Maintenance Note

On Friday, August 31, 2018 at 12:00 pm Eastern Time, JMIR will be completing a server migration to improve site stability and user experience. We expect to be back online Friday, August 31, 2018 at 5:00 pm Eastern Time. Should any problems arise our technical team will be using the weekend to resolve them, and users will be able to access our site by Sunday, September 2, 2018 at 1:00pm Eastern Time.

Who will be affected?


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:

  • A patient and the physician discussing Internet health information. Source: Pxhere; Copyright:; URL:; License: Public Domain (CC0).

    Relationship Between Internet Health Information and Patient Compliance Based on Trust: Empirical Study


    Background: The internet has become a major mean for acquiring health information; however, Web-based health information is of mixed quality and may markedly affect patients’ health-related behavior and decisions. According to the social information processing theory, patients’ trust in their physicians may potentially change due to patients’ health-information-seeking behavior. Therefore, it is important to identify the relationship between internet health information and patient compliance from the perspective of trust. Objective: The objective of our study was to investigate the effects of the quality and source of internet health information on patient compliance using an empirical study based on the social information processing theory and social exchange theory. Methods: A Web-based survey involving 336 valid participants was conducted in China. The study included independent variables (internet health information quality and source of information), 2 mediators (cognition-based trust [CBT] and affect-based trust [ABT]), 1 dependent variable (patient compliance), and 3 control variables (gender, age, and job). All variables were measured using multiple-item scales from previously validated instruments, and confirmative factor analysis as well as structural equation modeling was used to test hypotheses. Results: The questionnaire response rate was 77.16% (375/486), validity rate was 89.6% (336/375), and reliability and validity were acceptable. We found that the quality and source of internet health information affect patient compliance through the mediation of CBT and ABT. In addition, internet health information quality has a stronger influence on patient compliance than the source of information. However, CBT does not have any direct effect on patient compliance, but it directly affects ABT and then indirectly impacts patient compliance. Therefore, the effect of ABT seems stronger than that of CBT. We found an unexpected, nonsignificant relationship between the source of internet health information and ABT. Conclusions: From patients’ perspective, internet health information quality plays a stronger role than its source in impacting their trust in physicians and the consequent compliance with physicians. Therefore, patient compliance can be improved by strengthening the management of internet health information quality. The study findings also suggest that physicians should focus on obtaining health information from health websites, thereby expanding their understanding of patients’ Web-based health-information-seeking preferences, and enriching their knowledge structure to show their specialization and reliability in the communication with patients. In addition, the mutual demonstration of care and respect in the communication between physicians and patients is important in promoting patients’ ABT in their physicians.

  • Browsing ratings of a physician. Source: The Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Developing Embedded Taxonomy and Mining Patients’ Interests From Web-Based Physician Reviews: Mixed-Methods Approach


    Background: Web-based physician reviews are invaluable gold mines that merit further investigation. Although many studies have explored the text information of physician reviews, very few have focused on developing a systematic topic taxonomy embedded in physician reviews. The first step toward mining physician reviews is to determine how the natural structure or dimensions is embedded in reviews. Therefore, it is relevant to develop the topic taxonomy rigorously and systematically. Objective: This study aims to develop a hierarchical topic taxonomy to uncover the latent structure of physician reviews and illustrate its application for mining patients’ interests based on the proposed taxonomy and algorithm. Methods: Data comprised 122,716 physician reviews, including reviews of 8501 doctors from a leading physician review website in China (, collected between 2007 and 2015. Mixed methods, including a literature review, data-driven-based topic discovery, and human annotation were used to develop the physician review topic taxonomy. Results: The identified taxonomy included 3 domains or high-level categories and 9 subtopics or low-level categories. The physician-related domain included the categories of medical ethics, medical competence, communication skills, medical advice, and prescriptions. The patient-related domain included the categories of the patient profile, symptoms, diagnosis, and pathogenesis. The system-related domain included the categories of financing and operation process. The F-measure of the proposed classification algorithm reached 0.816 on average. Symptoms (Cohen d=1.58, Δu=0.216, t=229.75, and P<.001) are more often mentioned by patients with acute diseases, whereas communication skills (Cohen d=−0.29, Δu=−0.038, t=−42.01, and P<.001), financing (Cohen d=−0.68, Δu=−0.098, t=−99.26, and P<.001), and diagnosis and pathogenesis (Cohen d=−0.55, Δu=−0.078, t=−80.09, and P<.001) are more often mentioned by patients with chronic diseases. Patients with mild diseases were more interested in medical ethics (Cohen d=0.25, Δu 0.039, t=8.33, and P<.001), operation process (Cohen d=0.57, Δu 0.060, t=18.75, and P<.001), patient profile (Cohen d=1.19, Δu 0.132, t=39.33, and P<.001), and symptoms (Cohen d=1.91, Δu=0.274, t=62.82, and P<.001). Meanwhile, patients with serious diseases were more interested in medical competence (Cohen d=−0.99, Δu=−0.165, t=−32.58, and P<.001), medical advice and prescription (Cohen d=−0.65, Δu=−0.082, t=−21.45, and P<.001), financing (Cohen d=−0.26, Δu=−0.018, t=−8.45, and P<.001), and diagnosis and pathogenesis (Cohen d=−1.55, Δu=−0.229, t=−50.93, and P<.001). Conclusions: This mixed-methods approach, integrating literature reviews, data-driven topic discovery, and human annotation, is an effective and rigorous way to develop a physician review topic taxonomy. The proposed algorithm based on Labeled-Latent Dirichlet Allocation can achieve impressive classification results for mining patients’ interests. Furthermore, the mining results reveal marked differences in patients’ interests across different disease types, socioeconomic development levels, and hospital levels.

  • Set-up of the intervention in the waiting room. Source: Image created by the Authors; Copyright: Titus Josef Brinker; URL:; License: Creative Commons Attribution (CC-BY).

    A Face-Aging App for Smoking Cessation in a Waiting Room Setting: Pilot Study in an HIV Outpatient Clinic


    Background: There is strong evidence for the effectiveness of addressing tobacco use in health care settings. However, few smokers receive cessation advice when visiting a hospital. Implementing smoking cessation technology in outpatient waiting rooms could be an effective strategy for change, with the potential to expose almost all patients visiting a health care provider without preluding physician action needed. Objective: The objective of this study was to develop an intervention for smoking cessation that would make use of the time patients spend in a waiting room by passively exposing them to a face-aging, public morphing, tablet-based app, to pilot the intervention in a waiting room of an HIV outpatient clinic, and to measure the perceptions of this intervention among smoking and nonsmoking HIV patients. Methods: We developed a kiosk version of our 3-dimensional face-aging app Smokerface, which shows the user how their face would look with or without cigarette smoking 1 to 15 years in the future. We placed a tablet with the app running on a table in the middle of the waiting room of our HIV outpatient clinic, connected to a large monitor attached to the opposite wall. A researcher noted all the patients who were using the waiting room. If a patient did not initiate app use within 30 seconds of waiting time, the researcher encouraged him or her to do so. Those using the app were asked to complete a questionnaire. Results: During a 19-day period, 464 patients visited the waiting room, of whom 187 (40.3%) tried the app and 179 (38.6%) completed the questionnaire. Of those who completed the questionnaire, 139 of 176 (79.0%) were men and 84 of 179 (46.9%) were smokers. Of the smokers, 55 of 81 (68%) said the intervention motivated them to quit (men: 45, 68%; women: 10, 67%); 41 (51%) said that it motivated them to discuss quitting with their doctor (men: 32, 49%; women: 9, 60%); and 72 (91%) perceived the intervention as fun (men: 57, 90%; women: 15, 94%). Of the nonsmokers, 92 (98%) said that it motivated them never to take up smoking (men: 72, 99%; women: 20, 95%). Among all patients, 102 (22.0%) watched another patient try the app without trying it themselves; thus, a total of 289 (62.3%) of the 464 patients were exposed to the intervention (average waiting time 21 minutes). Conclusions: A face-aging app implemented in a waiting room provides a novel opportunity to motivate patients visiting a health care provider to quit smoking, to address quitting at their subsequent appointment and thereby encourage physician-delivered smoking cessation, or not to take up smoking.

  • Source: Ciran /; Copyright: The Authors; URL:; License: Licensed by JMIR.

    Effectiveness of Serious Gaming During the Multidisciplinary Rehabilitation of Patients With Complex Chronic Pain or Fatigue: Natural Quasi-Experiment


    Background: Current evidence for the effectiveness of specialist multidisciplinary programs for burdensome chronic pain and functional somatic syndromes drives the effort to improve approaches, strategies, and delivery modes. It remains unknown to what extent and in what respect serious gaming during the regular outpatient rehabilitation can contribute to health outcomes. Objective: The objectives of our study were to determine the effect of additional serious gaming on (1) physical and emotional functioning in general; (2) particular outcome domains; and (3) patient global impressions of change, general health, and functioning and to determine (4) the dependency of serious gaming effects on adherence. Methods: We conducted a naturalistic quasi-experiment using embedded qualitative methods. The intervention group patients received an additional guided (mindfulness-based) serious gaming intervention during weeks 9-12 of a 16-week rehabilitation program at 2 sites of a Dutch rehabilitation clinic. Simultaneously, 119 control group patients followed the same program without serious gaming at 2 similar sites of the same clinic. Data consisted of 10 semistructured patient interviews and routinely collected patient self-reported outcomes. First, multivariate linear mixed modeling was used to simultaneously estimate a group effect on the outcome change between weeks 8 and 16 in 4 primary outcomes: current pain intensity, fatigue, pain catastrophizing, and psychological distress. Second, similar univariate linear mixed models were used to estimate effects on particular (unstandardized) outcomes. Third, secondary outcomes (ie, global impression of change, general health, functioning, and treatment satisfaction) were compared between the groups using independent t tests. Finally, subgroups were established according to the levels of adherence using log data. Influences of observed confounding factors were considered throughout analyses. Results: Of 329 eligible patients, 156 intervention group and 119 control group patients (N=275) with mostly chronic back pain and concomitant psychosocial problems participated in this study. Of all, 119 patients played ≥75% of the game. First, the standardized means across the 4 primary outcomes showed a significantly more favorable degree of change during the second part of the treatment for the intervention group than for the control group (beta=−0.119, SE=0.046, P=.009). Second, the intervention group showed a greater outcome change in depressive mood (b=−2.748, SE=1.072, P=.011) but not in “insufficiency” or concentration problems. Third, no significant group effects on secondary outcomes were found. Fourth, adherence was generally high and invariant. Conclusions: The findings of this study suggest a very small favorable average effect on relevant health outcomes of additional serious gaming during multidisciplinary rehabilitation. The indication that serious gaming could be a relatively time-efficient component warrants further research into if, when, how, and for which patients serious gaming could be cost-effective in treatment and why. Trial Registration: Netherlands Trial Registry NTR6020; (Archived by WebCite at

  • Source: Vaping360 (; Copyright: Vaping360; URL:; License: Creative Commons Attribution + NoDerivatives (CC-BY-ND).

    Understanding Users’ Vaping Experiences from Social Media: Initial Study Using Sentiment Opinion Summarization Techniques


    Background: E-liquid is one of the main components in electronic nicotine delivery systems (ENDS). ENDS review comments could serve as an early warning on use patterns and even function to serve as an indicator of problems or adverse events pertaining to the use of specific e-liquids—much like types of responses tracked by the Food and Drug Administration (FDA) regarding medications. Objective: This study aimed to understand users’ “vaping” experience using sentiment opinion summarization techniques, which can help characterize how consumers think about specific e-liquids and their characteristics (eg, flavor, throat hit, and vapor production). Methods: We collected e-liquid reviews on JuiceDB from June 27, 2013 to December 31, 2017 using its public application programming interface. The dataset contains 27,070 reviews for 8058 e-liquid products. Each review is accompanied by an overall rating and a set of 4 aspect ratings of an e-liquid, each on a scale of 1-5: flavor accuracy, throat hit, value, and cloud production. An iterative dichotomiser 3 (ID3)-based influential aspect analysis model was adopted to learn the key elements that impact e-liquid use. Then, fine-grained sentiment analysis was employed to mine opinions on various aspects of vaping experience related to e-liquids. Results: We found that flavor accuracy and value were the two most important aspects that affected users’ sentiments toward e-liquids. Of reviews in JuiceDB, 67.83% (18,362/27,070) were positive, while 12.67% (3430/27,070) were negative. This indicates that users generally hold positive attitudes toward e-liquids. Among the 9 flavors, fruity and sweet were the two most popular. Great and sweet tastes, reasonable value, and strong throat hit made users satisfied with fruity and sweet flavors, whereas “strange” tastes made users dislike those flavors. Meanwhile, users complained about some e-liquids’ steep or expensive prices, bad quality, and harsh throat hit. There were 2342 fruity e-liquids and 2049 sweet e-liquids. There were 55.81% (1307/2342) and 59.83% (1226/2049) positive sentiments and 13.62% (319/2342) and 12.88% (264/2049) negative sentiments toward fruity e-liquids and sweet e-liquids, respectively. Great flavors and good vapors contributed to positive reviews of fruity and sweet products. However, bad tastes such as “sour” or “bitter” resulted in negative reviews. These findings can help businesses and policy makers to further improve product quality and formulate effective policy. Conclusions: This study provides an effective mechanism for analyzing users’ ENDS vaping experience based on sentiment opinion summarization techniques. Sentiment opinions on aspect and products can be found using our method, which is of great importance to monitor e-liquid products and improve work efficiency.

  • Source: Pexels; Copyright:; URL:; License: Licensed by the authors.

    Information and Communication Technologies Interest, Access, and Use: Cross-Sectional Survey of a Community Sample of Urban, Predominantly Black Women


    Background: Information and communication technologies (ICT) offer the potential for delivering health care interventions to low socioeconomic populations who often face barriers in accessing health care. However, most studies on ICT for health education and interventions have been conducted in clinical settings. Objective: The aim of this study was to examine access to and use of mobile phones and computers, as well as interest in, using ICT for receipt of behavioral health information among a community sample of urban, predominately black, women with low socioeconomic status. Methods: Participants (N=220) were recruited from hair salons and social service centers and completed audio-computer assisted self-interviews. Results: The majority of the participants (212/220, 96.3%) reported use of a cell phone at least weekly, of which 89.1% (189/212) used smartphones and 62.3% (137/220) reported computer use at least weekly. Of the women included in the study, 51.9% (107/206) reported using a cell phone and 39.4% (74/188) reported using a computer to access health and/or safety information at least weekly. Approximately half of the women expressed an interest in receiving information about stress management (51%-56%) or alcohol and health (45%-46%) via ICT. Smartphone ownership was associated with younger age (odds ratio [OR] 0.92, 95% CI 0.87-0.97) and employment (OR 5.12, 95% CI 1.05-24.95). Accessing health and safety information weekly by phone was associated with younger age (OR 0.96, 95% CI 0.94-0.99) and inversely associated with higher income (OR 0.42, 95% CI 0.20-0.92). Conclusions: Our findings suggest that ICT use, particularly smartphone use, is pervasive among predominantly black women with low socioeconomic status in urban, nonclinical settings. These results show that ICT is a promising modality for delivering health information to this population. Further exploration of the acceptability, feasibility, and effectiveness of using ICT to disseminate behavioral health education and intervention is warranted.

  • Source:; Copyright: Intel Free Press; URL:; License: Creative Commons Attribution (CC-BY).

    Social Media Use in Interventions for Diabetes: Rapid Evidence-Based Review


    Background: Health authorities recommend educating diabetic patients and their families and initiating measures aimed at improving self-management, promoting a positive behavior change, and reducing the risk of complications. Social media could provide valid channel to intervene in and deliver diabetes education. However, it is not well known whether the use of these channels in such interventions can help improve the patients’ outcomes. Objective: The objective of our study was to review and describe the current existing evidence on the use of social media in interventions targeting people affected with diabetes. Methods: A search was conducted across 4 databases (PubMed, Scopus, EMBASE, and Cochrane Library).The quality of the evidence of the included primary studies was graded according to the Grading of Recommendations Assessment, Development and Evaluation criteria, and the risk of bias of systematic reviews was assessed by drawing on AMSTAR (A MeaSurement Tool to Assess systematic Reviews) guidelines. The outcomes reported by these studies were extracted and analyzed. Results: We included 20 moderate- and high-quality studies in the review: 17 primary studies and 3 systematic reviews. Of the 16 publications evaluating the effect on glycated hemoglobin (HbA1c) of the interventions using social media, 13 reported significant reductions in HbA1c values. The 5 studies that measured satisfaction with the interventions using social media found positive effects. We found mixed evidence regarding the effect of interventions using social media on health-related quality of life (2 publications found positive effects and 3 found no differences) and on diabetes knowledge or empowerment (2 studies reported improvements and 2 reported no significant changes). Conclusions: There is very little good-quality evidence on the use of social media in interventions aimed at helping people with diabetes. However, the use of these channels is mostly linked to benefits on patients’ outcomes. Public health institutions, clinicians, and other stakeholders who aim at improving the knowledge of diabetic patients could consider the use of social media in their interventions.

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

    Differences in the Effect of Internet-Based Cognitive Behavioral Therapy for Improving Nonclinical Depressive Symptoms Among Workers by Time Preference:...


    Background: Previous randomized controlled trials (RCTs) have shown a significant intervention effect of internet-based computerized cognitive behavioral therapy (iCBT) on improving nonclinical depressive symptoms among healthy workers and community residents in a primary prevention setting. Time preference is one’s relative valuation for having a reward (eg, money) at present than at a later date. Time preference may affect the effectiveness of cognitive behavioral therapy. Objective: This RCT aimed to test the difference of intervention effect of an iCBT program on improving nonclinical depressive symptoms between two subgroups classified post-hoc on the basis of time preference among workers in Japan. Methods: All workers in one corporate group (approximate n=20,000) were recruited. Participants who fulfilled the inclusion criteria were randomly allocated to either intervention or control groups. Participants in the intervention group completed 6 weekly lessons and homework assignments within the iCBT program. The Beck Depression Inventory-II (BDI-II) and Kessler’s Psychological Distress Scale (K6) measures were obtained at baseline and 3-, 6-, and 12-month follow-ups. Two subgroups were defined by the median of time preference score at baseline. Results: Only few (835/20,000, 4.2%) workers completed the baseline survey. Of the 835 participants, 706 who fulfilled the inclusion criteria were randomly allocated to the intervention or control group. Participants who selected irrational time preference options were excluded (21 and 18 participants in the intervention and control groups, respectively). A three-way interaction (group [intervention/control] × time [baseline/follow-up] × time preference [higher/lower]) effect of iCBT was significant for BDI-II (t1147.42=2.33, P=.02) and K6 (t1254.04=2.51, P=.01) at the 3-month follow-up, with a greater effect of the iCBT in the group with higher time preference. No significant three-way interaction was found at the 6- and 12-month follow-ups. Conclusions: The effects of the iCBT were greater for the group with higher time preference at the shorter follow-up, but it was leveled off later. Workers with higher time preference may change their cognition or behavior more quickly, but these changes may not persist. Trial Registration: UMIN Clinical Trials Registry UMIN000014146; recptno=R000016466 (Archived by WebCite at

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

    Internet of Things Buttons for Real-Time Notifications in Hospital Operations: Proposal for Hospital Implementation


    Background: Hospital staff frequently performs the same process hundreds to thousands of times a day. Customizable Internet of Things buttons are small, wirelessly-enabled devices that trigger specific actions with the press of an integrated button and have the potential to automate some of these repetitive tasks. In addition, IoT buttons generate logs of triggered events that can be used for future process improvements. Although Internet of Things buttons have seen some success as consumer products, little has been reported on their application in hospital systems. Objective: We discuss potential hospital applications categorized by the intended user group (patient or hospital staff). In addition, we examine key technological considerations, including network connectivity, security, and button management systems. Methods: In order to meaningfully deploy Internet of Things buttons in a hospital system, we propose an implementation framework grounded in the Plan-Do-Study-Act method. Results: We plan to deploy Internet of Things buttons within our hospital system to deliver real-time notifications in public-facing tasks such as restroom cleanliness and critical supply restocking. We expect results from this pilot in the next year. Conclusions: Overall, Internet of Things buttons have significant promise; future rigorous evaluations are needed to determine the impact of Internet of Things buttons in real-world health care settings.

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

    Using Mobile Apps to Assess and Treat Depression in Hispanic and Latino Populations: Fully Remote Randomized Clinical Trial


    Background: Most people with mental health disorders fail to receive timely access to adequate care. US Hispanic/Latino individuals are particularly underrepresented in mental health care and are historically a very difficult population to recruit into clinical trials; however, they have increasing access to mobile technology, with over 75% owning a smartphone. This technology has the potential to overcome known barriers to accessing and utilizing traditional assessment and treatment approaches. Objective: This study aimed to compare recruitment and engagement in a fully remote trial of individuals with depression who either self-identify as Hispanic/Latino or not. A secondary aim was to assess treatment outcomes in these individuals using three different self-guided mobile apps: iPST (based on evidence-based therapeutic principles from problem-solving therapy, PST), Project Evolution (EVO; a cognitive training app based on cognitive neuroscience principles), and health tips (a health information app that served as an information control). Methods: We recruited Spanish and English speaking participants through social media platforms, internet-based advertisements, and traditional fliers in select locations in each state across the United States. Assessment and self-guided treatment was conducted on each participant's smartphone or tablet. We enrolled 389 Hispanic/Latino and 637 non-Hispanic/Latino adults with mild to moderate depression as determined by Patient Health Questionnaire-9 (PHQ-9) score≥5 or related functional impairment. Participants were first asked about their preferences among the three apps and then randomized to their top two choices. Outcomes were depressive symptom severity (measured using PHQ-9) and functional impairment (assessed with Sheehan Disability Scale), collected over 3 months. Engagement in the study was assessed based on the number of times participants completed active surveys. Results: We screened 4502 participants and enrolled 1040 participants from throughout the United States over 6 months, yielding a sample of 348 active users. Long-term engagement surfaced as a key issue among Hispanic/Latino participants, who dropped from the study 2 weeks earlier than their non-Hispanic/Latino counterparts (P<.02). No significant differences were observed for treatment outcomes between those identifying as Hispanic/Latino or not. Although depressive symptoms improved (beta=–2.66, P=.006) over the treatment course, outcomes did not vary by treatment app. Conclusions: Fully remote mobile-based studies can attract a diverse participant pool including people from traditionally underserved communities in mental health care and research (here, Hispanic/Latino individuals). However, keeping participants engaged in this type of “low-touch” research study remains challenging. Hispanic/Latino populations may be less willing to use mobile apps for assessing and managing depression. Future research endeavors should use a user-centered design to determine the role of mobile apps in the assessment and treatment of depression for this population, app features they would be interested in using, and strategies for long-term engagement. Trial Registration: NCT01808976; (Archived by WebCite at

  • Wechat interface of a tertiary referral hospital. Source: Image created by the authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Social Media Landscape of the Tertiary Referral Hospitals in China: Observational Descriptive Study


    Background: Social media has penetrated all walks of life. Chinese health care institutions are increasingly utilizing social media to connect with their patients for better health service delivery. Current research has focused heavily on the use of social media in developed countries, with few studies exploring its usage in the context of developing countries, such as China. Tertiary hospitals in China are usually located in city centers, and they serve as medical hubs for multiple regions, with comprehensive and specialized medical care being provided. These hospitals are assumed to be the pioneers in creating official social media accounts to connect with their patients due to the fact that they appear to have more resources to support this innovative approach to communication and health care education. Objective: The objective of our study was to examine China’s best tertiary hospitals, as recognized by The National Health Commission of the People’s Republic of China (NHCPRC), and to map out the landscape of current social media usage by hospitals when engaging with patients. Methods: We examined the best 705 tertiary hospitals in China by collecting and analyzing data regarding their usage of popular Chinese social media apps Sina Weibo and WeChat. The specific data included (1) hospital characteristics (ie, time since established, number of beds, hospital type, and regions or localities) and (2) status of social media usage regarding two of the most popular local social media platforms in China (ie, time of initiation, number of followers, and number of tweets or posts). We further used a logistic regression model to test the association between hospital characteristics and social media adoption. Results: Of all, 76.2% (537/705) tertiary referral hospitals have created official accounts on either Sina Weibo or WeChat, with the latter being more popular among the two. In addition, our study suggests that larger and newer hospitals with greater resources are more likely to adopt social media, while hospital type and affiliation with universities are not significant predictors of social media adoption among hospitals. Conclusions: Our study demonstrated that hospitals are more inclined to use WeChat. The move by hospitals from Sina Weibo to WeChat indicates that patients are not satisfied by mere communication and that they now place more value on health service delivery. Meanwhile, utilizing social media requires comprehensive thinking from the hospital side. Once adopted, hospitals are encouraged to implement specific rules regarding social media usage. In the future, a long journey still lies ahead for hospitals in terms of operating their official social media accounts.

  • Source: iStock by Getty Images; Copyright: AlexRaths; URL:; License: Creative Commons Attribution (CC-BY).

    Cloud Computing for Infectious Disease Surveillance and Control: Development and Evaluation of a Hospital Automated Laboratory Reporting System


    Background: Outbreaks of several serious infectious diseases have occurred in recent years. In response, to mitigate public health risks, countries worldwide have dedicated efforts to establish an information system for effective disease monitoring, risk assessment, and early warning management for international disease outbreaks. A cloud computing framework can effectively provide the required hardware resources and information access and exchange to conveniently connect information related to infectious diseases and develop a cross-system surveillance and control system for infectious diseases. Objective: The objective of our study was to develop a Hospital Automated Laboratory Reporting (HALR) system based on such a framework and evaluate its effectiveness. Methods: We collected data for 6 months and analyzed the cases reported within this period by the HALR and the Web-based Notifiable Disease Reporting (WebNDR) systems. Furthermore, system evaluation indicators were gathered, including those evaluating sensitivity and specificity. Results: The HALR system reported 15 pathogens and 5174 cases, and the WebNDR system reported 34 cases. In a comparison of the two systems, sensitivity was 100% and specificity varied according to the reported pathogens. In particular, the specificity for Streptococcus pneumoniae, Mycobacterium tuberculosis complex, and hepatitis C virus were 99.8%, 96.6%, and 97.4%, respectively. However, the specificity for influenza virus and hepatitis B virus were only 79.9% and 47.1%, respectively. After the reported data were integrated with patients’ diagnostic results in their electronic medical records (EMRs), the specificity for influenza virus and hepatitis B virus increased to 89.2% and 99.1%, respectively. Conclusions: The HALR system can provide early reporting of specified pathogens according to test results, allowing for early detection of outbreaks and providing trends in infectious disease data. The results of this study show that the sensitivity and specificity of early disease detection can be increased by integrating the reported data in the HALR system with the cases’ clinical information (eg, diagnostic results) in EMRs, thereby enhancing the control and prevention of infectious diseases.

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  • The messages presented in electronic cigarette related social media promotions and discussions: A scoping review

    Date Submitted: Aug 16, 2018

    Open Peer Review Period: Aug 20, 2018 - Oct 15, 2018

    Background: There has been a rapid rise in the popularity of electronic cigarettes (e-cigarettes) over the last decade, with growth predicted to continue. The uptake of these devices has escalated des...

    Background: There has been a rapid rise in the popularity of electronic cigarettes (e-cigarettes) over the last decade, with growth predicted to continue. The uptake of these devices has escalated despite inconclusive evidence of their efficacy as a smoking cessation device and unknown long term health consequences. As tobacco smoking rates continue to drop or plateau in many well developed countries transnational tobacco companies have transitioned into the vaping industry and are now using social media to promote their products. Evidence suggests that e-cigarettes are being marketed on social media as a harm reduction alternative with retailers and manufactures utilising marketing techniques historically used by the tobacco industry. Objective: To identify and describe the messages presented in e-cigarette related social media (Twitter, YouTube, Instagram and Pinterest) promotions and discussions, and identify future directions for research, surveillance and regulation. Methods: Data sources included Medline, Scopus, ProQuest, Informit, Journal of Medical Internet Research and Google Scholar. Studies must have been published in English between 2007 and 2017, analyse content captured from e-cigarette related social media promotions or discussions, and report results for e-cigarettes separately to other forms of tobacco and nicotine delivery. Database searching ceased October 2017. Initial searching identified 536 studies. Two reviewers screened studies by title and abstract. One reviewer examined 71 full texts to determine eligibly and identified 25 studies for inclusion. This process was undertaken with the assistance of the online screening and data extraction tool - Covidence. The review was registered with The Joanna Briggs Institute Systematic Reviews database and followed the methodology for JBI Scoping Reviews. Results: Several key messages are being used to promote e-cigarettes including as a safer alternative to cigarettes, efficacy as a smoking cessation aid, and for use where smoking is prohibited. Other major marketing efforts aimed at capturing a larger market involve promotion of innovative flavouring and highlighting the public performance of vaping. Discussion and promotion of these devices appears to be predominantly occurring among everyday people and those with vested interests such as retailers and manufacturers. There is a noticeable silence from the public health and government sector in these discussions on social media. Conclusions: The social media landscape is being dominated by pro-vaping messages disseminated by the vaping industry and vaping proponents. The uncertainty surrounding e-cigarette regulation expressed within the public health field appears not to be reflected in ongoing social media dialogues, and highlights the need for public health professionals to interact with the public to actively influence social media conversations and create a more balanced discussion. With the vaping industry changing so rapidly real time monitoring and surveillance of how these devices are discussed, promoted, and reportedly used on social media is necessary in conjunction with evidence published in academic journals.

  • Time2bHealthy – an internet-based childhood obesity prevention program for parents of preschool-aged children: outcomes of a randomized controlled trial

    Date Submitted: Aug 16, 2018

    Open Peer Review Period: Aug 20, 2018 - Oct 15, 2018

    Background: E-Health obesity programs offer benefits to traditionally-delivered programs and have shown promise in improving obesity-related behaviors in children. Objective: To assess the efficacy of...

    Background: E-Health obesity programs offer benefits to traditionally-delivered programs and have shown promise in improving obesity-related behaviors in children. Objective: To assess the efficacy of a parent-focussed internet-based healthy lifestyle program for preschool-aged children, who are overweight or at risk of becoming overweight, on child BMI, obesity-related behaviors, parent modelling and parent self-efficacy. Methods: The Time2bHealthy randomized controlled trial was conducted in the Illawarra and surrounding areas of New South Wales and Melbourne, Victoria, Australia during 2016-2017. Participants were recruited both online and through more traditional means. Parent/carer and child (aged 2-5 years) dyads were randomized into an intervention or comparison group. Intervention participants received an 11-week internet-based healthy lifestyle program, which was underpinned by Social Cognitive Theory, followed by fortnightly emails for 3-months thereafter. Intervention participants were encouraged to access and contribute to a closed (secret) Facebook group to communicate with other participants and a dietitian. Comparison participants received email communication only. Child BMI was the primary outcome, which was objectively measured. Secondary outcomes included objectively-measured physical activity, parent- and objectively-measured sleep habits, and parent-reported dietary intake, screen-time, child feeding, parent modelling and parent self-efficacy. All data were collected at face-to-face appointments at baseline, 3- and 6-months by blinded data collectors. Randomization was conducted using a computerized random number generator post-baseline data collection. Results: Eighty-six dyads were recruited, with 42 randomized to the intervention group and 44 to the comparison group. 78 dyads attended the 3- and 6-month follow-ups, with 7 lost to follow-up and one withdrawing. Mean child age was 3.46 years and 91% were in the healthy weight range. Sixty-nine percent of participants completed at least 5 of the 6 modules. Intention-to-treat analyses found no significant outcomes for change in BMI between groups. Compared to children in the comparison group, those in the intervention group reduced frequency of discretionary food intake (estimate -0.360, 95% CI -2.272 to -0.447, P=0.00), and parents showed improvement in child feeding pressure to eat practices (-0.304, 95% CI 0.061 to -0.003, P=0.048) and nutrition self-efficacy (0.429, 95% CI 0.096 to 0.763, P=0.01). No significant time by group interaction was found for other outcomes. Conclusions: The trial demonstrated that a parent-focussed eHealth childhood obesity prevention program can provide support to improve dietary-related practices and self-efficacy but was not successful in reducing BMI. The target sample size was not achieved, which would have affected statistical power. Clinical Trial: Australian and New Zealand Clinical Trials Registry (12616000119493).

  • How is my Child’s Asthma?”: Digital Phenotype and Actionable Insights for Pediatric Asthma

    Date Submitted: Aug 20, 2018

    Open Peer Review Period: Aug 20, 2018 - Aug 29, 2018

    Background: Asthma is a multifactorial chronic disease that severely affects a child’s daily activity and quality of life. Pediatric asthma causes reduced playtime, disturbed sleep, and missed schoo...

    Background: Asthma is a multifactorial chronic disease that severely affects a child’s daily activity and quality of life. Pediatric asthma causes reduced playtime, disturbed sleep, and missed school days -- impacting a child’s long-term academic and physical growth. In this paper, we discuss the use of the kHealth system for continuous and comprehensive monitoring of child’s symptoms, activity, sleep pattern, environmental triggers, and compliance. The kHealth system helps in deriving actionable insights to help manage asthma both at personal and at the cohort level. Specifically, the two of scores introduced here are also the basis of ongoing work on addressing personalized asthma care, and to answer questions such as: how can I help my child better adhere to care instructions and reduce future exacerbation? Objective: In traditional asthma management protocol, a child meets with a clinician infrequently, say once in 3 to 6 months. The child provides information about his/her recent asthma condition by answering a set of questions to assess asthma control which may include the validated Asthma Control Test questionnaire. The elicited information may be inadequate for precise diagnosis of the cause for the timely determination of asthma control and compliance and for assessing the effectiveness of the treatment plan. The continuous monitoring and improved tracking of child’s symptoms, activities, sleep, treatment adherence, and the environmental conditions allow more precise determination of asthma triggers, and reliable assessment of medication compliance and its effectiveness. Methods: The kHealth system comprises of kHealth kit, kHealth cloud, and kHealth dashboard. Digital Phenotype Score and Controller Compliance Score are defined to summarize the child’s condition from the data collected using the kHealth kit to provide better actionable insights. Digital Phenotype Score formalizes the asthma control level using data about symptoms, rescue medication usage, activity level, and sleep pattern. The Compliance Score captures how well the child is complying with the treatment protocol. We monitored and analyzed data for 95 children, each recruited for a 1 or 3-month long study. Results: At cohort level, we found 36.6% of the children’s asthma was Very Poorly Controlled, 25.6% was Not Well Controlled, and 37.8% was Well Controlled. Among the Very Poorly Controlled children (n=30), we found 30% were Highly Compliant towards their controller medication intake suggesting their re-evaluation for change in medication/dosage, while 50% were Poorly Compliant and candidates for more timely intervention to improve compliance to mitigate their situation. Conclusions: We observed that a patient’s Digital Phenotype Score and Controller Compliance Score computed based on continuous digital monitoring provides clinician a more timely and detailed evidence of patient’s asthma-related condition compared to the Asthma Control Test scores taken infrequently during clinic visits.

  • Profiles of health information seeking and the current digital divide within a California population-based sample

    Date Submitted: Aug 14, 2018

    Open Peer Review Period: Aug 19, 2018 - Oct 14, 2018

    Background: Internet use for health information is important given the rise of eHealth integrating technology into healthcare. Despite perceived widespread use of the Internet, a persistent “digital...

    Background: Internet use for health information is important given the rise of eHealth integrating technology into healthcare. Despite perceived widespread use of the Internet, a persistent “digital divide” exists in which many individuals have ready access to the Internet and others do not. To date, most published reports have compared characteristics of Internet users seeking health information vs. non-users. However, there is little understanding of the differences between Internet users seeking health information online and users who do not. Understanding these differences could enable targeted outreach for health interventions and promotion of eHealth technologies. Objective: This study aims to assess population-level characteristics associated with types of Internet use, particularly for seeking online health information. Methods: The 2015-2016 California Health Interview Survey (CHIS) datasets were used for this study. Internet use was classified as: those who have never used the Internet (Never use), those who have ever used the Internet but not to search for health information in the last 12-months (Use not for health), and those who have ever used the Internet and have used it to search for health information in the last 12-months (Use for health). Weighted multinomial logistic regression was used to assess sociodemographic and health characteristics associated with types of Internet use. Findings are reported as odds ratios (OR) with 95% confidence intervals (CI). Results: Among 42,087 participants (weighted sample of 29,236,426), 19% reported Never Use of the Internet, 27.9% reported Use not for health, and 53.1% reported Use for health. Compared to Never Use, Use for health individuals were more likely to be younger (OR: 0.1 [CI: 0.1, 0.2] for ≥60 years vs <60 years), female (OR: 1.6 [CI: 1.3, 1.9] compared to males), non-Hispanic White (OR: 0.54 [CI: 0.4, 0.7] and OR: 0.2 [CI: 0.2, 0.4] for Latinos and African Americans, respectively), and have a higher socioeconomic status (400%+ Federal Poverty Guidelines OR: 1.3 [CI: 1.4, 2.4]). Overall, characteristics for the Use not for health and Use for health groups were similar, except for those with lower levels of education and respondents not having visited a physician in the last year. For these two characteristics Use not for health were more similar to Never Use. Conclusions: Findings indicate that a digital divide characterized by sociodemographic and health information exists across three types of users. Results reflect previous studies about the divide specifically with regards to disparities in use and access related to age, race/ ethnicity, and socioeconomic status. Disparities in health seeking online may reflect existing disparities in healthcare access extending into a new era of health technology. Findings support the need for interventions to target Internet access and health literacy among Never Use and Use not for health groups.

  • Data mining in the development of mHealth apps: assessing in-app navigation through Markov Chain analysis

    Date Submitted: Aug 16, 2018

    Open Peer Review Period: Aug 16, 2018 - Oct 11, 2018

    Background: Background: Mobile applications generate vast amounts of user data. In the mHealth domain, researchers are increasingly discovering the opportunities of these data to assess the engagement...

    Background: Background: Mobile applications generate vast amounts of user data. In the mHealth domain, researchers are increasingly discovering the opportunities of these data to assess the engagement levels of their developed mobile applications. To date however, the analysis of these data is often limited to descriptive statistics. Using the right data mining techniques, application log data can offer significantly deeper insights. Objective: Objective: The purpose of this study was to assess how more advanced data mining techniques offer an opportunity to dig deeper into the data and afford to discover application mHealth app usage patterns using Markov Chain and sequence clustering analysis. Methods: Methods: A transition matrix between the nine pages of the app was composed from which a Markov Chain was constructed, enabling intuitive user behavior analysis. Results: Results: Five session types of app use were distinguished through the analysis, two of which represented usage of the main intended functions as envisioned by the developers. The two main functions were further automatically reconstructed by means of sequence clustering. Conclusions: Conclusions: Using Markov Chains to assess in-app navigation presents an innovative method to evaluate mHealth interventions. The insights can be used to improve the navigation in the app, the flow between behavior change techniques and placement of features in the app. 

  • History Repeating Itself? Concerns about [Genetic] Testing in Primary Care

    Date Submitted: Aug 13, 2018

    Open Peer Review Period: Aug 15, 2018 - Oct 10, 2018

    Primary care physicians report low confidence in their genetic testing knowledge. Practitioners in the early 20th century similarly reported hesitations towards now-routine chemistry-based blood tests...

    Primary care physicians report low confidence in their genetic testing knowledge. Practitioners in the early 20th century similarly reported hesitations towards now-routine chemistry-based blood tests, e.g. counts, glucose, and cholesterol measurement. Physicians in 2017 say the same things about genetic testing: it is hard to incorporate into daily practice, not consistently useful, less important than other priorities, and technically difficult. Technologies that are now commonplace in clinical practice were approached overcautiously at inception, similar to genetic testing today.