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

The validity of two widely used commercial and research-grade activity monitors, during resting, household and activity behaviours

Wearable devices are increasingly prevalent in research environments for the estimation of energy expenditure (EE) and heart rate (HR). The aim of this study was to validate the HR and EE estimates of the Fitbit charge 2 (FC2), and the EE estimates of the Sensewear armband mini (SWA). We recruited 59 healthy adults to participate in walking, running, cycling, sedentary and household tasks. Estimates of HR from the FC2 were compared to a HR chest strap (Polar) and EE to a stationary metabolic cart (Vyntus CPX). The SWA overestimated overall EE by 0.03 kcal/min−1 and was statistically equivalent to the criterion measure, with a mean absolute percentage error (MAPE) of 29%. In contrast, the FC2 was not equivalent overall (MAPE = 44%). In household tasks, MAPE values of 93% and 83% were observed for the FC2 and SWA, respectively. The FC2 HR estimates were equivalent to the criterion measure overall. The SWA is more accurate than the commercial-grade FC2. Neither device is consistently accurate across the range of activities used in this study. The HR data obtained from the FC2 is more accurate than its EE estimates and future research may focus more on this variable

Imprinting methylation in SNRPN and MEST1 in adult blood predicts cognitive ability

Genomic imprinting is important for normal brain development and aberrant imprinting has been associated with impaired cognition. We studied the imprinting status in selected imprints (H19, IGF2, SNRPN, PEG3, MEST1, NESPAS, KvDMR, IG-DMR and ZAC1) by pyrosequencing in blood samples from longitudinal cohorts born in 1936 (n = 485) and 1921 (n = 223), and anterior hippocampus, posterior hippocampus, periventricular white matter, and thalamus from brains donated to the Aberdeen Brain Bank (n = 4). MEST1 imprint methylation was related to childhood cognitive ability score (-0.416 95% CI -0.792,-0.041; p = 0.030), with the strongest effect evident in males (-0.929 95% CI -1.531,-0.326; p = 0.003). SNRPN imprint methylation was also related to childhood cognitive ability (+0.335 95%CI 0.008,0.663; p = 0.045). A significant association was also observed for SNRPN methylation and adult crystallised cognitive ability (+0.262 95%CI 0.007,0.517; p = 0.044). Further testing of significant findings in a second cohort from the same region, but born in 1921, resulted in similar effect sizes and greater significance when the cohorts were combined (MEST1; -0.371 95% CI -0.677,-0.065; p = 0.017; SNRPN; +0.361 95% CI 0.079,0.643; p = 0.012). For SNRPN and MEST1 and four other imprints the methylation levels in blood and in the five brain regions were similar. Methylation of the paternally expressed, maternally methylated genes SNRPN and MEST1 in adult blood was associated with cognitive ability in childhood. This is consistent with the known importance of the SNRPN containing 15q11-q13 and the MEST1 containing 7q31-34 regions in cognitive function. These findings, and their sex specific nature in MEST1, point to new mechanisms through which complex phenotypes such as cognitive ability may be inherited. These mechanisms are potentially relevant to both the heritable and non-heritable components of cognitive ability. The process of epigenetic imprinting-within SNRPN and MEST1 in particular-and the factors that influence it, are worthy of further study in relation to the determinants of cognitive ability

Appetite Control across the Lifecourse: The Acute Impact of Breakfast Drink Quantity and Protein Content. The Full4Health Project

Understanding the mechanisms of hunger, satiety and how nutrients affect appetite control is important for successful weight management across the lifecourse. The primary aim of this study was to describe acute appetite control across the lifecourse, comparing age groups (children, adolescents, adults, elderly), weight categories, genders and European sites (Scotland and Greece). Participants (n = 391) consumed four test drinks, varying in composition (15% (normal protein, NP) and 30% (high protein, HP) of energy from protein) and quantity (based on 100% basal metabolic rate (BMR) and 140% BMR), on four separate days in a double-blind randomized controlled study. Ad libitum energy intake (EI), subjective appetite and biomarkers of appetite and metabolism (adults and elderly only) were measured. The adults’ appetite was significantly greater than that of the elderly across all drink types (p < 0.004) and in response to drink quantities (p < 0.001). There were no significant differences in EI between age groups, weight categories, genders or sites. Concentrations of glucagon-like peptide 1 (GLP-1) and peptide YY (PYY) were significantly greater in the elderly than the adults (p < 0.001). Ghrelin and fasting leptin concentrations differed significantly between weight categories, genders and sites (p < 0.05), while GLP-1 and PYY concentrations differed significantly between genders only (p < 0.05). Compared to NP drinks, HP drinks significantly increased postprandial GLP-1 and PYY (p < 0.001). Advanced age was concomitant with reduced appetite and elevated anorectic hormone release, which may contribute to the development of malnutrition. In addition, appetite hormone concentrations differed between weight categories, genders and geographical locations

The NuGO proof of principle study package: a collaborative research effort of the European Nutrigenomics Organisation

Acknowledgments This project is funded by the Nutrigenomics Organisation, EC funded Network of Excellence, grant nr.FOOD- 2004-506360.Peer reviewedPublisher PD

Prevention of acute kidney injury and protection of renal function in the intensive care unit

Acute renal failure on the intensive care unit is associated with significant mortality and morbidity. To determine recommendations for the prevention of acute kidney injury (AKI), focusing on the role of potential preventative maneuvers including volume expansion, diuretics, use of inotropes, vasopressors/vasodilators, hormonal interventions, nutrition, and extracorporeal techniques. A systematic search of the literature was performed for studies using these potential protective agents in adult patients at risk for acute renal failure/kidney injury between 1966 and 2009. The following clinical conditions were considered: major surgery, critical illness, sepsis, shock, and use of potentially nephrotoxic drugs and radiocontrast media. Where possible the following endpoints were extracted: creatinine clearance, glomerular filtration rate, increase in serum creatinine, urine output, and markers of tubular injury. Clinical endpoints included the need for renal replacement therapy, length of stay, and mortality. Studies are graded according to the international Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) group system Several measures are recommended, though none carries grade 1A. We recommend prompt resuscitation of the circulation with special attention to providing adequate hydration whilst avoiding high-molecular-weight hydroxy-ethyl starch (HES) preparations, maintaining adequate blood pressure using vasopressors in vasodilatory shock. We suggest using vasopressors in vasodilatory hypotension, specific vasodilators under strict hemodynamic control, sodium bicarbonate for emergency procedures administering contrast media, and periprocedural hemofiltration in severe chronic renal insufficiency undergoing coronary intervention