Using affective avatars and rich multimedia content for education of children with autism

Autism is a communication disorder that mandates early and continuous educational interventions on various levels like the everyday social, communication and reasoning skills. Computer-aided education has recently been considered as a likely intervention method for such cases, and therefore different systems have been proposed and developed worldwide. In more recent years, affective computing applications for the aforementioned interventions have also been proposed to shed light on this problem. In this paper, we examine the technological and educational needs of affective interventions for autistic persons. Enabling affective technologies are visited and a number of possible exploitation scenarios are illustrated. Emphasis is placed in covering the continuous and long term needs of autistic persons by unobtrusive and ubiquitous technologies with the engagement of an affective speaking avatar. A personalised prototype system facilitating these scenarios is described. In addition the feedback from educators for autistic persons is provided for the system in terms of its usefulness, efficiency and the envisaged reaction of the autistic persons, collected by means of an anonymous questionnaire. Results illustrate the clear potential of this effort in facilitating a very promising autism intervention

HindiPersonalityNet: Personality Detection in Hindi Conversational Data using Deep Learning with Static Embedding

Personality detection along with other behavioural and cognitive assessment can essentially explain why people act the way they do and can be useful to various online applications such as recommender systems, job screening, matchmaking, and counselling. Additionally, psychometric NLP relying on textual cues and distinctive markers in writing style within conversational utterances reveal signs of individual personalities. This work demonstrates a text-based deep neural model, HindiPersonalityNet of classifying conversations into three personality categories {ambivert, extrovert, introvert} for detecting personality in Hindi conversational data. The model utilizes GRU with BioWordVec embeddings for text classification and is trained/tested on a novel dataset, शख्सियत (pronounced as Shakhsiyat) curated using dialogues from an Indian crime-thriller drama series, Aarya. The model achieves an F1-score of 0.701 and shows the potential for leveraging conversational data from various sources to understand and predict a person's personality traits. It exhibits the ability to capture semantic as well as long-distance dependencies in conversations and establishes the effectiveness of our dataset as a benchmark for personality detection in Hindi dialogue data. Further, a comprehensive comparison of various static and dynamic word embedding is done on our standardized dataset to ascertain the most suitable embedding method for personality detection

Application of Mobile Health Services to Support Patient Self-Management of Chronic Conditions

Background: Chronic conditions are the leading cause of ill-health, disability and premature death, adding huge health and socioeconomic burden to the healthcare system. Although mobile health (mHealth) services have the potential to provide patients with a timely, ubiquitous, and cost-effective means to access healthcare services, to date, much remains to be revealed for their application in chronic condition management. Aim: This doctoral project aims to comprehensively understand the application of mHealth services to support patient self-management of chronic conditions. This aim is achieved through four objectives: (1) to synthesise research evidence about health outcomes of applying mHealth services to support patient self-management of chronic conditions and the essential components to achieve these outcomes, (2) to determine the mechanism for applying mHealth services to support patient self-management of chronic conditions, (3) to explore critical factors and how these factors influence patients\u27 intention to continuously use mHealth services, and (4) to apply the above findings to guide the design of a prototype mHealth service. Methods: To increase the generalisability of the findings, three chronic conditions that could benefit from mHealth services were purposively studied to address the research objectives within the feasibility of available study sites and resources at different stages of the project. First, two literature review studies were conducted to achieve Objective 1. One was a systematic review to investigate health outcomes of mHealth services to support patient self-management of one chronic condition, unhealthy alcohol use, and the essential components to achieve these outcomes. The other was a rapid review on using behavioural theory to guide the design of mHealth services that support patient self-management of another chronic condition, hypertension. Second, two field studies were conducted to achieve Objectives 2 and 3, respectively. One was an interview study that explored patients\u27 perceptions of a mHealth service to support their self-management of hypertension in China. The other was a questionnaire survey study conducted on the same site that explored critical factors influencing patients\u27 intention to continuously use the mHealth service. Third, a clinician-led, experience-based co-design approach was implemented to apply the above-mentioned learning experience to the development practice of a mHealth service that supports patient self-management of obesity before elective surgery in Australia, achieving Objective 4. Results: Literature reviews identify five structural components - context, theory, content, delivery mode, and implementation procedure - which are essential for mHealth services to achieve three health outcomes - behavioural, physiological, and cognitive outcomes. Inductive synthesis of the interview findings lead to a 6A framework that summarises the mechanisms for mHealth services: access, assessment, assistance, awareness, ability, and activation. Mobile health services provide patients with easy access to health assessment and healthcare assistance to increase their self-management awareness and ability, thereby activating their self-management behaviours. Questionnaire survey study finds that patients\u27 intention to continuously use mHealth services can be influenced by the information quality, system quality and service quality by influencing their perceived usefulness and satisfaction with the mHealth services. Guided by Social Cognitive Theory, the developed prototype mHealth service provide patients with functions of automatic push notifications, online resources, goal setting and monitoring, and interactive health-related exchanges that encourage their physical activity, healthy eating, psychological preparation, and a positive outlook for elective surgery. The patients\u27 requirements in two focus group discussions enabled the research team to improve the mHealth service design. Conclusion: Mobile health services guided by behavioural theories can provide patients with easy access to health assessment and healthcare assistance to increase their self-management awareness and ability, thereby activating their self-management behaviours. The effort for designing mHealth services needs to be placed on crafting content (to improve information quality), developing useful functions and selecting a proper delivery mode (to improve system quality), and establishing effective implementation procedures (to improve service quality). These will ensure patients\u27 perceived usefulness and satisfaction with mHealth services, increase their intention to continuously use such services, thus supporting long-term patient self-management of chronic conditions. As demonstrated by the design case, the findings of this PhD project can be generalised to guide the design of other mHealth services that aim to support patient self-management of chronic conditions

AIDS Behav

Mobile health (mHealth) technology can be a valuable tool in the management of chronic illnesses, including HIV. Qualitative research methods were used to identify the desired content and features of a mobile app for meeting and improving the healthcare needs of persons living with HIV (PLWH). We conducted six focus group sessions with 50 English-or Spanish-speaking PLWH in New York City. To inform data analysis and to illustrate how mHealth technology can be used as a persuasive strategy for improving the health of PLWH, we integrated Fogg's functional role triad for computing technology model with the self-determination theory to illustrate how mHealth technology can be used as a persuasive strategy for improving the health of PLWH. Participants suggested several tools for meeting their healthcare needs, including: reminders/alerts, lab results tracking, and notes on health status. mHealth technology can function as a social actor by providing chat boxes/forums, testimonials of lived experiences, and personal outreach. Examples of media that can be used as a persuasive technology include games/virtual rewards, coding of health tasks, and simulations on how to connect with PLWH. Findings from these focus groups can be used to design a mobile app for PLWH\uc2\ua0that is targeted to meet their healthcare needs.1U01PS00371501/PS/NCHHSTP CDC HHS/United StatesK12 RR017648/RR/NCRR NIH HHS/United States2016-06-01T00:00:00Z25572830PMC449793

Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review

Background: The public health impact of health and well-being digital interventions is dependent upon sufficient real-world uptake and engagement. Uptake is currently largely dependent on popularity indicators (eg, ranking and user ratings on app stores), which may not correspond with effectiveness, and rapid disengagement is common. Therefore, there is an urgent need to identify factors that influence uptake and engagement with health and well-being apps to inform new approaches that promote the effective use of such tools. Objective: This review aimed to understand what is known about influences on the uptake of and engagement with health and well-being smartphone apps among adults. Methods: We conducted a systematic review of quantitative, qualitative, and mixed methods studies. Studies conducted on adults were included if they focused on health and well-being smartphone apps reporting on uptake and engagement behavior. Studies identified through a systematic search in Medical Literature Analysis and Retrieval System Online, or MEDLARS Online (MEDLINE), EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsychINFO, Scopus, Cochrane library databases, DataBase systems and Logic Programming (DBLP), and Association for Computing Machinery (ACM) Digital library were screened, with a proportion screened independently by 2 authors. Data synthesis and interpretation were undertaken using a deductive iterative process. External validity checking was undertaken by an independent researcher. A narrative synthesis of the findings was structured around the components of the capability, opportunity, motivation, behavior change model and the theoretical domains framework (TDF). Results: Of the 7640 identified studies, 41 were included in the review. Factors related to uptake (U), engagement (E), or both (B) were identified. Under capability, the main factors identified were app literacy skills (B), app awareness (U), available user guidance (B), health information (E), statistical information on progress (E), well-designed reminders (E), features to reduce cognitive load (E), and self-monitoring features (E). Availability at low cost (U), positive tone, and personalization (E) were identified as physical opportunity factors, whereas recommendations for health and well-being apps (U), embedded health professional support (E), and social networking (E) possibilities were social opportunity factors. Finally, the motivation factors included positive feedback (E), available rewards (E), goal setting (E), and the perceived utility of the app (E). Conclusions: Across a wide range of populations and behaviors, 26 factors relating to capability, opportunity, and motivation appear to influence the uptake of and engagement with health and well-being smartphone apps. Our recommendations may help app developers, health app portal developers, and policy makers in the optimization of health and well-being apps

Advances in Teaching & Learning Day Abstracts 2005

Proceedings of the Advances in Teaching & Learning Day Regional Conference held at The University of Texas Health Science Center at Houston in 2005

Information management to enable personalized medicine: stakeholder roles in building clinical decision support

<p>Abstract</p> <p>Background</p> <p>Advances in technology and the scientific understanding of disease processes are presenting new opportunities to improve health through individualized approaches to patient management referred to as personalized medicine. Future health care strategies that deploy genomic technologies and molecular therapies will bring opportunities to prevent, predict, and pre-empt disease processes but will be dependent on knowledge management capabilities for health care providers that are not currently available. A key cornerstone to the potential application of this knowledge will be effective use of electronic health records. In particular, appropriate clinical use of genomic test results and molecularly-targeted therapies present important challenges in patient management that can be effectively addressed using electronic clinical decision support technologies.</p> <p>Discussion</p> <p>Approaches to shaping future health information needs for personalized medicine were undertaken by a work group of the American Health Information Community. A needs assessment for clinical decision support in electronic health record systems to support personalized medical practices was conducted to guide health future development activities. Further, a suggested action plan was developed for government, researchers and research institutions, developers of electronic information tools (including clinical guidelines, and quality measures), and standards development organizations to meet the needs for personalized approaches to medical practice. In this article, we focus these activities on stakeholder organizations as an operational framework to help identify and coordinate needs and opportunities for clinical decision support tools to enable personalized medicine.</p> <p>Summary</p> <p>This perspective addresses conceptual approaches that can be undertaken to develop and apply clinical decision support in electronic health record systems to achieve personalized medical care. In addition, to represent meaningful benefits to personalized decision-making, a comparison of current and future applications of clinical decision support to enable individualized medical treatment plans is presented. If clinical decision support tools are to impact outcomes in a clear and positive manner, their development and deployment must therefore consider the needs of the providers, including specific practice needs, information workflow, and practice environment.</p

Healthcare 4.0 and Health Management

Industry 4.0, which is rapidly developing and changing in today's world, has also heavily influenced the health sector and is gathered under Health 4.0. This study was conducted to discuss what Health 4.0 applications are and their importance in terms of health management within the scope of their contribution to health services and to make suggestions. In this context, Health 4.0 technologies and applications in the world and in Turkey are first explained. Subsequently, its importance in terms of Health Management was mentioned and suggestions were made. In terms of health management, it can be said that Health 4.0 primarily contributes to accessibility in health services, increases the comprehensiveness of health services, reduces health expenses, and is beneficial in terms of time and effectiveness in accurate diagnosis and treatment. In addition, it was emphasized that studies on the adaptation of health systems, health human resources and society to this rapid change are also important

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke

The goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided

Technologie en innovatie in Vlaanderen: Prioriteiten.

Vlaanderen;