scholarly journals Personas for Perfectly Tailored eHealth Technologies: Segmenting Heart Failure Patients using the Persona Approach Twente (Preprint)

2020 ◽  
Author(s):  
Iris ten Klooster ◽  
Jobke Wentzel ◽  
Floor Sieverink ◽  
Gerard Linssen ◽  
Robin Wesselink ◽  
...  
Keyword(s):  
End Users ◽  
Full Potential ◽  
User Centered Design ◽  
Related Data ◽  
Partitioning Around Medoids ◽  
Health Related ◽  
Phases Of Development ◽  
Persona Development ◽  
Structured Approach ◽  
Mixed Types

BACKGROUND The full potential of eHealth technologies to support self- and disease-management for patients with chronic diseases, is not being reached. A possible explanation for these lacking results is that during the development process, insufficient attention is paid to the needs, wishes and context of the prospective end-users. To overcome such issues, the User-Centered Design (UCD) practice of creating personas is widely accepted as a means to ensure the fit between a technology and the target group or end-users throughout all phases of development. OBJECTIVE In the current study, we integrate several approaches to persona-development into the Persona Approach Twente (PAT), to attain a structured approach that aligns with the iterative process of eHealth development. METHODS In three steps, different parts from the dataset were analyzed using the Partitioning Around Medoids clustering method. First, we used health-related EPR data only. Secondly, we added person-related data that was gathered through interviews and questionnaires. Thirdly, we added log data. RESULTS In the first step, two clusters were found, with average silhouette widths of 0.12, and 0.27. In the second step, again two clusters were found, with average silhouette widths of 0.08, and 0.12. In the third step, three clusters were identified, with average silhouette widths of 0.09, 0.12, and 0.04. CONCLUSIONS The Persona Approach Twente is applicable for mixed types of data, and allows alignment of this UCD method to the iterative approach of eHealth development. Challenges lie in data quality and fitness for (quantitative) clustering.

Health data sharing and use explored

Impact ◽  
2021 ◽  
Vol 2021 (8) ◽  
pp. 4-5
Author(s):  
Lucy Annette
Keyword(s):  
Data Sharing ◽  
Data Protection ◽  
Positive Impact ◽  
Digital Health ◽  
Health Data ◽  
Full Potential ◽  
Health And Wellness ◽  
Related Data ◽  
Health Related ◽  
The Right

Three expert roundtables took place as part of DigitalHealthEurope (DHE), with discussions surrounding health data sharing and use. In the first roundtable, the implementation of GDPR was explored and the experts delved into possible remaining challenges associated with understanding the way in which health related data may be used. Legal issues and the importance of data protection and citizen protection were discussed, as was the need for more human resources regarding data protection, which could be rectified by the provision of education in this area. The introduction of a new EU body responsible for data legislative needs was an idea that was put forward. Next, the law as an enabler of data use was discussed, along with the protection of citizens and data. It was highlighted that in order for the full potential of digital health to be realised, data literacy and skills are paramount. The experts also discussed how data can be used to protect citizens, without compromising a right to privacy, as well as the importance of generating the right data to ensure that it can be used to protect citizens' health and wellness. A further topic of discussion was how the development of a range of skills among data stakeholders would lead to the better use of data and that this would have a positive impact on health and wellness.

Novel Visualization of Large Health Related Data Sets

2015 ◽  
Author(s):  
William E. Hammond ◽  
Vivian L. West ◽  
David Borland ◽  
Igor Akushevich ◽  
Eugenia M. Heinz
Keyword(s):  
Data Sets ◽  
Related Data ◽  
Health Related

scholarly journals Assessment of Dietary Habits, Physical Activity and Lifestyle in Medical University Students

2021 ◽  
Vol 13 (6) ◽  
pp. 3572
Author(s):  
Lavinia-Maria Pop ◽  
Magdalena Iorga ◽  
Iulia-Diana Muraru ◽  
Florin-Dumitru Petrariu
Keyword(s):  
Physical Activity ◽  
Medical Students ◽  
Fast Food ◽  
Dietary Habits ◽  
Healthy Life ◽  
Related Data ◽  
The Third ◽  
Health Related ◽  
Medical University ◽  
Busy Schedule

A busy schedule and demanding tasks challenge medical students to adjust their lifestyle and dietary habits. The aim of this study was to identify dietary habits and health-related behaviours among students. A number of 403 students (80.40% female, aged M = 21.21 ± 4.56) enrolled in a medical university provided answers to a questionnaire constructed especially for this research, which was divided into three parts: the first part collected socio-demographic, anthropometric, and medical data; the second part inquired about dietary habits, lifestyle, sleep, physical activity, water intake, and use of alcohol and cigarettes; and the third part collected information about nutrition-related data and the consumption of fruit, vegetables, meat, eggs, fish, and sweets. Data were analysed using SPSS v24. Students usually slept M = 6.71 ± 1.52 h/day, and one-third had self-imposed diet restrictions to control their weight. For both genders, the most important meal was lunch, and one-third of students had breakfast each morning. On average, the students consumed 1.64 ± 0.88 l of water per day and had 220 min of physical activity per week. Data about the consumption of fruit, vegetables, meat, eggs, fish, sweets, fast food, coffee, tea, alcohol, or carbohydrate drinks were presented. The results of our study proved that medical students have knowledge about how to maintain a healthy life and they practice it, which is important for their subsequent professional life.

Understanding Data Collection Mechanisms Used by Health and Wellness Applications (Preprint)

2021 ◽  
Author(s):  
Ben Philip ◽  
Mohamed Abdelrazek ◽  
Alessio Bonti ◽  
Scott Barnett ◽  
John Grundy
Keyword(s):  
Data Collection ◽  
User Experience ◽  
Health Data ◽  
Domain Model ◽  
Health And Wellness ◽  
Related Services ◽  
Related Data ◽  
Medical Apps ◽  
App Development ◽  
Health Related

UNSTRUCTURED Our objective is to better understand health-related data collection across different mHealth app categories. This would help in developing a health domain model for mHealth apps to facilitate app development and data sharing between these apps to improve user experience and reduce redundancy in data collection. We identified app categories listed in a curated library which was then used to explore the Google Play Store for health/medical apps that were then filtered using our inclusion criteria. We downloaded and analysed these apps using a script we developed around the popular AndroGuard tool. We analysed the use of Bluetooth peripherals and built-in sensors to understand how a given app collects/generates health data. We retrieved 3,251 applications meeting our criteria, and our analysis showed that only 10.7% of these apps requested permission for Bluetooth access. We found 50.9% of the Bluetooth Service UUIDs to be known in these apps, with the remainder being vendor specific. The most common health-related services using the known UUIDs were Heart Rate, Glucose and Body Composition. App permissions show the most used device module/sensor to be the camera (20.57%), closely followed by GPS (18.39%). Our findings are consistent with previous studies in that not many health apps were found to use built-in sensors or peripherals for collecting health data. The use of more peripherals and automated data collection along with integration with other apps could increase usability and convenience which would eventually also improve user experience and data reliability.

Secure Cross-Border Exchange of Health Related Data: The KONFIDO Approach

2019 ◽  
pp. 318-327
Author(s):  
Sotiris Diamantopoulos ◽  
Dimitris Karamitros ◽  
Luigi Romano ◽  
Luigi Coppolino ◽  
Vassilis Koutkias ◽  
...  
Keyword(s):  
Related Data ◽  
Cross Border ◽  
Health Related

scholarly journals Data safe havens to combine health and genomic data: benefits and challenges

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Kerina H Jones ◽  
Arron S Lacey ◽  
Brian L Perkins ◽  
Mark I Rees
Keyword(s):  
Association Studies ◽  
Genomic Data ◽  
Population Level ◽  
Data Availability ◽  
Genome Wide Association Studies ◽  
Related Data ◽  
Research Areas ◽  
Individual Privacy ◽  
Access Controls ◽  
Health Related

ABSTRACTObjectivesData safe havens can bring together and combine a rich array of anonymised person-based data for research and policy evaluation within a secure setting. To date, the majority of available datasets have been structured micro-data derived from routine health-related records. Possibilities are opening up for the greater reuse of genomic data such as Genome Wide Association studies (GWAS) and Whole Exome/Genome Sequencing (WES or WGS). However, there are considerable challenges to be addressed if the benefits of using these data in combination with health-related data are to be realized safely. ApproachWe explore the benefits and challenges of using genomic datasets with health-related data, and using the Secure Anonymised Information Linkage (SAIL) system as a case study, the implications and way forward for Data Safe Havens in seeking to incorporate genomic data for use with health-related data. ResultsThe benefits of using GWAS, WES and WGS data in conjunction with health-related data include the potential to explore genetics at a population level and open up novel research areas. These include the ability to increasingly stratify and personalize how medical indications are detected and treated through precision medicine by understanding rare conditions and adding socioeconomic and environmental context to genomic data. Among the challenges are: data availability, computing capacity, technical solutions, legal and regulatory frameworks, public perceptions, individual privacy and organizational risk. Many of the challenges within these areas are common to person-based data in general, and often Data Safe Havens have been designed to address these. But there are also aspects of these challenges, and other challenges, specific to genomic data. These include issues due to the unknown clinical significance of genomic information now or in the future, with corresponding risks for privacy and impact on individuals. ConclusionGenomic data sets contain vast amounts of valuable information, some of which is currently undefined, but which may have direct bearing on individual health at some point. The use of these data in combination with health-related data has the potential to bring great benefits, better clinical trial stratification, epidemiology project design and clinical improvements. It is, therefore, essential that such data are surrounded by a properly-designed, robust governance framework including technical and procedural access controls that enable the data to be used safely.

scholarly journals All Our Data Will Be Health Data One Day: The Need for Universal Data Protection and Comprehensive Consent

10.2196/16879 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e16879 ◽  
Author(s):  
Christophe Olivier Schneble ◽  
Bernice Simone Elger ◽  
David Martin Shaw
Keyword(s):  
Health Status ◽  
Data Protection ◽  
Health Data ◽  
Future Health ◽  
Related Data ◽  
Legal Implications ◽  
Health Related ◽  
Behavior Health

Tremendous growth in the types of data that are collected and their interlinkage are enabling more predictions of individuals’ behavior, health status, and diseases. Legislation in many countries treats health-related data as a special sensitive kind of data. Today’s massive linkage of data, however, could transform “nonhealth” data into sensitive health data. In this paper, we argue that the notion of health data should be broadened and should also take into account past and future health data and indirect, inferred, and invisible health data. We also lay out the ethical and legal implications of our model.

scholarly journals A user-friendly tool for cloud-based whole slide image segmentation, with examples from renal histopathology

2021 ◽  
Author(s):  
Brendon R Lutnick ◽  
David Manthey ◽  
Jan U Becker ◽  
Brandon Ginley ◽  
Katharina Moos ◽  
...  
Keyword(s):  
Machine Learning ◽  
User Interface ◽  
Interstitial Fibrosis ◽  
End Users ◽  
Great Promise ◽  
Histological Structure ◽  
Full Potential ◽  
Learning Tools ◽  
Tubular Atrophy ◽  
Slide Image

Image-based machine learning tools hold great promise for clinical applications in nephropathology and kidney research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often face prohibitive challenges in using these tools to their full potential, including the lack of technical expertise, suboptimal user interface, and limited computation power. We have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in murine models of aging, diabetic nephropathy, and HIV associated nephropathy. The ability to access this tool over the internet will facilitate widespread use by computational non-experts. Histo-Cloud is open source and adaptable for segmentation of any histological structure regardless of stain. Histo-Cloud will greatly accelerate and facilitate the generation of datasets for machine learning in the analysis of kidney histology, empowering computationally novice end-users to conduct deep feature analysis of tissue slides.

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