Instructional technologies used to be optional and supplemental pedagogical tools until the global health crisis of 2020 compelled education systems to rely on digital devices and services to guarantee academic continuity. Suddenly, the contemporary principles and practices utilized in delivering health education curricula were insufficient and ineffective. Acknowledging the vital role of technology in shaping the future of education, there is now a greater demand to foster innovative interventions and continuous improvement in strategies, methodologies, and systems to empower learners, educators, and leaders in the digital age. This paradigm shift requires a fundamental transformation in the way we approach teaching and learning, and a willingness to embrace new approaches and tools that can enhance the quality of education and support student success. The Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines provides comprehensive coverage of innovative methods and strategies to produce the next generation of health professionals. The book lays the groundwork for implementable teaching and learning models that facilitate knowledge acquisition, enhance perceptual variation, improve skill coordination, and develop a scientific and technological mindset. Each chapter provides an in-depth examination of instructional technologies contextualized in various medical and health domains, including nursing, physiotherapy, radiology, neurophysiology, physical health, dentistry, clinical medicine, and more. This reference work is a must-read for all stakeholders in health education and related fields, including educators, students, researchers, administrators, and healthcare professionals.

The book Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines provides comprehensive coverage of innovative methods and strategies to produce the next generation of health professionals. It lays the groundwork for implementable teaching and learning models that facilitate knowledge acquisition, enhance perceptual variation, improve skill coordination, and develop a scientific and technological mindset. It is designed to provide an in-depth look at the latest advancements and best practices in the field, with a specific emphasis on improving student learning outcomes and professional development. Each chapter provides an in-depth examination of instructional technologies, including artificial intelligence (AI), machine learning, gamification, telemedicine, chatbots, virtual and augmented realities, bioprinting, computer-assisted instruction (CAI), mobile health, smart learning environments, intelligent tutoring systems (ITS), and more. In addition, these chapters illustrate how these educational technologies can be effectively developed and implemented in real-world settings that are contextualized in various medical and health domains, including nursing, physiotherapy, radiology, neurophysiology, physical health, dentistry, clinical medicine, and more.

Children often make unhealthy food choices because they lack nutrition knowledge. This chapter consequently assessed the potential employment of a nutrition intelligent tutoring system (NutritionITS) for teaching and learning basic nutrition concepts in primary education. Following the K-12 Health Curriculum Guide by the Department of Education, NutritionITS incorporated the first quarter content in the grade 1 level. Using an exploratory sequential mixed methods design, it was evaluated by teachers and parents. The qualitative phase served as the participatory design process that extracted the features needed by teachers. On the other hand, the quantitative phase served as the prototype evaluation using the technology acceptance model constructs. The results presented in this chapter may assist educational leaders, teachers, parents, and students in achieving a better learning outcome in nutrition education. In addition to its contribution to the literature of educational research, this is the first study to develop an intelligent tutoring system in the field of nutrition science.

Empirical evidence has demonstrated the benefits of physical activity in preventing chronic diseases and premature death. Unfortunately, there is a global trend of insufficient physical activity, which was aggravated by the recent global pandemic. Although physical education is often used to promote physical activity, the transition to online education made it difficult to teach fitness and exercise from a distance due to several limiting factors. This chapter aims to respond to these challenges by implementing a school-based public health intervention using a computer-assisted instruction (CAI) tool called VD-CAI. Through an experimental approach, it was found that VD-CAI as an instructional technology shows a performance advantage compared to other pedagogies. Specifically, students who used VD-CAI in their physical education courses received higher scores and exhibited a more positive attitude. This chapter contributes to the growing scientific evidence of the effectiveness of school-based health education programs as well as the expanding literature on CAI and physical education.

Over the past decade, several studies have been conducted to discover a better-performing multipath clustering technique. Developing a multipath clustering technique with better accuracy performance is a big challenge considering the varying properties of the multipath propagations that change over time. In this study, several clustering techniques have been investigated and compared to the newly-developed technique for performance analysis. Using the Jaccard score as a metric for the accuracy of grouping correctly the wireless multipaths, the performance of the different clustering techniques has been determined and compared to the newly-developed technique. The proposed clustering algorithm shows improved performance in the indoor channel scenarios but needs further investigation in the semi-urban environment.

An attachable exoskeleton in backpacks for postural correction by using pressure sensors has been designed and implemented. The system determines the changes in body posture and the selsyn control for the proper posture of shoulder and spine correction. The design of the prototype is based on an exoskeleton which is a backpack, having straps with a selsyn motor, pressure sensors, and portable battery or power bank. The system's functionality and reliability were tested by recording the slouching angle of five test subjects. The study also aims to monitor the improvement of posture in the duration of 2 weeks upon the usage of the system. Pre-test and post-test of posture were evaluated by the physical therapist for improvement in posture and showed a significant reduction of the number of slouches per day for the five test subjects. The four test subjects failed to reach more than 40% reduction of number of slouches, but one subject attained 39.4% reduction. The system is an active device in reminding the subjects to maintain proper posture while most of the posture correctors available in the market are passive. The prototype was able to accomplish what it was designed and created for, which is to reduce the number of slouches for the improvement of the user's posture.

E-commerce shows steady growth in the marketplace since it makes the lives of people easier. Today’s generation is more inclined toward convenience in purchasing goods. The COVID-19 shut down many food establishments across the Philippines, resulting in an online bakeries boom. Pastries are one of the most sought goods online, especially with the pandemic surge where physical stores are seldomly open. E-commerce with a recommender system is the trend that helps customers choose products, which helps in decision-making on what to purchase. On the other hand, a mobile application counterpart could increase brand recognition and customer engagement as it is now the most effective, direct, and personalized way to deliver product information. In this study, the descriptive research method was used, with a questionnaire using the functionality, usability, reliability, performance and supportability (FURPS) serving as the instrument for testing the acceptability. The overall quality of the system was given an acceptable rating with a weighted mean of 4.07, indicating that the system’s functions were well integrated, that navigation was simple, performed consistently, and that the system was accessible regardless of device.

While several content management systems (CMS) and audience analytics tools are available for digital signage in the market, they are often sold separately and can be expensive. Therefore, this project aims to design a cost-effective and unified web-based solution for digital signage that combines content management and audience analytics functions, reducing the need for multiple purchases. This can be achieved by utilizing Raspberry Pi technology, known for its cost-effectiveness and versatility in integrations, along with a face recognition camera and machine learning methods. This proof of concept demonstrates the integration of these components to create a dynamic digital signage system. Overall, this project has the potential to offer an affordable solution for companies aiming to efficiently manage and optimize their digital marketing strategies, especially in the Digital Out-of-Home (DOOH) Advertising space.

Relative humidity is an important environmental parameter and is widely used in various fields. Prediction of humidity levels is crucial for climate modeling, heat stress, air quality forecasting, and public health. Machine learning techniques have shown potential for predicting humidity due to their nonlinear nature. However, there is a research gap in humidity prediction in the Philippines, specifically the lack of studies utilizing the available parameters provided by PAGASA, presenting an opportunity for further investigation and development of models for predicting humidity levels in the country. In this study, the researchers used a publicly available dataset from PAGASA containing weather measurements from 2000 to 2022 in the Philippines. Various machine learning models were trained and tested, with hyperparameter tuning performed using Bayesian optimization. The Gaussian Process Regression model with optimized hyperparameters achieved the best performance in predicting relative humidity, with the lowest RMSE and highest R-squared values. This study provides a reliable way to predict humidity levels in the Philippines based on weather parameters.

Online shopping has become one of the most prominent forms of retail for businesses. This is due to the advancement of web services, and mobile applications that have become accessible, and effective with the utilization of the Internet. Accordingly, this study aims to further scrutinize the discussed application of online shopping. Therefore, an online retail system with mobile application through Android was developed, deployed with the purpose of managing the products, and services that are offered by the company, with the standardization of data forecasting to make accurate prediction of future trends. To standardize and validate the attributes of the said system, a descriptive research method that used a survey instrument based on the Likert scale, and the functionality, usability, reliability, performance, and supportability (FURPS) model. The said survey instrument collected 200 responses with purposeful sampling treatment and converted into distinct inputs with the use of the weighted mean formula. The functionality, usability, and reliability were rated as acceptable, with weighted means of 4.5, 4.5, and 4.5, respectively. The performance and supportability were rated as perfectly acceptable, with weighted mean scores of 4.7 and 4.6, accordingly. The system’s overall attributes were rated perfectly acceptable, with a weighted mean of 4.6, suggesting that it managed and analyzed sales, services, and inventory data.