Sea Horse Optimization-Deep Neural Network: A Medication Adherence Monitoring System Based on Hand Gesture Recognition.

Medication adherence is an essential aspect of healthcare for patients and is important for achieving medical objectives. However, the lack of standard techniques for measuring adherence is a global concern, making it challenging to accurately monitor and measure patient medication regimens. The use of sensor technology for medication adherence monitoring has received much attention lately since it makes it possible to continuously observe patients' medication adherence behavior. Sensor devices or smart wearables utilize state-of-the-art machine learning (ML) methods to analyze intricate data patterns and provide predictions accurately. The key aim of this work is to develop a sensor-based hand gesture recognition model to predict medication activities. In this research, a smart sensor device-based hand gesture prediction model is developed to recognize medication intake activities. The device includes a tri-axial gyroscope, geometric, and accelerometer sensors to sense and gather data from hand gestures. A smartphone application gathers hand gesture data from the sensor device, which is then stored in the cloud database in a .csv format. These data are collected, processed, and classified to recognize the medication intake activity using the proposed novel neural network model called Sea Horse Optimization-Deep Neural Network (SHO-DNN). The SHO technique is implemented to update the biases and weights and the number of hidden layers in the DNN model. By updating these parameters, the DNN model is improved in classifying the samples of hand gestures to identify the medication activities. The research model demonstrates impressive performance, with an accuracy of 98.59%, sensitivity of 97.82%, precision of 98.69%, and an F1 score of 98.48%. Hence, the proposed model outperformed the most available models in all the aforementioned aspects. The results indicate that this model is a promising approach for medication adherence monitoring in healthcare applications, instilling confidence in its effectiveness.

Practical guide: Glucagon-like peptide-1 and dual glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 receptor agonists in diabetes mellitus.

In 2005, exenatide became the first approved glucagon-like peptide-1 receptor agonist (GLP-1 RA) for type 2 diabetes mellitus (T2DM). Since then, numerous GLP-1 RAs have been approved, including tirzepatide, a novel dual glucose-dependent insulinotropic polypeptide (GIP)/GLP-1 RA, which was approved in 2022. This class of drugs is considered safe with no hypoglycemia risk, making it a common second-line choice after metformin for treating T2DM. Various considerations can make selecting and switching between different GLP-1 RAs challenging. Our study aims to provide a comprehensive guide for the usage of GLP-1 RAs and dual GIP and GLP-1 RAs for the management of T2DM.

Adherence to Advisory Committee on Immunization Practices in diabetes mellitus patients in Saudi Arabia: A multicenter retrospective study.

BACKGROUND: Patients with diabetes mellitus (DM) are predisposed to an increased risk of infection signifying the importance of vaccination to protect against its potentially severe complications. The Centers for Disease Control and Prevention/Advisory Committee on Immunization Practices (CDC/ACIP) issued immunization re-commendations to protect this patient population. AIM: To assess the adherence of patients with DM to the CDC/ACIP immunization recommendations in Saudi Arabia and to identify the factors associated with the vaccine adherence rate. METHODS: An observational retrospective study conducted in 2023 was used to collect data on the vaccination records from 13 diabetes care centers in Saudi Arabia with 1000 eligible patients in phase I with data collected through chart review and 709 patients in phase II through online survey. RESULTS: Among participants, 10.01% (n = 71) had never received any vaccine, while 85.89% (n = 609) received at least one dose of the coronavirus disease 2019 (COVID-19) vaccine, and 34.83% (n = 247) had received the annual influenza vaccine. Only 2.96% (n = 21), 2.11% (n = 15), and 1.12% (n = 8) received herpes zoster, tetanus, diphtheria, and pertussis (Tdap), and human papillomavirus (HPV) vaccines, respectively. For patients with DM in Saudi Arabia, the rate of vaccination for annual influenza and COVID-19 vaccines was higher compared to other vaccinations such as herpes zoster, Tdap, pneumococcal, and HPV. Factors such as vaccine recommendations provided by family physicians or specialists, site of care, income level, DM-related hospitalization history, residency site, hemoglobin A1c (HbA1c) level, and health sector type can significantly influence the vaccination rate in patients with DM. Among non-vaccinated patients with DM, the most reported barriers were lack of knowledge and fear of side effects. This signifies the need for large-scale research in this area to identify additional factors that might facilitate adherence to CDC/ACIP vaccine recommendations in patients with DM. CONCLUSION: In Saudi Arabia, patients with DM showed higher vaccination rates for annual influenza and COVID-19 vaccines compared to other vaccinations such as herpes zoster, Tdap, pneumococcal, and HPV. Factors such as vaccine recommendations provided by family physicians or specialists, the site of care, income level, DM-related hospitalization history, residency site, HbA1c level, and health sector type can significantly influence the vaccination rate in patients with DM.