Design of an mHealth System for Maternal and Children HIV care.

While progress has been made to slow down its spread and increase uptake of treatment, human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV/AIDS) is still a highly significant health problem for many low- and middle-income countries (LMICs). Specifically, almost half of new HIV patients in Asia Pacific were children. The prevention of mother-to-child transmission faces complex socioeconomic and other problems. With the increasing growth of mobile technologies in LMICs, especially in Asia Pacific, mHealth, the application of mobile technology for health applications, has a significant potential to help alleviate these problems. In this paper, we propose the design of an mHealth System for Maternal and Children HIV care. It includes specialized portals for patients, family/community members, healthcare providers, healthcare referral system, payers and drug supply chain. While each portal is customized towards the needs of a particular actor, such as treatment scheduling and education for patients, and epidemiological data management for healthcare referrals, all the different elements are integrated through a central server to form an integrated system with a secured data exchange environment. This proposed integrative design is aimed to facilitate efficient, timely and coordinated information dissemination, analysis, and care across the healthcare system, and is intended for application in developing countries, especially in the Asia Pacific region.

Tablet-Based Well-Being Check for the Elderly: Development and Evaluation of Usability and Acceptability.

BACKGROUND: Many elderly people prefer to live at home independently. One of the major concerns raised by the family members is the safety and well-being of their elderly family members when living independently in a home environment. To address this issue, assistive technology solutions have been available in the market. Despite their availability and proliferation, these types of solutions are not popular with the elderly due to their intrusive nature, privacy-related issues, social stigma, and fear of losing human interaction. This study shares the experience in the development of a digital photo frame system that helps family members to check the well-being of the elderly, exploiting their desire to remain socially connected. OBJECTIVES: The aim of this study was to iteratively design, implement, and assess the usability, user friendliness, and acceptability of a tablet-based system to check the well-being of the elderly. METHODS: Our study methodology comprises three separate stages: initial system development, contextual assessment, and comparative case study evaluation. RESULTS: In the first stage, requirements were elicited from the elderly to design a well-being check prototype. In the second stage, areas for improvements (eg, privacy features) were identified. Also, additional features (such as medication prompts or food reminders) were suggested to help aged and health care service providers with effective but subtle monitoring of the elderly. These would lower their operating cost by reducing visits by care providers to the homes of the elderly. In the third stage, the results highlighted the difference (between users in India and Australia) in the levels of familiarity of the elderly with this technology. Some elderly participants at the Kalyani Institute for Study, Planning and Action for Rural Change, India latched onto this technology quickly while a few refused to use the system. However, in all cases, the support of family members was crucial for their willingness to use the technology. CONCLUSIONS: This project has three major outcomes. First, a picture frame prototype was tested with the elderly to leverage the benefits of social communication. Second, the project helped us test and implement the "Silvercare" model for supporting the elderly through young retired people residing in the area. Finally, the project helped formalize the agile three-stage design methodology to develop information technology solutions for the elderly. Also, the project contributed to an ongoing European Union Project called Victoryahome, which involves more than 50 sites across 5 countries (Norway, Sweden, Netherlands, Portugal, and Australia) to assess the use of telepresence robots, wearable fall detectors, and medication dispensers for the elderly living independently.

Emerging ICT implementation issues in aged care.

BACKGROUND: Demand for aged care services continues to soar as a result of an aging population. This increasing demand requires more residential aged care facilities and healthcare workforce. One recommended solution is to keep older people in their homes longer and support their independent life through the use of information and communication technologies (ICT). However, the aged care sector is still in the early stages of adopting ICT. OBJECTIVE: The aim of this study was to identify the key issues that affect the adoption of ICT in the aged care sector. METHODS: A systematic literature review was undertaken and involved four steps. The first two steps aimed to identify and select relevant articles. Data was then extracted from the selected articles and identified issues were analyzed and grouped into three major categories. RESULTS: ICT adoption issues were categorized into different perspectives, representing older people, health professionals and management. Our findings showed that all three groups were mostly concerned with issues around behavior, cost and lack of technical skills. DISCUSSION AND CONCLUSIONS: Findings reported in this study will help decision makers at aged care settings to systematically understand issues related to ICT adoption and thus proactively introduce interventions to improve use of ICT in this sector. On the basis of our findings, we suggest future research focus on the examination of aged care workflow and assessment of return on ICT investment.

Design of an unobtrusive system for fall detection in multiple occupancy residences.

A small trial was conducted to examine the feasibility of detecting falls using a combination of ambient passive infrared (PIR) and pressure mat (PM) sensors in a home with multiple occupants. The key tracking method made use of graph theoretical concepts to track each individual in the residence and to monitor them independently for falls. The proposed algorithm attempts to recognize falls where the subject experiences a hard fall on an indoor surface that leads to loss of consciousness or an inability to get up from the floor without assistance, due to severe injuries. The sensitivity, specificity and accuracy of the algorithm in detecting falls are 85.00%, 80.00% and 82.86%, respectively.

Simulated unobtrusive falls detection with multiple persons.

One serious issue related to falls among the elderly living at home or in a residential care facility is the "long lie" scenario, which involves being unable to get up from the floor after a fall for 60 min or more. This research uses a simulated environment to investigate the potential effectiveness of using wireless ambient sensors (dual-technology (microwave/infrared) motion detectors and pressure mats) to track the movement of multiple persons and to unobtrusively detect falls when they occur, therefore reducing the rate of occurrence of "long lie" scenarios. A path-finding algorithm (A*) is used to simulate the movement of one or more persons through the residential area. For analysis, the sensor network is represented as an undirected graph, where nodes in the graph represent sensors, and edges between nodes in the graph imply that these sensors share an overlapping physical region in their area of sensitivity. A second undirected graph is used to represent the physical adjacency of the sensors (even where they do not overlap in their monitored regions). These graphical representations enable the tracking of multiple subjects/groups within the environment, by analyzing the sensor activation and adjacency profiles, hence allowing individuals/groups to be isolated when multiple persons are present, and subsequently monitoring falls events. A falls algorithm, based on a heuristic decision tree classifier model, was tested on 15 scenarios, each including one or more persons; three scenarios of activity of daily living, and 12 different types of falls (four types of fall, each with three postfall scenarios). The sensitivity, specificity, and accuracy of the falls algorithm are 100.00%, 77.14%, and 89.33%, respectively.

Software simulation of unobtrusive falls detection at night-time using passive infrared and pressure mat sensors.

Falls and their related injuries are a major challenge facing elderly people. One serious issue related to falls among the elderly living at home is the 'long-lie' scenario, which is the inability to get up from the floor after a fall, followed by lying on the floor for 60 minutes, or more. Several studies of accelerometer and gyroscope-based wearable falls detection devices have been cited in the literature. However, when the subject moves around at night-time, such as making a trip from the bedroom to the toilet, it is unlikely that they will remember or even feel an inclination to wear such a device. This research will investigate the potential usefulness of an unobtrusive fall detection system, based on the use of passive infrared sensors (PIRs) and pressure mats (PMs), that will detect falls automatically by recognizing unusual activity sequences in the home environment; hence, decreasing the number of subjects suffering the 'long-lie' scenario after a fall. A Java-based wireless sensor network (WSN) simulation was developed. This simulation reads the room coordinates from a residential map, a path-finding algorithm (A*) simulates the subject's movement through the residential environment, and PIR and PM sensors respond in a binary manner to the subject's movement. The falls algorithm was tested for four scenarios; one scenario including activities of daily living (ADL) and three scenarios simulating falls. The simulator generates movements for ten elderly people (5 female and 5 male; age: 50-70 years; body mass index: 25.85-26.77 kg/m(2)). A decision tree based heuristic classification model is used to analyze the data and differentiate falls events from normal activities. The sensitivity, specificity and accuracy of the algorithm are 100%, 66.67% and 90.91%, respectively, across all tested scenarios.