mHealth Innovation Research Articles

Cardiovascular Disease Prevention With mHealth Innovations: Transforming Wellness Through Wireless.

Cardiovascular Disease Prevention With mHealth Innovations: Transforming Wellness Through Wireless.

M-Health terhadap Kepatuhan Pengobatan pada Pasien Hipertensi

This research aims to provide an opportunity for hypertensive patients to receive education and information related to hypertension and medication from health workers via their cell phones. The method used to search for articles relevant to the study in September-October 2023 was carried out using three stages, namely: planning, conducting, and reporting. The results obtained show that as many as 10 international articles analyzed in this paper have passed screening according to the inclusion and exclusion criteria. The journals or articles reviewed include the year of publication, database, research design, and location. In conclusion, the use of M-Health has been proven to be effective in making sufferers more compliant with every treatment carried out, especially when treatment is hampered by geographical problems. This M-Health innovation can help the process of effective management and control of hypertension. Keywords: M-Health, innovation, medication adherence, hypertension

"Endless opportunities": A qualitative exploration of facilitators and barriers to scale-up of two-way texting follow-up after voluntary medical male circumcision in Zimbabwe.

In Zimbabwe, the ZAZIC consortium employs two-way, text-based (2wT) follow-up to strengthen post-operative care for voluntary medical male circumcision (VMMC). 2wT scaled nationally with evidence of client support and strengthened follow-up. However, 2wT uptake among healthcare providers remains suboptimal. Understanding the gap between mobile health (mHealth) potential for innovation expansion and scale-up realization is critical for 2wT and other mHealth innovations. Therefore, we conducted an exploratory qualitative study with the objective of identifying 2wT program strengths, challenges, and suggestions for scale up as part of routine VMMC services. A total of 16 in-depth interviews (IDIs) with diverse 2wT stakeholders were conducted, including nurses, monitoring & evaluation teams, and technology partners-a combination of perspectives that provide new insights. We used both inductive and deductive coding for thematic analysis. Among 2wT drivers of expansion success, interviewees noted: 2wT care benefits for clients; effective hands-on 2wT training; ease of app use for providers; 2wT saved time and money; and 2wT strengthened client/provider interaction. For 2wT scale-up challenges, staff shortages; network infrastructure constraints; client costs; duplication of paper and electronic reporting; and complexity of digital tools integration. To improve 2wT robustness, respondents suggested: more staff training to offset turnover; making 2wT free for clients; using 2wT to replace paper VMMC reporting; integrating with routine VMMC reporting systems; and expanding 2wT to other health areas. High stakeholder participation in app design, implementation strengthening, and evaluation were appreciated. Several 2wT improvements stemmed from this study, including enrollment of multiple people on one number to account for phone sharing; 2wT inclusion of minors ages 15+; clients provided with $1 to offset SMS costs; and reduced SMS messages to clients. Continued 2wT mentoring for staff, harmonization of 2wT with Ministry e-health data systems, and increased awareness of 2wT's client and provider benefits will help ensure successful 2wT scale-up.

MOBILE HEALTH (MHEALTH) INNOVATIONS FOR PUBLIC HEALTH FEEDBACK: A GLOBAL PERSPECTIVE

In recent years, Mobile Health (mHealth) innovations have emerged as a transformative force in the realm of public health, revolutionizing the way healthcare is delivered and monitored worldwide. This review presents a comprehensive overview of the latest advancements in mHealth technologies, focusing particularly on their role in soliciting and utilizing public health feedback on a global scale. The proliferation of smartphones and mobile applications has paved the way for novel approaches to healthcare delivery, monitoring, and data collection. Through leveraging the ubiquity and connectivity of mobile devices, mHealth initiatives have facilitated enhanced communication between healthcare providers and patients, enabling real-time monitoring of health metrics, adherence to treatment regimens, and timely interventions. One of the key aspects of mHealth innovations is their capacity to engage diverse populations and solicit feedback regarding various aspects of public health, ranging from disease outbreaks to healthcare service quality. Through interactive platforms and user-friendly interfaces, individuals can provide valuable insights, report symptoms, and participate in surveys, thereby contributing to the generation of actionable data for public health interventions. Furthermore, mHealth solutions have demonstrated significant promise in overcoming barriers to healthcare access, particularly in underserved and remote communities. By delivering health information, diagnostic tools, and remote consultations via mobile platforms, these innovations have expanded the reach of essential healthcare services, thereby bridging gaps in healthcare delivery and improving health outcomes on a global scale. However, challenges such as privacy concerns, technological literacy, and disparities in digital access persist, underscoring the importance of equitable deployment and user-centered design in mHealth initiatives. Moreover, the integration of mHealth solutions into existing healthcare infrastructure requires careful coordination among stakeholders, including policymakers, healthcare providers, and technology developers. Mobile Health (mHealth) innovations hold immense potential to revolutionize public health feedback mechanisms on a global scale. By harnessing the power of mobile technologies, these initiatives can empower individuals, enhance healthcare delivery, and inform evidence-based public health policies for a healthier future.
 Keywords: Mobile Health, mHealth, Public Health, Healthcare, Innovation, Review.

Neonatal jaundice detection in low-income Mexican settings: is there scope for mHealth innovation?

Abstract Introduction Neonatal jaundice (NNJ) is a common and usually harmless condition, but undetected severe cases can cause brain damage and disabilities in thousands of babies each year. Low- and middle-income countries often lack accurate methods for detection and rely primarily on visual assessment (VA), resulting in a higher incidence of consequences. Norwegian researchers developed an easy-to-use and affordable smartphone app (Picterus) to detect NNJ that has shown higher accuracy than VA in Norwegian and Mexican neonates. We evaluated the feasibility of implementing Picterus in low-income settings in Mexico to improve outcomes for affected newborns. Methods Qualitative methods, including 1 focus group, 15 semi structured interviews, and 4 observations, were used in urban and rural health facilities in Oaxaca, Mexico, with medical doctors, nurses, and health authorities as participants. Results Four themes were identified through thematic analysis guided by the Consolidated Framework for Implementation Research: (1) the current process to detect NNJ, (2) participants’ needs to improve it, (3) feasibility of implementing Picterus in the health system, and (4) users’ expectations from the app. The findings indicate deficiencies in the current process of NNJ detection and a desire among participants for a more accurate tool. Picterus was perceived as an easy-to-use and useful tool compatible with the work routine; however, barriers to adoption were identified, primarily related to internet deficiencies and costs. Conclusions Picterus has the potential to improve NNJ detection but contextual barriers must be addressed for a successful implementation in the setting. Additionally, there is scope to optimize VA to strengthen NNJ detection. Key messages • Prevalence of severe consequences of NNJ is higher in LMIC compared to high-income ones reflecting an unjust but preventable health inequity with significant health, social and economic costs. • Picterus offers a reliable and accessible tool to bridge the gap in NNJ detection, potentially improving outcomes for affected newborns in all settings.

Development, usability, formative assessment, and story immersion of Nutrigame, a mHealth nutrition education app

The replacement of unprocessed and minimally processed foods with processed and ultra-processed ones can be a contributor to the increased prevalence of obesity amongst adults and adolescents. Mobile health (mHealth) innovations, such as mobile applications (apps), especially games, can be used to improve health behaviors and increase adolescent knowledge. The aim of this qualitative research was to describe the development, assessment, and story immersion of a mHealth nutrition education app developed to improve the food knowledge of adolescents. This study employed the stepwise approach model to the mHealth app development, according to the person-based approach and evidence-based behavior change techniques. The mHealth app was based on the concepts of the NOVA system food classification, present in the Brazilian dietary guidelines. The developed app, Nutrigame – your food guide, is a story-based serious game set in the food routine of an adolescent who needs to choose what to eat, when, where, and with whom the meals are taken. The first version of the app was tested by a group of adolescents (convenience sample, n=6, mean age: 15.8 ± 0.9 years) for 30 days. To assess usability, feasibility, engagement (user testing), and formative evaluation from the user's perspective, five individual interviews. After the last interview, the participants were required to complete a 13-item immersion scale, adapted from the narrative transportation scale to assess story immersion game. The final version of the game was adapted to meet the suggestions presented by the adolescents. All steps used for the app development are described. The gamification elements chosen led to the comprehension of the main learning contents. The mean story immersion score (30.3 ± 1.9) demonstrates the participant's involvement with the game's narrative. This study can provide useful insights to public health researchers and nutrition educators who are planning to develop a mHealth nutrition education app from a practical perspective.

Assessing Technology Innovation of Mobile Health Apps for Medical Care Providers

In recent years, mobile health applications (mHealth apps) have created a new social context in the clinical diagnosis process based on technology innovation. To improve understanding of digital transformation in healthcare and determinants for its implementation, this article examines the available mHealth apps addressed to medical practitioners and explores these apps features’ trustworthiness, quality, and popularity. Based on the normalization process theory, we explain the process from the introduction of mHealth innovation in the clinical practice to the intention of professional engagement. We conduct content analysis of the apps’ descriptions, functions, and user reviews; measured available app features and performance metrics; and applied statistical analysis to understand features intercorrelations and users’ preferences. We observe moderate levels of reliability and quality in the existing mHealth apps, revealing margins for potential improvement. The mHealth app star rating, quality score, and downloads are all highly intercorrelated. The apps with more features score higher in quality, especially those with credible sources, feedback opportunities, and “how to use” instructions. App development from credible sources can increase their trustworthiness. Further, technological advances in these apps can greatly benefit health professionals’ everyday practice and, thus, healthcare for society. This article contributes to the information systems research by using social theories to reveal the importance of app features to the adoption of professional health applications and underline the sets of policies/practices that should apply in the app development process to strengthen clinical efficiency and effectiveness.

Improving community health worker treatment for malaria, diarrhoea, and pneumonia in Uganda through inSCALE community and mHealth innovations: A cluster randomised controlled trial.

The inSCALE cluster randomised controlled trial in Uganda evaluated two interventions, mHealth and Village Health Clubs (VHCs) which aimed to improve Community Health Worker (CHW) treatment for malaria, diarrhoea, and pneumonia within the national Integrated Community Case Management (iCCM) programme. The interventions were compared with standard care in a control arm. In a cluster randomised trial, 39 sub-counties in Midwest Uganda, covering 3167 CHWs, were randomly allocated to mHealth; VHC or usual care (control) arms. Household surveys captured parent-reported child illness, care seeking and treatment practices. Intention-to-treat analysis estimated the proportion of appropriately treated children with malaria, diarrhoea, and pneumonia according to WHO informed national guidelines. The trial was registered at ClinicalTrials.gov (NCT01972321). Between April-June 2014, 7679 households were surveyed; 2806 children were found with malaria, diarrhoea, or pneumonia symptoms in the last one month. Appropriate treatment was 11% higher in the mHealth compared to the control arm (risk ratio [RR] 1.11, 95% CI 1.02, 1.21; p = 0.018). The largest effect was on appropriate treatment for diarrhoea (RR 1.39; 95% CI 0.90, 2.15; p = 0.134). The VHC intervention increased appropriate treatment by 9% (RR 1.09; 95% CI 1.01, 1.18; p = 0.059), again with largest effect on treatment of diarrhoea (RR 1.56, 95% CI 1.04, 2.34, p = 0.030). CHWs provided the highest levels of appropriate treatment compared to other providers. However, improvements in appropriate treatment were observed at health facilities and pharmacies, with CHW appropriate treatment the same across the arms. The rate of CHW attrition in both intervention arms was less than half that of the control arm; adjusted risk difference mHealth arm -4.42% (95% CI -8.54, -0.29, p = 0.037) and VHC arm -4.75% (95% CI -8.74, -0.76, p = 0.021). Appropriate treatment by CHWs was encouragingly high across arms. The inSCALE mHealth and VHC interventions have the potential to reduce CHW attrition and improve the care quality for sick children, but not through improved CHW management as we had hypothesised. Trial Registration:ClinicalTrials.gov (NCT01972321).

Clinical utility of a mHealth assisted intervention for activity modulation in chronic pain: The pilot implementation of pain ROADMAP.

Being able to successfully self-regulate one's activity levels is a key adaptation strategy for many people with chronic pain. This study aimed to explore the clinical utility of a mobile health platform (Pain ROADMAP) for assisting with the delivery of a tailored activity modulation intervention for people with persistent pain. Twenty adults with chronic pain undertook 1-week monitoring intervals which involved wearing an Actigraph activity monitor and entering pain, opioid use, and activity participation data into a custom-made phone app. The Pain ROADMAP online portal integrated and analysed the data to detect activities that caused a severe pain exacerbation and provided summary statistics pertaining the data collected. As part of a 15-week treatment protocol, participants received feedback from three dispersed Pain ROADMAP monitoring periods. Treatment focused on adapting pain-provoking activities, gradually increase goal-related activity and optimizing routine. Results revealed good participant acceptability of monitoring procedures and reasonable adherence to both monitoring procedures and clinical follow-up appointments. Preliminary efficacy was established through clinically meaningful decreases in overactivity behaviour, pain variation, opioid use, depression, activity avoidance, and increases in productivity. No adverse outcomes were observed. The results of this study provide initial support for the clinical utility of mHealth assisted activity modulation interventions that involve remote monitoring. This is the first study to demonstrate how mHealth innovations that utilise ecological momentary assessment can be successfully integrated with wearable technologies to provide a tailored activity modulation intervention that is both highly valued by people with chronic pain and assists individuals to make constructive behavioural changes. Adaptions such as low costs sensors, increased customisability and gamification may be important for enhanced uptake, adherence and scalability.

Usability of a mHealth Solution using Speech Recognition for Point-of-care Diagnostic Management

The administrative burden for physicians in the hospital can affect the quality of patient care. The Service Center Medical Informatics (SMI) of the University Hospital Würzburg developed and implemented the smartphone-based mobile application (MA) ukw.mobile1 that uses speech recognition for the point-of-care ordering of radiological examinations. The aim of this study was to examine the usability of the MA workflow for the point-of-care ordering of radiological examinations. All physicians at the Department of Trauma and Plastic Surgery at the University Hospital Würzburg, Germany, were asked to participate in a survey including the short version of the User Experience Questionnaire (UEQ-S) and the Unified Theory of Acceptance and Use of Technology (UTAUT). For the analysis of the different domains of user experience (overall attractiveness, pragmatic quality and hedonic quality), we used a two-sided dependent sample t-test. For the determinants of the acceptance model, we employed regression analysis. Twenty-one of 30 physicians (mean age 34 ± 8 years, 62% male) completed the questionnaire. Compared to the conventional desktop application (DA) workflow, the new MA workflow showed superior overall attractiveness (mean difference 2.15 ± 1.33), pragmatic quality (mean difference 1.90 ± 1.16), and hedonic quality (mean difference 2.41 ± 1.62; all p < .001). The user acceptance measured by the UTAUT (mean 4.49 ± 0.41; min. 1, max. 5) was also high. Performance expectancy (beta = 0.57, p = .02) and effort expectancy (beta = 0.36, p = .04) were identified as predictors of acceptance, the full predictive model explained 65.4% of its variance. Point-of-care mHealth solutions using innovative technology such as speech-recognition seem to address the users’ needs and to offer higher usability in comparison to conventional technology. Implementation of user-centered mHealth innovations might therefore help to facilitate physicians’ daily work.

A Study on mHealth Innovations through the Lens of Patent Analysis

A Study on mHealth Innovations through the Lens of Patent Analysis

PREDICTORS OF MHEALTH MINDFULNESS USE IN OLDER ADULT CAREGIVER POPULATION

Abstract Caregivers of older adults with MCI have multiple competing demands that limit their ability to seek mental health care, despite being at high risk for stress, anxiety, burnout, and depression. MHealth mindfulness interventions have preliminary efficacy to reduce distress for caregivers, but it is unknown which caregivers are most likely to use and benefit from mHealth mental health interventions. Understanding older adult caregiver mHealth use is an important step in providing personalized mental health care. This study investigated caregiver characteristics that predicted mHealth mindfulness mobile application use in a sample of n=58 caregivers of older adults with MCI. Using linear regression models, it was determined that years spent caregiving, having someone to help with caregiving tasks and education level were all significant predictors of the total amount of minutes spent using the mindfulness application. Our results contribute to the growing body of literature on mHealth strategies to address caregiver distress. Future work should address the expansion of mHealth innovations for older adults and their care persons.

Analysis of Factors Influencing the Development of mHealth Innovation Based on Data Mining Algorithms

Data mining algorithms combine expertise in machine algorithm learning, software modeling pattern recognition, statistical analysis principles, database construction, and artificial intelligence. With the rapid development of Internet technology and the common application of cell phones, mobile medical, a new medical method based on this technology, has been spawned, which greatly facilitates multiple aspects of medical services such as doctor diagnosis, patient treatment, disease care, and health management of critically ill patients and also alleviates the imbalance of medical resources. This paper firstly starts from the background of rapid development of information technology and mobile technology, combines the theoretical knowledge of data mining algorithm and mobile medical, as well as previous research reviews, and presents the main research content of this paper: analysis of factors influencing the development of mobile medical innovation based on data mining algorithm. Based on the K-means algorithm in the data mining algorithm and the Apriori algorithm in the association algorithm, this paper analyzes the current situation and problems of mobile medical development in China based on the algorithm model, analyzes the influencing factors of mobile medical innovation development in China based on the algorithm model, and summarizes and concludes the influencing factors of mobile medical innovation development and concludes that there are four categories of mHealth innovation development influencing factors: demand influence, policy orientation, technological innovation, and capital injection.

Expert appraisal and prioritization of barriers to mHealth use for older adults living with Alzheimer's disease and related Dementias: A Delphi study

IntroductionOlder adults living with Alzheimer's disease and related dementias (ADRD) can benefit from mHealth innovations in (daily) care. However, successful implementation and adoption of such innovations can be hindered by a lack of inclusive design. Inclusive design can be challenging, due to the variety of ADRD- and aging-related symptoms that can pose barriers to using mHealth. Previously, a literature-based model with 53 barriers to mHealth use for this population has been developed (“MHealth for OLder adults living with DEMentia – USability“ or MOLDEM-US). In this study, we aim to prioritize these through a Delphi study with ADRD experts (case managers, informal caregivers, hospital healthcare professionals, district nurses, and researchers). Methods: In the first round, participant characteristics and potentially new insights into barriers to mHealth use for older adults living with ADRD were gathered. The consensus questionnaire was submitted in the second round, containing barriers to mHealth use for this population (from MOLDEM-US) with questions inquiring its impact and frequency. In the third round, participants rejudged those barriers for which no consensus (<51 %) or minor consensus (51 % − 60 %) was reached. Results: Thirty-seven participants completed the three rounds of the study. Consensus was reached for eleven barriers after the second round, all having major impact and frequency: integration of functions during daily activities, perceived complexity, efficiency in seeing benefits, trust in own ability, restlessness and agitation, computer literacy, self confidence in using wearables, learnability, working memory, and visual acuity. Conclusion: After round three, consensus was achieved for all 53 barriers. Twenty-six barriers are considered to majorly affect mHealth use, most of which relate to cognition and frame of mind. This study contributes to the development of mHealth design guidelines that take into account the progressive and diverse ADRD- and aging-related symptoms negatively affecting mHealth implementation and adoption.

Searching for Sustainability in Health Systems: Toward a Multidisciplinary Evaluation of Mobile Health Innovations

Mobile health (mHealth) innovations are considered by governments as game changers toward more sustainable health systems. The existing literature focuses on the clinical aspects of mHealth but lacks an integrated framework on its sustainability. The foundational idea for this paper is to include disciplinary complementarities into a multi-dimensional vision to evaluate the non-clinical aspects of mHealth innovations. We performed a targeted literature review to find how the sustainability of mHealth innovations was appraised in each discipline. We found that each discipline considers a different outcome of interest and adopts different time horizons and perspectives for the evaluation. This article reflects on how the sustainability of mHealth innovation can be assessed at both the level of the device itself as well as the level of the health system. We identify some of the challenges ahead of researchers working on mobile health innovations in contributing to shaping a more sustainable health system.

The Influence of Design and Implementation Characteristics on the Use of Maternal Mobile Health Interventions in Kenya: Systematic Literature Review.

BackgroundThe growth of mobile technology in developing countries, coupled with pressing maternal health care challenges, has led to a widespread implementation of maternal mobile health (mHealth) innovations. However, reviews generating insights on how the characteristics of the interventions influence use are scarce.ObjectiveThis study aims to review maternal mHealth interventions in Kenya to explore the influence of intervention design and implementation characteristics on use by maternal health clients. We also provide a starting inventory for maternal mHealth interventions in the country.MethodsUsing a systematic approach, we retrieved a total of 1100 citations from both peer-reviewed and gray sources. Articles were screened on the basis of an inclusion and exclusion criterion, and the results synthesized by categorizing and characterizing the interventions presented in the articles. The first phase of the literature search was conducted between January and April 2019, and the second phase was conducted between April and June 2021.ResultsA total of 16 articles were retrieved, comprising 13 maternal mHealth interventions. The study highlighted various mHealth design and implementation characteristics that may influence the use of these interventions.ConclusionsIn addition to elaborating on insights that would be useful in the design and implementation of future interventions, this study contributes to a local inventory of maternal mHealth interventions that may be useful to researchers and implementers in mHealth. This study highlights the need for explanatory studies to elucidate maternal mHealth use, while complementing existing evidence on mHealth effectiveness.

Factors motivating end-users’ behavioural intention to recommend m-health innovation: multi-group analysis

mHealth innovation is health innovation that is gaining adoption in developing countries. The COVID-19 impact and the brain drain of health practitioners in Nigeria are indicating higher importance of the innovation. However, while the literatures on adoption are rising there is paucity of literature on intention to recommend mHealth innovation particularly with gender context. The study considered determinants of end-users’ behavioural intention to recommend mHealth innovation by considering multi-group analysis. Theory of reasoned action was modified with variables from health belief model and unified theory of acceptance and use of innovation to structure the intention to recommend mHealth model. Structured questionnaire with adapted items from extant studies were scaled on 7 point likert scale while snowball sampling technique was adopted in data collection. A total of 291 questionnaires were used in the analysis which was done through IBM Statistics version 23 and AMOS 23. The study used descriptive statistics, structural equation model and an invariant analysis to check the difference between the groups. The model showed good fit using a set of fit indices and coefficient of codetermination that indicates high predictive capacity. The SEM analysis show that subjective norm is the most vital factor that influences end-users’ intention to recommend mHealth innovation to others. Generally, it indicates that male users have higher intention to recommend mHealth innovation than female users of mHealth innovation. The result of the analysis also show that the correlations between the exogenous factors are positive and at least moderate across gender thus indicating the direction for adoption of marketing communication strategies to deepen the awareness of the innovation and encourage recommendation to others. The recommendation suggested center on marketing communication strategies that involves use of role models, influencers, celebrities, health practitioners and other referents to encourage the awareness and intention to recommend the innovation. The key limitation of the study is on sample size and the use of cross-sectional survey design which could pose danger in generalisation of the findings. However, it is concluded in the study that male users of mHealth innovation have higher intention to recommend the innovation than the female users of the innovation.

Notice

British Journal of Clinical PsychologyVolume 61, Issue S1 p. 136-136 NoticeFree Access Notice This article corrects the following: Using supervised machine learning on neuropsychological data to distinguish OCD patients with and without sensory phenomena from healthy controls Caitlin A. Stamatis, Marcelo C. Batistuzzo, Tais Tanamatis, Euripedes C. Miguel, Marcelo Q. Hoexter, Kiara R. Timpano, Volume 60Issue 1British Journal of Clinical Psychology pages: 77-98 First Published online: December 10, 2020 First published: 04 January 2022 https://doi.org/10.1111/bjc.12352AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat The article listed below, intended for publication in British Journal of Clinical Psychology, Volume 61, Issue S1, a special issue entitled ‘mHealth and Technology Innovations for Anxiety and OC Spectrum Disorders’ was accidentally published in a regular issue. This article should be cited as shown below. References Stamatis, C. A., Batistuzzo, M. C., Tanamatis, T., Miguel, E. C., Hoexter, M. Q., & Timpano, K. R. (2021). Using supervised machine learning on neuropsychological data to distinguish OCD patients with and without sensory phenomena from healthy controls. British Journal of Clinical Psychology, 60(1), 77– 98. https://doi.org/10.1111/bjc.12272Wiley Online LibraryPubMedWeb of Science®Google Scholar Volume61, IssueS1Special Issue: mHealth and Technology Innovations for Anxiety and OC Spectrum DisordersJanuary 2022Pages 136-136 ReferencesRelatedInformation

The implementation of a maternal mHealth project in South Africa: Lessons for taking mHealth innovations to scale

MomConnect is a mHealth programme in South Africa targeted at improving antenatal and maternal health. This study explored the barriers and facilitators to implementing the MomConnect programme in South Africa and the applicable lessons for scaling mHealth programmes in Africa. We reviewed the published literature and collected data through semi-structured interviews of project partners and leaders who worked on the MomConnect project. We asked study participants to identify any barriers and/or facilitators in the implementation of the MomConnect project and how they would overcome those barriers and strengthen the facilitators. We employed the Consolidated Framework for Implementation Research (CFIR) to inform the identification of a priori codes for the directed content analysis. Based on the analysis, the key components that supported the implementation of the MomConnect project included (1) strategic partnership and coordination across partner levels, (2) cost-effective technology and sustainable funding measures, (3) adequate adaptation of the innovation to local and national settings, and (4) guiding mHealth policy and legislation frameworks. The study’s results suggest that strong political will and a robust partnership are essential to lead the strategic implementation process of mHealth projects. Such mHealth projects thrive in a policy framework that can support the planning and implementation process. In addition, understanding the accessibility needs of the project’s target population as identified in this study is critical to developing mHealth projects that are sustainable in the long term by adapting to cost-effective technologies.

Mobile "mHealth" Interventions in Mental Health Care.

Back to table of contents Previous article Editor's ChoiceFull AccessMobile “mHealth” Interventions in Mental Health CareAlexia Polillo, Ph.D., Nicole Kozloff, M.D., S.M., and Lisa B. Dixon, M.D., M.P.H.Alexia PolilloSearch for more papers by this author, Ph.D., Nicole KozloffSearch for more papers by this author, M.D., S.M., and Lisa B. DixonSearch for more papers by this author, M.D., M.P.H.Published Online:4 Nov 2021https://doi.org/10.1176/appi.ps.721103AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail This Editor’s Choice collection was published online in October 2021. To review all collections, please visit the Editor’s Choice section of ps.psychiatryonline.org.Mobile health or “mHealth” interventions using text messaging, apps, and websites have the potential to reduce barriers to mental health care, including difficulties navigating, accessing, and engaging in services. While evidence supports the feasibility and acceptability of these interventions in mental health treatment, continued research is needed to evaluate their efficacy and implementation, both as stand-alone interventions and to augment traditional services. There is a risk that mHealth innovation could inadvertently marginalize vulnerable populations that already face difficulties accessing needed treatment, including people experiencing poverty or lacking digital literacy skills. Codesign of interventions with people with lived experience can help ensure that mHealth interventions meet the needs of the populations they intend to support.This collection examines innovative mHealth interventions that are changing mental health care. The first section highlights some of the latest mobile apps that have been evaluated in the journal. The next section presents articles that describe challenges that may arise in mHealth intervention implementation. The final section discusses the future of mHealth interventions, their efficacy, and their anticipated role in enhancing accessibility to mental health services.Examples of mobile mental health interventionsMobile Health (mHealth) Versus Clinic-Based Group Intervention for People With Serious Mental Illness: A Randomized Controlled TrialBen-Zeev D, Brian RM, Jonathan G, et al.Psychiatr Serv 2018; 69:978–985Augmenting Evidence-Based Care With a Texting Mobile Interventionist: A Pilot Randomized Controlled TrialBen-Zeev D, Buck B, Meller S, et al.Psychiatr Serv 2020; 71:1218–1224Virtual Hope Box: Randomized Controlled Trial of a Smartphone App for Emotional Regulation and Coping With DistressBush NE, Smolenski DJ, Denneson LM, et al.Psychiatr Serv 2016; 68:330–336Randomized Trial of a Mobile Personal Health Record for Behavioral Health HomesDruss BG, Li J, Tapscott S, et al.Psychiatr Serv 2020; 71:803–809Mobile Behavioral Sensing for Outpatients and Inpatients With SchizophreniaBen-Zeev D, Wang R, Abdullah S, et al.Psychiatr Serv 2015; 67:558–561Evaluating and implementing mobile mental health interventionsA Hierarchical Framework for Evaluation and Informed Decision Making Regarding Smartphone Apps for Clinical CareTorous JB, Chan SR, Gipson SY-MT, Kim JW, et al.Psychiatr Serv 2018; 69:498–500Mental Health App Evaluation: Updating the American Psychiatric Association’s Framework Through a Stakeholder-Engaged WorkshopLagan S, Emerson MR, King D, et al.Psychiatr Serv 2021; 73:1095–1098Three Problems With Current Digital Mental Health Research . . . and Three Things We Can Do About ThemMohr DC, Weingardt KR, Reddy M, et al.Psychiatr Serv 2017; 68:427–429Smartphone Apps for College Mental Health: A Concern for Privacy and Quality of Current OfferingsMelcher J, Torous JPsychiatr Serv 2020; 71:1114–1119Curating the Digital Mental Health Landscape With a Guide to Behavioral Health Apps: A County-Driven ResourceMontgomery RM, Brandysky L, Neary M, et al.Psychiatr Serv 2021; 72:1229–1232User Engagement in Mental Health Apps: A Review of Measurement, Reporting, and ValidityNg MM, Firth J, Minen M, et al.Psychiatr Serv 2019; 70:538–544The future of mobile mental health interventionsFocusing on the Future of Mobile Mental Health and Smartphone InterventionsTorous JBPsychiatr Serv 2018; 69:945–945Banbury Forum Consensus Statement on the Path Forward for Digital Mental Health TreatmentMohr DC, Azocar F, Bertagnolli A, et al.Psychiatr Serv 2021; 72:677–683Technology in Mental Health: Creating New Knowledge and Inventing the Future of ServicesBen-Zeev DPsychiatr Serv 2017; 68:107–108Enhancing Standards and Principles in Digital Mental Health With Recovery-Focused Guidelines for Mobile, Online, and Remote Monitoring TechnologiesFortuna KL, Walker R, Fisher DB, et al.Psychiatr Serv 2019; 70:1080–1081 FiguresReferencesCited byDetailsCited ByNone Volume 72Issue 11 November 01, 2021Pages 1363-1364 Metrics PDF download History Published online 4 November 2021 Published in print 1 November 2021