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Journal Description

The Journal of Medical Internet Research (JMIR), now in its 20th year, is the pioneer open access eHealth journal and is the flagship journal of JMIR Publications. It is the leading digital health journal globally in terms of quality/visibility (Impact Factor 2017: 4.671, ranked #1 out of 22 journals) and in terms of size (number of papers published). The journal focuses on emerging technologies, medical devices, apps, engineering, and informatics applications for patient education, prevention, population health and clinical care. As leading high-impact journal in its' disciplines (health informatics and health services research), it is selective, but it is now complemented by almost 30 specialty JMIR sister journals, which have a broader scope. Peer-review reports are portable across JMIR journals and papers can be transferred, so authors save time by not having to resubmit a paper to different journals. 

As open access journal, we are read by clinicians, allied health professionals, informal caregivers, and patients alike, and have (as all JMIR journals) a focus on readable and applied science reporting the design and evaluation of health innovations and emerging technologies. We publish original research, viewpoints, and reviews (both literature reviews and medical device/technology/app reviews).

We are also a leader in participatory and open science approaches, and offer the option to publish new submissions immediately as preprints, which receive DOIs for immediate citation (eg, in grant proposals), and for open peer-review purposes. We also invite patients to participate (eg, as peer-reviewers) and have patient representatives on editorial boards.

Be a widely cited leader in the digitial health revolution and submit your paper today!

 

Recent Articles:

  • Health Care and Cybersecurity: Bibliometric Analysis of the Literature

    Abstract:

    Background: Over the past decade, clinical care has become globally dependent on information technology. The cybersecurity of health care information systems is now an essential component of safe, reliable, and effective health care delivery. Objective: The objective of this study was to provide an overview of the literature at the intersection of cybersecurity and health care delivery. Methods: A comprehensive search was conducted using PubMed and Web of Science for English-language peer-reviewed articles. We carried out chronological analysis, domain clustering analysis, and text analysis of the included articles to generate a high-level concept map composed of specific words and the connections between them. Results: Our final sample included 472 English-language journal articles. Our review results revealed that majority of the articles were focused on technology: Technology–focused articles made up more than half of all the clusters, whereas managerial articles accounted for only 32% of all clusters. This finding suggests that nontechnological variables (human–based and organizational aspects, strategy, and management) may be understudied. In addition, Software Development Security, Business Continuity, and Disaster Recovery Planning each accounted for 3% of the studied articles. Our results also showed that publications on Physical Security account for only 1% of the literature, and research in this area is lacking. Cyber vulnerabilities are not all digital; many physical threats contribute to breaches and potentially affect the physical safety of patients. Conclusions: Our results revealed an overall increase in research on cybersecurity and identified major gaps and opportunities for future work.

  • Source: Kadena Air Base (Tara A Williamson); Copyright: US Air Force; URL: https://www.kadena.af.mil/News/Article-Display/Article/417911/heart-health-makes-happy-humans/; License: Public Domain (CC0).

    Patient-Level Prediction of Cardio-Cerebrovascular Events in Hypertension Using Nationwide Claims Data

    Abstract:

    Background: Prevention and management of chronic diseases are the main goals of national health maintenance programs. Previously widely used screening tools, such as Health Risk Appraisal, are restricted in their achievement this goal due to their limitations, such as static characteristics, accessibility, and generalizability. Hypertension is one of the most important chronic diseases requiring management via the nationwide health maintenance program, and health care providers should inform patients about their risks of a complication caused by hypertension. Objective: Our goal was to develop and compare machine learning models predicting high-risk vascular diseases for hypertensive patients so that they can manage their blood pressure based on their risk level. Methods: We used a 12-year longitudinal dataset of the nationwide sample cohort, which contains the data of 514,866 patients and allows tracking of patients’ medical history across all health care providers in Korea (N=51,920). To ensure the generalizability of our models, we conducted an external validation using another national sample cohort dataset, comprising one million different patients, published by the National Health Insurance Service. From each dataset, we obtained the data of 74,535 and 59,738 patients with essential hypertension and developed machine learning models for predicting cardiovascular and cerebrovascular events. Six machine learning models were developed and compared for evaluating performances based on validation metrics. Results: Machine learning algorithms enabled us to detect high-risk patients based on their medical history. The long short-term memory-based algorithm outperformed in the within test (F1-score=.772, external test F1-score=.613), and the random forest-based algorithm of risk prediction showed better performance over other machine learning algorithms concerning generalization (within test F1-score=.757, external test F1-score=.705). Concerning the number of features, in the within test, the long short-term memory-based algorithms outperformed regardless of the number of features. However, in the external test, the random forest-based algorithm was the best, irrespective of the number of features it encountered. Conclusions: We developed and compared machine learning models predicting high-risk vascular diseases in hypertensive patients so that they may manage their blood pressure based on their risk level. By relying on the prediction model, a government can predict high-risk patients at the nationwide level and establish health care policies in advance.

  • Viewing multiple digital data sets. Source: iStock by Getty; Copyright: YakobchukOlena; URL: https://www.istockphoto.com/gb/photo/male-doing-estimation-on-screen-gm817442226-132225929; License: Licensed by the authors.

    A Framework for Analyzing and Measuring Usage and Engagement Data (AMUsED) in Digital Interventions: Viewpoint

    Abstract:

    Trials of digital interventions can yield extensive, in-depth usage data, yet usage analyses tend to focus on broad descriptive summaries of how an intervention has been used by the whole sample. This paper proposes a novel framework to guide systematic, fine-grained usage analyses that better enables understanding of how an intervention works, when, and for whom. The framework comprises three stages to assist in the following: (1) familiarization with the intervention and its relationship to the captured data, (2) identification of meaningful measures of usage and specifying research questions to guide systematic analyses of usage data, and (3) preparation of datasheets and consideration of available analytical methods with which to examine the data. The framework can be applied to inform data capture during the development of a digital intervention and/or in the analysis of data after the completion of an evaluation trial. We will demonstrate how the framework shaped preparation and aided efficient data capture for a digital intervention to lower transmission of cold and flu viruses in the home, as well as how it informed a systematic, in-depth analysis of usage data collected from a separate digital intervention designed to promote self-management of colds and flu. The Analyzing and Measuring Usage and Engagement Data (AMUsED) framework guides systematic and efficient in-depth usage analyses that will support standardized reporting with transparent and replicable findings. These detailed findings may also enable examination of what constitutes effective engagement with particular interventions.

  • Source: Image created by the Authors; Copyright: The Authors; URL: http://www.jmir.org/2018/2/e10441/; License: Creative Commons Attribution (CC-BY).

    Mitigation of Participant Loss to Follow-Up Using Facebook: All Our Families Longitudinal Pregnancy Cohort

    Abstract:

    Background: Facebook, a popular social media site, allows users to communicate and exchange information. Social media sites can also be used as databases to search for individuals, including cohort participants. Retaining and tracking cohort participants are essential for the validity and generalizability of data in longitudinal research. Despite numerous strategies to minimize loss to follow-up, maintaining contact with participants is time-consuming and resource-intensive. Social media may provide alternative methods of contacting participants who consented to follow-up but could not be reached, and thus are potentially “lost to follow-up.” Objective: The aim of this study was to determine if Facebook was a feasible method for identifying and contacting participants of a longitudinal pregnancy cohort who were lost to follow-up and re-engaging them without selection bias. Methods: This study used data from the All Our Families cohort. Of the 2827 mother-child dyads within the cohort, 237 participants were lost to follow-up. Participants were considered lost to follow-up if they had agreed to participate in additional research, completed at least one of the perinatal questionnaires, did not complete the 5-year postpartum questionnaire, and could not be contacted after numerous attempts via phone, email, or mail. Participants were considered to be matched to a Facebook profile if 2 or more characteristics matched information previously collected. Participants were sent both a friend request and a personal message through the study’s Facebook page and were invited to verify their enrollment in the study. The authors deemed a friend request was necessary because of the reduced functionality of nonfriend direct messaging at the time. If the participant accepted the study’s friend request, then a personalized message was sent. Participants were considered reconnected if they accepted the friend request or responded to any messages. Participants were considered re-engaged if they provided up-to-date contact information. Results: Compared with the overall cohort, participants who were lost to follow-up (n=237) were younger (P=.003), nonmarried (P=.02), had lower household income (P<.001), less education (P<.001), and self-identified as being part of an ethnic minority (P=.02). Of the 237 participants considered lost to follow-up, 47.7% (113/237) participants were identified using Facebook. Among the 113 identified participants, 77.0% (87/113) were contacted, 32.7% (37/113) were reconnected, and 17.7% (20/113) were re-engaged. No significant differences were found between those identified on Facebook (n=113) and those who were not able to be identified (n=124). Conclusions: Facebook identified 47.6% (113/237) of participants who were considered lost to follow-up, and the social media site may be a practical tool for reconnecting with participants. The results from this study demonstrate that social networking sites, such as Facebook, could be included in the development of retention practices and can be implemented at any point in cohort follow-up.

  • Source: Freepik; Copyright: jannoon028; URL: https://www.freepik.com/free-photo/doctor-with-tablet-hands_978248.htm; License: Licensed by JMIR.

    Digital Education in Health Professions: The Need for Overarching Evidence Synthesis

    Abstract:

    Synthesizing evidence from randomized controlled trials of digital health education poses some challenges. These include a lack of clear categorization of digital health education in the literature; constantly evolving concepts, pedagogies, or theories; and a multitude of methods, features, technologies, or delivery settings. The Digital Health Education Collaboration was established to evaluate the evidence on digital education in health professions; inform policymakers, educators, and students; and ultimately, change the way in which these professionals learn and are taught. The aim of this paper is to present the overarching methodology that we use to synthesize evidence across our digital health education reviews and to discuss challenges related to the process. For our research, we followed Cochrane recommendations for the conduct of systematic reviews; all reviews are reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidance. This included assembling experts in various digital health education fields; identifying gaps in the evidence base; formulating focused research questions, aims, and outcome measures; choosing appropriate search terms and databases; defining inclusion and exclusion criteria; running the searches jointly with librarians and information specialists; managing abstracts; retrieving full-text versions of papers; extracting and storing large datasets, critically appraising the quality of studies; analyzing data; discussing findings; drawing meaningful conclusions; and drafting research papers. The approach used for synthesizing evidence from digital health education trials is commonly regarded as the most rigorous benchmark for conducting systematic reviews. Although we acknowledge the presence of certain biases ingrained in the process, we have clearly highlighted and minimized those biases by strictly adhering to scientific rigor, methodological integrity, and standard operating procedures. This paper will be a valuable asset for researchers and methodologists undertaking systematic reviews in digital health education.

  • Source: CATCH at the University of Sheffield; Copyright: CATCH at the University of Sheffield; URL: http://www.jmir.org/2019/2/e11694/; License: Creative Commons Attribution (CC-BY).

    Older Adults’ Perspectives on Using Digital Technology to Maintain Good Mental Health: Interactive Group Study

    Abstract:

    Background: A growing number of apps to support good mental health and well-being are available on digital platforms. However, very few studies have examined older adults’ attitudes toward the use of these apps, despite increasing uptake of digital technologies by this demographic. Objective: This study sought to explore older adults’ perspectives on technology to support good mental health. Methods: A total of 15 older adults aged 50 years or older, in two groups, participated in sessions to explore the use of digital technologies to support mental health. Interactive activities were designed to capture participants’ immediate reactions to apps and websites designed to support mental health and to explore their experiences of using technology for these purposes in their own lives. Template analysis was used to analyze transcripts of the group discussions. Results: Older adults were motivated to turn to technology to improve mood through mechanisms of distraction, normalization, and facilitated expression of mental states, while aiming to reduce burden on others. Perceived barriers to use included fear of consequences and the impact of low mood on readiness to engage with technology, as well as a lack of prior knowledge applicable to digital technologies. Participants were aware of websites available to support mental health, but awareness alone did not motivate use. Conclusions: Older adults are motivated to use digital technologies to improve their mental health, but barriers remain that developers need to address for this population to access them.

  • MOTECH Ghana. Source: Grameen Foundation Ghana; Copyright: Grameen Foundation Ghana; URL: http://www.jmir.org/2019/2/e11268/; License: Creative Commons Attribution (CC-BY).

    Mobile Technology for Community Health in Ghana: Is Maternal Messaging and Provider Use of Technology Cost-Effective in Improving Maternal and Child Health...

    Abstract:

    Background: Mobile technologies are emerging as tools to enhance health service delivery systems and empower clients to improve maternal, newborn, and child health. Limited evidence exists on the value for money of mobile health (mHealth) programs in low- and middle-income countries. Objective: This study aims to forecast the incremental cost-effectiveness of the Mobile Technology for Community Health (MOTECH) initiative at scale across 170 districts in Ghana. Methods: MOTECH’s “Client Data Application” allows frontline health workers to digitize service delivery information and track the care of patients. MOTECH’s other main component, the “Mobile Midwife,” sends automated educational voice messages to mobile phones of pregnant and postpartum women. We measured program costs and consequences of scaling up MOTECH over a 10-year analytic time horizon. Economic costs were estimated from informant interviews and financial records. Health effects were modeled using the Lives Saved Tool with data from an independent evaluation of changes in key services coverage observed in Gomoa West District. Incremental cost-effectiveness ratios were presented overall and for each year of implementation. Uncertainty analyses assessed the robustness of results to changes in key parameters. Results: MOTECH was scaled in clusters over a 3-year period to reach 78.7% (170/216) of Ghana’s districts. Sustaining the program would cost US $17,618 on average annually per district. Over 10 years, MOTECH could potentially save an estimated 59,906 lives at a total cost of US $32 million. The incremental cost per disability-adjusted life year averted ranged from US $174 in the first year to US $6.54 in the tenth year of implementation and US $20.94 (95% CI US $20.34-$21.55) over 10 years. Uncertainty analyses suggested that the incremental cost-effectiveness ratio was most sensitive to changes in health effects, followed by personnel time. Probabilistic sensitivity analyses suggested that MOTECH had a 100% probability of being cost-effective above a willingness-to-pay threshold of US $50. Conclusions: This is the first study to estimate the value for money of the supply- and demand-side of an mHealth initiative. The adoption of MOTECH to improve MNCH service delivery and uptake represents good value for money in Ghana and should be considered for expansion. Integration with other mHealth solutions, including e-Tracker, may provide opportunities to continue or combine beneficial components of MOTECH to achieve a greater impact on health.

  • Source: Freepik; Copyright: katemangostar; URL: https://www.freepik.com/free-photo/reading-adult-person-entrepreneur-job_1072594.htm; License: Licensed by the authors.

    Use of Learning Analytics Data in Health Care–Related Educational Disciplines: Systematic Review

    Abstract:

    Background: While the application of learning analytics in tertiary education has received increasing attention in recent years, a much smaller number have explored its use in health care-related educational studies. Objective: This systematic review aims to examine the use of e-learning analytics data in health care studies with regards to how the analytics is reported and if there is a relationship between e-learning analytics and learning outcomes. Methods: We performed comprehensive searches of papers from 4 electronic databases (MEDLINE, EBSCOhost, Web of Science, and ERIC) to identify relevant papers. Qualitative studies were excluded from this review. Papers were screened by 2 independent reviewers. We selected qualified studies for further investigation. Results: A total of 537 papers were screened, and 19 papers were identified. With regards to analytics undertaken, 11 studies reported the number of connections and time spent on e-learning. Learning outcome measures were defined by summative final assessment marks or grades. In addition, significant statistical results of the relationships between e-learning usage and learning outcomes were reported in 12 of the identified papers. In general, students who engaged more in e-learning resources would get better academic attainments. However, 2 papers reported otherwise with better performing students consuming less e-learning videos. A total of 14 papers utilized satisfaction questionnaires for students, and all were positive in their attitude toward e-learning. Furthermore, 6 of 19 papers reported descriptive statistics only, with no statistical analysis. Conclusions: The nature of e-learning activities reported in this review was varied and not detailed well. In addition, there appeared to be inadequate reporting of learning analytics data observed in over half of the selected papers with regards to definitions and lack of detailed information of what the analytic was recording. Although learning analytics data capture is popular, a lack of detail is apparent with regards to the capturing of meaningful and comparable data. In particular, most analytics record access to a management system or particular e-learning materials, which may not necessarily detail meaningful learning time or interaction. Hence, learning analytics data should be designed to record the time spent on learning and focus on key learning activities. Finally, recommendations are made for future studies.

  • Source: Image created by the Authors; Copyright: The Authors; URL: http://www.jmir.org/2018/0/e0/; License: Creative Commons Attribution (CC-BY).

    Evaluation of Mothers’ Perceptions of a Technology-Based Supportive Educational Parenting Program (Part 2): Qualitative Study

    Abstract:

    Background: Transitioning into parenthood can be stressful as parents struggle to cope with new parenting responsibilities. Although perinatal care in hospitals aims to improve parental outcomes, there is a general consensus that it is suboptimal and insufficient. Therefore, many studies have designed intervention methods to supplement support for parents during this stressful period. However, studies often focus on parental outcomes as indicators of their interventions’ success and effectiveness. Studies evaluating participants’ experiences and feedback are limited. Objective: This study aimed to examine the experiences and perceptions of participants who participated in a supportive education parenting program intervention study. Methods: A qualitative semistructured interview was conducted with 16 mothers (6 control and 10 intervention) from a randomized controlled trial. The supportive education parenting program received by the intervention group included 2 phone-based perinatal educational sessions, a phone-based educational session after childbirth, and a 1-month postpartum access to a mobile health app. The interviews were approximately 30- to 60-min long, audiotaped and transcribed verbatim, and analyzed using thematic analysis. Study findings were reported according to the Consolidated Criteria for Reporting Qualitative Research checklist. Results: The 3 main themes evaluating mothers’ experiences and perceptions were generated: (1) changed perspective toward parenthood, (2) journey from pregnancy to after birth, and (3) a way forward. Mothers from the intervention group mostly had good perinatal experiences with sufficient support received, which elevated their emotional well-being and increased parenting involvement. Mothers in the control group, although satisfied with the hospital care received, were more stressed and shared a need for professional advice and extra support. Apart from technical enhancements, mothers also requested extended social support during early pregnancy up to 1 year postpartum, taking into consideration Asian cultural practices. Conclusions: Mothers who received the intervention were overall satisfied with the support provided by the technology-based supportive educational parenting program. The success of the educational program in this study highlights the need to supplement standard care in hospitals with technology-based educational programs. Future research should include fathers’ perceptions to attain an in-depth understanding of overall participants’ experiences and needs in the future development of supportive and educational programs.

  • Source: Flickr; Copyright: alexxis; URL: https://www.flickr.com/photos/alexxis/5868483370/; License: Creative Commons Attribution + NoDerivatives (CC-BY-ND).

    Effectiveness of a Technology-Based Supportive Educational Parenting Program on Parental Outcomes (Part 1): Randomized Controlled Trial

    Abstract:

    Background: Transitioning into parenthood can be stressful for new parents, especially with the lack of continuity of care from health care professionals during the postpartum period. Short hospital stays limit the availability of support and time parents need to be well equipped with parenting and infant care skills. Poor parental adjustment may, in turn, lead to negative parental outcomes and adversely affect the child’s development. For the family’s future well-being, and to facilitate a smoother transition into parenthood, there is a need for easily accessible, technology-based educational programs to support parents during the crucial perinatal period. Objective: This study aimed to examine the effectiveness of a technology-based supportive educational parenting program (SEPP) on parenting outcomes during the perinatal period in couples. Methods: A randomized, single-blinded, parallel-armed, controlled trial was conducted. The study recruited 236 parents (118 couples) from an antenatal clinic of a tertiary hospital in Singapore. Eligible parents were randomly assigned to the intervention group (n=118) or the control group (n=118). The SEPP is based on Bandura’s self-efficacy theory and Bowlby’s theory of attachment. Components of the intervention include 2 telephone-based educational sessions (1 antenatal and 1 immediately postnatal) and a mobile health app follow-up for 1 month. The control group only received routine perinatal care provided by the hospital. Outcome measures including parenting self-efficacy (PSE), parental bonding, perceived social support, parenting satisfaction, postnatal depression (PND), and anxiety were measured using reliable and valid instruments. Data were collected over 6 months at 4 time points: during pregnancy (third trimester), 2 days postpartum, 1 month postpartum, and 3 months postpartum. Outcomes were standardized using baseline means and SDs. Linear mixed models were used to compare the groups for postpartum changes in the outcome variables. Results: The intervention group showed significantly better outcome scores than the control group from baseline to 3 months postpartum for PSE (mean difference, MD, 0.37; 95% CI 0.06 to 0.68; P=.02), parental bonding (MD −1.32; 95% CI −1.89 to −0.75; P<.001), self-perceived social support (MD 0.69; 95% CI 0.18 to 1.19; P=.01), parenting satisfaction (MD 1.40; 95% CI 0.86 to 1.93; P<.001), and PND (MD −0.91; 95% CI −1.34 to −0.49; P<.001). Postnatal anxiety (PNA) scores of the intervention group were only significantly better after adjusting for covariates (MD −0.82; 95% CI −1.15 to −0.49; P<.001). Conclusions: The technology-based SEPP is effective in enhancing parental bonding, PSE, perceived social support and parental satisfaction, and in reducing PND and PNA. Health care professionals could incorporate it with existing hands-on infant care classes and routine care to better meet parents’ needs and create positive childbirth experiences, which may in turn encourage parents to have more children. Trial Registration: ISRCTN Registry ISRCTN48536064; http://www.isrctn.com/ISRCTN48536064 (Archived by WebCite at http://www.webcitation.org/6wMuEysiO).

  • Students engaged in mLearning. Source: Image created by the Authors; Copyright: The Authors; URL: http://www.jmir.org/2019/2/e12937/; License: Creative Commons Attribution (CC-BY).

    Mobile Digital Education for Health Professions: Systematic Review and Meta-Analysis by the Digital Health Education Collaboration

    Abstract:

    Background: There is a pressing need to implement efficient and cost-effective training to address the worldwide shortage of health professionals. Mobile digital education (mLearning) has been mooted as a potential solution to increase the delivery of health professions education as it offers the opportunity for wide access at low cost and flexibility with the portability of mobile devices. To better inform policy making, we need to determine the effectiveness of mLearning. Objective: The primary objective of this review was to evaluate the effectiveness of mLearning interventions for delivering health professions education in terms of learners’ knowledge, skills, attitudes, and satisfaction. Methods: We performed a systematic review of the effectiveness of mLearning in health professions education using standard Cochrane methodology. We searched 7 major bibliographic databases from January 1990 to August 2017 and included randomized controlled trials (RCTs) or cluster RCTs. Results: A total of 29 studies, including 3175 learners, met the inclusion criteria. A total of 25 studies were RCTs and 4 were cluster RCTs. Interventions comprised tablet or smartphone apps, personal digital assistants, basic mobile phones, iPods, and Moving Picture Experts Group-1 audio layer 3 player devices to deliver learning content. A total of 20 studies assessed knowledge (n=2469) and compared mLearning or blended learning to traditional learning or another form of digital education. The pooled estimate of studies favored mLearning over traditional learning for knowledge (standardized mean difference [SMD]=0.43, 95% CI 0.05-0.80, N=11 studies, low-quality evidence). There was no difference between blended learning and traditional learning for knowledge (SMD=0.20, 95% CI –0.47 to 0.86, N=6 studies, low-quality evidence). A total of 14 studies assessed skills (n=1097) and compared mLearning or blended learning to traditional learning or another form of digital education. The pooled estimate of studies favored mLearning (SMD=1.12, 95% CI 0.56-1.69, N=5 studies, moderate quality evidence) and blended learning (SMD=1.06, 95% CI 0.09-2.03, N=7 studies, low-quality evidence) over traditional learning for skills. A total of 5 and 4 studies assessed attitudes (n=440) and satisfaction (n=327), respectively, with inconclusive findings reported for each outcome. The risk of bias was judged as high in 16 studies. Conclusions: The evidence base suggests that mLearning is as effective as traditional learning or possibly more so. Although acknowledging the heterogeneity among the studies, this synthesis provides encouraging early evidence to strengthen efforts aimed at expanding health professions education using mobile devices in order to help tackle the global shortage of health professionals.

  • Source: Freepik; Copyright: rawpixel.com; URL: https://www.freepik.com/free-photo/doctor-suggesting-hospital-program-patient_2767780.htm; License: Licensed by JMIR.

    Implementing Blockchains for Efficient Health Care: Systematic Review

    Abstract:

    Background: The decentralized nature of sensitive health information can bring about situations where timely information is unavailable, worsening health outcomes. Furthermore, as patient involvement in health care increases, there is a growing need for patients to access and control their data. Blockchain is a secure, decentralized online ledger that could be used to manage electronic health records (EHRs) efficiently, therefore with the potential to improve health outcomes by creating a conduit for interoperability. Objective: This study aimed to perform a systematic review to assess the feasibility of blockchain as a method of managing health care records efficiently. Methods: Reviewers identified studies via systematic searches of databases including PubMed, MEDLINE, Scopus, EMBASE, ProQuest, and Cochrane Library. Suitability for inclusion of each was assessed independently. Results: Of the 71 included studies, the majority discuss potential benefits and limitations without evaluation of their effectiveness, although some systems were tested on live data. Conclusions: Blockchain could create a mechanism to manage access to EHRs stored on the cloud. Using a blockchain can increase interoperability while maintaining privacy and security of data. It contains inherent integrity and conforms to strict legal regulations. Increased interoperability would be beneficial for health outcomes. Although this technology is currently unfamiliar to most, investments into creating a sufficiently user-friendly interface and educating users on how best to take advantage of it would lead to improved health outcomes. International Registered Report Identifier (IRRID): RR2-10.2196/10994

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    Date Submitted: Feb 15, 2019

    Open Peer Review Period: Feb 15, 2019 - Feb 25, 2019

    Background: Some hospitalized patients rapidly deteriorate due to either disease progression or imperfect triage and level of care assignment after their admission. An EWS to identify patients at high...

    Background: Some hospitalized patients rapidly deteriorate due to either disease progression or imperfect triage and level of care assignment after their admission. An EWS to identify patients at high risk of subsequent intra-hospital death can be an effective tool to benefit patient safety and quality of care, and also reduce avoidable harm and costs. Objective: To prospectively validate a real-time early warning system (EWS) with the capacity to predict patients at high risk of in-hospital mortality during their inpatient episodes. Methods: Data were collected from the system-wide electronic medical record (EMR) of two acute Berkshire Health System hospitals, comprised of 54,246 inpatient admissions from January 01, 2015 to September 30, 2017, of which 3.97% resulted in intra-hospital deaths. After constructing the model using statistical methods and modern machine learning algorithms, we prospectively validated the algorithms as a real-time inpatient early warning system (EWS) for mortality. Results: Our EWS algorithm scored patients’ daily and long-term risk of in-hospital mortality probability after admission and stratified them into distinct risk groups. In the prospective validation, our EWS prospectively attained a c-statistic of 0.884, where 99 encounters were captured in the highest risk group, 68.69% of which died during the episodes. Furthermore, our real-time EWS successfully forecasted the top 13.33% of the expired patients at least 40.8 hours before death. Important clinical utilization features, together with coded diagnoses, vital signs and laboratory test results were recognized as impactful predictors in the final EWS. Conclusions: In this study, we prospectively demonstrated our new EWS’s capability of real-time monitoring and alerting clinicians to patients at high risk of in-hospital death, providing opportunities for timely interventions. This real-time EWS is able to assist clinical decision making and enable more actionable and effective individualized care for better patients’ health outcomes in target medical facilities.

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    Date Submitted: Feb 11, 2019

    Open Peer Review Period: Feb 14, 2019 - Apr 11, 2019

    As Facebook continues to grow its number of active users, the potential to harness data generated by Facebook users also grows. Because the nature of many interactions on Facebook consists of creating...

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    Date Submitted: Feb 11, 2019

    Open Peer Review Period: Feb 14, 2019 - Apr 11, 2019

    Background: Ethnic minority populations exhibit disproportionately high rates and poor control of type 2 diabetes mellitus (T2DM). eHealth tools may be used to facilitate the cultural adaptation and t...

    Background: Ethnic minority populations exhibit disproportionately high rates and poor control of type 2 diabetes mellitus (T2DM). eHealth tools may be used to facilitate the cultural adaptation and tailoring of T2DM education and management. Objective: To develop an adaptable Interactive lifestyle Assessment, Counseling and Education (I-ACE) software to support dietitian-delivered lifestyle counselling among ethnic-minority patients with T2DM, and evaluate its effect of on diabetes-related dietary knowledge and management. Methods: The dietician-assisted I-ACE software was developed in consultation with experienced clinical dieticians and an endocrinologist. It incorporated evidence-based dietary and physical activity recommendations and educational materials. The features and behavioral change techniques included: quantitative lifestyle (dietary intake and physical activity) assessment and simulation, individually-tailored education and recommendations, motivational interviewing, the Pareto approach to achieving maximum impact with minimum change, goal-setting, and tracking progress. For the unblinded pilot trial, 50 overweight/obese Arab adults (aged 40-62 y) with poorly-controlled T2DM were recruited from local primary care clinics and randomly assigned to receive 4 in-person, dietician-delivered counseling sessions over the course of 6 months either: 1) using the I-ACE tool (experimental arm), or 2) using standard lifestyle advice (SLA) methods (comparison arm). All outcome assessments were face-to-face. Diabetes-related dietary knowledge (primary outcome) was measured at baseline, 3, 6, and 12 months. Lifestyle behaviors, anthropometric parameters and HbA1c were measured before, during and after the intervention. Multiple linear regression and repeated-measures linear mixed models were used to compare change in study outcomes, and explore time trends in between- and within-group change. Results: Twenty-five participants were enrolled in each arm, of whom 24 and 21 completed the final assessment of the primary outcome in the I-ACE and SLA arms, respectively. DM-related lifestyle knowledge increased more rapidly in the I-ACE than the SLA arm (P for study arm*time interaction=.023). Added sugar intake was lower in the I-ACE than SLA arm at 12 months (mean±SE difference: -1.9±0.9% of total energy; P=.050). Within the I-ACE arm, the mean±SE differences in added sugar and dietary fiber intakes from baseline to 12 months were -2.6±1.0% of total energy (P=.025) and 2.7±0.0 g/1000 kcal (P=.003), respectively. The odds of engaging in any leisure physical activity at 12 months in the I-ACE vs SLA arms tended to be higher, but did not reach statistical significance (OR: 2.8; 95% CI: 0.7-11.6; P=.157). Both arms exhibited significant reductions in HbA1c (P for change over time<.001). Conclusions: Use of the culturally-adapted I-ACE software in a 6-month, 4-session lifestyle counselling intervention improved the efficiency of lifestyle education, as compared to SLA, among low-SES, ethnic minority patients with T2DM. This pilot trial provides justification for conducting a large-scale trial to evaluate its effectiveness and applicability in routine clinical care among ethnically diverse populations. Clinical Trial: ClinicalTrials.gov NCT01858506

  • Feasibility, acceptability, and effectiveness of a web-based therapeutic intervention for post-traumatic stress disorder among American Indian/Alaska Native adults in primary care

    Date Submitted: Feb 11, 2019

    Open Peer Review Period: Feb 14, 2019 - Apr 11, 2019

    Background: Posttraumatic stress disorder (PTSD) is a major public health concern among American Indian and Alaska Native (AI/AN) populations. Primary care clinics are often the first point of contact...

    Background: Posttraumatic stress disorder (PTSD) is a major public health concern among American Indian and Alaska Native (AI/AN) populations. Primary care clinics are often the first point of contact for AI/AN people seeking health care and are feasible locations for trauma-focused interventions. Objective: Web-based therapeutic interventions (WBTI) for PTSD have the potential to reduce PTSD symptoms in AI/AN primary care patients by offering culturally tailored psychoeducation and symptom self-management tools. In this study, we investigate the feasibility and acceptability in two AI/AN serving primary care sites and effectiveness of a WBTI on trauma symptom changes in a 12-week period. Methods: A community-based participatory research process was used to refine the WBTI adaptations and content, and conduct a feasibility test of the resultant 16-guide intervention “Health is Our Tradition: Balance and Harmony after Trauma” within two AI/AN healthcare settings. AI/AN people ages 18 years and older who were not in crisis and scored positive on the primary care PTSD instrument completed baseline measures and were trained on WBTI usage. Participants were provided weekly tip via text message during the 12-week intervention. Content was devised to reinforce website use, complement website content, and remind participants about follow-up visits. At each visit, participants completed follow-up versions of all baseline measures (except demographics) including the PC-PTSD and a satisfaction/acceptability questionnaire. Electronic health records were collected for the periods extending to 12 months prior to study enrollment and 3 months following study enrollment. Changes in perceptions of acceptability/feasibility between the 6-week and 12-week follow-up were examined with paired t-tests. Analysts explored changes in symptomatology over the 12-week intervention with one-way ANOVAs for repeated measures or repeated measures logistic regression tests. To examine the effect of the intervention on service utilization, analysts compared clinic visit frequency from the health record data in the 12 months before the intervention (divided by 4 for comparison) and the 3 months after it with paired t-tests. The Wilcoxon Signed Rank Test for non-parametric data was used to test significance for non-normally distributed data. Results: In a sample of N=24, the WBTI was well received with no difference in use, engagement, satisfaction or technical skills needed for use by age or gender. Website usage decreased significantly over the course of the 12-week intervention period yet participants reported significant reductions in PTSD, depression, and physical symptoms related to PTSD, and problematic alcohol use over the same period of WBTI usage. Conclusions: The website shows promise for integration into primary care and behavioral health settings to augment and improve access to treatment of the health consequences of trauma exposure among adult AI/AN primary care patients.

  • Linking Phenotypes and Genotypes with Matrix Factorizations

    Date Submitted: Feb 10, 2019

    Open Peer Review Period: Feb 13, 2019 - Apr 10, 2019

    Background: Background: Phenotype is defined as the composite of an organism’s observable characteristics or traits, such as human’s eye colors, behaviors and disease symptoms. Genotype is the gen...

    Background: Background: Phenotype is defined as the composite of an organism’s observable characteristics or traits, such as human’s eye colors, behaviors and disease symptoms. Genotype is the genetic makeup of a cell, an organism, or an individual usually with reference to a specific characteristic under consideration. Thus phenotype can be regarded as the macroscopic description of an organism while genotype is its microscopic expression. Objective: Objective: Identification of phenotype-genotype associations is the primary step explaining the pathogenesis of human complex diseases. It is also of key importance for the development of Genomic medicine, sometimes also known as personalized medicine, which is a way to customize medical care to an individual body’s unique genetic makeup. Methods: Methods: In this paper, we propose a unified computational framework, called PheGe , to bridge phenotypes and genotypes. PheGe utilizes phenotype similarity network, genotype similarity network and known phenotype-genotype associations to explore the potential associations among other unlinked phenotypes and genotypes. Results: Results: As by-products, PheGe can also discover the phenotype and genotype groups, such that the phenotypes or genotypes within the same group are highly correlated with each other. We also validate the effectiveness of PheGe on a real-world data set, where we discover some interesting phenotype-genotype associations and phenotype/genotype groups. Conclusions: Conclusions: Our method can reveal potential phenotype clusters and genotype clusters and their unknown associations through a variety of phenotype similarities, genotype similarities, as well as known phenotype-genotype associations.

  • Leaning Forward and Self-Disclosure by a Healthcare Robot Increase User Attention and Engagement: An Experiment

    Date Submitted: Feb 9, 2019

    Open Peer Review Period: Feb 12, 2019 - Apr 9, 2019

    Background: For robots to be effectively employed in health applications, they need to display appropriate social behaviours. A fundamental requirement in all social interactions is the ability to eng...

    Background: For robots to be effectively employed in health applications, they need to display appropriate social behaviours. A fundamental requirement in all social interactions is the ability to engage, maintain and demonstrate attention. Attentional behaviours include leaning forward, self-disclosure, and changes in voice pitch. Objective: The aim of this research was to examine the effect of robot attentional behaviours on user perceptions and behaviours in a simulated healthcare interaction. Methods: A between-subjects experimental design was employed in a laboratory setting. Participants were randomised to one of four experimental conditions before engaging in a scripted interaction with a ‘medical receptionist’ robot. Experimental conditions included a self-disclosure condition, voice pitch change condition, forward lean condition, as well as a neutral condition. Participants completed post-interaction measures relating to engagement, perceived robot attention, and perceived robot empathy. Interactions were video recorded and coded for participant attentional behaviours. Results: 181 participants were recruited from the University. Participants who interacted with the robot in the forward lean and self-disclosure conditions found the robot to be significantly more stimulating that those who interacted with the robot in the voice pitch or the neutral conditions (p = .03). Participants in the forward lean, self-disclosure, and neutral conditions found the robot to be significantly more interesting compared to those in the voice pitch condition (p = .002). Participants in the forward lean and self-disclosure conditions spent significantly more time looking at the robot than participants in the neutral condition (p = <.001). Significantly more participants in the self-disclosure condition laughed during the interaction (p = .01), and significantly more participants in the forward-lean condition leant towards the robot during the interaction (p = <.001). Conclusions: The use of self-disclosure and forward lean by a healthcare robot can increase human engagement and attentional behaviours. Voice pitch changes did not increase attention or engagement. The small effects with regards to participant perceptions are potentially due to limitations in self-report measures, or a lack of comparison for most participants who had never interacted with a robot before. Further research could explore the use of self-disclosure and forward lean using a within-subjects design, and in real healthcare settings.

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