Background Health care 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 an 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 are 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 an 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 an 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 eight tentative items were suggested. Only two of them were finally added after Round 2. Eleven 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).

Conclusion The Benjumea privacy scale is a new robust tool to assess the quality of an mHealth app privacy policy, providing a deeper and complementary analysis to other scales. Also, this robust scale provides a guideline for the development of high-quality privacy policies of mHealth apps.

J.B. directed the study and took the lead in conducting the Delphi process. J.B. also supported the data analysis, the calculation of statistical values, and the interpretation of the data. J.R. took the lead in drafting the manuscript, supported by J.B., E.D.-Z., O.R.-R., and A.C. J.R. also participated in study direction, contributed to the calculation of statistical values, and supported the data analysis and interpretation of the data. E.D.-Z. participated in study direction, data analysis, and interpretation of the data. O.R.-R. supported the Delphi process, resolved discrepancies, and reviewed the final version of the manuscript. A.C. resolved discrepancies, acquired funding through a research project, participated in the interpretation of data, and reviewed the final version of the manuscript. J.B., J.R., E.D.-Z., O.R.-R., and A.C. selected and contacted experts for the Delphi process.

Received: 06 July 2022

Accepted: 11 July 2023

Accepted Manuscript online:
17 August 2023

Article published online:
22 December 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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