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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:

  • Telehealth offers options for support for families living with dementia. Source: Oregon Health & Science University/Fritz Liedtke; Copyright: The Authors; URL: https://www.jmir.org/2019/5/e13599; License: Licensed by JMIR.

    Using Technology to Facilitate Fidelity Assessments: The Tele-STAR Caregiver Intervention

    Abstract:

    Background: Families living with Alzheimer disease and related dementias have more access to support thanks to the development of effective telehealth-based programs. However, as technological science grows, so does the risk that these technology-based interventions will diverge from foundational protocols, diluting their efficacy. Strategies that ensure programs are delivered as intended, with fidelity to guiding protocols, are needed across the intervention spectrum—from development to wide-scale implementation. Few papers address fidelity in their technology-based work. Here, we present our translated telehealth intervention, Tele-STAR, with our fidelity findings. Objective: This study aimed to assess the preliminary efficacy of Tele-STAR on reducing family caregiver burden and depression. Across the implementation phases, we assessed the fidelity of a caregiver education intervention, STAR-C, as it was translated into a telehealth option (Tele-STAR). Methods: A total of 13 family caregivers consented to participate in an 8-week, videoconference-based intervention (Tele-STAR). Tele-STAR efficacy in reducing the affective burden of caregiving was assessed using pre- and postintervention paired t tests. Content experts assessed program fidelity by reviewing and rating Tele-STAR materials for adherence to the original STAR-C protocol. These experts assessed treatment fidelity by viewing videos of the intervention and rating adherence on a checklist. Results: Tele-STAR reduced caregiver burden and retained good program and treatment fidelity to STAR-C. Conclusions: We found Tele-STAR reduced caregiver burden and had good fidelity to the original protocol. Assessing fidelity is a complex process that requires incorporation of these procedures early in the research process. The technology used in this study facilitated the accrual of informative data about the fidelity of our translated intervention, Tele-STAR.

  • The Pubmender system. Source: The Authors / Placeit; Copyright: The Authors; URL: https://www.jmir.org/2019/5/e12957; License: Creative Commons Attribution (CC-BY).

    The Deep Learning–Based Recommender System “Pubmender” for Choosing a Biomedical Publication Venue: Development and Validation Study

    Abstract:

    Background: It is of great importance for researchers to publish research results in high-quality journals. However, it is often challenging to choose the most suitable publication venue, given the exponential growth of journals and conferences. Although recommender systems have achieved success in promoting movies, music, and products, very few studies have explored recommendation of publication venues, especially for biomedical research. No recommender system exists that can specifically recommend journals in PubMed, the largest collection of biomedical literature. Objective: We aimed to propose a publication recommender system, named Pubmender, to suggest suitable PubMed journals based on a paper’s abstract. Methods: In Pubmender, pretrained word2vec was first used to construct the start-up feature space. Subsequently, a deep convolutional neural network was constructed to achieve a high-level representation of abstracts, and a fully connected softmax model was adopted to recommend the best journals. Results: We collected 880,165 papers from 1130 journals in PubMed Central and extracted abstracts from these papers as an empirical dataset. We compared different recommendation models such as Cavnar-Trenkle on the Microsoft Academic Search (MAS) engine, a collaborative filtering–based recommender system for the digital library of the Association for Computing Machinery (ACM) and CiteSeer. We found the accuracy of our system for the top 10 recommendations to be 87.0%, 22.9%, and 196.0% higher than that of MAS, ACM, and CiteSeer, respectively. In addition, we compared our system with Journal Finder and Journal Suggester, which are tools of Elsevier and Springer, respectively, that help authors find suitable journals in their series. The results revealed that the accuracy of our system was 329% higher than that of Journal Finder and 406% higher than that of Journal Suggester for the top 10 recommendations. Our web service is freely available at https://www.keaml.cn:8081/. Conclusions: Our deep learning–based recommender system can suggest an appropriate journal list to help biomedical scientists and clinicians choose suitable venues for their papers.

  • Source: The Authors / Placeit; Copyright: JMIR Publications; URL: http://www.jmir.org/2019/5/e12793/; License: Creative Commons Attribution (CC-BY).

    Recruiting to a Randomized Controlled Trial of a Web-Based Program for People With Type 2 Diabetes and Depression: Lessons Learned at the Intersection of...

    Abstract:

    Background: E-mental health (eMH) interventions are now widely available and they have the potential to revolutionize the way that health care is delivered. As most health care is currently delivered by primary care, there is enormous potential for eMH interventions to support, or in some cases substitute, services currently delivered face to face in the community setting. However, randomized trials of eMH interventions have tended to recruit participants using online recruitment methods. Consequently, it is difficult to know whether participants who are recruited online differ from those who attend primary care. Objective: This paper aimed to document the experience of recruiting to an eMH trial through primary care and compare the characteristics of participants recruited through this and other recruitment methods. Methods: Recruitment to the SpringboarD randomized controlled trial was initially focused on general practices in 2 states of Australia. Over 15 months, we employed a comprehensive approach to engaging practice staff and supporting them to recruit patients, including face-to-face site visits, regular contact via telephone and trial newsletters, and development of a Web-based patient registration portal. Nevertheless, it became apparent that these efforts would not yield the required sample size, and we therefore supplemented recruitment through national online advertising and promoted the study through existing networks. Baseline characteristics of participants recruited to the trial through general practice, online, or other sources were compared using the analysis of variance and chi square tests. Results: Between November 2015 and October 2017, 780 people enrolled in SpringboarD, of whom 740 provided information on the recruitment source. Of these, only 24 were recruited through general practice, whereas 520 were recruited online and 196 through existing networks. Key barriers to general practice recruitment included perceived mismatch between trial design and diabetes population, prioritization of acute health issues, and disruptions posed by events at the practice and community level. Participants recruited through the 3 different approaches differed in age, gender, employment status, depressive symptoms, and diabetes distress, with online participants being distinguished from those recruited through general practice or other sources. However, most differences reached only a small effect size and are unlikely to be of clinical importance. Conclusions: Time, labor, and cost-intensive efforts did not translate into successful recruitment through general practice in this instance, with barriers identified at several different levels. Online recruitment yielded more participants, who were broadly similar to those recruited via general practice.

  • Source: Freepik; Copyright: senivpetro; URL: https://www.freepik.com/free-photo/arabian-woman-hijab-inside-cafe-working-laptop_4410718.htm; License: Licensed by JMIR.

    Assessing Electronic Health Literacy in the State of Kuwait: Survey of Internet Users From an Arab State

    Abstract:

    Background: The internet and social media have become an important source for health information. In 2017, the State of Kuwait ranked first in mobile subscription penetration in the Arab world; nearly 90% of its population uses the internet. Electronic health (eHealth) literacy is important in populations that have easy and affordable access to internet resources to more effectively manage health conditions as well as improve general population health. Objective: The aim of this study was to assess eHealth literacy levels across internet users in Kuwait and identify demographic characteristics that influence eHealth literacy. Furthermore, the study aimed to identify the reasons and type of information that people seek online. Finally, this study examined the utilization of various social media channels for accessing online health information. The social media platforms considered were as follows: WhatsApp, Twitter, Instagram, YouTube, Facebook, and Snapchat. Methods: A cross-sectional anonymous Web-based survey was used to collect data about eHealth literacy and related information. The eHealth literacy scale (eHEALS), originally developed by Norman and Skinner, is measured using 8 Likert-type scales. A linear regression model estimates the effect of demographic variables such as age, gender, and education on eHealth literacy while controlling for participants’ perceived usefulness and importance of the internet. Participants were also surveyed about their frequency in using social media platforms for seeking health information. Results: Kuwait’s composite eHEALS, based on a sample of 386 participants, was 28.63, which is very similar to eHEALS observed among adult populations in other developed countries. Females in Kuwait demonstrated a higher average eHEALS compared with males. Among the social media platforms, the survey results indicated that YouTube is the most frequently used to seek health information, with Facebook being the least frequently used. Conclusions: Internet users in Kuwait appear confident in their ability to search for health-related information online compared with other populations, as indicated by aggregate eHEALS scores. Considering this finding, government and health care organizations should shift more efforts from traditional media toward online health information, focusing on the social media outlets that people in Kuwait find more useful for seeking health information.

  • Source: Image created by the Authors; Copyright: The Authors; URL: http://www.jmir.org/2019/5/e13868; License: Creative Commons Attribution (CC-BY).

    Digital Education of Health Professionals on the Management of Domestic Violence: Systematic Review and Meta-Analysis by the Digital Health Education...

    Abstract:

    Background: The World Health Organization states that 35% of women experience domestic violence at least once during their lifetimes. However, approximately 80% of health professionals have never received any training on management of this major public health concern. Objective: The objective of this study was to evaluate the effectiveness of health professions digital education on domestic violence compared to that of traditional ways or no intervention. Methods: Seven electronic databases were searched for randomized controlled trials from January 1990 to August 2017. The Cochrane Handbook guideline was followed, and studies reporting the use of digital education interventions to educate health professionals on domestic violence management were included. Results: Six studies with 631 participants met our inclusion criteria. Meta-analysis of 5 studies showed that as compared to control conditions, digital education may improve knowledge (510 participants and 5 studies; standardized mean difference [SMD] 0.67, 95% CI 0.38-0.95; I2=59%; low certainty evidence), attitudes (339 participants and 3 studies; SMD 0.67, 95% CI 0.25-1.09; I2=68%; low certainty evidence), and self-efficacy (174 participants and 3 studies; SMD 0.47, 95% CI 0.16-0.77; I2=0%; moderate certainty evidence). Conclusions: Evidence of the effectiveness of digital education on health professionals’ understanding of domestic violence is promising. However, the certainty of the evidence is predominantly low and merits further research. Given the opportunity of scaled transformative digital education, both further research and implementation within an evaluative context should be prioritized.

  • Source: Freepik; Copyright: Freepik; URL: https://www.freepik.com/free-photo/elderly-businessman-working-while-having-coffee_4203751.htm; License: Licensed by JMIR.

    A Novel Instrument for Measuring Older People’s Attitudes Toward Technology (TechPH): Development and Validation

    Abstract:

    Background: The use of health technology by older people is coming increasingly in focus with the demographic changes. Health information technology is generally perceived as an important factor in enabling increased quality of life and reducing the cost of care for this group. Age-appropriate design and facilitation of technology adoption are important to ensure functionality and removal of various barriers to usage. Development of assessment tools and instruments for evaluating older persons’ technology adoption and usage as well as measuring the effects of the interventions are of high priority. Both usability and acceptance of a specific technology or service are important factors in evaluating the impact of a health information technology intervention. Psychometric measures are seldom included in evaluations of health technology. However, basic attitudes and sentiments toward technology (eg, technophilia) could be argued to influence both the level of satisfaction with the technology itself as well as the perception of the health intervention outcome. Objective: The purpose of this study is to develop a reduced and refined instrument for measuring older people's attitudes and enthusiasm for technology based on relevant existing instruments for measuring technophilia. A requirement of the new instrument is that it should be short and simple to make it usable for evaluation of health technology for older people. Methods: Initial items for the TechPH questionnaire were drawn from a content analysis of relevant existing technophilia measure instruments. An exploratory factor analysis was conducted in a random selection of persons aged 65 years or older (N=374) on eight initial items. The scale was reduced to six items, and the internal consistency and reliability of the scale were examined. Further validation was made by a confirmatory factor analysis (CFA). Results: The exploratory factor analysis resulted in two factors. These factors were analyzed and labeled techEnthusiasm and techAnxiety. They demonstrated relatively good internal consistency (Cronbach alpha=.72 and .68, respectively). The factors were confirmed in the CFA and showed good model fit (χ28=21.2, χ2/df=2.65, comparative fit index=0.97, adjusted goodness-of-fit index=0.95, root mean square error of approximation=0.067, standardized root mean square residual=0.036). Conclusions: The construed TechPH score showed expected relations to external real-world criteria, and the two factors showed interesting internal relations. Different technophilia personality traits distinguish clusters with different behaviors of adaptation as well as usage of new technology. Whether there is an independent association with the TechPH score against outcomes in health technology projects needs to be shown in further studies. The instrument must also be validated in different contexts, such as other countries.

  • Source: The Authors / Placeit; Copyright: JMIR Publications; URL: http://www.jmir.org/2019/5/e13668/; License: Creative Commons Attribution (CC-BY).

    Validity of Online Screening for Autism: Crowdsourcing Study Comparing Paid and Unpaid Diagnostic Tasks

    Abstract:

    Background: Obtaining a diagnosis of neuropsychiatric disorders such as autism requires long waiting times that can exceed a year and can be prohibitively expensive. Crowdsourcing approaches may provide a scalable alternative that can accelerate general access to care and permit underserved populations to obtain an accurate diagnosis. Objective: We aimed to perform a series of studies to explore whether paid crowd workers on Amazon Mechanical Turk (AMT) and citizen crowd workers on a public website shared on social media can provide accurate online detection of autism, conducted via crowdsourced ratings of short home video clips. Methods: Three online studies were performed: (1) a paid crowdsourcing task on AMT (N=54) where crowd workers were asked to classify 10 short video clips of children as “Autism” or “Not autism,” (2) a more complex paid crowdsourcing task (N=27) with only those raters who correctly rated ≥8 of the 10 videos during the first study, and (3) a public unpaid study (N=115) identical to the first study. Results: For Study 1, the mean score of the participants who completed all questions was 7.50/10 (SD 1.46). When only analyzing the workers who scored ≥8/10 (n=27/54), there was a weak negative correlation between the time spent rating the videos and the sensitivity (ρ=–0.44, P=.02). For Study 2, the mean score of the participants rating new videos was 6.76/10 (SD 0.59). The average deviation between the crowdsourced answers and gold standard ratings provided by two expert clinical research coordinators was 0.56, with an SD of 0.51 (maximum possible SD is 3). All paid crowd workers who scored 8/10 in Study 1 either expressed enjoyment in performing the task in Study 2 or provided no negative comments. For Study 3, the mean score of the participants who completed all questions was 6.67/10 (SD 1.61). There were weak correlations between age and score (r=0.22, P=.014), age and sensitivity (r=–0.19, P=.04), number of family members with autism and sensitivity (r=–0.195, P=.04), and number of family members with autism and precision (r=–0.203, P=.03). A two-tailed t test between the scores of the paid workers in Study 1 and the unpaid workers in Study 3 showed a significant difference (P<.001). Conclusions: Many paid crowd workers on AMT enjoyed answering screening questions from videos, suggesting higher intrinsic motivation to make quality assessments. Paid crowdsourcing provides promising screening assessments of pediatric autism with an average deviation <20% from professional gold standard raters, which is potentially a clinically informative estimate for parents. Parents of children with autism likely overfit their intuition to their own affected child. This work provides preliminary demographic data on raters who may have higher ability to recognize and measure features of autism across its wide range of phenotypic manifestations.

  • Ethics of participatory disease surveillance. Source: Image created by the Authors; Copyright: The Authors; URL: https://www.jmir.org/2019/5/e12273/; License: Creative Commons Attribution (CC-BY).

    Participatory Disease Surveillance Systems: Ethical Framework

    Abstract:

    Advances in information technology are changing public health at an unprecedented rate. Participatory surveillance systems are contributing to public health by actively engaging digital (eg, Web-based) communities of volunteer citizens to report symptoms and other pertinent information on public health threats and also by empowering individuals to promptly respond to them. However, this digital model raises ethical issues on top of those inherent in traditional forms of public health surveillance. Research ethics are undergoing significant changes in the digital era where not only participants’ physical and psychological well-being but also the protection of their sensitive data have to be considered. In this paper, the digital platform of Influenzanet is used as a case study to illustrate those ethical challenges posed to participatory surveillance systems using digital platforms and mobile apps. These ethical challenges include the implementation of electronic consent, the protection of participants’ privacy, the promotion of justice, and the need for interdisciplinary capacity building of research ethics committees. On the basis of our analysis, we propose a framework to regulate and strengthen ethical approaches in the field of digital public health surveillance.

  • Source: Freepik; Copyright: jcomp; URL: https://www.freepik.com/free-photo/doctor-is-working-with-tablet-white-background_3763235.htm#index=44; License: Licensed by JMIR.

    Data Challenges With Real-Time Safety Event Detection And Clinical Decision Support

    Abstract:

    Background: The continued digitization and maturation of health care information technology has made access to real-time data easier and feasible for more health care organizations. With this increased availability, the promise of using data to algorithmically detect health care–related events in real-time has become more of a reality. However, as more researchers and clinicians utilize real-time data delivery capabilities, it has become apparent that simply gaining access to the data is not a panacea, and some unique data challenges have emerged to the forefront in the process. Objective: The aim of this viewpoint was to highlight some of the challenges that are germane to real-time processing of health care system–generated data and the accurate interpretation of the results. Methods: Distinct challenges related to the use and processing of real-time data for safety event detection were compiled and reported by several informatics and clinical experts at a quaternary pediatric academic institution. The challenges were collated from the experiences of the researchers implementing real-time event detection on more than half a dozen distinct projects. The challenges have been presented in a challenge category-specific challenge-example format. Results: In total, 8 major types of challenge categories were reported, with 13 specific challenges and 9 specific examples detailed to provide a context for the challenges. The examples reported are anchored to a specific project using medication order, medication administration record, and smart infusion pump data to detect discrepancies and errors between the 3 datasets. Conclusions: The use of real-time data to drive safety event detection and clinical decision support is extremely powerful, but it presents its own set of challenges that include data quality and technical complexity. These challenges must be recognized and accommodated for if the full promise of accurate, real-time safety event clinical decision support is to be realized.

  • Source: Image created by the Authors; Copyright: The Authors; URL: https://www.jmir.org/2019/5/e13131; License: Creative Commons Attribution (CC-BY).

    Perspectives of English, Chinese, and Spanish-Speaking Safety-Net Patients on Clinician Computer Use: Qualitative Analysis

    Abstract:

    Background: Safety-net systems serve patients with limited health literacy and limited English proficiency (LEP) who face communication barriers. However, little is known about how diverse safety-net patients feel about increasing clinician electronic health record (EHR) use. Objective: The aim of this study was to better understand how safety-net patients, including those with LEP, view clinician EHR use. Methods: We conducted focus groups in English, Spanish, and Cantonese (N=37) to elicit patient perspectives on how clinicians use EHRs during clinic visits. Using a grounded theory approach, we coded transcripts to identify key themes. Results: Across multiple language groups, participants accepted multitasking and silent clinician EHR use if focused on their care. However, participants desired more screen share and eye contact, especially when demonstrating physical concerns. All participants, including LEP participants, wanted clinicians to include them in EHR use. Conclusions: Linguistically diverse patients accept the value of EHR use during outpatient visits but desire more eye contact, verbal warnings before EHR use, and screen-sharing. Safety-net health systems should support clinicians in completing EHR-related tasks during the visit using patient-centered strategies for all patients.

  • Source: Flickr; Copyright: EdTech Stanford University School of Medicine; URL: https://www.flickr.com/photos/stanfordedtech/8034110512/in/album-72157631646125281/; License: Creative Commons Attribution + Noncommercial + NoDerivatives (CC-BY-NC-ND).

    Theories Predicting End-User Acceptance of Telemedicine Use: Systematic Review

    Abstract:

    Background: Only a few telemedicine applications have made their way into regular care. One reason is the lack of acceptance of telemedicine by potential end users. Objective: The aim of this systematic review was to identify theoretical predictors that influence the acceptance of telemedicine. Methods: An electronic search was conducted in PubMed and PsycINFO in June 2018 and supplemented by a hand search. Articles were identified using predefined inclusion and exclusion criteria. In total, two reviewers independently assessed the title, abstract, and full-text screening and then individually performed a quality assessment of all included studies. Results: Out of 5917 potentially relevant titles (duplicates excluded), 24 studies were included. The Axis Tool for quality assessment of cross-sectional studies revealed a high risk of bias for all studies except for one study. The most commonly used models were the Technology Acceptance Model (n=11) and the Unified Theory of Acceptance and Use of Technology (n=9). The main significant predictors of acceptance were perceived usefulness (n=11), social influences (n=6), and attitude (n=6). The results show a superiority of technology acceptance versus original behavioral models. Conclusions: The main finding of this review is the applicability of technology acceptance models and theories on telemedicine adoption. Characteristics of the technology, such as its usefulness, as well as attributes of the individual, such as his or her need for social support, inform end-user acceptance. Therefore, in the future, requirements of the target group and the group’s social environment should already be taken into account when planning telemedicine applications. The results support the importance of theory-guided user-centered design approaches to telemedicine development.

  • Source: Flickr; Copyright: Army Medicine; URL: https://www.flickr.com/photos/armymedicine/8643039512; License: Creative Commons Attribution (CC-BY).

    Health Data Processes: A Framework for Analyzing and Discussing Efficient Use and Reuse of Health Data With a Focus on Patient-Reported Outcome Measures

    Abstract:

    The collection and use of patient health data are central to any kind of activity in the health care system. These data may be produced during routine clinical processes or obtained directly from the patient using patient-reported outcome (PRO) measures. Although efficiency and other reasons justify data availability for a range of potentially relevant uses, these data are nearly always collected for a single specific purpose. The health care literature reflects this narrow scope, and there is limited literature on the joint use of health data for daily clinical use, clinical research, surveillance, and administrative purposes. The aim of this paper is to provide a framework for discussing the efficient use of health data with a specific focus on the role of PRO measures. PRO data may be used at an individual patient level to inform patient care or shared decision making and to tailor care to individual needs or group-level needs as a complement to health record data, such as that on mortality and readmission, in order to inform service delivery and measure the real-world effectiveness of treatment. PRO measures may be used either for their own sake, to provide valuable information from the patient perspective, or as a proxy for clinical data that would otherwise not be feasible to collect. We introduce a framework to analyze any health care activity that involves health data. The framework consists of four data processes (patient identification, data collection, data aggregation and data use), further structured into two dichotomous dimensions in each data process (level: group vs patient; timeframe: ad hoc vs systematic). This framework is used to analyze various health activities with respect to joint use of data, considering the technical, legal, organizational, and logistical challenges that characterize each data process. Finally, we propose a model for joint use of health data with data collected during follow-up as a base. Demands for health data will continue to increase, which will further add to the need for the concerted use and reuse of PRO data for parallel purposes. Repeated and uncoordinated PRO data collection for the same patient for different purposes results in misuse of resources for the patient and the health care system as well as reduced response rates owing to questionnaire fatigue. PRO data can be routinely collected both at the hospital (from inpatients as well as outpatients) and outside of hospital settings; in primary or social care settings; or in the patient’s home, provided the health informatics infrastructure is in place. In the future, clinical settings are likely to be a prominent source of PRO data; however, we are also likely to see increased remote collection of PRO data by patients in their own home (telePRO). Data collection for research and quality surveillance will have to adapt to this circumstance and adopt complementary data capture methods that take advantage of the utility of PRO data collected during daily clinical practice. The European Union’s regulation with respect to the protection of personal data—General Data Protection Regulation—imposes severe restrictions on the use of health data for parallel purposes, and steps should be taken to alleviate the consequences while still protecting personal data against misuse.

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  • The development of a drug classification system for antihypertensive medications utilizing electronic health record based data: A methodology comparison

    Date Submitted: May 21, 2019

    Open Peer Review Period: May 24, 2019 - Jul 19, 2019

    Background: Computable phenotypes have the ability to utilize data within the electronic health record (EHR) in order to identify patients with certain characteristics. Many computable phenotypes rel...

    Background: Computable phenotypes have the ability to utilize data within the electronic health record (EHR) in order to identify patients with certain characteristics. Many computable phenotypes rely on multiple types of data within the EHR including prescription drug information, which is the case for resistant hypertension (RHTN). RHTN is a phenotype that is dependent on the correct classification of antihypertensive prescription drug information, as well as corresponding diagnoses and blood pressure information. Objective: We sought to create an antihypertensive drug classification system to be utilized with EHR based data as part of our RHTN computable phenotype. Methods: We compared four different antihypertensive drug classification systems based off of four different methodologies and terminologies, including three RxNorm Concept Unique Identifier (RxCUI) based classifications, and one medication name based classification. The RxCUI based classifications utilized data from 1) the Drug Ontology (DrOn), 2) the new Medication Reference Terminology (MED-RT), and 3) the Anatomical Therapeutic Chemical (ATC) Classification System and DrugBank, while the medication name classification relied on antihypertensive drug names. Each classification system was applied to EHR based prescription drug data from hypertensive patients in the OneFlorida Data Trust. Results: We observed broad overlap between the four methods, with 84-95% of terms overlapping pairwise between the different classification methods. Key differences arose from drug products with multiple dosage forms (e.g. oral and ophthalmic, oral and topical), drug products with an indication of benign prostatic hyperplasia, drug products that contain more than one ingredient (combination products), and terms within the classification systems corresponding to retired or obsolete RxCUIs. Conclusions: We have constructed two antihypertensive drug classifications, one based on RxCUIs, and one based on medication name that can be used in future computable phenotypes that require anti-hypertensive drug classifications.

  • Using Technology to Communicate Prenatal Health Messages to Pregnant Women: A Mixed Methods analysis of the Knowledge, Attitudes, and Perceptions of Text4Baby in Urban Immigrant Pregnant Women

    Date Submitted: May 17, 2019

    Open Peer Review Period: May 23, 2019 - Jul 18, 2019

    Background: The Text4baby™ mobile health (mhealth) program has received national attention and is acclaimed to provide pregnant women with greater access to prenatal healthcare, resources, and infor...

    Background: The Text4baby™ mobile health (mhealth) program has received national attention and is acclaimed to provide pregnant women with greater access to prenatal healthcare, resources, and information. However, without sufficient piloting, little is known whether urban and Afro-Caribbean immigrant pregnant women are open and receptive to such innovative health communication methods, or of the cultural and systematic barriers that inhibit their behavioral intent to use Text4baby. Objective: We aimed to understand the lived experiences of urban and immigrant pregnant women with accessing prenatal health care and information in Brooklyn New York; and to utilize behavioral and technology assimilation theoretical constructs to measure their knowledge, perceptions and behavioral intent to use the Text4baby program. Methods: This exploratory mixed methods study first used a phenomenological approach to explore and describe the lived experiences of pregnant women while receiving prenatal care at University Hospital of Brooklyn at Downstate prenatal health clinic. Data from the qualitative arm led to the development of a theoretically inspired survey instrument that was then used in a repeated measures pre-post test design to evaluate changes in participants’ knowledge, attitudes, beliefs and perceptions of Text4baby after a minimum of four weeks exposure to the program’s messages. Results: Three themes emerged during the focus groups and interviews and were major factors affecting participant experiences: (1) patient-provider engagement, (2) social support, and (3) acculturation. With time as a barrier to quality care; inadequate patient provider engagement often left many participants with feelings of indifference regarding the prenatal care and information they received in the clinical setting. However, pregnant women reported strongly positive attitudes towards the use of mobile health and Text4baby with 63% of survey respondents reporting strong agreement with Text4baby providing them extra support during their pregnancy. Overall, on a scale of 1 -5, participant perception of the usefulness of Text4baby ranked at 4.26, and their perception of the compatibility and relative advantage of using Text4baby ranked 4.41 and 4.15 respectively. There was a 14% increase in participants reporting their intent to use Text4baby; and a 28% increase from pre and post-test in pregnant women strongly agreeing to speak more with their doctor about the information learned through Text4baby. Conclusions: Urban and immigrant pregnant women in Brooklyn endure a number of social determinants of health that create barriers when accessing quality prenatal health care and information which negatively impacts prenatal outcomes. Low-income expecting mothers often lack access to vital information about pregnancy, preparation for birth, and best practices when caring for their newborn. Our study indicates a number of systematic, political, and other macrosystem-level factors that perpetuate health disparities in our study population. In addition, traditional, cultural, and environmental patterns also perpetuate suboptimal prenatal behaviors and practices that influence access to quality care and prenatal outcomes.

  • Serious gaming during multidisciplinary rehabilitation for patients with chronic pain or fatigue symptoms: realistic process evaluation

    Date Submitted: May 20, 2019

    Open Peer Review Period: May 23, 2019 - Jul 18, 2019

    Background: Serious gaming could support patients in learning to cope with chronic pain or functional somatic syndromes and reduce symptom burdens. Objective: To realize this potential, insight is nee...

    Background: Serious gaming could support patients in learning to cope with chronic pain or functional somatic syndromes and reduce symptom burdens. Objective: To realize this potential, insight is needed into how this could work, why, for whom, and in what actual treatment circumstances. Methods: Inspired by a realist approach, process evaluation methods were embedded before, during, and after a two-armed natural quasi-experiment (n=275). Changes in health outcome over time were compared between two groups of patients with interfering chronic pain or fatigue symptoms: 1) those who received a short additional blended mindfulness-based serious gaming intervention during a regular multidisciplinary rehabilitation program and 2) those who did not. Prior to inspecting outcome data, stakeholder focus group and patient semi-structured interview data were coded for configurations of intervention characteristics (I) in context (C) that activate mechanisms (M) for producing outcomes (O). Subsequently, hypotheses were formulated that could be tested on quantitative data using multiple regression and (moderated) mediation models. Results: Qualitative methods showed that self-discrepancies perceived by patients during serious gaming were a necessary trigger (M) for learning results with respect to self-awareness in moments of daily social interaction (O). Characteristics of serious gaming intervention (I) in context (C) that recipients considered important for gaming acceptance or learning results included design qualities, the relative advantage of an experiential learning opportunity, compatibility within rehabilitation treatment with a consistent approach and distinctive modality, (limited) flexibility to adjust to the personal preferences and contexts of the users, patients’ age and styles of managing stress or pain, provider role perceptions, and local intervention planning and facilitating processes. Quantitative methods showed that very small study group differences in self-reported depression, pain and fatigue changes (-.07<β<-.17, all 95% confidence interval upper bounds <0) were mediated by group differences in mindfulness (β=.26, 95% CI=.02-.51). Mindfulness changes were also positively associated with patient involvement in serious gaming (n=114, β=.36, P=001). Acceptance of serious gaming was lower in older patients. Outcome changes were stronger in patients that reported lower active coping with stress and pain coping before serious gaming. Finally, learning results and acceptance varied by indicators of local planning and facilitation of serious gaming. Conclusions: This study developed a tentative program theory about how and why serious gaming can additionally support learning about coping for reducing burdens of chronic pain or fatigue symptoms in certain patients and actual intervention delivery conditions. Future research can elucidate which findings are fallible, extendable, and transferable across future serious gaming contexts. This supports decisions for designing, allocating and tailoring serious gaming for optimal patient chances of important health benefit. Clinical Trial: This study was registered in the Netherlands Trial Registry NTR6020 on June 10th, 2016.

  • Analyzing the Performance of a Preeclampsia Prediction Software by Customization of Maternal Ethnicity: Cross Sectional Study

    Date Submitted: May 20, 2019

    Open Peer Review Period: May 23, 2019 - Jul 18, 2019

    Background: There is a pioneer algorithm developed by Fetal Medicine Foundation (FMF) to predict preeclampsia based on maternal characteristics combined with biophysical and/or biochemical markers. T...

    Background: There is a pioneer algorithm developed by Fetal Medicine Foundation (FMF) to predict preeclampsia based on maternal characteristics combined with biophysical and/or biochemical markers. The Afro-Caribbean ethnicity is the second risk factor, in magnitude, found in populations tested by FMF, which was not confirmed in a Brazilian scenario. Objective: This study aimed to analyze the performance of preeclampsia (PE) prediction software by customization of maternal ethnicity. Methods: It was an observational, cross-sectional study, with secondary evaluation of data from FMF first trimester screening tests of singleton pregnancies. Risk scores were calculated from maternal characteristics and biophysical markers and were presented as the risk for PE development before 34 and 37 weeks. The following steps were followed: (1)identification of women characterized as black ethnicity; (2)calculation of early and preterm PE risk, reclassifying them as white, which generated a new score; (3)comparison of the proportions of women categorized as high risk between the original and new scores; (4)construction of the receiver operator characteristic(ROC)curve; (5)calculation of the area under the curve(AUC), sensitivity and false positive rate(FPR); (6)comparison of the AUC, sensitivity and FPR of the original with the “new”risk by chi-square test. Results: A total of 1531 cases composed the final sample, with 14.3%(95%CI: 12.54-16.06) and 11.88%(95% CI: 10.26 - 13.51) were respectively classified as high risk for PE development, originally and after recalculating the new risk. The comparison of FMF2012 predictive model performance between the originally estimated risks and the estimated “new risks”, showed that the difference was not significant for sensitivity and AUC, but it was significant for FPR. Conclusions: We concluded that black ethnicity classification of Brazilian pregnant women by FMF2012 algorithm increases the false positive rate. Suppressing ethnicity effect didn’t improve the test sensitivity. By modifying demographic characteristics it is possible to improve some performance aspects of clinical prediction tests. Clinical Trial: No trial registration

  • Comparative Effectiveness of a Web-Based Patient Decision Aid for Therapeutic Options for Sickle Cell Disease

    Date Submitted: May 22, 2019

    Open Peer Review Period: May 23, 2019 - May 29, 2019

    Background: Hydroxyurea, chronic blood transfusion, and bone marrow transplantation are efficacious disease-modifying therapies for sickle cell disease (SCD) but are associated with a significant deci...

    Background: Hydroxyurea, chronic blood transfusion, and bone marrow transplantation are efficacious disease-modifying therapies for sickle cell disease (SCD) but are associated with a significant decisional dilemma because of the inherent risk-benefit tradeoffs and the lack of comparative studies. A web-based patient decision aid (PtDA) has the potential to provide patients with high-quality information about their treatment options and associated risks and benefits, help them clarify their values, and allow them to share in the process of informed medical decision making. Objective: The objective of this study was to develop a literacy-sensitive, web-based, PtDA using the conceptual framework of the Ottawa decision support framework, and to estimate in a randomized clinical trial the effectiveness of the PtDA in improving patient knowledge, and involvement in decision-making. Methods: We conducted population decisional needs assessment in a nationwide sample of patients, caregivers, stakeholders, and health care providers using qualitative interviews to identify decisional conflict (uncertainty); knowledge and expectations; values (what is important to patients); support and resources; decision types, timing, stages, and learning; and personal clinical characteristics. Interview transcripts were coded using QSR NVivo 10. Prototype PtDA underwent Alpha testing to establish usability and the accuracy of the information that it conveyed. Stakeholders participated in iterative cycles of beta testing. We conducted a randomized clinical trial of adults and of caregivers of pediatric patients to evaluate the efficacy of the PtDA. Results: A total of 223 stakeholders participated in decisional needs assessment and provided their preferences which guided the development of the PtDA (www.sickleoptions.org) which was then refined and finalized with alpha testing by 30 patients and 38 healthcare providers, and iterative cycles of beta testing by 87 stakeholders. To evaluate the efficacy of the PtDA, we enrolled 120 participants (60 in the decision aid and 60 in the usual care arm) in a randomized clinical trial. Qualitative interviews revealed high levels of usability, acceptability, and utility in education, values clarification, and preparation for decision making. The PtDA met most of the international patient decision aid collaboration standards for content, development process, and efficacy. While statistically significant improvement was observed in decisional self-efficacy and preparation for decision-making, as well reduction in overall decisional conflict score, informed sub-score and values clarification sub-score, the large amount of missing data in the completion of follow-up surveys limits the ability to draw conclusions about the effectiveness of the decision aid in improving patient knowledge and involvement in decision-making. Conclusions: We have developed a PtDA for SCD with extensive input from stakeholders. Qualitative data and surveys established the acceptability and utility of the PtDA in education and, decision making, but missing survey data limit conclusions about the effectiveness of the PtDA in Improving patient knowledge and involvement in decision making. Clinical Trial: NCT02326597

  • A cross-sectional study on quality of diabetes information identified from the Internet

    Date Submitted: May 19, 2019

    Open Peer Review Period: May 22, 2019 - Jul 17, 2019

    Background: Increasingly people seek health information from the Internet, in particular, health information on diseases that require intensive self-management, such as diabetes. However, the Internet...

    Background: Increasingly people seek health information from the Internet, in particular, health information on diseases that require intensive self-management, such as diabetes. However, the Internet is largely unregulated and the quality of online health information may not be credible. Objective: To assess the quality of online information on diabetes identified from the Internet. Methods: We used the single term “diabetes” or equivalent Chinese characters to search Google and Baidu respectively. The first 50 websites retrieved from each of the two search engines were screened for eligibility using pre-determined inclusion and exclusion criteria. Included websites were assessed on four domains: accessibility, content coverage, validity and readability. Results: We included 26 websites from Google search engine and 34 from Baidu search engine. There were significant differences in website provider (P<0.0001), but not in targeted population (P=0.832) and publication types (P=0.378), between the two search engines. The website accessibility was not statistically significantly different between the two search engines, although there were significant differences in items regarding website content coverage. There was no statistically significant difference in website validity between the Google and Baidu search engines (mean Discern score 3.3 vs 2.9, p=0.156). The results to appraise readability for English website showed that that Flesch Reading Ease scores ranged from 23.1 to 73.0 and the mean score of Flesch-Kincaid Grade Level ranged range from 5.7 to 19.6. Conclusions: The content coverage of the health information for patients with diabetes in English search engine tended to be more comprehensive than that from Chinese search engine. There was a lack of websites provided by health organisations in China. The quality of online health information for people with diabetes needs to be improved to bridge the knowledge gap between website service and public demand.

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