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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:

  • The online messaging portal of the Partner in Balance program (montage). Source: The Authors /; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    Effectiveness of a Blended Care Self-Management Program for Caregivers of People With Early-Stage Dementia (Partner in Balance): Randomized Controlled Trial


    Background: The benefits of electronic health support for dementia caregivers are increasingly recognized. Reaching caregivers of people with early-stage dementia could prevent high levels of burden and psychological problems in the later stages. Objective: The current study evaluates the effectiveness of the blended care self-management program, Partner in Balance, compared to a control group. Methods: A single-blind randomized controlled trial with 81 family caregivers of community-dwelling people with mild dementia was conducted. Participants were randomly assigned to either the 8-week, blended care self-management Partner in Balance program (N=41) or a waiting-list control group (N=40) receiving usual care (low-frequent counseling). The program combines face-to-face coaching with tailored Web-based modules. Data were collected at baseline and after 8 weeks in writing by an independent research assistant who was blinded to the treatment. The primary proximal outcome was self-efficacy (Caregiver Self-Efficacy Scale) and the primary distal outcome was symptoms of depression (Center for Epidemiological Studies Depression Scale). Secondary outcomes included mastery (Pearlin Mastery Scale), quality of life (Investigation Choice Experiments for the Preferences of Older People), and psychological complaints (Hospital Anxiety and Depression Scale-Anxiety and Perceived Stress Scale). Results: A significant increase in favor of the intervention group was demonstrated for self-efficacy (care management, P=.002; service use P=.001), mastery (P=.001), and quality of life (P=.032). Effect sizes were medium for quality of life (d=0.58) and high for self-efficacy care management and service use (d=0.85 and d=0.93, respectively) and mastery (d=0.94). No significant differences between the groups were found on depressive symptoms, anxiety, and perceived stress. Conclusions: This study evaluated the first blended-care intervention for caregivers of people with early-stage dementia and demonstrated a significant improvement in self-efficacy, mastery, and quality of life after receiving the Partner in Balance intervention, compared to a waiting-list control group receiving care as usual. Contrary to our expectations, the intervention did not decrease symptoms of depression, anxiety, or perceived stress. However, the levels of psychological complaints were relatively low in the study sample. Future studies including long-term follow up could clarify if an increase in self-efficacy results in a decrease or prevention of increased stress and depression. To conclude, the program can provide accessible preventative care to future generations of caregivers of people with early-stage dementia. Trial Registration: Netherlands Trial Register NTR4748; (Archived by WebCite at

  • A physician at the Nashik Kumbh Mela, a mass gathering in western India, uses a 3G enabled tablet computer, cloud computing, and remote analytics for real-time tracking of disease outbreaks. Thousands of healthcare providers in India are already using mobile devices for logging patient health information. Source: Image created by the authors; Copyright: The Authors; License: Licensed by JMIR.

    Reimagining Health Data Exchange: An Application Programming Interface–Enabled Roadmap for India


    In February 2018, the Government of India announced a massive public health insurance scheme extending coverage to 500 million citizens, in effect making it the world’s largest insurance program. To meet this target, the government will rely on technology to effectively scale services, monitor quality, and ensure accountability. While India has seen great strides in informational technology development and outsourcing, cellular phone penetration, cloud computing, and financial technology, the digital health ecosystem is in its nascent stages and has been waiting for a catalyst to seed the system. This National Health Protection Scheme is expected to provide just this impetus for widespread adoption. However, health data in India are mostly not digitized. In the few instances that they are, the data are not standardized, not interoperable, and not readily accessible to clinicians, researchers, or policymakers. While such barriers to easy health information exchange are hardly unique to India, the greenfield nature of India’s digital health infrastructure presents an excellent opportunity to avoid the pitfalls of complex, restrictive, digital health systems that have evolved elsewhere. We propose here a federated, patient-centric, application programming interface (API)–enabled health information ecosystem that leverages India’s near-universal mobile phone penetration, universal availability of unique ID systems, and evolving privacy and data protection laws. It builds on global best practices and promotes the adoption of human-centered design principles, data minimization, and open standard APIs. The recommendations are the result of 18 months of deliberations with multiple stakeholders in India and the United States, including from academia, industry, and government.

  • Source: Image created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Barriers to and Facilitators of Engagement With Remote Measurement Technology for Managing Health: Systematic Review and Content Analysis of Findings


    Background: Remote measurement technology refers to the use of mobile health technology to track and measure change in health status in real time as part of a person’s everyday life. With accurate measurement, remote measurement technology offers the opportunity to augment health care by providing personalized, precise, and preemptive interventions that support insight into patterns of health-related behavior and self-management. However, for successful implementation, users need to be engaged in its use. Objective: Our objective was to systematically review the literature to update and extend the understanding of the key barriers to and facilitators of engagement with and use of remote measurement technology, to guide the development of future remote measurement technology resources. Methods: We conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines involving original studies dating back to the last systematic review published in 2014. We included studies if they met the following entry criteria: population (people using remote measurement technology approaches to aid management of health), intervention (remote measurement technology system), comparison group (no comparison group specified), outcomes (qualitative or quantitative evaluation of the barriers to and facilitators of engagement with this system), and study design (randomized controlled trials, feasibility studies, and observational studies). We searched 5 databases (MEDLINE, IEEE Xplore, EMBASE, Web of Science, and the Cochrane Library) for articles published from January 2014 to May 2017. Articles were independently screened by 2 researchers. We extracted study characteristics and conducted a content analysis to define emerging themes to synthesize findings. Formal quality assessments were performed to address risk of bias. Results: A total of 33 studies met inclusion criteria, employing quantitative, qualitative, or mixed-methods designs. Studies were conducted in 10 countries, included male and female participants, with ages ranging from 8 to 95 years, and included both active and passive remote monitoring systems for a diverse range of physical and mental health conditions. However, they were relatively short and had small sample sizes, and reporting of usage statistics was inconsistent. Acceptability of remote measurement technology according to the average percentage of time used (64%-86.5%) and dropout rates (0%-44%) was variable. The barriers and facilitators from the content analysis related to health status, perceived utility and value, motivation, convenience and accessibility, and usability. Conclusions: The results of this review highlight gaps in the design of studies trialing remote measurement technology, including the use of quantitative assessment of usage and acceptability. Several processes that could facilitate engagement with this technology have been identified and may drive the development of more person-focused remote measurement technology. However, these factors need further testing through carefully designed experimental studies. Trial Registration: International Prospective Register of Systematic Reviews (PROSPERO) CRD42017060644; (Archived by WebCite at

  • Cloud computing in health care. Source: Flickr; Copyright: NEC Corporation of America; URL:; License: Creative Commons Attribution (CC-BY).

    Rethinking the Meaning of Cloud Computing for Health Care: A Taxonomic Perspective and Future Research Directions


    Background: Cloud computing is an innovative paradigm that provides users with on-demand access to a shared pool of configurable computing resources such as servers, storage, and applications. Researchers claim that information technology (IT) services delivered via the cloud computing paradigm (ie, cloud computing services) provide major benefits for health care. However, due to a mismatch between our conceptual understanding of cloud computing for health care and the actual phenomenon in practice, the meaningful use of it for the health care industry cannot always be ensured. Although some studies have tried to conceptualize cloud computing or interpret this phenomenon for health care settings, they have mainly relied on its interpretation in a common context or have been heavily based on a general understanding of traditional health IT artifacts, leading to an insufficient or unspecific conceptual understanding of cloud computing for health care. Objective: We aim to generate insights into the concept of cloud computing for health IT research. We propose a taxonomy that can serve as a fundamental mechanism for organizing knowledge about cloud computing services in health care organizations to gain a deepened, specific understanding of cloud computing in health care. With the taxonomy, we focus on conceptualizing the relevant properties of cloud computing for service delivery to health care organizations and highlighting their specific meanings for health care. Methods: We employed a 2-stage approach in developing a taxonomy of cloud computing services for health care organizations. We conducted a structured literature review and 24 semistructured expert interviews in stage 1, drawing on data from theory and practice. In stage 2, we applied a systematic approach and relied on data from stage 1 to develop and evaluate the taxonomy using 14 iterations. Results: Our taxonomy is composed of 8 dimensions and 28 characteristics that are relevant for cloud computing services in health care organizations. By applying the taxonomy to classify existing cloud computing services identified from the literature and expert interviews, which also serves as a part of the taxonomy, we identified 7 specificities of cloud computing in health care. These specificities challenge what we have learned about cloud computing in general contexts or in traditional health IT from the previous literature. The summarized specificities suggest research opportunities and exemplary research questions for future health IT research on cloud computing. Conclusions: By relying on perspectives from a taxonomy for cloud computing services for health care organizations, this study provides a solid conceptual cornerstone for cloud computing in health care. Moreover, the identified specificities of cloud computing and the related future research opportunities will serve as a valuable roadmap to facilitate more research into cloud computing in health care.

  • Source: Freepik; Copyright: boryanam; URL:; License: Licensed by JMIR.

    Examining the Complexity of Patient-Outpatient Care Team Secure Message Communication: Qualitative Analysis


    Background: The value of secure messaging in streamlining routine patient care activities is generally agreed upon. However, the differences in how patients use secure messaging, including for communicating both routine and nonroutine issues, and the implications of these differences in use are less well understood. Objective: The purpose of this study was to examine secure messaging use to extend current knowledge of how this tool is being used in outpatient care settings and generate new research questions to improve our understanding of the role of secure messaging in the patient-provider communication toolbox. Methods: We conducted an in-depth qualitative analysis of secure message threads in 12 US Department of Veterans Affairs outpatient clinics in south Texas. We analyzed 70 secure message threads with a total of 179 unique communications between patients and their outpatient teams for patterns in communication and secure message content. We used theories from information systems and complexity science in organizations to explain our observations. Results: Analysis identified content relating to 3 main themes: (1) information management, (2) uncertainty management, and (3) patient safety and engagement risks and opportunities. Within these themes, we identified 2 subcategories of information management (information exchange and problem solving), 2 subcategories of uncertainty management (relationship building and sensemaking), and 3 subcategories of patient safety and engagement risks and opportunities (unresolved issues, tone mismatch, and urgent medical issues). Secure messages were most often used to communicate routine issues (eg, information exchange and problem solving). However, the presence of subcategories pertaining to nonroutine issues (eg, relationship building, sensemaking, tone mismatch, urgent issues, and unresolved issues) requires attention, particularly for improving opportunities in outpatient care settings using secure messaging. Conclusions: Patients use secure messaging for both routine and nonroutine purposes. Our analysis sheds light on potentially new patient safety concerns, particularly when using secure messaging to address some of the more complex issues patients are communicating with providers. Secure messaging is an asynchronous communication information system operated by patients and providers who are often characterized as having significant differences in knowledge, experience and expectations. As such, justification for its use beyond routine purposes is limited—yet this occurs, presenting a multifaceted dilemma for health care organizations. Secure messaging use in outpatient care settings may be more nuanced, and thus more challenging to understand and manage than previously recognized. New information system designs that acknowledge the use of secure messaging for nonroutine and complex health topics are needed.

  • Researcher studying users' posting activity over the course of one week in the British Lung Foundation community and the effect of superusers (montage). Source: The Authors /; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    How Online Communities of People With Long-Term Conditions Function and Evolve: Network Analysis of the Structure and Dynamics of the Asthma UK and British...


    Background: Self-management support can improve health and reduce health care utilization by people with long-term conditions. Online communities for people with long-term conditions have the potential to influence health, usage of health care resources, and facilitate illness self-management. Only recently, however, has evidence been reported on how such communities function and evolve, and how they support self-management of long-term conditions in practice. Objective: The aim of this study is to gain a better understanding of the mechanisms underlying online self-management support systems by analyzing the structure and dynamics of the networks connecting users who write posts over time. Methods: We conducted a longitudinal network analysis of anonymized data from 2 patients’ online communities from the United Kingdom: the Asthma UK and the British Lung Foundation (BLF) communities in 2006-2016 and 2012-2016, respectively. Results: The number of users and activity grew steadily over time, reaching 3345 users and 32,780 posts in the Asthma UK community, and 19,837 users and 875,151 posts in the BLF community. People who wrote posts in the Asthma UK forum tended to write at an interval of 1-20 days and six months, while those in the BLF community wrote at an interval of two days. In both communities, most pairs of users could reach one another either directly or indirectly through other users. Those who wrote a disproportionally large number of posts (the superusers) represented 1% of the overall population of both Asthma UK and BLF communities and accounted for 32% and 49% of the posts, respectively. Sensitivity analysis showed that the removal of superusers would cause the communities to collapse. Thus, interactions were held together by very few superusers, who posted frequently and regularly, 65% of them at least every 1.7 days in the BLF community and 70% every 3.1 days in the Asthma UK community. Their posting activity indirectly facilitated tie formation between other users. Superusers were a constantly available resource, with a mean of 80 and 20 superusers active at any one time in the BLF and Asthma UK communities, respectively. Over time, the more active users became, the more likely they were to reply to other users’ posts rather than to write new ones, shifting from a help-seeking to a help-giving role. This might suggest that superusers were more likely to provide than to seek advice. Conclusions: In this study, we uncover key structural properties related to the way users interact and sustain online health communities. Superusers’ engagement plays a fundamental sustaining role and deserves research attention. Further studies are needed to explore network determinants of the effectiveness of online engagement concerning health-related outcomes. In resource-constrained health care systems, scaling up online communities may offer a potentially accessible, wide-reaching and cost-effective intervention facilitating greater levels of self-management.

  • Source: iStock by Getty Images; Copyright: luoman; URL:; License: Licensed by the authors.

    A Decade of Veteran Voices: Examining Patient Portal Enhancements Through the Lens of User-Centered Design


    Background: Health care systems have entered a new era focused on patient engagement. Patient portals linked to electronic health records are recognized as a promising multifaceted tool to help achieve patient engagement goals. Achieving significant growth in adoption and use requires agile evaluation methods to complement periodic formal research efforts. Objective: This paper describes one of the implementation strategies that the Department of Veterans Affairs (VA) has used to foster the adoption and sustained use of its patient portal, My HealtheVet, over the last decade: an ongoing focus on user-centered design (UCD). This strategy entails understanding the users and their tasks and goals and optimizing portal design and functionality accordingly. Using a case study approach, we present a comparison of early user demographics and preferences with more recent data and several examples to illustrate how a UCD can serve as an effective implementation strategy for a patient portal within a large integrated health care system. Methods: VA has employed a customer experience analytics (CXA) survey on its patient portal since 2007 to enable ongoing direct user feedback. In a continuous cycle, a random sample of site visitors is invited to participate in the Web-based survey. CXA model questions are used to track and trend satisfaction, while custom questions collect data about users’ characteristics, needs, and preferences. In this case study, we performed analyses of descriptive statistics comparing user characteristics and preferences from FY2008 (wherein “FY” means “fiscal year”) to FY2017 and user trends regarding satisfaction with and utilization of specific portal functions over the last decade, as well as qualitative content analysis of user’s open-ended survey comments. Results: User feedback has guided the development of enhancements to core components of the My HealtheVet portal including available features, content, interface design, prospective functional design, and related policies. Ten-year data regarding user characteristics and portal utilization demonstrate trends toward greater patient engagement and satisfaction. Administration of a continuous voluntary Web-based survey is an efficient and effective way to capture veterans’ voices about who they are, how they use the patient portal, needed system improvements, and desired additional services. Conclusions: Leveraging “voice-of-the-customer” techniques as part of patient portal implementation can ensure that such systems meet users’ needs in ways that are agile and most effective. Through this strategy, VA has fostered significant adoption and use of My HealtheVet to engage patients in managing their health.

  • Source: Flickr; Copyright: Gianluca Carnicella; URL:; License: Creative Commons Attribution + Noncommercial + NoDerivatives (CC-BY-NC-ND).

    Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and Validation Study


    Background: Dementia is increasing in prevalence worldwide, yet frequently remains undiagnosed, especially in low- and middle-income countries. Population-based surveys represent an underinvestigated source to identify individuals at risk of dementia. Objective: The aim is to identify participants with high likelihood of dementia in population-based surveys without the need of the clinical diagnosis of dementia in a subsample. Methods: Unsupervised machine learning classification (hierarchical clustering on principal components) was developed in the Health and Retirement Study (HRS; 2002-2003, N=18,165 individuals) and validated in the Survey of Health, Ageing and Retirement in Europe (SHARE; 2010-2012, N=58,202 individuals). Results: Unsupervised machine learning classification identified three clusters in HRS: cluster 1 (n=12,231) without any functional or motor limitations, cluster 2 (N=4841) with walking/climbing limitations, and cluster 3 (N=1093) with both functional and walking/climbing limitations. Comparison of cluster 3 with previously published predicted probabilities of dementia in HRS showed that it identified high likelihood of dementia (probability of dementia >0.95; area under the curve [AUC]=0.91). Removing either cognitive or both cognitive and behavioral measures did not impede accurate classification (AUC=0.91 and AUC=0.90, respectively). Three clusters with similar profiles were identified in SHARE (cluster 1: n=40,223; cluster 2: n=15,644; cluster 3: n=2335). Survival rate of participants from cluster 3 reached 39.2% (n=665 deceased) in HRS and 62.2% (n=811 deceased) in SHARE after a 3.9-year follow-up. Surviving participants from cluster 3 in both cohorts worsened their functional and mobility performance over the same period. Conclusions: Unsupervised machine learning identifies high likelihood of dementia in population-based surveys, even without cognitive and behavioral measures and without the need of clinical diagnosis of dementia in a subsample of the population. This method could be used to tackle the global challenge of dementia.

  • Source: Rawpixel; Copyright: Rawpixel; URL:; License: Public Domain (CC0).

    Electronic Health Literacy Across the Lifespan: Measurement Invariance Study


    Background: Electronic health (eHealth) information is ingrained in the healthcare experience to engage patients across the lifespan. Both eHealth accessibility and optimization are influenced by lifespan development, as older adults experience greater challenges accessing and using eHealth tools as compared to their younger counterparts. The eHealth Literacy Scale (eHEALS) is the most popular measure used to assess patient confidence locating, understanding, evaluating, and acting upon online health information. Currently, however, the factor structure of the eHEALS across discrete age groups is not well understood, which limits its usefulness as a measure of eHealth literacy across the lifespan. Objective: The purpose of this study was to examine the structure of eHEALS scores and the degree of measurement invariance among US adults representing the following generations: Millennials (18-35-year-olds), Generation X (36-51-year-olds), Baby Boomers (52-70-year-olds), and the Silent Generation (71-84-year-olds). Methods: Millennials (N=281, mean 26.64 years, SD 5.14), Generation X (N=164, mean 42.97 years, SD 5.01), and Baby Boomers/Silent Generation (N=384, mean 62.80 years, SD 6.66) members completed the eHEALS. The 3-factor (root mean square error of approximation, RMSEA=.06, comparative fit index, CFI=.99, Tucker-Lewis index, TLI=.98) and 4-factor (RMSEA=.06, CFI=.99, TLI=.98) models showed the best global fit, as compared to the 1- and 2-factor models. However, the 4-factor model did not have statistically significant factor loadings on the 4th factor, which led to the acceptance of the 3-factor eHEALS model. The 3-factor model included eHealth Information Awareness, Search, and Engagement. Pattern invariance for this 3-factor structure was supported with acceptable model fit (RMSEA=.07, Δχ2=P>.05, ΔCFI=0). Compared to Millennials and members of Generation X, those in the Baby Boomer and Silent Generations reported less confidence in their awareness of eHealth resources (P<.001), information seeking skills (P=.003), and ability to evaluate and act on health information found on the Internet (P<.001). Results: Young (18-48-year olds, N=411) and old (49-84-year olds, N=419) adults completed the survey. A 3-factor model had the best fit (RMSEA=.06, CFI=.99, TLI=.98), as compared to the 1-factor, 2-factor, and 4-factor models. These 3-factors included eHealth Information Awareness (2 items), Information Seeking (2 items), and Information and Evaluation (4 items). Pattern invariance was supported with the acceptable model fit (RMSEA=.06, Δχ2=P>.05, ΔCFI=0). Compared with younger adults, older adults had less confidence in eHealth resource awareness (P<.001), information seeking skills (P<.01), and ability to evaluate and act upon online health information (P<.001). Conclusions: The eHEALS can be used to assess, monitor uniquely, and evaluate Internet users’ awareness of eHealth resources, information seeking skills, and engagement abilities. Configural and pattern invariance was observed across all generation groups in the 3-factor eHEALS model. To meet gold the standards for factor interpretation (ie, 3 items or indicators per factor), future research is needed to create and assess additional eHEALS items. Future research is also necessary to identify and test items for a fourth factor, one that captures the social nature of eHealth.

  • Tweets about measles (montage). Source: Twitter /; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    Public Perception Analysis of Tweets During the 2015 Measles Outbreak: Comparative Study Using Convolutional Neural Network Models


    Background: Timely understanding of public perceptions allows public health agencies to provide up-to-date responses to health crises such as infectious diseases outbreaks. Social media such as Twitter provide an unprecedented way for the prompt assessment of the large-scale public response. Objective: The aims of this study were to develop a scheme for a comprehensive public perception analysis of a measles outbreak based on Twitter data and demonstrate the superiority of the convolutional neural network (CNN) models (compared with conventional machine learning methods) on measles outbreak-related tweets classification tasks with a relatively small and highly unbalanced gold standard training set. Methods: We first designed a comprehensive scheme for the analysis of public perception of measles based on tweets, including 3 dimensions: discussion themes, emotions expressed, and attitude toward vaccination. All 1,154,156 tweets containing the word “measles” posted between December 1, 2014, and April 30, 2015, were purchased and downloaded from Two expert annotators curated a gold standard of 1151 tweets (approximately 0.1% of all tweets) based on the 3-dimensional scheme. Next, a tweet classification system based on the CNN framework was developed. We compared the performance of the CNN models to those of 4 conventional machine learning models and another neural network model. We also compared the impact of different word embeddings configurations for the CNN models: (1) Stanford GloVe embedding trained on billions of tweets in the general domain, (2) measles-specific embedding trained on our 1 million measles related tweets, and (3) a combination of the 2 embeddings. Results: Cohen kappa intercoder reliability values for the annotation were: 0.78, 0.72, and 0.80 on the 3 dimensions, respectively. Class distributions within the gold standard were highly unbalanced for all dimensions. The CNN models performed better on all classification tasks than k-nearest neighbors, naïve Bayes, support vector machines, or random forest. Detailed comparison between support vector machines and the CNN models showed that the major contributor to the overall superiority of the CNN models is the improvement on recall, especially for classes with low occurrence. The CNN model with the 2 embedding combination led to better performance on discussion themes and emotions expressed (microaveraging F1 scores of 0.7811 and 0.8592, respectively), while the CNN model with Stanford embedding achieved best performance on attitude toward vaccination (microaveraging F1 score of 0.8642). Conclusions: The proposed scheme can successfully classify the public’s opinions and emotions in multiple dimensions, which would facilitate the timely understanding of public perceptions during the outbreak of an infectious disease. Compared with conventional machine learning methods, our CNN models showed superiority on measles-related tweet classification tasks with a relatively small and highly unbalanced gold standard. With the success of these tasks, our proposed scheme and CNN-based tweets classification system is expected to be useful for the analysis of tweets about other infectious diseases such as influenza and Ebola.

  • Patient accessing their medical record online. Source: Pexels; Copyright: Bruce Mars; URL:; License: Licensed by JMIR.

    The Impact of Patient Online Access to Computerized Medical Records and Services on Type 2 Diabetes: Systematic Review


    Background: Online access to computerized medical records has the potential to improve convenience, satisfaction, and care for patients, and to facilitate more efficient organization and delivery of care. Objective: The objective of this review is to explore the use and impact of having online access to computerized medical records and services for patients with type 2 diabetes mellitus in primary care. Methods: Multiple international databases including Medline, Embase, CINAHL, PsycINFO and the Cochrane Library were searched between 2004 and 2016. No limitations were placed on study design, though we applied detailed inclusion and exclusion criteria to each study. Thematic analysis was used to synthesize the evidence. The Mixed Methods Appraisal Toolkit was used to appraise study quality. Results: A search identified 917 studies, of which 28 were included. Five themes were identified: (1) disparities in uptake by age, gender, ethnicity, educational attainment, and number of comorbidities, with young men in full-time employment using these services most; (2) improved health outcomes: glycemic control was improved, but blood pressure results were mixed; (3) self-management support from improved self-care and shared management occurred especially soon after diagnosis and when complications emerged. There was a generally positive effect on physician-patient relationships; (4) accessibility: patients valued more convenient access when online access to computerized medical records and services work; and (5) technical challenges, barriers to use, and system features that impacted patient and physician use. The Mixed Methods Appraisal Toolkit rated 3 studies as 100%, 19 studies as 75%, 4 studies as 50%, and 1 study scored only 25%. Conclusions: Patients valued online access to computerized medical records and services, although in its current state of development it may increase disparities. Online access to computerized medical records appears to be safe and is associated with improved glycemic control, but there was a lack of rigorous evidence in terms of positive health outcomes for other complications, such as blood pressure. Patients remain concerned about how these systems work, the rules, and timeliness of using these systems.

  • A nurse giving telemedicine advice. Source: iStock by Getty Images; Copyright: Steve Debenport; URL:; License: Licensed by the authors.

    Determinants of Successful eHealth Coaching for Consumer Lifestyle Changes: Qualitative Interview Study Among Health Care Professionals


    Background: Success with lifestyle change, such as weight loss, tobacco cessation, and increased activity level, using electronic health (eHealth) has been demonstrated in numerous studies short term. However, evidence on how to maintain the effect long-term has not been fully explored, even though there is a pressing need for long-term solutions. Recent studies indicate that weight loss can be achieved and maintained over 12 and 20 months in a primary care setting using a collaborative eHealth tool. The effect of collaborative eHealth in promoting lifestyle changes depends on competent and skilled dieticians, nurses, physiotherapists, and occupational therapists acting as eHealth coaches. How such health care professionals perceive delivering asynchronous eHealth coaching and which determinants they find to be essential to achieving successful long-term lifestyle coaching have only been briefly explored and deserve further exploration. Objective: The aim of this study is to analyze how health care professionals perceive eHealth coaching and to explore what influences successful long-term lifestyle change for patients undergoing hybrid eHealth coaching using a collaborative eHealth tool. Methods: A total of 10 health care professionals were recruited by purposive sampling. They were all women aged 36 to 65 years of age with a mean age of 48 years of age. A total of 8/10 (80%) had more than 15 years of experience in their field, and all had more than six months of experience providing eHealth lifestyle coaching using a combination of face-to-face meetings and asynchronous eHealth coaching. They worked in 5 municipalities in the Region of Southern Denmark. We performed individual, qualitative, semistructured, in-depth interviews in their workplace about their experiences with health coaching about lifestyle change, both for their patients and for themselves, and mainly how they perceived using a collaborative eHealth solution as a part of their work. Results: The health care professionals all found establishing and maintaining an empathic relationship essential and that asynchronous eHealth lifestyle coaching challenged this compared to face-to-face coaching. The primary reason was that unlike typical in-person encounters in health care, they did not receive immediate feedback from the patients. We identified four central themes relevant to the health care professionals in their asynchronous eHealth coaching: (1) establishing an empathic relationship, (2) reflection in asynchronous eHealth coaching, (3) identifying realistic goals based on personal barriers, and (4) staying connected in asynchronous coaching. Conclusions: Establishing and maintaining an empathic relationship is probably the most crucial factor for successful subsequent eHealth coaching. It was of paramount importance to get to know the patient first, and the asynchronous interaction aspect presented challenges because of the delay in response times (both ways). It also presented opportunities for reflection before answering. The health care professionals found they had to provide both relational communication and goal-oriented coaching when using eHealth solutions. Going forward, the quality of the health care professional–patient interaction will need attention if patients are to benefit from collaborative eHealth coaching fully.

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    Open Peer Review Period: Jul 13, 2018 - Jul 21, 2018

    Background: Technology companies with large data sets have the potential to fuel discovery in the health sciences and gain valuable insights for their own businesses by sharing their data with academi...

    Background: Technology companies with large data sets have the potential to fuel discovery in the health sciences and gain valuable insights for their own businesses by sharing their data with academic researchers. However, many companies are wary about such sharing due to concerns about ethics, data security, logistics, and public relations. To date, neither the academic literature nor the technology community has provided guidance for companies considering academic data sharing. Without such guidance, few companies are willing to take on the potential work and risks involved in non-commercial data sharing, and the scientific and societal potential for their data consequently goes unrealized. Objective: In this paper, we describe the 18-month long pilot of a data sharing program led by Crisis Text Line (CTL), a not-for-profit technology company that provides a free, 24/7 text-line for people in crisis. The primary goal of the pilot was to design and develop a rigorous framework of principles for the safe and ethical sharing of user data. By implementing and studying a pilot program based on these principles, CTL aimed to gain experience that would help them optimize their own future data sharing programs and to make public the results of the study in order to inform similar decisions made by others. Methods: CTL used a stakeholder-based policy process to develop a feasible and ethical data sharing program. The process comprised: forming a data ethics committee; identifying policy challenges and solutions; announcing the program and generating interest; and revising the policy and launching the program. Once the pilot was complete, CTL examined how well the program ran and compared it with other potential program models. Results: The policy development process yielded key principles and protocols for addressing core ethical challenges and these were then used to govern a pilot research program which assessed over 100 applications, of which 20 were accepted. An internal evaluation determined that the pilot was successful in the goals of sharing scientific data and protecting client confidentiality. Financial and human capital requirements for maintaining this particular form of open data program were challenging. However, alternative models that could enact the same principles were identified. Conclusions: The set of principles derived from this pilot provided a secure foundation on which CTL was able to further iterate in the development of new data sharing programs. The principles offer a framework that other organizations can use to create safe and ethical programs of their own. Open data programs are important and feasible to establish, but they require significant long-term commitments to support the responsible sharing of data. Organizations considering developing open data programs should evaluate the value and sensitivity of the data as well as available resources for program start-up and maintenance when selecting their path forward.

  • CLIPS: An interactive retrieval system for clinical trial studies with context-dependent protocol elements

    Date Submitted: Jul 9, 2018

    Open Peer Review Period: Jul 12, 2018 - Sep 6, 2018

    Background: Clinical trial protocol includes all the steps that should be conducted in clinical trial and all clinical trial start with development of the protocol. And the protocol is kinds of proced...

    Background: Clinical trial protocol includes all the steps that should be conducted in clinical trial and all clinical trial start with development of the protocol. And the protocol is kinds of procedure manual that consists of appropriate combination of related elements, so one element selection generally affects the next element selection. To development the protocol, researchers refer a published guides or electronic protocol templates. The detail elements of the protocol are designed based on the previous data which is retrieved separately in public database of clinical trial. Specifically, researchers retrieve for preliminary information using text based user input keywords, then they extract the elements. However, the current retrieve approaches do not provide a method to interactively select for a combination of the elements. Objective: The purpose of our study is to provide context-dependent protocol element selection system for the development of objective and successful clinical trial protocols. In detail, we construct a database that can retrieve protocols by combined analysis of elements. Moreover, we develop a web-based interactive protocol element wise selection application using constructed database. Methods: We have constructed a key-value type database for searching element combinations. To build the database, we have defined structure of protocol within five factors; design, subject, variables, statistical issue and descriptions. In this study, five factors are defined as a subset of protocol elements and we have manually classified them from collected information in public database. Then, we have developed a web application to implement interactive method for retrieving selected combination of protocol element. The application in the form of a connected tree provides options to select the next element according to the decision of previous element. Therefore, a researcher can retrieve the structure of the combined protocol. Also, the application supports a function for retrieve various selected protocol structure at the same time. Results: We have developed a database and search application for protocol structure retrieval. The database is built on individual protocol information extracted from previous 184,634 clinical trials and provides 13,210 integrated structural information. Furthermore, the database contains various semantic information of the protocols to filter protocols in search application. We did technical validation for evaluating of the database with ‘Cancer and Other Neoplasms’ category of By comparing with the our method has better performance in predicting phenotypic features. Our F1 score was 0.515, while F1 score of was 0.377. Finally, we developed web application, CLinical trial protocol database System(CLIPS), which provides users with search our database interactively based on protocol elements. CLIPS is available at Conclusions: We have developed database and application to interactively search clinical trial protocols efficiently. We believe that our system is helpful for conducting a new clinical trial. Furthermore, we expect that the database would be utilized as meta-analysis in clinical trials for various purposes. Clinical Trial: None

  • Effectiveness of a behavior change technique-based smartphone game to improve intrinsic motivation and physical activity adherence in type 2 diabetes patients – A randomized controlled trial.

    Date Submitted: Jul 10, 2018

    Open Peer Review Period: Jul 12, 2018 - Sep 6, 2018

    Background: Regular physical activity (PA) is an essential component of a successful type 2 diabetes treatment. However, despite the manifest evidence for the numerous health benefits of regular PA, m...

    Background: Regular physical activity (PA) is an essential component of a successful type 2 diabetes treatment. However, despite the manifest evidence for the numerous health benefits of regular PA, most patients with type 2 diabetes remain inactive, often due to low motivation and lack of PA enjoyment. A recent and promising approach to help overcome these PA barriers and motivate inactive individuals to change their PA behavior is PA-promoting smartphone games. While short-term results of these games are encouraging, the long-term success in effectively changing PA behavior has to date not been confirmed. It is possible that an insufficient incorporation of motivational elements or flaws in gameplay and storyline in these games affect the long-term motivation to play and thereby prevent sustained changes in PA behavior. We aimed to address these design challenges by developing a PA-promoting smartphone game that incorporates established behavior change techniques and specifically targets inactive type 2 diabetes patients. Objective: To investigate if a self-developed, behavior change technique-based smartphone game designed by an interdisciplinary team is able to motivate inactive individuals with type 2 diabetes for regular use and thereby increase their intrinsic PA motivation. Methods: Thirty-six inactive, overweight type 2 diabetes patients (45-70 years of age) were randomly assigned to either the intervention group or the control group (one-time lifestyle counseling). Participants were instructed to play the smartphone game or to implement the recommendations from the lifestyle counseling autonomously during the 24-week intervention period. Intrinsic PA motivation was assessed with an abridged 12-item version of the Intrinsic Motivation Inventory (IMI) before and after the intervention. In addition, adherence to the game-proposed PA recommendations during the intervention period was assessed in the intervention group via the phone-recorded game usage data. Results: Intrinsic PA motivation (IMI total score) increased significantly in the intervention group (+6.4 (SD 4.2; P < .001) points) while it decreased by 1.9 (SD 16.5; P = .623) points in the control group. The adjusted difference between both groups was 8.1 (95% CI 0.9, 15.4; P = .029) points. The subscales ‘interest/enjoyment’ (+2.0 (SD 1.9) points, P < .001) and ‘perceived competence’ (+2.4 (SD 2.4) points, P < .001) likewise increased significantly in the intervention group while they did not change significantly in the control group. The usage data revealed that participants in the intervention group used the game for an average of 131.1 (SD 48.7) minutes of in-game walking and for an average of 15.3 (SD 24.6) minutes of strength training per week. Conclusions: In inactive individuals with type 2 diabetes, a novel smartphone game incorporating established motivational elements and personalized PA recommendations elicits significant increases in intrinsic PA motivation that are accompanied by de-facto improvements in PA adherence over 24 weeks. Clinical Trial: (NCT02657018), registered on January 15th, 2016, last status update on April 27th, 2018.

  • Service Use History of Individuals Enrolling in an Online Suicidal Ideation Treatment Trial

    Date Submitted: Jul 9, 2018

    Open Peer Review Period: Jul 11, 2018 - Sep 5, 2018

    Background: A significant recent innovation is the development of internet-based psychological treatments for suicidal thinking. However, we know very little about individuals experiencing suicidal id...

    Background: A significant recent innovation is the development of internet-based psychological treatments for suicidal thinking. However, we know very little about individuals experiencing suicidal ideation who seek help through online services, and in particular, their previous health service usage patterns. Objective: The current study aimed to examine service use history and its correlates amongst adults experiencing suicidal ideation who enrolled in an online suicidal ideation treatment trial. Methods: Participants (N = 418) at pre-intervention reported demographic information, clinical characteristics, and health service use over the previous six-months. Results: Participants had a high rate of service use in the six months before enrolling in the treatment trial. The two most common contact points were General Practitioners (GPs) and mental health professionals. Notably, those with a previous single suicide attempt had lower odds of contact with any service than those with no attempt. Furthermore, those living in rural or remote areas had lower odds of contacting GPs or mental health professionals than those living in metropolitan areas. Conclusions: Our study shows that individuals enrolling in an e-health intervention trial have often received treatment from GPs or mental health professionals. These services can therefore play an important role in preventing the escalation of suicidal thinking. Enrolment in our online treatment trial suggests though that face-to-face health services may not be enough. Finally, our study highlights the need to improve the provision of coordinated and assertive care after a suicide attempt, as well as health service availability and utilisation for those living in rural and remote areas.

  • Review of Use of Digital Health in Melanoma Post-Treatment Care for Rural and Remote Communities

    Date Submitted: Jul 11, 2018

    Open Peer Review Period: Jul 11, 2018 - Jul 19, 2018

    Background: The melanoma incidence and mortality rates in rural and remote communities are exponentially higher than in urban areas. Digital health could be used to close the urban/rural gap for melan...

    Background: The melanoma incidence and mortality rates in rural and remote communities are exponentially higher than in urban areas. Digital health could be used to close the urban/rural gap for melanoma and improve access to post-treatment and support care services. Objective: To understand how digital health is currently used for melanoma post-treatment care and determine its benefits for Australian rural and remote areas. Methods: A systematic search of PubMed, Medline, Google Scholar, Scopus was conducted in March 2018. Findings were clustered per type of intervention and related-direct outcomes. Results: Five studies met the inclusion criteria, but none of them investigated the benefits of digital health for melanoma post-treatment care in rural and remote areas of Australia. A number of empirical studies demonstrated consumers’ acceptance toward digital intervention for post-treatment care. Findings did not take into consideration individual, psychological and socioeconomic factors, even though studies show their significant impacts on melanoma quality of aftercare. Conclusions: Digital interventions may to be used as an adjunct service by clinicians during melanoma post-treatment care, especially in regions that are lower-resourced by practitioners and health infrastructure, such as rural and remote Australia. Technology could be used to reduce the disparity in melanoma incidence, mortality rates and accessibility to post-treatment care management between urban and rural/remote populations.