Mobile Health Context Research Articles

How Interprofessional Community Mobile Healthcare and Service-Learning Work Together to Identify and Address Chronic Health Disparities.

Residents of the State of Delaware experience high levels of health inequities. Service-learning programs provided jointly by universities and community partners can address health disparities through documentation of disparities and service provision that sees patients where they are. Benefits accrue for both students and communities experiencing health inequities. HEALTH for All (H4A) mobile unit clients can receive a variety of services at sites co-located with community based organizations (CBOs). Between September 2023 and January 2024, H4A clients had their blood pressure assessed by a trained healthcare provider. Demographic and ZIP Code of residence data were collected by a trained graduate student. Data were recorded and analyzed using Microsoft Excel Version 16.5 (Redmond, WA, USA). All documentation was reviewed and approved by the University of Delaware's Institutional Review Board (IRB #1567044-3). Between September 2023 and January 2024, 152 clients participated. Most participants were female (72.27%; 104 of 143) and identified as White (68.66%; 92 of 134). The largest group of clients were in Stage 1 Hypertension (34.21%; 52 of 152), followed by Elevated (23.68%; 36 of 152), Normal (22.37%; 34 of 152), and Stage 2 Hypertension (19.74%; 30 of 152). Black or African American clients had higher systolic and diastolic blood pressure compared to other racial and ethnic groups. There were also differences in the share of clients with hypertension by ZIP Code of residence. Interprofessional service-learning in a mobile health context provides students with practical field experience and real-world insights into community perspectives and needs, including addressing health inequities. Academic-community partnerships and mobile health programs should be prioritized in the future to address health inequities and foster the development of socially engaged, community-minded future professionals.

Conceptualizing psychological well‐being as a dynamic process: Implications for research on mobile health interventions

AbstractWe introduce a theoretical framework for conceptualizing Psychological Well‐Being (PWB) as a process that unfolds over short and longer time‐scales. We argue that this framework can be especially useful for studying the change mechanisms in PWB within the context of mobile Health (mHealth) interventions. Four lines of research are considered within this framework to inform the scientific exploration of PWB in the context of mHealth interventions. First, we explore key dynamic characteristics of change in PWB functioning. Second, we discuss PWB intervention response as a learning process (i.e., meaningful and transformational changes in state), reflected as change in key dynamic characteristics of PWB. Third, we explain mechanisms of change in PWB intervention practices through an underlying process of skill development. Fourth, we discuss intervention response heterogeneity within this framework. The approach we outline is intended to articulate currently unanswered, process‐oriented research questions about PWB interventions, how they work, and a methodological path forward for exploring them.

Linking self-congruence and functional congruence to mobile health apps

PurposeConsumers often start using mobile health apps but quit using them after a brief period of time. However, app providers can only ensure their long-term existence in the market if their app is used a long period, so that they can thus generate long-term revenue from advertising, subscriptions and sponsorships. Therefore, this study aims to gain a deeper understanding of the determinants of consumers’ continuous usage intention.Design/methodology/approachBased on a sample of 274 current mobile health app users, this study tests whether ideal self-congruence and/or functional congruence strengthens consumers’ continuous usage intention.FindingsThe results reveal that ideal self-congruence and functional congruence positively affect consumers’ continuous usage intention. Furthermore, an initial favorable attitude toward a mobile health app (i.e. ideal self-congruence) leads to a more favorable evaluation of the functional attributes of the app regardless of consumers’ objective evaluation of these attributes.Practical implicationsProviders should specifically take consumers’ ideal self-concept into consideration to increase consumers’ continuous usage intention of mobile health apps. Matching consumers’ ideal self-concept further leads consumers to a more favorable evaluation of the functional attributes of mobile health apps.Originality/valueOnly a few studies have examined factors influencing the continuous usage intention of mobile health apps; moreover, these studies have largely neglected the symbolic dimension of consumption behavior. Therefore, this study introduces congruence theory into the context of mobile health apps to provide a holistic view of the influence of the symbolic (i.e. ideal self-congruence) and utilitarian (i.e. functional congruence) dimensions on mobile health app consumption.

Prioritization of Quality Principles for Health Apps Using the Kano Model: Survey Study.

BackgroundHealth apps are often used without adequately taking aspects related to their quality under consideration. This may partially be due to inadequate awareness about necessary criteria and how to prioritize them when evaluating an app.ObjectiveThe aim of this study was to introduce a method for prioritizing quality attributes in the mobile health context. To this end, physicians were asked about their assessment of nine app quality principles relevant in health contexts and their responses were used as a basis for designing a method for app prioritization. Ultimately, the goal was to aid in making better use of limited resources (eg, time) by assisting with the decision as to the specific quality principles that deserve priority in everyday medical practice and those that can be given lower priority, even in cases where the overall principles are rated similarly.MethodsA total of 9503 members of two German professional societies in the field of orthopedics were invited by email to participate in an anonymous online survey over a 1-month period. Participants were asked to rate a set of nine app quality principles using a Kano survey with functional and dysfunctional (ie, positively and negatively worded) questions. The evaluation was based on the work of Kano (baseline), supplemented by a self-designed approach.ResultsAmong the 9503 invited members, 382 completed relevant parts of the survey (return rate of 4.02%). These participants were equally and randomly assigned to two groups (test group and validation group, n=191 each). Demographic characteristics did not significantly differ between groups (all P>.05). Participants were predominantly male (328/382, 85.9%) and older than 40 years (290/382, 75.9%). Given similar ratings, common evaluation strategies for Kano surveys did not allow for conclusive prioritization of the principles, and the same was true when using the more elaborate approach of satisfaction and dissatisfaction indices following the work of Timko. Therefore, an extended, so-called “in-line-of-sight” method was developed and applied for this evaluation. Modified from the Timko method, this approach is based on a “point of view” (POV) metric, which generates a ranking coefficient. Although the principles were previously almost exclusively rated as must-be (with the exception of resource efficiency), which was not conducive to their prioritization, the new method applied from the must-be POV resulted in identical rankings for the test and validation groups: (1) legal conformity, (2) content validity, (3) risk adequacy, (4) practicality, (5) ethical soundness, (6) usability, (7) transparency, (8) technical adequacy, and (9) resource efficiency.ConclusionsEstablished survey methodologies based on the work of Kano predominantly seek to categorize the attributes to be evaluated. The methodology presented here is an interesting option for prioritization, and enables focusing on the most important criteria, thus saving valuable time when reviewing apps for use in the medical field, even with otherwise largely similar categorization results. The extent to which this approach is applicable beyond the scenario presented herein requires further investigation.

A fuzzy mobile decision support system for diagnosing of the angiographic status of heart disease

In estimating the risk of heart disease due to non-deterministic risk factors, often diagnosis of angiographic disease is difficult for physicians. For modeling and application of this uncertain and imprecise modality, decision support systems based on fuzzy logic are appropriate and an effective approach. Smartphone-based applications can facilitate the efficient use of these systems in form of evidence-based medicine. The aim of this applied study was developing and evaluating an application to diagnose angiographic disease conditions and the severity of heart attacks through a smartphone-based clinical decision support system. Android application development environment was utilized after extraction of linguistic rules and definition of membership functions needed for decisions. A smartphone-based application was designed using these guidelines and utilizing fuzzy modeling. Then, the app was evaluated in terms of accuracy using the popular Cleveland dataset. According to the results, 10 fuzzy rules for modeling, seven input variables and one decision variable were extracted. Programming was done in Eclipse. The evaluation results indicate that the accuracy of the program was 94%. Decision support systems within the context of mobile health have become some of the most efficient and influential information technology tools in recent years. They can improve patient-care management, provide easier access to the information needed for healthcare decisions and ultimately reduce healthcare costs while improving its quality.

Applicability of the User Engagement Scale to Mobile Health: A Survey-Based Quantitative Study.

BackgroundThere has recently been exponential growth in the development and use of health apps on mobile phones. As with most mobile apps, however, the majority of users abandon them quickly and after minimal use. One of the most critical factors for the success of a health app is how to support users’ commitment to their health. Despite increased interest from researchers in mobile health, few studies have examined the measurement of user engagement with health apps.ObjectiveUser engagement is a multidimensional, complex phenomenon. The aim of this study was to understand the concept of user engagement and, in particular, to demonstrate the applicability of a user engagement scale (UES) to mobile health apps.MethodsTo determine the measurability of user engagement in a mobile health context, a UES was employed, which is a psychometric tool to measure user engagement with a digital system. This was adapted to Ada, developed by Ada Health, an artificial intelligence–powered personalized health guide that helps people understand their health. A principal component analysis (PCA) with varimax rotation was conducted on 30 items. In addition, sum scores as means of each subscale were calculated.ResultsSurvey data from 73 Ada users were analyzed. PCA was determined to be suitable, as verified by the sampling adequacy of Kaiser-Meyer-Olkin=0.858, a significant Bartlett test of sphericity (χ2300=1127.1; P<.001), and communalities mostly within the 0.7 range. Although 5 items had to be removed because of low factor loadings, the results of the remaining 25 items revealed 4 attributes: perceived usability, aesthetic appeal, reward, and focused attention. Ada users showed the highest engagement level with perceived usability, with a value of 294, followed by aesthetic appeal, reward, and focused attention.ConclusionsAlthough the UES was deployed in German and adapted to another digital domain, PCA yielded consistent subscales and a 4-factor structure. This indicates that user engagement with health apps can be assessed with the German version of the UES. These results can benefit related mobile health app engagement research and may be of importance to marketers and app developers.

Distributed Architecture With Double-Phase Microaggregation for the Private Sharing of Biomedical Data in Mobile Health

In this paper, we present the concept of double-phase microaggregation as an improvement of classical microaggregation for the protection of privacy in distributed scenarios without fully trusted parties. We apply this new concept in the context of mobile health and we show that a distributed architecture consisting of patients and several intermediate entities can apply it to protect the privacy of patients, whose data are released to third parties for secondary use. After recalling some fundamental concepts of statistical disclosure control and microaggregation, we detail the distributed architecture that allows the private gathering, storage, and sharing of biomedical data. We show that double-phase multivariate microaggregation properly fits the needs for privacy preservation of biomedical data in the distributed context of mobile health. Moreover, we show that double-phase microaggregation performs similarly to classical microaggregation in terms of information loss, disclosure risk, and correlation preservation, while avoiding the limitations of a centralized approach.