.

A novel scaling methodology to reduce the biases associated with missing data from commercial activity monitors

Background

Commercial physical activity monitors have wide utility in the assessment of physical activity in research and clinical settings, however, the removal of devices results in missing data and has the potential to bias study conclusions. This study aimed to evaluate methods to address missingness in data collected from commercial activity monitors.

Methods

This study utilised 1526 days of near complete data from 109 adults participating in a European weight loss maintenance study (NoHoW). We conducted simulation experiments to test a novel scaling methodology (NoHoW method) and alternative imputation strategies (overall/individual mean imputation, overall/individual multiple imputation, Kalman imputation and random forest imputation). Methods were compared for hourly, daily and 14-day physical activity estimates for steps, total daily energy expenditure (TDEE) and time in physical activity categories. In a second simulation study, individual multiple imputation, Kalman imputation and the NoHoW method were tested at different positions and quantities of missingness. Equivalence testing and root mean squared error (RMSE) were used to evaluate the ability of each of the strategies relative to the true data.

Results

The NoHoW method, Kalman imputation and multiple imputation methods remained statistically equivalent (p<0.05) for all physical activity metrics at the 14-day level. In the second simulation study, RMSE tended to increase with increased missingness. Multiple imputation showed the smallest RMSE for Steps and TDEE at lower levels of missingness (<19%) and the Kalman and NoHoW methods were generally superior for imputing time in physical activity categories.

Conclusion

Individual centred imputation approaches (NoHoW method, Kalman imputation and individual Multiple imputation) offer an effective means to reduce the biases associated with missing data from activity monitors and maximise data retention.

Estimating physical activity and sedentary behaviour in a free-living environment: A comparative study between Fitbit Charge 2 and Actigraph GT3X

BACKGROUND

Activity trackers such as the Fitbit Charge 2 enable users and researchers to monitor physical activity in daily life, which could be beneficial for changing behaviour. However, the accuracy of the Fitbit Charge 2 in a free-living environment is largely unknown.

OBJECTIVE

To investigate the agreement between Fitbit Charge 2 and ActiGraph GT3X for the estimation of steps, energy expenditure, time in sedentary behaviour, and light and moderate-to-vigorous physical activity under free-living conditions, and further examine to what extent placing the ActiGraph on the wrist as opposed to the hip would affect the findings.

METHODS

41 adults (n = 10 males, n = 31 females) were asked to wear a Fitbit Charge 2 device and two ActiGraph GT3X devices (one on the hip and one on the wrist) for seven consecutive days and fill out a log of wear times. Agreement was assessed through Bland-Altman plots combined with multilevel analysis.

RESULTS

The Fitbit measured 1,492 steps/day more than the hip-worn ActiGraph (limits of agreement [LoA] = -2,250; 5,234), while for sedentary time, it measured 25 min/day less (LoA = -137; 87). Both Bland-Altman plots showed fixed bias. For time in light physical activity, the Fitbit measured 59 min/day more (LoA = -52;169). For time in moderate-to-vigorous physical activity, the Fitbit measured 31 min/day less (LoA = -132; 71) and for activity energy expenditure it measured 408 kcal/day more than the hip-worn ActiGraph (LoA = -385; 1,200). For the two latter outputs, the plots indicated proportional bias. Similar or more pronounced discrepancies, mostly in opposite direction, appeared when comparing to the wrist-worn ActiGraph.

CONCLUSION

Moderate to substantial differences between devices were found for most outputs, which could be due to differences in algorithms. Caution should be taken if replacing one device with another and when comparing results.