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Journal Description

The Journal of Medical Internet Research (JMIR), now in its 21st 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 2019: 5.03, ranking Q1 in the medical informatics category. The journal focuses on emerging technologies, medical devices, apps, engineering, and informatics applications for patient education, prevention, population health and clinical care. As a 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 an open access journal, we are read by clinicians, allied health professionals, informal caregivers, and patients alike, and have (as with 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: Freepik; Copyright: xb100; URL:; License: Licensed by JMIR.

    Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media


    Background: High-quality medical resources are in high demand worldwide, and the application of artificial intelligence (AI) in medical care may help alleviate the crisis related to this shortage. The development of the medical AI industry depends to a certain extent on whether industry experts have a comprehensive understanding of the public’s views on medical AI. Currently, the opinions of the general public on this matter remain unclear. Objective: The purpose of this study is to explore the public perception of AI in medical care through a content analysis of social media data, including specific topics that the public is concerned about; public attitudes toward AI in medical care and the reasons for them; and public opinion on whether AI can replace human doctors. Methods: Through an application programming interface, we collected a data set from the Sina Weibo platform comprising more than 16 million users throughout China by crawling all public posts from January to December 2017. Based on this data set, we identified 2315 posts related to AI in medical care and classified them through content analysis. Results: Among the 2315 identified posts, we found three types of AI topics discussed on the platform: (1) technology and application (n=987, 42.63%), (2) industry development (n=706, 30.50%), and (3) impact on society (n=622, 26.87%). Out of 956 posts where public attitudes were expressed, 59.4% (n=568), 34.4% (n=329), and 6.2% (n=59) of the posts expressed positive, neutral, and negative attitudes, respectively. The immaturity of AI technology (27/59, 46%) and a distrust of related companies (n=15, 25%) were the two main reasons for the negative attitudes. Across 200 posts that mentioned public attitudes toward replacing human doctors with AI, 47.5% (n=95) and 32.5% (n=65) of the posts expressed that AI would completely or partially replace human doctors, respectively. In comparison, 20.0% (n=40) of the posts expressed that AI would not replace human doctors. Conclusions: Our findings indicate that people are most concerned about AI technology and applications. Generally, the majority of people held positive attitudes and believed that AI doctors would completely or partially replace human ones. Compared with previous studies on medical doctors, the general public has a more positive attitude toward medical AI. Lack of trust in AI and the absence of the humanistic care factor are essential reasons why some people still have a negative attitude toward medical AI. We suggest that practitioners may need to pay more attention to promoting the credibility of technology companies and meeting patients’ emotional needs instead of focusing merely on technical issues.

  • A woman using a smartphone. Source: Pexels; Copyright:; URL:; License: Licensed by the authors.

    Adherence to Established Treatment Guidelines Among Unguided Digital Interventions for Depression: Quality Evaluation of 28 Web-Based Programs and Mobile Apps


    Background: Web-based interventions for depression have been widely tested for usability and functioning. However, the few studies that have addressed the therapeutic quality of these interventions have mainly focused on general aspects without consideration of specific quality factors related to particular treatment components. Clinicians and scientists are calling for standardized assessment criteria for web-based interventions to enable effective and trustworthy patient care. Therefore, an extensive evaluation of web-based interventions at the level of individual treatment components based on therapeutic guidelines and manuals is needed. Objective: The objective of this study was to evaluate the quality of unguided web-based interventions for depression at the level of individual treatment components based on their adherence to current gold-standard treatment guidelines and manuals. Methods: A comprehensive online search of popular app stores and search engines in January 2018 revealed 11 desktop programs and 17 smartphone apps that met the inclusion criteria. Programs and apps were included if they were available for German users, interactive, unguided, and targeted toward depression. All programs and apps were tested by three independent researchers following a standardized procedure with a predefined symptom trajectory. During the testing, all web-based interventions were rated with a standardized list of criteria based on treatment guidelines and manuals for depression. Results: Overall interrater reliability for all raters was substantial with an intraclass correlation coefficient of 0.73 and Gwet AC1 value of 0.80. The main features of web-based interventions included mood tracking (24/28, 86%), psychoeducation (21/28, 75%), cognitive restructuring (21/28, 75%), crisis management (20/28, 71%), behavioral activation (19/29, 68%), and relaxation training (18/28, 64%). Overall, therapeutic meaningfulness was rated higher for desktop programs (mean 4.13, SD 1.17) than for smartphone apps (mean 2.92, SD 1.46). Conclusions: Although many exercises from manuals are included in web-based interventions, the necessary therapeutic depth of the interventions is often not reached, and risk management is frequently lacking. There is a need for further research targeting general principles for the development and evaluation of therapeutically sound web-based interventions for depression.

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

    Effectiveness and Safety of Using Chatbots to Improve Mental Health: Systematic Review and Meta-Analysis


    Background: The global shortage of mental health workers has prompted the utilization of technological advancements, such as chatbots, to meet the needs of people with mental health conditions. Chatbots are systems that are able to converse and interact with human users using spoken, written, and visual language. While numerous studies have assessed the effectiveness and safety of using chatbots in mental health, no reviews have pooled the results of those studies. Objective: This study aimed to assess the effectiveness and safety of using chatbots to improve mental health through summarizing and pooling the results of previous studies. Methods: A systematic review was carried out to achieve this objective. The search sources were 7 bibliographic databases (eg, MEDLINE, EMBASE, PsycINFO), the search engine “Google Scholar,” and backward and forward reference list checking of the included studies and relevant reviews. Two reviewers independently selected the studies, extracted data from the included studies, and assessed the risk of bias. Data extracted from studies were synthesized using narrative and statistical methods, as appropriate. Results: Of 1048 citations retrieved, we identified 12 studies examining the effect of using chatbots on 8 outcomes. Weak evidence demonstrated that chatbots were effective in improving depression, distress, stress, and acrophobia. In contrast, according to similar evidence, there was no statistically significant effect of using chatbots on subjective psychological wellbeing. Results were conflicting regarding the effect of chatbots on the severity of anxiety and positive and negative affect. Only two studies assessed the safety of chatbots and concluded that they are safe in mental health, as no adverse events or harms were reported. Conclusions: Chatbots have the potential to improve mental health. However, the evidence in this review was not sufficient to definitely conclude this due to lack of evidence that their effect is clinically important, a lack of studies assessing each outcome, high risk of bias in those studies, and conflicting results for some outcomes. Further studies are required to draw solid conclusions about the effectiveness and safety of chatbots. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42019141219;

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

    Similarities and Differences in COVID-19 Awareness, Concern, and Symptoms by Race and Ethnicity in the United States: Cross-Sectional Survey


    Background: Existing health disparities based on race and ethnicity in the United States are contributing to disparities in morbidity and mortality during the coronavirus disease (COVID-19) pandemic. We conducted an online survey of American adults to assess similarities and differences by race and ethnicity with respect to COVID-19 symptoms, estimates of the extent of the pandemic, knowledge of control measures, and stigma. Objective: The aim of this study was to describe similarities and differences in COVID-19 symptoms, knowledge, and beliefs by race and ethnicity among adults in the United States. Methods: We conducted a cross-sectional survey from March 27, 2020 through April 1, 2020. Participants were recruited on social media platforms and completed the survey on a secure web-based survey platform. We used chi-square tests to compare characteristics related to COVID-19 by race and ethnicity. Statistical tests were corrected using the Holm Bonferroni correction to account for multiple comparisons. Results: A total of 1435 participants completed the survey; 52 (3.6%) were Asian, 158 (11.0%) were non-Hispanic Black, 548 (38.2%) were Hispanic, 587 (40.9%) were non-Hispanic White, and 90 (6.3%) identified as other or multiple races. Only one symptom (sore throat) was found to be different based on race and ethnicity (P=.003); this symptom was less frequently reported by Asian (3/52, 5.8%), non-Hispanic Black (9/158, 5.7%), and other/multiple race (8/90, 8.9%) participants compared to those who were Hispanic (99/548, 18.1%) or non-Hispanic White (95/587, 16.2%). Non-Hispanic White and Asian participants were more likely to estimate that the number of current cases was at least 100,000 (P=.004) and were more likely to answer all 14 COVID-19 knowledge scale questions correctly (Asian participants, 13/52, 25.0%; non-Hispanic White participants, 180/587, 30.7%) compared to Hispanic (108/548, 19.7%) and non-Hispanic Black (25/158, 15.8%) participants. Conclusions: We observed differences with respect to knowledge of appropriate methods to prevent infection by the novel coronavirus that causes COVID-19. Deficits in knowledge of proper control methods may further exacerbate existing race/ethnicity disparities. Additional research is needed to identify trusted sources of information in Hispanic and non-Hispanic Black communities and create effective messaging to disseminate correct COVID-19 prevention and treatment information.

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

    Diagnosing Parkinson Disease Through Facial Expression Recognition: Video Analysis


    Background: The number of patients with neurological diseases is currently increasing annually, which presents tremendous challenges for both patients and doctors. With the advent of advanced information technology, digital medical care is gradually changing the medical ecology. Numerous people are exploring new ways to receive a consultation, track their diseases, and receive rehabilitation training in more convenient and efficient ways. In this paper, we explore the use of facial expression recognition via artificial intelligence to diagnose a typical neurological system disease, Parkinson disease (PD). Objective: This study proposes methods to diagnose PD through facial expression recognition. Methods: We collected videos of facial expressions of people with PD and matched controls. We used relative coordinates and positional jitter to extract facial expression features (facial expression amplitude and shaking of small facial muscle groups) from the key points returned by Face++. Algorithms from traditional machine learning and advanced deep learning were utilized to diagnose PD. Results: The experimental results showed our models can achieve outstanding facial expression recognition ability for PD diagnosis. Applying a long short-term model neural network to the positions of the key features, precision and F1 values of 86% and 75%, respectively, can be reached. Further, utilizing a support vector machine algorithm for the facial expression amplitude features and shaking of the small facial muscle groups, an F1 value of 99% can be achieved. Conclusions: This study contributes to the digital diagnosis of PD based on facial expression recognition. The disease diagnosis model was validated through our experiment. The results can help doctors understand the real-time dynamics of the disease and even conduct remote diagnosis.

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

    Evaluation of Volume of News Reporting and Opioid-Related Deaths in the United States: Comparative Analysis Study of Geographic and Socioeconomic Differences


    Background: News media coverage is a powerful influence on public attitude and government action. The digitization of news media covering the current opioid epidemic has changed the landscape of coverage and may have implications for how to effectively respond to the opioid crisis. Objective: This study aims to characterize the relationship between volume of online opioid news reporting and opioid-related deaths in the United States and how these measures differ across geographic and socioeconomic county-level factors. Methods: Online news reports from February 2018 to April 2019 on opioid-related events in the United States were extracted from Google News. News data were aggregated at the county level and compared against opioid-related death counts. Ordinary least squares regression was used to model opioid-related death rate and opioid news coverage with the inclusion of socioeconomic and geographic explanatory variables. Results: A total of 35,758 relevant news reports were collected representing 1789 counties. Regression analysis revealed that opioid-related death rate was positively associated with news reporting. However, opioid-related death rate and news reporting volume showed opposite correlations with educational attainment and rurality. When controlling for variation in death rate, counties in the Northeast were overrepresented by news coverage. Conclusions: Our results suggest that regional variation in the volume of opioid-related news reporting does not reflect regional variation in opioid-related death rate. Differences in the amount of media attention may influence perceptions of the severity of opioid epidemic. Future studies should investigate the influence of media reporting on public support and action on opioid issues.

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

    Blockchain in Health Care: Hope or Hype?


    There has been an increasing interest in blockchain technology from the health care sector in the last couple of years. The value proposition for using blockchain technology in the health care sector is to share sensitive patient data among health care entities securely and to empower patients. Blockchain technology allows patients to have an active role in developing and updating their own patient data. However, is blockchain technology really the silver bullet it seems to be? With this paper, we aim to understand the benefits and challenges of blockchain technology in the health care sector. We discuss innovation and security implications concerning blockchain technology in health care. Furthermore, we show that there is a need for more use cases to ensure the secure sharing of data within the health care sector. In our opinion, blockchain technology will not solve the issues encountered by the health care sector; in fact, it may raise more issues than it will solve.

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

    Causal Effect of Honorary Titles on Physicians’ Service Volumes in Online Health Communities: Retrospective Study


    Background: An OHC online health community (OHC) is an interactive platform for virtual communication between patients and physicians. Patients can typically search, seek, and share their experience and rate physicians, who may be involved in giving advice. Some OHC providers provide incentives in form of honorary titles to encourage the web-based involvement of physicians, but it is unclear whether the award of honorary titles has an impact on their consultation volume in an OHC. Objective: This study is designed to identify the differential treatment effect of the incentive policy on the service volumes for the subgroups of treatment and control in an OHC. This study aims to answer the following questions: Does an honorary title for physicians impact their service volumes in an OHC? During the period of discontinuity, can we identify the sharp effect of the incentive award on the outcomes of physicians’ service volumes? Methods: We acquired the targeted samples based on treatment, namely, physicians with an honorary title or not and outcomes measured before and after the award of the 2 subgroups. A regression discontinuity design was applied to investigate the impact of the honorary titles incentive as a treatment in an OHC. There was a sharply discontinuous effect of treatment on physicians’ online health service performance. The experimental data set consisted of 346 physicians in the treatment group (with honorary titles). Applying the propensity score matching method, the same size of physicians (n=346) was matched and selected as the control group. Results: A sharp discontinuity was found at the time of the physician receiving the honorary title. The results showed that the parametric estimates of the coefficient were significantly positively (P<.001) associated with monthly home page views. The jump in the monthly volumes of home page views was much sharper than that of the monthly consultations. Conclusions: The changes in the volumes of monthly consultations and home page views reflect the differential treatment effect of honorary titles on physicians’ service volumes. The effect of the incentive policy with honorary titles is objectively estimated from both the perspective of online and offline medical services in an OHC. Being named with honorary titles significantly multiplied monthly home page views, yet it did not significantly impact monthly consultations. This may be because consultation capacity is limited by the physician's schedule for consultations.

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

    Evaluating the Impact of the Grading and Assessment of Predictive Tools Framework on Clinicians and Health Care Professionals’ Decisions in Selecting...


    Background: While selecting predictive tools for implementation in clinical practice or for recommendation in clinical guidelines, clinicians and health care professionals are challenged with an overwhelming number of tools. Many of these tools have never been implemented or evaluated for comparative effectiveness. To overcome this challenge, the authors developed and validated an evidence-based framework for grading and assessment of predictive tools (the GRASP framework). This framework was based on the critical appraisal of the published evidence on such tools. Objective: The aim of the study was to examine the impact of using the GRASP framework on clinicians’ and health care professionals’ decisions in selecting clinical predictive tools. Methods: A controlled experiment was conducted through a web-based survey. Participants were randomized to either review the derivation publications, such as studies describing the development of the predictive tools, on common traumatic brain injury predictive tools (control group) or to review an evidence-based summary, where each tool had been graded and assessed using the GRASP framework (intervention group). Participants in both groups were asked to select the best tool based on the greatest validation or implementation. A wide group of international clinicians and health care professionals were invited to participate in the survey. Task completion time, rate of correct decisions, rate of objective versus subjective decisions, and level of decisional conflict were measured. Results: We received a total of 194 valid responses. In comparison with not using GRASP, using the framework significantly increased correct decisions by 64%, from 53.7% to 88.1% (88.1/53.7=1.64; t193=8.53; P<.001); increased objective decision making by 32%, from 62% (3.11/5) to 82% (4.10/5; t189=9.24; P<.001); decreased subjective decision making based on guessing by 20%, from 49% (2.48/5) to 39% (1.98/5; t188=−5.47; P<.001); and decreased prior knowledge or experience by 8%, from 71% (3.55/5) to 65% (3.27/5; t187=−2.99; P=.003). Using GRASP significantly decreased decisional conflict and increased the confidence and satisfaction of participants with their decisions by 11%, from 71% (3.55/5) to 79% (3.96/5; t188=4.27; P<.001), and by 13%, from 70% (3.54/5) to 79% (3.99/5; t188=4.89; P<.001), respectively. Using GRASP decreased the task completion time, on the 90th percentile, by 52%, from 12.4 to 6.4 min (t193=−0.87; P=.38). The average System Usability Scale of the GRASP framework was very good: 72.5% and 88% (108/122) of the participants found the GRASP useful. Conclusions: Using GRASP has positively supported and significantly improved evidence-based decision making. It has increased the accuracy and efficiency of selecting predictive tools. GRASP is not meant to be prescriptive; it represents a high-level approach and an effective, evidence-based, and comprehensive yet simple and feasible method to evaluate, compare, and select clinical predictive tools.

  • Betel nuts arranged on beach in Guam. Source: Image created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    An Instagram-Based Study to Understand Betel Nut Use Culture in Micronesia: Exploratory Content Analysis


    Background: A 2012 World Health Organization report recognizes betel nut use as an urgent public health threat faced by the Western Pacific region. However, compared with other addictive substances, little is known about how betel nuts are depicted on social media platforms. In particular, image-based social media platforms can be powerful tools for health communication. Studying the content of substance use on visual social media may provide valuable insights into public health interventions. Objective: This study aimed to explore and document the ways that betel nut is portrayed on the photo-sharing site Instagram. The analysis focuses on the hashtag #pugua, which refers to the local term for betel nut in Guam and other parts of Micronesia. Methods: An exploratory content analysis of 242 Instagram posts tagged #pugua was conducted based on previous research on substance use and Instagram and betel nut practices in Micronesia. In addition, the study examined the social engagement of betel nut content on the image-based platform. Results: The study findings revealed content themes referencing the betel nut or betel nut tree, betel nut preparation practices, and the unique social and cultural context surrounding betel nut activity in Guam and Micronesia. In addition, certain practices and cultural themes encouraged social engagement on Instagram. Conclusions: The findings from this study emphasize the cultural relevance of betel nut use in Micronesia. These findings provide a basis for empirically testing hypotheses related to the etiological roles of cultural identity and pride in shaping betel nut use behavior among Micronesians, particularly youths and young adults. Such research is likely to inform the development of culturally relevant betel nut prevention and cessation programs.

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

    Benefits of Massive Open Online Course Participation: Deductive Thematic Analysis


    Background: Massive open online courses (MOOCs), as originally conceived, promised to provide educational access to anyone with an internet connection. However, the expansiveness of MOOC education has been found to be somewhat limited. Nonetheless, leading universities continue to offer MOOCs, including many in the health sciences, on a number of private platforms. Therefore, research on online education must include thorough understanding of the role of MOOCs. To date, studies on MOOC participants have focused mainly on learners’ assessment of the course. It is known that MOOCs are not reaching the universal audiences that were predicted, and much knowledge has been gained about learners’ perceptions of MOOCs. However, there is little scholarship on what learners themselves gain from participating in MOOCs. Objective: As MOOC development persists and expands, scholars and developers should be made aware of the role of MOOCs in education by examining what these courses do offer their participants. The objective of this qualitative synthesis of a set of MOOC evaluation studies was to explore outcomes for MOOC learners, that is, how the learners themselves benefit from participating in MOOCs. Methods: To explore MOOC learners’ outcomes, we conducted a qualitative synthesis in the form of a deductive thematic analysis, aggregating findings from 17 individual studies selected from an existing systematic review of MOOC evaluation methods. We structured our inquiry using the Kirkpatrick model, considering Kirkpatrick levels 2, 3, and 4 as potential themes in our analysis. Results: Our analysis identified six types of Kirkpatrick outcomes in 17 studies. Five of these outcomes (learning/general knowledge, skills, attitudes, confidence, and commitment) fit into Kirkpatrick Level 2, while Kirkpatrick Level 3 outcomes concerning behavior/application were seen in four studies. Two additional themes were identified outside of the Kirkpatrick framework: culture and identity outcomes and affective/emotional outcomes. Kirkpatrick Level 4 was not represented among the outcomes we examined. Conclusions: Our findings point to some gains from MOOCs. While we can expect MOOCs to persist, how learners benefit from the experience of participating in MOOCs remains unclear.

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

    The Effects of Telemonitoring on Patient Compliance With Self-Management Recommendations and Outcomes of the Innovative Telemonitoring Enhanced Care Program...


    Background: Telemonitoring enables care providers to remotely support outpatients in self-managing chronic heart failure (CHF), but the objective assessment of patient compliance with self-management recommendations has seldom been studied. Objective: This study aimed to evaluate patient compliance with self-management recommendations of an innovative telemonitoring enhanced care program for CHF (ITEC-CHF). Methods: We conducted a multicenter randomized controlled trial with a 6-month follow-up. The ITEC-CHF program comprised the provision of Bluetooth-enabled scales linked to a call center and nurse care services to assist participants with weight monitoring compliance. Compliance was defined a priori as weighing at least 4 days per week, analyzed objectively from weight recordings on the scales. The intention-to-treat principle was used to perform the analysis. Results: A total of 184 participants (141/184, 76.6% male), with a mean age of 70.1 (SD 12.3) years, were randomized to receive either ITEC-CHF (n=91) or usual care (control; n=93), of which 67 ITEC-CHF and 81 control participants completed the intervention. For the compliance criterion of weighing at least 4 days per week, the proportion of compliant participants in the ITEC-CHF group was not significantly higher than that in the control group (ITEC-CHF: 67/91, 74% vs control: 56/91, 60%; P=.06). However, the proportion of ITEC-CHF participants achieving the stricter compliance standard of at least 6 days a week was significantly higher than that in the control group (ITEC-CHF: 41/91, 45% vs control: 23/93, 25%; P=.005). Conclusions: ITEC-CHF improved participant compliance with weight monitoring, although the withdrawal rate was high. Telemonitoring is a promising method for supporting both patients and clinicians in the management of CHF. However, further refinements are required to optimize this model of care. Trial Registration: Australian New Zealand Clinical Trial Registry ACTRN12614000916640;

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  • The academic viewpoint on patient data ownership in the context of Big Data – a Scoping review

    Date Submitted: Jul 6, 2020

    Open Peer Review Period: Jul 11, 2020 - Sep 11, 2020

    Background: Ownership of patient information in the context of Big Data is a relatively new problem, apparently not yet fully recognized by the medical academic community. The problem is interdiscipli...

    Background: Ownership of patient information in the context of Big Data is a relatively new problem, apparently not yet fully recognized by the medical academic community. The problem is interdisciplinary, incorporating legal, ethical, medical and aspects of information and communication technologies and a more sophisticated analysis of the issue is needed. However, no previous scoping review has mapped existing studies on the subject. Objective: The aim of this study is to determine how the medical academic community perceives the issue of ownership of patient information in the context of Big Data, its possible solutions and implemented practical applications. Methods: A scoping review, based on the five-stage framework outlined by Arksey and O’Malley and further developed by Levac, Colquhoun and O’Brien was conducted. The organization and the reporting of the results of the scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and its extensions for Scoping Reviews (PRISMA-ScR). A systematic and comprehensive search of four scientific information databases, PubMed, ScienceDirect, Scopus and Springer was performed for studies published between January 2000 and October 2019. Two authors independently assessed eligibility and extracted data. Results: The review included 32 eligible articles authored by academicians and corresponding to three focus areas: problem (ownership); area (healthcare); context (Big Data). Five major aspects were studied: scientific area of publications, aspects and academicians’ perception of ownership in the context of Big Data, proposed solutions and practical applications for data ownership issues in the context of Big Data. The aspects in which publications consider ownership of medical data are not clearly distinguished, but can be summarized as: ethical, legal, political and managerial. Ownership of patient data is perceived primarily as a challenge being fundamental to conducting medical research including data sales and sharing, and in a smaller degree as a means of control, problem, threat and opportunity also in view of primarily medical research. Although numerous solutions are falling into three categories: technology, law and policy are proposed, only three real applications are discussed. Conclusions: The issue of ownership of patient information in the context of Big Data is poorly researched, is not addressed consistently and in its integrity, and there is no consensus on the ethical requirement for justice and the necessary legal regulations. What is missing is, in fact, adequate policy decisions expressed through the relevant legal framework. And in order to develop appropriate policies and regulations, ethical principles must be known, understood and upheld.

  • Knowledge, attitudes, and practices towards COVID-19 among primary and middle school students during the COVID-19 outbreak period in Beijing: An online cross-sectional survey

    Date Submitted: Jul 8, 2020

    Open Peer Review Period: Jul 8, 2020 - Sep 2, 2020

    Background: The distribution and influence factors of knowledge, attitudes and practices (KAP) towards coronavirus disease 2019 (COVID-19) among children and adolescents remain unknown. Objective: Thi...

    Background: The distribution and influence factors of knowledge, attitudes and practices (KAP) towards coronavirus disease 2019 (COVID-19) among children and adolescents remain unknown. Objective: This study aimed to investigate the KAP towards COVID-19 and their influencing factors among primary and middle school students during the self-quarantine period in Beijing. Methods: This was a cross-sectional study among students from 18 primary and middle schools in Beijing during March 2020. Stratified cluster sampling was conducted. Demographic and KAP-related COVID-19 information was collected through an online questionnaire. The influencing factors were analyzed by multivariable logistic regression. Results: A total of 7,377 students were included. The overall correct rate for COVID-19 knowledge was 74.1%, while only 31.5% and 40.5% could identify the high-risk places of cross-infection and warning body temperature. Although 94.5% of respondents believed the epidemic could be controlled, over 50% expressed various concerns about the epidemic. The compliance rates for basic preventing behaviors were all over 80%, while those for "rational and effective ventilation" (39.2%) and "dinning separately" (38.6%) were low. The KAP levels were significantly differed according to various school categories of students. The COVID-19 knowledge (OR= 3.309, 95% CI: 2.921, 3.748) and attitude (OR=1.145, 95% CI: 1.003, 1.308) were associated with preventive practices. Besides, female, urban students, those with a healthy lifestyle, and those with the willingness to engage in healthcare tended to have better preventive practices. Conclusions: Most students in Beijing hold a high level of knowledge, optimistic attitudes and have appropriate practices towards COVID-19. However, targeted interventions are still necessary, especially for students with high-risk characteristics. Keywords: COVID-19; primary and middle school students; knowledge; attitude; practice Clinical Trial: Because the study is not a clinical study, there is no trial ID for this study.

  • Development and External Validation of Diagnostic Model for Periprocedural Bradycardia during Primary Percutaneous Coronary Intervention: Algorithm Development and Validation

    Date Submitted: Jul 7, 2020

    Open Peer Review Period: Jul 7, 2020 - Sep 1, 2020

    Background: Periprocedural bradycardia weaks the benefit of primary percutaneous coronary intervention (PPCI) and has deleterious effects on organ perfusion of patients with acute ST elevation myocard...

    Background: Periprocedural bradycardia weaks the benefit of primary percutaneous coronary intervention (PPCI) and has deleterious effects on organ perfusion of patients with acute ST elevation myocardial infarction (STEMI). Objective: The objective of our study was to develop and externally validate a diagnostic model of periprocedural bradycardia. . Methods: Design: Multivariate logistic regression of a cohort of acute STEMI patients. Setting: Emergency department ward of a university hospital. Participants: Diagnostic model development: Totally 1820 acute STEMI patients who were consecutively treated with PPCI from November 2007 to December 2015 in Beijing Anzhen Hospital, Capital Medical University. External validation: Totally 716 acute STEMI patients who were treated with PPCI from January 2016 to June 2018 in Beijing Anzhen Hospital, Capital Medical University. Outcomes: Periprocedural bradycardia during PPCI. Periprocedural bradycardia was defined as preoperative heart rate ≥ 50 times / min, intraoperative heart rate <50 times / min persistent or transient. We used logistic regression analysis to analyze the risk factors of periprocedural bradycardia in the development data set. We developed a diagnostic model of periprocedural bradycardia and constructed a nomogram.We assessed the predictive performance of the diagnostic model in the validation data sets by examining measures of discrimination, calibration, and decision curve analysis (DCA). Results: Periprocedural bradycardia occurred in 332 out of 1,820 participants (18.2%) in the development dataset. The strongest predictors of periprocedural bradycardia were intra-procedural hypotension, the culprit vessel was not left anterior descending (LAD), using thrombus aspiration devices during procedure, sex, history of coronary artery disease, total occlusion of culprit vessel, and no-reflow. We developed a diagnostic model of periprocedural bradycardia.The area under the receiver operating characteristic(ROC) curve(AUC) was was.8384 ±.0122, 95% confidence interval(CI)=.81460~.86225in the development set. We constructed a nomogram based on predictors of periprocedural bradycardia. Periprocedural bradycardia occurred in 102 out of 716 participants (14.2%)in the validation dataset. The AUC was was .8437 ±.0203, 95% CI= .80390 ~ .88357. Discrimination, calibration, and DCA were satisfactory. Date of approved by ethic committee:16 May 2019. Date of data collection start: 1 June 2019. Numbers recruited as of submission of the manuscript:2,536. Conclusions: We developed and externally validated a diagnostic model of periprocedural bradycardia during PPCI. Clinical Trial: We registered this study with WHO International Clinical Trials Registry Platform(ICTRP). Registration number: ChiCTR1900023214. Registered Date :16 May 2019.

  • A COVID-19 Contact Tracing Self-Confirmation System for the General Population in Japan: Design and Implementation Evaluation

    Date Submitted: Jul 4, 2020

    Open Peer Review Period: Jul 4, 2020 - Aug 29, 2020

    Background: The global spread of coronavirus disease (COVID-19) has attracted extensive research concerns. It is an infectious disease resulting from a novel virus termed severe acute respiratory synd...

    Background: The global spread of coronavirus disease (COVID-19) has attracted extensive research concerns. It is an infectious disease resulting from a novel virus termed severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). The joint collaboration of Ministry of Health, Labor and Welfare (MHLW) in Japan and new coronavirus infection control team has led to the formal issuance of a Bluetooth-based mobile app, which integrates privacy and security protection with methodical processing required for effective contact tracing of exposure to COVID-19. Objective: Due to the demand for a contact tracing instrument that timely keeps track of exposure, teams of professionals and authority experts contributed to the design and implementation of a mobile app intended to provide the general population with updates of contacts at the individual level, including both the infected and the exposed, to manage the risk of close contacts in Japan. This study aims to evaluate the development and distribution of a Bluetooth-based mobile contact-confirming application (COCOA) for COVID-19 to integrate the efforts by healthcare practitioners, the infected and the exposed to contain the spread of COVID-19. Methods: The Exposure Notification Framework (AGF) co-provided by Apple and Google is used in the provision of service. It distributes validated incremental information of COVID-19 that is closely related but might be unware to the individuals. Great emphasis is placed on the correct understanding of seven major steps (issue and confirm process code, report infection status, request information of the infected, calculate and compare exposure summary, provision of response guidance) needed in the process. Results: COCOA consists of three major components: the two mobile apps for the infected and exposed individuals respectively and the notification system that is used to manage and broadcast the information of infection and exposure. Users can self-confirm the risk of exposure to the COVID-19 by periodically fetching the outbreak data and choose provided available response to COVID-19. Conclusions: COCOA is a mobile-based self-confirmation telehealth system developed to assist not only the government and healthcare providers, but also the infected and the exposed individuals to contain COVID-19 through rapid responses and effective non-contact cooperation. The design and the mechanism presents a desirable capacity to promote non-contact cooperation, forecast the potential risk of being infected and facilitate consequential measures to prevent further spread.

  • Internet-based and mobile-supported stress management as a universal prevention approach – Effectiveness and moderators from a large pragmatic randomized-controlled trial

    Date Submitted: Jul 3, 2020

    Open Peer Review Period: Jul 3, 2020 - Aug 28, 2020

    Background: Emerging evidence indicates the effectiveness of Internet-based mobile supported stress management (iSMIs) in highly stressed employees. It is yet unclear, however, whether iSMIs are also...

    Background: Emerging evidence indicates the effectiveness of Internet-based mobile supported stress management (iSMIs) in highly stressed employees. It is yet unclear, however, whether iSMIs are also effective without a preselection process in a universal prevention approach which more closely resembles routine occupational health care. Moreover, evidence for whom iSMIs might be suitable and for whom not is scarce. Objective: The present study aims to evaluate the iSMI GET.ON Stress without baseline inclusion criteria and examine moderators of intervention effects. Methods: 396 employees were randomly assigned to the intervention condition (IC) or the six-month waiting list control condition (WLC). The iSMI consisted of seven sessions and one booster session with and offered no therapeutic guidance. Self-report data were assessed at baseline, seven weeks, and at six months following randomization. The primary outcome was perceived stress (PSS-10). Several a priori defined moderators were explored as potential effect modifiers. Results: Participants of the IC reported significantly lower perceived stress at post-treatment (d=0.71) and six-month follow-up (d=0.61) compared to the WLC. Significant differences with medium to large effect sizes were found for all mental health and most work-related outcomes. Resilience, agreeableness, psychological strain and self-regulation moderated intervention effects. Conclusions: This study indicates that iSMIs can be effective in a broad range of employees with no need for pre-selection to achieve substantial effects. The subgroups that might not profit all had extreme values on the respective measures and represented only a very small proportion of the investigated sample, indicating a broad applicability of GET.ON Stress.

  • Noncommunicable chronic disease and the risk of COVID-19: a population-based case-control study

    Date Submitted: Jul 2, 2020

    Open Peer Review Period: Jul 2, 2020 - Aug 27, 2020

    Objective: To investigate the association of the non-communicable chronic disease (NCD) with the risk of coronavirus disease 2019 (COVID-19). Methods: A case-control study was conducted. The cases we...

    Objective: To investigate the association of the non-communicable chronic disease (NCD) with the risk of coronavirus disease 2019 (COVID-19). Methods: A case-control study was conducted. The cases were laboratory-confirmed COVID-19 who were treated in the Union Hospital in Wuhan. The healthy controls were randomly selected from the participants of the Hunan Government Employee Cohort study who were not infected with COVID-19, matching by age and sex. NCDs including hypertension, diabetes, coronary heart disease, chronic pulmonary disease, and cancer were determined by self-reportings, use of medications, measurements, and/or laboratory testings. The severity of COVID-19 was determined by physicians according to the guideline. Logistic regression was used to estimate the association, in terms of odds ratio (OR). Results: A total of 468 cases and 1404 controls (1:3) were included in the analysis with a mean age of 59.1±12.8 years and 51.7% male. The case group comprised 134 moderately ill, 275 severely ill, and 59 critically ill COVID-19 patients. Patients with diabetes (OR=3.23, P<0.001), chronic pulmonary disease (OR=5.99, P<0.001), and hypertension (OR=1.45, P=0.001) showed a significantly increased risk of COVID-19 infection compared to the healthy controls. Additionally, diabetes, chronic pulmonary disease, hypertension, and the number of comorbid NCDs were associated with the severity of COVID-19 dose-dependently. Conclusions: Patients with diabetes, hypertension, and chronic pulmonary disease are at a higher risk of having COVID-19 and developing severe type of the disease.