Investigating data accessibility of personal health apps

Personal Health Data Tracking by Blind and Low-Vision People: A Survey Study (Preprint)

BACKGROUND

Personal health technologies, including wearable tracking devices and mobile apps, have great potential to equip the general population with the ability to monitor and manage their health. However, being designed for sighted people, much of their functionality is largely inaccessible to the blind and low-vision (BLV) population, threatening the equitable access to personal health data (PHD) and health care services.

OBJECTIVE

This study aims to understand why and how BLV people collect and use their PHD and the obstacles they face in doing so. Such knowledge can inform accessibility researchers and technology companies of the unique self-tracking needs and accessibility challenges that BLV people experience.

METHODS

We conducted a web-based and phone survey with 156 BLV people. We reported on quantitative and qualitative findings regarding their PHD tracking practices, needs, accessibility barriers, and work-arounds.

RESULTS

BLV respondents had strong desires and needs to track PHD, and many of them were already tracking their data despite many hurdles. Popular tracking items (ie, exercise, weight, sleep, and food) and the reasons for tracking were similar to those of sighted people. BLV people, however, face many accessibility challenges throughout all phases of self-tracking, from identifying tracking tools to reviewing data. The main barriers our respondents experienced included suboptimal tracking experiences and insufficient benefits against the extended burden for BLV people.

CONCLUSIONS

We reported the findings that contribute to an in-depth understanding of BLV people's motivations for PHD tracking, tracking practices, challenges, and work-arounds. Our findings suggest that various accessibility challenges hinder BLV individuals from effectively gaining the benefits of self-tracking technologies. On the basis of the findings, we discussed design opportunities and research areas to focus on making PHD tracking technologies accessible for all, including BLV people.

Willingness to Share Wearable Device Data for Research Among Mechanical Turk Workers: Web-Based Survey Study

Background

Wearable devices that are used for observational research and clinical trials hold promise for collecting data from study participants in a convenient, scalable way that is more likely to reach a broad and diverse population than traditional research approaches. Amazon Mechanical Turk (MTurk) is a potential resource that researchers can use to recruit individuals into studies that use data from wearable devices.

Objective

This study aimed to explore the characteristics of wearable device users on MTurk that are associated with a willingness to share wearable device data for research. We also aimed to determine whether compensation was a factor that influenced the willingness to share such data.

Methods

This was a secondary analysis of a cross-sectional survey study of MTurk workers who use wearable devices for health monitoring. A 19-question web-based survey was administered from March 1 to April 5, 2018, to participants aged ≥18 years by using the MTurk platform. In order to identify characteristics that were associated with a willingness to share wearable device data, we performed logistic regression and decision tree analyses.

Results

 A total of 935 MTurk workers who use wearable devices completed the survey. The majority of respondents indicated a willingness to share their wearable device data (615/935, 65.8%), and the majority of these respondents were willing to share their data if they received compensation (518/615, 84.2%). The findings from our logistic regression analyses indicated that Indian nationality (odds ratio [OR] 2.74, 95% CI 1.48-4.01, P=.007), higher annual income (OR 2.46, 95% CI 1.26-3.67, P=.02), over 6 months of using a wearable device (OR 1.75, 95% CI 1.21-2.29, P=.006), and the use of heartbeat and pulse tracking monitoring devices (OR 1.60, 95% CI 0.14-2.07, P=.01) are significant parameters that influence the willingness to share data. The only factor associated with a willingness to share data if compensation is provided was Indian nationality (OR 0.47, 95% CI 0.24-0.9, P=.02). The findings from our decision tree analyses indicated that the three leading parameters associated with a willingness to share data were the duration of wearable device use, nationality, and income.

Conclusions

Most wearable device users indicated a willingness to share their data for research use (with or without compensation; 615/935, 65.8%). The probability of having a willingness to share these data was higher among individuals who had used a wearable for more than 6 months, were of Indian nationality, or were of American (United States of America) nationality and had an annual income of more than US $20,000. Individuals of Indian nationality who were willing to share their data expected compensation significantly less often than individuals of American nationality (P=.02).

Updating HIPAA for the electronic medical record era

With advances in technology, patients increasingly expect to access their health information on their phones and computers seamlessly, whenever needed, to meet their clinical needs. The 1996 passage of the Health Insurance Portability and Accountability Act (HIPAA), modifications made by the Health Information Technology for Economic and Clinical Health Act (HITECH), and the recent 21st Century Cures Act (Cures) promise to make patients' health information available to them without special effort and at no cost. However, inconsistencies among these policies' definitions of what is included in "health information", widespread variation in electronic health record system capabilities, and differences in local health system policies around health data release have created a confusing landscape for patients, health care providers, and third parties who reuse health information. In this article, we present relevant regulatory history, describe challenges to health data portability and fluidity, and present the authors' policy recommendations for lawmakers to consider so that the vision of HIPAA, HITECH, and Cures may be fulfilled.

Sleep apps: current limitations and challenges

Sleep app ownership is increasing exponentially, due to their accessibility and ease-of-use. However, there are several concerns regarding the use of sleep apps. Few sleep apps demonstrate empirical evidence to support their claims, and if they do, this evidence can be based on significant methodological limitations. In addition, there are data privacy concerns with regards to sleep apps, which share sensitive user data with business and marketing partners, unbeknownst to their users. Moreover, sleep apps may increase engagement with healthcare professionals, which may place additional strain on under-pressure sleep services. This would be compounded by the fact that some sleep apps produce many false positives, and clinicians would need more time to analyze the data provided by these apps. In the future, sleep apps must undergo rigorous validation studies and grant more autonomy to their users over how their data is shared.

Mobile Applications for Caregivers of Individuals with Chronic Conditions and/or Diseases: Quantitative Content Analysis

BACKGROUND

Mobile health applications can help empowering caregivers and promote their wellbeing and their quality of life.

OBJECTIVE

To analyze the technical and functional characteristics of mobile health applications designed for caregivers of individuals with chronic conditions and/or diseases.

MATERIALS AND METHODS

Systematic search of mobile health applications for smartphones (apps) based on the PRISMA standard for systematic reviews on the App Store and Google Play store during May and June 2018. A second search was carried out on the Pubmed and Google Scholar database to determine whether the applications had been tested or evaluated with results published in scientific journals and then a third search was performed on the Spanish health apps catalogs to evaluate the quality and security of the selected apps.

RESULTS

746 available health apps were identified and 43 were included in this paper. 67% (n = 29) of the apps were aimed exclusively at informal caregivers, 51% (n = 22) were designed to support caregivers and only 21% (n = 9) of them provided any sort of social or emotional support. The screening in Pubmed and Google Scholar determined that the apps analyzed lacked published papers and most of the apps (84%; n = 36) lacked approval from official agencies supporting their usage.

DISCUSSION

The apps available on the market that meet the actual needs of caregivers are limited. Most of the apps were aimed at improving the care of the individual with a chronic illness.

CONCLUSION

Despite the remarkable benefits of mHealth regarding the care of chronic diseases, a relatively poor contribution has been made to support caregivers. Customized apps, interventions assessing their effectiveness and adequate evidence are needed to understand the impact of this digital tool on caregivers' health.

Emerging health data platforms: From individual control to collective data governance

Health data have enormous potential to transform healthcare, health service design, research, and individual health management. However, health data collected by institutions tend to remain siloed within those institutions limiting access by other services, individuals or researchers. Further, health data generated outside health services (e.g., from wearable devices) may not be easily accessible or useable by individuals or connected to other parts of the health system. There are ongoing tensions between data protection and the use of data for the public good (e.g., research). Concurrently, there are a number of data platforms that provide ways to disrupt these traditional health data siloes, giving greater control to individuals and communities. Through four case studies, this paper explores platforms providing new ways for health data to be used for personal data sharing, self-health management, research, and clinical care. The case-studies include data platforms: PatientsLikeMe, Open Humans, Health Record Banks, and unforgettable.me. These are explored with regard to what they mean for data access, data control, and data governance. The case studies provide insight into a shift from institutional to individual data stewardship. Looking at emerging data governance models, such as data trusts and data commons, points to collective control over health data as an emerging approach to issues of data control. These shifts pose challenges as to how “traditional” health services make use of data collected on these platforms. Further, it raises broader policy questions regarding how to decide what public good data should be put towards.

Deploying Patient-Facing Application Programming Interfaces: Thematic Analysis of Health System Experiences

BACKGROUND

Health systems have recently started to activate patient-facing application programming interfaces (APIs) to facilitate patient access to health data and other interactions.

OBJECTIVE

This study sought to ascertain health systems' understanding, strategies, governance, and organizational infrastructure around patient-facing APIs, as well as their business drivers and barriers, to facilitate national learning, policy, and progress toward adoption.

METHODS

We performed a content analysis of semistructured interviews with a convenience sample of 10 health systems known to be leading adopters of health technology, having either implemented or planning to implement patient-facing APIs.

RESULTS

Of the 10 health systems, eight had operational patient-facing APIs, with organizational strategy driven most by federal policy, the emergence of Health Records on iPhone, and feelings of ethical obligation. The two priority use cases identified were enablement of a patient's longitudinal health record and digital interactions with the health system. The themes most frequently cited as barriers to the increased use of patient-facing APIs were security concerns, an immature app ecosystem that does not currently offer superior functionality compared with widely adopted electronic health record (EHR)-tethered portals, a lack of business drivers, EHR vendor hesitation toward data sharing, and immature technology and standards.

CONCLUSIONS

Our findings reveal heterogeneity in health system understanding and approaches to the implementation and use of patient-facing APIs. Ongoing study, targeted policy interventions, and sharing of best practices appear necessary to achieve successful national implementation.