Categorizing Mobile Health Project Evaluation Techniques

Mobile health has received some serious attention in research and development community. Although very promising, the evaluation of mobile health is one major challenge without much guidance on what evaluation techniques are appropriate and when in numerous scenarios. To address this challenge, we create a taxonomy of evaluation techniques from a sample of 64 mobile health (mHealth) projects. The research problem and scope is first defined through a literature review on the fields of mobile health and project evaluation. This is followed by a description of the methodology of taxonomy development and a description of the categorization process of the observed evaluation techniques from the sample. Following creation of an initial taxonomy, we present the findings from the categorization process and discuss their implications on both the mHealth and project evaluation fields

Discovering mHealth Users’ Privacy and Security Concerns through Social Media Mining

The purpose of this study is to explore the various privacy and security concerns conveyed by social media users in relation to the use of mHealth wearable technologies, using Grounded Theory and Text Mining methodologies. The results of the emerging theory explain that the concerns of users can be categorized as relating to data management, data surveillance, data invasion, technical safety, or legal & policy issues. The results show that over time, mHealth users are still concerned about areas such as security breaches, real-time data invasion, surveillance, and how companies use the data collected from these devices. Further, the results from the emotion and sentiment analyses revealed that users generally exhibited anger and fear, and sentiments that were negatively expressed. Theoretically, the results also support the literature on user acceptance of mHealth wearables as influenced by the distrust of companies and their utilization of personally harvested data

Discovering mHealth Users’ Privacy and Security Concerns through Social Media Mining

The purpose of this study is to explore the various privacy and security concerns conveyed by social media users in relation to the use of mHealth wearable technologies, using Grounded Theory and Text Mining methodologies. The results of the emerging theory explain that the concerns of users can be categorized as relating to data management, data surveillance, data invasion, technical safety, or legal & policy issues. The results show that over time, mHealth users are still concerned about areas such as security breaches, real-time data invasion, surveillance, and how companies use the data collected from these devices. Further, the results from the emotion and sentiment analyses revealed that users generally exhibited anger and fear, and sentiments that were negatively expressed. Theoretically, the results also support the literature on user acceptance of mHealth wearables as influenced by the distrust of companies and their utilization of personally harvested data

Privacy and Security Concerns Associated with mHealth Technologies: A Computational Social Science Approach

mHealth technologies seek to improve personal wellness; however, there are still significant privacy and security challenges. The purpose of this study is to analyze tweets through social media mining to understand user-reported concerns associated with mHealth devices. Triangulation was conducted on a representative sample to confirm the results of the topic modeling using manual coding. The results of the emotion analysis showed 67% of the posts were largely associated with anger and fear, while 71% revealed an overall negative sentiment. The findings demonstrate the viability of leveraging computational techniques to understand the social phenomenon in question and confirm concerns such as accessibility of data, lack of data protection, surveillance, misuse of data, and unclear policies. Further, the results extend existing findings by highlighting critical concerns such as users’ distrust of these mHealth hosting companies and the inherent lack of data control

A Bleeding Digital Heart: Identifying Residual Data Generation from Smartphone Applications Interacting with Medical Devices

The integration of medical devices in everyday life prompts the idea that these devices will increasingly have evidential value in civil and criminal proceedings. However, the investigation of these devices presents new challenges for the digital forensics community. Previous research has shown that mobile devices provide investigators with a wealth of information. Hence, mobile devices that are used within medical environments potentially provide an avenue for investigating and analyzing digital evidence from such devices. The research contribution of this paper is twofold. First, it provides an empirical analysis of the viability of using information from smartphone applications developed to complement a medical device, as digital evidence. Second, it includes documentation on the artifacts that are potentially useful in a digital forensics investigation of smartphone applications that interact with medical devices

Parents unwittingly leak their children's data:a GDPR time bomb?

There are many apps available for parents that are designed to help them monitor their pregnancy or child’s development. These apps require parents to share information about themselves or their children in order to utilise many of the apps’ features. However, parents remain concerned about their children’s privacy, indicating a privacy paradox between concerns and actions. The research presented here conducted an analysis of parenting apps alongside a survey of parents to determine if their concerns regarding sharing information about their children was at odds with their use of parenting apps.A survey of 75 parents found that they had strong concerns around the availability of information about their children but were using apps within which they shared this information. Parents were not giving consideration to the information requested when using apps. This should be of concern to developers given the growing awareness of users’ rights in relation to managing their data.We propose new guidelines for app developers to better protect children’s privacy and to improve trust relationships between developers and users

Forensic Taxonomy of Popular Android mHealth Apps

Mobile health applications (or mHealth apps, as they are commonly known) are increasingly popular with both individual end users and user groups such as physicians. Due to their ability to access, store and transmit personally identifiable and sensitive information (e.g. geolocation information and personal details), they are potentially an important source of evidentiary materials in digital investigations. In this paper, we examine 40 popular Android mHealth apps. Based on our findings, we propose a taxonomy incorporating artefacts of forensic interest to facilitate the timely collection and analysis of evidentiary materials from mobile devices involving the use of such apps. Artefacts of forensic interest recovered include user details and email addresses, chronology of user locations and food habits. We are also able to recover user credentials (e.g. user password and four-digit app login PIN number), locate user profile pictures and identify timestamp associated with the location of a user

Understanding Users’ Health Information Privacy Concerns for Health Wearables

Health information privacy concerns (HIPC) are commonly cited as primary barrier to the ongoing growth of health wearables (HW) for private users. However, little is known about the driving factors of HIPC and the nature of users’ privacy perception. Seven semi-structured focus groups with current users of HWs were conducted to empirically explore factors driving users’ HIPC. Based on an iterative thematic analysis approach, where the interview codes were systematically matched with literature, I develop a thematic map that visualizes the privacy perception of HW users. In particular this map uncovers three central factors (Dilemma of Forced Acceptance, State-Trait Data Sensitivity and Transparency) on HIPC, which HW users have to deal with