Opportunities and challenges in the use of personal health data for health research

Bietz, Matthew J.; Bloss, Cinnamon S.; Calvert, Scout; Godino, Job G.; Gregory, Judith; Claffey, Michael P.; Sheehan, Jerry; Patrick, Kevin

doi:10.1093/jamia/ocv118

Cited by 118 publications

(117 citation statements)

References 7 publications

(5 reference statements)

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“…Likewise, recent literature has deemed the informed consent process an ineffective formality, as participants often “do not have the time nor the expertise to decipher what is actually being said” or read [27]. Even when a privacy policy does explicitly state intended uses of personal health information, consumers are often unaware of the nature and extent of data sharing to which they are consenting [15]. For instance, a recent study of privacy policies found that multiple health tracking apps share sensitive user information with third parties, largely unbeknownst to consumers [24].…”

Section: Discussionmentioning

confidence: 99%

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Privacy Attitudes among Early Adopters of Emerging Health Technologies

et al. 2016

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IntroductionAdvances in health technology such as genome sequencing and wearable sensors now allow for the collection of highly granular personal health data from individuals. It is unclear how people think about privacy in the context of these emerging health technologies. An open question is whether early adopters of these advances conceptualize privacy in different ways than non-early adopters.PurposeThis study sought to understand privacy attitudes of early adopters of emerging health technologies.MethodsTranscripts from in-depth, semi-structured interviews with early adopters of genome sequencing and health devices and apps were analyzed with a focus on participant attitudes and perceptions of privacy. Themes were extracted using inductive content analysis.ResultsAlthough interviewees were willing to share personal data to support scientific advancements, they still expressed concerns, as well as uncertainty about who has access to their data, and for what purpose. In short, they were not dismissive of privacy risks. Key privacy-related findings are organized into four themes as follows: first, personal data privacy; second, control over personal information; third, concerns about discrimination; and fourth, contributing personal data to science.ConclusionEarly adopters of emerging health technologies appear to have more complex and nuanced conceptions of privacy than might be expected based on their adoption of personal health technologies and participation in open science. Early adopters also voiced uncertainty about the privacy implications of their decisions to use new technologies and share their data for research. Though not representative of the general public, studies of early adopters can provide important insights into evolving attitudes toward privacy in the context of emerging health technologies and personal health data research.

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Section: Discussionmentioning

confidence: 99%

“…Individuals drawn from this group (N = 11) were appropriate for our current study given that they were early adopters of personal health tracking technologies. The original study has been previously described [15], and additional information about the HDE Project can be found online at hdexplore.calit2.net.…”

Section: Methodsmentioning

confidence: 99%

Privacy Attitudes among Early Adopters of Emerging Health Technologies

et al. 2016

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“…Terms of Service and other end-user agreements governing these applications tend to permit collection, aggregation, and analysis of usage and behavioural data without clear indications of how data will be used in the future, beyond general statements about third party access. H-IoT devices can generate 'invisible data' for which the user is unaware of the scope or granularity of parameters being measured (Peppet 2014;Denecke et al 2015;Bietz et al 2016). A lack of an explicit informed consent mechanism in end-user agreements between H-IoT manufacturers and users gives cause for concern (Fairfield and Shtein 2014), even when 'participants' are 'de-identified' (Ioannidis 2013), when the data generated are intended to be re-purposed for medical research or comparable consumer analytics (Taddeo 2016).…”

Section: Consent and The Uncertain Value Of H-iot Datamentioning

confidence: 99%

“…The uncertain risks of H-IoT should, however, be considered next to the potential benefits of aggregation and re-use, both for the user's direct healthcare and wellbeing and for the development of medical knowledge (Bietz et al 2016). …”

Section: Consent and The Uncertain Value Of H-iot Datamentioning

confidence: 99%

Ethics of the health-related internet of things: a narrative review

Mittelstadt

2017

Ethics Inf Technol

104

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“…In the context of a chronic disease, there are a large number of data points that may be self-tracked. They fall into a set of well-known data types, or dimensions, which include signs and symptoms of the disease, biomarkers and behavioral markers like physical activity, treatments, selfmanagement strategies, as well as potential environmental factors [15,98]. Designers traditionally rely on current scientific knowledge about the disease to identify which of these data types, and which specific variables among them, to incorporate in their self-tracking tools.…”

Section: Introductionmentioning

confidence: 99%

Designing in the Dark

McKillop

Mamykina

Elhadad

2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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The design of personal health informatics tools has traditionally been explored in self-monitoring and behavior change. There is an unmet opportunity to leverage selftracking of individuals and study diseases and health conditions to learn patterns across groups. An open research question, however, is how to design engaging self-tracking tools that also facilitate learning at scale. Furthermore, for conditions that are not well understood, a critical question is how to design such tools when it is unclear which data types are relevant to the disease. We outline the process of identifying design requirements for self-tracking endometriosis, a highly enigmatic and prevalent disease, through interviews (N=3), focus groups (N=27), surveys (N=741), and content analysis of an online endometriosis community (1500 posts, N=153 posters) and show value in triangulating across these methods. Finally, we discuss tensions inherent in designing self-tracking tools for individual use and population analysis, making suggestions for overcoming these tensions.

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Opportunities and challenges in the use of personal health data for health research

Cited by 118 publications

References 7 publications

Privacy Attitudes among Early Adopters of Emerging Health Technologies

Privacy Attitudes among Early Adopters of Emerging Health Technologies

Ethics of the health-related internet of things: a narrative review

Designing in the Dark

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