Appropriate support in the home may not be readily available for people living in the community with mental illness and physical comorbidities. This mixed-method study evaluated a smart home technology intervention for individuals within this population as well as providing health care providers with health monitoring capabilities. The study recruited 13 participants who were offered a smartphone, a touchscreen monitor, and health devices, including smartwatches, weigh scales, and automated medication dispensers. Healthcare providers were able to track health device data, which were synchronized with the Lawson Integrated DataBase. Participants completed interviews at baseline as well as at 6-month and 12-month follow-ups. Focus groups with participants and care providers were conducted separately at 6-month and 12-month time points. As the sample size was too small for meaningful statistical inference, only descriptive statistics were presented. However, the qualitative analyses revealed improvements in physical and mental health, as well as enhanced communication with care providers and friends/family. Technical difficulties and considerations are addressed. Ethics analyses revealed advancement in equity and fairness, while policy analyses revealed plentiful opportunities for informing policymakers. The economic costs are also discussed. Further studies and technological interventions are recommended to explore and expand upon in-home technologies that can be easily implemented into the living environment.

This study evaluated a smart technology intervention in the home as a support for individuals with severe mental illness. This study recruited 13 participants in a variety of community-based homes. Participants were offered a smartphone, a touchscreen monitor and health devices such as smartwatches, weigh-scales, and automated medication dispensers. Data was exported to the Lawson Integrated DataBase for care providers to monitor/track. Interviews with participants and focus groups with participants and care providers were conducted at baseline, 6-months and 12-months, and survey instruments were used to collect quantitative data about different dimensions of health and social determinants. Descriptive statistics from these outcome measures are presented as the sample size was too small for meaningful statistical inference. Qualitative analyses revealed a high degree of acceptability of the devices and motivation for healthy living, communication and mental health. Health Care Providers also noted improvements to client health. This study proves the feasibility of deploying smart technologies to support individuals with severe mental illness. Future scale-up would further our understanding of their impacts.

Smart home technologies present an unprecedented opportunity to improve health and health care by providing greater communication and connectivity with services and care providers and by supporting the daily activities of people managing both mental and physical health problems. Based on our experience from conducting smart technology health studies, including a smart home intervention, we provide guidance on developing and implementing such interventions. First, we describe the need for an overarching principle of security and privacy that must be attended to in all aspects of such a project. We then describe 4 key steps in developing a successful smart home innovation for people with mental and physical health conditions. These include (1) setting up the digital infrastructure, (2) ensuring the components of the system communicate, (3) ensuring that the system is designed for the intended population, and (4) engaging stakeholders. Recommendations on how to approach each of these steps are provided along with suggested literature that addresses additional considerations, guidelines, and equipment selection in more depth.

The overall objective of this research was to develop and test the use of smart technology in delivering safe, effective mental health services before expanding into community homes. A system was created that linked multiple screen devices such as smartphones and tablets and health monitoring devices with a central secure database for data to be funneled and stored for monitoring and tracking. In order to assess the feasibility of this technological innovation, the research team installed equipment in two prototype apartments at two inpatient psychiatric hospitals and in up to eight community homes operated by the Canadian Mental Health Association and London Middlesex Community Housing. The results indicate that most participants found the technology acceptable, and that the system was successfully able to export data securely.

SummaryA data providing service (DPS) in service‐oriented architecture is tasked only with the retrieval of data that are annotated over a domain ontology. One particular motivating application of DPSs is their use within collaborative environments. An important characteristic for the enterprises of such a collaborative environment is the ability to employ data sharing with one another. A major concern in this situation is the protection of each enterprise's privacy while still permitting data sharing. One potential solution is to provide filtered data through access control. This work describes how to implement access control through fine‐grained filtering of DPS response messages; it is accomplished using a filtering ontology and relations between the domain ontology of DPS and the proposed filtering ontology. Therefore, enterprises can write enterprise‐specific access control policies referencing a common filtering ontology defined within a collaborative environment, enabling access control‐based data sharing within the environment. This work additionally illustrates the implementation of our general solution to data providing web services, interpreted by an eXtensible Access Control Markup Language‐based access control framework. The implementation is further evaluated in a case study of real world data, provided by a health research institute in London, Canada. Copyright © 2013 John Wiley & Sons, Ltd.

This work describes how a fine-grained filtering Web service is generated using an extended XACML architecture for Data Providing Web Services (DPWSs). A DPWS is a Web service tasked with the retrieval of data that is annotated using a domain ontology. This work extends XACML policies to reference filtering classes from a filtering ontology instead of directly referencing fine-grained XML elements. This paper then contributes a language to state relations between the filtering ontology and the DPWS's domain ontology. It also contributes an algorithm to interpret these relations to determine if a given XML element from a DPWS is a member of a filtering class. This algorithm is then used by the extended XACML architecture to infer how a DPWS shall be filtered. This approach provides a clear separation of concerns between a DPWS's data domain and the filtering domain.