Dataset Information

Investigating data accessibility of personal health apps.

ABSTRACT: OBJECTIVE:Despite the potential values self-tracking data could offer, we have little understanding of how much access people have to "their" data. Our goal of this article is to unveil the current state of the data accessibility-the degree to which people can access their data-of personal health apps in the market. MATERIALS AND METHODS:We reviewed 240 personal health apps from the App Store and selected 45 apps that support semi-automated tracking. We characterized the data accessibility of these apps using two dimensions-data access methods and data types. RESULTS:More than 90% of our sample apps (n?=?41) provide some types of data access support, which include synchronizing data with a health platform (ie, Apple Health), file download, and application program interfaces. However, the two approachable data access methods for laypeople-health platform and file download-typically put a significant limit on data format, granularity, and amount, which constrains people from easily repurposing the data. DISCUSSION:Personal data should be accessible to the people who collect them, but existing methods lack sufficient support for people in accessing the fine-grained data. Lack of standards in personal health data schema as well as frequent changes in market conditions are additional hurdles to data accessibility. CONCLUSIONS:Many stakeholders including patients, healthcare providers, researchers, third-party developers, and the general public rely on data accessibility to utilize personal data for various goals. As such, improving data accessibility should be considered as an important factor in designing personal health apps and health platforms.

SUBMITTER: Kim Y

PROVIDER: S-EPMC6433179 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Investigating data accessibility of personal health apps.

Kim Yoojung Y Lee Bongshin B Choe Eun Kyoung EK

Journal of the American Medical Informatics Association : JAMIA 20190501 5

<h4>Objective</h4>Despite the potential values self-tracking data could offer, we have little understanding of how much access people have to "their" data. Our goal of this article is to unveil the current state of the data accessibility-the degree to which people can access their data-of personal health apps in the market.<h4>Materials and methods</h4>We reviewed 240 personal health apps from the App Store and selected 45 apps that support semi-automated tracking. We characterized the data acce ...[more]

PMID: 30861531

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Project description:BackgroundHealth care technologies can help improve workers' health and productivity by supporting workplace health promotion. A personal health record app is used to manage medical data such as results from medical checkups, which facilitates decision making for medical personnel. However, an analysis of users' technology acceptance is required to provide appropriate services based on personal health record apps.ObjectiveThe purpose of this study was to analyze the factors influencing the behavioral intention of health experts and workers to use an app in workers' health centers and to examine differences in their perception of the main variables.MethodsThe study involved health experts and workers who visited 21 workers' health centers in Korea to verify a research model in which perceived risk was added to the unified theory of acceptance and use of technology, a representative theory of information technology acceptance. After receiving ethical approval from the Korea National Institute for Bioethics Policy, 1050 questionnaires were distributed over 7 weeks with cooperation of the Korea Occupational Safety and Health Agency. A multiple linear regression analysis and multigroup path analysis were performed to verify the hypotheses, and independent samples t tests were performed to analyze differences between workers' and health experts' perception of the main variables.ResultsThe analysis included data from 866 respondents (687 workers and 179 health experts). Effort expectancy (beta=.08, P=.03), social influence (beta=.43, P<.001), performance expectancy (beta=.07, P=.008), and facilitating conditions (beta=.13, P<.001) exerted significant positive effects on behavioral intention, whereas perceived risk (beta=-.29, P<.001) exerted a significant negative effect on behavioral intention. Performance expectancy had a significant effect on path differences depending on gender (critical ratio=-3.38) and age (critical ratio=1.97). Workers' mean scores for the main variables were higher relative to those of health experts for all remaining variables except perceived risk, and significant differences were observed for all remaining variables except facilitating condition.ConclusionsSocial influence exerted the strongest effect on behavioral intention to use the personal health record app. Consequently, it is necessary to coordinate health promotion activities in the workplace as well as the operational direction of community institutions such as in workers' health centers to allow workers to manage their own health via continuous use of the app. In addition, the app should be developed based on a requirement analysis of the balance between both interest groups in consideration of differences in perspective between consumers and service providers.

Project description:BackgroundThe Health Avatar Platform provides a mobile health environment with interconnected patient Avatars, physician apps, and intelligent agents (termed IoA3) for data privacy and participatory medicine; however, its fully decentralized architecture has come at the expense of decentralized data management and data provenance.ObjectiveThe introduction of blockchain and smart contract technologies to the legacy Health Avatar Platform with a clinical metadata registry remarkably strengthens decentralized health data integrity and immutable transaction traceability at the corresponding data-element level in a privacy-preserving fashion. A crypto-economy ecosystem was built to facilitate secure and traceable exchanges of sensitive health data.MethodsThe Health Avatar Platform decentralizes patient data in appropriate locations (ie, on patients' smartphones and on physicians' smart devices). We implemented an Ethereum-based hash chain for all transactions and smart contract-based processes to guarantee decentralized data integrity and to generate block data containing transaction metadata on-chain. Parameters of all types of data communications were enumerated and incorporated into 3 smart contracts, in this case, a health data transaction manager, a transaction status manager, and an application programming interface transaction manager. The actual decentralized health data are managed in an off-chain manner on appropriate smart devices and authenticated by hashed metadata on-chain.ResultsMetadata of each data transaction are captured in a Health Avatar Platform blockchain node by the smart contracts. We provide workflow diagrams each of the 3 use cases of data push (from a physician app or an intelligent agents to a patient Avatar), data pull (request to a patient Avatar by other entities), and data backup transactions. Each transaction can be finely managed at the corresponding data-element level rather than at the resource or document levels. Hash-chained metadata support data element-level verification of data integrity in subsequent transactions. Smart contracts can incentivize transactions for data sharing and intelligent digital health care services.ConclusionsHealth Avatar Platform and interconnected patient Avatars, physician apps, and intelligent agents provide a decentralized blockchain ecosystem for health data that enables trusted and finely tuned data sharing and facilitates health value-creating transactions with smart contracts.

Project description:BackgroundHome assessment is a critical component of successful home modifications, enabling individuals with functional limitations to age in place comfortably. A high-quality home assessment tool should facilitate a valid and reliable assessment involving health care and housing professionals, while also engaging and empowering consumers and their caregivers who may be dealing with multiple functional limitations. Unlike traditional paper-and-pencil assessments, which require extensive training and expert knowledge and can be alienating to consumers, mobile health (mHealth) apps have the potential to engage all parties involved, empowering and activating consumers to take action. However, little is known about which apps contain all the necessary functionality, quality appraisal, and accessibility.ObjectiveThis study aimed to assess the functionality, overall quality, and accessibility of mHealth home assessment apps.MethodsmHealth apps enabling home assessment for aging in place were identified through a comprehensive search of scholarly articles, the Apple (iOS) and Google Play (Android) stores in the United States, and fnd.io. The search was conducted between November 2022 and January 2023 following a method adapted from PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Reviewers performed a content analysis of the mobile app features to evaluate their functionality, overall quality, and accessibility. The functionality assessment used a home assessment component matrix specifically developed for this study. For overall quality, the Mobile Application Rating Scale (MARS) was used to determine the apps' effectiveness in engaging and activating consumers and their caregivers. Accessibility was assessed using the Web Content Accessibility Guidelines (WCAG) 2.1 (A and AA levels). These 3 assessments were synthesized and visualized to provide a comprehensive evaluation.ResultsA total of 698 apps were initially identified. After further screening, only 6 apps remained. Our review revealed that none of the apps used thoroughly tested assessment tools, offered all the functionality required for reliable home assessment, achieved the "good" quality threshold as measured by the MARS, or met the accessibility criteria when evaluated against WCAG 2.1. However, DIYModify received the highest scores in both the overall quality and accessibility assessments. The MapIt apps also showed significant potential due to their ability to measure the 3D environment and the inclusion of a desktop version that extends the app's functionality.ConclusionsOur review revealed that there are very few apps available within the United States that possess the necessary functionality, engaging qualities, and accessibility to effectively activate consumers and their caregivers for successful home modification. Future app development should prioritize the integration of reliable and thoroughly tested assessment tools as the foundation of the development process. Furthermore, efforts should be made to enhance the overall quality and accessibility of these apps to better engage and empower consumers to take necessary actions to age in place.

Project description:BackgroundMental disorders are normally diagnosed exclusively on the basis of symptoms, which are identified from patients' interviews and self-reported experiences. To make mental health diagnoses and monitoring more objective, different solutions have been proposed such as digital phenotyping of mental health (DPMH), which can expand the ability to identify and monitor health conditions based on the interactions of people with digital technologies.ObjectiveThis article aims to identify and characterize the sensing applications and public data sets for DPMH from a technical perspective.MethodsWe performed a systematic review of scientific literature and data sets. We searched 8 digital libraries and 20 data set repositories to find results that met the selection criteria. We conducted a data extraction process from the selected articles and data sets. For this purpose, a form was designed to extract relevant information, thus enabling us to answer the research questions and identify open issues and research trends.ResultsA total of 31 sensing apps and 8 data sets were identified and reviewed. Sensing apps explore different context data sources (eg, positioning, inertial, ambient) to support DPMH studies. These apps are designed to analyze and process collected data to classify (n=11) and predict (n=6) mental states/disorders, and also to investigate existing correlations between context data and mental states/disorders (n=6). Moreover, general-purpose sensing apps are developed to focus only on contextual data collection (n=9). The reviewed data sets contain context data that model different aspects of human behavior, such as sociability, mood, physical activity, sleep, with some also being multimodal.ConclusionsThis systematic review provides in-depth analysis regarding solutions for DPMH. Results show growth in proposals for DPMH sensing apps in recent years, as opposed to a scarcity of public data sets. The review shows that there are features that can be measured on smart devices that can act as proxies for mental status and well-being; however, it should be noted that the combined evidence for high-quality features for mental states remains limited. DPMH presents a great perspective for future research, mainly to reach the needed maturity for applications in clinical settings.

Dataset Information

Investigating data accessibility of personal health apps.

Publications

Investigating data accessibility of personal health apps.

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