109,053 research outputs found

    Barriers to older adults’ uptake of mobile-based mental health interventions

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    Background To address increasing demand of mental healthcare treatments for older adults and the need to reduce delivery costs, healthcare providers are turning to mobile applications. The importance of psychological barriers have been highlighted in the uptake of mobile-based mental health interventions and efforts have been made to identify these barriers in order to facilitate initial uptake and acceptance. However, limited research has focused on older adults’ awareness of these applications and factors that might be hindering their use. Objective The purpose of this study was to explore the perceived barriers that older adults experience in the uptake of mobile-based mental health interventions. Methods Semi-structured interviews were conducted with a sample of 10 older adults, 50 years or older (female = 7, mean age = 68 years), who experienced periods of low mood. National Health Service applications were demonstrated to facilitate conversation and explore participants’ understanding of mental health and mobile-based mental health interventions. Thematic analysis was used to analyse the interview transcripts. Results The social ecological model was adopted as an organising framework for the thematic analysis which identified six distinct barriers to older adults’ uptake of mobile-based mental health interventions: mental electronic-health (e-health) awareness, interaction with technology, discontinuation, ‘seeing’ facilitates therapeutic alliance, incongruent role of the general practitioner and privacy and confidentiality. Conclusions Older adults experience a number of barriers to uptake ranging from the individual level to a macro, organisational level. The practical implications of these barriers are discussed such as the need for increased awareness of mobile-based mental health interventions among older adults

    RoboChain: A Secure Data-Sharing Framework for Human-Robot Interaction

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    Robots have potential to revolutionize the way we interact with the world around us. One of their largest potentials is in the domain of mobile health where they can be used to facilitate clinical interventions. However, to accomplish this, robots need to have access to our private data in order to learn from these data and improve their interaction capabilities. Furthermore, to enhance this learning process, the knowledge sharing among multiple robot units is the natural step forward. However, to date, there is no well-established framework which allows for such data sharing while preserving the privacy of the users (e.g., the hospital patients). To this end, we introduce RoboChain - the first learning framework for secure, decentralized and computationally efficient data and model sharing among multiple robot units installed at multiple sites (e.g., hospitals). RoboChain builds upon and combines the latest advances in open data access and blockchain technologies, as well as machine learning. We illustrate this framework using the example of a clinical intervention conducted in a private network of hospitals. Specifically, we lay down the system architecture that allows multiple robot units, conducting the interventions at different hospitals, to perform efficient learning without compromising the data privacy.Comment: 7 pages, 6 figure

    Information Accountability Framework for a Trusted Health Care System

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    Trusted health care outcomes are patient centric. Requirements to ensure both the quality and sharing of patients’ health records are a key for better clinical decision making. In the context of maintaining quality health, the sharing of data and information between professionals and patients is paramount. This information sharing is a challenge and costly if patients’ trust and institutional accountability are not established. Establishment of an Information Accountability Framework (IAF) is one of the approaches in this paper. The concept behind the IAF requirements are: transparent responsibilities, relevance of the information being used, and the establishment and evidence of accountability that all lead to the desired outcome of a Trusted Health Care System. Upon completion of this IAF framework the trust component between the public and professionals will be constructed. Preservation of the confidentiality and integrity of patients’ information will lead to trusted health care outcomes

    Privacy in crowdsourcing:a systematic review

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    The advent of crowdsourcing has brought with it multiple privacy challenges. For example, essential monitoring activities, while necessary and unavoidable, also potentially compromise contributor privacy. We conducted an extensive literature review of the research related to the privacy aspects of crowdsourcing. Our investigation revealed interesting gender differences and also differences in terms of individual perceptions. We conclude by suggesting a number of future research directions.</p

    MOSAIC roadmap for mobile collaborative work related to health and wellbeing.

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    The objective of the MOSAIC project is to accelerate innovation in Mobile Worker Support Environments. For that purpose MOSAIC develops visions and illustrative scenarios for future collaborative workspaces involving mobile and location-aware working. Analysis of the scenarios is input to the process of road mapping with the purpose of developing strategies for R&D leading to deployment of innovative mobile work technologies and applications across different domains. One of the application domains where MOSAIC is active is health and wellbeing. This paper builds on another paper submitted to this same conference, which presents and discusses health care and wellbeing specific scenarios. The aim is to present an early form of a roadmap for validation

    Architecture and Implementation of a Trust Model for Pervasive Applications

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    Collaborative effort to share resources is a significant feature of pervasive computing environments. To achieve secure service discovery and sharing, and to distinguish between malevolent and benevolent entities, trust models must be defined. It is critical to estimate a device\u27s initial trust value because of the transient nature of pervasive smart space; however, most of the prior research work on trust models for pervasive applications used the notion of constant initial trust assignment. In this paper, we design and implement a trust model called DIRT. We categorize services in different security levels and depending on the service requester\u27s context information, we calculate the initial trust value. Our trust value is assigned for each device and for each service. Our overall trust estimation for a service depends on the recommendations of the neighbouring devices, inference from other service-trust values for that device, and direct trust experience. We provide an extensive survey of related work, and we demonstrate the distinguishing features of our proposed model with respect to the existing models. We implement a healthcare-monitoring application and a location-based service prototype over DIRT. We also provide a performance analysis of the model with respect to some of its important characteristics tested in various scenarios
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