Investigating Privacy and Security Challenges of mHealth Applications

Privacy and mHealth are fast becoming an important influence on the U.S. healthcare system. The most visible element of mHealth is the profusion of mobile phone applications, especially ones related to wellness. Before researchers can fully examine the impact of mHealth on healthcare, barriers to use need to be addressed. One of the barriers most cited by medical professionals and patients is lack of adequate privacy and security policies and regulation for mHealth apps. In this paper the current state of data security in mobile apps is investigated by conducting a physical forensics analysis of several widely used mHealth applications. We report on the kinds of personal data that can be uncovered both before and after applications are removed and/or secured on a mobile device. These results can be used to develop a set of recommendations that can help to inform users, developers and policy stakeholders of best practices in this area. We also introduce a policy framework for mHealth apps and discuss future work

Evaluation of two mobile health apps in the context of smoking cessation: qualitative study of cognitive behavioral therapy (CBT) versus non-CBT-based digital solutions.

BACKGROUND: Mobile health (mHealth) apps can offer users numerous benefits, representing a feasible and acceptable means of administering health interventions such as cognitive behavioral therapy (CBT). CBT is commonly used in the treatment of mental health conditions, where it has a strong evidence base, suggesting that it represents an effective method to elicit health behavior change. More importantly, CBT has proved to be effective in smoking cessation, in the context of smoking-related costs to the National Health Service (NHS) having been estimated to be as high as £2.6bn in 2015. Although the evidence base for computerized CBT in mental health is strong, there is limited literature on its use in smoking cessation. This, combined with the cost-effectiveness of mHealth interventions, advocates a need for research into the effectiveness of CBT-based smoking cessation apps. OBJECTIVE: The objective of this study was, first, to explore participants' perceptions of 2 mHealth apps, a CBT-based app, Quit Genius, and a non-CBT-based app, NHS Smokefree, over a variety of themes. Second, the study aimed to investigate the perceptions and health behavior of users of each app with respect to smoking cessation. METHODS: A qualitative short-term longitudinal study was conducted, using a sample of 29 smokers allocated to one of the 2 apps, Quit Genius or Smokefree. Each user underwent 2 one-to-one semistructured interviews, 1 week apart. Thematic analysis was carried out, and important themes were identified. Descriptive statistics regarding participants' perceptions and health behavior in relation to smoking cessation are also provided. RESULTS: The thematic analysis resulted in five higher themes and several subthemes. Participants were generally more positive about Quit Genius's features, as well as about its design and information engagement and quality. Quit Genius users reported increased motivation to quit smoking, as well as greater willingness to continue using their allocated app after 1 week. Moreover, these participants demonstrated preliminary changes in their smoking behavior, although this was in the context of our limited sample, not yet allowing for the finding to be generalizable. CONCLUSIONS: Our findings underscore the use of CBT in the context of mHealth apps as a feasible and potentially effective smoking cessation tool. mHealth apps must be well developed, preferably with an underlying behavioral change mechanism, to promote positive health behavior change. Digital CBT has the potential to become a powerful tool in overcoming current health care challenges. The present results should be replicated in a wider sample using the apps for a longer period so as to allow for generalizability. Further research is also needed to focus on the effect of greater personalization on behavioral change and on understanding the psychological barriers to the adoption of new mHealth solutions

Analysis of the Adherence of mHealth Applications to HIPAA Technical Safeguards

The proliferation of mobile health technology, or mHealth apps, has made it essential to protect individual health details. People now have easy access to digital platforms that allow them to save, share, and access their medical data and treatment information as well as easily monitor and manage health-related issues. It is crucial to make sure that protected health information (PHI) is effectively and securely transmitted, received, created, and maintained in accordance with the rules outlined by the Health Insurance Portability and Accountability Act (HIPAA), as the use of mHealth apps increases. Unfortunately, many mobile app developers, particularly those of mHealth apps, do not completely understand the HIPAA security and privacy requirements. This offers a unique opportunity for research to create an analytical framework that can help programmers maintain safe and HIPAA-compliant source code while also educating users about the security and privacy of private health information. The plan is to develop a framework which will serve as the foundation for developing an integrated development environment (IDE) plugin for mHealth app developers and a web-based interface for mHealth app consumers. This will help developers identify and address HIPAA compliance issues during the development process and provide consumers with a tool to evaluate the privacy and security of mHealth apps before downloading and using them. The goal is to encourage the development of secure and compliant mHealth apps that safeguard personal health information

A technology acceptance analysis for mhealth apps: the case of Turkey

The acceptance of mHealth (mobile health) apps has been on the increase throughout the world as well as in Turkey. There are two main indicators of mHealth success and acceptance, such as mHealth apps users’ satisfaction level and intention to use mHealth apps. In this context, the factors, including ease of use, trust, privacy, usefulness, and information quality are critical to analyze how they affect the acceptance of the mHealth apps by the Turkish users, and their satisfaction level with mHealth apps. Thus, the main objectives of this study are to (1) to explain how users perceive and use mHealth apps with technology acceptance analysis, (2) investigate whether the usefulness or uselessness of mHealth apps depends on user feelings about mHealth apps, (3) analyze the impacts of ease of use, trust, privacy, usefulness and information quality on mHealth users’ satisfaction and intention, and (4) identify users’ attitudes towards mHealth apps and their satisfaction level with mHealth apps in Turkey. A total of 282 participants from Turkey completed a survey analyzing the ease of use, trust, privacy, usefulness and information quality of mHealth apps to specify the reasons for mHealth acceptance. Statistical techniques were employed for data analysis. This study provides some managerial implications and scholarly recommendations to increase the acceptance of mHealth apps as well as helping mHealth apps designers to recognize the factors that influence the intention to adopt mHealth

Patient Generated Health Data: Framework for Decision Making

Patient information is a major part of healthcare decision making. Although currently scattered due to multiple sources and diverse formats, decision making can be improved if the patient information is readily available in a unified manner. Mobile technologies can improve decision making by integrating patient information from multiple sources. This study explores how patient generated health data (PGHD) from multiple sources can lead to improved healthcare decision making. A semi-systematic review is conducted to analyze research articles for transparency, clarity, and complete reporting. We conceptualize the data generated by healthcare professional as primarily from EHR/EMR and the data generated by patient as primarily from mobile apps and wearables. Eight themes led to the development of Convergence Model for Patient Data (CMPD). A framework was developed to illustrate several scenarios, to identify quality and timeliness requirements in mobile healthcare environment, and to provide necessary decision support