Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?


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, and is also the largest journal in the field. The journal focuses on emerging technologies, medical devices, apps, engineering, telehealth 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, and which together receive over 6.000 submissions a year. 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 journal but can simply transfer it between 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: Pexels; Copyright: Samson Katt; URL:; License: Licensed by JMIR.

    Effects of Objective and Subjective Health Literacy on Patients’ Accurate Judgment of Health Information and Decision-Making Ability: Survey Study


    Background: Interpreting health information and acquiring health knowledge have become more important with the accumulation of scientific medical knowledge and ideals of patient autonomy. Health literacy and its tremendous success as a concept can be considered an admission that not all is well in the distribution of health knowledge. The internet makes health information much more easily accessible than ever, but it introduces its own problems, of which health disinformation is a major one. Objective: The objective of this study was to determine whether objective and subjective health literacy are independent concepts and to test which of the two was associated more strongly with accurate judgments of the quality of a medical website and with behavioral intentions beneficial to health. Methods: A survey on depression and its treatments was conducted online (n=362). The Newest Vital Sign was employed to measure objective, performance-based health literacy, and the eHealth Literacy Scale was used to measure subjective, perception-based health literacy. Correlations, comparisons of means, linear and binary logistic regression, and mediation models were used to determine the associations. Results: Objective and subjective health literacy were weakly associated with one another (r=0.06, P=.24). High objective health literacy levels were associated with an inclination to behave in ways that are beneficial to one’s own or others’ health (Exp[B]=2.068, P=.004) and an ability to recognize low-quality online sources of health information (β=–.4698, P=.005). The recognition also improved participants’ choice of treatment (β=–.3345, P<.001). Objective health literacy helped people to recognize misinformation on health websites and improved their judgment on their treatment for depression. Conclusions: Self-reported, perception-based health literacy should be treated as a separate concept from objective, performance-based health literacy. Only objective health literacy appears to have the potential to prevent people from becoming victims of health disinformation.

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

    The Role of Technology and Social Media Use in Sleep-Onset Difficulties Among Italian Adolescents: Cross-sectional Study


    Background: The use of technology and social media among adolescents is an increasingly prevalent phenomenon. However, there is a paucity of evidence on the relationship between frequency of use of electronic devices and social media and sleep-onset difficulties among the Italian population. Objective: The aim of this study is to investigate the association between the use of technology and social media, including Facebook and YouTube, and sleep-onset difficulties among adolescents from Lombardy, the most populous region in Italy. Methods: The relationship between use of technology and social media and sleep-onset difficulties was investigated. Data came from the 2013-2014 wave of the Health Behavior in School-aged Children survey, a school-based cross-sectional study conducted on 3172 adolescents aged 11 to 15 years in Northern Italy. Information was collected on difficulties in falling asleep over the last 6 months. We estimated the odds ratios (ORs) for sleep-onset difficulties and corresponding 95% CIs using logistic regression models after adjustment for major potential confounders. Results: The percentage of adolescents with sleep-onset difficulties was 34.3% (1081/3151) overall, 29.7% (483/1625) in boys and 39.2% (598/1526) in girls. It was 30.3% (356/1176) in 11-year-olds, 36.2% (389/1074) in 13-year-olds, and 37.3% (336/901) in 15-year-olds. Sleep-onset difficulties were more frequent among adolescents with higher use of electronic devices, for general use (OR 1.50 for highest vs lowest tertile of use; 95% CI 1.21-1.85), use for playing games (OR 1.35; 95% CI 1.11-1.64), use of online social networks (OR 1.40 for always vs never or rarely; 95% CI 1.09-1.81), and YouTube (OR 2.00; 95% CI 1.50-2.66). Conclusions: This study adds novel information about the relationship between sleep-onset difficulties and technology and social media in a representative sample of school-aged children from a geographical location that has not been included in studies of this type previously. Exposure to screen-based devices and online social media is significantly associated with adolescent sleep-onset difficulties. Interventions to create a well-coordinated parent- and school-centered strategy, thereby increasing awareness on the unfavorable effect of evolving technologies on sleep among adolescents, are needed.

  • Source: flickr; Copyright: Forth With Life; URL:; License: Creative Commons Attribution (CC-BY).

    Determining the Intellectual Structure and Academic Trends of Smart Home Health Care Research: Coword and Topic Analyses


    Background: With the rapid development of information and communication technologies, smart homes are being investigated as effective solutions for home health care. The increasing academic attention on smart home health care has primarily been on the development and application of smart home technologies. However, comprehensive studies examining the general landscape of diverse research areas for smart home health care are still lacking. Objective: This study aims to determine the intellectual structure of smart home health care in a time series by conducting a coword analysis and topic analysis. Specifically, it investigates (1) the intellectual basis of smart home health care through overall academic status, (2) the intellectual foci through influential keywords and their evolutions, and (3) intellectual trends through primary topics and their evolutions. Methods: Analyses were conducted in 5 steps: (1) data retrieval from article databases (Web of Science, Scopus, and PubMed) and the initial dataset preparation of 6080 abstracts from the year 2000 to the first half of 2019; (2) data preprocessing and refinement extraction of 25,563 words; (3) a descriptive analysis of the overall academic status and period division (ie, 4 stages of 3-year blocks); (4) coword analysis based on word co-occurrence networks for the intellectual foci; and (5) topic analysis for the intellectual trends based on latent Dirichlet allocation (LDA) topic modeling, word-topic networks, and researcher workshops. Results: First, regarding the intellectual basis of smart home health care, recent academic interest and predominant journals and research domains were verified. Second, to determine the intellectual foci, primary keywords were identified and classified according to the degree of their centrality values. Third, 5 themes pertaining to the topic evolution emerged: (1) the diversification of smart home health care research topics; (2) the shift from technology-oriented research to technological convergence research; (3) the expansion of application areas and system functionality of smart home health care; (4) the increased focus on system usability, such as service design and experiences; and (5) the recent adaptation of the latest technologies in health care. Based on these findings, the pattern of technology diffusion in smart home health care research was determined as the adaptation of technologies, the proliferation of application areas, and an extension into system design and service experiences. Conclusions: The research findings provide academic and practical value in 3 aspects. First, they promote a comprehensive understanding of the smart home health care domain by identifying its multifaceted intellectual structure in a time series. Second, they can help clinicians discern the development and dispersion level of their respective disciplines. Third, the pattern of technology diffusion in smart home health care could help scholars comprehend current and future research trends and identify research opportunities based on upcoming research waves of newly adapted technologies in smart home health care.

  • The photo shows the directions for patients to use the Cloud SYSUCC remote pharmacy platform. Source: Image created by the authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Use of a Remote Oncology Pharmacy Service Platform for Patients With Cancer During the COVID-19 Pandemic: Implementation and User Acceptance Evaluation


    Background: The COVID-19 outbreak has increased challenges associated with health management, especially cancer management. In an effort to provide continuous pharmaceutical care to cancer patients, Sun Yat-sen University Cancer Center (SYSUCC) implemented a remote pharmacy service platform based on its already existing web-based hospital app known as Cloud SYSUCC. Objective: The aim of this study was to investigate the characteristics, acceptance, and initial impact of the Cloud SYSUCC app during a COVID-19 outbreak in a tertiary cancer hospital in China. Methods: The total number of online prescriptions and detailed information on the service were obtained during the first 6 months after the remote service platform was successfully set up. The patients’ gender, age, residence, primary diagnosis, drug classification, weekly number of prescriptions, and prescribed drugs were analyzed. In addition, a follow-up telephonic survey was conducted to evaluate patients’ satisfaction in using the remote prescription service. Results: A total of 1718 prescriptions, including 2022 drugs for 1212 patients, were delivered to 24 provinces and municipalities directly under the Central Government of China between February 12, 2020, and August 11, 2020. The majority of patients were female (841/1212, 69.39%), and 90.18% (1093/1212) of them were aged 31-70 years old. The top 3 primary diagnoses for which remote medical prescriptions were made included breast cancer (599/1212, 49.42%), liver cancer (249/1212, 20.54%), and thyroid cancer (125/1212, 10.31%). Of the 1718 prescriptions delivered, 1435 (83.5%) were sent to Guangdong Province and 283 (16.5%) were sent to other provinces in China. Of the 2022 drugs delivered, 1012 (50.05%) were hormonal drugs. The general trend in the use of the remote prescription service declined since the 10th week. A follow-up telephonic survey found that 88% (88/100) of the patients were very satisfied, and 12% (12/100) of the patients were somewhat satisfied with the remote pharmacy service platform. Conclusions: The remote pharmacy platform Cloud SYSUCC is efficient and convenient for providing continuous pharmaceutical care to patients with cancer during the COVID-19 crisis. The widespread use of this platform can help to reduce person-to-person transmission as well as infection risk for these patients. Further efforts are needed to improve the quality and acceptance of the Cloud SYSUCC platform, as well as to regulate and standardize the management of this novel service.

  • Patients are using mobile devices to visit Internet hospitals for consultation. Source: Image created by the authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Assessment of Internet Hospitals in China During the COVID-19 Pandemic: National Cross-Sectional Data Analysis Study


    Background: Internet hospitals in China are being rapidly developed as an innovative approach to providing health services. The ongoing COVID-19 pandemic has triggered the development of internet hospitals that promote outpatient service delivery to the public via internet technologies. To date, no studies have assessed China's internet hospitals during the COVID-19 pandemic. Objective: This study aimed to elucidate the characteristics of China's internet hospitals and assess the health service capacity of these hospitals. Methods: Data on 711 internet hospitals were collected from official websites, the WeChat (Tencent Inc) platform, smartphone apps, and the Baidu search engine until July 16, 2020. Results: As of July 16, 2020, 711 internet hospitals were developed in mainland China. More than half of these internet hospitals (421/711, 59.2%) were established during 2019 (206/711, 29%) and 2020 (215/711, 30.2%). Furthermore, about one-third (215/711, 30.2%) of internet hospitals were established at the beginning of 2020 as an emergency response to the COVID-19 epidemic. The 711 internet hospitals consisted of the following 3 types of hospitals: government-oriented (42/711, 5.91%), hospital-oriented (143/711, 20.11%), and enterprise-oriented internet hospitals (526/711, 73.98%). The vast majority of internet hospitals were traditional hospitals (526/711, 74%). Nearly 46.1% (221/711) of internet hospitals requested doctors to provide health services at a specific web clinic. Most patients (224/639, 35.1%) accessed outpatient services via WeChat. Internet hospitals’ consulting methods included SMS text messaging consultations involving the use of graphics (552/570, 96.8%), video consultations (248/570, 43.5%), and telephone consultations (238/570, 41.8%). The median number of available web-based doctors was 43, and the median consultation fees of fever clinics and other outpatient clinics were ¥0 (US $0) per consultation and ¥6 (US $0.93) per consultation, respectively. Internet hospitals have provided various services during the COVID-19 pandemic, including medical prescription, drug delivery, and medical insurance services. Conclusions: The dramatic increase of internet hospitals in China has played an important role in the prevention and control of COVID-19. Internet hospitals provide different and convenient medical services for people in need.

  • Screenshot of EVO. © 2020-2021, Akili Interactive Labs, Inc. All rights reserved. Source: Akili Interactive Labs; Copyright: Akili Interactive Labs; URL:; License: Licensed by the authors.

    Application of an Adaptive, Digital, Game-Based Approach for Cognitive Assessment in Multiple Sclerosis: Observational Study


    Background: Cognitive impairment is one of the most debilitating manifestations of multiple sclerosis. Currently, the assessment of cognition relies on a time-consuming and extensive neuropsychological examination, which is only available in some centers. Objective: To enable simpler, more accessible cognitive screening, we sought to determine the feasibility and potential assessment sensitivity of an unsupervised, adaptive, video game–based digital therapeutic to assess cognition in multiple sclerosis. Methods: A total of 100 people with multiple sclerosis (33 with cognitive impairment and 67 without cognitive impairment) and 24 adults without multiple sclerosis were tested with the tablet game (EVO Monitor) and standard measures, including the Brief International Cognitive Assessment for Multiple Sclerosis (which included the Symbol Digit Modalities Test [SDMT]) and Multiple Sclerosis Functional Composite 4 (which included the Timed 25-Foot Walk test). Patients with multiple sclerosis also underwent neurological evaluations and contributed recent structural magnetic resonance imaging scans. Group differences in EVO Monitor performance and the association between EVO Monitor performance and standard measures were investigated. Results: Participants with multiple sclerosis and cognitive impairment showed worse performance in EVO Monitor compared with participants without multiple sclerosis (P=.01) and participants with multiple sclerosis without cognitive impairment (all P<.002). Regression analyses indicated that participants with a lower SDMT score showed lower performance in EVO Monitor (r=0.52, P<.001). Further exploratory analyses revealed associations between performance in EVO Monitor and walking speed (r=–0.45, P<.001) as well as brain volumetric data (left thalamic volume: r=0.47, P<.001; right thalamic volume: r=0.39, P=.002; left rostral middle frontal volume: r=0.28, P=.03; right rostral middle frontal volume: r=0.27, P=.03). Conclusions: These findings suggest that EVO Monitor, an unsupervised, video game–based digital program integrated with adaptive mechanics, is a clinically valuable approach to measuring cognitive performance in patients with multiple sclerosis. Trial Registration: NCT03569618;

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

    Factors Affecting the Implementation, Use, and Adoption of Real-Time Location System Technology for Persons Living With Cognitive Disabilities in Long-term...


    Background: As the aging population continues to grow, the number of adults living with dementia or other cognitive disabilities in residential long-term care homes is expected to increase. Technologies such as real-time locating systems (RTLS) are being investigated for their potential to improve the health and safety of residents and the quality of care and efficiency of long-term care facilities. Objective: The aim of this study is to identify factors that affect the implementation, adoption, and use of RTLS for use with persons living with dementia or other cognitive disabilities in long-term care homes. Methods: We conducted a systematic review of the peer-reviewed English language literature indexed in MEDLINE, Embase, PsycINFO, and CINAHL from inception up to and including May 5, 2020. Search strategies included keywords and subject headings related to cognitive disability, residential long-term care settings, and RTLS. Study characteristics, methodologies, and data were extracted and analyzed using constant comparative techniques. Results: A total of 12 publications were included in the review. Most studies were conducted in the Netherlands (7/12, 58%) and used a descriptive qualitative study design. We identified 3 themes from our analysis of the studies: barriers to implementation, enablers of implementation, and agency and context. Barriers to implementation included lack of motivation for engagement; technology ecosystem and infrastructure challenges; and myths, stories, and shared understanding. Enablers of implementation included understanding local workflows, policies, and technologies; usability and user-centered design; communication with providers; and establishing policies, frameworks, governance, and evaluation. Agency and context were examined from the perspective of residents, family members, care providers, and the long-term care organizations. Conclusions: There is a striking lack of evidence to justify the use of RTLS to improve the lives of residents and care providers in long-term care settings. More research related to RTLS use with cognitively impaired residents is required; this research should include longitudinal evaluation of end-to-end implementations that are developed using scientific theory and rigorous analysis of the functionality, efficiency, and effectiveness of these systems. Future research is required on the ethics of monitoring residents using RTLS and its impact on the privacy of residents and health care workers. Trial Registration:

  • Source: Pixabay; Copyright: Chuck Underwood; URL:; License: Licensed by the authors.

    Implementation of Telehealth Services to Assess, Monitor, and Treat Neurodevelopmental Disorders: Systematic Review


    Background: In response to COVID-19, there has been increasing momentum in telehealth development and delivery. To assess the anticipated exponential growth in telehealth, it is important to accurately capture how telehealth has been used in specific mental health fields prior to the pandemic. Objective: This systematic review aimed to highlight how telehealth has been used with clinical samples in the neurodevelopmental field, including patients with neurodevelopmental disorders (NDDs), their families, and health care professionals. To identify which technologies show the greatest potential for implementation into health services, we evaluated technologies for effectiveness, economic impact, and readiness for clinical adoption. Methods: A systematic search of literature was undertaken in April 2018 and updated until December 2019, by using the Medline, Web of Science, Scopus, CINAHL Plus, EMBASE, and PsycInfo databases. Extracted data included the type of technology, how the technology was used (ie, assessment, treatment, and monitoring), participant characteristics, reported outcomes and authors’ views on clinical effectiveness, user impact (ie, feasibility and acceptability), economic impact, and readiness for clinic adoption. A quality review of the research was performed in accordance with the Oxford Centre for Evidence-Based Medicine Levels of Evidence. Results: A total of 42 studies met the inclusion criteria. These studies included participants and family members with autism spectrum disorders (21/42, 50%), attention deficit hyperactivity disorders (8/42, 19%), attention deficit hyperactivity or autism spectrum disorders (3/42, 7%), communication disorders (7/42, 17%), and tic disorders (2/42, 5%). The focus of most studies (33/42, 79%) was on treatment, rather than assessment (4/42, 10%) or monitoring (5/42, 12%). Telehealth services demonstrated promise for being clinically effective, predominantly in relation to diagnosing and monitoring NDDs. In terms of NDD treatment, telehealth services were usually equivalent to control groups. There was some evidence of positive user and economic impacts, including increased service delivery efficiency (eg, increased treatment availability and decreased waiting times). However, these factors were not widely recorded across the studies. Telehealth was demonstrated to be cost-effective in the few studies that considered cost-effectiveness. Study quality varied, as many studies had small sample sizes and inadequate control groups. Of the 42 studies, only 11 (26%) were randomized controlled trials, 12 (29%) were case studies or case series, 6 (14%) were qualitative studies, and 5 (12%) were noncomparative trials. Conclusions: Telehealth has the potential to increase treatment availability, decrease diagnosis waiting times, and aid in NDD monitoring. Further research with more robust and adequately powered study designs that consider cost-effectiveness and increased efficiency is needed. This systematic review highlights the extent of telehealth technology use prior to the COVID-19 pandemic and the movement for investing in remote access to treatments. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42018091156;

  • Source: Adobe Stock; Copyright: sepy; URL:; License: Licensed by JMIR.

    Patient Perspectives on Health Data Privacy and Implications for Adverse Drug Event Documentation and Communication: Qualitative Study


    Background: Adverse drug events are unintended and harmful effects of medication use. Using existing information and communication technologies (ICTs) to increase information sharing about adverse drug events may improve patient care but can introduce concerns about data privacy. Objective: This study aims to examine the views of patients and their caregivers about data protection when using ICTs to communicate adverse drug event information to improve patient safety. Methods: We conducted an exploratory qualitative study. A total of 4 focus groups were held among patients who had experienced or were at risk of experiencing an adverse drug event, their family members, and their caregivers. We recruited participants through multiple avenues and iteratively analyzed the data using situational analysis. Results: Of the 47 participants recruited, 28 attended our focus groups. We identified 3 primary themes. First, participants felt that improved information sharing about adverse drug events within their circle of care would likely improve care. Second, participants were concerned about data handling and inappropriate access but believed that the benefits of information sharing outweighed the risks of privacy breaches. Finally, participants were more concerned about data privacy in the context of stigmatized health conditions. Conclusions: Current conditions for maintaining health data privacy are consistent with participants’ preferences, despite the fact that health data are susceptible to breaches and mismanagement. Information sharing that increases patient safety may justify potential privacy risks. Greater attention to patient concerns and the effect of social and contextual concerns in the design and implementation of health information technologies may increase patient confidence in the privacy of their information.

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

    Prevalence of Health Misinformation on Social Media: Systematic Review


    Background: Although at present there is broad agreement among researchers, health professionals, and policy makers on the need to control and combat health misinformation, the magnitude of this problem is still unknown. Consequently, it is fundamental to discover both the most prevalent health topics and the social media platforms from which these topics are initially framed and subsequently disseminated. Objective: This systematic review aimed to identify the main health misinformation topics and their prevalence on different social media platforms, focusing on methodological quality and the diverse solutions that are being implemented to address this public health concern. Methods: We searched PubMed, MEDLINE, Scopus, and Web of Science for articles published in English before March 2019, with a focus on the study of health misinformation in social media. We defined health misinformation as a health-related claim that is based on anecdotal evidence, false, or misleading owing to the lack of existing scientific knowledge. We included (1) articles that focused on health misinformation in social media, including those in which the authors discussed the consequences or purposes of health misinformation and (2) studies that described empirical findings regarding the measurement of health misinformation on these platforms. Results: A total of 69 studies were identified as eligible, and they covered a wide range of health topics and social media platforms. The topics were articulated around the following six principal categories: vaccines (32%), drugs or smoking (22%), noncommunicable diseases (19%), pandemics (10%), eating disorders (9%), and medical treatments (7%). Studies were mainly based on the following five methodological approaches: social network analysis (28%), evaluating content (26%), evaluating quality (24%), content/text analysis (16%), and sentiment analysis (6%). Health misinformation was most prevalent in studies related to smoking products and drugs such as opioids and marijuana. Posts with misinformation reached 87% in some studies. Health misinformation about vaccines was also very common (43%), with the human papilloma virus vaccine being the most affected. Health misinformation related to diets or pro–eating disorder arguments were moderate in comparison to the aforementioned topics (36%). Studies focused on diseases (ie, noncommunicable diseases and pandemics) also reported moderate misinformation rates (40%), especially in the case of cancer. Finally, the lowest levels of health misinformation were related to medical treatments (30%). Conclusions: The prevalence of health misinformation was the highest on Twitter and on issues related to smoking products and drugs. However, misinformation on major public health issues, such as vaccines and diseases, was also high. Our study offers a comprehensive characterization of the dominant health misinformation topics and a comprehensive description of their prevalence on different social media platforms, which can guide future studies and help in the development of evidence-based digital policy action plans.

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

    Adherence of Mobile App-Based Surveys and Comparison With Traditional Surveys: eCohort Study


    Background: eCohort studies offer an efficient approach for data collection. However, eCohort studies are challenged by volunteer bias and low adherence. We designed an eCohort embedded in the Framingham Heart Study (eFHS) to address these challenges and to compare the digital data to traditional data collection. Objective: The aim of this study was to evaluate adherence of the eFHS app-based surveys deployed at baseline (time of enrollment in the eCohort) and every 3 months up to 1 year, and to compare baseline digital surveys with surveys collected at the research center. Methods: We defined adherence rates as the proportion of participants who completed at least one survey at a given 3-month period and computed adherence rates for each 3-month period. To evaluate agreement, we compared several baseline measures obtained in the eFHS app survey to those obtained at the in-person research center exam using the concordance correlation coefficient (CCC). Results: Among the 1948 eFHS participants (mean age 53, SD 9 years; 57% women), we found high adherence to baseline surveys (89%) and a decrease in adherence over time (58% at 3 months, 52% at 6 months, 41% at 9 months, and 40% at 12 months). eFHS participants who returned surveys were more likely to be women (adjusted odds ratio [aOR] 1.58, 95% CI 1.18-2.11) and less likely to be smokers (aOR 0.53, 95% CI 0.32-0.90). Compared to in-person exam data, we observed moderate agreement for baseline app-based surveys of the Physical Activity Index (mean difference 2.27, CCC=0.56), and high agreement for average drinks per week (mean difference 0.54, CCC=0.82) and depressive symptoms scores (mean difference 0.03, CCC=0.77). Conclusions: We observed that eFHS participants had a high survey return at baseline and each 3-month survey period over the 12 months of follow up. We observed moderate to high agreement between digital and research center measures for several types of surveys, including physical activity, depressive symptoms, and alcohol use. Thus, this digital data collection mechanism is a promising tool to collect data related to cardiovascular disease and its risk factors.

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

    A Newly Developed Web-Based Resource on Genetic Eye Disorders for Users With Visual Impairment (Gene.Vision): Usability Study


    Background: Despite the introduction of the Web Content Accessibility Guidelines and legislations, many websites remain poorly accessible to users with disability, especially those with visual impairment, as the internet has become a more visually complex environment. With increasing reliance on the internet and almost 2 million people in the United Kingdom being affected by vision loss, it is important that they are not overlooked when developing web-based materials. A significant proportion of those affected have irreversible vision loss due to rare genetic eye disorders, and many of them use the internet as a primary source of information for their conditions. However, access to high-quality web-based health information with an inclusive design remains a challenge for many. We have developed a new web-based resource for genetic eye disorders called Gene.Vision that aims to provide a holistic guide for patients, relatives, and health care professionals. by sight loss, it is important that they are not overlooked when developing web-based materials. A significant proportion of those affected have irreversible sight loss due to rare genetic eye disorders, and many of them use the internet as a primary source of information for their conditions. However, access to high-quality web-based health information with an inclusive design remains a challenge for many. Objective: Through a usability testing session of our website prototype, this study aims to identify key web-based accessibility features for internet users with vision impairment and to explore whether the contents provided in Gene.Vision are relevant and comprehensible. Methods: A face-to-face testing session with 8 participants (5 patients, 2 family members, and 1 member of the public) and 8 facilitators was conducted on a prototype website. Remote testing was performed with another patient due to COVID-19 restrictions. Home page design, navigation, content layout and quality, language, and readability were explored through direct observation and task completion using the think-aloud method. A patient focus group was organized to elicit further feedback. Qualitative data were gathered and analyzed to identify core themes through open and axial coding. Results: All participants had good computer literacy; 6 patients with visual impairment used visual aid software including iOS VoiceOver and Speak Screen, iOS Classic Invert, ZoomText 2020, Job Access With Speech, and Nonvisual Desktop Access. The features identified by the participants that will enhance accessibility and usability for users with visual impairment were a consistent website layout, a structured information hierarchy with a clear description of links, good chromatic and luminance contrast, a simple home page with predictable and easy navigation, adaptability to various assistive software, and readable and relevant content. They reported that dynamic content (such as carousels) and large empty spaces reduced accessibility. Information on research, support available, practical advice, and links to charities were incentives for repeated website visits. Conclusions: We demonstrated the importance of developing a website with a user-based approach. Through end user testing, we identified several key web-based accessibility features for people with visual impairment. Target end users should always be involved early and throughout the design process to ensure their needs are met. Many of these steps can be implemented easily and will aid in search engine optimization.

Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Latest Submissions Open for Peer-Review:

View All Open Peer Review Articles
  • A Pathway-driven Coordinated Telehealth System for Management of Patients with Single or Multiple Chronic Diseases in China: System Development and Retrospective Study

    Date Submitted: Jan 17, 2021

    Open Peer Review Period: Jan 17, 2021 - Mar 14, 2021

    Background: Integrated care enhanced with information technology has emerged as a means to transform health services to meet long-term care needs of chronic disease patients. However, the feasibility...

    Background: Integrated care enhanced with information technology has emerged as a means to transform health services to meet long-term care needs of chronic disease patients. However, the feasibility of applying integrated care to the emerging “three-manager” mode in China remains to be explored. Moreover, only few studies attempted to integrate multiple types of chronic diseases into one system. Objective: This study aimed to present a coordinated telehealth system that supports the management of single or multiple chronic diseases, meanwhile addresses the existing challenges of “three-manager” mode in China. Methods: The system was designed based on a tailored integrated care model. The model was constructed from an individual scale, mainly concentrating on specifying the management roles and their responsibilities through a universal care pathway. A custom ontology was developed to represent the knowledge contained in the model. The system consisted of a service engine for data storage and decision support, as well as different forms of clients for care providers and patients. Currently, the system supports management of three single chronic diseases (hypertension, type 2 diabetes mellitus, and chronic obstructive pulmonary disease) and one type of multiple chronic conditions (hypertension with type 2 diabetes mellitus). A retrospective study was performed based on the long-term observational data extracted from the system to analyze system usability, treatment effect, and quality of care. Results: The retrospective analysis involved 6,964 chronic disease patients and 249 care providers who have registered in our system since the deployment in 2015. A total of 519,598 self-monitoring records have been submitted by the patients. The engine was able to generate different types of records regularly according to the specific care pathway. Based on the comparison tests and casual inference results, a part of patient outcomes improved after receiving intervention through the system, especially the systolic blood pressure of hypertensive patients (P<.001 in all comparison tests and an approximately 5 mmHg decrease after intervention via causal inference). The regional case study shows that the work efficiency of care providers differed individually. Conclusions: Our system has the potential to provide effective management support for single or multiple chronic conditions simultaneously. The tailored closed-loop care pathway was feasible and effective under the “three-manager” mode in China. One direction for future work is to introduce advanced artificial intelligence techniques to construct a more personalized care pathway.

  • Cabernet: A Question-and-Answer System to Extract Data from Free-Text Pathology Reports

    Date Submitted: Jan 16, 2021

    Open Peer Review Period: Jan 15, 2021 - Mar 12, 2021

    Background: Information in pathology reports is critical for cancer care. Natural language processing (NLP) systems to extract information from pathology reports are often narrow in scope or require e...

    Background: Information in pathology reports is critical for cancer care. Natural language processing (NLP) systems to extract information from pathology reports are often narrow in scope or require extensive tuning. Consequently, there is growing interest in automated deep learning approaches. A powerful new NLP algorithm, Bidirectional Encoder Representations from Transformers (BERT), was published in late 2018. BERT set new performance standards on tasks as diverse as question-answering, named entity recognition, speech recognition, and more. Objective: to develop a BERT-based system to automatically extract detailed tumor site and histology information from free text pathology reports. Methods: We pursued three specific aims: 1) extract accurate tumor site and histology descriptions from free-text pathology reports; 2) accommodate the diverse terminology used to indicate the same pathology; and 3) provide accurate standardized tumor site and histology codes for use by downstream applications. We first trained a base language-model to comprehend the technical language in pathology reports. This involved unsupervised learning on a training corpus of 275,605 electronic pathology reports from 164,531 unique patients that included 121 million words. Next, we trained a Q&A “head” that would connect to, and work with, the pathology language model to answer pathology questions. Our Q&A system was designed to search for the answers to two predefined questions in each pathology report: 1) “What organ contains the tumor?”; and, 2) “What is the kind of tumor or carcinoma?”. This involved supervised training on 8,197 pathology reports, each with ground truth answers to these two questions determined by Certified Tumor Registrars. The dataset included 214 tumor sites and 193 histologies. The tumor site and histology phrases extracted by the Q&A model were used to predict ICD-O-3 site and histology codes. This involved fine-tuning two additional BERT models: one to predict site codes, and the second to predict histology codes. Our final system includes a network of 3 BERT-based models. We call this caBERTnet (pronounced “Cabernet”). We evaluated caBERnet using a sequestered test dataset of 2,050 pathology reports with ground truth answers determined by Certified Tumor Registrars. Results: caBERTnet’s accuracies for predicting group-level site and histology codes were 93.5% and 97.7%, respectively. The top-5 accuracies for predicting fine-grained ICD-O-3 site and histology codes with 5 or more samples each in the training dataset were 93.6% and 95.4%, respectively. Conclusions: This is the first time an NLP system has achieved expert-level performance predicting ICD-O-3 codes across a broad range of tumor sites and histologies. Our new system could help reduce treatment delays, increase enrollment in clinical trials of new therapies, and improve patient outcomes.

  • The challenge of integrating eHealth into healthcare; a systematic literature review on structure, process and outcomes (Donabedian model)

    Date Submitted: Jan 14, 2021

    Open Peer Review Period: Jan 14, 2021 - Jan 25, 2021

    Background: Healthcare organisations increasingly work with eHealth. However, the integration of eHealth into regular healthcare is challenging. It requires organisations to change the way they work....

    Background: Healthcare organisations increasingly work with eHealth. However, the integration of eHealth into regular healthcare is challenging. It requires organisations to change the way they work. The organisation’s structure and care processes need to be adapted to ensure that eHealth supports the attainment of the desired outcomes. Objective: The aims of this study were to investigate whether there are identifiable indicators in the structure, process and outcome categories related to a successful integration of eHealth in regular healthcare, and to investigate which indicators of structure and process are related to outcome indicators. Methods: A systematic literature review was conducted, using Donabedian’s Structure-Process-Outcome framework (SPO), to identify indicators that are related to the integration of eHealth into healthcare organisations. Data extraction sheets were designed to provide an overview of the study characteristics, the eHealth characteristics, and the indicators. The extracted indicators were organised into themes and subthemes of the structure, process and outcome categories. Results: Eleven studies were included, covering a variety of study designs, diseases and eHealth tools. All studies identified structure, process and outcome indicators that were potentially related to the integration of eHealth. The number of indicators found in structure, process, outcomes was respectively 175, 84, and 88. The themes with the most-noted indicators and their mutual interaction were the inner setting (51 indicators, 16 interactions), care receiver (40 indicators, 11 interactions) and technology (38 indicators, 12 interactions) themes, all three in the structure category, the healthcare actions theme (38 indicators, 15 interactions) in the process category and the efficiency theme (30 indicators, 15 interactions) in the outcome category. In-depth examination showed four most-reported indicators, namely indicator ‘deployment of human resources’ (n=11) of the inner setting theme in the structure category, the ‘ease of use’ (n=16) and ‘technical issue’ (n=10) indicators, both in the technology theme within the structure category, and the ‘health logistics’ (n=26) indicator in the efficiency theme within the outcome category. Conclusions: This study showed that three principles are important for the successful integration of eHealth into healthcare. First, the role of the care receiver needs to be incorporated into the organisational structure and daily care process. Second, the technology must be well attuned to the organisational structure and daily care process. Third, the deployment of human resources in the daily care processes needs to be aligned with the desired end results. Not adhering to these points could negatively affect the organisation, daily process, or the end results.

  • Temporal Dynamics of Emotions during the COVID-19 Pandemic at the Center of Outbreak: A Sentimental Analysis of Weibo Tweets from Wuhan

    Date Submitted: Jan 10, 2021

    Open Peer Review Period: Jan 10, 2021 - Mar 7, 2021

    Background: The ongoing COVID-19 pandemic increased the general public's anxiety, depression, post-traumatic stress disorder (PTSD), psychological stress in various degrees around the world. Objective...

    Background: The ongoing COVID-19 pandemic increased the general public's anxiety, depression, post-traumatic stress disorder (PTSD), psychological stress in various degrees around the world. Objective: This study aims to detect the temporal patterns of emotional fluctuation, the significant events that affected the emotional changes and variations, and the hourly variations of the emotions within a day. Methods: Based on a longitudinal dataset of 816,556 posts tweeted by 27,912 Weibo users in Wuhan from December 31, 2019 to April 31, 2020, we processed general sentiment inclination rating and the type of sentiments of Weibo tweets by relevant Python libraries and the Naive Bayes Classifier algorithm. We also grouped the hours into five-time groups to measure the netizens’ sentimental changes during different periods in a day. Results: Overall, negative emotions like surprise, fear, and anger are the salient emotions on the social media platform. Milestone events, such as the confirmation of human-to-human transmission, etc., are the primary events that ignited the emotions. Emotions varied within a day. Although all emotions are more prevalent in the afternoon and night, fear and anger are more dominant in the morning and afternoon, while depression is more salient during the night. Conclusions: Milestone events during the pandemic are the primary events that ignited the citizens’ emotions. In addition, the emotions varied within a day. Better-tailored mental health services and interventions could be conducted accordingly.

  • Search trends of online interest in LUTS enquiry, diagnosis and treatment in China

    Date Submitted: Jan 8, 2021

    Open Peer Review Period: Jan 7, 2021 - Mar 4, 2021

    Background: Lower urinary tract symptoms (LUTS) are one of the most described urination disorders worldwide. Previous investigations have focused predominantly on the prospective identification of cas...

    Background: Lower urinary tract symptoms (LUTS) are one of the most described urination disorders worldwide. Previous investigations have focused predominantly on the prospective identification of cases that meets the researchers criteria, the genuine demand as regard to LUTS and related issues from patients may thus be neglected. Objective: To examine the online search trend and behaviours related to LUTS on a national and regional scale using the dominant major search engine in mainland China. Methods: The Baidu Index was queried using the LUTS related terms for the period 2011.01–2020.09. The search volume for each term was recorded to analyze the search trend and demographic distributions. For user interest, the data of demand graph and trend data were collected and analyzed. Results: Of the 13 LUTS symptom domains, 11 domains are available in Baidu index database. The BSI for each LUTS domains varies from 37.78% to 1.47%. the search trend of urinary frequency (APC = 7.82%; p < .05; 2011-2018), incomplete emptying (APC = 17.74%; p < .05; 2011-2016), nocturia (APC = 11.54%; p < .05; 2011-2018), dysuria (APC = 20.77%; p < .05; 2017-2020) and incontinence (APC = 13.39%; p < .05; 2011-2016). The search index trends for the weak stream (APC = -4.68%; p < .05; 2011-2017, APC = 9.32%, p = 2.35, 2017-2020), split stream (APC = 9.50%; p = .44, 2011-2013, APC = 2.05%, p = .71, 2013-2020), urgency (APC = -2.63%; p = 1.17, 2011-2018, APC = 8.58%; p = .19, 2018-2020), nocturnal enuresis (APC = -4.20%; p = .62, 2011-2017, APC = 20.77%, p < .05, 2017-2020). The age distribution of the population of each LUTS symptom enquiries shows that the population aged 20 to 40 years comprised over 65% of the total search enquiries. Seconded is 5.2%- 21.50 % in the age group 40-49 years. People from the east part of china made over 50% of the total search queries. Further, most of these searches for LUTS symptom entries are related to those for urinary diseases in varying degrees. Conclusions: Online interest in LUTS term fluctuated wildly and was reflected timely by Baidu index in china mainland. The online search popularity for each LUTS terms varie significantly and is differed from personal interest, population concerns, regional variations and gender. These data can be used by providers to track LUTS prevalence and population interests in guiding establish disease-specific healthcare policies and optimize the physician-patient healthcare sessions.

  • Prediction Model of Perioperative Blood Transfusion for Cardiovascular Surgery Patients Based on Machine Learning: Retrospective Study Using Electronic Medical Records

    Date Submitted: Jan 6, 2021

    Open Peer Review Period: Jan 6, 2021 - Mar 3, 2021

    Background: Blood transfusion was related to postoperative adverse events and increased medical costs in patients underwent cardiovascular surgery. Predicting transfusion risk or major bleeding risk w...

    Background: Blood transfusion was related to postoperative adverse events and increased medical costs in patients underwent cardiovascular surgery. Predicting transfusion risk or major bleeding risk will help reduce transfusion. Machine learning (ML) methods show good performance at predicting risk, but transfusion risk prediction based on ML models among Chinese population were unavailable. Objective: To establish and validate prediction models using ML methods for perioperative transfusion risk of patients undergoing cardiovascular surgery in the Chinese population. Methods: Analysis was performed using electronic medical records from patients underwent cardiovascular surgery in Fuwai hospital between January 1, 2016 and June 30, 2019. Based on the 66402 unique patients, a retrospective cohort (N=61892) and a prospective cohort (N=4510) were formed for model derivation and validation. Four ML algorithms including eXtreme Gradient Boosting (XGBoost), random forest, naive Bayes, logistic regression with least absolute shrinkage and selection operator were adopted using 10-folds cross-validation to build prediction models of perioperative blood, red blood cell, plasma and platelet transfusion. According to the model evaluation in the validation cohort, the optimal perioperative blood transfusion prediction model was selected to compare with the Association of Cardiothoracic Anaesthetists perioperative risk of blood transfusion score (the ACTA-PORT score) established in previous research. Results: Among ML models, the XGBoost(area under the receiver-operating characteristic curve[AUC]:0.823; 95% confidence interval[CI]: 0.810 to 0.836) outperformed other models for perioperative blood transfusion and showed better prediction ability than ACTA-PORT score (AUC:0.690; 95% CI: 0.673 to 0.707; P<.001) in the validation cohort. While ML prediction models for perioperative red blood cell transfusion, plasma transfusion and platelet transfusion, achieving good model performance as AUC levels were 0.836(95% CI: 0.823 to 0.849), 0.766(95% CI: 0.745 to 0.787) and 0.948(95% CI: 0.937 to 0.959) respectively. Conclusions: The study retrospectively developed and prospectively validated discriminative perioperative transfusion prediction models, which may promote the early warning and intervention against perioperative transfusion, and benefit patient blood management.