The mechanical efficiency of human locomotion has been studied extensively. The mechanical efficiency of the whole body occasionally exceeds muscle efficiency during bouncing type gaits. It is thought to occur due to elasticity and stiffness of the tendinomuscular system and neuromuscular functions, especially stretch reflexes. In addition, the lower limb joint kinetics affect mechanical efficiency. We investigated the impact of varying external work on mechanical efficiency and lower limb kinetics during repeated sledge jumping. Fifteen male runners performed sledge jumping for 4 minutes at 3 different sledge inclinations. Lower limb kinematics, ground reaction forces, and expired gases were analyzed. Mechanical efficiency did not differ according to sledge inclination. Mechanical efficiency correlated positively with the positive mechanical work of the knee and hip joints and the negative contribution of the hip joints. Conversely, it correlated negatively with both the positive and negative contributions of the ankle joint. This may be attributable to the greater workload in this study versus previous studies. To achieve greater external work, producing more mechanical energy at the proximal joint and transferring it to the distal joint could be an effective strategy for improving mechanical efficiency because of the greater force-generating capability of distal joint muscles.

Minibus taxis, a form of informal shared mobility that carries up to 16 passengers, is the main mode of public transport in sub-Saharan Africa, and given global trends, a large-scale shift to electric paratransit is imminent in the coming decades. Modeling the energy consumption (kWh/km) of electric vehicle (EV) fleets is a pre-requisite for planning for fleet deployment, especially in energy-constrained contexts. Given the paucity of EVs in sub-Saharan Africa, ground-truth data on the energy consumption of electric paratransit does not exist for many developing contexts. Consequently, GPS tracking data on internal combustion engine (ICE) versions of these vehicles is often used to estimate the energy consumption of an electric equivalent. To date, only per-minute GPS tracking data has been captured on these vehicles and used for energy consumption estimates. But this sampling frequency is insufficient for accurate energy consumption estimates, especially given the unique micro-mobility patterns of minibus taxis that are characterized by many rapid acceleration/deceleration events in quick succession. Although simulators can be used to interpolate between the dataset, they have been shown to be inaccurate in the regional context. This article presents a dataset of high-fidelity micromobility data captured on minibus taxis in transit on four typical route types: inter-city, intra-city, uphill, and downhill. The main objective was to estimate energy requirements for the eventual electrification of these vehicles, the data was thus processed according to an electro-kinetic model. This high-fidelity mobility data was captured by \"standardised passengers\" with bespoke GPS-location logging devices sampling at 1 Hz. Trips on the four route types were recorded and saved in six folders - three routes, each in two directions, with one route being uphill in one direction and downhill in another. Each of the six folders have subfolders for time of day - morning, afternoon, and evening. In total 62 trips were recorded with varying durations, depending on the traffic and route length.

BACKGROUND: Sedentary behavior has been identified as a significant risk factor for Metabolic Syndrome (MetS). However, it is unclear if the sedentary pattern measurement approach (posture vs. movement) impacts observed associations or if associations differ for Hispanic/Latino communities, who have higher risk of MetS.
METHODS: Participants from the Community of Mine (CoM) study (N = 602) wore hip-based accelerometers for 14 days and completed MetS-associated biomarker assessment (triglycerides, blood pressure, fasting glucose, HDL cholesterol, waist circumference). Sedentary patterns were classified using both cutpoints (movement-based) and the Convolutional Neural Network Hip Accelerometer Posture (CHAP) algorithm (posture-based). We used logistic regression to estimate associations between MetS with sedentary patterns overall and stratified by Hispanic/Latino ethnicity.
RESULTS: CHAP and cutpoint sedentary patterns were consistently associated with MetS. When controlling for total sedentary time and moderate to vigorous physical activity, only CHAP-measured median sedentary bout duration (OR = 1.15, CI: 1.04, 1.28) was significant. In stratified analysis, CHAP-measured median bout duration and time spent in sedentary bouts ≥ 30 min were each associated with increased odds of MetS, but the respective associations were stronger for Hispanic/Latino ethnicity (OR = 1.71 and 1.48; CI = 1.28-2.31 and 1.12-1.98) than for non-Hispanic/Latino ethnicity (OR = 1.43 and 1.40; CI = 1.10-1.87 and 1.06-1.87).
CONCLUSIONS: The way sedentary patterns are measured can impact the strength and precision of associations with MetS. These differences may be larger in Hispanic/Latino ethnic groups and warrants further research to inform sedentary behavioral interventions in these populations.

Obesity is a chronic disease caused primarily by the imbalance between the amount of calories supplied to the body and energy expenditure. Not only does it deteriorate the quality of life, but most importantly it increases the risk of cardiovascular diseases and the development of type 2 diabetes mellitus, leading to reduced life expectancy. In this review, we would like to present the molecular pathomechanisms underlying obesity, which constitute the target points for the action of anti-obesity medications. These include the central nervous system, brain-gut-microbiome axis, gastrointestinal motility, and energy expenditure. A significant part of this article is dedicated to incretin-based drugs such as GLP-1 receptor agonists (e.g., liraglutide and semaglutide), as well as the brand new dual GLP-1 and GIP receptor agonist tirzepatide, all of which have become \"block-buster\" drugs due to their effectiveness in reducing body weight and beneficial effects on the patient\'s metabolic profile. Finally, this review article highlights newly designed molecules with the potential for future obesity management that are the subject of ongoing clinical trials.

Indirect methods of measuring the energy expenditure of grazing animals using heartbeat variation or accelerometers are very convenient due to their low cost and low intrusiveness, allowing animals to maintain their usual routine. In the case of accelerometers, it is possible to use them to measure activity, as well as to classify animal behavior, allowing their usage in other scenarios. Despite the obvious convenience of use, it is important to evaluate the measurement error and understand the validity of the measurement through a simplistic method. In this paper, data from accelerometers were used to classify behavior and measure animal activity, and an algorithm was developed to calculate the energy expended by sheep. The results of the energy expenditure calculations were subsequently compared with the values reported in the literature, and it was verified that the values obtained were within the reference ranges. Although it cannot be used as a real metering of energy expended, the method is promising, as it can be integrated with other complementary sources of information, such as the evolution of the animal\'s weight and ingestion time, thus providing assistance in animals\' dietary management.

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.

Evidence is growing that the individual adjustment of energy targets guided by indirect calorimetry (IC) can improve outcome. With the development of a new generation of devices that are easier to use and rapid, it appears important to share knowledge and expertise that may be used to individualize nutrition care. Despite the focus of this tutorial being on one contemporary device, the principles of IC apply across existing devices and can assist tailoring the nutrition prescription and in assessing response to nutrition therapy. The present tutorial addresses its clinical application in intubated mechanically ventilated and spontaneously breathing adult patients (canopy), i.e. it covers the range from critical illness to outpatients. The cases that are presented show how the measured energy expenditure (mEE), and the respiratory quotient (RQ), i.e. the ratio of expired CO2 to consumed O2, should be applied in different cases, to adapt and individualize nutrition prescription, as it is a good marker of over- or underfeeding at the different stages of disease. The RQ also informs about the patient\'s body\'s capacity to use different substrates: the variations of RQ indicating the metabolic changes revealing insufficient or excessive feeding. The different cases reflect the use of a new generation device as a metabolic monitor that should be combined with other clinical observations and laboratory biomarkers. The tutorial also points to some shortcomings of the method, proposing alternatives.

An imbalance between energy intake and energy expenditure predisposes obesity and its related metabolic diseases. Soluble dietary fiber has been shown to improve metabolic homeostasis mainly via microbiota reshaping. However, the application and metabolic effects of insoluble fiber are less understood. Herein, we employed nanotechnology to design citric acid-crosslinked carboxymethyl cellulose nanofibers (CL-CNF) with a robust capacity of expansion upon swelling. Supplementation with CL-CNF reduced food intake and delayed digestion rate in mice by occupying stomach. Besides, CL-CNF treatment mitigated diet-induced obesity and insulin resistance in mice with enhanced energy expenditure, as well as ameliorated inflammation in adipose tissue, intestine and liver and reduced hepatic steatosis, without any discernible signs of toxicity. Additionally, CL-CNF supplementation resulted in enrichment of probiotics such as Bifidobacterium and decreased in the relative abundances of deleterious microbiota expressing bile salt hydrolase, which led to increased levels of conjugated bile acids and inhibited intestinal FXR signaling to stimulate the release of GLP-1. Taken together, our findings demonstrate that CL-CNF administration protects mice from diet-induced obesity and metabolic dysfunction by reducing food intake, enhancing energy expenditure and remodeling gut microbiota, making it a potential therapeutic strategy against metabolic diseases.

OBJECTIVE: Accurately estimating resting energy requirements is crucial for optimizing energy intake, particularly in the context of patients with varying energy needs, such as individuals with cancer. We sought to evaluate the agreement between resting energy expenditure (REE) predicted by 40 equations and that measured by reference methods in women undergoing active breast cancer treatment stage (I-IV) and post-completion (i.e., survivors).
METHODS: Data from 4 studies were combined. REE values estimated from 40 predictive equations identified by a systematic search were compared with REE assessed by indirect calorimetry (IC) using a metabolic cart (MC-REE N = 46) or a whole-room indirect calorimeter (WRIC-REE N = 44). Agreement between methods was evaluated using Bland-Altman and Lin\'s concordance coefficient correlation (Lin\'s CCC).
RESULTS: Ninety participants (24 % survivors, 61.1% had early-stage breast cancer I or II, mean age: 56.8 ± 11 years; body mass index: 28.7 ± 6.4 kg/m2) were included in this analysis. Mean MC-REE and WRIC-REE values were 1389 ± 199 kcal/day and 1506 ± 247 kcal/day, respectively. Limits of agreement were wide for all equations compared to both MC and WRIC (∼300 kcal for both methods), including the most commonly used ones, such as Harris-Benedict and Mifflin ST. Jeor equations; none had a bias within ±10% of measured REE, and all had low agreement per Lin\'s CCC analysis (<0.90). The Korth equation exhibited the best performance against WRIC and the Lvingston-Kohlstadt equation against MC. Similar patterns of bias were observed between survivors and patients and between patients with stages I-III versus IV cancer.
CONCLUSIONS: Most equations failed to accurately predict REE at the group level, and none were effective at the individual level. This inaccuracy has significant implications for women with or surviving breast cancer, who may experience weight gain, maintenance, or loss due to inaccurate energy needs estimations. Therefore, our research underscores the need for further efforts to improve REE estimation.

Brown adipose tissue (BAT) activation is an emerging target for obesity treatments due to its thermogenic properties stemming from its ability to shuttle energy through uncoupling protein 1 (Ucp1). Recent rodent studies show how BAT and white adipose tissue (WAT) activity can be modulated to increase the expression of thermogenic proteins. Consequently, these alterations enable organisms to endure cold-temperatures and elevate energy expenditure, thereby promoting weight loss. In humans, BAT is less abundant in obese subjects and impacts of thermogenesis are less pronounced, bringing into question whether energy expending properties of BAT seen in rodents can be translated to human models. Our review will discuss pharmacological, hormonal, bioactive, sex-specific and environmental activators and inhibitors of BAT to determine the potential for BAT to act as a therapeutic strategy. We aim to address the feasibility of utilizing BAT modulators for weight reduction in obese individuals, as recent studies suggest that BAT\'s contributions to energy expenditure along with Ucp1-dependent and -independent pathways may or may not rectify energy imbalance characteristic of obesity.