mHealth Research Research Articles

Implementing text-messaging to support and enhance delivery of health behavior change interventions in low- to middle-income countries: case study of the Lifestyle Africa intervention

The prevalence of non-communicable diseases, such as diabetes and cardiovascular disease, is rising in low- and middle-income countries (LMICs). Health behavior change (HBC) interventions such as the widely used Diabetes Prevention Program (DPP) are effective at reducing chronic disease risk, but have not been adapted for LMICs. Leveraging mobile health (mHealth) technology such as text messaging (SMS) to enhance reach and participant engagement with these interventions has great promise, yet we lack evidence-informed approaches to guide the integration of SMS specifically to support HBC interventions in LMIC contexts. To address this gap, we integrated guidance from the mHealth literature with expertise and first-hand experience to establish specific development steps for building and implementing SMS systems to support HBC programming in LMICs. Specifically, we provide real-world examples of each development step by describing our experience in designing and delivering an SMS system to support a culturally-adapted DPP designed for delivery in South Africa. We outline eight key SMS development steps, including: 1) determining if SMS is appropriate; 2) developing system architecture and programming; 3) developing theory-based messages; 4) developing SMS technology; 5) addressing international SMS delivery; 6) testing; 7) system training and technical support; and 8) cost considerations. We discuss lessons learned and extractable principles that may be of use to other mHealth and HBC researchers working in similar LMIC contexts.Trial registration Clinicaltrials.gov, NCT03342274. Registered 10 November 2017.

Influencing Factors to mHealth Uptake With Indigenous Populations: Qualitative Systematic Review.

The advancements and abundance of mobile phones and portable health devices have created an opportunity to use mobile health (mHealth) for population health systems. There is increasing evidence for the feasibility and acceptance of mHealth with Indigenous populations. Providing a synthesis of qualitative findings of mHealth with Indigenous populations will gain insights into the strengths and challenges to mHealth use in Indigenous populations. This review aimed to identify and synthesize qualitative data pertaining to the experiences and perceptions of mHealth from the perspectives of end users (patients and service providers) living in the colonial settler democracies of Canada, Australia, New Zealand, the United States, the Pacific Islands, and the Sápmi region of northern Europe. In May 2021, systematic searches of peer-reviewed, scientific papers were conducted across the 5 databases of PubMed, CINAHL, Embase, PsycINFO, and Web of Science. Qualitative or mixed method studies were included where a mHealth intervention was the primary focus for responding to health challenges with Indigenous populations. Two authors independently screened papers for eligibility and assessed the risk of bias using a modified version of the Critical Appraisal Skills Programme. A meta-aggregative approach was used to analyze the findings of included studies. Seventeen papers met the eligibility criteria, 8 studies with patients, 7 studies with service providers, and 2 studies that included both patients and service providers. Studies were conducted in Australia (n=10), Canada (n=2), New Zealand (n=2), Papua New Guinea (n=1), the United States (n=1), and Samoa (n=1). Our interpretation of these qualitative findings shows commonalities between Indigenous patients' and service providers' perceptions of mHealth. We summarize our findings in six themes: (1) mHealth literacy, (2) mHealth as a facilitator for connection and support, (3) mHealth content needed to be culturally relevant, (4) mHealth security and confidentiality, (5) mHealth supporting rather than replacing service providers, and (6) workplace and organizational capacity. This research suggests that mHealth can meet the needs of both patients and service providers when the mHealth intervention is culturally relevant, accounts for digital and health literacy, incorporates interactive components, is supported by workplaces, fits into health provider workflows, and meets security and confidentiality standards. Future mHealth research with Indigenous populations should partner with key representatives (eg, patients, service providers, and executive leaders) in the mHealth design appropriate to the purpose, people, setting, and delivery.

Use of Framework Matrix and Thematic Coding Methods in Qualitative Analysis for mHealth: NIRUDAK Study Data.

Framework Matrix Analysis (FMA) and Applied Thematic Analysis (ATA) are qualitative methods that have not been as widely used/cited compared to content analysis or grounded theory. This paper compares methods of FMA with ATA for mobile health (mHealth) research. The same qualitative data were analyzed separately, using each methodology. The methods, utility, and results of each are compared, and recommendations made for their effective use. Formative qualitative data were collected in eight focus group discussions with physicians and nurses from three hospitals in Bangladesh. Focus groups were conducted via video conference in the local language, Bangla, and audio recorded. Audio recordings were used to complete a FMA of participants' opinions about key features of a novel mHealth application (app) designed to support clinical management in patients with acute diarrhea. The resulting framework matrix was shared with the app design team and used to guide iterative development of the product for a validation study of the app. Subsequently, focus group audio recordings were transcribed in Bangla then translated into English for ATA; transcripts and codes were entered into NVivo qualitative analysis software. Code summaries and thematic memos explored the clinical utility of the mHealth app including clinicians' attitudes about using this decision support tool. Each of the two methods contributes differently to the research goal and have different implications for an mHealth research timeline. Recommendations for the effective use of each method in app development include: using FMA for data reduction where specific outcomes are needed to make programming and design decisions and using ATA to capture the more nuanced issues that guide use, product implementation, training, and workflow. By describing how both analytical methods were used in this context, this paper provides guidance and an illustration for use of these two methods, specifically in mHealth design.

Rating analysis and BERTopic modeling of consumer versus regulated mHealth app reviews in Germany

Germany introduced prescription-based mobile health (mHealth) apps in October 2020, becoming the first country to offer them fully reimbursed by health insurance. These regulated apps, known as DiGAs, undergo a rigorous approval process similar to pharmaceuticals, including data protection measures and sometimes clinical trials. This study compares the user experience of DiGAs with non-prescription mHealth apps in Germany, analyzing both average app store ratings and written reviews. Our study pioneers the use of BERTopic for sentiment analysis and topic modeling in the mHealth research domain. The dataset comprises 15 DiGAs and 50 comparable apps, totaling 17,588 German-language reviews. Results reveal that DiGAs receive higher contemporary ratings than non-regulated apps (Android: 3.82 vs. 3.77; iOS: 3.78 vs. 3.53; p < 0.01; non-parametric Mann–Whitney–Wilcoxon test). Key factors contributing to positive user experience with DiGAs are customer service and personalization (15%) and ease of use (13%). However, challenges for DiGAs include software bugs (24%) and a cumbersome registration process (20%). Negative user reviews highlight concerns about therapy effectiveness (11%). Excessive pricing is the main concern for the non-regulated group (27%). Data privacy and security receive limited attention from users (DiGAs: 0.5%; comparators: 2%). In conclusion, DiGAs are generally perceived positively based on ratings and sentiment analysis of reviews. However, addressing pricing concerns in the non-regulated mHealth sector is crucial. Integrating user experience evaluation into the review process could improve adherence and health outcomes.

University students' opinions towards mobile sensing data collection: A qualitative analysis.

mHealth researchers can now collect a wealth of data using "life tracking apps" (LTAs), which are smartphone applications that use mobile sensing to capture and summarise a multitude of data channels (e.g., location, movement, keyword use, sleep, exercise, and so on). The combined wealth of information can create digital signatures of individuals, which hold immense promise for mental health research and interventions by allowing new insights into moment-to-moment changes in behaviour and mental states. However, little is known about what a common research demographic (university students) thinks about these apps and what might factor into their decisions to participate in research using a LTA. This qualitative study ran five focus group sessions (21 students in total) to explore students' experiences, beliefs, and opinions about LTAs to generate insights into what would make them more or less likely to participate in research involving LTAs. Transcripts were coded and examined for categories using qualitative content analysis. Important categories that emerged were privacy (although this varied based on the individual and data being collected), data security, inconvenience, intrusiveness, financial compensation, and the perceived nature of the research team responsible. On the basis of these categories, we derived seven key insights to increase student participation in research using LTAs: strengthen and communicate privacy and data security, design the app to be as convenient as possible to users, maximise passive data collection, think cautiously before tracking data perceived as "creepy" such as messages, offer suitable financial compensation, be transparent about goals and justification for data being collection to build trust, and attract participants by highlighting how the app can help them achieve their goals. With these insights, mHealth researchers can maximise their participant pool and improve this nascent and promising field.

Associations of Country-Specific and Sociodemographic Factors With Self-Reported COVID-19-Related Symptoms: Multivariable Analysis of Data From the CoronaCheck Mobile Health Platform.

The COVID-19 symptom-monitoring apps provide direct feedback to users about the suspected risk of infection with SARS-CoV-2 and advice on how to proceed to prevent the spread of the virus. We have developed the CoronaCheck mobile health (mHealth) platform, the first free app that provides easy access to valid information about the risk of infection with SARS-CoV-2 in English and German. Previous studies have suggested that the clinical characteristics of individuals infected with SARS-CoV-2 vary by age, gender, and viral variant; however, potential differences between countries have not been adequately studied. The aim of this study is to describe the characteristics of the users of the CoronaCheck mHealth platform and to determine country-specific and sociodemographic associations of COVID-19-related symptoms and previous contacts with individuals infected with COVID-19. Between April 8, 2020, and February 3, 2022, data on sociodemographic characteristics, symptoms, and reports of previous close contacts with individuals infected with COVID-19 were collected from CoronaCheck users in different countries. Multivariable logistic regression analyses were performed to examine whether self-reports of COVID-19-related symptoms and recent contact with a person infected with COVID-19 differed between countries (Germany, India, South Africa), gender identities, age groups, education, and calendar year. Most app users (N=23,179) were from Germany (n=8116, 35.0%), India (n=6622, 28.6%), and South Africa (n=3705, 16.0%). Most data were collected in 2020 (n=19,723, 85.1%). In addition, 64% (n=14,842) of the users were male, 52.1% (n=12,077) were ≥30 years old, and 38.6% (n=8953) had an education level of more than 11 years of schooling. Headache, muscle pain, fever, loss of smell, loss of taste, and previous contacts with individuals infected with COVID-19 were reported more frequently by users in India (adjusted odds ratios [aORs] 1.3-8.3, 95% CI 1.2-9.2) and South Africa (aORs 1.1-2.6, 95% CI 1.0-3.0) than those in Germany. Cough, general weakness, sore throat, and shortness of breath were more frequently reported in India (aORs 1.3-2.6, 95% CI 1.2-2.9) compared to Germany. Gender-diverse users reported symptoms and contacts with confirmed COVID-19 cases more often compared to male users. Patterns of self-reported COVID-19-related symptoms and awareness of a previous contact with individuals infected with COVID-19 seemed to differ between India, South Africa, and Germany, as well as by gender identity in these countries. Viral symptom-collecting apps, such as the CoronaCheck mHealth platform, may be promising tools for pandemics to support appropriate assessments. Future mHealth research on country-specific differences during a pandemic should aim to recruit representative samples.

Strengthening the evidence base for mHealth in clinical practice: Conducting research with standalone or interoperable systems - a viewpoint.

The aim of this viewpoint is to inform mobile health (mHealth) evidence development in using standalone or interoperable systems in hospital practice. There is a gap between mHealth research and its widespread uptake in clinical practice. Evidence generation is not keeping up with the introduction and implementation of technologies. This is partly a consequence of the technology characteristics and the way research is conducted in a clinical setting. Research and development of mHealth technology can be conducted standalone in a laboratory like setting, standalone in a clinical setting or interoperable with already existing technology in hospital practice. Standalone systems operate relatively independent from an organizations' existing infrastructure. Using laboratory settings does not reflect the complexity of real-life, but in clinical practice this may be suitable for research assessing usability, feasibility or even clinical and process outcomes at a small scale. Realizing research and development on interoperable mHealth technology solutions, especially with operational EMR systems, is a challenging, time- and resource intensive process and requires large(r) investments, as it is often complicated by a myriad of interfering factors. Interoperable systems are however a more sustainable option in the long run, and generated evidence reflects the real hospital care setting and this option may therefore facilitate dissemination. Choosing either a standalone or interoperable setting affects the research design, the implementation pace and ultimately widespread adoption of the mHealth technology. We recommend to include these technology characteristics in implementation frameworks and think of evaluation research designs in an early phase.

S-21-3: EFFECTIVENESS AND ACCEPTABILITY OF MOBILE PHONE TEXT MESSAGING INTERVENTION TO IMPROVE BLOOD PRESSURE CONTROL (TEXT4BP) AMONG HYPERTENSIVE PATIENTS IN NEPAL

Objectives: In LMICs, the rapid uptake and availability of mobile phones provide excellent opportunities for host of an intervention. However, research on the effectiveness and acceptability of mHealth in these settings remains limited. The main objective of this feasibility study was to assess the effectiveness and acceptability of a mobile phone text messaging intervention (TEXT4BP) to improve blood pressure control and treatment adherence among hypertensive patients in Nepal. Design and Methods: The TEXT4BP study was a two-arm, parallel-group, unblinded, randomized controlled pilot trial that recruited 200 clinically diagnosed hypertensive patients aged 18–69 years visiting at a tertiary referral hospital in Kathmandu, Nepal. We co-designed the contextual text messages informed by the formative qualitative studies that used COM-B (capability, opportunity, motivation, and behavior) model and Technology Acceptance Model (TAM) model. Patients in the intervention arm (n = 100) received text messages containing information on hypertension, diet, medication, and physical activity three times per week for three months. The control arm (n = 100) received standard care. Primary outcomes were changes in blood pressure and medication adherence at three months. Secondary outcomes included blood pressure control, medication adherence self-efficacy, and hypertension knowledge. Feasibility measures, such as attrition rate, was calculated. A nested qualitative study assessed the acceptability of the intervention. Results: Over the 12 weeks of follow-up, the retention rate was 77%. The intervention group had a greater reduction in systolic and diastolic BP vs usual care [-7.09/-5.86 (p = 0.003) vs -0.77/-1.35 (p = 0.28) mmHg] [adjusted difference: systolic B = -6.50 (95% CI, -12.6; -0.33) and diastolic BP B = -4.60 (95% CI, -8.16; -1.04)] (fig 1), coupled with a greater proportion achieving target BP (70% vs 48%, p = 0.006) at three months. The intervention arm showed an improvement in compliance to antihypertensive therapy (p &lt; 0.001), medication adherence (p &lt; 0.001), medication adherence self-efficacy (p = 0.023), and knowledge on hypertension and its treatment (p = 0.013). In addition, intervention participants expressed a high rate of acceptability and desire to continue the TEXT4BP intervention in the future too. Conclusion: The TEXT4BP study has generated promising evidence that text messaging intervention is feasible, acceptable, and effective in improving blood pressure control in low-resource settings like Nepal. The findings will inform further large-scale mHealth research and contribute to clinical practice with evidence-based hypertension guidelines in Nepal. Trial reg: ACTRN12619001213134

Factors Affecting mHealth Technology Adoption in Developing Countries: The Case of Egypt

Mobile health apps are seeing rapid growth in the potential to improve access to healthcare services for disadvantaged communities, while enhancing the efficiency of the healthcare delivery value chain. Still, the adoption of mHealth apps is relatively low, especially in developing countries. In Egypt, an initiative for national-level healthcare coverage was launched in 2021, accompanied by a rise in mHealth start-ups. However, many of these projects did not progress beyond the pilot stage, with very little known about the antecedents of mHealth adoption for the Egyptian user. Semi-structured interviews were conducted with 22 Egyptians, aiming to uncover factors affecting the use of mHealth apps for Egyptian citizens. Some of these factors were introduced by previous studies, such as Perceived Service Quality, Perceived Risk, Perceived Ease of Use, and Trust. Others were not well established in the mHealth research strand, such as Perceived Reputation and Perceived Familiarity, while Governance, Personalized Experience, Explain-ability, Interaction, Language, and Cultural Issues, are novel factors introduced by the current research. The effect of these suggested independent variables on the willingness to adopt mHealth apps was validated using a survey administered to 150 Egyptians, confirming the significant positive effect of most of these factors on mHealth adoption in Egypt. This research contributes to methodology by introducing novel constructs in the mHealth research context, which might be specific to the target developing country. Practical implications were suggested for designers and healthcare service providers might increase the adoption of their apps in developing countries, such as Egypt.

The feasibility of using Apple's ResearchKit for recruitment and data collection: Considerations for mental health research.

In 2015, Apple launched an open-source software framework called ResearchKit. ResearchKit provides an infrastructure for conducting remote, smartphone-based research trials through the means of Apple's App Store. Such trials may have several advantages over conventional trial methods including the removal of geographic barriers, frequent assessments of participants in real-life settings, and increased inclusion of seldom-heard communities. The aim of the current study was to explore the feasibility of participant recruitment and the potential for data collection in the non-clinical population in a smartphone-based trial using ResearchKit. As a case example, an app called eMovit, a behavioural activation (BA) app with the aim of helping users to build healthy habits was used. The study was conducted over a 9-month period. Any iPhone user with access to the App Stores of The Netherlands, Belgium, and Germany could download the app and participate in the study. During the study period, the eMovit app was disseminated amongst potential users via social media posts (Twitter, Facebook, LinkedIn), paid social media advertisements (Facebook), digital newsletters and newspaper articles, blogposts and other websites. In total, 1,788 individuals visited the eMovit landing page. A total of 144 visitors subsequently entered Apple's App Store through that landing page. The eMovit product page was viewed 10,327 times on the App Store. With 79 installs, eMovit showed a conversion rate of 0.76% from product view to install of the app. Of those 79 installs, 53 users indicated that they were interested to participate in the research study and 36 subsequently consented and completed the demographics and the participants quiz. Fifteen participants completed the first PHQ-8 assessment and one participant completed the second PHQ-8 assessment. We conclude that from a technological point of view, the means provided by ResearchKit are well suited to be integrated into the app process and thus facilitate conducting smartphone-based studies. However, this study shows that although participant recruitment is technically straightforward, only low recruitment rates were achieved with the dissemination strategies applied. We argue that smartphone-based trials (using ResearchKit) require a well-designed app dissemination process to attain a sufficient sample size. Guidelines for smartphone-based trial designs and recommendations on how to work with challenges of mHealth research will ensure the quality of these trials, facilitate researchers to do more testing of mental health apps and with that enlarge the evidence-base for mHealth.

How mHealth Can Contribute to Improving the Continuum of Care: A Scoping Review Approach to the Case of Human Immunodeficiency Virus in Sub-Saharan Africa.

Objectives: To determine mHealth’s contribution to improving the continuum of care in sub-Saharan Africa towards achieving treatment targets for human immunodeficiency virus (HIV) endorsed by the 2016 Political Declaration on ending acquired immunodeficiency syndrome (AIDS). Methods: PubMed, Medline, Embase, Web of Science Core Collection and Cochrane databases; three observatories and four repositories were searched to identify and select relevant articles, projects and guidelines published from 1 January 2017, to 30 April 2021. Records focusing on the use of mHealth related to HIV treatment cascade or healthcare provider/patient relationship were considered. Results: From 574 identified records, 381 (206 scientific manuscripts and 175 mHealth projects) were considered. After screening, 36 articles (nine randomized control trials, five cohort studies, 19 qualitative studies, and three economic studies) and 23 projects were included. Conclusion: The cross-cutting benefits of mHealth that enhance patient empowerment have been identified. Important challenges such as gaps between research and implementation, lack of transdisciplinary collaboration, and lack of economic evidence were identified to support future mHealth research and accelerate the achievement of treatment targets for HIV.

A "No-Code" App Design Platform for Mobile Health Research: Development and Usability Study.

BackgroundA challenge facing researchers conducting mobile health (mHealth) research is the amount of resources required to develop mobile apps. This can be a barrier to generating relevant knowledge in a timely manner. The recent rise of “no-code” software development platforms may overcome this challenge and enable researchers to decrease the cost and time required to develop mHealth research apps.ObjectiveWe aimed to describe the development process and the lessons learned to build Pathverse, a no-code mHealth app design platform.MethodsThe study took place between November 2019 and December 2021. We used a participatory research framework to develop the mHealth app design platform. In phase 1, we worked with researchers to gather key platform feature requirements and conducted an exploratory literature search to determine needs related to this platform. In phase 2, we used an agile software framework (Scrum) to develop the platform. Each development sprint cycle was 4 weeks in length. We created a minimum viable product at the end of 7 sprint cycles. In phase 3, we used a convenience sample of adults (n=5) to gather user feedback through usability and acceptability testing. In phase 4, we further developed the platform based on user feedback, following the V-model software development process.ResultsOur team consulted end users (ie, researchers) and utilized behavior change technique taxonomy and behavior change models (ie, the multi-process action control framework) to guide the development of features. The first version of the Pathverse platform included features that allowed researchers to (1) design customized multimedia app content (eg, interactive lessons), (2) set content delivery logic (eg, only show new lessons when completing the previous lesson), (3) implement customized participant surveys, (4) provide self-monitoring tools, (5) set personalized goals, and (6) customize app notifications. Usability and acceptability testing revealed that researchers found the platform easy to navigate and that the features were intuitive to use. Potential improvements include the ability to deliver adaptive interventions and add features such as community group chat.ConclusionsTo our knowledge, Pathverse is the first no-code mHealth app design platform for developing mHealth interventions for behavior. We successfully used behavior change models and the behavior change technique taxonomy to inform the feature requirements of Pathverse. Overall, the use of a participatory framework, combined with the agile and hybrid-agile software development process, enabled our team to successfully develop the Pathverse platform.

Developing a Consensus for Adolescent and Young Adult mHealth HIV Prevention Interventions in the United States: A Delphi Method Study.

BackgroundEngaging adolescents and young adults (AYAs) who are at elevated risk for HIV acquisition or who are living with HIV in health care has posed a major challenge in HIV prevention and care efforts. Mobile health (mHealth) interventions are a popular and accessible strategy to support AYA engagement despite barriers to care present along the HIV care continuum. Even with progress in the field of mHealth research, expert recommendations for the process of designing, evaluating, and implementing HIV-related mHealth interventions are underdeveloped.ObjectiveThe aim of this study was to compile expert recommendations on the development, evaluation, and implementation of AYA-focused HIV prevention and care mHealth interventions.MethodsExperts from adolescent mHealth HIV research networks and investigators of recently funded HIV mHealth projects and programs were identified and invited to complete a series of electronic surveys related to the design, implementation, and evaluation of HIV-related mHealth interventions. A modified Delphi method was used to ask experts to score 35 survey items on a 4-point Likert scale from not important to very important and encouraged experts to leave additional comments in textboxes. Responses were reviewed by the researchers, a team of 4 HIV mHealth intervention experts. The average importance ratings from survey responses were calculated and then categorized as retained (high importance), flagged (mid-level importance), or dropped (no/low importance). Additionally, thematic analysis of expert comments helped modify survey items for the next survey round. An evaluation of the level of agreement among experts on the most important items followed each round until consensus was reached.ResultsOf the 35 invited experts, 23 completed the first survey representing a variety of roles within a research team. Following two rounds of Delphi surveys, experts scored 24 of the 28 (86%) survey items included in round two as important to very important. The final consensus items included 24 recommendations related to the mHealth intervention design process (n=15), evaluation (n=2), and implementation (n=7). The 3 survey items with the highest average scores focused on the design process, specifically, (1) the creation of a diverse team including researchers, app software developers, and youth representation; (2) the importance of AYA-focused content; and (3) the value of an iterative process. Additionally, experts highlighted the importance of establishing the best ways to collect data and the types of data for collection during the evaluation process as well as constructing a plan for participant technology disruption when implementing an mHealth intervention.ConclusionsThe modified Delphi method was a useful tool to convene experts to determine recommendations for AYA-focused HIV prevention and care mHealth interventions. These recommendations can inform future mHealth interventions. To ensure the acceptability, feasibility, and efficacy of these AYA HIV prevention interventions, the focus must be on the specific needs of AYAs by including representation of AYAs in the process, including consistent and relevant content, ensuring appropriate data is collected, and considering technology and health accessibility barriers.

Electronic Health Record-Based Recruitment and Retention and Mobile Health App Usage: Multisite Cohort Study.

BackgroundTo address the obesity epidemic, there is a need for novel paradigms, including those that address the timing of eating and sleep in relation to circadian rhythms. Electronic health records (EHRs) are an efficient way to identify potentially eligible participants for health research studies. Mobile health (mHealth) apps offer available and convenient data collection of health behaviors, such as timing of eating and sleep.ObjectiveThe aim of this descriptive analysis was to report on recruitment, retention, and app use from a 6-month cohort study using a mobile app called Daily24.MethodsUsing an EHR query, adult patients from three health care systems in the PaTH clinical research network were identified as potentially eligible, invited electronically to participate, and instructed to download and use the Daily24 mobile app, which focuses on eating and sleep timing. Online surveys were completed at baseline and 4 months. We described app use and identified predictors of app use, defined as 1 or more days of use, versus nonuse and usage categories (ie, immediate, consistent, and sustained) using multivariate regression analyses.ResultsOf 70,661 patients who were sent research invitations, 1021 (1.44%) completed electronic consent forms and online baseline surveys; 4 withdrew, leaving a total of 1017 participants in the analytic sample. A total of 53.79% (n=547) of the participants were app users and, of those, 75.3% (n=412), 50.1% (n=274), and 25.4% (n=139) were immediate, consistent, and sustained users, respectively. Median app use was 28 (IQR 7-75) days over 6 months. Younger age, White race, higher educational level, higher income, having no children younger than 18 years, and having used 1 to 5 health apps significantly predicted app use (vs nonuse) in adjusted models. Older age and lower BMI predicted early, consistent, and sustained use. About half (532/1017, 52.31%) of the participants completed the 4-month online surveys. A total of 33.5% (183/547), 29.3% (157/536), and 27.1% (143/527) of app users were still using the app for at least 2 days per month during months 4, 5, and 6 of the study, respectively.ConclusionsEHR recruitment offers an efficient (ie, high reach, low touch, and minimal participant burden) approach to recruiting participants from health care settings into mHealth research. Efforts to recruit and retain less engaged subgroups are needed to collect more generalizable data. Additionally, future app iterations should include more evidence-based features to increase participant use.

Viewing Mobile Health Technology Design Through the Lens of Amplification Theory.

Digital health interventions designed to promote health equity can be valuable tools in the delivery of health care to hardly served patient populations. But if the design of these technologies and the interventions in which they are deployed do not address the myriad structural barriers to care that minoritized patients, patients in rural areas, and patients who have trouble paying for care often face, their impact may be limited. Drawing on our mobile health (mHealth) research in the arena of cardiovascular care and blood pressure management, this viewpoint argues that health care providers and researchers should tend to structural barriers to care as a part of their digital health intervention design. Our 3-step predesign framework, informed by the Amplification Theory of Technology, offers a model that interventionists can follow to address these concerns.

MHealth Research for Weight Loss, Physical Activity, and Sedentary Behavior: Bibliometric Analysis.

BackgroundResearch into mobile health (mHealth) technologies on weight loss, physical activity, and sedentary behavior has increased substantially over the last decade; however, no research has been published showing the research trend in this field.ObjectiveThe purpose of this study was to provide a dynamic and longitudinal bibliometric analysis of recent trends of mHealth research for weight loss, physical activity, and sedentary behavior.MethodsA comprehensive search was conducted through Web of Science to retrieve all existing relevant documents published in English between January 1, 2010, and November 1, 2021. We developed appropriate research questions; based on the proven bibliometric approaches, a search strategy was formulated to screen the title for eligibility. Finally, we conducted bibliometric analyses to explore the growth rate of publications; publication patterns; and the most productive authors, institutions, and countries, and visualized the trends in the field using a keyword co-occurrence network.ResultsThe initial search identified 8739 articles, of which 1035 were included in the analyses. Our findings show an exponential growth trend in the number of annual publications of mHealth technology research in these fields. JMIR mHealth and uHealth (n=214, 20.67%), Journal of Medical Internet Research (n=71, 6.86%), and BMC Public Health (n=36, 3.47%) were the top 3 journals, publishing higher numbers of articles. The United States remained the leading contributor in these areas (n=405, 39.13%), followed by Australia (n=154, 14.87%) and England (n=125, 12.07%). Among the universities, the University of Sydney (n=36, 3.47%) contributed the most mHealth technology research in these areas; however, Deakin University (n=25, 2.41%) and the National University of Singapore (n=23, 2.22%) were in the second and third positions, respectively.ConclusionsAlthough the number of papers published on mobile technologies for weight loss, physical activity, and sedentary behavior was initially low, there has been an overall increase in these areas in recent years. The findings of the study indicate that mobile apps and technologies have substantial potential to reduce weight, increase physical activity, and change sedentary behavior. Indeed, this study provides a useful overview of the publication trends and valuable guidance on future research directions and perspectives in this rapidly developing field.

Development of a mHealth Intervention (TechCare) for First Episode Psychosis: A Focus Group Study With Mental Health Professionals

AimsResearch in the area of mHealth, has shown much promise in the development of mobile phone interventions which look at the assessment and treatment in real-time of psychiatric disorders. Within the context of Severe Mental Illnesses (SMI), such as psychosis, communication and understanding between health professionals and service users in the reporting of distress and reoccurrence of symptoms is essential in reducing the chances of relapse. An alternative pathway which uses mobile technology to engage with services, may hold the key to gaining a deeper understanding of the lived experiences of those with mental health difficulties, in particular experiences of recovery from SMI's. AIM: The aim of the study was to explore the perspectives and opinions of health professionals on the development and refinement of the TechCare App for psychosis. A qualitative approach was adopted for data collection, which provided an understanding of factors in relation to the development of the intervention.MethodsA total of two focus groups were held with health professionals to elicit their views on optimising the utility of the TechCare App. The total sample size for the focus groups was n = 16 with a total of 6 males and 10 females. This qualitative study was part of a feasibility study, investigating a novel intervention (TechCare) (Husain et al., 2016; Gire et al., 2021) which monitored participants symptoms and provided a tailored psychosocial response in real-time. Data obtained from the focus groups was transcribed. Framework analysis were used to analyse the data for emerging themes, focusing on feasibility, acceptability and further development.ResultsThe key themes that emerged from the data were; access and usage of digital technologies, implications for clinical practice, challenges &amp; barriers to delivery and development and refinement considerations for the TechCare App.ConclusionResults of the focus group with health professionals provided a unique perspective of conducting mHealth research within an EIS context, and the differing challenges professionals anticipated facing in delivering the TechCare App intervention. The main finding of the focus group was that professionals saw the potential for the TechCare App to increase access to digital technologies, providing service users with an alternative medium to communicate with EIS health professionals. However, the participants felt that despite mHealth Apps being a useful platform to deliver interventions, face-to-face contact should remain an important aspect of routine care.

Design Frameworks for Mobile Health Technology

The World Health Organization envisions that mobile health (mHealth) technologies will revolutionize healthcare to help people around the world. This review examined recent literature to assess the state-of-the-art of mHealth technology design frameworks and guidelines. Through multiple rounds of searches and screenings, twelve eligible research publications were selected from MEDLINE and Web of Science for full analyses, including selected bibliometrics, categorical meta-trend analyses and inductive content analysis. This review reveals the current state of mHealth research, highlights impactful design frameworks and guidelines, generates the current action cycle for mHealth technology research, and discusses on practical implications as well as future directions for mHealth research and design.

Introduction to Topical Issue on mHealth for Brain Injury Rehabilitation.

Introduction to Topical Issue on mHealth for Brain Injury Rehabilitation.

Development of a Mobile App for Clinical Research: Challenges and Implications for Investigators.

Advances in mobile app technologies offer opportunities for researchers to feasibly collect a large amount of patient data that were previously inaccessible through traditional clinical research methods. Collection of data via mobile devices allows for several advantages, such as the ability to continuously gather data outside of research facilities and produce a greater quantity of data, making these data much more valuable to researchers. Health services research is increasingly incorporating mobile health (mHealth), but collecting these data in current research institutions is not without its challenges. Our paper uses a specific example to depict specific challenges of mHealth research and provides recommendations for investigators looking to incorporate digital app technologies and patient-collected digital data into their studies. Our experience describes how clinical researchers should be prepared to work with variable software and mobile app development timelines; research institutions that are interested in participating in mHealth research need to invest in supporting information technology infrastructures in order to be a part of the growing field of mHealth and gain access to valuable patient-collected data.