Adamson University

Introduction: Medication non-adherence remains a major challenge in hypertension management among middleaged and older adults. Reminder systems can support timely medication use, but many existing tools lack usability and healthcare workflow integration. REM, derived from the word 'reminder', was developed as an integrated medication adherence support system connecting patients, physicians, and pharmacists through a mobile application, smart pillbox, and wearable device. This study explored stakeholders' perceived usefulness, ease of use, behavioral intention to use REM, and recommendations for improvement. Methods: This qualitative formative study used semi-structured interviews with hypertensive middle-aged and older adult patients (n = 20), physicians (n = 10), pharmacists (n =10), and information technology (IT) experts (n = 10). The interview guide was informed by the Technology Acceptance Model and examined perceptions of system usefulness, ease of use, intention to use, feasibility, and recommended refinements. Data were coded and organized using MAXQDA. Results: Participants perceived the REM system as useful and easy to use for supporting medication adherence and patient monitoring. Most participants were willing to use or recommend REM, although acceptance depended on physician endorsement, affordability, insurance coverage, and workflow compatibility. Suggested refinements included secure communication, improved portability, offline functionality, and enhanced data security. Conclusion: REM was perceived as a useful and easy-to-use digital health intervention with potential to support medication adherence among middle-aged and older adults with hypertension. Findings highlight the importance of user-centered design, stakeholder integration, affordability, workflow compatibility, and robust data security in promoting future adoption and implementation. Public interest summary: Middle-aged to older adults with high blood pressure often forget to take their medication on time. To address this, the REM system was developed to improve their medication compliance. REM was derived from the word "reminder". The system consists of a mobile application, a smart pillbox, and a wearable device that work together to help patients remember their medication. Physicians and pharmacists can also access patients' prescriptions and medication intake schedules to monitor their progress and compliance. In this study, patients, physicians, and pharmacists, including IT experts, were interviewed to express their thoughts about REM. Most participants were also willing to use and recommend REM for its usefulness. Suggested improvements included chat support, offline accessibility, portability, and stronger privacy and security features. In conclusion, the REM system shows potential as a user-friendly tool for improving medication adherence and supporting better health outcomes.

This study investigated the behavior of concrete-filled steel tubes (CFST) under extreme fire conditions, focusing on two key fire performance indicators: the fire resistance rating (FRR) and residual strength index (RSI). Advanced prediction models were developed using neural networks optimized with a particle swarm optimization algorithm. A comprehensive experimental database and a diverse range of neural network architectures were utilized. The models demonstrated superior predictive accuracy, as validated through multiple performance metrics and comparisons with existing prediction equations. Furthermore, causal inference techniques were applied to identify the influence and relative importance of each variable. Visualization tools were instrumental in uncovering patterns and correlations that would be difficult to detect through numerical data alone. The proposed FRR and RSI models offer a cost-effective, non-destructive method for assessing and designing CFST elements in concrete structures.

BackgroundGender-affirming hormone therapy (GAHT) is a critical component of gender-affirming care that supports transgender individuals in developing physical characteristics aligned with their gender identity and improving psychosocial well-being. While global evidence highlights its benefits, limited research has examined the lived experiences of transgender individuals undergoing GAHT in the Philippines, where access remains constrained by structural and sociocultural barriers.MethodsThis qualitative study employed an interpretative phenomenological approach to explore the lived experiences of Filipino transgender individuals undergoing GAHT. Eighteen transgender adults (11 trans men and 7 trans women), aged 20-40 years, were recruited using purposive sampling. In-depth semi-structured interviews were conducted between March and May 2024. Data were analyzed using reflexive thematic analysis, supported by MAXQDA software.ResultsThree major themes emerged. Navigating structural barriers and embodied uncertainty captured financial constraints, limited access to care, stigma, and reliance on self-directed hormone use. Stepping into a second puberty reflected participants' experiences of bodily, emotional, and sexual changes following hormone initiation, including voice changes, breast development, libido shifts, and mood fluctuations, often requiring trial-and-error adjustments. Becoming the true self highlighted the transformative effects of GAHT, including enhanced self-recognition, improved well-being, and strengthened engagement in transgender communities.ConclusionGAHT was experienced as both a medically and socially transformative process. Despite persistent structural barriers, it enabled participants to achieve greater alignment between their bodies and identities. Expanding accessible, culturally competent gender-affirming care and strengthening community support systems are essential to improving the safety and continuity of GAHT in the Philippines.

The Internet of Things (IoT) has gained significant popularity through wearable technologies. Several IoT devices are used in educational institutions, smart homes, and security management. Their widespread use in sports training applications using IoT (WS-IoT)-based wearable sensors is noteworthy. Meanwhile, artificial intelligence (AI) has evolved rapidly, and its applications have expanded across many diverse fields. Over the past few years, China has placed greater emphasis on physical education to enhance students' physical fitness and overall capabilities. A significant number of colleges and universities have adopted artificial intelligence to transform traditional single-mode sports training methods. This paper introduces a continuous monitoring system for sports individuals using the WS-IoT. The goal of coaches is to maximize the results of athlete training across various sports events using AI technology and their sports expertise. In this study, we examine the use of AI technology in sports and assess the practical implications of its implementation. To achieve the objective function, we used a Bayesian deep classifier and deep learning algorithms that utilize adaptive optimization. Utilizing artificial intelligence in Chinese educational institutions improves training efficiency and effectiveness. The system was then rigorously tested and analyzed in terms of the quality-of-service parameters, including communication collisions and packet loss rates. It is shown that packet loss rates increase with increasing packet numbers, but collision rates remain unchanged. Furthermore, a practical application of the system in colleges and universities showed that 14.4% of the students were extremely satisfied with the use of teaching materials, 24.5% were satisfied with the materials, 30.1% were satisfied with the training methods, and 17.9% were dissatisfied.

Beam failure due to shear is particularly important in deteriorating structures, highlighting the need for prediction models to assess the impact of reinforcement corrosion on shear strength. Hence, this study focused on developing a robust predictive model to predict the shear strength of corrosion damaged beam. Machine learning approach was employed to capture the dominating material interactions and to accommodate a broader range of shear strength parameters. Parametric investigation was employed to systematically examine the relative contribution of each parameter on the overall behavior of shear strength. Various performance measures revealed that the proposed hybrid neuro-swarm model was found to be superior to other existing shear prediction equations. Using the causal inference procedure and parametric analysis, the effects and relative importance of each parameter on shear strength were explored. The parametric investigation offered clear and intuitive analysis that facilitated the identification of underlying patterns and correlations, which would have been challenging to recognize or interpret accurately through numerical analysis alone. The neuro-swarm model can be used to reasonably predict the shear strength of corroded beams and is vital in assessing the durability of deteriorating reinforced concrete structures. The results of the model can be used as a reliable baseline information for implementing appropriate remedial measures to prolong the service life of deteriorating structures. This is study is the first to employ machine learning techniques, particularly neuro-swarm algorithm, to develop a robust model to predict the shear strength of corroded reinforced concrete beam.