mHealth Usage Research Articles

Determinants of the intention to use mHealth in the future: Evidence from an intervention study of patients with chronic diseases in Slovenia

BackgroundMobile health (mHealth) implementation is crucial for developing sustainable healthcare systems, but it faces the challenge of user acceptance. Extending traditional acceptance models allows for the cognitive, emotional and social aspects of engaging with mHealth to be captured, creating a more comprehensive understanding of users’ intentions to use it in the future. User-centred intervention studies based on users’ real experiences with mHealth are essential for accurate assessments and for improving upon studies that rely merely on anticipated mHealth use. MethodsAn intervention study was conducted with 103 patients with at least one chronic condition (type 2 diabetes and/or arterial hypertension) who had used an mHealth service for three months. They were recruited through purposive sampling at a community health centre in Slovenia. Path analysis was applied to the survey data collected after a three-month testing period to validate an explanatory model with eight hypotheses. ResultsThe intensity of mHealth use affected usability, which in turn affected acceptability, the psychosocial impacts of engagement with mHealth and intention for future use. The results showed that the intensity of mHealth use did not affect mHealth acceptability. Likewise, acceptability did not affect the psychosocial impacts of engagement with mHealth or the intention for its future use. Notably, perceptions of the psychosocial impacts of mHealth had no significant effect on the intention for future use. ConclusionUsability and intensity of use play a central role in the post-intervention usage of mHealth, offering valuable insights for policymakers and healthcare providers involved in the delivery of mHealth-based treatment to patients with chronic diseases.

Barriers and benefits of mHealth for community health workers in integrated community case management of childhood diseases in Banda Parish, Kampala, Uganda: a cross-sectional study

BackgroundLow-quality data presents a significant challenge for community health workers (CHWs) in low and middle-income countries (LMICs). Mobile health (mHealth) applications offer a solution by enabling CHWs to record and submit data electronically. However, the barriers and benefits of mHealth usage among CHWs in informal urban settlements remain poorly understood. This study sought to determine the barriers and benefits of mHealth among CHWs in Banda parish, Kampala.MethodsThis qualitative study involved 12 key informant interviews (KIIs) among focal persons from Kampala City Council Authority (KCCA) and NGOs involved in data collected by CHWs, and officials from the Ministry of Health (MOH) and two mixed-sex Focused Group Discussions (FGDs) of CHWs from Banda parish, Kampala district. Data analysis utilised Atlas Ti Version 7.5.7. Thematic analysis was conducted, and themes were aligned with the social-ecological model.ResultsThree themes of institutional and policy, community and interpersonal, and individual aligning to the Social ecological model highlighted the factors contributing to barriers and the benefits of mHealth among CHWs for iCCM. The key barriers to usability, acceptability and sustainability included high training costs, CHW demotivation, infrastructure limitations, data security concerns, community awareness deficits, and skill deficiencies. Conversely, mHealth offers benefits such as timely data submission, enhanced data quality, geo-mapping capabilities, improved CHW performance monitoring, community health surveillance, cost-effective reporting, and CHW empowering with technology.ConclusionDespite limited mHealth experience, CHWs expressed enthusiasm for its potential. Implementation was viewed as a solution to multiple challenges, facilitating access to health information, efficient data reporting, and administrative processes, particularly in resource-constrained settings. Successful mHealth implementation requires addressing CHWs’ demotivation, ensuring reliable power and network connectivity, and enhancing capacity for digital data ethics and management. By overcoming these barriers, mHealth can significantly enhance healthcare delivery at the community level, leveraging technology to optimize resource utilization and improve health outcomes. mHealth holds promise for transforming CHW practices, yet its effective integration necessitates targeted interventions to address systemic challenges and ensure sustainable implementation in LMIC contexts.

Association Between the Composite Cardiovascular Risk and mHealth Use Among Adults in the 2017-2020 Health Information National Trends Survey: Cross-Sectional Study.

Numerous studies have suggested that the relationship between cardiovascular disease (CVD) risk and the usage of mobile health (mHealth) technology may vary depending on the total number of CVD risk factors present. However, whether higher CVD risk is associated with a greater likelihood of engaging in specific mHealth use among US adults is currently unknown. We aim to assess the associations between the composite CVD risk and each component of mHealth use among US adults regardless of whether they have a history of CVD or not. This study used cross-sectional data from the 2017 to 2020 Health Information National Trends Survey. The exposure was CVD risk (diabetes, hypertension, smoking, physical inactivity, and overweight or obesity). We defined low, moderate, and high CVD risk as having 0-1, 2-3, and 4-5 CVD risk factors, respectively. The outcome variables of interest were each component of mHealth use, including using mHealth to make health decisions, track health progress, share health information, and discuss health decisions with health providers. We used multivariable logistic regression models to examine the association between CVD risk and mHealth use adjusted for demographic factors. We included 10,531 adults, with a mean age of 54 (SD 16.2) years. Among the included participants, 50.2% were men, 65.4% were non-Hispanic White, 41.9% used mHealth to make health decisions, 50.8% used mHealth to track health progress toward a health-related goal, 18.3% used mHealth to share health information with health providers, and 37.7% used mHealth to discuss health decisions with health providers (all are weighted percentages). Adults with moderate CVD risk were more likely to use mHealth to share health information with health providers (adjusted odds ratio 1.49, 95% CI 1.24-1.80) and discuss health decisions with health providers (1.22, 95% CI 1.04-1.44) compared to those with low CVD risk. Similarly, having high CVD risk was associated with higher odds of using mHealth to share health information with health providers (2.61, 95% CI 1.93-3.54) and discuss health decisions with health providers (1.56, 95% CI 1.17-2.10) compared to those with low CVD risk. Upon stratifying by age and gender, we observed age and gender disparities in the relationship between CVD risk and the usage of mHealth to discuss health decisions with health providers. Adults with a greater number of CVD risk factors were more likely to use mHealth to share health information with health providers and discuss health decisions with health providers. These findings suggest a promising avenue for enhancing health care communication and advancing both primary and secondary prevention efforts related to managing CVD risk factors through the effective usage of mHealth technology.

Preferences, Acceptability and Usage of Mobile Health Applications Among Undergraduate Nursing Students: A Multisite, Cross-Sectional Survey.

Mobile health applications (mHealth) can bring revolutionary changes to health care by making it more accessible, cost-effective, and efficient. The successful adoption of mHealth is largely determined by its acceptance by providers and end users. The acceptance of mHealth among nursing students, who are the future nurses, needs to be understood. This study aimed to determine the preferences, acceptability, and usage of mHealth applications among undergraduate nursing students. A descriptive cross-sectional study was conducted in two selected universities in southern and northern India. A convenience sample of 567 undergraduate nursing students was recruited for the study. The data were collected using a structured questionnaire via Google Forms. mHealth applications were presently used by 32.9% of the students. The most common types of mHealth applications used were diet related (27.6%) and lifestyle related (23.4%). The majority of nursing students were willing to use mobile phones for consulting health professionals (78.8%) for prevention and management of health and disease. (83.1%), and for emergencies (73.3%). While more than half of the students were willing to recommend mHealth applications for patients, those students who were using two or more mHealth apps were more likely to recommend the same for the patients. The results of the study highlight about the usage of mobile health applications among the nursing students. The students consider mHealth applications to be useful for delivering health-related services to end users. The study underscores the need to sensitize students about the possible usage of mHealth in healthcare delivery.

Racial Disparities in Shared Decision-Making and the Use of mHealth Technology Among Adults With Hypertension in the 2017-2020 Health Information National Trends Survey: Cross-Sectional Study in the United States.

Mobile health (mHealth) technology has the potential to support shared decision-making (SDM) and improve hypertension control. However, our understanding of the variations in individuals' involvement in SDM and mHealth usage across different racial and ethnic groups in the United States is still limited. This study aimed to investigate the extent of involvement in SDM and the usage of mHealth technology in health-related activities among US adults with hypertension from diverse racial and ethnic backgrounds and to examine whether the mHealth usage differed by individuals' level of engagement in SDM. This study used cross-sectional data from the 2017 to 2020 Health Information National Trends Survey, which was conducted on US adults with self-reported hypertension, and race and ethnicity data were included. The exposure of interest was race and ethnicity. The outcomes were SDM and mHealth usage. SDM was assessed using an item: "In the past 12 months, how often did your health professional: involve you in decisions about your healthcare as much as you wanted?" mHealth usage was defined as using a smartphone or tablet to engage in (1) making health decisions, (2) discussing health decisions with health providers, (3) tracking health progress, and (4) sharing health information. Weighted multivariable logistic regression models were used to examine the association between race and ethnicity and SDM or mHealth usage adjusted for covariates and stratified by the level of engagement in SDM. This study included 4893 adults with hypertension, and the mean age was 61 (SD 13) years. The sample was 53% female, 61% (n=3006) non-Hispanic White, 19% (n=907) non-Hispanic Black or African American, 12% (n=605) Hispanic, 4% (n=193) non-Hispanic Asian, and 4% (n=182) non-Hispanic other. Compared to the non-Hispanic White adults, non-Hispanic Black adults were more likely to use mHealth to make health decisions (adjusted odds ratio [aOR] 1.70, 95% CI 1.23-2.34), share health information (aOR 1.46, 95% CI 1.02-2.08), and discuss health decisions with health providers (aOR 1.38, 95% CI 1.02-1.87). Significant associations were observed specifically among those who were always involved in SDM. Asian adults were less likely to be involved in SDM (aOR 0.51, 95% CI 0.26-0.99) and were more likely to use mHealth to track progress on a health-related goal (aOR 2.07, 95% CI 1.28-3.34) than non-Hispanic White adults. Hispanic adults were less likely to use mHealth to share health information (aOR 0.47, 95% CI 0.33-0.67) and discuss health decisions with health providers (aOR 0.65, 95% CI 0.46-0.94) compared to non-Hispanic White adults. This study observed racial and ethnic disparities in SDM and mHealth usage among US adults with hypertension. These findings emphasize the significance of comprehending the involvement of SDM and the usage of mHealth technology within racially and ethnically diverse populations.

Exploring experiences of pharmacists and pharmacy students using mHealth apps: A qualitative study of user experience

ABSTRACT Purpose Pharmacists can enhance mHealth delivery by promoting the awareness and use of health apps, while pharmacy students and early career pharmacists allow broader perspective for further development. This study aimed to explore the experiences and attitudes of pharmacy students and pharmacists, on the use, advantages and possible improvement of available mHealth platforms in Serbia. Methods One-on-one interviews were conducted online in two phases, during March and April 2020 (n = 40) and during March 2021 (n = 20), using a published, validated interview guide which was adapted for this study. Interviews were transcribed ad verbatim, coded and thematically analysed. Results Although participants recognized room for optimization, most apps had satisfactory user experience. Participants used health apps as a source of updated information and tools in education and work, but also for self-development. During working hours, participants mostly used medication-related apps, however, it was shown that they use different health apps in order to provide the best possible care to patients. Conclusions Greater insight into usage, expectations, benefits and challenges of mHealth in pharmacy was obtained and can lead into better informed design of health apps to encourage students and pharmacists, both as professionals and individuals, on the path of their continuous improvement.

Modeling Consumer Acceptance and Usage Behaviors of m-Health: An Integrated Model of Self-Determination Theory, Task-Technology Fit, and the Technology Acceptance Model.

Although mobile health (m-health) has great potential to reduce the cost of medical care and improve its quality and efficiency, it is not widely accepted by consumers. In addition, there is still a lack of comprehensive insight into m-health acceptance, especially among consumers with different demographic characteristics. This study aimed to explore the factors affecting consumers' acceptance and usage behaviors of m-health and to examine whether their roles differ by demographic characteristics. A comprehensive m-health acceptance model was proposed by integrating factors from the Self-Determination Theory, Task-Technology Fit, and Technology Acceptance Model. Survey data were collected from 623 Chinese adults with at least 6 months of m-health usage experience and analyzed using structural equation modeling techniques. Multi-group analyses were performed to assess whether the model relationships were different across gender, age, and usage experience. The results indicated that relatedness and competence were significant motivational antecedents of perceived ease of use. Task-technology fit and the perceived ease of use significantly affected the perceived usefulness. The perceived ease of use and perceived usefulness were significant determinants of consumer usage behaviors of m-health and together explained 81% of its variance. Moreover, the relationships among autonomy, perceived usefulness, and usage behaviors of m-health were moderated by gender. Consumer usage behaviors of m-health were affected by factors such as self-motivation (i.e., relatedness and competence), technology perceptions (i.e., perceived ease of use and perceived usefulness), and task-technology fit. These findings provide a theoretical underpinning for future research on m-health acceptance and provide empirical evidence for practitioners to promote the better design and use of m-health for healthcare activities.

Usage behavior of mHealth service users in Australia: do user demographics matter?

PurposeMobile health (mHealth) service is an Australian Government initiative aiming to improve the quality of health-care services. However, little is known about Australian health consumers’ willingness to adopt mHealth. The purpose of this paper is to study the usage behavior of mHealth service users in Australia. While various factors may impact users’ willingness to accept mHealth, this research investigates factors influencing the mHealth adoption decisions of the Australian health-care consumers, and the moderating impact of demographic factors on the usage behavior (UB) of mHealth services which has been rarely addressed in an Australian setting in the past.Design/methodology/approachThe theoretical framework is based on the technology acceptance model (TAM). Data were collected from residents of the Australian Capital Territory using a survey questionnaire and examined using partial least squares structural equation modeling.FindingsThe proposed mHealth usage model demonstrated a good fit and indicated that perceived usefulness, perceived ease of use, social influence, service quality and government influence are critical issues shaping mHealth UB. The moderation analysis revealed that users’ demographics, namely, gender, age and education are instrumental in broadening the understanding of UB of mHealth service in Australia.Practical implicationsThe findings will inform health-care service providers about the critical importance of the key factors driving the usage of mHealth services. Health-care providers and relevant authorities can develop targeted communication strategies that maximize the acceptance of mHealth services. Furthermore, deeper understanding of users’ demographic profiles would enable health-care service providers to promote their services to the right clients.Originality/valueThe above findings on the factors and user demographics informing the usage of mHealth services have unique practical, contextual and theoretical implications.

Exploring Factors that Influence Physicians’ Usage of mHealth on Physician-Patient Communication

Background: A desirable physician-patient communication is broadly concluded to be able to reduce waste of medical resources and increase the patients’ confidence in recovery. Along with the rapid development of internet of things, hospitals have largely used mHealth as relationship structure to increase information exchange and relationship be- tween bilateral parties. Drawing on the theories of social exchange and social capital, this study extends technology acceptance model (TAM) to investigate social effects on mHealth adoption. Methods: 120 valid hypertension patient samples were collected with the attending aids of a medical center in Taiwan. Results: The result indicates that the conventional TAM constructs of perceived ease of use (PEOU) and perceived usefulness (PU) of mHealth technology are effective in constructing the social capital (mainly relational capital). The social capital (SC) is found to be significant that generated by PEOU and PU in affecting the mHealth adoption. However, negative capital (NC) effect due mainly to excessive structure capital that annoys patients will decrease adoption. Conclusions: While most studies focused so far on the positive outcomes of building relationship with doctors, we have to revealed its dark side as well. And we proof that patients would really suffered in overwhelmed relationships.

The potential of mHealth for older adults on dialysis and their care partners: What's been done and where do we go from here?

Self-care, or the dynamic, daily process of becoming actively involved in one's own care, is paramount to prevent and manage complications of end-stage kidney disease. However, many older dialysis patients face distinctive challenges to adequate engagement in self-care. One promising strategy for facilitating self-care among older dialysis patients and their care partners is the utilization of mobile health (mhealth). mHealth encompasses mobile and wireless communication devices used to improve healthcare delivery, patient and care partner outcomes, and patient care. In other disease populations, mHealth has been linked to maintenance of or improvements in self-management, medication compliance, patient education, and patient-provider communication, all of which can slow disease progression. Although mHealth is considered feasible, acceptable, and clinically useful, this technology has predominately targeted younger patients. Thus, there is a need to develop mHealth for older dialysis patients and their care partners. In this article, we describe current mHealth usage in older dialysis patients, including promising findings, challenges, and research gaps. Given the lack of research on mHealth among care partners of older dialysis patients, we highlight lessons learned from other disease populations to inform the future design and implementation of mHealth for these key stakeholders. We also propose that leveraging care partners represents an opportunity to meaningfully tailor mHealth applications and, by extension, improve care partner physical and mental health and decrease caregiver burden. We conclude with a summary of future directions to help older dialysis patients and their care partners receive recognition as target end-users amid the constant evolution of mHealth.

Rediscovering Allergic Rhinitis: The Use of a Novel mHealth Solution to Describe and Monitor Health-Related Quality of Life in Elderly Patients

Introduction: Allergic rhinitis (AR) is a disease characterized by IgE-mediated hypersensitivity responses akin to allergic asthma. Although common in children and young adults, AR can be particularly vexing in the elderly: several studies have underlined its impact on the patient’s self-perceived health-related quality of life (HR-QoL). Available literature data on AR-affected elderly patients remain sparse and often focused on specific characteristics. mHealth solutions such as MASK-air® can be used in assessing salient clinical characteristics and unique shifts in self-perceived HR-QoL in old age people. With this pilot study, we aim to assess these variables in two cohorts of AR-affected elderly patients – one actively involved in the daily use of mHealth applications and the other having never used such a solution – by applying a widespread, validated, and standardized tool. Methods: AR-affected patients aged ≥65 years accessing the outpatient clinic of the Bari Geriatric Immuno-allergology Unit between March and July 2021 were enrolled and assigned to “mHealth” (MASK-air®) and “non-mHealth” cohorts accordingly. Each participant was given a 19-item questionnaire delivered via a custom software solution, with the EuroQoL EQ-5D-5L used to assess HR-QoL. Results: 93 patients (51 mHealth users, 43 non-mHealth users) were enrolled. AR was often either standalone or associated with asthma and conjunctivitis, and 57.4% of the participants reported a negative influence of AR on daily activities. Analysis of HR-QoL showed significantly worse scores in mobility and anxiety/depression dimensions for female patients regardless of app usage, while male non-mHealth users had worse self-care scores. Female mHealth users had worse scores for the self-care and activity dimensions, whereas female non-mHealth users showed worse scores on the pain scale. In general, mHealth users showed a greater degree of anxiety/depression when compared to non-mHealth users, relating to a greater awareness of their health status. Conclusion: The use of an mHealth solution, along with a concise, clinically-validated, comprehensive HR-QoL assessment toolset such as the EQ-5D, can prove beneficial in defining the unique characteristics of AR in the elderly. It can enable a detailed exploration of the impact on specific aspects of quality of life in old age. Raising patient awareness towards a health condition can improve compliance to treatment as well as follow-up. A lack of uniformity in approach, along with missing data pertaining to the general population are critical issues that require further studies. A more thorough diffusion of mHealth usage is also necessary among the geriatric population.

Leveraging mHealth usage logs to inform health worker performance in a Resource-Limited setting: Case example of mUzima use for a chronic disease program in Western Kenya

Health systems in low- and middle-income countries (LMICs) can be strengthened when quality information on health worker performance is readily available. With increasing adoption of mobile health (mHealth) technologies in LMICs, there is an opportunity to improve work-performance and supportive supervision of workers. The objective of this study was to evaluate usefulness of mHealth usage logs (paradata) to inform health worker performance. This study was conducted at a chronic disease program in Kenya. It involved 23 health providers serving 89 facilities and 24 community-based groups. Study participants, who already used an mHealth application (mUzima) during clinical care, were consented and equipped with an enhanced version of the application that captured usage logs. Three months of log data were used to determine work performance metrics, including: (a) number of patients seen; (b) days worked; (c) work hours; and (d) length of patient encounters. Pearson correlation coefficient for days worked per participant as derived from logs as well as from records in the Electronic Medical Record system showed a strong positive correlation between the two data sources (r(11) = .92, p < .0005), indicating mUzima logs could be relied upon for analyses. Over the study period, only 13 (56.3%) participants used mUzima in 2,497 clinical encounters. 563 (22.5%) of encounters were entered outside of regular work hours, with five health providers working on weekends. On average, 14.5 (range 1-53) patients were seen per day by providers. mHealth-derived usage logs can reliably inform work patterns and augment supervision mechanisms made particularly challenging during the COVID-19 pandemic. Derived metrics highlight variabilities in work performance between providers. Log data also highlight areas of suboptimal use, of the application, such as for retrospective data entry for an application meant for use during the patient encounter to best leverage built-in clinical decision support functionality.

Factors influencing mHealth adoption and its impact on mental well-being during COVID-19 pandemic: A SEM-ANN approach

The objectives of this study are to examine the factors affecting the intention and actual usage behavior on mHealth adoption, investigate the effect of actual usage behavior of mHealth on mental well-being of the end-users, and investigate the moderating role of self-quarantine on the intention–actual usage of mHealth under the coronavirus disease (COVID-19) pandemic situation. The required primary data were gathered from the end-users of mHealth in Bangladesh. Using the Unified Theory of Acceptance and Use of Technology (UTAUT2), this study has confirmed that performance expectancy, effort expectancy, social influence, hedonic motivation, and facilitating conditions have a positive influence on behavioral intention whereas health consciousness has an impact on both intention and actual usage behavior. mHealth usage behavior has an affirmative and meaningful effect on the mental well-being of the service users. Moreover, self-quarantine has strong influence on actual usage behavior but does not moderate the intention-behavior relationship. In addition, due to the existence of a non-linearity problem in the data set, the Artificial Neural Network (ANN) approach was engaged to sort out relatively significant predictors acquired from Structural Equation Modeling (SEM). However, this study contributes to the emergent mHealth literature by revealing how the use of the mHealth services elevates the quality of patients' mental well-being under this pandemic situation.

Mobile health (mHealth) usage, barriers, and technological considerations in persons with multiple sclerosis: a literature review.

ObjectivesPersons with multiple sclerosis (MS) can face a number of potential healthcare-related barriers, for which mobile health (mHealth) technology can be potentially beneficial. This review aimed to understand the frequency, current uses, and potential barriers with mHealth usage among persons with MS.MethodsA query string was used to identify articles on PubMed, MEDLINE, CINAHL, and IEEE Xplore that were published in English between January 2010 and December 2019. s were reviewed and selected based on a priori inclusion and exclusion criteria. Fifty-nine peer-reviewed research studies related to the study questions are summarized.ResultsThe majority of persons with MS were reported as using smartphones, although rates of mHealth utilization varied widely. mHealth usage was grouped into 3 broad categories: (1) disability and symptom measurement; (2) interventions and symptom management; and (3) tracking and promoting adherence. While there have been an increasing number of mHealth options, certain limitations associated with MS (eg, poor dexterity, memory problems) may affect usage, although including persons with MS in the design process can address some of these issues.DiscussionGiven the increased attention to mHealth in this population and the current need for telehealth and at home devices, it is important that persons with MS and healthcare providers are involved in the development of new mHealth tools to ensure that the end product meets their needs. Considerations for addressing the potential mHealth use barriers in persons with MS are discussed.

Effect of mHealth tool on knowledge regarding reproductive health of school going adolescent girls: a before-after quasi-experimental study

ObjectiveThis study was executed to (1) assess the effect of mHealth (mobile health) tool on knowledge regarding reproductive health (RH) of adolescent girls and (2) determine the utilisation of mHealth...

A novel interactive mobile health support system for pediatric obesity treatment: a randomized controlled feasibility trial

BackgroundIn order to achieve improved weight status, behavioral pediatric obesity treatment is resource intensive. Mobile Health (mHealth) is more accessible than standard care but effective approaches are scarce. Therefore, the aim of this feasibility trial was to study trial design, mHealth usage, compliance, and acceptability of a novel mHealth approach in pediatric obesity treatment.MethodsThis six-month parallel two-arm feasibility trial took place at three pediatric outpatient clinics in Stockholm, Sweden. Participants, 5–12 years, starting obesity treatment were randomized to using an mHealth support system as an addition to standard care (intervention) or to standard care alone (control). The intervention included daily self-monitoring of weight transferred to a mobile application (app) used by parents, a website in which clinicians could track treatment progress, prespecified treatment goals for change in degree of obesity shown in the app and on the website, and text message interactions between clinicians and parents. The main outcome was description of feasibility. Height and weight were measured at baseline, three, and 6 months to explore changes in body mass index standard deviation score (BMI SDS).ResultsOf 40 children eligible for inclusion, 28 agreed to participate (54% girls) and were randomized to intervention (n = 15) or control (n = 13). Weight was measured at home regularly throughout the entire trial period by 12/15 children in the intervention group. Attendance at appointments were better in the intervention group (p = 0.024). Both parents and clinicians had a positive experience and found the mHealth support system accessible. At 6 months the intervention group had a greater reduction of 0.24 units in BMI SDS than standard care (− 0.23 vs. 0.01, p = 0.002).ConclusionsThe mHealth support system was a feasible and innovative treatment approach which, in addition to standard care, generated better treatment results than standard care alone. Future research should evaluate the treatment effects over a longer follow-up time in a larger study sample.Trial registrationThis trial was retrospectively registered at ClinicalTrials.gov, ID: NCT03380598, on November 8, 2017.

Mobile Health Usage, Preferences, Barriers, and eHealth Literacy in Rheumatology: Patient Survey Study.

BackgroundMobile health (mHealth) defines the support and practice of health care using mobile devices and promises to improve the current treatment situation of patients with chronic diseases. Little is known about mHealth usage and digital preferences of patients with chronic rheumatic diseases.ObjectiveThe aim of the study was to explore mHealth usage, preferences, barriers, and eHealth literacy reported by German patients with rheumatic diseases.MethodsBetween December 2018 and January 2019, patients (recruited consecutively) with rheumatoid arthritis, psoriatic arthritis, and axial spondyloarthritis were asked to complete a paper-based survey. The survey included questions on sociodemographics, health characteristics, mHealth usage, eHealth literacy using eHealth Literacy Scale (eHEALS), and communication and information preferences.ResultsOf the patients (N=193) who completed the survey, 176 patients (91.2%) regularly used a smartphone, and 89 patients (46.1%) regularly used social media. Patients (132/193, 68.4%) believed that using medical apps could be beneficial for their own health. Out of 193 patients, only 8 (4.1%) were currently using medical apps, and only 22 patients (11.4%) stated that they knew useful rheumatology websites/mobile apps. Nearly all patients (188/193, 97.4%) would agree to share their mobile app data for research purposes. Out of 193 patients, 129 (66.8%) would regularly enter data using an app, and 146 patients (75.6%) would welcome official mobile app recommendations from the national rheumatology society. The preferred duration for data entry was not more than 15 minutes (110/193, 57.0%), and the preferred frequency was weekly (59/193, 30.6%). Medication information was the most desired app feature (150/193, 77.7%). Internet was the most frequently utilized source of information (144/193, 74.6%). The mean eHealth literacy was low (26.3/40) and was positively correlated with younger age, app use, belief in benefit of using medical apps, and current internet use to obtain health information.ConclusionsPatients with rheumatic diseases are very eager to use mHealth technologies to better understand their chronic diseases. This open-mindedness is counterbalanced by low mHealth usage and competency. Personalized mHealth solutions and clear implementation recommendations are needed to realize the full potential of mHealth in rheumatology.

MHealth and Engagement Concerning Persons With Chronic Somatic Health Conditions: Integrative Literature Review

BackgroundChronic somatic health conditions are a global public health challenge. Being engaged in one’s own health management for such conditions is important, and mobile health (mHealth) solutions are often suggested as key to promoting engagement.ObjectiveThe aim of this study was to review, critically appraise, and synthesize the available research regarding engagement through mHealth for persons with chronic somatic health conditions.MethodsAn integrative literature review was conducted. The PubMed, CINAHL, and Inspec databases were used for literature searches. Quality assessment was done with the guidance of Critical Appraisal Skills Programme (CASP) checklists. We used a self-designed study protocol comprising 4 engagement aspects—cognitive, behavioral and emotional, interactional, and the usage of mHealth—as part of the synthesis and analysis.ResultsA total of 44 articles met the inclusion criteria and were included in the analysis. mHealth usage was the most commonly occurring engagement aspect, behavioral and emotional aspects the second, cognitive aspects the third, and interactional aspects of engagement the least common aspect in the included articles. The results showed that there is a mix of enablers and barriers to engagement in relation to the 4 engagement aspects. The perceived meaningfulness and need for the solution and its content were important to create and maintain engagement. When perceived as meaningful, suitable, and usable, mHealth can support knowledge gain and learning, facilitate emotional and behavioral aspects such as a sense of confidence, and improve interactions and communications with health care professionals.ConclusionsmHealth solutions have the potential to support health care engagement for persons with chronic somatic conditions. More research is needed to further understand how, by which means, when, and among whom mHealth could further improve engagement for this population.

Perception of Mobile Health Maternal Healthcare Services among Pregnant Women in Nigeria

The daunting challenge of maternal deaths resulting from preventable causes has remained a major public health issue in the developing countries of Africa and it is yet to be fully tackled. Empirical knowledge of the awareness of mobile health (m-health) maternal healthcare services among pregnant women in these parts, therefore, holds an important key to achieving success in reproductive health issues across the affected populations. Despite the introduction of mobile health services, the desirable state of reduced maternal mortality figures is yet to be achieved. This study sought to ascertain the level of awareness, extent of adoption and the challenges of m-health maternal services in southeast Nigeria. Our analysis of questionnaire-survey data on pregnant women from three states of Nigeria shows that 89.7% of respondents were aware of m-health in the study areas. However, awareness of the existence of m-health for maternal healthcare is different from usage of m-health in maternal healthcare delivery. The major challenges to its use are network failure from service providers and lack of funds for subscription, which may also mean that mobile phone ownership alone does not determine the success of m-health maternal health services in these parts. This leads to one of the recommendations that those who design mobile health application should consider offline mechanism as an alternative to the recurring network failure, for ease of use of such technology.

Technical and Psychosocial Challenges of mHealth Usage for Antiretroviral Therapy Adherence Among People Living With HIV in a Resource-Limited Setting: Case Series.

BackgroundMobile communication has been found to improve antiretroviral therapy (ART) adherence among people living with HIV. In an ongoing randomized clinical trial, 2 mobile communication strategies (ie, sending SMS text messages and real-time medication monitoring [RTMM]) were used to improve adherence to ART among people living with HIV in Tanzania. We noticed remarkable discrepancies between self-reported adherence and adherence recorded by SMS text messaging or RTMM among some of the first trial participants.ObjectiveOur objective was to describe these cases and the observed discrepancies in more detail, to serve as a useful illustration of some of the challenges in using mobile health in resource-limited settings.MethodsIn an ongoing randomized trial, adults living with HIV from two HIV treatment centers in Tanzania who were suspected of low levels of adherence were randomly assigned in a 1:1:1 ratio to receive (1) SMS text message reminders, (2) an RTMM device, or (3) no additional intervention to standard HIV care. During bimonthly study visits, the participants self-reported their level of adherence, received feedback about their level of adherence based on SMS text messaging or RTMM, and discussed strategies to overcome adherence problems with nurses providing HIV care. For the purpose of this report, we selected people living with HIV who had completed 5 follow-up visits and consistently reported more than 95% adherence, while SMS text messaging or RTMM recorded lower than 75% adherence. The participants were invited for a short, face-to-face in-depth interview to explore reasons for this discrepancy.ResultsAt the time of this analysis, 26 participants had completed follow-up. Six of these evidenced the above-mentioned discrepancies, with an average adherence of 46% based on SMS text messaging or RTMM, while self-reported adherence was 98%. Five of these 6 participants insisted that their adherence to ART was good, with 4 reporting that their adherence to properly using the monitoring device was low. Three participants mentioned concerns about involuntary disclosure of HIV status as a main reason for low adherence to using the device. Two participants were still depending on other reminder cues despite receiving SMS text message or RTMM reminders. Poor network coverage caused low adherence in 1 participant.ConclusionsPsychosocial barriers were reported as importantly contributing to low adherence, both with respect to use of ART and proper use of the adherence-monitoring device. This case series illustrates that when introducing new digital adherence monitoring technology, researchers should consider psychosocial barriers and distinguish between adherence to device use and adherence to treatment.Trial RegistrationPan African Clinical Trials Registry PACTR201712002844286; https://tinyurl.com/y98q4p3l