Volume 02 · Issue 04 · 2022

Malnutrition (excess or defect) and sedentariness act as an accelerator in the older people frailty process. A systemic solution has been developed to engage older people in a healthier lifestyle using serious games and food monitoring. The study aimed to evaluate protocol influence on variables related to unhealthy behaviors improving dietary habits through a remote nutritional coaching approach and stimulating the population to increase physical activity through Exergames.

Thirty-two subjects (25 Treatments and 7 Controls, aging 65–80 years), of which 15 (11 Treatments and 4 Controls) living in the UK (ACCORD and ExtraCare Villages placed in Shenley Wood (Milton Keynes), St. Crispin (Northampton), and Showell Court (Wolverhampton)) and 17 (14 Treatments and 3 Controls) in Italy (Genoa, Liguria), were recruited and characterized in terms of nutritional status, physical, somatometric, hemodynamic and biochemical measurements, and body composition. Participants were stimulated to adopt the Mediterranean dietary pattern, by a food diary diet-app, and perform regular physical activity, by the Exergame app, for three months. At the end of the trial, users underwent the same test battery. Data were tested for normality of distribution by the Shapiro-Wilk test. Comparisons between groups were performed at baseline by unpaired Student’s t-test for continuous variables, chi-square test, or Fisher’s exact test for categorical variables. Analysis of Variance (ANOVA) for repeated measures was used to analyze the significance of changes over time between groups.

At the end of the trial, significant reductions of systolic (15 mmHg, P = 0.001), diastolic (5 mmHg, P = 0.025), mean (10 mmHg, P = 0.001) blood pressure, and rate-pressure product (RPP) (1,105 mmHg^*bpm, P = 0.017) values were observed in DOREMI users. A trend of improvement of physical performance by the short physical performance battery (SPPB) was observed for balance and walk subtests. A significant decrease (0.91 kg, P = 0.043) in Body Mass Index (BMI) was observed in overweight subjects (BMI >25 kg/m²) after DOREMI intervention in the entire population. The Mini Nutritional Assessment (MNA) score (1, P = 0.004) significantly increased after intervention, while waist measure (3 cm, P <0.001) significantly decreased in the DOREMI users. A reduction in glycated hemoglobin (Hb) was registered (0.20%, P = 0.018) in the DOREMI UK users.

Improvement of healthy behavior by technological tools, providing feedback between user and remote coach and increasing user’s motivation, appears potentially effective. This information and communication technologies (ICT) approach offers an innovative solution to stimulate healthy eating and lifestyle behaviors, supporting clinicians in patient management.

Hepatitis C virus (HCV) has a high prevalence worldwide, and the progression of the disease can cause irreversible damage to severe liver damage or even death. Therefore, developing prediction models using machine learning techniques is beneficial. This study was conducted to classify suspected patients with HCV infection using different classification models.

The study was conducted using a dataset derived from the University of California, Irvine (UCI) Machine Learning Repository. Since the HCV dataset was imbalanced, the synthetic minority oversampling technique (SMOTE) was applied to balance the dataset. After cleaning the dataset, it was divided into training and test data for developing six classification models. These six algorithms included the support vector machine (SVM), Gaussian Naïve Bayes (NB), decision tree (DT), random forest (RF), logistic regression (LR), and K-nearest neighbors (KNN) algorithm. The Python programming language was used to develop the classifiers. Receiver operating characteristic curve analysis and other metrics were used to evaluate the performance of the proposed models.

After the evaluation of the models using different metrics, the RF classifier had the best performance among the six methods. The accuracy of the RF classifier was 97.29%. Accordingly, the area under the curve (AUC) for LR, KNN, DT, SVM, Gaussian NB, and RF models were 0.921, 0.963, 0.953, 0.972, 0.896, and 0.998, respectively, RF showing the best predictive performance.

Various machine learning techniques for classifying healthy and unhealthy patients were used in this study. Additionally, the developed models might identify the stage of HCV based on trained data.

Continuous blood pressure (BP) monitoring provides additional information about how changes in BP may correlate with daily activities and sleep patterns. Recommendations from the American Heart Association and American College of Cardiology strongly suggest confirming a diagnosis of hypertension with continuous BP monitoring. Non-invasive and non-intrusive detection of haemodynamic parameters is emerging as a norm, based on self-monitoring wearable medical devices. Researchers have carried out several studies using non-invasive and continuous BP measurements as an alternative to conventional cuff-based measurements. In this work, we proposed a novel method for cuffless estimation of BP using impedance cardiography (ICG).

We conducted a single-centre, cross-sectional study of 104 subjects (of whom 30 were categorized as controls and the remaining 74 as the disease group) at the Medical College and Hospital, Kolkata. The disease group consisted of patients with confirmed coronary artery disease, while the individuals in the control group were deemed to be healthy. All subjects underwent electrocardiogram recording by on-duty doctors in order to determine their health status. A custom-made device based on the principle of impedance plethysmography was designed to record impedance changes due to subjects’ peripheral blood flow. The device was used to record ICG signals. In this study, we developed a novel auto-adaptive algorithm based on ICG signals for non-invasive, cuffless, continuous monitoring of BP and heart rate. Separate mathematical models were developed for all the estimated parameters (BP and heart rate) for both the study groups (control and disease). The developed models were auto-adaptive and did not require subject-specific calibration. Performance indicators including, r², error percentage, standard deviation, and mean difference were used to quantify the performance of the models.

The ICG signal recorded by the device was used to extract features and compute the augmentation index. The calculated augmentation index values showed strong correlations with systolic BP (r = 0.99, P < 0.05), diastolic BP (r = 0.95, P < 0.05), and heart rate (r = 0.78, P < 0.05). The models were also shown to have a high degree of accuracy for systolic and diastolic BP. Error margins were in the range ±2.33 and ±1.79 mmHg for systolic BP in disease and control subjects, respectively, and ±3.60 and ±1.82 mmHg for diastolic BP. However, the accuracy was lower in the prediction of heart rate in disease subjects, with a reported r² value of 0.72 and an error margin of ±2.88 beats per min; for healthy subjects, the results were marginally better, with an error margin of ±1.82 beats per min. All statistical analyses were performed using MATLAB (R2017a, MathWorks^®, USA).

In this study, we developed a non-invasive cuffless approach for estimation of systemic peripheral BP and heart rate using ICG. The proposed methodology eliminated any discomfort to patients caused by inflation of the cuff (in the case of cuff-based BP monitoring) or the need to constantly wear a fingertip photoplethysmography device (in the case of cuffless BP monitoring). The results obtained appeared promising and increased the potential scope of ICG for monitoring other haemodynamic parameters related to heart function.