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The population of dementia patients is on the rise, as society undergoes rapid aging. This led to an expansion of dementia-related data. This study aims to develop a comprehensive dementia ontology to facilitate the collection and analysis of high-quality dementia data. We followed an ontology building process from Ontology Development 101 and the content of the dementia ontology was validated by experts in dementia care and dementia-related research.

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Korean National Institute of Health initiated data harmonization across cohorts with the aim to ensure semantic interoperability of data and to create a common database of standardized data elements for future collaborative research. With this aim, we reviewed code books of cohorts and identified common data items and values which can be combined for data analyses. We then mapped data items and values to standard health terminologies such as SNOMED CT. Preliminary results of this ongoing data harmonization work will be presented.

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Robotic systems are used to support inpatients and healthcare professionals and to improve the efficiency and quality of nursing. There is a lack of scientific literature on how applied robotic systems can be used to support inpatients. This study uses surveys and focus group interviews to identify the necessary aspects and functions of bedside robots for inpatients. A total of 90 healthcare professionals and 108 inpatients completed the questionnaire, and four physicians and five nurses participated in the focus group interviews. The most highly desired functionalities were related to patient care and monitoring, including alerting staff, measuring vital signs, and sensing falls. Nurses and physicians reported different needs for human-robot interaction. Nurses valued robotic functions such as nonverbal expression recognition, automatic movement, content suggestion, and emotional expressions. The results of the patients' open-ended questions and healthcare professionals' focus groups indicate that the purpose of the robots should primarily be treatment and nursing. Participants believe bedside robots would be helpful but have concerns regarding safety and utility. This study attempts to determine which aspects of robots may increase their acceptance. Our findings suggest that if robots are used in healthcare institutions, they may improve the effectiveness of care.

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OBJECTIVES: This study analyzed the health technology trends and sentiments of users using Twitter data in an attempt to examine the public's opinions and identify their needs. METHODS: Twitter data related to health technology, from January 2010 to October 2016, were collected. An ontology related to health technology was developed. Frequently occurring keywords were analyzed and visualized with the word cloud technique. The keywords were then reclassified and analyzed using the developed ontology and sentiment dictionary. Python and the R program were used for crawling, natural language processing, and sentiment analysis. RESULTS: In the developed ontology, the keywords are divided into 'health technology' and 'health information'. Under health technology, there are are six subcategories, namely, health technology, wearable technology, biotechnology, mobile health, medical technology, and telemedicine. Under health information, there are four subcategories, namely, health information, privacy, clinical informatics, and consumer health informatics. The number of tweets about health technology has consistently increased since 2010; the number of posts in 2014 was double that in 2010, which was about 150 thousand posts. Posts about mHealth accounted for the majority, and the dominant words were 'care', 'new', 'mental', and 'fitness'. Sentiment analysis by subcategory showed that most of the posts in nearly all subcategories had a positive tone with a positive score. CONCLUSIONS: Interests in mHealth have risen recently, and consequently, posts about mHealth were the most frequent. Examining social media users' responses to new health technology can be a useful method to understand the trends in rapidly evolving fields.

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Wearable activity trackers (WAT) are electronic monitoring devices that enable users to track and monitor their health-related physical fitness metrics including steps taken, level of activity, walking distance, heart rate, and sleep patterns. Despite the proliferation of these devices in various contexts of use and rising research interests, there is limited understanding of the broad research landscape. The purpose of this systematic review is therefore to synthesize the existing wealth of research on WAT, and to provide a comprehensive summary based on common themes and approaches. This article includes academic work published between 2013 and 2017 in PubMed, Embase, Scopus, Web of Science, ACM Digital Library, and Google Scholar. A final list of 463 articles was analyzed for this review. Topic modeling methods were used to identify six key themes (topics) of WAT research, namely: (1) Technology Focus, (2) Patient Treatment and Medical Settings, (3) Behavior Change, (4) Acceptance and Adoption (Abandonment), (5) Self-monitoring Data Centered, and (6) Privacy. We take an interdisciplinary approach to wearable activity trackers to propose several new research questions. The most important research gap we identify is to attempt to understand the rich human-information interaction that is enabled by WAT adoption.

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Recent rapid development of medical and information technology has enabled the use of appropriate techniques for the delivery of healthcare. This project involved prediction of the meaning and structure of future health services, which are now commonly described through various keywords, without establishment of the concepts. The objectives of this study were to identify key concepts and values about future health services and to categorize the prediction of those from the perspectives of the concerned professionals using Q-methodology with 50 selected Q-statements. A total of 53 participants performed the Q-sort task using the 50 statements; collected data were analyzed using an associated program, pc-QUANL. Fifty Q-samples were selected to sort the concepts, and 53 professionals sorted the Q-samples. Six concepts were summarized, namely, the Optimistic Innovation Positive Type, Pessimistic Resistance to Technology-Driven Medicine Type, Intelligent Information Technology Centered Type, Bio-technology Centered Type, Personal Health Data Centered Type, and Customized Care Centered Type. The results could be used in the future design of consumer-centered health services. Advanced technology may accommodate the individual needs of different stakeholders and carve an ecosystem-wide suite of interacting complex adaptive systems.

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We reviewed the effect sizes of pediatric obesity intervention studies using mobile technology. Ten databases (Cochrane CENTRAL, CINAHL, EMBASE, PubMed/Medline, KoreaMED, KMBASE, KISS, NDSL, KSITI, and RISS) were reviewed, and four studies were included in a qualitative synthesis. To obtain significant change in obesity-related outcomes among elementary school students, including parents and utilizing text messages in interventions are recommended. Furthermore, devices such as accelerometers may aid obesity management. A meta-analysis of four studies indicated that the mobile intervention positively influenced dropout rates but was ineffective for outcomes of weight control, exercise, and sugar-sweetened beverage intake.

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This study was conducted on verifying whether variables such as prior health related behaviors, health literacy, interpersonal influence, perceived ease of use and usefulness of health information, and behavioral intention could predict actual health promoting behaviors of consumers using health information with mobile in the future. The research model was based on Technology Acceptance Model. Data were collected from 199 mobile health information seekers. Participants' actual health promoting behaviors were checked after 3 months from pre-data collection. The final modified model had good fit indices.

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