mHealth Context Research Articles

Privacy paradox resolution in mHealth: the moderating effect of rationality degree

PurposeThe collection and use of personal medical information for mobile health (mHealth) service raise significant privacy concerns. In this context, this study aims to explore the privacy paradox and its impact from the perspective of paradox resolution.Design/methodology/approachBased on social support theory and privacy calculus theory, this study first studies the effect of social support on perceived benefits, and explores the moderating effect of perceived health status on the privacy trade-off process. Secondly, the study examines the path of “privacy concerns – disclosure intention – disclosure behavior” to verify the existence of the privacy paradox. Following this, based on rational choice theory, the rationality degree is introduced as a moderating variable to investigate both its impact on the central route and the strength of this impact on the privacy paradox.FindingsEmpirical results show that informational support and emotional support influence perceived benefits significantly. Perceived benefits significantly influence privacy concerns, and perceived health status has a significant positive moderating effect. The authors further find that there is a privacy paradox within the mHealth context, and the privacy paradox is moderated negatively by rationality degree. The findings indicate that the impact strength of the privacy paradox will decrease with increases in rationality degree.Research limitations/implicationsThe findings indicate that it is crucial to evaluate the privacy paradox and its impact from the perspective of paradox resolution.Originality/valueThis study offers a complete comprehension of the privacy paradox in mHealth and provides several valuable recommendations for enhancing both mHealth services and privacy controls.

An empirical study on secure usage of mobile health apps: The attack simulation approach

: Mobile applications (apps) have proven their usefulness in enhancing service provisioning across a multitude of domains that range from smart healthcare, to mobile commerce, and areas of context-sensitive computing. In smart healthcare context, mobile health (mHealth) apps - representing a specific genre of mobile apps that manage health information - face some critical challenges relating to security and privacy of device and user data. In recent years, a number of empirically grounded, survey-based studies have been conducted to investigate secure usage of mHealth apps. However, such studies rely on self-reported behaviors documented via interviews or survey questions that lack practical approaches that can simulate attack scenario for monitoring users’ actions and behaviors while using mHealth apps. : Our objective was to conduct an empirical study - engaging participants with attack simulation scenarios and analyze their actions - for investigating the security awareness of mHealth app users. : We simulated some common security attack scenarios in mHealth context and engaged a total of 105 app users to monitor their actions and analyze their behavior. We analyzed users' data with statistical analysis including correlations test, descriptive analysis, and qualitative data analysis (i.e., thematic analysis method). : Our results indicate that whilst the minority of our participants perceived access permissions positively, the majority had negative views. Users provide their consent, granting permissions, without a careful review of privacy policies that leads to undesired or malicious access to health data. Findings also indicated that 73.3% of our participants had denied at least one access permission, and 36% of our participants preferred no authentication method. : The study complements existing research on secure usage of mHealth apps, simulates security threats to monitor users’ actions, and provides empirically grounded guidelines for secure development and usage of mobile health systems.

Increasing mobile health application usage among Generation Z members: evidence from the UTAUT model

PurposeThe acceptance of mobile health (m-health) applications, especially those of a preventive nature, by individuals, is not well understood. Despite the benefits offered by m-health applications in improving and sustaining health and well-being through various avenues, widespread adoption is yet to be seen. Within this context, this study aims to reveal the enabling factors and barriers that influence the use of m-health applications among Generation Z (Gen-Z).Design/methodology/approachThe Unified Theory of Acceptance and Use of Technology (UTAUT) was extended with e-health literacy, trust and enjoyment constructs. Data from a survey study on 312 Gen-Z members were analysed via structural equation modelling, shedding light on the reasons why new generations adopt m-health apps.FindingsThe findings indicate that social influence and enjoyment are the most significant factors influencing the use of m-health apps. The significant impact of performance and effort expectancy on intentions was also confirmed by the results. Moreover, privacy risk was identified as a barrier to adoption. The results also indicated that the strong influence of trust on privacy risk can be used to offset those privacy concerns.Practical implicationsThe findings highlight that hedonic motivation, which is commonly overlooked in health settings, plays an important role in m-health app use. Thus, promoting mobile app features that provide enjoyment will be influential in attracting the younger generation.Originality/valueThe context of the study differs from the norm and focuses on a regional health tourism hub, Turkey, situated at the crossroads of Europe and Asia. UTAUT model is modified with relevant constructs, namely, enjoyment, e-health literacy and privacy risk, to better fit the m-health context.

Predicting mHealth Acceptance Using the UTAUT2 Technology Acceptance Model: A Mixed-Methods Approach.

Mobile health (mHealth) apps are increasingly used in healthcare to support people with chronic diseases such as diabetes. mHealth acceptance is crucial for using them. Due to acceptance problems, however, mHealth apps are not used by all chronic disease patients. To predict user acceptance, technology acceptance models such as UTAUT2 are used. However, UTAUT2 was not explicitly developed for the mHealth context. This study investigates if additional health-related constructs could increase the predictive power of the UTAUT2 model. A mixed-methods design, comprising an initial qualitative methods triangulation study that consisted of a literature search, expert interviews, and patient interviews, and a subsequent quantitative cross-sectional survey with 413 patients was used. The mixed-methods study revealed and validated two new constructs relevant for predicting mHealth acceptance not represented in the UTAUT2 model: "perceived disease threat" and "trust". The UTAUT2 model was successfully extended by two new constructs relevant to the mHealth context.

Suitability of the Unified Theory of Acceptance and Use of Technology 2 Model for Predicting mHealth Acceptance Using Diabetes as an Example: Qualitative Methods Triangulation Study.

BackgroundIn recent years, the use of mobile health (mHealth) apps to manage chronic diseases has increased significantly. Although mHealth apps have many benefits, their acceptance is still low in certain areas and groups. Most mHealth acceptance studies are based on technology acceptance models. In particular, the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model was developed to predict technology acceptance in a consumer context. However, to date, only a few studies have used the UTAUT2 model to predict mHealth acceptance and confirm its suitability for the health sector. Thus, it is unclear whether the UTAUT2 model is suitable for predicting mHealth acceptance and whether essential variables for a health-related context are missing.ObjectiveThis study aims to validate the suitability of UTAUT2 for predicting mHealth acceptance.MethodsIn this study, diabetes was used as an example as mHealth apps are a significant element of diabetes self-management. In addition, diabetes is one of the most common chronic diseases affecting young and older people worldwide. An explorative literature review and guided interviews with 11 mHealth or technology acceptance experts and 8 mHealth users in Austria and Germany were triangulated to identify all relevant constructs for predicting mHealth acceptance. The interview participants were recruited by purposive sampling until theoretical saturation was reached. Data were analyzed using structured content analysis based on inductive and deductive approaches.ResultsThis study was able to confirm the relevance of all exogenous UTAUT2 constructs. However, it revealed two additional constructs that may also need to be considered to better predict mHealth acceptance: trust and perceived disease threat.ConclusionsThis study showed that the UTAUT2 model is suitable for predicting mHealth acceptance. However, the model should be extended to include 2 additional constructs for use in the mHealth context.

Using Co-design in Mobile Health System Development: A Qualitative Study With Experts in Co-design and Mobile Health System Development.

BackgroundThe proliferation of mobile devices has enabled new ways of delivering health services through mobile health systems. Researchers and practitioners emphasize that the design of such systems is a complex endeavor with various pitfalls, including limited stakeholder involvement in design processes and the lack of integration into existing system landscapes. Co-design is an approach used to address these pitfalls. By recognizing users as experts of their own experience, co-design directly involves users in the design process and provides them an active role in knowledge development, idea generation, and concept development.ObjectiveDespite the existence of a rich body of literature on co-design methodologies, limited research exists to guide the co-design of mobile health (mHealth) systems. This study aims to contextualize an existing co-design framework for mHealth applications and construct guidelines to address common challenges of co-designing mHealth systems.MethodsTapping into the knowledge and experience of experts in co-design and mHealth systems development, we conducted an exploratory qualitative study consisting of 16 semistructured interviews. Thereby, a constructivist ontological position was adopted while acknowledging the socially constructed nature of reality in mHealth system development. Purposive sampling across web-based platforms (eg, Google Scholar and ResearchGate) and publications by authors with co-design experience in mHealth were used to recruit co-design method experts (n=8) and mHealth system developers (n=8). Data were analyzed using thematic analysis along with our objectives of contextualizing the co-design framework and constructing guidelines for applying co-design to mHealth systems development.ResultsThe contextualized framework captures important considerations of the mHealth context, including dedicated prototyping and implementation phases, and an emphasis on immersion in real-world contexts. In addition, 7 guidelines were constructed that directly pertain to mHealth: understanding stakeholder vulnerabilities and diversity, health behavior change, co-design facilitators, immersion in the mHealth ecosystem, postdesign advocates, health-specific evaluation criteria, and usage data and contextual research to understand impact.ConclusionsSystem designers encounter unique challenges when engaging in mHealth systems development. The contextualized co-design framework and constructed guidelines have the potential to serve as a shared frame of reference to guide the co-design of mHealth systems and facilitate interdisciplinary collaboration at the nexus of information technology and health research.

Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data.

BackgroundThe use of wearables facilitates data collection at a previously unobtainable scale, enabling the construction of complex predictive models with the potential to improve health. However, the highly personal nature of these data requires strong privacy protection against data breaches and the use of data in a way that users do not intend. One method to protect user privacy while taking advantage of sharing data across users is federated learning, a technique that allows a machine learning model to be trained using data from all users while only storing a user’s data on that user’s device. By keeping data on users’ devices, federated learning protects users’ private data from data leaks and breaches on the researcher’s central server and provides users with more control over how and when their data are used. However, there are few rigorous studies on the effectiveness of federated learning in the mobile health (mHealth) domain. ObjectiveWe review federated learning and assess whether it can be useful in the mHealth field, especially for addressing common mHealth challenges such as privacy concerns and user heterogeneity. The aims of this study are to describe federated learning in an mHealth context, apply a simulation of federated learning to an mHealth data set, and compare the performance of federated learning with the performance of other predictive models. MethodsWe applied a simulation of federated learning to predict the affective state of 15 subjects using physiological and motion data collected from a chest-worn device for approximately 36 minutes. We compared the results from this federated model with those from a centralized or server model and with the results from training individual models for each subject.ResultsIn a 3-class classification problem using physiological and motion data to predict whether the subject was undertaking a neutral, amusing, or stressful task, the federated model achieved 92.8% accuracy on average, the server model achieved 93.2% accuracy on average, and the individual model achieved 90.2% accuracy on average. ConclusionsOur findings support the potential for using federated learning in mHealth. The results showed that the federated model performed better than a model trained separately on each individual and nearly as well as the server model. As federated learning offers more privacy than a server model, it may be a valuable option for designing sensitive data collection methods.

Privacy paradox in mHealth applications: An integrated elaboration likelihood model incorporating privacy calculus and privacy fatigue

As people’s health awareness and standard of living improve, mHealth applications are being increasingly used. However, mHealth application services are mainly based on the collection of personal and behavioral data, which conflicts with users’ growing privacy concerns. In that context, this study considers the privacy paradox phenomenon, in which privacy concerns co-exist with disclosure behavior. This study explores the privacy paradox in mHealth applications using an integrated elaboration likelihood model (ELM) from the perspective of privacy calculus and privacy fatigue. Results from the quasi-experiment and partial least squares structural equation modeling reveal that, compared with privacy concerns, perceived benefits have a greater impact on users’ disclosure intention, which further supports the existence of the privacy paradox in the mHealth context; this process is found to originate in users’ privacy calculus. However, privacy fatigue is found to have an insignificant impact on users’ disclosure intention, which may be due to the low sunk cost of users’ investment in mHealth applications. The results indicate that designers of mHealth applications should optimize their interaction functions to enhance benefits to users.

Insights into mobile health application market via a content analysis of marketplace data with machine learning.

Despite the benefits offered by an abundance of health applications promoted on app marketplaces (e.g., Google Play Store), the wide adoption of mobile health and e-health apps is yet to come. This study aims to investigate the current landscape of smartphone apps that focus on improving and sustaining health and wellbeing. Understanding the categories that popular apps focus on and the relevant features provided to users, which lead to higher user scores and downloads will offer insights to enable higher adoption in the general populace. This study on 1,000 mobile health applications aims to shed light on the reasons why particular apps are liked and adopted while many are not. User-generated data (i.e. review scores) and company-generated data (i.e. app descriptions) were collected from app marketplaces and manually coded and categorized by two researchers. For analysis, Artificial Neural Networks, Random Forest and Naïve Bayes Artificial Intelligence algorithms were used. The analysis led to features that attracted more download behavior and higher user scores. The findings suggest that apps that mention a privacy policy or provide videos in description lead to higher user scores, whereas free apps with in-app purchase possibilities, social networking and sharing features and feedback mechanisms lead to higher number of downloads. Moreover, differences in user scores and the total number of downloads are detected in distinct subcategories of mobile health apps. This study contributes to the current knowledge of m-health application use by reviewing mobile health applications using content analysis and machine learning algorithms. The content analysis adds significant value by providing classification, keywords and factors that influence download behavior and user scores in a m-health context.

Appropriation of Mobile Health for Diabetes Self-Management: Lessons From Two Qualitative Studies.

BackgroundTo achieve clarity on mobile health’s (mHealth’s) potential in the diabetes context, it is necessary to understand potential users’ needs and expectations, as well as the factors determining their mHealth use. Recently, a few studies have examined the user perspective in the mHealth context, but their explanatory value is constrained because of their limitation to adoption factors.ObjectiveThis paper uses the mobile phone appropriation model to examine how individuals with type 1 or type 2 diabetes integrate mobile technology into their everyday self-management. The study advances the field beyond mere usage metrics or the simple dichotomy of adoption versus rejection.MethodsData were gathered in 2 qualitative studies in Singapore and Germany, with 21 and 16 respondents, respectively. Conducting semistructured interviews, we asked respondents about their explicit use of diabetes-related apps, their general use of varied mobile technologies to manage their disease, and their daily practices of self-management.ResultsThe analysis revealed that although some individuals with diabetes used dedicated diabetes apps, most used tools across the entire mobile-media spectrum, including lifestyle and messaging apps, traditional health information websites and forums. The material indicated general barriers to usage, including financial, technical, and temporal restrictions.ConclusionsIn sum, we find that use patterns differ regarding users’ evaluations, expectancies, and appropriation styles, which might explain the inconclusive picture of effects studies in the diabetes mHealth context.

Mixed-Methods Research in Diabetes Management via Mobile Health Technologies: A Scoping Review.

BackgroundConsidering the increasing incidence and prevalence of diabetes worldwide and the high level of patient involvement it requires, diabetes self-management is a serious issue. The use of mobile health (mHealth) in diabetes self-management has increased, but so far research has not provided sufficient information about the uses and effectiveness of mHealth-based interventions. Alternative study designs and more rigorous methodologies are needed. Mixed-methods designs may be particularly useful because both diabetes self-management and mHealth studies require integrating theoretical and methodological approaches.ObjectiveThis scoping review aimed to examine the extent of the use of mixed-methods research in mHealth-based diabetes management studies. The methodological approaches used to conduct mixed-methods studies were analyzed, and implications for future research are provided.MethodsGuided by Arksey and O’Malley’s framework, this scoping review implemented a comprehensive search strategy including reviewing electronic databases, key journal searches, Web-based research and knowledge centers, websites, and handsearching reference lists of the studies. The studies focusing on mHealth technologies and diabetes management were included in the review if they were primary research papers published in academic journals and reported using a combination of qualitative and quantitative methods. The key data extracted from the reviewed studies include purpose of mixing, design type, stage of integration, methods of legitimation, and data collection techniques.ResultsThe final sample (N=14) included studies focused on the feasibility and usability of mHealth diabetes apps (n=7), behavioral measures related to the mHealth apps (n=6), and challenges of intervention delivery in the mHealth context (n=1). Reviewed studies used advanced forms of mixed-methods designs where integration occurred at multiple points and data were collected using multiple techniques. However, the majority of studies did not identify a specific mixed-methods design or use accepted terminology; nor did they justify using this approach.ConclusionsThis review provided important insights into the use of mixed methods in studies focused on diabetes management via mHealth technologies. The prominent role of qualitative methods and tailored measures in diabetes self-management studies was confirmed, and the importance of using multiple techniques and approaches in this field was emphasized. This review suggests defining specific mixed-methods questions, using specific legitimation methods, and developing research designs that overcome sampling and other methodological problems in future studies.

A new SmartSMS protocol for secure SMS communication in m-health environment

In the present m-health scenario, a patient and a doctor may engage in an SMS conversation regarding the sensitive medical information about the body of a patient and the corresponding treatment for it. Hence, designing a secure protocol for SMS communication for this context of m-health has become mandatory. Though a number of protocols have been defined in the literature, each protocol exposes some drawbacks which makes it unsuitable for the secure SMS communication. Protocols proposed earlier such as SMSSec, PK-SIM have more computation and communication overheads. Despite being secure, EasySMS protocol suffers from Denial of Service (DoS) attacks and Distributed Denial of Service (DDoS) attacks. Thus, an efficient protocol called SmartSMS to ensure the end-to-end secure SMS communication in m-health environment while being resistant to attacks has been proposed in this work. The main contribution of this proposed SmartSMS protocol is to minimize the computational complexity of the Mobile Station (MS) and the Authentication Server (AS). In addition, the proposed work also minimizes the communication complexity involved in secure SMS communications. Through extensive simulations, it has been proved that the proposed protocol shows lower computational and communication overheads compared to other protocols in the literature and shows promising results.

Impact of capacity and discharging rate on battery life time: A stochastic model to support mobile device autonomy planning

Mobile computing devices have improved substantially. Such a development fosters the usage of those devices for various purposes, including health care as a prominent example. Mobile health (mHealth) is a subfield of electronic health (eHealth) that refers to the practice of medicine and public health supported by mobile devices. A specific concern for mHealth is the limitation of power supply for the mobile devices, since interruptions on power supply can result in critical situations. This paper proposes analytical models to identify the mean time of one life cycle of the battery, and thus identify the average battery lifetime considering some device utilization characteristics. We evaluate the impact of both network connectivity (local and wide areas) and communication protocols (Polling, Long polling, WebSockets, and XMPP) on the discharging rate, and subsequently on the lifetime of batteries with different power capacities. We observed a noteworthy impact of these factors on battery usage and, thus, on mHealth applications. We use a hierarchical modeling approach that enables efficient representation of system complexity and provides accurate results for planning the autonomy of mobile devices in an mHealth context.

Nonlinearities in personalization-privacy paradox in mHealth adoption: the mediating role of perceived usefulness and attitude.

Personalization in healthcare refers to individualizing services and products based on patients' health conditions and interests. In order to deliver highly personalized offerings, mHealth providers need to use patients' health information, which provokes patients' concerns over personal health information leakage. So the personalization-privacy paradox is an important issue in the mHealth context. To gain a better understanding of this paradox, we take the personalization and privacy paradox factors as independent variables, incorporating the nonlinear relationships between personalization and privacy, and take attitude and perceived usefulness as middle variables to study mHealth adoption. The hypothesized model is tested through an empirical research of a 489-respondent sample in China. PLS is used for data analysis. (1) Personalization and privacy are found to influence mHealth adoption intention via attitude and perceived usefulness; (2) there is a substitution relationship, also called negative synergy between personalization and privacy in mHealth contexts; (3) attitude mediates the effect of perceived usefulness on intention, indicating a significant role of attitude.

Development and validation of an instrument to measure user perceived service quality of mHealth

The role of service quality in fostering the growth of mHealth services has gained much attention in the academic and practitioner communities. However, empirical research in this area has been beset by inadequate conceptualization and the lack of a validated scale. This study addresses these limitations by theoretically conceptualizing and empirically validating a multidimensional service quality scale in the mHealth context. The findings show that mHealth service quality is a hierarchical, multidimensional, and reflective construct, which consists of three primary dimensions and eight subdimensions. The results also confirm that the mHealth service quality scale is more effective at predicting satisfaction and continuance in a nomological network.

Continuance of mHealth services at the bottom of the pyramid: the roles of service quality and trust

Continued usage of information systems (or, IS continuance) has proven to be a critical success parameter for ICT implementation at the top of the global economic pyramid. However, there are few studies which have explored continued IS usage at the bottom of the economic pyramid (BOP) though it represents the majority of the world’s population. To fill this knowledge gap, this study develops an mHealth continuance model at the BOP framing the impact of two post adoption expectation beliefs (i.e., perceived service quality and perceived trust). This study extends ECM (expectation confirmation model) perspective synthesizing the extant literature on continued IS usage, service quality and consumer trust. The proposed model was empirically tested within the context of mHealth (mobile health) services at the BOP, applying PLS (partial least squares) under a cross sectional study. The findings confirm that both perceived service quality and perceived trust have significant explanatory power under an integrated ECM providing superior prediction of continuance intentions. The study concludes by discussing conceptual contributions, practical implications, limitations and future research directions.