Data sharing practices of medicines related apps and the mobile ecosystem: traffic, content, and network analysis


Objectives To investigate whether and how user data are shared by top rated medicines related mobile applications (apps) and to characterise privacy risks to app users, both clinicians and consumers.

Design Traffic, content, and network analysis.

Setting Top rated medicines related apps for the Android mobile platform available in the Medical store category of Google Play in the United Kingdom, United States, Canada, and Australia.

Participants 24 of 821 apps identified by an app store crawling program. Included apps pertained to medicines information, dispensing, administration, prescribing, or use, and were interactive.

Interventions Laboratory based traffic analysis of each app downloaded onto a smartphone, simulating real world use with four dummy scripts. The app’s baseline traffic related to 28 different types of user data was observed. To identify privacy leaks, one source of user data was modified and deviations in the resulting traffic observed.

Main outcome measures Identities and characterisation of entities directly receiving user data from sampled apps. Secondary content analysis of company websites and privacy policies identified data recipients’ main activities; network analysis characterised their data sharing relations.

Results 19/24 (79%) of sampled apps shared user data. 55 unique entities, owned by 46 parent companies, received or processed app user data, including developers and parent companies (first parties) and service providers (third parties). 18 (33%) provided infrastructure related services such as cloud services. 37 (67%) provided services related to the collection and analysis of user data, including analytics or advertising, suggesting heightened privacy risks. Network analysis revealed that first and third parties received a median of 3 (interquartile range 1-6, range 1-24) unique transmissions of user data. Third parties advertised the ability to share user data with 216 “fourth parties”; within this network (n=237), entities had access to a median of 3 (interquartile range 1-11, range 1-140) unique transmissions of user data. Several companies occupied central positions within the network with the ability to aggregate and re-identify user data.

Conclusions Sharing of user data is routine, yet far from transparent. Clinicians should be conscious of privacy risks in their own use of apps and, when recommending apps, explain the potential for loss of privacy as part of informed consent. Privacy regulation should emphasise the accountabilities of those who control and process user data. Developers should disclose all data sharing practices and allow users to choose precisely what data are shared and with whom.


Journalists recently revealed that Australia’s most popular medical appointment booking app, HealthEngine, routinely shared 100s of users’ private medical information to personal injury law firms as part of a referral partnership contract.1 Although the company claimed this was only done with users’ consent, these practices were not included in the privacy policy but in a separate “collection notice,” and there was no opportunity for users to opt-out if they wished to use the application (app).1

Mobile health apps are a booming market targeted at both patients and health professionals.2 These apps claim to offer tailored and cost effective health promotion, but they pose unprecedented risk to consumers’ privacy given their ability to collect user data, including sensitive information. Health app developers routinely, and legally, share consumer data with third parties in exchange for services that enhance the user’s experience (eg, connecting to social media) or to monetise the app (eg, hosted advertisements).34 Little transparency exists around third party data sharing, and health apps routinely fail to provide privacy assurances, despite collecting and transmitting multiple forms of personal and identifying information.56789

Third parties may collate data on an individual from multiple sources. Threats to privacy are heightened when data are aggregated across multiple sources and consumers have no way to identify whether the apps or websites they use share their data with the same third party providers.3 Collated data are used to populate proprietary algorithms that promise to deliver “insights” into consumers. Thus, the sharing of user data ultimately has real world consequences in the form of highly targeted advertising or algorithmic decisions about insurance premiums, employability, financial services, or suitability for housing. These decisions may be discriminatory or made on the basis of incomplete or inaccurate data, with little recourse for consumers.1011

Apps that provide medicines related information and services may be particularly likely to share or sell data, given that these apps collect sensitive, specific medical information of high value to third parties.12 For example, drug information and clinical decision support apps that target health professionals are of particular interest to pharmaceutical companies, which can offer tailored advertising and glean insights into prescribing habits.13 Drug adherence apps targeting consumers can deliver a detailed account of a patient’s health history and behaviours related to the use of medicines.14

We investigated the nature of data transmission to third parties among top rated medicines related apps, including the type of consumer data and the number and identities of third parties, and we characterised the relations among third parties to whom consumer data are transmitted.


We carried out this study in two phases: the first was a traffic analysis of the data sharing practices of the apps and the second was a content and network analysis to characterise third parties and their interrelations (box 1).


We purposefully sampled medicines related apps that were considered prominent owing to being highly downloaded, rated in the top 100, or endorsed by credible organisations. During 17 October to 17 November 2017, we triangulated two sampling strategies to identify apps. In the first strategy we used a crawling program that interacted directly with the app store’s application programming interface. This program systematically sampled the metadata for the top 100 ranked free and paid apps from the Medical store category of the United Kingdom, United States, Australian, and Canadian Google Play stores on a weekly basis. In the second strategy we screened for recommended or endorsed apps on the website of an Australian medicines related not-for-profit organisation, a curated health app library, a published systematic review, and personal networks of practising pharmacists.

One investigator screened 821 apps for any app names that were potentially related to medicines (ie, managing drugs, adherence, medicines or prescribing information) and excluded apps with irrelevant names (eg, “Pregnancy Calendar,” “Gray’s Anatomy–Atlas,” “Easy stop Smoking,” “Breathing Zone”) (fig 1). Two investigators then independently screened 67 app store descriptions according to the following inclusion criteria:

Fig 1

 Pertains to medicines, such as managing drugs, adherence, medicines or prescribing information

● Available for the Android mobile platform in Google Play to an Australian consumer

● Requests at least one “dangerous” permission, as defined by Google Play,15 or claims to collect or share user data

● Has some degree of interactivity with the user, defined as requiring user input.

We excluded apps if they were available exclusively to customers of a single company (pharmacy, insurance plan, or electronic health record), were targeted at or restricted to use in a single country (ie, a formulary app for UK health professionals employed by the National Health Service), were prohibitively expensive (>$100; >£76; >€88), or were no longer available during the analysis period.

Data collection

Traffic analysis

The methods of the traffic analysis are described in detail elsewhere.16 For this analysis we made use of Agrigento, a tool for detecting obfuscated privacy leaks such as encoding or encryption in Android apps. In a laboratory setting, between November and December 2017, we downloaded each app onto a Google Pixel 1 smartphone running Android 7.1. We purchased subscriptions when required (in the form of in-app purchases).

Between December 2017 and January 2018 we simulated real world, in-depth use of the app using four dummy scripted user profiles (one doctor, one pharmacist, and two consumers; see supplementary file), including logging in and interacting with the app while it was running, which involved manually clicking on all buttons, adjusting all settings, and inputting information from the dummy profile when applicable. As all apps were available to the public, we randomised the dummy user profiles irrespective of the app’s target user group.

Using one randomly assigned dummy scripted user profile for each app, we ran the app 14 times to observe its “normal” network traffic related to 28 different prespecified types of user data, such as Android ID, birthday, email, precise location, or time zone. Fourteen executions of the app were required to establish a baseline and to minimise the occurrence of false positives.16 Then we modified one aspect of the user’s profile (eg, location) and ran the app a 15th time to evaluate any change in the network traffic. This differential analysis allowed the detection of an incidence of user data sharing by observing any deviations in network traffic. Change in traffic during the 15th run indicated that the modified aspect of the user’s profile was communicated by the app to the external network, meaning that user data were shared with a third party. We repeated the 15th run for each of the 28 prespecified types of user information, altering one type of data for each run.

The results of the traffic analysis included a list of domain names and respective IP addresses receiving user data and the specific types of user data they received. We identified the recipients of user data by integrating Agrigento with Shodan, a search engine for servers, to obtain geographical information for IP addresses. To reveal the identity of the entities involved, we used the public WHOIS service, a database of domain registrations. Leveraging these tools, we were able to obtain information about the hosts that receive data from the apps, such as location and owner of the remote server.

Content analysis

For each of the entities receiving user data in the traffic analysis, two investigators independently examined their Crunchbase profile, company website, and linked documents such as privacy policies, terms and conditions, or investor prospectus. The investigators extracted data related to the company’s mission, main activities, data sharing partnerships, and privacy practices related to user data into an open ended form in RedCap.17 Data were extracted between 1 February 2018 and 15 July 2018; one investigator extracted data before, and the other after, the General Data Protection Rules (GDPR) were implemented in the European Union in May 2018, which meant that some developers disclosed additional data sharing partnerships in their privacy policies.18 Any discrepancies were resolved through consensus or consolidation and by taking the more recent information as accurate.

Data analysis

We classified entities receiving user data into three categories: first parties, when the app transmitted user data to the developer or parent company (users are considered second parties); third parties, when the app directly transmitted user data to external entities; and fourth parties, companies with which third parties reported the ability to further share user data. We calculated descriptive statistics in Excel 2016 (Microsoft) for all app and company characteristics. Using NVivo 11 (QSR International), we coded unstructured data inductively, and iteratively categorised each company based on its main activities and self reported business models.

Network analysis

We combined data on apps and their associated first, third, and fourth parties into two networks. Network analysis was conducted using R, and the igraph (1.0.1) library for network analysis and tidygraph (1.1.1) for visualisation.1920 The first network represented apps and entities that directly received data (first and third parties), as identified by our traffic and privacy policy analysis. We use descriptive statistics to describe the network’s data sharing potential.

The second network represents the potential sharing of user data within the mobile ecosystem, including to fourth parties. To simplify the representation, we grouped apps, their developers, and parent companies into “families” based on shared ownership, and we removed ties to third parties that only provided infrastructure services as they did not report further data sharing partnerships with fourth parties. We report third and fourth parties’ direct and indirect access to app users’ data and summarise the scope of data potentially available to third and fourth parties through direct and indirect channels. This simulation assumes that the same person uses all apps in our sample and it shows how her or his data get distributed and multiplied across the network, identifying the most active distributors of data and the companies that occupy favourable positions in the network, enabling each to gather and aggregate user data from multiple sources.

Patient and public involvement

We undertook this research from the perspective of an Australian app user and in partnership with the Australian Communications Consumer Action Network (ACCAN), the peak body for consumer representation in the telecommunications sector. In continuation of an existing partnership,21 we jointly applied for funding from the Sydney Policy Lab, a competition designed to support and deepen policy partnerships. A representative from ACCAN was involved in preparing the funding application; designing the study protocol, including identifying outcomes of interest; team meetings related to data collection and analysis; preparing dissemination materials targeted at consumers; and designing a dissemination strategy to consumers and regulators.


Overall, 24 apps were included in the study (table 1). Although most (20/24, 83%) appeared free to download, 30% (6/20) of the “free” apps” offered in-app purchases and 30% (6/20) contained advertising as identified in the Google Play store. Of the for-profit companies (n=19), 13 had a Crunchbase profile (68%).

Data sharing practices

As per developer self report in the Google Play store, apps requested on average 4 (range 0-10) “dangerous” permissions—that is, data or resources that involve the user’s private information or stored data or can affect the operation of other apps.15 Most commonly, apps requested permission to read or write to the device’s storage (19/24, 79%), view wi-fi connections (11/24, 46%), read the list of accounts on the device (7/24, 29%), read phone status and identity, including the phone number of the device, current cellular network information, and when the user is engaged in a call (7/24, 29%), and access approximate (6/24, 25%) or precise location (6/24, 25%).

In our traffic analysis, most apps transmitted user data outside of the app (17/24, 71%). Of the 28 different types of prespecified user data, apps most commonly shared a user’s device name, operating system version, browsing behaviour, and email address (table 2). Out of 104 detected transmissions, aggregated by type of user data for each app, 98 (94%) were encrypted and six (6%) occurred in clear text. Out of 24 sampled apps, three (13%) leaked at least one type of user data in clear text, whereas the remainder 14 (58%) only transmitted encrypted user data (over HTTPS) or did not transmit user data in the traffic analysis (7/24, 29%). After implementation of the GDPR, developers disclosed additional data sharing relations within privacy policies, including for two additional apps that had not transmitted any user data during the traffic analysis. Thus, a total of 19/24 (79%) sampled apps shared user data (see supplementary table 2).

Table 3 displays the data sharing practices of the apps (see supplementary table 2 for overview of data sharing practices) detected in the traffic analysis and screening of privacy policies. We categorised first and third parties receiving user data as infrastructure providers or analysis providers. Infrastructure related entities provided services such as cloud computing, networks, servers, internet, and data storage. Analysis entities provided services related to the collection, collation, analysis, and commercialisation of user data in some capacity.

Recipients of user data

Through traffic and privacy policy analysis, we identified 55 unique entities that received or processed user data, which included app developers, their parent companies, and third parties. We classified app developers and their parent companies as “first parties”; these entities have access to user data through app or company ownership, or both. Although first parties collected user data to deliver and improve the app experience, some of these companies also described commercialising these data through advertising or selling deidentified and aggregated data or analyses to pharmaceutical companies, health insurers, or health services.

Developers engaged a range of third parties who directly received user data and provided services, ranging from error reporting to in-app advertising to processing customer service tickets. Most of these services were provided on a “freemium” basis, meaning that basic services are free to developers, but that higher levels of use or additional features are charged.

Third parties typically reserved the right to collect deidentified and aggregated data from app users for their own commercial purposes and to share these data among their commercial partners or to transfer data as a business asset in the event of a sale. For example, Flurry analytics, offered by Yahoo! helps developers to track new users, active users, sessions, and the performance of the app, and offers this service free of charge. In exchange, developers grant Flurry “the right for any purpose, to collect, retain, use, and publish in an aggregate manner . . . characteristics and activities of end users of your applications.”22 In our sample, Flurry collected Android ID, device name, and operating system version from one app; however, its privacy policy states that it may also collect data about users, including users’ activity on other sites and apps, from their parent company Verizon Communications, advertisers, publicly available sources, and other companies. These aggregated and pseudonymous (eg, identified by Android ID) data are used to match and serve targeted advertising and to associate the user’s activity across services and devices, and these data might be shared with business affiliates.22

We categorised 18 entities (18/55, 33%) as infrastructure providers, which included cloud services (Amazon Web Services, Microsoft Azure), content delivery networks (Amazon CloudFront, CloudFlare), managed cloud providers (Bulletproof, Rackspace, Tier 3), database platforms (MongoDB Cloud Services), and data storage centres (Google). Developers relied on the services of infrastructure related third parties to securely store or process user data, thus the risks to privacy are lower. However, sharing with infrastructure related third parties represents additional attack surfaces in terms of cybersecurity. Several companies providing cloud services also offered a full suite of services to developers that included data analytics or app optimisation, which would involve accessing, aggregating, and analysing app user data. The privacy policies of these entities, however, stated this would occur within the context of a relationship with the developer-as-client and thus likely does not involve commercialising app user data for third party purposes.

We categorised 37 entities (37/55, 67%) as analysis providers, which involved the collection, collation, analysis, and commercialisation of user data in some capacity. Table 4 characterises these analysis providers based on their main business activities.

A systems view of privacy

While certain data sources are clearly sensitive, personal, or identifying (eg, date of birth, drug list), others may seem irrelevant from a privacy perspective (eg, device name, Android ID). When combined, however, such information can be used to uniquely identify a user, even if not by name. Thus, we conducted a network analysis to understand how user data might be aggregated. We grouped the 55 entities identified in the traffic analysis into 46 “families” based on shared ownership, presuming that data as an asset was shared among acquiring, subsidiary, and affiliated companies as was explicitly stated in most privacy policies.23 For example, the family “Alphabet,” named for the parent company, is comprised of, Google Analytics, Crashlytics, and AdMob by Google.

Third party sharing

Supplementary figure 1 displays the results of the network analysis containing apps, and families of first and third parties that receive user data and are owned by the same parent company. The size of the entity indicates the volume of user data it sends or receives. We differentiated among apps (orange), companies whose main purpose in receiving data was for analysis, including tracking, advertising, or other analytics (grey), and companies whose main purpose in receiving data was infrastructure related, including data storage, content delivery networks, and cloud services (blue).

From the sampled apps, first and third parties received a median of 3 (interquartile range 1-6, range 1-24) unique transmissions of user data, defined as sharing of a unique type of data (eg, Android ID, birthdate, location) with a first or third party. and Alphabet (the parent company of Google) received the highest volume of user data (both received n=24), followed by Microsoft (n=14). First and third parties received a median of 3 (interquartile range 1-5; range 1-18) different types of user data from the sampled apps. and Microsoft, two cloud service providers, received the greatest variety of user data (18 and 14 types, respectively), followed by the app developers Talking Medicines (n=10), Ada Health (n=9), and MedAdvisor International (n=8).

Fourth party sharing

Supplementary figure 2 displays the results of a network analysis conducted to understand the hypothetical data sharing that might occur within the mobile ecosystem at the discretion of app developers, owners, or third parties. Analysis of the websites and privacy policies of third parties revealed additional possibilities for sharing app users’ data, described as “integrations” or monetisation practices related to data (eg, Facebook disclosed sharing end user data with data brokers for targeted advertising). Integrations allowed developers to access and export data through linked accounts (eg, linking a third party analytics and advertising service); however, privacy policies typically stipulated that once data were sent to the integration partner, the data were subject to the partner’s terms and conditions.

App developers typically engage third party companies to collect and analyse user data (derived from use of the app) for app analytics or advertising purposes. The privacy policies of third parties, however, define a relationship with the app developer and disclose how the developer’s data (as a customer of the third party) will be treated. App users are informed that the collection and sharing of their data are defined by the developer’s and not by the third party’s privacy policy, and thus are referred to the app developer in the event of a privacy complaint.

Supplementary figure 2 displays the network including fourth parties. All the companies in the fourth party network receive user data for the purposes of analysis, including user behaviour analytics, error tracking, and advertising. We classified entities in the fourth party network by sector, based on their keywords in Crunchbase, to understand how health related app data might travel and to what end.

The fourth party network included 237 entities including 17 app families (apps, developers, and their parent companies in orange) (17/237, 7%), 18 third parties (18/237, 8%), and 216 fourth parties (216/237, 91%); 14 third parties were also identified as fourth parties (14/237, 6%) meaning that these third parties identified in the traffic analysis could also receive data from other third parties identified in the traffic analysis. Supplementary figure 2 shows that most third and fourth parties in the network (blue) could be broadly characterised as software and technology companies (120/220, 55%), whereas 33% (72/220) were explicitly digital advertising companies (grey), 8% (17/220) were owned by private equity and venture capital firms (yellow), 7 (3%) were major telecommunications corporations (dark grey), and 1 (1%) was a consumer credit reporting agency (purple). Only three entities could be characterised predominantly as belonging to the health sector (1%) (brown). Entities in the fourth party network potentially had access to a median of 3 (interquartile range 1-11, range 1-140) unique transmissions of user data from the sampled apps.

The fourth parties that are positioned in the network to receive the highest volume and most varied user data are multinational technology companies, including Alphabet, Facebook, and Oracle, and the data sharing partners of these companies (table 5). For example, Alphabet is the parent company of Google, which owns the third parties Crashlytics, Google Analytics, and AdMob By Google identified in our analysis. In its privacy policy, Google reports data sharing partnerships with Nielsen, comScore, Kanta, and RN SSI Group for the purpose of “advertising and ad measurement purposes, using their own cookies or similar technologies.”24 These partners “can collect or receive non-personally identifiable information about your browser or device when you use Google sites and apps.”24Table 6 exemplifies the risks to privacy as a result of data aggregation within the fourth party network.


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