Designing Disease-Specific mHealth Apps for Clinical Value

mHealth apps for patient use are promising but continue to face a plateau in usage. Current apps work for a limited segment of the patient population, i.e., those who enjoy tracking for intrinsic rewards. There are many opportunities to support patient care in between health care provider visits that are not currently being met for many diseases and patient types (personas). This is an area of great potential growth for mHealth apps and could contribute greatly to patient health and wellness. In this chapter, we propose a framework for how to think about the between-visit needs of patients that would motivate continued use of mhealth apps. We view the app design process from the following perspectives: 1) disease-specific needs, 2) non-disease specific needs, 3) behavioral theoretical aspects of app usage and 4) app-intrinsic usage motivators. Myasthenia gravis serves as the use case for illustrating these perspectives and how to use them in designing a disease-specific mHealth app.

Systematic Review of Mobile Applications to Monitor Symptoms of COVID-19 Using the Mobile Application Rating Scale (Preprint)

BACKGROUND Monitoring acute and long-term symptoms of COVID-19 is critical for personal and public health. Mobile health (mHealth) applications (apps) can be used to support symptom monitoring at the point of need for patients with COVID-19. OBJECTIVE To systematically review and evaluate mHealth apps for quality, functionality, and consistency with guidelines for monitoring symptoms of COVID-19. METHODS We conducted a systematic review of apps for COVID-19 symptom monitoring by searching in two major app stores. The final apps were independently assessed using the Mobile Application Rating Scale (MARS), IMS Institute for Healthcare Informatics functionality score, and guidelines from the Center for Disease Control and World Health Organization. Interrater reliability between the reviewers was calculated. RESULTS A total of 1,017 mobile apps were reviewed and 20 met the inclusion criteria. The majority of the apps (90%, n=18) were designed to track acute COVID-19 symptoms, and only two addressed long-term symptoms. Overall, the apps scored high on quality, with an overall MARS rating of 3.94. The most common functionality among all apps was the instruct function (95%, n=19). The most common symptoms included in the apps for tracking were: fever and dry cough (n=18), aches and pains (n=17), difficulty breathing (n=17), tiredness, sore throat, headache, loss of taste, or smell (n=16), and diarrhea (n=15). CONCLUSIONS mHealth apps designed to monitor symptoms of COVID-19 had high quality, but the majority of apps focused almost exclusively on acute symptoms. Future apps should also incorporate monitoring long-term symptoms of COVID-19. CLINICALTRIAL N/A

Piloting an Innovative Concept of e–Mental Health and mHealth Workshops With Medical Students Using a Participatory Co-design Approach and App Prototyping: Case Study

Background Medical students show low levels of e–mental health literacy. Moreover, there is a high prevalence of common mental illnesses among medical students. Mobile health (mHealth) apps can be used to maintain and promote medical students’ well-being. To date, the potential of mHealth apps for promoting mental health among medical students is largely untapped because they seem to lack familiarity with mHealth. In addition, little is known about medical students’ preferences regarding mHealth apps for mental health promotion. There is a need for guidance on how to promote competence-based learning on mHealth apps in medical education. Objective The aim of this case study is to pilot an innovative concept for an educative workshop following a participatory co-design approach and to explore medical students’ preferences and ideas for mHealth apps through the design of a hypothetical prototype. Methods We conducted a face-to-face co-design workshop within an elective subject with 26 participants enrolled at a medical school in Germany on 5 consecutive days in early March 2020. The aim of the workshop was to apply the knowledge acquired from the lessons on e–mental health and mHealth app development. Activities during the workshop included group work, plenary discussions, storyboarding, developing personas (prototypical users), and designing prototypes of mHealth apps. The workshop was documented in written and digitalized form with the students’ permission. Results The participants’ feedback suggests that the co-design workshop was well-received. The medical students presented a variety of ideas for the design of mHealth apps. Among the common themes that all groups highlighted in their prototypes were personalization, data security, and the importance of scientific evaluation. Conclusions Overall, this case study indicates the feasibility and acceptance of a participatory design workshop for medical students. The students made suggestions for improvements at future workshops (eg, use of free prototype software, shift to e-learning, and more time for group work). Our results can be (and have already been) used as a starting point for future co-design workshops to promote competence-based collaborative learning on digital health topics in medical education.

Factors affecting physicians using mobile health applications: an empirical study

Background Mobile health applications (mHealth apps) have created innovative service channels for patients with chronic diseases. These innovative service channels require physicians to actively use mHealth apps. However, few studies investigate physicians’ participation in mHealth apps. Objective This study aims to empirically explore factors affecting physicians’ usage behaviors of mHealth apps. Based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and mHealth apps features, we propose a research model including altruism, cognitive trust, and online ratings. Methods We collected data from physicians who have used mHealth apps and conducted a factor analysis to verify the convergence and discriminative effects. We used a hierarchical regression method to test the path coefficients and statistical significance of our research model. In addition, we adopted bootstrapping approach and further analyzed the mediating effects of behavioral intention between all antecedent variables and physicians’ usage behavior. Finally, we conducted three robustness analyses to test the validity of results and tested the constructs to verify the common method bias. Results Our results support the effects of performance expectancy, effort expectancy, social influence, and altruism on the behavioral intentions of physicians using mHealth apps. Moreover, facilitating conditions and habits positively affect physicians using mHealth apps through the mediating effort of behavioral intention. Physicians’ cognitive trust and online rating have significant effects on their usage behaviors through the mediating efforts of behavioral intention. Conclusions This study contributes to the existing literature on UTAUT2 extension of physicians’ acceptance of mHealth apps by adding altruism, cognitive trust, and online ratings. The results of this study provide a novel perspective in understanding the factors affecting physicians’ usage behaviors on mHealth apps in China and provide such apps’ managers with an insight into the promotion of physicians’ active acceptance and usage behaviors.

Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia

A series of mitigation efforts were implemented in response to the COVID-19 pandemic in Saudi Arabia, including the development of mobile health applications (mHealth apps) for the public. Assessing the acceptability of mHealth apps among the public is crucial. This study aimed to use Twitter to understand public perceptions around the use of six Saudi mHealth apps used during COVID-19: “Sehha”, “Mawid”, “Sehhaty”, “Tetamman”, “Tawakkalna”, and “Tabaud”. We used two methodological approaches: network and sentiment analysis. We retrieved Twitter data using specific mHealth apps-related keywords. After including relevant tweets, our final mHealth app networks consisted of a total of 4995 Twitter users and 8666 conversational relationships. The largest networks in size (i.e., the number of users) and volume (i.e., the conversational relationships) among all were “Tawakkalna” followed by “Tabaud”, and their conversations were led by diverse governmental accounts. In contrast, the four remaining mHealth networks were mainly led by the health sector and media. Our sentiment analysis approach included five classes and showed that most conversations were neutral, which included facts or information pieces and general inquires. For the automated sentiment classifier, we used Support Vector Machine with AraVec embeddings as it outperformed the other tested classifiers. The sentiment classifier showed an accuracy, precision, recall, and F1-score of 85%. Future studies can use social media and real-time analytics to improve mHealth apps’ services and user experience, especially during health crises.

Continued use of mobile apps for health promotion (Preprint)

BACKGROUND Mobile health (mhealth) applications (apps) promotion has been increasingly used year after year. They have recently played an important role in preventing an unhealthy society. Mhealth apps have been developed in different health areas, increasingly satisfying user goals. OBJECTIVE This study aimed to explore and understand important factors that contribute to use behavior and the intention to continue using mhealth apps, in combination with the self-determination theory, and produce well-being and consciousness in health. This study extends the findings to continue usage, along with the intrinsic motivation of each consumer that drives well-being. METHODS The proposed model was empirically tested using data from a survey. We obtained 306 valid responses through an online questionnaire created in Qualtrics from participants that use mhealth apps. We applied the partial least squares structural equation modelling (PLS-SEM) technique to test the research model. RESULTS The results show that the drivers of intrinsic motivation with statistical significance were perceived autonomy ((β ) ̂= 0.23; P < .001), perceived relatedness (β ̂ = 0.11; P = 0.049) and perceived competence (β ̂ = 0.50; P < .001). The drivers of perceived usefulness with statistical significance were confirmation (β ̂ = 0.74; P < .001). The precursors of satisfaction that were statistical significance were confirmation (β ̂ = 0.66; P < .001) and perceived usefulness ((β ) ̂= 0.22; P < .001). Use behavior was significantly influenced by intrinsic motivation (β ̂ = 0.58; P < .001) and perceived usefulness (β ̂ = 0.32; P < .001). The drivers of continuance intention to use with statistical significance were intrinsic motivation (β ̂ = 0.12; P = .018), perceived usefulness (β ̂ = 0.14; P = .004), satisfaction (β ̂ = 0.48; P < .001) and use behavior (β ̂ = 0.22; P < .001). Well-being was significantly influenced by use behavior (β ̂ = 0.62; P < .001) and continuance intention to use (β ̂ = 0.19; P = .001). The moderation effect of health consciousness in the relationship between use behavior and well-being (β ̂ = 0.14; P = .006) was statistically significant but the relationship between continuance intention to use and well-being (β ̂ = -0.06; P = .22), was not statistically significant. Therefore, the model explained over 50.8% of the total variance in intrinsic motivation, 54,4% of the variance in perceived usefulness, 69.6% of the variance satisfaction, 62.2% of the variance use behavior, 73.6% of the variance continuance intention to use, and 65.6% of the variance well-being. CONCLUSIONS Our study contributes to the continuance theory of the use of mhealth apps. The model found that intrinsic motivation, satisfaction, perceived usefulness, and user behavior significantly affect continuance intention. Our study demonstrates a significant role in a society that uses mhealth apps and that their use reflects well-being with the moderation effect of health consciousness.

Factors Influencing Adherence to mHealth Apps among Adults with Non-communicable Disease: A Systematic Review. (Preprint)

BACKGROUND Mobile health applications show vast potential in supporting patients and health care systems with the globally increasing prevalence and economic costs of non-communicable diseases. However, despite the availability of evidence-based mHealth apps, a substantial proportion of users does not adhere to them as intended and may consequently not receive treatment. Therefore, understanding factors that act as barriers or facilitators to adherence is a fundamental concern to prevent intervention dropouts and increase the effectiveness of digital health interventions. OBJECTIVE This review aims to identify intervention- and patient-related factors influencing the continued use of mHealth applications targeting non-communicable diseases (NCDs). We further derive quantified adherence scores for different health domains, which may help stakeholders plan, develop, and evaluate mHealth apps. METHODS A comprehensive systematic literature search (January 2007- December 2020) was conducted in MEDLINE, Embase, Web of Science, Scopus, and ACM Digital Library. Data on intended use, actual use, and factors influencing adherence were extracted. Intervention-related and patient-related factors with a positive or negative influence on adherence are presented separately for the health domains NCD-Self-Management, Mental Health, Substance Use, Nutrition, Physical Activity, Weight Loss, Multicomponent Lifestyle Interventions, Mindfulness, and other NCDs. Quantified adherence measures, calculated as the ratio between estimated intended and actual use, were derived for each study and compared with qualitative findings. RESULTS The literature search yielded 2862 potentially relevant articles, of which 99 were included as part of the inclusion criteria. Four intervention-related factors indicated positive effects on adherence across all health domains: (1) personalization or tailoring the content of the mHealth app to the individual needs of the user, (2) reminders in the form of individualized push notifications, (3) a user-friendly and technically stable app design, and (4) personal support complementary to the digital intervention. Social and gamification features were also identified as drivers of app adherence across several health domains. A wide variety of patient-related factors like user characteristics or user recruitment channels further affects adherence. Derived adherence scores of included mHealth apps averaged 56.0%. CONCLUSIONS This study contributes to the scarce scientific evidence on factors positively or negatively influencing adherence to mHealth apps and is the first to compare adherence relative to the intended use of various health domains quantitatively. As underlying studies mostly have a pilot character with short study durations, research on factors influencing adherence to mHealth apps is still limited. To facilitate future research on mHealth app adherence, researchers should clearly outline and justify the app's intended use, report objective data on actual use relative to the intended use, and ideally, provide long-term usage and retention data.

Effectivness of specific mobile health applications (mHealth-apps) in gestational diabtetes mellitus: a systematic review

Background Gestational diabetes mellitus (GDM) emerges worldwide and is closely associated with short- and long-term health issues in women and their offspring, such as pregnancy and birth complications respectively comorbidities, Type 2 Diabetes (T2D), metabolic syndrome as well as cardiovascular diseases. Against this background, mobile health applications (mHealth-Apps) do open up new possibilities to improve the management of GDM. Therefore, we analyzed the clinical effectiveness of specific mHealth-Apps on clinical health-related short and long-term outcomes in mother and child. Methods A systematic literature search in Medline (PubMed), Cochrane Library, Embase, CINAHL and Web of Science Core Collection databases as well as Google Scholar was performed. We selected studies published 2008 to 2020 analyzing women diagnosed with GDM using specific mHealth-Apps. Controlled clinical trials (CCT) and randomized controlled trials (RCT) were included. Study quality was assessed using the Effective Public Health Practice Project (EPHPP) tool. Results In total, n = 6 publications (n = 5 RCTs, n = 1 CCT; and n = 4 moderate, n = 2 weak quality), analyzing n = 408 GDM patients in the intervention and n = 405 in the control groups, were included. Compared to control groups, fasting blood glucose, 2-h postprandial blood glucose, off target blood glucose measurements, delivery mode (more vaginal deliveries and fewer (emergency) caesarean sections) and patient compliance showed improving trends. Conclusion mHealth-Apps might improve health-related outcomes, particularly glycemic control, in the management of GDM. Further studies need to be done in more detail.

Health Promotion through Monetary Incentives: Evaluating the Impact of Different Reinforcement Schedules on Engagement Levels with a mHealth App

Background: Financial rewards can be employed in mHealth apps to effectively promote health behaviors. However, the optimal reinforcement schedule—with a high impact, but relatively low costs—remains unclear. Methods: We evaluated the impact of different reinforcement schedules on engagement levels with a mHealth app in a six-week, three-arm randomized intervention trial, while taking into account personality differences. Participants (i.e., university staff and students, N = 61) were awarded virtual points for performing health-related activities. Their performance was displayed via a dashboard, leaderboard, and newsfeed. Additionally, participants could win financial rewards. These rewards were distributed using a fixed schedule in the first study arm, and a variable schedule in the other arms. Furthermore, payouts were immediate in the first two arms, whereas payouts in the third arm were delayed. Results: All three reinforcement schedules had a similar impact on user engagement, although the variable schedule with immediate payouts was reported to have the lowest cost per participant. Additionally, the impact of financial rewards was affected by personal characteristics. Especially, individuals that were triggered by the rewards had a greater ability to defer gratification. Conclusion: When employing financial rewards in mHealth apps, variable reinforcement schedules with immediate payouts are preferred from the perspective of cost and impact.