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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:

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

    Web-Based Intervention Using Behavioral Activation and Physical Activity for Adults With Depression (The eMotion Study): Pilot Randomized Controlled Trial


    Background: Physical activity is a potentially effective treatment for depression and depressive relapse. However, promoting physical activity in people with depression is challenging. Interventions informed by theory and evidence are therefore needed to support people with depression to become more physically active. eMotion is a Web-based intervention combining behavioral activation and physical activity promotion for people in the community with symptoms of depression. Objective: The objectives were to assess the feasibility and acceptability of delivering eMotion to people in the community with symptoms of depression and to explore outcomes. Methods: Participants with elevated depressive symptoms were recruited from the community through various methods (eg, social media) and randomized to eMotion or a waiting list control group for 8 weeks. eMotion is an administratively supported weekly modular program that helps people use key behavior change techniques (eg, graded tasks, action planning, and self-monitoring) to re-engage in routine, pleasurable, and necessary activities, with a focus on physical activities. Feasibility data were collected that included the following: recruitment and trial retention rates; fidelity of intervention delivery, receipt, and enactment; and acceptability of the intervention and data collection procedures. Data were collected for the primary (depression) and secondary outcomes (eg, anxiety, physical activity, fidelity, and client satisfaction) at baseline and 2 months postrandomization using self-reported Web-based questionnaires and accelerometers. Delivery fidelity (logins, modules accessed, time spent) was tracked using Web usage statistics. Exploratory analyses were conducted on the primary and secondary outcomes. Results: Of the 183 people who contacted the research team, 62 were recruited and randomized. The mean baseline score was 14.6 (SD 3.2) on the 8-item Patient Health Questionnaire depression scale (PHQ-8). Of those randomized, 52 participants provided accelerometer-recorded physical activity data at baseline that showed a median of 35.8 (interquartile range [IQR] 0.0-98.6) minutes of moderate-to-vigorous physical activity (MVPA) recorded in at least 10-minute bouts per week, with only 13% (7/52) people achieving guideline levels (150 minutes of MVPA per week). In total, 81% (50/62) of participants provided follow-up data for the primary outcome (PHQ-8), but only 39% (24/62) provided follow-up accelerometer data. Within the intervention group, the median number of logins, modules accessed, and total minutes spent on eMotion was 3 (IQR 2.0-8.0), 3 (IQR 2.0-5.0), and 41.3 (IQR 18.9-90.4), respectively. Acceptability was mixed. Exploratory data analysis showed that PHQ-8 levels were lower for the intervention group than for the control group at 2 months postrandomization (adjusted mean difference −3.6, 95% CI −6.1 to −1.1). Conclusions: It was feasible to deliver eMotion in UK communities to inactive populations. eMotion has the potential to be effective and is ready for testing in a full-scale trial. Further work is needed to improve engagement with both the intervention and data collection procedures. Trial Registration: NCT03084055; (Archived by WebCite at

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

    Creating Low-Cost 360-Degree Virtual Reality Videos for Hospitals: A Technical Paper on the Dos and Don’ts


    This article will provide a framework for producing immersive 360-degree videos for pediatric and adult patients in hospitals. This information may be useful to hospitals across the globe who may wish to produce similar videos for their patients. Advancements in immersive 360-degree technologies have allowed us to produce our own “virtual experience” where our children can prepare for anesthesia by “experiencing” all the sights and sounds of receiving and recovering from an anesthetic. We have shown that health care professionals, children, and their parents find this form of preparation valid, acceptable and fun. Perhaps more importantly, children and parents have self-reported that undertaking our virtual experience has led to a reduction in their anxiety when they go to the operating room. We provide definitions, and technical aspects to assist other health care professionals in the development of low-cost 360-degree videos.

  • 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.

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  • Towards managing a platform of mental health apps: A Secondary Analysis of the IntelliCare Field Trial

    Date Submitted: Jul 12, 2018

    Open Peer Review Period: Jul 16, 2018 - Sep 10, 2018

    Background: People using apps for mental health and wellbeing are likely to try multiple apps over time. In general, people use apps to meet immediate needs, and often use a variety of apps to meet l...

    Background: People using apps for mental health and wellbeing are likely to try multiple apps over time. In general, people use apps to meet immediate needs, and often use a variety of apps to meet larger goals (for example people may have multiple apps to manage various transportation needs). IntelliCare is a mental health app platform that was designed with these principles in mind: the apps are elemental in that each app targets a different change strategy; they are simple and brief to use; and they are eclectic, allowing the user to select which strategies are useful to them. While this may improve engagement, it creates the same challenges faced by users of app stores. Thus, mental health app platforms will require navigation aids, such as recommender systems that can quickly get a person to an app that is useful. Objective: As a first step towards developing navigation and recommender tools, this study explored app use patterns across the IntelliCare platform, and their relationship to depression and anxiety outcomes. Methods: This secondary analysis of the IntelliCare Field Trial recruited people with depression and/or anxiety. Participants received 8 weeks of coaching, primarily by text, and weekly recommendations for apps. App use metrics included frequency of use and lifetime use were defined. Depression and anxiety, measured using the PHQ-9 and GAD-7, were assessed at baseline and end of treatment. Ordinal logistic regression models, log-rank tests and cluster analysis were utilized to determine patterns of use, and if these use metrics alone, or in combination, predicted improvement or remission(I/R) in depression or anxiety. Results: The analysis included 96 people with depression and/or anxiety. Peole generally followed recommendations to download and try new apps each week. Apps clustered into 5 groups: Thinking (apps that targeted or relied on thinking), Calming (relaxation and insomnia), Checklists (apps that used checklists), Activity (behavioral activation and activity), and Other. Both overall frequency of use and lifetime use were predictive in response for depression and anxiety. The Thinking, Calming, and Checklist clusters were associated with improvement in depression and anxiety, and the Activity cluster was associated with improvement in Anxiety only. However, the use of clusters was not more strongly associated with improvement than individual app use. Conclusions: Participants in a field trial remained engaged with a suite of apps for the full eight weeks of the trial. App use patterns did fall into clusters, suggesting knowing something about use of one app may be useful in helping select another app that the person is more likely to use. Clinical Trial: NCT02176226

  • Using the Facebook Advertisement Platform to Recruit Chinese, Korean, and Latinx Cancer Survivors for Psychosocial Research

    Date Submitted: Jul 12, 2018

    Open Peer Review Period: Jul 16, 2018 - Sep 10, 2018

    Background: Ethnic minority cancer survivors remain an understudied and underrepresented population in cancer research in part due to the challenge of low participant recruitment rates. Therefore, ide...

    Background: Ethnic minority cancer survivors remain an understudied and underrepresented population in cancer research in part due to the challenge of low participant recruitment rates. Therefore, identifying effective recruitment strategies is imperative for reducing cancer health disparities among this population. With the widespread use of social media, health researchers have turned to Facebook as a potential source of recruitment. Objective: We evaluated the feasibility and effectiveness of purchasing ads on Facebook to recruit Chinese, Korean, and Latinx cancer survivors residing in the United States. We assessed their experience with participating in the present online survey and their interest for future research. Methods: Five purchased ads in English, Simplified Chinese, Traditional Chinese, Korean, and Spanish were shown on Facebook. Participants who clicked on the Facebook ad were directed to the study website and asked to submit their emails to receive the link to the 30-minute online survey. Inclusion criteria included being of Asian or Latinx heritage, 18 years or older, having a cancer diagnosis, and being within five years of cancer treatment. Participants who completed the survey were sent a $10 USD Walmart eGiftcard. Results: The Facebook ads were shown for 48 consecutive days for a total spending of $1,200.46 ($25/day budget). Overall, 11 East Asian and 15 Latinx cancer survivors completed the study, resulting in an average cost per participant of $46.17 USD. The East Asian and Latinx cancer survivors did not significantly differ in age, years lived in the United States, education level, generation status, and time since diagnosis. However, Latinx cancer survivors were marginally more likely to have limited English proficiency and lower annual income than East Asian cancer survivors. Both Latinx and East Asian cancer survivors reported that they enjoyed participating in the present study and indicated an interest in participating in future psychosocial research studies. Conclusions: The use of Facebook ads successfully resulted in the recruitment of East Asian and Latinx cancer survivors with different cancer diagnoses who resides in various geographic regions of the United States. We found that East Asian and Latinx cancer survivors recruited from Facebook were interested in participating in future psychosocial research, thereby providing support for the feasibility and effectiveness of using Facebook as a source of recruitment for ethnic minority cancer survivors.

  • Development and Usability of a Fall Risk Mobile Health Application for Older Adults

    Date Submitted: Jul 12, 2018

    Open Peer Review Period: Jul 16, 2018 - Sep 10, 2018

    Background: Falls are the leading cause of injury related death in older adults. Due to various constraints, objective fall risk screening is seldom performed in clinical settings. Smartphones offer h...

    Background: Falls are the leading cause of injury related death in older adults. Due to various constraints, objective fall risk screening is seldom performed in clinical settings. Smartphones offer high potential to provide fall risk screening for older adults in home settings. However, there is limited understanding of whether smartphone technology for falls screening is usable by older adults who present age-related changes in perceptual, cognitive, and motor capabilities. Objective: The aims of this study were to develop a fall risk mobile health application (app) and to determine the usability of the fall risk app in healthy, older adults. Methods: A fall risk app was developed that consists of health history questionnaires and five mobility tasks to measure individual fall risk. An iterative design-evaluation process of semi-structured interviews were performed to determine the usability of the app on a smartphone and tablet. Participants also completed a Systematic Usability Scale (SUS). Six older adults participated in the first round of interviews, and five older adults participated in the second round. Interviews were videotaped and transcribed, and the data was coded to create themes. Average SUS scores were calculated for the smartphone and tablet. Results: Two themes were identified from the first round of interviews related to perceived ease of use and perceived usefulness. While instructions for balance tasks were difficult to understand, participants found it beneficial to learn about their risk for falls, found the app easy to follow, and reported confidence in using the app on their own. Modifications were made to the app, and following a second round of interviews, participants reported high ease of use and usefulness in learning about their risk of falling. Little differences were reported between using a smartphone or tablet. Average SUS scores ranged from 79 to 84. Conclusions: Our fall risk app was found to be highly usable by older adults as reported from interviews and high scores on the SUS. When designing a mobile health app for older adults, developers should include clear and simple instructions and preventative strategies to improve health. Furthermore, if the design accommodates for age-related sensory changes, smartphones can be as effective as tablets. A mobile app to assess fall risk has potential to be used in home settings by older adults.

  • Evaluation and implementation of ListeningTime; a web-based preparatory communication tool for elderly cancer patients and their healthcare providers

    Date Submitted: Jul 12, 2018

    Open Peer Review Period: Jul 16, 2018 - Sep 10, 2018

    Background: Effective patient-provider communication is an important condition to deliver optimal care and it supports patients in coping with their disease. The complex and emotionally loaded setting...

    Background: Effective patient-provider communication is an important condition to deliver optimal care and it supports patients in coping with their disease. The complex and emotionally loaded setting of oncology care challenges both healthcare providers and patients in reaching effective communication. ListeningTime is developed for elderly cancer patients and their oncological healthcare providers to help them (better) prepare the clinical encounter and overcome communication barriers. ListeningTime is a web-based preparatory communication tool including modeling videos and has an audio-facility to listen back to recorded encounters. Objective: To evaluate the usability, perceived usefulness and actual use of ListeningTime, through the eyes of elderly cancer patients and their oncological healthcare providers. If evaluated positively, the ultimate goal is to make ListeningTime publicly available. Methods: First, members of a panel of elderly (ex-)cancer patients (≥ 65 years) were approached to evaluate ListeningTime via an online questionnaire. Usability and perceived usefulness were assessed. Second, ListeningTime was evaluated in real-life practice through a pilot study in three Dutch hospitals. In these hospitals, elderly cancer patients and their oncological healthcare providers were approached to evaluate ListeningTime via a similar online questionnaires, measuring perceived usefulness. Additionally, we examined log files and user statistics to get insight in how the program was used. Results: Thirty (ex-)cancer patients from the patient panel, and seventeen patients and eight healthcare providers from the hospitals, evaluated ListeningTime. Overall, both the panel members and the hospital patients were positive about the ListeningTime website, the audio-facility and the video fragments. Some patients suggested improvements with respect to the actors’ performances in the video fragments and believed that ListeningTime is mainly suitable for non-experienced patients. Healthcare providers were also positive about ListeningTime. They valued the video fragments for patients and the audio-facility for patients and themselves. However, providers did not listen back to their own recorded encounters. Patients did use the audio-facility to listen back to their encounters. Conclusions: ListeningTime was evaluated positively, both by patients and their oncological healthcare providers. As a result, the video fragments of ListeningTime are now made publicly available for elderly cancer patients through the Dutch website ‘’. Clinical Trial: not applicable

  • Motivation predicts change in nurses’ physical activity levels during a web-based worksite intervention: results from a randomized trial

    Date Submitted: Jul 11, 2018

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

    Background: Low physical activity levels can negatively affect nurses’ health. Given the low physical activity levels nurses report, the need for brief and economical interventions designed to incre...

    Background: Low physical activity levels can negatively affect nurses’ health. Given the low physical activity levels nurses report, the need for brief and economical interventions designed to increase physical activity in this population is clear. We developed a web-based intervention which utilized motivational strategies to increase nurses’ physical activity levels. The intervention provided nurses with feedback from an activity monitor coupled with a web-based individual, friend or team physical activity challenge. Objective: In this parallel-group randomized trial, we examined whether nurses’ motivation at baseline predicted changes in objectively-measured physical activity during the 6-week intervention. Methods: Participants were 76 nurses (97% female; mean age=46 years, SD=11) randomly assigned to one of three physical activity challenge conditions: 1) individual, 2) friend, or 3) team. Nurses completed a questionnaire online assessing motivational regulations for physical activity prior to the intervention and wore a Tractivity® activity monitor prior to and during the 6-week intervention. We analyzed data using multilevel modeling for repeated measures. Results: Nurses’ physical activity levels increased (linear estimate=10.30, SE=3.15), but the rate of change decreased over time (quadratic estimate=-2.06, SE=0.52). External and identified regulations, but not intrinsic and introjected regulations, predicted changes in nurses’ physical activity levels. Conclusions: Our findings provide evidence that an intervention incorporating self-monitoring and physical activity challenges can be effective in increasing nurses’ physical activity levels. Also, largely consistent with motivational theories and prior research, they suggest interventions incorporating strategies promoting motivation for physical activity should be developed and tested. registration: This trial was not registered with like other trials conducted during the same enrollment period as registration was not required by the study sponsor. Clinical Trial: This trial was not registered with like other trials conducted during the same enrollment period as registration was not required by the study sponsor.

  • Digital marketing to promote healthy weight gain among pregnant women in Alberta

    Date Submitted: Jul 10, 2018

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

    Background: As the use of digital media for health promotion becomes increasingly common, descriptive studies exploring current and innovative marketing strategies will enhance understanding of effect...

    Background: As the use of digital media for health promotion becomes increasingly common, descriptive studies exploring current and innovative marketing strategies will enhance understanding of effective strategies and best practices. Objective: To describe the implementation of a provincial digital media campaign using complementary advertising platforms to promote healthy pregnancy weight gain messages and direct an online audience to a credible website. Methods: The digital media campaign occurred in three phases, each for eight weeks, and consisted of search engine marketing using Google AdWords and social media advertising through Facebook. All advertising materials directed users to evidence-based pregnancy-related weight gain content on the Healthy Parents, Healthy Children website. Results: Google ads received a total of 43,449 impressions, 2,522 clicks and an average click through rate (CTR) of 5.80%. Of the people who clicked on a Google ad, 78.9% completed an action on the website. Across all Facebook ads there was a total of 772,263 impressions, 14,482 clicks and an average CTR of 1.88%. The highest performing ad was an image of a group of diverse pregnant women with the headline: Pregnancy weight is not the same for every woman. Conclusions: This study supports the use of digital marketing as an important avenue for delivering health messages and directing online users to credible sources of information. The opportunity to reach large, yet targeted audiences, along with the ability to monitor and evaluate metrics in order to optimize activities throughout a campaign is a powerful advantage over traditional marketing tactics. Health organizations can use the results and insights of this study to help inform the design and implementation of similar online activities.