Assessment of the Fairness of Privacy Policies of Mobile Health Apps: Scale Development and Evaluation in Cancer Apps (Preprint)

Benjumea, Jaime; Ropero, Jorge; Rivera-Romero, Octavio; Dorronzoro, Enrique; Carrasco, Alejandro

doi:10.2196/preprints.17134

Cited by 3 publications

(8 citation statements)

References 14 publications

(18 reference statements)

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“…We decided to follow the modified method because we had identified the initial discussion points in our previous work 23 and, therefore, our study started collecting opinions on these pre-defined voting questions. Traditional Delphi method normally starts identifying the items to be evaluated in the later rounds using open-ended questions 44 .…”

Section: Methodsmentioning

confidence: 99%

“…In the field of mHealth, some authors have studied privacy policies in apps, since privacy policies represent the contract by which the data controller agrees to maintain the user privacy. Some papers have analyzed privacy policies in apps from different health conditions, like headache 19 , chronic insomnia 20 , depression 21 , blood pressure and diabetes 22 , cancer 23 , and mHealth apps in general [24][25][26] . The existence 27,28 , content [19][20][21][23][24][25][26][29][30][31][32][33][34] , and even the legibility of a privacy policy 22,26,34,35 were studied by researchers.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Some papers have analyzed privacy policies in apps from different health conditions, like headache 19 , chronic insomnia 20 , depression 21 , blood pressure and diabetes 22 , cancer 23 , and mHealth apps in general [24][25][26] . The existence 27,28 , content [19][20][21][23][24][25][26][29][30][31][32][33][34] , and even the legibility of a privacy policy 22,26,34,35 were studied by researchers. Results about the existence of a privacy policy were varied.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Results about the existence of a privacy policy were varied. Some studies stated that 90 % of the apps had a privacy policy 25,27 , others concluded that between 69 and 75 % of the apps had a privacy policy 23,32,36 . Other authors stated than less than half the apps had a privacy policy 21,26 .…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…are some proposals to assess privacy policies in mobile health applications, these are heterogeneous and even not very objective. Trying to fill this gap, we proposed a methodology for assessing privacy policies based on the GDPR 23 . This methodology, specifically designed for privacy policies in mobile health applications, consists of verifying, as a checklist, not only the mere existence or not of certain items in the assessed privacy policy, but also verifying that information is given in a transparent way, avoiding vague descriptions.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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A Proposal for a Robust Validated Weighted General Data Protection Regulation-Based Scale to Assess the Quality of Privacy Policies of Mobile Health Applications: An eDelphi Study

Benjumea,

Ropero,

Dorronzoro-Zubiete

et al. 2023

Methods Inf Med

Self Cite

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Background: Healthcare services are undergoing a digital transformation in which the Participatory Health Informatics field has a key role. Within this field, studies aimed to assess the quality of digital tools, including mHealth apps, are conducted. Privacy is one dimension of the quality of a mHealth app. Privacy consists of several components, including organizational, technical and legal safeguards. Within legal safeguards, giving transparent information to the users on how their data is handled is crucial. This information is usually disclosed to users through the privacy policy document. Assessing the quality of a privacy policy is a complex task and several scales supporting this process have been proposed in the literature. However, these scales are heterogeneous and even not very objective. In our previous study, we proposed a checklist of items guiding the assessment of the quality of a mHealth app privacy policy, based on the General Data Protection Regulation. Objective: To refine the robustness of our General Data Protection Regulation-based privacy scale to assess the quality of a mHealth app privacy policy, to identify new items, and to assign weights for every item in the scale. Methods: A two-round modified eDelphi study was conducted involving a privacy expert panel. Results: After the Delphi process, all the items in the scale were considered "important" or "very important" (4 and 5 in a 5-point Likert scale, respectively) by most of the experts. One of the original items was suggested to be reworded, while 8 tentative items were suggested. Only 2 of them were finally added after Round 2. 11 of the 16 items in the scale were considered "very important" (weight of 1), while the other 5 were considered "important" (weight of 0.5). Conclusions: The Benjumea privacy scale is a new robust tool to assess the quality of a mHealth app privacy policy, providing a deeper and complementary analysis to other scales that assesses the general quality. Also, this robust scale provides a guideline for the development of high-quality privacy policies of mHealth apps. Keywords: participatory health informatics; mHealth apps; privacy policies; assessment scale; GDPR;

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Section: Methodsmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

See 3 more Smart Citations

A Proposal for a Robust Validated Weighted General Data Protection Regulation-Based Scale to Assess the Quality of Privacy Policies of Mobile Health Applications: An eDelphi Study

Benjumea,

Ropero,

Dorronzoro-Zubiete

et al. 2023

Methods Inf Med

Self Cite

View full text Add to dashboard Cite

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Post-COVID Public Health Surveillance and Privacy Expectations in the United States: Scenario-Based Interview Study

Seberger¹,

Patil²

2021

JMIR Mhealth Uhealth

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Background Smartphone-based apps designed and deployed to mitigate the COVID-19 pandemic may become infrastructure for postpandemic public health surveillance in the United States. Through the lenses of privacy concerns and user expectations of digital pandemic mitigation techniques, we identified possible long-term sociotechnical implications of such an infrastructure. Objective We explored how people in the United States perceive the possible routinization of pandemic tracking apps for public health surveillance in general. Our interdisciplinary analysis focused on the interplay between privacy concerns, data practices of surveillance capitalism, and trust in health care providers. We conducted this analysis to achieve a richer understanding of the sociotechnical issues raised by the deployment and use of technology for pandemic mitigation. Methods We conducted scenario-based, semistructured interviews (n=19) with adults in the United States. The interviews focused on how people perceive the short- and long-term privacy concerns associated with a fictional smart thermometer app deployed to mitigate the “outbreak of a contagious disease.” In order to elicit future-oriented discussions, the scenario indicated that the app would continue functioning “after the disease outbreak has dissipated.” We analyzed interview transcripts using reflexive thematic analysis. Results In the context of pandemic mitigation technology, including app-based tracking, people perceive a core trade-off between public health and personal privacy. People tend to rationalize this trade-off by invoking the concept of “the greater good.” The interplay between the trade-off and rationalization forms the core of sociotechnical issues that pandemic mitigation technologies raise. Participants routinely expected that data collected through apps related to public health would be shared with unknown third parties for the financial gain of the app makers. This expectation suggests a perceived alignment between an app-based infrastructure for public health surveillance and the broader economics of surveillance capitalism. Our results highlight unintended and unexpected sociotechnical impacts of routinizing app-based tracking on postpandemic life, which are rationalized by invoking a nebulous concept of the greater good. Conclusions While technologies such as app-based tracking could be useful for pandemic mitigation and preparedness, the routinization of such apps as a form of public health surveillance may have broader, unintentional sociotechnical implications for individuals and the societies in which they live. Although technology has the potential to increase the efficacy of pandemic mitigation, it exists within a broader network of sociotechnical concerns. Therefore, it is necessary to consider the long-term implications of pandemic mitigation technologies beyond the immediate needs of addressing the COVID-19 pandemic. Potential negative consequences include the erosion of patient trust in health care systems and providers, grounded in concerns about privacy violations and overly broad surveillance.

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Post-COVID Public Health Surveillance and Privacy Expectations in the United States: Scenario-Based Interview Study (Preprint)

Seberger¹,

Patil²

2021

Preprint

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BACKGROUND Smartphone-based apps designed and deployed to mitigate the ongoing COVID-19 pandemic are poised to become an infrastructure for post-pandemic public health surveillance. Yet people frequently identify deep-seated privacy concerns about such apps, invoking rationalizations such as contributing to ‘the greater good’ to justify their privacy-related discomfort. We adopt a future-oriented lens and consider participant perceptions of the potential routinization of such apps as a general public health surveillance infrastructure. This work focuses on the need to temper the surveillant achievement of public health with consideration for potential colonization of public health by the exploitative mechanisms of surveillance capitalism. OBJECTIVE This study develops an understanding of people’s perceptions of the potential routinization of apps as an infrastructure for public health surveillance after the COVID-19 pandemic has ended. METHODS We conducted scenario-based interviews (n = 19) with adults in the United States in order to understand how people perceive the short- and long-term privacy concerns associated with a fictional smart-thermometer app deployed to mitigate the ‘outbreak of a contagious disease.’ The scenario indicated that the app would continue functioning ‘after the disease outbreak as dissipated.’ We analyzed participant interviews using reflexive thematic analysis (TA). RESULTS Participants contextualized their perceptions of the app in a core trade-off between public health and personal privacy. They further evidenced the widespread expectation that data collected through health-surveillant apps would be shared with unknown third parties for financial gain. This expectation suggests a perceived alignment between health surveillant technologies and the broader economics of surveillance capitalism. Because of such expectations, participants routinely rationalized the use of the fictional app, which they viewed as always already privacy-invasive, by invoking ‘the greater good.’ We uncover that ‘the greater good’ is multi-faceted and self-contradictory, evidencing participants’ worry that health surveillance apps will contribute to an expansion of exploitative forms of surveillance. CONCLUSIONS While apps may be an effective means of pandemic-mitigation and preparedness, such apps are not exclusively beneficial in their outcomes. The potential routinization of apps as an infrastructure of general public health surveillance fosters end-user exploitation. Through its alignment with surveillance capitalism, such exploitation potentially erodes patient trust in the health care systems and providers that care for them. The inroads to such exploitation are present in participants’ manifestation of digital resignation, hyperbolic scaling, expectation of an infrastructure that works ‘too well,’ and generalized privacy fatalism.

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Assessment of the Fairness of Privacy Policies of Mobile Health Apps: Scale Development and Evaluation in Cancer Apps (Preprint)

Cited by 3 publications

References 14 publications

A Proposal for a Robust Validated Weighted General Data Protection Regulation-Based Scale to Assess the Quality of Privacy Policies of Mobile Health Applications: An eDelphi Study

A Proposal for a Robust Validated Weighted General Data Protection Regulation-Based Scale to Assess the Quality of Privacy Policies of Mobile Health Applications: An eDelphi Study

Post-COVID Public Health Surveillance and Privacy Expectations in the United States: Scenario-Based Interview Study

Post-COVID Public Health Surveillance and Privacy Expectations in the United States: Scenario-Based Interview Study (Preprint)

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