Rules of engagement in mobile health: what does mobile health bring to research and theory?

Accuracy of Artificial Intelligence Based Chatbots in Analyzing Orthopedic Pathologies: An Experimental Multi-Observer Analysis

Background and Objective: The rapid development of artificial intelligence (AI) is impacting the medical sector by offering new possibilities for faster and more accurate diagnoses. Symptom checker apps show potential for supporting patient decision-making in this regard. Whether the AI-based decision-making of symptom checker apps shows better performance in diagnostic accuracy and urgency assessment compared to physicians remains unclear. Therefore, this study aimed to investigate the performance of existing symptom checker apps in orthopedic and traumatology cases compared to physicians in the field. Methods: 30 fictitious case vignettes of common conditions in trauma surgery and orthopedics were retrospectively examined by four orthopedic and traumatology specialists and four different symptom checker apps for diagnostic accuracy and the recommended urgency of measures. Based on the estimation provided by the doctors and the individual symptom checker apps, the percentage of correct diagnoses and appropriate assessments of treatment urgency was calculated in mean and standard deviation [SD] in [%]. Data were analyzed statistically for accuracy and correlation between the apps and physicians using a nonparametric Spearman's correlation test (p < 0.05). Results: The physicians provided the correct diagnosis in 84.4 ± 18.4% of cases (range: 53.3 to 96.7%), and the symptom checker apps in 35.8 ± 1.0% of cases (range: 26.7 to 54.2%). The agreement in the accuracy of the diagnoses varied from low to high (Physicians vs. Physicians: Spearman's ρ: 0.143 to 0.538; Physicians vs. Apps: Spearman's ρ: 0.007 to 0.358) depending on the different physicians and apps. In relation to the whole population, the physicians correctly assessed the urgency level in 70.0 ± 4.7% (range: 66.7 to 73.3%) and the apps in 20.6 ± 5.6% (range: 10.8 to 37.5%) of cases. The agreement on the accuracy of estimating urgency levels was moderate to high between and within physicians and individual apps. Conclusions: AI-based symptom checker apps for diagnosis in orthopedics and traumatology do not yet provide a more accurate analysis regarding diagnosis and urgency evaluation than physicians. However, there is a broad variation in the accuracy between different digital tools. Altogether, this field of AI application shows excellent potential and should be further examined in future studies.

The impact of mobile health interventions on service users' health outcomes and the role of health professions: a systematic review of systematic reviews-protocol

BACKGROUND

Mobile health tools have gained prominence in global health care in recent years. Mobile health (mHealth) interventions have demonstrated their impact on managing healthcare service users' health. A pilot search revealed many systematic reviews on the effectiveness of mobile health tools on service users' health outcomes. However, how the role of healthcare professionals in promoting the adoption of mobile health may lead to improved outcomes needs to be clarified. Therefore, this systematic review aims to synthesise existing systematic reviews that examine both the impact of mobile health interventions on service users' outcomes and the role of healthcare professionals in facilitating the adoption of mobile health solutions.

METHODS

Five electronic databases will be searched: EMBASE, CINHAL Plus, MEDLINE, Web of Science, and the Cochrane Library for systematic reviews exploring the impact of mobile health interventions on service users' outcomes and the role of healthcare professionals in facilitating the adoption of mobile health solutions. Systematic reviews published in English dated from January 2015 to June 2024 will be included. Screening and selection of the reviews against inclusion and exclusion criteria will be performed by three independent reviewers, as well as data extraction and quality assessment.

DISCUSSION

Current systematic reviews in mHealth have primarily focused on assessing the effectiveness of mHealth interventions for managing a range of conditions. While these reviews provide valuable input into the outcomes for mHealth, more is needed to know about the impact of the involvement of health professions on service users' outcomes when adopting mHealth. This systematic review of systematic reviews aims to bridge this critical gap in the literature by critically appraising and synthesising the evidence of mHealth interventions' impact on service user outcomes and the level of involvement of health professionals.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD 42023414435.

The Intersection of Persuasive System Design and Personalization in Mobile Health: Statistical Evaluation

Background

Persuasive technology is an umbrella term that encompasses software (eg, mobile apps) or hardware (eg, smartwatches) designed to influence users to perform preferable behavior once or on a long-term basis. Considering the ubiquitous nature of mobile devices across all socioeconomic groups, user behavior modification thrives under the personalized care that persuasive technology can offer. However, there is no guidance for developing personalized persuasive technologies based on the psychological characteristics of users.

Objective

This study examined the role that psychological characteristics play in interpreted mobile health (mHealth) screen perceived persuasiveness. In addition, this study aims to explore how users’ psychological characteristics drive the perceived persuasiveness of digital health technologies in an effort to assist developers and researchers of digital health technologies by creating more engaging solutions.

Methods

An experiment was designed to evaluate how psychological characteristics (self-efficacy, health consciousness, health motivation, and the Big Five personality traits) affect the perceived persuasiveness of digital health technologies, using the persuasive system design framework. Participants (n=262) were recruited by Qualtrics International, Inc, using the web-based survey system of the XM Research Service. This experiment involved a survey-based design with a series of 25 mHealth app screens that featured the use of persuasive principles, with a focus on physical activity. Exploratory factor analysis and linear regression were used to evaluate the multifaceted needs of digital health users based on their psychological characteristics.

Results

The results imply that an individual user’s psychological characteristics (self-efficacy, health consciousness, health motivation, and extraversion) affect interpreted mHealth screen perceived persuasiveness, and combinations of persuasive principles and psychological characteristics lead to greater perceived persuasiveness. The F test (ie, ANOVA) for model 1 was significant (F9,6540=191.806; P<.001), with an adjusted R2 of 0.208, indicating that the demographic variables explained 20.8% of the variance in perceived persuasiveness. Gender was a significant predictor, with women having higher perceived persuasiveness (P=.008) relative to men. Age was a significant predictor of perceived persuasiveness with individuals aged 40 to 59 years (P<.001) and ≥60 years (P<.001). Model 2 was significant (F13,6536=341.035; P<.001), with an adjusted R2 of 0.403, indicating that the demographic variables self-efficacy, health consciousness, health motivation, and extraversion together explained 40.3% of the variance in perceived persuasiveness.

Conclusions

This study evaluates the role that psychological characteristics play in interpreted mHealth screen perceived persuasiveness. Findings indicate that self-efficacy, health consciousness, health motivation, extraversion, gender, age, and education significantly influence the perceived persuasiveness of digital health technologies. Moreover, this study showed that varying combinations of psychological characteristics and demographic variables affected the perceived persuasiveness of the primary persuasive technology category.

Achieving the potential of mHealth in medicine requires challenging the ethos of care delivery

Mobile Health (mHealth) interventions have received a mix of praise and excitement, as well as caution and even opposition over recent decades. While the rapid adoption of mHealth solutions due to the COVID-19 pandemic has weakened resistance to integrating these digital approaches into practice and generated renewed interest, the increased reliance on mHealth signals a need for optimizing development and implementation. Despite an historically innovation-resistant medical ethos, mHealth is becoming a normalized supplement to clinical practice, highlighting increased demand. Reaching the full potential of mHealth requires new thinking and investment. The current challenge to broaden mHealth adoption and to ensure equity in access may be overcoming a “design purgatory,” where innovation fails to connect to practice. We recommend leveraging the opportunity presented by the COVID-19 pandemic to disrupt routine practice and with a new focus on theory-driven replicability of mHealth tools and strategies aimed at medical education and professional organizations.

Adoption of enterprise architecture for healthcare in AeHIN member countries

Background

Healthcare organisations are undergoing a major transformational shift in the use of information and digital health technologies. Enterprise architecture (EA) has been incrementally adopted in many healthcare organisations globally to facilitate this change. EA can increase the effectiveness of an organisation’s digital health capabilities and resources. However, little is known about the status of EA adoption in low-income and middle-income countries. This study aimed to evaluate the challenges, goals and benefits associated with adoption of EA for healthcare in the Asia eHealth Information Network (AeHIN) member countries.

Methods

We developed an EA Adoption Evaluation framework with four principal layers: governance, strategy, EA and performance. The framework guided the development of a questionnaire to investigate the goals, challenges and benefits faced before and during EA adoption by healthcare organisations.

Sample

26 participants from 18 healthcare organisations in the Asia-Pacific region representing 11 countries. Organisations included Ministries of Health, Universities, Non-Governmental Organisations and Technical Advisory Groups.

Findings

Only 5 of the 18 organisations had begun adopting EA. The goals expressed for EA adoption were to address issues such as interoperability, lack of technical infrastructure and poor alignment of business and information technology strategies. Cost reduction was less emphasised. The main challenges to adopting EA was the lack of EA knowledge, leadership and involvement of senior management.

Conclusion

The adoption of EA is incipient in AeHIN member healthcare organisations. To encourage EA adoption, these organisations need to invest in internal capacity building, senior management training and seek independent EA expert advice to systematically identify and address the barriers to adopting EA.

From obligation to opportunity: future of patient-reported outcome measures at the Veterans Health Administration

Patient-reported outcome (PRO) measures are particularly important in mental health services because patients are the central and essential source of information about their mental health status. PRO measures have the potential to engage patients in meaningful and focused conversations during clinical encounters, but unfortunately they often do not serve this purpose in mental health care. Administration of routine outcome measures has often been viewed by clinicians as an obligatory quality improvement process that takes time away from the clinical encounter. This commentary describes current practical barriers to using PRO measures in practice. Then, focusing specifically on the Veterans Health Administration, a unit of the U.S. Department of Veterans Affairs (VA), we propose processes within which PRO measures in mental health services could support the clinical encounter and enhance patient-centered mental health care. With the increasing number of Accountable Care Organizations and other integrated health-care systems that focus on mental health-care delivery, VA has an opportunity to leverage its long-standing electronic medical record technologies and integrated health system to serve as a model for incorporating PRO measures into mental health-care practices. This commentary provides a vision for the future of mental health delivery by incorporating PRO measures at the VA and in other health-care systems.

Who Are the People in Your Neighborhood? Personas Populating Unregulated mHealth Research

A key feature of unregulated mHealth research is the diversity of participants in this space. Applying an approach drawn from user experience design, we describe a set of archetypal unregulated mHealth researcher "personas," which range from individuals who seek empowerment or have philanthropic objectives to those who are primarily motivated by financial gain or have misanthropic objectives. These descriptions are useful for evaluating policies applicable to mHealth to understand how they will impact various stakeholders.

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

BACKGROUND

There 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.

OBJECTIVE

User 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.

METHODS

To 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.

RESULTS

Survey 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.

CONCLUSIONS

Although 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.