Physical Activity In Humans Research Articles

Enhancing Virtual Walking Sensation Using Self-Avatar in First-Person Perspective and Foot Vibrations

Walking is a fundamental physical activity in humans. Various virtual walking systems have been developed using treadmill or leg-support devices. Using optic flow, foot vibrations simulating footsteps, and a walking avatar, we propose a virtual walking system that does not require limb action for seated users. We aim to investigate whether a full-body or hands-and-feet-only walking avatar with either the first-person (experiment 1) or third-person (experiment 2) perspective can convey the sensation of walking in a virtual environment through optic flows and foot vibrations. The viewing direction of the virtual camera and the head of the full-body avatar were linked to the actual user's head motion. We discovered that the full-body avatar with the first-person perspective enhanced the sensations of walking, leg action, and telepresence, either through synchronous or asynchronous foot vibrations. Although the hands-and-feet-only avatar with the first-person perspective enhanced the walking sensation and telepresence, compared with the no-avatar condition, its effect was less prominent than that of the full-body avatar. However, the full-body avatar with the third-person perspective did not enhance the sensations of walking and leg action; rather, it impaired the sensations of self-motion and telepresence. Synchronous or rhythmic foot vibrations enhanced the sensations of self-motion, waking, leg action, and telepresence, irrespective of the avatar condition. These results suggest that the full-body or hands-and-feet avatar is effective for creating virtual walking experiences from the first-person perspective, but not the third-person perspective, and that the foot vibrations simulating footsteps are effective, regardless of the avatar condition.

Monitoring Real-Time Personal Locomotion Behaviors Over Smart Indoor-Outdoor Environments Via Body-Worn Sensors

The monitoring of human physical activities using wearable sensors, such as inertial-based sensors, plays a significant role in various current and potential applications. These applications include physical health tracking, surveillance systems, and robotic assistive technologies. Despite the wide range of applications, classification and recognition of human activities remains imprecise and this may contribute to unfavorable reactions and responses. To improve the recognition of human activities, we designed a dataset in which ten participants (five male and five female) performed 11 different activities wearing three body-worn inertial sensors in different locations on the body. Our model extracts data via a hierarchical feature-based technique. These features include time, wavelet, and time-frequency domains, respectively. Stochastic gradient descent (SGD) is then introduced to optimize selective features. The selected features with optimized patterns are further processed by multi-layered kernel sliding perceptron to develop adaptive learning for the classification of physical human activities. Our proposed model was experimentally evaluated and applied on three benchmark datasets: IM-WSHA, a self-annotated dataset, PAMAP2 dataset which is comprised of daily living activities, and an HuGaDB, a dataset which contains physical activities for aging people. The experimental results show that the proposed method achieves better results and outperforms others in terms of recognition accuracy, achieving an accuracy rate of 83.18%, 94.16%, and 92.50% respectively, when IM-WSHA, PAMAP2, and HuGaDB datasets are applied.

Smartphone and Smartwatch for Human Activity Recognition

Human activity recognition (HAR) systems are developed as aspect of a model to allow continual assessment of human behaviors in IoT environments in the areas of ambient assisted living, sports injury detection, elderly care, rehabilitation, and entertainment and close monitoring. Smartphones are already used to recognize activity. Most of the research done in this field placed a restriction on fixing the smartphone securely in a certain location on the human body, along with the machine learning system, to promote the process of classifying raw data from smartphone sensors to human activities. Smartwatches solve this limitation by placing them in a consistent position, which becomes steady and precisely sensitive to body movements. For this experiment, we evaluate both the accelerometer and the gyroscope sensor on the smartphone and the smartwatch, and decide which sensors hybrid does superiorly. Five daily physical human activities are evaluated using five classifiers from WEKA, in addition to Artificial Neural Network (ANN), K- Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms builtin MATLAB 2018a. We used confusion matrix and random simulation to compare the accuracy and efficiency of those models. The results showed that the accelerometer sensors combination has the highest accuracy among other combinations and achieved an overall accuracy of 97.7% using SVM that gives the best performance among all other classifiers.

Walking for subjects with type 2 diabetes: a systematic review and joint AMD/SID/SISMES evidence-based practical guideline

AimsRegular exercise is considered a cornerstone in the management of type 2 diabetes mellitus (T2DM). It improves glucose control and cardiovascular risk factors, contributes to weight loss, and also improves general well-being, likely playing a role in the prevention of chronic complications of diabetes. However, compliance to exercise recommendations is generally inadequate in subjects with T2DM. Walking is the most ancestral form of physical activity in humans, easily applicable in daily life. It may represent, in many patients, a first simple step towards lifestyle changes. Nevertheless, while most diabetic patients do not engage in any weekly walking, exercise guidelines do not generally detail how to improve its use. The aims of this document are to conduct a systematic review of available literature on walking as a therapeutic tool for people with T2DM, and to provide practical, evidence-based clinical recommendations regarding its utilization in these subjects.Data synthesisAnalysis of available RCTs proved that regular walking training, especially when supervised, improves glucose control in subjects with T2DM, with favorable effects also on cardiorespiratory fitness, body weight, and blood pressure. Moreover, some recent studies have shown that even short bouts of walking, used for breaking prolonged sitting, can ameliorate glucose profiles in diabetic patients with sedentary behavior.ConclusionsThere is sufficient evidence to recognize that walking is a useful therapeutic tool for people with T2DM. This document discusses theoretical and practical issues for improving its use.

A Review of Physical Human Activity Recognition Chain Using Sensors

In the era of Internet of Medical Things (IoMT), healthcare monitoring has gained a vital role nowadays. Moreover, improving lifestyle, encouraging healthy behaviours, and decreasing the chronic diseases are urgently required. However, tracking and monitoring critical cases/conditions of elderly and patients is a great challenge. Healthcare services for those people are crucial in order to achieve high safety consideration. Physical human activity recognition using wearable devices is used to monitor and recognize human activities for elderly and patient. The main aim of this review study is to highlight the human activity recognition chain, which includes, sensing technologies, preprocessing and segmentation, feature extractions methods, and classification techniques. Challenges and future trends are also highlighted.

Walking for subjects with type 2 diabetes: A systematic review and joint AMD/SID/SISMES evidence-based practical guideline

AimsRegular exercise is considered a cornerstone in the management of type 2 diabetes mellitus (T2DM). It improves glucose control and cardiovascular risk factors, contributes to weight loss, and also improves general well-being, likely playing a role in the prevention of chronic complications of diabetes. However, compliance to exercise recommendations is generally inadequate in subjects with T2DM. Walking is the most ancestral form of physical activity in humans, easily applicable in daily life. It may represent, in many patients, a first simple step towards lifestyle changes. Nevertheless, while most diabetic patients do not engage in any weekly walking, exercise guidelines do not generally detail how to improve its use. The aims of this document are to conduct a systematic review of available literature on walking as a therapeutic tool for people with T2DM, and to provide practical, evidence-based clinical recommendations regarding its utilization in these subjects. Data synthesisAnalysis of available RCTs proved that regular walking training, especially when supervised, improves glucose control in subjects with T2DM, with favorable effects also on cardiorespiratory fitness, body weight and blood pressure. Moreover, some recent studies have shown that even short bouts of walking, used for breaking prolonged sitting, can ameliorate glucose profiles in diabetic patients with sedentary behavior. ConclusionsThere is sufficient evidence to recognize that walking is a useful therapeutic tool for people with T2DM. This document discusses theoretical and practical issues for improving its use.This article is co-published in the journals Sport Sciences for Health and Nutrition, Metabolism and Cardiovascular Diseases.

Mobile Apps to Quantify Aspects of Physical Activity: a Systematic Review on its Reliability and Validity.

The purpose of this study was to systematically review and evaluate the evidence on the accuracy (validity) and consistency (reliability) of mobile apps used to quantify physical activity. Systematic literature searches were conducted in Pubmed, Science Direct, Web of Science, Physiotherapy Evidence Database (PEDro), Academic Search Complete and IEEE Xplore. Studies were included if they reported on the validity and/or reliability of a mobile application aiming primarily at measuring physical activity in humans with or without pathology. The reference lists of included articles were also screened for reports not identified through electronic searches. The methodological quality of included studies was assessed by 2 independent reviewers and data extracted by one reviewer and checked for accuracy by a second reviewer. A total of 25 articles were included in this review, of which 18 refer to validity and 7 to both validity and reliability. Mean percentage difference was used as an indicator of validity and varied between 0.1% and 79.3%. Intraclass Correlation Coefficients varied between 0.02 and 0.99 indicating poor to excellent reliability. There is conflicting and insufficient evidence on the validity and reliability, respectively, of apps for measuring physical activity. Nevertheless, velocity and the place where the smartphone is carried seem to have an impact on validity.

"Muscle-Gut-Brain Axis": Can Physical Activity Help Patients with Alzheimer's Disease Due to Microbiome Modulation?

Alzheimer's disease (AD) is one of the most common forms of dementia, which cannot be cured at the moment. Therefore, researchers also look for the alternative approaches to its treatment. It is suggested that changes in human gut microbiome mediated by exercise could influence the development and progression of AD and a new term "muscle-gut-brain axis" is introduced. There is much evidence to support this assumption. The gut microbiology is closely related to a wide range of diseases of the nervous system and therefore any negative qualitative and quantitative changes in the composition of the gut microbiota can potentially contribute to the pathophysiology of AD. Research shows that the treatment of intestinal dysbiosis with probiotics/synbiotics/eubiotics can prevent or alleviate the symptoms of these chronic neurological diseases. Studies also point to the positive effects of movement on the health of seniors. A positive correlation can be found between cognitive functions and physical stress, both in the elderly and in AD patients. Even short-term interventions with a relatively low frequency seem to produce positive results, while physical activities can be performed by using relatively simple and cost-effective means. In addition, physical activity can significantly modulate gut microbiome. Thus, it can be concluded that physical activity in humans seems to correlate with gut microbiome, which can prevent the incidence and development of AD.

Memory-related perceptual illusions directly affect physical activity in humans.

Perceptual illusions help us understand deficits in human perception, but they also have the potential to serve as treatment methods; e.g., to alleviate phantom limb pain. Treatment effects are usually the direct result of a mismatch between false visual feedback and somatosensory/proprioceptive feedback. We aimed to influence physical activity (walking distance) using a memory-related perceptual illusion that relies on a mismatch between a spatially manipulated virtual reality environment and a weakness of memory for a similar, previously experienced environment. Participants' main task was to reproduce a baseline distance three times, by walking on a treadmill while moving through a virtual reality environment. Depending on condition, the environment was either stretched or compressed relative to the previous session, but participants were not informed about these manipulations. Because false, suggestive information can lead to alterations in memory, especially when conveyed through ‘rich’ forms of media such as virtual reality, we expected each manipulation to alter memory for the previous environment(s) and we hypothesized that this would influence walking distance. The results for the first time showed that memory-related perceptual illusions can directly affect physical activity in humans. The effects we found are substantial; stretching previously experienced virtual environments led participants to almost double their initial walking distance, whereas compressing the environments resulted in about half of the initial distance. Possible clinical applications arising from these findings are discussed.

High-Phosphate Diet Induces Exercise Intolerance and Impairs Fatty Acid Metabolism in Mice.

Inorganic phosphate (Pi) is used extensively as a preservative and a flavor enhancer in the Western diet. Physical inactivity, a common feature of Western societies, is associated with increased cardiovascular morbidity and mortality. It is unknown whether dietary Pi excess contributes to exercise intolerance and physical inactivity. To determine an association between Pi excess and physical activity in humans, we assessed the relationship between serum Pi and actigraphy-determined physical activity level, as well as left ventricular function by cardiac magnetic resonance imaging, in DHS-2 (Dallas Heart Study phase 2) participants after adjusting for relevant variables. To determine direct effects of dietary Pi on exercise capacity, oxygen uptake, serum nonesterified fatty acid, and glucose were measured during exercise treadmill test in C57/BL6 mice fed either a high-Pi (2%) or normal-Pi (0.6%) diet for 12 weeks. To determine the direct effect of Pi on muscle metabolism and expression of genes involved in fatty acid metabolism, additional studies in differentiated C2C12 myotubes were conducted after subjecting to media containing 1 to 3 mmol/L Pi (pH 7.0) to simulate in vivo phosphate conditions. In participants of the DHS-2 (n=1603), higher serum Pi was independently associated with reduced time spent in moderate to vigorous physical activity ( P=0.01) and increased sedentary time ( P=0.004). There was no association between serum Pi and left ventricular ejection fraction or volumes. In animal studies, compared with the control diet, consumption of high-Pi diet for 12 weeks did not alter body weight or left ventricular function but reduced maximal oxygen uptake, treadmill duration, spontaneous locomotor activity, fat oxidation, and fatty acid levels and led to downregulation of genes involved in fatty acid synthesis, release, and oxidation, including Fabp4, Hsl, Fasn, and Pparγ, in muscle. Similar results were recapitulated in vitro by incubating C2C12 myotubes with high-Pi media. Our data demonstrate a detrimental effect of dietary Pi excess on skeletal muscle fatty acid metabolism and exercise capacity that is independent of obesity and cardiac contractile function. Dietary Pi may represent a novel and modifiable target to reduce physical inactivity associated with the Western diet.

Segmentation and Recognition of Basic and Transitional Activities for Continuous Physical Human Activity

Human activity recognition (HAR) is a hot research topic which aims to understand human behavior and can be applied in various applications. However, transitions between activities are usually disregarded due to their low incidence and short duration when compared against other activities, while in fact, transitions can affect the performance of the recognition system if not dealt with properly. In this paper, we propose and implement a systematic human activity recognition method to recognize basic activities (BA) and transitional activities (TA) in a continuous sensor data stream. First, raw sensor data are segmented into fragments with sliding window and the features are constructed based on window segmentation. Then, cluster analysis with K-Means is used to aggregate activity fragments into periods. Next, generally, realize the classification of BA and TA according to the shortest duration of the BA, and then deal with the hidden phenomenon of BA. Third, the fragments between adjacent BA are evaluated to decide whether they are TA or disturbance process. Finally, random forest classifier is used to accurately recognize BA and TA. The proposed method is evaluated on the public dataset SBHAR. The results demonstrate that our method effectively recognizes different activities and can deliver high accuracy with all activities considered.

Industry 4.0: Employers Expectations of Accounting Graduates and its Implications on Teaching and Learning Practices

This study aims to examine employers’ expectations of knowledge and skills in accounting graduates before hiring them. Using a qualitative approach on accounting practitioners and academics, this study shows that the expectations employers place on accounting students in the Industry 4.0 environment is set to change significantly, particularly in technologies used in the preparation, reporting and dissemination of financial information. Specifically, this study showcases the strong views expressed by a panel of experts comprising accounting practitioners, particularly on the subjects of job redundancies and the degree of replacement of human activities by electronic devices and machines. The replacement of physical human activities by machines is therefore expected. In addition, accounting practitioners see the need for accounting graduates to focus on continuous learning and education, particularly in acquiring knowledge and skills in IT. Hence, there is a need for academics and universities to incorporate various accounting technologies and simulations that are applicable to the Industry 4.0 environment, in their teaching and learning activities. This study contributes to existing education literature by providing new findings on the expectations of employers of future accounting graduates. In addition, this study contributes to the further understanding of academics and universities in strategizing ways and innovating new teaching and learning approaches so that accounting graduates’ employability can be enhanced and the needs of the current market for accountants can be accommodated.

Human Activity Recognition from Body Sensor Data using Deep Learning.

In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring elderly and physical impaired people at Smart home to improve their rehabilitation processes. However, it is not easy to accurately and automatically recognize physical human activity through wearable sensors due to the complexity and variety of body activities. In this paper, we address the human activity recognition problem as a classification problem using wearable body sensor data. In particular, we propose to utilize a Deep Belief Network (DBN) model for successful human activity recognition. First, we extract the important initial features from the raw body sensor data. Then, a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) are performed to further process the features and make them more robust to be useful for fast activity recognition. Finally, the DBN is trained by these features. Various experiments were performed on a real-world wearable sensor dataset to verify the effectiveness of the deep learning algorithm. The results show that the proposed DBN outperformed other algorithms and achieves satisfactory activity recognition performance.

Human Activities Recognition Via Smartphones Using Supervised Machine Learning Classifiers

This paper presents a way of detecting twelve daily physical human activities such as sitting, laying, standing, attaching to table, walking, jogging, running, jumping, pushups, stairs down, going up stairs, and cycling with acceleration and gyroscope sensors data resulted from using android smart mobile phones. An android application was developed to collect raw data from the sensors. The subjects preformed the twelve activities with smart phones where it is installed. Five of the samples had been selected as train data, while the rest ten samples selected as test data. In order to classify the subjects’ raw data, a program in Matlab R2016a was developed that applies twelve supervised classification algorithms models, and then compare between them in term of accuracy and speed factors. The twelve models are divided into two categories: Six of them under support vector machine (SVM); while the other six are under the k-nearest neighbor (k-NN). Finally, this study has the following results: The overall average accuracy rate with SVM cases is 89.79% in comparison with 87.81% for k-NN. The average speed rate is 47 seconds in SVM cases whereas it is 39 seconds in k-NN cases. With expansion of the number of activities up to 12 human actions, the result of the study showed that a good performance in terms of accuracy and speed was gained without losing an accuracy level achieved in the previous studies where maximum 8 activities were handled.

Neuromodulation of orexin neurons reduces diet-induced adiposity

Background/Objectives:Low levels of orexin are associated with obesity and reduced physical activity in humans and animals.Subjects/Methods:Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) selectively activated orexin neurons in mouse lateral hypothalamus (LH) to measure effects on spontaneous physical activity (SPA). DREADD targeting was achieved by stereotaxic injection of AAV vectors into caudal lateral LH of heterozygous orexin-Cre or C57/B6J mice. In one set of studies, excitation of orexin neurons was examined (virus: AAV2-EF1a-DIO-hM3Dq-mCherry), and test sessions began 3-4 h after light cycle onset. In a study examining the inhibition of orexin neurons (virus: AAV2-hSyn-DIO-hM4Di-mCherry), testing began 15 min prior to dark cycle onset. Clozapine n-oxide (CNO; 1 or 5 mg/kg) or saline was injected intraperitoneally and time spent moving in open field chambers was recorded for 2 h. Follow-up studies in separate mouse cohorts quantified SPA in parallel with changes in energy expenditure (EE) and chow intake using indirect calorimetry chambers (SableSystem™). Following acclimation, testing sessions (saline and/or CNO) took place over the course of ~1 week, with injections administered every day. Changes in SPA, EE, chow intake, fecal boli, and body composition (EchoMRI™) were measured. Additional mice cohorts were fed a high-fat diet (HFD) and injected with CNO daily up to 10 days to assess the potential for orexin activation to prevent diet-induced obesity.Results:Activation of orexin resulted in increases in SPA in male and female mice, and was accompanied by increases in energy expenditure without changes in overall chow intake. When orexin activation occurred in the context of high fat diet, weight gain and adiposity were significantly attenuated. SPA was decreased when DREADDs were used to inhibit orexin activity.Conclusion:These results demonstrate that orexin neurons play a critical role in mediating physical activity and suggest a novel therapeutic target for treating obesity.

A robust human activity recognition system using smartphone sensors and deep learning

In last few decades, human activity recognition grabbed considerable research attentions from a wide range of pattern recognition and human–computer interaction researchers due to its prominent applications such as smart home health care. For instance, activity recognition systems can be adopted in a smart home health care system to improve their rehabilitation processes of patients. There are various ways of using different sensors for human activity recognition in a smartly controlled environment. Among which, physical human activity recognition through wearable sensors provides valuable information about an individual’s degree of functional ability and lifestyle. In this paper, we present a smartphone inertial sensors-based approach for human activity recognition. Efficient features are first extracted from raw data. The features include mean, median, autoregressive coefficients, etc. The features are further processed by a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) to make them more robust. Finally, the features are trained with a Deep Belief Network (DBN) for successful activity recognition. The proposed approach was compared with traditional expression recognition approaches such as typical multiclass Support Vector Machine (SVM) and Artificial Neural Network (ANN) where it outperformed them.

Exercise training response heterogeneity: physiological and molecular insights.

The overall beneficial effects of exercise are well studied, but why some people do not respond favourably to exercise is less understood. The National Institutes of Health Common Fund has recently launched the large-scale discovery project 'Molecular Transducers of Physical Activity in Humans' to examine the physiological and molecular (i.e. genetic, epigenetic, lipidomic, metabolomic, proteomic, etc.) responses to exercise training. A nationwide, multicentre clinical trial such as this one also provides a unique opportunity to robustly investigate the non-response to exercise in thousands of individuals that have undergone supervised aerobic- and resistance-based exercise training interventions. The term 'non-responder' is used here to address the lack of a response (to an exercise intervention) in an outcome specified a priori. Cardiorespiratory fitness ([Formula: see text]) as an exercise response variable was recently reviewed; thus, this review focuses on metabolic aspects of the non-response to exercise training. Integrated -omics platforms are discussed as an approach to disentangle the complicated relationships between endogenous and exogenous factors that drive the lack of a response to exercise in some individuals. Harnessing the power of combined -omics platforms with deep clinical phenotyping of human study participants will advance the field of exercise metabolism and shift the paradigm, allowing exercise interventions to be targeted at those most likely to benefit and identifying novel approaches to treat those who do not.

Effects of knee sleeves on coordination of lower-limb segments in healthy adults during level walking and one-leg hopping.

The evaluation of multisegment coordination is important in gaining a better understanding of the gait and physical activities in humans. Therefore, this study aims to verify whether the use of knee sleeves affects the coordination of lower-limb segments during level walking and one-leg hopping. Eleven healthy male adults participated in this study. They were asked to walk 10 m on a level ground and perform one-leg hops with and without a knee sleeve. The segment angles and the response velocities of the thigh, shank, and foot were measured and calculated by using a motion analysis system. The phases between the segment angle and the velocity were then calculated. Moreover, the continuous relative phase (CRP) was calculated as the phase of the distal segment subtracted from the phase of the proximal segment and denoted as CRPTS (thigh–shank), CRPSF (shank–foot), and CRPTF (thigh–foot). The root mean square (RMS) values were used to evaluate the in-phase or out-of-phase states, while the standard deviation (SD) values were utilized to evaluate the variability in the stance and swing phases during level walking and in the preflight, flight, and landing phases during one-leg hopping. The walking velocity and the flight time improved when the knee sleeve was worn (p < 0.05). The segment angles of the thigh and shank also changed when the knee sleeve was worn during level walking and one-leg hopping. The RMS values of CRPTS and CRPSF in the stance phase and the RMS values of CRPSF in the preflight and landing phases changed (p < 0.05 in all cases). Moreover, the SD values of CRPTS in the landing phase and the SD values of CRPSF in the preflight and landing phases increased (p < 0.05 in all cases). These results indicated that wearing a knee sleeve caused changes in segment kinematics and coordination.

Accelerator-Based Human Activity Recognition Using Voting Technique with NBTree and MLP Classifiers

In evolution and ubiquitous computing systems, accelerometer-based human activity recognition has huge potential in a large number of application domains. Accelerometer-based human activity recognition aims to identify physical activities performed by human using accelerometer; a sensor device attached to the body and returns an actual valued estimate of acceleration along the x-, y- and z-axes from which the sensor location can be estimated. In this study, an accelerator-based activity recognition model using voting technique was proposed. Two machine learning classifiers, Naive Bayes Tree (NBTree) and Multilayer Perceptron (MLP), were used as ensemble classifiers in the voting technique. To evaluate the proposed voting technique, the performance of selected individual classifiers and existing voting technique was first examined, followed by the experiment to determine the performance of the proposed model. All of the experiments were performed using a standard dataset called Wireless Sensor Data Mining involving six physical human activities; jogging, walking, walking towards upstairs, walking towards downstairs, sitting and stand still. Results showed that the proposed voting technique with NBTree and MLP ensemble classifiers outperformed other individual classifiers and another previously suggested voting technique for accelerometer-based human activity recognition.

Advances in Exercise, Fitness, and Performance Genomics in 2015.

This review of the exercise genomics literature encompasses the highest-quality articles published in 2015 across seven broad topics: physical activity behavior, muscular strength and power, cardiorespiratory fitness and endurance performance, body weight and adiposity, insulin and glucose metabolism, lipid and lipoprotein metabolism, and hemodynamic traits. One study used a quantitative trait locus for wheel running in mice to identify single nucleotide polymorphisms (SNPs) in humans associated with physical activity levels. Two studies examined the association of candidate gene ACTN3 R577X genotype on muscular performance. Several studies examined gene-physical activity interactions on cardiometabolic traits. One study showed that physical inactivity exacerbated the body mass index (BMI)-increasing effect of an FTO SNP but only in individuals of European ancestry, whereas another showed that high-density lipoprotein cholesterol (HDL-C) SNPs from genome-wide association studies exerted a smaller effect in active individuals. Increased levels of moderate-to-vigorous-intensity physical activity were associated with higher Matsuda insulin sensitivity index in PPARG Ala12 carriers but not Pro12 homozygotes. One study combined genome-wide and transcriptome-wide profiling to identify genes and SNPs associated with the response of triglycerides (TG) to exercise training. The genome-wide association study results showed that four SNPs accounted for all of the heritability of △TG, whereas the baseline expression of 11 genes predicted 27% of △TG. A composite SNP score based on the top eight SNPs derived from the genomic and transcriptomic analyses was the strongest predictor of ΔTG, explaining 14% of the variance. The review concludes with a discussion of a conceptual framework defining some of the critical conditions for exercise genomics studies and highlights the importance of the recently launched National Institutes of Health Common Fund program titled "Molecular Transducers of Physical Activity in Humans."