Unsupervised Clustering of Heartbeat Dynamics Allows for Real Time and Personalized Improvement in Cardiovascular Fitness

Assessment of the influence of chewing pattern on glucose homeostasis through linear regression model

OBJECTIVE

Maintaining plasma glucose homeostasis is vital for mammalian survival, but the masticatory function, which influences glucose regulation, has, to our knowledge, been overlooked.

RESEARCH METHODS AND PROCEDURES

In this study, we investigated the relationship between the glycemic response curve and chewing performance in a group of 8 individuals who consumed 80 g of apple. A device called "Chewing" utilizing electromyographic (EMG) technology quantitatively assesses chewing pattern, while glycemic response is analyzed using continuous glucose monitoring. We assessed chewing pattern characterizing chewing time (tchew), number of bites (nchew), work (w), power (wr), and chewing cycles (tcyc). Moreover, we measured the principal features of the glycemic response curve, including the area under the curve (α) and the mean time to reach the glycemic peak (tmean). We used linear regression models to examine the correlations between these variables.

RESULTS

tchew, nchew, and wr were correlated with α (R2 =  0.44,   P  <  0.05 for tchew and nchew, P  <  0.001 for wr), and tmean was correlated with tchew (R2  =  0.25,  P  <  0.05). These findings suggest that increasing chewing time and power, while reducing the number of chews, resulted in a wider glycemic curve and an earlier attainment of the glycemic peak.

CONCLUSIONS

These results emphasize the influence of proper chewing techniques on blood sugar levels. Implementing correct chewing habits could serve as an additional approach to managing the glycemic curve, particularly for individuals with diabetes.

Body composition and risk for sarcopenia in transgender women

OBJECTIVES

Body composition and strength of cisgender (cis) individuals are well established. However, those for transgender women (trans women) undergoing gender-affirming hormone therapy remain unclear. This study aimed to detect possible body composition and strength variations related to sarcopenia.

METHODS

This was a cross-sectional comparative study of 37 trans women, 34 cis men, and 34 cis women. Body composition was measured in all individuals by bioelectrical impedance analysis; prehensile strength by dynamometry was studied in trans women.

RESULTS

In this study, trans women had higher body mass index values than cis individuals (P < 0.01). Fat mass was 41% higher for trans women than cis men. Muscle mass (MM) was lower in trans women than cis men (-10%), and higher than cis women (24%). Bone mass was lower in trans women than cis men and higher in cis women (P < 0.01). Trans women's prehensile strengths were 25.26 kg for the right hand and 24.8 kg for the left. Appendicular skeletal muscle mass was 23.63 kg, and appendicular skeletal muscle mass index was 8.14 kg.

CONCLUSION

Trans women undergoing gender-affirming hormone therapy show a tendency to adapt body compartments to those of cis women with increased fat mass and reduced muscle mass. Prehensile strength in trans women was close to the cutoff points for sarcopenia risk. Nutrition, physical activity, strength, and body composition are important to avoid the possible risk for sarcopenia. More studies along these lines are necessary, especially in older adults.

Digital Biohacking Approach to Dietary Interventions: A Comprehensive Strategy for Healthy and Sustainable Weight Loss

The rising obesity epidemic requires effective and sustainable weight loss intervention strategies that take into account both of individual preferences and environmental impact. This study aims to develop and evaluate the effectiveness of an innovative digital biohacking approach for dietary modifications in promoting sustainable weight loss and reducing carbon footprint impact. A pilot study was conducted involving four participants who monitored their weight, diet, and activities over the course of a year. Data on food consumption, carbon footprint impact, calorie intake, macronutrient composition, weight, and energy expenditure were collected. A digital replica of the metabolism based on nutritional information, the Personalized Metabolic Avatar (PMA), was used to simulate weight changes, plan, and execute the digital biohacking approach to dietary interventions. The dietary modifications suggested by the digital biohacking approach resulted in an average daily calorie reduction of 236.78 kcal (14.24%) and a 15.12% reduction in carbon footprint impact (-736.48 gCO2eq) per participant. Digital biohacking simulations using PMA showed significant differences in weight change compared to actual recorded data, indicating effective weight reduction with the digital biohacking diet. Additionally, linear regression analysis on real data revealed a significant correlation between adherence to the suggested diet and weight loss. In conclusion, the digital biohacking recommendations provide a personalized and sustainable approach to weight loss, simultaneously reducing calorie intake and minimizing the carbon footprint impact. This approach shows promise in combating obesity while considering both individual preferences and environmental sustainability.

Unraveling the Gut Microbiome-Diet Connection: Exploring the Impact of Digital Precision and Personalized Nutrition on Microbiota Composition and Host Physiology

The human gut microbiome, an intricate ecosystem housing trillions of microorganisms within the gastrointestinal tract, holds significant importance in human health and the development of diseases. Recent advances in technology have allowed for an in-depth exploration of the gut microbiome, shedding light on its composition and functions. Of particular interest is the role of diet in shaping the gut microbiome, influencing its diversity, population size, and metabolic functions. Precision nutrition, a personalized approach based on individual characteristics, has shown promise in directly impacting the composition of the gut microbiome. However, to fully understand the long-term effects of specific diets and food components on the gut microbiome and to identify the variations between individuals, longitudinal studies are crucial. Additionally, precise methods for collecting dietary data, alongside the application of machine learning techniques, hold immense potential in comprehending the gut microbiome's response to diet and providing tailored lifestyle recommendations. In this study, we investigated the complex mechanisms that govern the diverse impacts of nutrients and specific foods on the equilibrium and functioning of the individual gut microbiome of seven volunteers (four females and three males) with an average age of 40.9 ± 10.3 years, aiming at identifying potential therapeutic targets, thus making valuable contributions to the field of personalized nutrition. These findings have the potential to revolutionize the development of highly effective strategies that are tailored to individual requirements for the management and treatment of various diseases.

Putting the Personalized Metabolic Avatar into Production: A Comparison between Deep-Learning and Statistical Models for Weight Prediction

Nutrition is a cross-cutting sector in medicine, with a huge impact on health, from cardiovascular disease to cancer. Employment of digital medicine in nutrition relies on digital twins: digital replicas of human physiology representing an emergent solution for prevention and treatment of many diseases. In this context, we have already developed a data-driven model of metabolism, called a "Personalized Metabolic Avatar" (PMA), using gated recurrent unit (GRU) neural networks for weight forecasting. However, putting a digital twin into production to make it available for users is a difficult task that as important as model building. Among the principal issues, changes to data sources, models and hyperparameters introduce room for error and overfitting and can lead to abrupt variations in computational time. In this study, we selected the best strategy for deployment in terms of predictive performance and computational time. Several models, such as the Transformer model, recursive neural networks (GRUs and long short-term memory networks) and the statistical SARIMAX model were tested on ten users. PMAs based on GRUs and LSTM showed optimal and stable predictive performances, with the lowest root mean squared errors (0.38 ± 0.16-0.39 ± 0.18) and acceptable computational times of the retraining phase (12.7 ± 1.42 s-13.5 ± 3.60 s) for a production environment. While the Transformer model did not bring a substantial improvement over RNNs in term of predictive performance, it increased the computational time for both forecasting and retraining by 40%. The SARIMAX model showed the worst performance in term of predictive performance, though it had the best computational time. For all the models considered, the extent of the data source was a negligible factor, and a threshold was established for the number of time points needed for a successful prediction.

Medical Image Classifications for 6G IoT-Enabled Smart Health Systems

As day-to-day-generated data become massive in the 6G-enabled Internet of medical things (IoMT), the process of medical diagnosis becomes critical in the healthcare system. This paper presents a framework incorporated into the 6G-enabled IoMT to improve prediction accuracy and provide a real-time medical diagnosis. The proposed framework integrates deep learning and optimization techniques to render accurate and precise results. The medical computed tomography images are preprocessed and fed into an efficient neural network designed for learning image representations and converting each image to a feature vector. The extracted features from each image are then learned using a MobileNetV3 architecture. Furthermore, we enhanced the performance of the arithmetic optimization algorithm (AOA) based on the hunger games search (HGS). In the developed method, named AOAHG, the operators of the HGS are applied to enhance the AOA's exploitation ability while allocating the feasible region. The developed AOAG selects the most relevant features and ensures the overall model classification improvement. To assess the validity of our framework, we conducted evaluation experiments on four datasets, including ISIC-2016 and PH2 for skin cancer detection, white blood cell (WBC) detection, and optical coherence tomography (OCT) classification, using different evaluation metrics. The framework showed remarkable performance compared to currently existing methods in the literature. In addition, the developed AOAHG provided results better than other FS approaches according to the obtained accuracy, precision, recall, and F1-score as performance measures. For example, AOAHG had 87.30%, 96.40%, 88.60%, and 99.69% for the ISIC, PH2, WBC, and OCT datasets, respectively.

Automatic Detection of Aerobic Threshold through Recurrence Quantification Analysis of Heart Rate Time Series

During exercise with increasing intensity, the human body transforms energy with mechanisms dependent upon actual requirements. Three phases of the body's energy utilization are recognized, characterized by different metabolic processes, and separated by two threshold points, called aerobic (AerT) and anaerobic threshold (AnT). These thresholds occur at determined values of exercise intensity(workload) and can change among individuals. They are considered indicators of exercise capacities and are useful in the personalization of physical activity plans. They are usually detected by ventilatory or metabolic variables and require expensive equipment and invasive measurements. Recently, particular attention has focused on AerT, which is a parameter especially useful in the overweight and obese population to determine the best amount of exercise intensity for weight loss and increasing physical fitness. The aim of study is to propose a new procedure to automatically identify AerT using the analysis of recurrences (RQA) relying only on Heart rate time series, acquired from a cohort of young athletes during a sub-maximal incremental exercise test (Cardiopulmonary Exercise Test, CPET) on a cycle ergometer. We found that the minima of determinism, an RQA feature calculated from the Recurrence Quantification by Epochs (RQE) approach, identify the time points where generic metabolic transitions occur. Among these transitions, a criterion based on the maximum convexity of the determinism minima allows to detect the first metabolic threshold. The ordinary least products regression analysis shows that values of the oxygen consumption VO₂, heart rate (HR), and Workload correspondent to the AerT estimated by RQA are strongly correlated with the one estimated by CPET (r > 0.64). Mean percentage differences are <2% for both HR and VO₂ and <11% for Workload. The Technical Error for HR at AerT is <8%; intraclass correlation coefficients values are moderate (≥0.66) for all variables at AerT. This system thus represents a useful method to detect AerT relying only on heart rate time series, and once validated for different activities, in future, can be easily implemented in applications acquiring data from portable heart rate monitors.

Two different smartwatches exhibit high accuracy in evaluating heart rate and peripheral oxygen saturation in cats when compared with the electrocardiography and transmittance pulse oximetry

OBJECTIVE

To evaluate the accuracy for 2 smartwatches with oximetry technology and optical wrist heart rate (HR) or single-lead Electrocardiography (ECG) technology (Fenix 5X Plus [GF5xp], Garmin Ltd and Apple Watch 6 [AppW6], Apple Inc, respectively) versus reference methods (ECG and transmittance pulse oximetry [TPO], respectively) in measuring HR and peripheral oxygen saturation of hemoglobin (SpO2) in cats.

ANIMALS

10 male client-owned cats aged 8 to 12 months and weighing 3.2 to 4.5 kg.

PROCEDURES

All cats that were presented for elective castration at the Atatürk University Animal Hospital between March 10 and April 15, 2022, were considered for enrollment. Monitoring of HR and SpO2 during anesthesia was performed with a 3-lead ECG and transmittance pulse oximetry, respectively, connected to a multiparameter monitor (reference methods) along with a GF5xp and a AppW6. Agreement between reference methods and the smartwatches were assessed by the Bland-Altman plot, in which the differences (%) between methods were plotted against their mean HR or SpO2 (reference method measurement - test device measurement) and the limits of agreement (mean ± 1.96 × SD).

RESULTS

Compared with ECG measurements of HR, GF5xp had superior bias (-0.1%) and limit of agreement (LoA, 3.0 to -3.3%) versus those of the AppW6 (bias, 0.2%; LoA, 3.7 to -3.4%). Compared with TPO measurements of SpO2, AppW6 had superior bias (0.2%) and LoA (3.0% and -2.5%) versus those of the GF5xp (bias, -2.1%; LoA, 0.2 to -4.4%).

CLINICAL RELEVANCE

Results indicated that the GF5xp and AppW6 exhibited high accuracy in evaluating HR and SpO2 in cats when compared with the reference methods. However, it should be noted that these comparisons were made in anesthetized patients without any systemic disease.

Personalized Metabolic Avatar: A Data Driven Model of Metabolism for Weight Variation Forecasting and Diet Plan Evaluation

Development of predictive computational models of metabolism through mechanistic models is complex and resource demanding, and their personalization remains challenging. Data-driven models of human metabolism would constitute a reliable, fast, and continuously updating model for predictive analytics. Wearable devices, such as smart bands and impedance balances, allow the real time and remote monitoring of physiological parameters, providing for a flux of data carrying information on user metabolism. Here, we developed a data-driven model of end-user metabolism, the Personalized Metabolic Avatar (PMA), to estimate its personalized reactions to diets. PMA consists of a gated recurrent unit (GRU) deep learning model trained to forecast personalized weight variations according to macronutrient composition and daily energy balance. The model can perform simulations and evaluation of diet plans, allowing the definition of tailored goals for achieving ideal weight. This approach can provide the correct clues to empower citizens with scientific knowledge, augmenting their self-awareness with the aim to achieve long-lasting results in pursuing a healthy lifestyle.

Resistance Training with Blood Flow Restriction and Ocular Health: A Brief Review

Despite the many health benefits of resistance training, it has been suggested that high-intensity resistance exercise is associated with acute increases in intraocular pressure which is a significant risk factor for the development of glaucomatous optic nerve damage. Therefore, resistance training using a variety of forms (e.g., resistance bands, free weights, weight machines, and bodyweight) may be harmful to patients with or at risk of glaucoma. An appropriate solution for such people may involve the combination of resistance training and blood flow restriction (BFR). During the last decade, the BFR (a.k.a. occlusion or KAATSU training) method has drawn great interest among health and sports professionals because of the possibility for individuals to improve various areas of fitness and performance at lower exercise intensities. In comparison to studies evaluating the efficiency of BFR in terms of physical performance and body composition changes, there is still a paucity of empirical studies concerning safety, especially regarding ocular health. Although the use of BFR during resistance training seems feasible for glaucoma patients or those at risk of glaucoma, some issues must be investigated and resolved. Therefore, this review provides an overview of the available scientific data describing the influence of resistance training combined with BFR on ocular physiology and points to further directions of research.