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.

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.

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

BACKGROUND: Cancer patients are increasingly using mobile health (mHealth) apps to take control of their health. Many studies have explored their efficiency, content, usability, and adherence; however, these apps have created a new set of privacy challenges, as they store personal and sensitive data. OBJECTIVE: The purpose of this study was to refine and evaluate a scale based on the General Data Protection Regulation and assess the fairness of privacy policies of mHealth apps. METHODS: Based on the experience gained from our previous work, we redefined some of the items and scores of our privacy scale. Using the new version of our scale, we conducted a case study in which we analyzed the privacy policies of cancer Android apps. A systematic search of cancer mobile apps was performed in the Spanish version of the Google Play website. RESULTS: The redefinition of certain items reduced discrepancies between reviewers. Thus, use of the scale was made easier, not only for the reviewers but also for any other potential users of our scale. Assessment of the privacy policies revealed that 29% (9/31) of the apps included in the study did not have a privacy policy, 32% (10/31) had a score over 50 out of a maximum of 100 points, and 39% (12/31) scored fewer than 50 points. CONCLUSIONS: In this paper, we present a scale for the assessment of mHealth apps that is an improved version of our previous scale with adjusted scores. The results showed a lack of fairness in the mHealth app privacy policies that we examined, and the scale provides developers with a tool to evaluate their privacy policies.

Privacy Assessment in Mobile Health Apps: Scoping Review.

BACKGROUND: Privacy has always been a concern, especially in the health domain. The proliferation of mobile health (mHealth) apps has led to a large amount of sensitive data being generated. Some authors have performed privacy assessments of mHealth apps. They have evaluated diverse privacy components; however, different authors have used different criteria for their assessments. OBJECTIVE: This scoping review aims to understand how privacy is assessed for mHealth apps, focusing on the components, scales, criteria, and scoring methods used. A simple taxonomy to categorize the privacy assessments of mHealth apps based on component evaluation is also proposed. METHODS: We followed the methodology defined by Arksey and O'Malley to conduct a scoping review. Included studies were categorized based on the privacy component, which was assessed using the proposed taxonomy. RESULTS: The database searches retrieved a total of 710 citations-24 of them met the defined selection criteria, and data were extracted from them. Even though the inclusion criteria considered articles published since 2009, all the studies that were ultimately included were published from 2014 onward. Although 12 papers out of 24 (50%) analyzed only privacy, 8 (33%) analyzed both privacy and security. Moreover, 4 papers (17%) analyzed full apps, with privacy being just part of the assessment. The evaluation criteria used by authors were heterogeneous and were based on their experience, the literature, and/or existing legal frameworks. Regarding the set of items used for the assessments, each article defined a different one. Items included app permissions, analysis of the destination, analysis of the content of communications, study of the privacy policy, use of remote storage, and existence of a password to access the app, among many others. Most of the included studies provided a scoring method that enables the comparison of privacy among apps. CONCLUSIONS: The privacy assessment of mHealth apps is a complex task, as the criteria used by different authors for their evaluations are very heterogeneous. Although some studies about privacy assessment have been conducted, a very large set of items to evaluate privacy has been used up until now. In-app information and privacy policies are primarily utilized by the scientific community to extract privacy information from mHealth apps. The creation of a scale based on more objective criteria is a desirable step forward for privacy assessment in the future.

Prevalencia de mastitis en vacas lecheras lactantes en el municipio de San Pedro de los Milagros, Antioquia / Prevalence of mastitis in milking cows in San Pedro de los Milagros, Antioquia, Colombia

En el período comprendido entre julio – septiembre de 1999, se realizó un muestreo de leche en el municipio de San Pedro de los Milagros, con el fin de medir la prevalencia de mastitis en la zona por medio de California Mastitis Test (CMT). El muestreo comprendió un grupo de 112 vacas lactantes escogidas al azar, provenientes de 15 veredas, a las cuales se les realizó C.M.T. por cuarto. A las vacas positivas o con un resultado sospechoso se les tomó muestra de leche para realizar recuento celular, cultivo y antibiograma. Los datos obtenidos se presentan en forma descriptiva; fue utilizada la prueba estadística chi cuadrado, para medir el grado de asociación de algunas variables. La prevalencia de mastitis por cuarto en el municipio fue de 12.3 por ciento (55 cuartos con resultado igual o mayor a trazas). Entre los cuartos afectados del total de los muestreados, el resultado del CMT más frecuentemente hallado fue el de dos cruces con un 4.4 por ciento. En los cuartos muestreados para cultivo, la bacteria más frecuentemente aislada fue Streptococcus agalactiae, seguida de Estafilococo coagulasa negativo y Staphylococccus aureus con un 47 por ciento, 14,6 por ciento, y 13 por ciento respectivamente. En el antibiograma realizado se encontró que del total de muestras positivas a Str. agalactiae un 19.3 por ciento mostró resistencia a la penicilina. La prevalencia de mastitis por cuarto en la zona no fue muy alta, si se tiene en cuenta que un porcentaje importante de productores no está realizando las mínimas medidas de higiene al ordeño como lavado y secado de la ubre (57.1 por ciento) y la desinfección postordeño (42.9 por ciento). Aunque las bacterias aisladas son las más frecuentemente reportadas en otros trabajos realizados en nuestro país y en resultados aislados reportados por clínicos de campo de la misma zona, es importante resaltar la resistencia que están adquiriendo las bacterias, para nuestro caso el Str. agalactiae a la penicilina; sin embargo...