Health figures: an open source JavaScript library for health data visualization

Ledesma, Andres; Al-Musawi, Mohammed; Nieminen, Hannu

doi:10.1186/s12911-016-0275-6

Cited by 25 publications

(9 citation statements)

References 48 publications

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“…Visualizations of data created problems in both CHF apps. Graphs and charts can be a valuable means of presenting health information (Alnosayan, Chatterjee, Alluhaidan, Lee, & Houston Feenstra, 2017;Ledesma, Al-Musawi, & Nieminen, 2016); however, in the current apps, ill-presented or complex visualizations were either interpreted incorrectly or were ignored. Although some users found the graphs helpful for viewing changes in information over time, few users could understand the information presented graphically, and many asked for clarity over what the graphs showed.…”

Section: Use Color Effectivelymentioning

confidence: 98%

Mobile Health Apps: Improving Usability for Older Adult Users

Morey¹,

Stuck²,

Chong³

et al. 2019

Ergonomics in Design

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With smartphone use among older populations on the rise, older adults have increased access to health-focused mobile apps. Despite their potential benefits for managing health, currently no guidelines exist for designing these apps specifically for older adult users. We evaluated the usability of one medication management app and two congestive heart failure management apps using cognitive walkthroughs, heuristic analysis, and user testing. We identified design issues that may affect usability for older users, including poor navigation, small button sizes, and inadequate data visualizations. We provide guidelines for developers of health apps to facilitate use by older adults.

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Section: Use Color Effectivelymentioning

confidence: 98%

Mobile Health Apps: Improving Usability for Older Adult Users

Morey¹,

Stuck²,

Chong³

et al. 2019

Ergonomics in Design

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“…Today's society is facing increasing pressure on medical needs and shrinking medical budgets [25], [26]. Data visualization as an important tool for data analysis not only plays an important role in hospital management, but also its powerful advantages are also reflected in limiting medical waste and control [27], [28]. The hospital has a wealth of data resources, including medical expenses data, electronic medical record data, medical imaging data, pathological parameter data, laboratory test data, etc.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Visualization Implementation of Medical Big Data Resource Sharing Mechanism Based on Deep Learning

Yang

Chen

2019

IEEE Access

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With the development of information technology, the informationization of the medical industry is also constantly developing rapidly, and medical data is growing exponentially. In the context of ''Big Data +'', people began to study the application of data visualization to medical data. Data visualization can make full use of the human sensory vision system to guide users through data analysis and present information hidden behind the data in an intuitive and easy-to-use manner. This paper first introduces the workflow of DBN, a deep learning algorithm, and summarizes the computational characteristics of the algorithm. The classification function is translated into an assembler using an instruction set-based assembly language, and the program is evaluated for performance. Secondly, based on the Hadoop ecosystem, this paper analyzes the BDMISS system for big data medical information resource sharing. Based on the system's requirements and functional positioning, from the medical information collection and sharing, data mining and knowledge management level, the big data medical service system is constructed. Based on the semantic network and ontology theory, big data mining technology and the design of ''medical cloud'', the resource sharing mechanism is analyzed. Based on the Spring MVC framework, using Echarts, HCharts and other data visualization technology, according to the design of specific modules, the visualization and display of medical data is realized, which has certain promotion effect on the research and development of medical big data visualization analysis.

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“…This is not answered in our paper, but we provide some innovative examples of visualization of this type of data. There are other studies looking in the visualization of Electronic Health Records (EHR) for helping decision making [ 34 ], also including Quantified Self data [ 35 – 37 ] but in those studies the QS data is limited to wearable sensors data. Folter et al studied the integration of health habit data (e.g., motion data for physical activity) to help decision making of healthcare professionals [ 36 ].…”

Section: Discussionmentioning

confidence: 99%

Implementing 360° Quantified Self for childhood obesity: feasibility study and experiences from a weight loss camp in Qatar

Fernández-Luque

Singh

Ofli

et al. 2017

BMC Med Inform Decis Mak

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BackgroundThe explosion of consumer electronics and social media are facilitating the rise of the Quantified Self (QS) movement where millions of users are tracking various aspects of their daily life using social media, mobile technology, and wearable devices. Data from mobile phones, wearables and social media can facilitate a better understanding of the health behaviors of individuals. At the same time, there is an unprecedented increase in childhood obesity rates worldwide. This is a cause for grave concern due to its potential long-term health consequences (e.g., diabetes or cardiovascular diseases). Childhood obesity is highly prevalent in Qatar and the Gulf Region. In this study we examine the feasibility of capturing quantified-self data from social media, wearables and mobiles within a weight lost camp for overweight children in Qatar.MethodsOver 50 children (9–12 years old) and parents used a wide range of technologies, including wearable sensors (actigraphy), mobile and social media (WhatsApp and Instagram) to collect data related to physical activity and food, that was then integrated with physiological data to gain insights about their health habits.In this paper, we report about the acquired data and visualization techniques following the 360° Quantified Self (360QS) methodology (Haddadi et al., ICHI 587–92, 2015).Results360QS allows for capturing insights on the behavioral patterns of children and serves as a mechanism to reinforce education of their mothers via social media. We also identified human factors, such as gender and cultural acceptability aspects that can affect the implementation of this technology beyond a feasibility study. Furthermore, technical challenges regarding the visualization and integration of heterogeneous and sparse data sets are described in the paper.ConclusionsWe proved the feasibility of using 360QS in childhood obesity through this pilot study. However, in order to fully implement the 360QS technology careful planning and integration in the health professionals’ workflow is needed.Trial RegistrationThe trial where this study took place is registered at ClinicalTrials.gov on 14 November 2016 (NCT02972164).

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Health figures: an open source JavaScript library for health data visualization

Cited by 25 publications

References 48 publications

Mobile Health Apps: Improving Usability for Older Adult Users

Mobile Health Apps: Improving Usability for Older Adult Users

Analysis and Visualization Implementation of Medical Big Data Resource Sharing Mechanism Based on Deep Learning

Implementing 360° Quantified Self for childhood obesity: feasibility study and experiences from a weight loss camp in Qatar

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