The Effects of Performance Expectancy, Effort Expectancy, Facilitating Condition, and Habit on Behavior Intention in Using Mobile Healthcare Application

South Tangerang Health Office had the responsibility in giving outstanding healthcare services to its resident’s despite of its limitation due Covid-19 pandemic. Some programs were initiated to reduce maternal, babies and toddler mortality, and in the same time reduce the number of malnourished children. The integrated healthcare mobile application called Si Pandai Kemas TangSel had been launched and can be downloaded easily through smartphone. The study is expected to measure the effectiveness of Si Pandai Kemas TangSel using UTAUT approach by measuring the influence of performance expectancy, effort expectancy, facilitating condition and habit toward behavior intention in using Si Pandai Kemas TangSel mobile application. The study showed that the effort expectancy and habit can increase the intention to use Si Pandai Kemas TangSel application. Meanwhile, performance expectancy and facilitating conditions did not affect behavioral intention in using Si Pandai Kemas TangSel application.

Dynamic Application Partitioning and Task-Scheduling Secure Schemes for Biosensor Healthcare Workload in Mobile Edge Cloud

Currently, the use of biosensor-enabled mobile healthcare workflow applications in mobile edge-cloud-enabled systems is increasing progressively. These applications are heavyweight and divided between a thin client mobile device and a thick server edge cloud for execution. Application partitioning is a mechanism in which applications are divided based on resource and energy parameters. However, existing application-partitioning schemes widely ignore security aspects for healthcare applications. This study devises a dynamic application-partitioning workload task-scheduling-secure (DAPWTS) algorithm framework that consists of different schemes, such as min-cut algorithm, searching node, energy-enabled scheduling, failure scheduling, and security schemes. The goal is to minimize the energy consumption of nodes and divide the application between local nodes and edge nodes by applying the secure min-cut algorithm. Furthermore, the study devises the secure-min-cut algorithm, which aims to migrate data between nodes in a secure form during application partitioning in the system. After partitioning the applications, the node-search algorithm searches optimally to run applications under their deadlines. The energy and failure schemes maintain the energy consumption of the nodes and the failure of the system. Simulation results show that DAPWTS outperforms existing baseline approaches by 30% in terms of energy consumption, deadline, and failure of applications in the system.

Korea Mobile Healthcare National Program on Risk Factors of Metabolic Syndrome: A Multicenter Retrospective Study (Preprint)

BACKGROUND Following the confirmation of the effect of the pilot of the Korea Mobile Healthcare National Program in 2016, the program has been conducted annually since 2018 to improve the risk factors for metabolic syndrome. However, since this implementation began, the program has not been evaluated. OBJECTIVE The purpose of this study was to retrospectively investigate the results of the Korea Mobile Healthcare National Program in 2018 and 2019. METHODS Health behavior of participants with risk factors for metabolic syndrome were managed using a mobile app for 24 weeks and feedback was provided. Paired t-test and chi squared test were used for comparing results before and after 24 weeks. RESULTS Of the 8,712 participants enrolled in 2018, 7,619 completed the 24 weeks program while 10,990 of the 12,447 participants enrolled in 2019 completed the program. After the program, over 60% participants had improvement in one or more risk factors (63.09% in 2018, 65.85% in 2019). There was a statistically significant improvement in the proportion of participants who had each risk factor (blood pressure, fasting plasma glucose, triglyceride, high density lipoprotein cholesterol, and waist circumference) in both 2018 and 2019 (p < .001). The average value of each risk factor also improved in a positive direction. CONCLUSIONS This study showed that the Korea Mobile Healthcare National Program may contribute to improvement in the risk factors for metabolic syndrome.

A highly smart MEMS acetone gas sensors in array for diet-monitoring applications

In the present work, gas sensor arrays consisted of four different sensing materials based on CuO and their depositions on the MEMS microheaters were designed, fabricated and characterized. The sensor array is consisted with CuO, CuO with Pt NPs, ZnO–CuO and ZnO–CuO with Au NPs and their gas sensing properties are characterized for detection of exhaled breath-related VOCs. Through MEMS microheaters, power consumption is considered for application to healthcare devices which requires ultrasensitive acetone gas sensitivity. Also, using the principal component analysis, it enables to discriminate acetone gas, a biomarker for fat burning during diet, with other VOCs gases. The device would be applicable for on-site diet monitoring in the field of mobile healthcare.

Resource Allocation with Sigmoidal Demands: Mobile Healthcare Units and Service Adoption

Problem definition: Achieving broad access to health services (a target within the sustainable development goals) requires reaching rural populations. Mobile healthcare units (MHUs) visit remote sites to offer health services to these populations. However, limited exposure, health literacy, and trust can lead to sigmoidal (S-shaped) adoption dynamics, presenting a difficult obstacle in allocating limited MHU resources. It is tempting to allocate resources in line with current demand, as seen in practice. However, to maximize access in the long term, this may be far from optimal, and insights into allocation decisions are limited. Academic/practical relevance: We present a formal model of the long-term allocation of MHU resources as the optimization of a sum of sigmoidal functions. We develop insights into optimal allocation decisions and propose pragmatic methods for estimating our model’s parameters from data available in practice. We demonstrate the potential of our approach by applying our methods to family planning MHUs in Uganda. Methodology: Nonlinear optimization of sigmoidal functions and machine learning, especially gradient boosting, are used. Results: Although the problem is NP-hard, we provide closed form solutions to particular cases of the model that elucidate insights into the optimal allocation. Operationalizable heuristic allocations, grounded in these insights, outperform allocations based on current demand. Our estimation approach, designed for interpretability, achieves better predictions than standard methods in the application. Managerial implications: Incorporating the future evolution of demand, driven by community interaction and saturation effects, is key to maximizing access with limited resources. Instead of proportionally assigning more visits to sites with high current demand, a group of sites should be prioritized. Optimal allocation among prioritized sites aims at equalizing demand at the end of the planning horizon. Therefore, more visits should generally be allocated to sites where the cumulative demand potential is higher and counterintuitively, often those where demand is currently lower.