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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 / Placeit.net; Copyright: JMIR Publications; URL: http://www.jmir.org/2018/7/e10017/; 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

    Abstract:

    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; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4748 (Archived by WebCite at http://www.webcitation.org/6vSb2t9Mg)

  • 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

    Abstract:

    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: http://www.jmir.org/2018/7/e10480/; 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

    Abstract:

    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; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=60644 (Archived by WebCite at http://www.webcitation.org/70K4mThTr)

  • Cloud computing in health care. Source: Flickr; Copyright: NEC Corporation of America; URL: https://www.flickr.com/photos/neccorp/16252514017/in/photolist-q6Pd1v-qL2kzb-nJ7Qx8-bnv3WG-qLbobV-o3oKiV-o3nusp-o3oKdz-doue9D-nYyThN-r3BdKn-r3wtjj-doune1-dounNJ-rpn5Yx-qL9xrK-o1BpjK-pVz5fX-25jDQyh-Bg7VkE-pVSWX5-Bg7Up1-BE7ATg-r3wkk3-ejraxX-dounGJ-nYxAvy-bV; License: Creative Commons Attribution (CC-BY).

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

    Abstract:

    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: https://www.freepik.com/free-photo/unhappy-woman-looking-at-her-smartphone_952362.htm; License: Licensed by JMIR.

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

    Abstract:

    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 / Placeit.net; Copyright: JMIR Publications; URL: http://www.jmir.org/2018/7/e238/; 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...

    Abstract:

    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: https://www.istockphoto.com/photo/brazilian-man-at-home-gm920698852-252961094; License: Licensed by the authors.

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

    Abstract:

    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: http://www.flickr.com/photos/22993384@N00/226893991; 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

    Abstract:

    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: https://www.rawpixel.com/image/413164/business-meeting-cafe; License: Public Domain (CC0).

    Electronic Health Literacy Across the Lifespan: Measurement Invariance Study

    Abstract:

    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 / Smartmockups.com; Copyright: JMIR Publications; URL: http://www.jmir.org/2018/7/e236/; License: Creative Commons Attribution (CC-BY).

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

    Abstract:

    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 DiscoverText.com. 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: https://www.pexels.com/photo/woman-wearing-tank-top-sitting-by-the-window-920381/; License: Licensed by JMIR.

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

    Abstract:

    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: https://www.istockphoto.com/gb/photo/senior-nurse-waves-while-participating-in-video-conference-gm696021828-128838715?clarity=false; License: Licensed by the authors.

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

    Abstract:

    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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    Date Submitted: Jul 10, 2018

    Open Peer Review Period: Jul 15, 2018 - Sep 9, 2018

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    Date Submitted: Jul 13, 2018

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

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

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

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

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

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