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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, 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:

  • Medly nurse coordinator introducing patient to the program. Source: Image created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Expanding Telemonitoring in a Virtual World: A Case Study of the Expansion of a Heart Failure Telemonitoring Program During the COVID-19 Pandemic


    Background: To minimize the spread and risk of a COVID-19 outbreak, societal norms have been challenged with respect to how essential services are delivered. With pressures to reduce the number of in-person ambulatory visits, innovative models of telemonitoring have been used during the pandemic as a necessary alternative to support access to care for patients with chronic conditions. The pandemic has led health care organizations to consider the adoption of telemonitoring interventions for the first time, while others have seen existing programs rapidly expand. Objective: At the Toronto General Hospital in Ontario, Canada, the rapid expansion of a telemonitoring program began on March 9, 2020, in response to COVID-19. The objective of this study was to understand the experiences related to the expanded role of a telemonitoring program under the changing conditions of the pandemic. Methods: A single-case qualitative study was conducted with 3 embedded units of analysis. Semistructured interviews probed the experiences of patients, clinicians, and program staff from the Medly telemonitoring program at a heart function clinic in Toronto, Canada. Data were analyzed using inductive thematic analysis as well as Eakin and Gladstone’s value-adding approach to enhance the analytic interpretation of the study findings. Results: A total of 29 participants were interviewed, including patients (n=16), clinicians (n=9), and operational staff (n=4). Four themes were identified: (1) providing care continuity through telemonitoring; (2) adapting telemonitoring operations for a more virtual health care system; (3) confronting virtual workflow challenges; and (4) fostering a meaningful patient-provider relationship. Beyond supporting virtual visits, the program’s ability to provide a more comprehensive picture of the patient’s health was valued. However, issues relating to the lack of system integration and alert-driven interactions jeopardized the perceived sustainability of the program. Conclusions: With the reduction of in-person visits during the pandemic, virtual services such as telemonitoring have demonstrated significant value. Based on our study findings, we offer recommendations to proactively adapt and scale telemonitoring programs under the changing conditions of an increasingly virtual health care system. These include revisiting the scope and expectations of telemedicine interventions, streamlining virtual patient onboarding processes, and personalizing the collection of patient information to build a stronger virtual relationship and a more holistic assessment of patient well-being.

  • Source:; Copyright: MS-R / Michael S-R; URL:; License: Creative Commons Attribution + ShareAlike (CC-BY-SA).

    Adequacy of Web-Based Activities as a Substitute for In-Person Activities for Older Persons During the COVID-19 Pandemic: Survey Study


    Background: Senior centers and other types of clubs provide activities for older adults to address boredom, social isolation, and loneliness. Due to the COVID-19 pandemic, most of these activities have been cancelled. A limited range of web-based activities have been offered as alternatives. However, the effectiveness of these web-based group activities for older adults has scarcely been researched. Objective: We aimed to understand the extent to which web-based activities for older adults provide an adequate substitute for in-person activities. Methods: In this telephone survey, we interviewed 105 older adults in Israel who had been offered the opportunity to participate in web-based activities after routine activities closed due to the COVID-19 pandemic. Of the total sample, 49/105 (46.7%) participated in the activities and 56/105 (53.3%) did not. We inquired about the respondents’ background characteristics, satisfaction with the activities, and reasons for participation or nonparticipation. Results: The respondents who participated in the web-based activities tended to be highly satisfied with at least some of them. They rated the enjoyment derived from the content of the activity as the most important motivator, followed by maintaining a routine and by enjoying the group and the presence of others. Over 50% of the participants (28/49, 57%) wished to continue with the exercise programming after the end of the COVID-19 pandemic, and 41% (20/49) wished to continue with the web-based lectures. Participants were more likely to report partaking in alternative activities than nonparticipants (P=.04). The most common reasons cited by nonparticipants were being unaware of the web-based program (24/56, 43%) despite a notification having been sent to the entire sample, lack of interest in the content (18/56, 32%), and technical issues (13/56, 23%), such as not owning or being able to fully use a computer. Both participants and nonparticipants were interested in a wide range of topics, with many being very particular about the topics they wished to access. Approximately half expressed willingness to pay for access; those who were willing to pay tended to have more years of education (P=.03). Conclusions: Our findings suggest a need for web-based activities for countering boredom and feelings of isolation. The main factors that influence the use, efficacy, and sustainability of online activities are access, motivational and need-fulfilling factors, and whether the activities are sufficiently tailored to individuals’ preferences and abilities. Challenges in substituting in-person services are promoting social relationships that are currently not sufficiently incorporated into most web-based programs, accommodating a wider range of topics, and increasing the accessibility of current programs to older adults, especially those who are homebound, both during and after the COVID-19 pandemic.

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

    Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set


    Background: In the United States, the rapidly evolving COVID-19 outbreak, the shortage of available testing, and the delay of test results present challenges for actively monitoring its spread based on testing alone. Objective: The objective of this study was to develop, evaluate, and deploy an automatic natural language processing pipeline to collect user-generated Twitter data as a complementary resource for identifying potential cases of COVID-19 in the United States that are not based on testing and, thus, may not have been reported to the Centers for Disease Control and Prevention. Methods: Beginning January 23, 2020, we collected English tweets from the Twitter Streaming application programming interface that mention keywords related to COVID-19. We applied handwritten regular expressions to identify tweets indicating that the user potentially has been exposed to COVID-19. We automatically filtered out “reported speech” (eg, quotations, news headlines) from the tweets that matched the regular expressions, and two annotators annotated a random sample of 8976 tweets that are geo-tagged or have profile location metadata, distinguishing tweets that self-report potential cases of COVID-19 from those that do not. We used the annotated tweets to train and evaluate deep neural network classifiers based on bidirectional encoder representations from transformers (BERT). Finally, we deployed the automatic pipeline on more than 85 million unlabeled tweets that were continuously collected between March 1 and August 21, 2020. Results: Interannotator agreement, based on dual annotations for 3644 (41%) of the 8976 tweets, was 0.77 (Cohen κ). A deep neural network classifier, based on a BERT model that was pretrained on tweets related to COVID-19, achieved an F1-score of 0.76 (precision=0.76, recall=0.76) for detecting tweets that self-report potential cases of COVID-19. Upon deploying our automatic pipeline, we identified 13,714 tweets that self-report potential cases of COVID-19 and have US state–level geolocations. Conclusions: We have made the 13,714 tweets identified in this study, along with each tweet’s time stamp and US state–level geolocation, publicly available to download. This data set presents the opportunity for future work to assess the utility of Twitter data as a complementary resource for tracking the spread of COVID-19.

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

    Socioeconomic Disparities in Social Distancing During the COVID-19 Pandemic in the United States: Observational Study


    Background: Eliminating disparities in the burden of COVID-19 requires equitable access to control measures across socio-economic groups. Limited research on socio-economic differences in mobility hampers our ability to understand whether inequalities in social distancing are occurring during the SARS-CoV-2 pandemic. Objective: We aimed to assess how mobility patterns have varied across the United States during the COVID-19 pandemic and to identify associations with socioeconomic factors of populations. Methods: We used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level in the United States between February and May 2020, the period during which social distancing was widespread in this country. Using linear mixed models, we assessed the associations between social distancing and socioeconomic variables, including the proportion of people in the population below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density. Results: We found that the speed, depth, and duration of social distancing in the United States are heterogeneous. We particularly show that social distancing is slower and less intense in counties with higher proportions of people below the poverty level and essential workers; in contrast, we show that social distancing is intensely adopted in counties with higher population densities and larger Black populations. Conclusions: Socioeconomic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of the COVID-19 pandemic in communities across the United States. These inequalities are likely to amplify existing health disparities and must be addressed to ensure the success of ongoing pandemic mitigation efforts.

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

    Social, Cognitive, and eHealth Mechanisms of COVID-19–Related Lockdown and Mandatory Quarantine That Potentially Affect the Mental Health of Pregnant Women...


    Background: Although lockdown and mandatory quarantine measures have played crucial roles in the sharp decrease of the number of newly confirmed/suspected COVID-19 cases, concerns have been raised over the threat that these measures pose to mental health, especially the mental health of vulnerable groups, including pregnant women. Few empirical studies have assessed whether and how these control measures may affect mental health, and no study has investigated the prevalence and impacts of the use of eHealth resources among pregnant women during the COVID-19 outbreak. Objective: This study investigated (1) the effects of lockdown and mandatory quarantine on mental health problems (ie, anxiety and depressive symptoms), (2) the potential mediation effects of perceived social support and maladaptive cognition, and (3) the moderation effects of eHealth-related factors (ie, using social media to obtain health information and using prenatal care services during the COVID-19 pandemic) on pregnant women in China. Methods: An online cross-sectional survey was conducted among 19,515 pregnant women from all 34 Chinese provincial-level administrative regions from February 25 to March 10, 2020. Results: Of the 19,515 participants, 12,209 (62.6%) were subjected to lockdown in their areas of residence, 737 (3.8%) were subjected to mandatory quarantine, 8712 (44.6%) had probable mild to severe depression, 5696 (29.2%) had probable mild to severe anxiety, and 1442 (7.4%) had suicidal ideations. Only 640 (3.3%) participants reported that they used online prenatal care services during the outbreak. Significant sociodemographic/maternal factors of anxiety/depressive symptoms included age, education, occupation, the area of residence, gestational duration, the number of children born, complication during pregnancy, the means of using prenatal care services, and social media use for obtaining health information. Multiple indicators multiple causes modeling (χ214=495.21; P<.05; comparative fit index=.99; nonnormed fit index=.98; root mean square error of approximation=.04, 90% CI 0.038-0.045) showed that quarantine was directly and indirectly strongly associated with poor mental health through decreased perceived social support and increased maladaptive cognition (B=.04; β=.02, 95% CI 0.01-0.02; P=.001), while lockdown was indirectly associated with mental health through increased social support and maladaptive cognition among pregnant women (B=.03; β=.03, 95% CI 0.02-0.03; P=.001). Multigroup analyses revealed that the use of social media for obtaining health information and the means of using prenatal care services were significant moderators of the model paths. Conclusions: Our findings provide epidemiological evidence for the importance of integrating mental health care and eHealth into the planning and implementation of control measure policies. The observed social and cognitive mechanisms and moderators in this study are modifiable, and they can inform the design of evidence-based mental health promotion among pregnant women.

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

    Efficacy of a Transdiagnostic Self-Help Internet Intervention for Reducing Depression, Anxiety, and Suicidal Ideation in Adults: Randomized Controlled Trial


    Background: Low-intensity self-guided mental health interventions that are delivered on the web may meet the needs and preferences of adults with mild to moderate symptoms. However, few clinical trials have examined the effectiveness of self-guided transdiagnostic interventions within a naturalistic setting. Objective: This randomized controlled trial (RCT) tests the effectiveness of the video-based transdiagnostic intervention FitMindKit in reducing depression symptoms (primary outcome), anxiety symptoms, disability, and suicidal ideation, relative to an attention-matched control condition called HealthWatch. Methods: The RCT was conducted with adults living in the Australian Capital Territory, Australia. Participants (n=1986) were recruited through the web using social media advertisements, screened for psychological distress, and then randomized to receive one of two 4-week programs: FitMindKit (12-module psychotherapy intervention) or HealthWatch (12-module program providing general health information). Participants were assessed at baseline and at 4 weeks postbaseline. To maintain the ecological validity of the trial, participants completed brief assessments and interventions without direct researcher contact or incentives. Results: Mixed model repeated-measures analyses of variance demonstrated that FitMindKit significantly improved depression symptoms (F1,701.7=3.97; P=.047), along with panic symptoms (F1,706.5=5.59; P=.02) and social anxiety symptoms (F1,680.0=12.37; P<.001), relative to the attention control condition. There were no significant effects on other outcomes. Conclusions: Self-guided transdiagnostic interventions can be beneficial when delivered directly to end users through the internet. Despite low adherence and small effect sizes, the availability of such interventions is likely to fill a critical gap in the accessibility of mental health services for the community. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12618001688279;

  • Paper-Pen vs Electronic Data Collection Tools. Source: Atinkut Alamirrew Zeleke; Copyright: Atinkut Alamirrew Zeleke; URL:; License: Creative Commons Attribution (CC-BY).

    Data Quality and Cost-effectiveness Analyses of Electronic and Paper-Based Interviewer-Administered Public Health Surveys: Systematic Review


    Background: A population-level survey (PLS) is an essential and standard method used in public health research that supports the quantification of sociodemographic events, public health policy development, and intervention designs. Data collection mechanisms in PLS seem to be a significant determinant in avoiding mistakes. Using electronic devices such as smartphones and tablet computers improves the quality and cost-effectiveness of public health surveys. However, there is a lack of systematic evidence to show the potential impact of electronic data collection tools on data quality and cost reduction in interviewer-administered surveys compared with the standard paper-based data collection system. Objective: This systematic review aims to evaluate the impact of the interviewer-administered electronic data collection methods on data quality and cost reduction in PLS compared with traditional methods. Methods: We conducted a systematic search of MEDLINE, CINAHL, PsycINFO, the Web of Science, EconLit, Cochrane CENTRAL, and CDSR to identify relevant studies from 2008 to 2018. We included randomized and nonrandomized studies that examined data quality and cost reduction outcomes, as well as usability, user experience, and usage parameters. In total, 2 independent authors screened the title and abstract, and extracted data from selected papers. A third author mediated any disagreements. The review authors used EndNote for deduplication and Rayyan for screening. Results: Our search produced 3817 papers. After deduplication, we screened 2533 papers, and 14 fulfilled the inclusion criteria. None of the studies were randomized controlled trials; most had a quasi-experimental design, for example, comparative experimental evaluation studies nested on other ongoing cross-sectional surveys. A total of 4 comparative evaluations, 2 pre-post intervention comparative evaluations, 2 retrospective comparative evaluations, and 4 one-arm noncomparative studies were included. Meta-analysis was not possible because of the heterogeneity in study designs, types, study settings, and level of outcome measurements. Individual paper synthesis showed that electronic data collection systems provided good quality data and delivered faster compared with paper-based data collection systems. Only 2 studies linked cost and data quality outcomes to describe the cost-effectiveness of electronic data collection systems. Field data collectors reported that an electronic data collection system was a feasible, acceptable, and preferable tool for their work. Onsite data error prevention, fast data submission, and easy-to-handle devices were the comparative advantages offered by electronic data collection systems. Challenges during implementation included technical difficulties, accidental data loss, device theft, security concerns, power surges, and internet connection problems. Conclusions: Although evidence exists of the comparative advantages of electronic data collection compared with paper-based methods, the included studies were not methodologically rigorous enough to combine. More rigorous studies are needed to compare paper and electronic data collection systems in public health surveys considering data quality, work efficiency, and cost reduction.

  • Digital epidemiology. Source: pxhere/Placeit; Copyright: pxhere/Placeit; URL:; License: Licensed by JMIR.

    Possibilities, Problems, and Perspectives of Data Collection by Mobile Apps in Longitudinal Epidemiological Studies: Scoping Review


    Background: The broad availability of smartphones and the number of health apps in app stores have risen in recent years. Health apps have benefits for individuals (eg, the ability to monitor one’s health) as well as for researchers (eg, the ability to collect data in population-based, clinical, and observational studies). Although the number of health apps on the global app market is huge and the associated potential seems to be great, app-based questionnaires for collecting patient-related data have not played an important role in epidemiological studies so far. Objective: This study aims to provide an overview of studies that have collected patient data using an app-based approach, with a particular focus on longitudinal studies. This literature review describes the current extent to which smartphones have been used for collecting (patient) data for research purposes, and the potential benefits and challenges associated with this approach. Methods: We conducted a scoping review of studies that used data collection via apps. PubMed was used to identify studies describing the use of smartphone app questionnaires for collecting data over time. Overall, 17 articles were included in the summary. Results: Based on the results of this scoping review, there are only a few studies that integrate smartphone apps into data-collection approaches. Studies dealing with the collection of health-related data via smartphone apps have mainly been developed with regard to psychosomatic, neurodegenerative, respiratory, and cardiovascular diseases, as well as malign neoplasm. Among the identified studies, the duration of data collection ranged from 4 weeks to 12 months, and the participants’ mean ages ranged from 7 to 69 years. Potential can be seen for real-time information transfer, fast data synchronization (which saves time and increases effectivity), and the possibility of tracking responses longitudinally. Furthermore, smartphone-based data-collection techniques might prevent biases, such as reminder bias or mistakes occurring during manual data transfers. In chronic diseases, real-time communication with physicians and early detection of symptoms enables rapid modifications in disease management. Conclusions: The results indicate that using mobile technologies can help to overcome challenges linked with data collection in epidemiological research. However, further feasibility studies need to be conducted in the near future to test the applicability and acceptance of these mobile apps for epidemiological research in various subpopulations.

  • EHRs are shared among different users such as doctors, patients, and family members. Source: Image created by the authors; Copyright: The Authors; URL:; License: Creative Commons Attribution + ShareAlike (CC-BY-SA).

    The HealthChain Blockchain for Electronic Health Records: Development Study


    Background: Health care professionals are required to maintain accurate health records of patients. Furthermore, these records should be shared across different health care organizations for professionals to have a complete review of medical history and avoid missing important information. Nowadays, health care providers use electronic health records (EHRs) as a key to the implementation of these goals and delivery of quality care. However, there are technical and legal hurdles that prevent the adoption of these systems, such as concerns about performance and privacy issues. Objective: This study aimed to build and evaluate an experimental blockchain for EHRs, named HealthChain, which overcomes the disadvantages of traditional EHR systems. Methods: HealthChain is built based on consortium blockchain technology. Specifically, three organizations, namely hospitals, insurance providers, and governmental agencies, form a consortium that operates under a governance model, which enforces the business logic agreed by all participants. Every peer node hosts an instance of the distributed ledger consisting of EHRs and an instance of chaincode regulating the permissions of participants. Designated orderers establish consensus on the order of EHRs and then disseminate blocks to peers. Results: HealthChain achieves functional and nonfunctional requirements. It can store EHRs in a distributed ledger and share them among different participants. Moreover, it demonstrates superior features, such as privacy preservation, security, and high throughput. These are the main reasons why HealthChain is proposed. Conclusions: Consortium blockchain technology can help to build new EHR systems and solve the problems that prevent the adoption of traditional systems.

  • Source: freepik; Copyright: master1305; URL:; License: Licensed by JMIR.

    Patient Care During the COVID-19 Pandemic: Use of Virtual Care


    Virtual care, the use of videoconferencing technology to connect with patients, has become critical in providing continuing care for patients during the current COVID-19 pandemic. Virtual care has now been adopted by health care providers across the spectrum, including physicians, residents, nurse practitioners, nurses, and allied health care professionals. Virtual care is novel and nuanced compared to in-person care. Most of the health care providers who are delivering or expected to deliver virtual care have little to no prior experience with it. The nuances of virtual care involve regulatory standards, platforms, technology and troubleshooting, patient selection, etiquette, and workflow, all of which comprise critical points in the provision of health care. It is important to consistently deliver high-quality, equitable, and professional virtual care to inspire patients with the trust they need to continue follow-up of their care in these difficult times. We have been adopting virtual care in our clinical practice for over two years. In partnership with Canada Health Infoway, we have assembled a primer for virtual care that can serve as a guide for any health care provider in Canada and globally, with the goal of providing seamless transitions between in-person and virtual care.

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

    Understanding eHealth Cognitive Behavioral Therapy Targeting Substance Use: Realist Review


    Background: There is a growing body of evidence regarding eHealth interventions that target substance use disorders. Development and funding decisions in this area have been challenging, due to a lack of understanding of what parts of an intervention work in which context. Objective: We conducted a realist review of the literature on electronic cognitive behavioral therapy (eCBT) programs for substance use with the goal of answering the following realist question: “How do different eCBT interventions for substance use interact with different contexts to produce certain outcomes?” Methods: A literature search of published and gray literature on eHealth programs targeting substance use was conducted. After data extraction, in order to conduct a feasible realist review in a timely manner, the scope had to be refined further and, ultimately, only included literature focusing on eCBT programs targeting substance use. We synthesized the available evidence from the literature into Context-Mechanism-Outcome configurations (CMOcs) in order to better understand when and how programs work. Results: A total of 54 papers reporting on 24 programs were reviewed. Our final results identified eight CMOcs from five unique programs that met criteria for relevance and rigor. Conclusions: Five strategies that may be applied to future eCBT programs for substance use are discussed; these strategies may contribute to a better understanding of mechanisms and, ultimately, may help design more effective solutions in the future. Future research on eCBT programs should try to understand the mechanisms of program strategies and how they lead to outcomes in different contexts.

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

    Risk Stratification for Early Detection of Diabetes and Hypertension in Resource-Limited Settings: Machine Learning Analysis


    Background: The impending scale up of noncommunicable disease screening programs in low- and middle-income countries coupled with limited health resources require that such programs be as accurate as possible at identifying patients at high risk. Objective: The aim of this study was to develop machine learning–based risk stratification algorithms for diabetes and hypertension that are tailored for the at-risk population served by community-based screening programs in low-resource settings. Methods: We trained and tested our models by using data from 2278 patients collected by community health workers through door-to-door and camp-based screenings in the urban slums of Hyderabad, India between July 14, 2015 and April 21, 2018. We determined the best models for predicting short-term (2-month) risk of diabetes and hypertension (a model for diabetes and a model for hypertension) and compared these models to previously developed risk scores from the United States and the United Kingdom by using prediction accuracy as characterized by the area under the receiver operating characteristic curve (AUC) and the number of false negatives. Results: We found that models based on random forest had the highest prediction accuracy for both diseases and were able to outperform the US and UK risk scores in terms of AUC by 35.5% for diabetes (improvement of 0.239 from 0.671 to 0.910) and 13.5% for hypertension (improvement of 0.094 from 0.698 to 0.792). For a fixed screening specificity of 0.9, the random forest model was able to reduce the expected number of false negatives by 620 patients per 1000 screenings for diabetes and 220 patients per 1000 screenings for hypertension. This improvement reduces the cost of incorrect risk stratification by US $1.99 (or 35%) per screening for diabetes and US $1.60 (or 21%) per screening for hypertension. Conclusions: In the next decade, health systems in many countries are planning to spend significant resources on noncommunicable disease screening programs and our study demonstrates that machine learning models can be leveraged by these programs to effectively utilize limited resources by improving risk stratification.

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  • Application of a user-centered usability evaluation framework to assess a handheld 12-lead electrocardiogram (ECG) device: user perceptions and experience in a clinical setting

    Date Submitted: Jan 19, 2021

    Open Peer Review Period: Jan 19, 2021 - Mar 16, 2021

    Background: Cardiac arrhythmias are a leading cause of death. Twelve-lead electrocardiogram (ECG) is the mainstay for diagnosing arrhythmias e.g. atrial fibrillation (AF) and cardiac conduction disord...

    Background: Cardiac arrhythmias are a leading cause of death. Twelve-lead electrocardiogram (ECG) is the mainstay for diagnosing arrhythmias e.g. atrial fibrillation (AF) and cardiac conduction disorders e.g. long QTc (LQTc). Handheld 12-lead ECG devices are emerging in the market. With emerging technology options to acquire an ECG in various clinical settings, evaluation of device usability should go beyond validation of the device in a controlled laboratory setting but also assess user perceptions and experience which are crucial for successful implementation in clinical practice. Objective: This study aimed to evaluate clinician and patient perceptions and experience regarding the usability of a handheld 12-lead ECG device compared to routinely used conventional 12-lead ECG machine. Methods: A mixed methods study was conducted with clinicians and patients in outpatient clinics and cardiology wards at Westmead Hospital, New South Wales, Australia. Each patient had two ECGs in two postures (supine and standing) acquired by each device in random sequence. The times taken by clinicians to acquire the first ECG (efficiency) using the devices were analysed using linear regression. ECG parameters (QT, QTc, HR, PR, QRS) and participant satisfaction survey were collected. The device reliability was assessed by evaluating the mean difference of QTc measurements within ± 15 milliseconds and intraclass correlation coefficients (ICC), and level of agreements (Kappa) of the devices in detecting AF and LQTc. Clinicians’ perceptions and feedback were assessed by semi-structured interviews drawing on the framework of the Technology Acceptance Model. Results: 100 patients (mean 57.9y (SD 15.2y), 64% male) participated with 783 ECGs acquired, and engaging 11 clinicians (90.9% of them acquired ECGs daily or weekly). Mean difference in QTc measurements of both devices were within ± 15 milliseconds with high ICC (ranged 0.90-0.96), and the devices had a good level of agreement in diagnosing AF and LQTc (Kappa ranged 0.68 – 0.93). Regardless of device, QTc at standing was lengthened compared with QTc at supine. Clinicians’ ECG acquisition times improved with usage (p for trend <0.001). Clinicians reported that device characteristics (small size, light weight, portability and wireless ECG transmission) were highly desired features. Most clinicians agreed that the handheld device could be used for clinician-led mass screening with enhancement in efficiency by increasing user training. Generally, patients reported that they felt comfortable when they were connected to both ECG devices. Conclusions: The handheld 12-lead ECG device was comparable to a conventional ECG machine in its reliability and usability. The user-centered evaluation approach helped us identify remediable action to improve efficiency of using the device, identified highly desirable device features which could potentially help mass screening and remote assessment of patients, and the approach could be applied to evaluate and better understand the acceptability and applicability of new medical devices. Clinical Trial: Not applicable.

  • The effectiveness of self-guided web-based physical activity and exercise interventions to improve health outcomes for people living with chronic health conditions: A systematic review and meta-analysis

    Date Submitted: Jan 19, 2021

    Open Peer Review Period: Jan 19, 2021 - Mar 16, 2021

    Background: The benefits of physical activity (PA) and exercise in people with chronic health conditions are well documented. However, there are barriers to accessing services and providing ongoing s...

    Background: The benefits of physical activity (PA) and exercise in people with chronic health conditions are well documented. However, there are barriers to accessing services and providing ongoing support for activity-based interventions over the duration of the condition. Digital health interventions, especially those with minimal human contact, offer a potential solution. Objective: The aim of this systematic review was to determine the effectiveness of self-guided web-based PA and exercise interventions to improve health outcomes for people living with chronic health conditions. Methods: A comprehensive and systematic search was conducted through CINAHL, MEDLINE, SPORTSDiscus, AMED, EBM, PsychINFO, and Scopus and Web of Science libraries for randomized trials up to September 2020 that evaluated the effect of self-guided web- or internet-based PA or exercise interventions on any health outcome. Only studies whose interventions had minimal human contact and whose interaction was automatically generated were included. All studies were screened for eligibility and relevant data was extracted. Two independent reviewers assessed the risk of bias using the Cochrane risk of bias tool. Standardized mean differences and 95% confidence intervals were calculated. PA data were pooled, and forest plots were generated. Results: Of the 7549 papers identified, 13 met the eligibility criteria and included a total of 1886 participants. There was wide variation in health conditions and intervention characteristics with regards to mode and parameters of delivery and in the use of theory and behavioral strategies applied. Self-reported PA in the intervention group was greater than controls at the end of the intervention (standardized mean difference (SMD) 0.18 95% CI=0.05, 0.31) and at follow up (SMD 0.29, 95% CI 0.13 to 0.45). The difference in objectively measured PA was moderate and non-significant (SMD 0.43 95% CI ‐0.16 to 1.02). All interventions included behavioral strategies and seven of the thirteen were underpinned by theory. Conclusions: Self-guided web-based PA and exercise interventions provided a positive effect on PA immediately after the intervention. An unexpected and positive finding was a sustained increase in PA at follow-up, particularly in those interventions that used behavioral strategies underpinned by a theoretical framework. Interventions with minimal contact have the potential to support sustained PA engagement at least as well as interventions with supervision. Clinical Trial: Protocol registration PROSPERO CRD42019132464

  • Segmental Signs and Spontaneous Pain in Acute Visceral Disease: An observational study

    Date Submitted: Jan 19, 2021

    Open Peer Review Period: Jan 19, 2021 - Mar 16, 2021

    Background: The differential diagnosis of acute visceral diseases is a challenging clinical problem. The older literature suggests that patients with acute visceral problems show segmental signs, such...

    Background: The differential diagnosis of acute visceral diseases is a challenging clinical problem. The older literature suggests that patients with acute visceral problems show segmental signs, such as hyperalgesia, skin resistance, or muscular defence, whose lateralization and segmental distribution may be used for differential diagnosis. Objective: This study aimed to investigate the lateralization and segmental distribution of spontaneous pain and segmental signs in acute visceral diseases using digital pain drawing technology. Methods: We recruited 208 emergency room patients that were presenting for acute medical problems. All patients underwent a structured 10-minute bodily examination to test for various segmental signs and were asked for spontaneous pain and segmental symptoms, such as nausea, meteorism, and urinary retention. We collected all findings as digital drawings on a tablet-PC. After the final diagnosis, patients were divided into groups according to the organ affected. Using statistical image analysis, we calculated average distributions of pain and segmental signs for the heart, lungs, stomach, liver/gallbladder, and kidneys/ureters analyzing their segmental distribution and lateralization. Results: 85 of 110 patients with a single-organ problem reported pain, while 81 had at least one segmental sign, the most frequent being hyperalgesia (n=46), and muscle resistance (n=39). While the pain was distributed along the body midline, segmental signs for the heart, stomach and liver/gallbladder appeared mostly ipsilateral to the affected organ. An unexpectedly high number of patients (n=37) further showed ipsilateral mydriasis. Conclusions: The present study underlines the usefulness of including digitally-recorded segmental signs in the bodily examination of patients with acute medical problems.

  • Effectiveness of a Game-Based School Program for Mental Health Literacy and Stigma on Depression in Adolescents (Moving Stories): A Cluster Randomized Controlled Trial

    Date Submitted: Jan 18, 2021

    Open Peer Review Period: Jan 18, 2021 - Mar 15, 2021

    Background: Depressive symptoms are highly prevalent among adolescents in Western countries. However, even though treatment for depressive symptoms is available, many adolescents do not seek help when...

    Background: Depressive symptoms are highly prevalent among adolescents in Western countries. However, even though treatment for depressive symptoms is available, many adolescents do not seek help when they need it. Important barriers for help-seeking among adolescents include low mental health literacy and high stigma. We have developed the game-based school program Moving Stories, which combines mental health literacy training for depression with contact with someone with lived experience, both in the digital and non-digital world. Objective: The goal of the present study was to conduct a first test on the effectiveness of the newly developed game-based program Moving Stories, by means of a cluster randomized controlled trial. Methods: A total of 185 adolescents participated, divided over 10 classes from four schools. Half of the classes were randomly selected to follow the Moving Stories program, while the other half were in the control group, where no intervention was provided. Adolescents filled out digital questionnaires at four time points with questions on mental health literacy, stigma, depressive symptoms and the program itself (pre-test, post-test, three-months follow-up, six-months follow-up). Using R, we ran linear mixed effects models for all continuous outcome variables, and generalized linear mixed effects models for all binary outcome variables. Results: Compared to the control group, participants in the Moving Stories group improved at post-test in symptom recognition (OR = 1.59, 95% CI = [0.95 – 2.73], z = 1.73, P = .08 ), personal stigma (b = -0.53, 95% CI = [-1.02 – -0.03], t(179.16) = -2.08, P = .04) and perceived stigma (b = -0.61, 95% CI = [-1.31 – 0.09], t(179.75) = -1.70, P = .09). Effects on personal stigma lasted over time (three-months follow-up: b = -0.57, 95% CI = [-1.11 – -0.03] , (174.39) = -2.07, P = .04, and six-months follow-up (trend), b = -0.49, 95% CI = [-1.01 – 0.03], t(169.35) = -1.83, P = .07). Most adolescents in the Moving Stories group participated in the introduction (98%) and discussion session (94%), played the game for four or five days (83%), and indicated they would recommend the game to peers (92%). Conclusions: The results of the current study show the potential of the Moving Stories program as a mental health literacy and stigma program. With changes in the program to improve long-term effects and effects on behavior, Moving Stories could be implemented in schools to improve help seeking in adolescents and reduce the negative consequences and burden of depressive symptoms. Clinical Trial: Dutch Trial Register NTR7033;

  • 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.