JMIR Publications

JMIR mHealth and uHealth

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.


Journal Description

JMIR mhealth and uhealth (mobile and ubiquitous health) (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2015: 4.532). For JMIR mHealth and uHealth we are expecting the first impact factor in 2017 which will be at least 2.84. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics.

JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research

JMIR mHealth and uHealth features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs.

JMIR mHealth and uHealth is indexed in PubMed Central/PubMed, and Thomson Reuters' Science Citation Index Expanded (SCIE), and is expecting its first official impact factor in July 2017.

JMIR mHealth and uHealth adheres to the same quality standards as JMIR and all articles published here are also cross-listed in the Table of Contents of JMIR, the worlds' leading medical journal in health sciences / health services research and health informatics.


Recent Articles:

  • Woman with smartphone and uMotif app in the rain. Collage created by JMIR Publications, based on Figure 1 in JMIR Mhealth Uhealth 2017;5(3):e37 and PlaceIt image. Source: PlaceIt / JMIR; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    Cloudy with a Chance of Pain: Engagement and Subsequent Attrition of Daily Data Entry in a Smartphone Pilot Study Tracking Weather, Disease Severity, and...


    Background: The increasing ownership of smartphones provides major opportunities for epidemiological research through self-reported and passively collected data. Objective: This pilot study aimed to codesign a smartphone app to assess associations between weather and joint pain in patients with rheumatoid arthritis (RA) and to study the success of daily self-reported data entry over a 60-day period and the enablers of and barriers to data collection. Methods: A patient and public involvement group (n=5) and 2 focus groups of patients with RA (n=9) supported the codesign of the app collecting self-reported symptoms. A separate “capture app” was designed to collect global positioning system (GPS) and continuous raw accelerometer data, with the GPS-linking providing local weather data. A total of 20 patients with RA were then recruited to collect daily data for 60 days, with entry and exit interviews. Of these, 17 were loaned an Android smartphone, whereas 3 used their own Android smartphones. Results: Of the 20 patients, 6 (30%) withdrew from the study: 4 because of technical challenges and 2 for health reasons. The mean completion of daily entries was 68% over 2 months. Patients entered data at least five times per week 65% of the time. Reasons for successful engagement included a simple graphical user interface, automated reminders, visualization of data, and eagerness to contribute to this easily understood research question. The main barrier to continuing engagement was impaired battery life due to the accelerometer data capture app. For some, successful engagement required ongoing support in using the smartphones. Conclusions: This successful pilot study has demonstrated that daily data collection using smartphones for health research is feasible and achievable with high levels of ongoing engagement over 2 months. This result opens important opportunities for large-scale longitudinal epidemiological research.

  • People in a pharmacy. Source: Wikimedia Commons; Copyright: Elmvh; URL:; License: Creative Commons Attribution + ShareAlike (CC-BY-SA).

    mHealth for Clinical Decision-Making in Sub-Saharan Africa: A Scoping Review


    Background: In a bid to deliver quality health services in resource-poor settings, mobile health (mHealth) is increasingly being adopted. The role of mHealth in facilitating evidence-based clinical decision-making through data collection, decision algorithms, and evidence-based guidelines, for example, is established in resource-rich settings. However, the extent to which mobile clinical decision support systems (mCDSS) have been adopted specifically in resource-poor settings such as Africa and the lessons learned about their use in such settings are yet to be established. Objective: The aim of this study was to synthesize evidence on the use of mHealth for point-of-care decision support and improved quality of care by health care workers in Africa. Methods: A scoping review of 4 peer-reviewed and 1 grey literature databases was conducted. No date limits were applied, but only articles in English language were selected. Using pre-established criteria, 2 reviewers screened articles and extracted data. Articles were analyzed using Microsoft Excel and MAXQDA. Results: We retained 22 articles representing 11 different studies in 7 sub-Saharan African countries. Interventions were mainly in the domain of maternal health and ranged from simple text messaging (short message service, SMS) to complex multicomponent interventions. Although health workers are generally supportive of mCDSS and perceive them as useful, concerns about increased workload and altered workflow hinder sustainability. Facilitators and barriers to use of mCDSS include technical and infrastructural support, ownership, health system challenges, and training. Conclusions: The use of mCDSS in sub-Saharan Africa is an indication of progress in mHealth, although their effect on quality of service delivery is yet to be fully explored. Lessons learned are useful for informing future research, policy, and practice for technologically supported health care delivery, especially in resource-poor settings.

  • Woman using Android phone. Copyright: Placeit; URL:; License: Licensed by JMIR.

    Investigating the Perceptions of Care Coordinators on Using Behavior Theory-Based Mobile Health Technology With Medicaid Populations: A Grounded Theory Study

    Authors List:


    Background: Medicaid populations are less engaged in their health care than the rest of the population, translating to worse health outcomes and increased health care costs. Since theory-based mobile health (mHealth) interventions have been shown to increase patient engagement, mobile phones may be an optimal strategy to reach this population. With increased development of theory-based mHealth technology, these interventions must now be evaluated with these medically underserved populations in a real-world setting. Objective: The aim of our study was to investigate care coordinators’ perceived value of using a health behavior theory-based mHealth platform with Medicaid clients. In particular, attention was paid to the perceived impact on patient engagement. This research was conducted using the patient-provider text messaging (short message service, SMS) platform, Sense Health (now Wellpass), which integrates the transtheoretical model (TTM), also called the stages of change model; social cognitive theory (SCT); supportive accountability; and motivational interviewing (MI). Methods: Interviews based in grounded theory methodology were conducted with 10 care managers to understand perceptions of the relationship between mHealth and patient engagement. Results: The interviews with care managers yielded a foundation for a grounded theory model, presenting themes that suggested 4 intertwined correlative relationships revolving around patient engagement: (1) A text messaging (short message service, SMS) platform supplements the client-care manager dynamic, which is grounded in high quality, reciprocal-communication to increase patient engagement; (2) Texting enhances the relationship between literacy and access to care for Medicaid patients, increasing low-literacy patients’ agency to access services; (3) Texting enhances communication, providing care managers with a new means to support their clients; and (4) Reminders augment client accountability, leading to both increased motivation and readiness to change behaviors, as well as an improved client-care manager relationship. Conclusions: Messaging platform features tied to health behavior theory appear to be effective in improving patient engagement. Two-way communication (supportive accountability), trusted relationships (supportive accountability, SCT), personalized messages (TTM), and patient input (TTM, SCT, MI) appeared as the most relevant components in achieving desired outcomes. Additionally, reminder messages were noted as especially useful in making Medicaid patients accountable and in turn engaging them in their health and health care. These findings convey suggested elements for inclusion in other mHealth interventions aiming to improve patient engagement in Medicaid populations.

  • Image Source: Copyright Shinya Suzuki via FLickR Licensed under Creative Commons Attribution 2.0.

    Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices


    Background: Physical activity tracking wearable devices have emerged as an increasingly popular method for consumers to assess their daily activity and calories expended. However, whether these wearable devices are valid at different levels of exercise intensity is unknown. Objective: The objective of this study was to examine heart rate (HR) and energy expenditure (EE) validity of 3 popular wrist-worn activity monitors at different exercise intensities. Methods: A total of 62 participants (females: 58%, 36/62; nonwhite: 47% [13/62 Hispanic, 8/62 Asian, 7/62 black/ African American, 1/62 other]) wore the Apple Watch, Fitbit Charge HR, and Garmin Forerunner 225. Validity was assessed using 2 criterion devices: HR chest strap and a metabolic cart. Participants completed a 10-minute seated baseline assessment; separate 4-minute stages of light-, moderate-, and vigorous-intensity treadmill exercises; and a 10-minute seated recovery period. Data from devices were compared with each criterion via two-way repeated-measures analysis of variance and Bland-Altman analysis. Differences are expressed in mean absolute percentage error (MAPE). Results: For the Apple Watch, HR MAPE was between 1.14% and 6.70%. HR was not significantly different at the start (P=.78), during baseline (P=.76), or vigorous intensity (P=.84); lower HR readings were measured during light intensity (P=.03), moderate intensity (P=.001), and recovery (P=.004). EE MAPE was between 14.07% and 210.84%. The device measured higher EE at all stages (P<.01). For the Fitbit device, the HR MAPE was between 2.38% and 16.99%. HR was not significantly different at the start (P=.67) or during moderate intensity (P=.34); lower HR readings were measured during baseline, vigorous intensity, and recovery (P<.001) and higher HR during light intensity (P<.001). EE MAPE was between 16.85% and 84.98%. The device measured higher EE at baseline (P=.003), light intensity (P<.001), and moderate intensity (P=.001). EE was not significantly different at vigorous (P=.70) or recovery (P=.10). For Garmin Forerunner 225, HR MAPE was between 7.87% and 24.38%. HR was not significantly different at vigorous intensity (P=.35). The device measured higher HR readings at start, baseline, light intensity, moderate intensity (P<.001), and recovery (P=.04). EE MAPE was between 30.77% and 155.05%. The device measured higher EE at all stages (P<.001). Conclusions: This study provides one of the first validation assessments for the Fitbit Charge HR, Apple Watch, and Garmin Forerunner 225. An advantage and novel approach of the study is the examination of HR and EE at specific physical activity intensities. Establishing validity of wearable devices is of particular interest as these devices are being used in weight loss interventions and could impact findings. Future research should investigate why differences between exercise intensities and the devices exist.

  • Fieldwork in rural Tibet conducted by Maoyi Tian. The community health worker is using a smartphone-based electronic decision support system. Image created by the author. Copyright Maoyi Tian.

    mHealth Interventions for Health System Strengthening in China: A Systematic Review


    Background: With rapidly expanding infrastructure in China, mobile technology has been deemed to have the potential to revolutionize health care delivery. There is particular promise for mobile health (mHealth) to positively influence health system reform and confront the new challenges of chronic diseases. Objective: The aim of this study was to systematically review existing mHealth initiatives in China, characterize them, and examine the extent to which mHealth contributes toward the health system strengthening in China. Furthermore, we also aimed to identify gaps in mHealth development and evaluation. Methods: We systematically reviewed the literature from English and Chinese electronic database and trial registries, including PubMed, EMBASE, Cochrane, China National Knowledge of Infrastructure (CNKI), and World Health Organization (WHO) International Clinical Trials Registry Platform. We used the English keywords of mHealth, eHealth, telemedicine, telehealth, mobile phone, cell phone, text messaging, and China, as well as their corresponding Chinese keywords. All articles using mobile technology for health care management were included in the study. Results: A total of 1704 articles were found using the search terms, and eventually 72 were included. Overall, few high quality interventions were identified. Most interventions were found to be insufficient in scope, and their evaluation was of inadequate rigor to generate scalable solutions and provide reliable evidence of effectiveness. Most interventions focused on text messaging for consumer education and behavior change. There were a limited number of interventions that addressed health information management, health workforce issues, use of medicines and technologies, or leadership and governance from a health system perspective. Conclusions: We provide four recommendations for future mHealth interventions in China that include the need for the development, evaluation and trials examining integrated mHealth interventions to guide the development of future mHealth interventions, target disadvantaged populations with mHealth interventions, and generate appropriate evidence for scalable and sustainable models of care.

  • Image Source: Copyright Piotr Adamowicz, licensed from shutterstock (URL:, license ID SSTK-060AA-57B8.

    Mobile App-Based Interventions to Support Diabetes Self-Management: A Systematic Review of Randomized Controlled Trials to Identify Functions Associated with...


    Background: Mobile health apps for diabetes self-management have different functions. However, the efficacy and safety of each function are not well studied, and no classification is available for these functions. Objective: The aims of this study were to (1) develop and validate a taxonomy of apps for diabetes self-management, (2) investigate the glycemic efficacy of mobile app-based interventions among adults with diabetes in a systematic review of randomized controlled trials (RCTs), and (3) explore the contribution of different function to the effectiveness of entire app-based interventions using the taxonomy. Methods: We developed a 3-axis taxonomy with columns of clinical modules, rows of functional modules and cells of functions with risk assessments. This taxonomy was validated by reviewing and classifying commercially available diabetes apps. We searched MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, the Chinese Biomedical Literature Database, and from January 2007 to May 2016. We included RCTs of adult outpatients with diabetes that compared using mobile app-based interventions with usual care alone. The mean differences (MDs) in hemoglobin A1c (HbA1c) concentrations and risk ratios of adverse events were pooled using a random-effects meta-analysis. After taxonomic classification, we performed exploratory subgroup analyses of the presence or absence of each module across the included app-based interventions. Results: Across 12 included trials involving 974 participants, using app-based interventions was associated with a clinically significant reduction of HbA1c (MD 0.48%, 95% CI 0.19%-0.77%) without excess adverse events. Larger HbA1c reductions were noted among patients with type 2 diabetes than those with type 1 diabetes (MD 0.67%, 95% CI 0.30%-1.03% vs MD 0.36%, 95% CI 0.08%-0.81%). Having a complication prevention module in app-based interventions was associated with a greater HbA1c reduction (with complication prevention: MD 1.31%, 95% CI 0.66%-1.96% vs without: MD 0.38%, 95% CI 0.09%-0.67%; intersubgroup P=.01), as was having a structured display (with structured display: MD 0.69%, 95% CI 0.32%-1.06% vs without: MD 0.16%, 95% CI 0.16%-0.48%; intersubgroup P=.03). However, having a clinical decision-making function was not associated with a larger HbA1c reduction (with clinical decision making: MD 0.18%, 95% CI 0.21%-0.56% vs without: MD 0.61%, 95% CI 0.27%-0.95%; intersubgroup P=.10). Conclusions: The use of mobile app-based interventions yields a clinically significant HbA1c reduction among adult outpatients with diabetes, especially among those with type 2 diabetes. Our study suggests that the clinical decision-making function needs further improvement and evaluation before being added to apps.

  • Image source: Created by the authors, from fig. 1 of Licensed under Creative Commons Attribution cc-by 2.0.

    Resting and Postexercise Heart Rate Detection From Fingertip and Facial Photoplethysmography Using a Smartphone Camera: A Validation Study


    Background: Modern smartphones allow measurement of heart rate (HR) by detecting pulsatile photoplethysmographic (PPG) signals with built-in cameras from the fingertips or the face, without physical contact, by extracting subtle beat-to-beat variations of skin color. Objective: The objective of our study was to evaluate the accuracy of HR measurements at rest and after exercise using a smartphone-based PPG detection app. Methods: A total of 40 healthy participants (20 men; mean age 24.7, SD 5.2 years; von Luschan skin color range 14-27) underwent treadmill exercise using the Bruce protocol. We recorded simultaneous PPG signals for each participant by having them (1) facing the front camera and (2) placing their index fingertip over an iPhone’s back camera. We analyzed the PPG signals from the Cardiio-Heart Rate Monitor + 7 Minute Workout (Cardiio) smartphone app for HR measurements compared with a continuous 12-lead electrocardiogram (ECG) as the reference. Recordings of 20 seconds’ duration each were acquired at rest, and immediately after moderate- (50%-70% maximum HR) and vigorous- (70%-85% maximum HR) intensity exercise, and repeated successively until return to resting HR. We used Bland-Altman plots to examine agreement between ECG and PPG-estimated HR. The accuracy criterion was root mean square error (RMSE) ≤5 beats/min or ≤10%, whichever was greater, according to the American National Standards Institute/Association for the Advancement of Medical Instrumentation EC-13 standard. Results: We analyzed a total of 631 fingertip and 626 facial PPG measurements. Fingertip PPG-estimated HRs were strongly correlated with resting ECG HR (r=.997, RMSE=1.03 beats/min or 1.40%), postmoderate-intensity exercise (r=.994, RMSE=2.15 beats/min or 2.53%), and postvigorous-intensity exercise HR (r=.995, RMSE=2.01 beats/min or 1.93%). The correlation of facial PPG-estimated HR was stronger with resting ECG HR (r=.997, RMSE=1.02 beats/min or 1.44%) than with postmoderate-intensity exercise (r=.982, RMSE=3.68 beats/min or 4.11%) or with postvigorous-intensity exercise (r=.980, RMSE=3.84 beats/min or 3.73%). Bland-Altman plots showed better agreement between ECG and fingertip PPG-estimated HR than between ECG and facial PPG-estimated HR. Conclusions: We found that HR detection by the Cardiio smartphone app was accurate at rest and after moderate- and vigorous-intensity exercise in a healthy young adult sample. Contact-free facial PPG detection is more convenient but is less accurate than finger PPG due to body motion after exercise.

  • Smartphone with a pink ribbon: Image source: Copyright, author Tamar Ginossar.

    Content, Usability, and Utilization of Plain Language in Breast Cancer Mobile Phone Apps: A Systematic Analysis


    Background: Breast cancer is one of the leading contributors to preventable illness and death among women. Although mobile phone apps provide unprecedented opportunity to engage women along the cancer continuum, little is known about the availability, content, and usability of breast cancer mobile phone apps. Objective: This study analyzed the content and adherence to literate design standards of all breast cancer-related apps available on the App Store and Google Play, as well as the relationship between their content, user ratings, and price. Methods: Following identification and downloading of all available breast cancer mobile phone apps in October 2015, 101 apps were confirmed as focusing on breast cancer. Based on prior research, we adapted and applied a content analysis scheme that was specific to breast cancer apps, including their main purpose, relevance to the cancer care continuum, and adherence to usability standards outlined by the Institute of Medicine (IOM). Results: The most common aim of apps was educational (73/101, 72.3%), followed by behavior change (24/101, 23.9%), fundraising (20/101, 19.8%), and advocacy (14/101, 13.9%). On the cancer continuum, primary prevention (strategies to prevent cancer cells from occurring) was mentioned in almost one-third of the apps (30/101, 29.7%). Less than half of the apps (46/101, 45.5%) presented information about mammography and/or breast clinical exam, and 53 apps (52.5%) discussed breast self-exam (which is no longer recommended). Symptoms of cancer prediagnosis, such as a lump, were discussed in almost half of the apps (48/101, 47.5%) and a similar number of apps included information about genetic risk for breast cancer (47/101, 46.5%). Information about breast cancer diagnosis was included in 42 apps (41.58%) and 43 (42.6%) apps discussed treatment options. Survivorship issues were addressed in 17 (16.8%) apps. Only one (1.0%) app discussed hospice. Adherence to usability recommendations was low. The median composite score was 3 (mean 2.60, SD 1.20) of the six recommended usability items. With eight plain language items, the median of the composite health literacy score was 5 (mean 5.06, SD 2.00). Most apps did not use easy-to-understand words (44/101, 43.6%) and few (24/101, 23.8%) defined key terms. Conclusions: Current breast cancer apps provide important information about breast cancer, but the most common topic covered is breast self-examination, a non-evidence-based screening strategy. Apps that focus on evidence-based strategies on the cancer continuum are needed, with a notable pressing need for apps that would address survivorship and end of life. Finally, developers of breast cancer apps should adhere to IOM standards to meet the needs of diverse populations and reduce current disparities.

  • Image source: Copyright Anna Bryukhanova via License purchased by the authors.

    Design Considerations for mHealth Programs Targeting Smokers Not Yet Ready to Quit: Results of a Sequential Mixed-Methods Study


    Background: Mobile health (mHealth) smoking cessation programs are typically designed for smokers who are ready to quit smoking. In contrast, most smokers want to quit someday but are not yet ready to quit. If mHealth apps were designed for these smokers, they could potentially encourage and assist more people to quit smoking. No prior studies have specifically examined the design considerations of mHealth apps targeting smokers who are not yet ready to quit. Objective: To inform the user-centered design of mHealth apps for smokers who were not yet ready to quit by assessing (1) whether these smokers were interested in using mHealth tools to change their smoking behavior; (2) their preferred features, functionality, and content of mHealth programs addressing smoking; and (3) considerations for marketing or distributing these programs to promote their uptake. Methods: We conducted a sequential exploratory, mixed-methods study. Qualitative interviews (phase 1, n=15) were completed with a demographically diverse group of smokers who were smartphone owners and wanted to quit smoking someday, but not yet. Findings informed a Web-based survey of smokers from across the United States (phase 2, n=116). Data were collected from April to September, 2016. Results: Findings confirmed that although smokers not yet ready to quit are not actively seeking treatment or using cessation apps, most would be interested in using these programs to help them reduce or change their smoking behavior. Among phase 2 survey respondents, the app features, functions, and content rated most highly were (1) security of personal information; (2) the ability to track smoking, spending, and savings; (3) content that adaptively changes with one’s needs; (4) the ability to request support as needed; (5) the ability to earn and redeem awards for program use; (6) guidance on how to quit smoking; and (7) content specifically addressing management of nicotine withdrawal, stress, depression, and anxiety. Results generally did not vary by stage of change for quitting smoking (precontemplation vs contemplation). The least popular feature was the ability to share progress via social media. Relevant to future marketing or distribution considerations, smokers were price-sensitive and valued empirically validated programs. Program source, expert recommendations, and user ratings were also important considerations. Conclusions: Smokers who are not yet ready to quit represent an important target group for intervention. Study findings suggest that many of these individuals are receptive to using mHealth tools to reduce or quit smoking, despite not having made a commitment to quit yet. The preferences for specific mHealth intervention features, functionality, and content outlined in this paper can aid addiction treatment experts, design specialists, and software developers interested in creating engaging interventions for smokers who want to quit in the future but are not yet committed to this important health goal.

  • Image Source: Montage of HIV test and condom ordering in HealthMindr app and man. Copyright JMIR, licensed under cc-by 2.0..

    Usability and Acceptability of a Mobile Comprehensive HIV Prevention App for Men Who Have Sex With Men: A Pilot Study


    Background: Men who have sex with men (MSM) are the group most impacted by the human immunodeficiency virus (HIV) epidemic and the only subgroup in the United States among which new HIV diagnoses are not decreasing. To achieve the US National HIV/AIDS (acquired immunodeficiency syndrome) Strategy goals of reducing new diagnoses by 25%, high (eg, 30-50%) coverage of multiple HIV prevention interventions is needed in both urban and rural areas. Mobile phone “apps” are an important channel through which prevention services could be provided at scale and at low marginal cost. Objective: The aim of this study was to evaluate the usability and acceptability of a theory-based Android mobile phone app for HIV prevention. Methods: The app included self-assessment tools; prevention recommendations; commodity (condoms, HIV self-tests) ordering; reminders to MSM for basic HIV prevention services, HIV testing, condom use, screening for preexposure prophylaxis (PrEP) and nonoccupational postexposure prophylaxis (nPEP); and prevention and treatment provider locators. The study recruited HIV-negative, Android-using MSM in Atlanta and Seattle who were asked to use the app for 4 months and complete a post-use survey. We measured the use of the app and its features, ordering of commodities, self-report of establishing an HIV testing plan, being HIV tested in the community, and starting PrEP or using nPEP. Usability was assessed using the system usability scale (SUS). Results: A total of 121 MSM were enrolled (59.5%, 72/121 from Atlanta; 40.5%, 49/121 from Seattle). Median age was 28. Nearly half (48.8%, 59/121) were nonwhite, and most (85.9%, 104/121) were gay-identified. Most had tested for HIV in the past (85.1%, 103/121), and 52 (43.0%, 52/121) had a plan to test for HIV regularly. Men used the app for an average of 17.7 minutes over the first 4 months. Over the 4-month period, over half ordered condoms (63.6%, 77/121) and HIV test kits (52.8%, 64/121) on the app. Eight of 86 (9%) PrEP-eligible MSM started PrEP during the 4-month period; of those, 6 of the 8 reported that the app influenced their decision to start PrEP. The mean SUS was 73 (above average). Conclusions: A theory-based mobile phone app was acceptable to MSM and was rated as having above-average usability. Most men used the commodity-ordering features of the app during the 4-month evaluation period, and nearly 1 in 10 PrEP-eligible men started PrEP, with most attributing their decision to start PrEP in part to the app. A broader, randomized controlled study of the impact of the app on uptake of prevention behaviors for MSM is warranted.

  • The equipment used in the remote monitoring group. Source: Figure 1 from; Copyright: the authors; License: Creative Commons Attribution (CC-BY).

    Remote Monitoring of Hypertension Diseases in Pregnancy: A Pilot Study


    Background: Although remote monitoring (RM) has proven its added value in various health care domains, little is known about the remote follow-up of pregnant women diagnosed with a gestational hypertensive disorders (GHD). Objective: The aim of this study was to evaluate the added value of a remote follow-up program for pregnant women diagnosed with GHD. Methods: A 1-year retrospective study was performed in the outpatient clinic of a 2nd level prenatal center where pregnant women with GHD received RM or conventional care (CC). Primary study endpoints include number of prenatal visits and admissions to the prenatal observation ward. Secondary outcomes include gestational outcome, mode of delivery, neonatal outcome, and admission to neonatal intensive care (NIC). Differences in continuous and categorical variables in maternal demographics and characteristics were tested using Unpaired Student’s two sampled t test or Mann-Whitney U test and the chi-square test. Both a univariate and multivariate analysis were performed for analyzing prenatal follow-up and gestational outcomes. All statistical analyses were done at nominal level, Cronbach alpha=.05. Results: Of the 166 patients diagnosed with GHD, 53 received RM and 113 CC. After excluding 5 patients in the RM group and 15 in the CC group because of the missing data, 48 patients in RM group and 98 in CC group were taken into final analysis. The RM group had more women diagnosed with gestational hypertension, but less with preeclampsia when compared with CC (81.25% vs 42.86% and 14.58% vs 43.87%). Compared with CC, univariate analysis in RM showed less induction, more spontaneous labors, and less maternal and neonatal hospitalizations (48.98% vs 25.00%; 31.63% vs 60.42%; 74.49% vs 56.25%; and 27.55% vs 10.42%). This was also true in multivariate analysis, except for hospitalizations. Conclusions: An RM follow-up of women with GHD is a promising tool in the prenatal care. It opens the perspectives to reverse the current evolution of antenatal interventions leading to more interventions and as such to ever increasing medicalized antenatal care.

  • Pediatric cardiovascular risk reduction smartphone application - photo montage by JMIR, licensed under cc-by 2.0, (c) JMIR Publications.

    A Mobile Clinical Decision Support Tool for Pediatric Cardiovascular Risk-Reduction Clinical Practice Guidelines: Development and Description


    Background: Widespread application of research findings to improve patient outcomes remains inadequate, and failure to routinely translate research findings into daily clinical practice is a major barrier for the implementation of any evidence-based guideline. Strategies to increase guideline uptake in primary care pediatric practices and to facilitate adherence to recommendations are required. Objective: Our objective was to operationalize the US National Heart, Lung, and Blood Institute’s Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents into a mobile clinical decision support (CDS) system for healthcare providers, and to describe the process development and outcomes. Methods: To overcome the difficulty of translating clinical practice guidelines into a computable form that can be used by a CDS system, we used a multilayer framework to convert the evidence synthesis into executable knowledge. We used an iterative process of design, testing, and revision through each step in the translation of the guidelines for use in a CDS tool to support the development of 4 validated modules: an integrated risk assessment; a blood pressure calculator; a body mass index calculator; and a lipid management instrument. Results: The iterative revision process identified several opportunities to improve the CDS tool. Operationalizing the integrated guideline identified numerous areas in which the guideline was vague or incorrect and required more explicit operationalization. Iterative revisions led to workable solutions to problems and understanding of the limitations of the tool. Conclusions: The process and experiences described provide a model for other mobile CDS systems that translate written clinical practice guidelines into actionable, real-time clinical recommendations.

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    Date Submitted: Mar 2, 2017

    Open Peer Review Period: Mar 3, 2017 - Apr 28, 2017

    Background: Demographic change 21st century, increasing number of elderly and diseade outbreaks that associated with this period, make new strategies for health care , change in behavior, and educatio...

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    Date Submitted: Feb 24, 2017

    Open Peer Review Period: Feb 26, 2017 - Apr 23, 2017

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    Open Peer Review Period: Feb 21, 2017 - Apr 18, 2017

    Background: Healthcare systems in Sub-Saharan Africa (SSA) are fraught with numerous governance and infrastructural issues including lack of access to quality care and health worker shortage. Policy m...

    Background: Healthcare systems in Sub-Saharan Africa (SSA) are fraught with numerous governance and infrastructural issues including lack of access to quality care and health worker shortage. Policy makers are becoming increasingly interested in adopting novel technologies such as web-based interventions within the scope of e-Health to bridge the gaps in care delivery in a cost-effective and sustainable manner. Successful implementation of these policies is reliant on evidences regarding people’s access to these technologies, which are scarce for countries in SSA. Objective: To 1) measure the prevalence of ever accessing computer and internet among adult population, 2) investigate the variation in the prevalence of accessing computer and internet across regional and socioeconomic groups, 3) assess association between ever accessing computer and internet and knowledge of routes and risk factors of HIV transmission in selected SSA countries. Methods: We analyzed cross-sectional datasets from UNICEF Multiple Indicator Cluster Surveys. Subjects were16,194 men and 39,121 women from Ghana, Guinea Bissau, Malawi and Zimbabwe. Main outcome variable was ever accessing computer and internet for any purpose. Associations were assessed by multivariable regression methods. Results: Lifetime experience of computer usage was respectively 21.5%, 13.4%, 12.3%, 28.4% among men, and 12.5%, 8.3%, 4.8%, 20.5% among women, and that of internet was 14.9%, 11.7%, 10.8%, 34% among men, and 6.4%, 6.9%, 4.2%, 21.6% among women in the aforementioned order. Participants who reported ever using computer and internet were more likely to have higher knowledge regarding the transmission of HIV compared to those who did not. Conclusions: Prevalence of lifetime access to computer and internet was considerably low in all four countries. Several socioeconomic factors appeared to be associated with the access to computer and internet, addressing which might prove beneficial for the successful expansion e-Health in these countries Clinical Trial: NA