ObjectivesTo 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.DesignTraffic, content, and network analysis.SettingTop 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.Participants24 of 821 apps identified by an app store crawling program. Included apps pertained to medicines information, dispensing, administration, prescribing, or use, and were interactive.InterventionsLaboratory 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 measuresIdentities 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.Results19/24 (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.ConclusionsSharing 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.
Old age is characterized by a complex pattern of multimorbidity and comorbidity. Single disease definitions do not account for the prevalence and complexity of multimorbidity in older people and a new lexicon may be needed to underpin research and health care interventions for older people.
Recent research has demonstrated that longitudinal integrated placements (LICs) are an alternative mode of clinical education to traditional placements. Extended student engagement in community settings provide the advantages of educational continuity as well as increased service provision in underserved areas. Developing and maintaining LICs require a differing approach to student learning than that for traditional placements. There has been little theoretically informed empirical research that has offered explanations of which are the important factors that promote student learning in LICs and the relationships between those factors. We explored the relationship between student learning, student perceptions of preparedness for practice and student engagement, in the context of a rural LIC. We used a sequential qualitative design employing thematic, comparative and relational analysis of data from student interviews (n = 18) to understand possible processes and mechanisms of student learning in the LIC. Through the theoretical lens of social learning systems, we identified two major themes; connectivity and preparedness for practice. Connectivity described engagement and relationship building by students, across formal and informal learning experiences, interprofessional interactions, social interactions with colleagues, interaction with patients outside of the clinical setting, and the extent of -016-9740-3 integration in the wider community. Preparedness for practice, reflected students' perceptions of having sufficient depth in clinical skills, personal and professional development, cultural awareness and understanding of the health system, to work in that system. A comparative analysis compared the nature and variation of learning across students. In a relational analysis, there was a positive association between connectivity and preparedness for practice. Connectivity is a powerful enabler of students' agentic engagement, collaboration, and learning within an LIC. It is related to student perceptions of preparedness for practice. These findings provide insight for institutions wishing to develop similar programmes, by encouraging health professional educators to consider all of the potential elements of the placements, which most promote connectivity.Adv in Health Sci Educ (2017) 22:1011-1029 DOI 10.1007/s10459
BackgroundA great deal of consumer data, collected actively through consumer reporting or passively through sensors, is shared among apps. Developers increasingly allow their programs to communicate with other apps, sensors, and Web-based services, which are promoted as features to potential users. However, health apps also routinely pose risks related to information leaks, information manipulation, and loss of information. There has been less investigation into the kinds of user data that developers are likely to collect, and who might have access to it.ObjectiveWe sought to describe how consumer data generated from mobile health apps might be distributed and reused. We also aimed to outline risks to individual privacy and security presented by this potential for aggregating and combining user data across apps.MethodsWe purposively sampled prominent health and fitness apps available in the United States, Canada, and Australia Google Play and iTunes app stores in November 2015. Two independent coders extracted data from app promotional materials on app and developer characteristics, and the developer-reported collection and sharing of user data. We conducted a descriptive analysis of app, developer, and user data collection characteristics. Using structural equivalence analysis, we conducted a network analysis of sampled apps’ self-reported sharing of user-generated data.ResultsWe included 297 unique apps published by 231 individual developers, which requested 58 different permissions (mean 7.95, SD 6.57). We grouped apps into 222 app families on the basis of shared ownership. Analysis of self-reported data sharing revealed a network of 359 app family nodes, with one connected central component of 210 app families (58.5%). Most (143/222, 64.4%) of the sampled app families did not report sharing any data and were therefore isolated from each other and from the core network. Fifteen app families assumed more central network positions as gatekeepers on the shortest paths that data would have to travel between other app families.ConclusionsThis cross-sectional analysis highlights the possibilities for user data collection and potential paths that data is able to travel among a sample of prominent health and fitness apps. While individual apps may not collect personally identifiable information, app families and the partners with which they share data may be able to aggregate consumer data, thus achieving a much more comprehensive picture of the individual consumer. The organizations behind the centrally connected app families represent diverse industries, including apparel manufacturers and social media platforms that are not traditionally involved in health or fitness. This analysis highlights the potential for anticipated and voluntary but also possibly unanticipated and involuntary sharing of user data, validating privacy and security concerns in mobile health.
Introduction. To maximize limited resources, many health promotion programs are designed to be delivered by volunteer lay leaders. But this model poses challenges to implementation in real-world settings and barriers to successfully scaling-up programs. This study examines the current lay leader training model for Walk With Ease, a Centers for Disease Control and Prevention–funded evidence-based arthritis program delivered at-scale. Method. Recruited volunteers (n = 106) opted into free online or in-person training and agreed to deliver one Walk With Ease program within the following year—only 49%, however, did. Using logistic regression models and qualitative interviews, we explored predictors of volunteer delivery. Results. Volunteers had higher odds of delivering programs if they trained online (odds ratio [OR] = 9.04, 95% confidence interval [CI: 2.30, 48.36]), previously taught health programs (OR = 15.52, 95% CI [3.51, 103.55]) or trained in the second year of implementation (OR = 27.08, 95% CI [2.63, 415.78]). Qualitative findings underscored that successful volunteers were readied by their previous health education experience. Conclusions. While online training modes appear effective to prepare experienced volunteers, lay leaders required additional support. This calls into question whether lay-led delivery models are suitable for scaling-up programs with limited resources. Given the many lay-led health interventions for chronic disease self-management, investing in common training and infrastructures for lay leader development could advance the quality and sustainability of real-world program delivery.
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