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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:

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

    Experiences of Psychotherapists With Remote Psychotherapy During the COVID-19 Pandemic: Cross-sectional Web-Based Survey Study


    Background: The current situation around the COVID-19 pandemic and the measures necessary to fight it are creating challenges for psychotherapists, who usually treat patients face-to-face with personal contact. The pandemic is accelerating the use of remote psychotherapy (ie, psychotherapy provided via telephone or the internet). However, some psychotherapists have expressed reservations regarding remote psychotherapy. As psychotherapists are the individuals who determine the frequency of use of remote psychotherapy, the potential of enabling mental health care during the COVID-19 pandemic in line with the protective measures to fight COVID-19 can be realized only if psychotherapists are willing to use remote psychotherapy. Objective: This study aimed to investigate the experiences of psychotherapists with remote psychotherapy in the first weeks of the COVID-19 lockdown in Austria (between March 24 and April 1, 2020). Methods: Austrian psychotherapists were invited to take part in a web-based survey. The therapeutic orientations of the psychotherapists (behavioral, humanistic, psychodynamic, or systemic), their rating of the comparability of remote psychotherapy (web- or telephone-based) with face-to-face psychotherapy involving personal contact, and potential discrepancies between their actual experiences and previous expectations with remote psychotherapy were assessed. Data from 1162 psychotherapists practicing before and during the COVID-19 lockdown were analyzed. Results: Psychotherapy conducted via telephone or the internet was reported to not be totally comparable to psychotherapy with personal contact (P<.001). Psychodynamic (P=.001) and humanistic (P=.005) therapists reported a higher comparability of telephone-based psychotherapy to in-person psychotherapy than behavioral therapists. Experiences with remote therapy (both web- and telephone-based) were more positive than previously expected (P<.001). Psychodynamic therapists reported more positive experiences with telephone-based psychotherapy than expected compared to behavioral (P=.03) and systemic (P=.002) therapists. In general, web-based psychotherapy was rated more positively (regarding comparability to psychotherapy with personal contact and experiences vs expectations) than telephone-based psychotherapy (P<.001); however, psychodynamic therapists reported their previous expectations to be equal to their actual experiences for both telephone- and web-based psychotherapy. Conclusions: Psychotherapists found their experiences with remote psychotherapy (ie, web- or telephone-based psychotherapy) to be better than expected but found that this mode was not totally comparable to face-to-face psychotherapy with personal contact. Especially, behavioral therapists were found to rate telephone-based psychotherapy less favorably than therapists with other theoretical backgrounds.

  • Quantitative measurement of damage to teeth over time using open access software. Source: The Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Influence of Scanner Precision and Analysis Software in Quantifying Three-Dimensional Intraoral Changes: Two-Factor Factorial Experimental Design


    Background: Three-dimensional scans are increasingly used to quantify biological topographical changes and clinical health outcomes. Traditionally, the use of 3D scans has been limited to specialized centers owing to the high cost of the scanning equipment and the necessity for complex analysis software. Technological advances have made cheaper, more accessible methods of data capture and analysis available in the field of dentistry, potentially facilitating a primary care system to quantify disease progression. However, this system has yet to be compared with previous high-precision methods in university hospital settings. Objective: The aim of this study was to compare a dental primary care method of data capture (intraoral scanner) with a precision hospital-based method (laser profilometer) in addition to comparing open source and commercial software available for data analysis. Methods: Longitudinal dental wear data from 30 patients were analyzed using a two-factor factorial experimental design. Bimaxillary intraoral digital scans (TrueDefinition, 3M, UK) and conventional silicone impressions, poured in type-4 dental stone, were made at both baseline and follow-up appointments (mean 36 months, SD 10.9). Stone models were scanned using precision laser profilometry (Taicaan, Southampton, UK). Three-dimensional changes in both forms of digital scans of the first molars (n=76) were quantitatively analyzed using the engineering software Geomagic Control (3D Systems, Germany) and freeware WearCompare (Leeds Digital Dentistry, UK). Volume change (mm3) was the primary measurement outcome. The maximum point loss (μm) and the average profile loss (μm) were also recorded. Data were paired and skewed, and were therefore compared using Wilcoxon signed-rank tests with Bonferroni correction. Results: The median (IQR) volume change for Geomagic using profilometry and using the intraoral scan was –0.37 mm3 (–3.75-2.30) and +0.51 mm3 (–2.17-4.26), respectively (P<.001). Using WearCompare, the median (IQR) volume change for profilometry and intraoral scanning was –1.21 mm3 (–3.48-0.56) and –0.39 mm3 (–3.96-2.76), respectively (P=.04). WearCompare detected significantly greater volume loss than Geomagic regardless of scanner type. No differences were observed between groups with respect to the maximum point loss or average profile loss. Conclusions: As expected, the method of data capture, software used, and measurement metric all significantly influenced the measurement outcome. However, when appropriate analysis was used, the primary care system was able to quantify the degree of change and can be recommended depending on the accuracy needed to diagnose a condition. Lower-resolution scanners may underestimate complex changes when measuring at the micron level.

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

    Self-Monitoring App Preferences for Sun Protection: Discrete Choice Experiment Survey Analysis


    Background: The availability and use of health apps continues to increase, revolutionizing the way mobile health interventions are delivered. Apps are increasingly used to prevent disease, improve well-being, and promote healthy behavior. On a similar rise is the incidence of skin cancers. Much of the underlying risk can be prevented through behavior change and adequate sun protection. Self-monitoring apps have the potential to facilitate prevention by measuring risk (eg, sun intensity) and encouraging protective behavior (eg, seeking shade). Objective: Our aim was to assess health care consumer preferences for sun protection with a self-monitoring app that tracks the duration and intensity of sun exposure and provides feedback on when and how to protect the skin. Methods: We conducted an unlabeled discrete choice experiment with 8 unique choice tasks, in which participants chose among 2 app alternatives, consisting of 5 preidentified 2-level attributes (self-monitoring method, privacy control, data sharing with health care provides, reminder customizability, and costs) that were the result of a multistep and multistakeholder qualitative approach. Participant preferences, and thus, the relative importance of attributes and their levels were estimated using conditional logit modeling. Analyses consisted of 200 usable surveys, yielding 3196 observations. Results: Our respondents strongly preferred automatic over manually operated self-monitoring (odds ratio [OR] 2.37, 95% CI 2.06-2.72) and no cost over a single payment of 3 Swiss francs (OR 1.72, 95% CI 1.49-1.99). They also preferred having over not having the option of sharing their data with a health care provider of their choice (OR 1.66, 95% CI 1.40-1.97), repeated over single user consents, whenever app data are shared with commercial thirds (OR 1.57, 95% CI 1.31-1.88), and customizable over noncustomizable reminders (OR 1.30, 95% CI 1.09-1.54). While most participants favored thorough privacy infrastructures, the attribute of privacy control was a relatively weak driver of app choice. The attribute of self-monitoring method significantly interacted with gender and perceived personal usefulness of health apps, suggesting that female gender and lower perceived usefulness are associated with relatively weaker preferences for automatic self-monitoring. Conclusions: Based on the preferences of our respondents, we found that the utility of a self-monitoring sun protection app can be increased if the app is simple and adjustable; requires minimal effort, time, or expense; and has an interoperable design and thorough privacy infrastructure. Similar features might be desirable for preventive health apps in other areas, paving the way for future discrete choice experiments. Nonetheless, to fully understand these preference dynamics, further qualitative or mixed method research on mobile self-monitoring-based sun protection and broader preventive mobile self-monitoring is required.

  • An opioid user is using Reddit. Source: Image created by the authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Detection of Suicidality Among Opioid Users on Reddit: Machine Learning–Based Approach


    Background: In recent years, both suicide and overdose rates have been increasing. Many individuals who struggle with opioid use disorder are prone to suicidal ideation; this may often result in overdose. However, these fatal overdoses are difficult to classify as intentional or unintentional. Intentional overdose is difficult to detect, partially due to the lack of predictors and social stigmas that push individuals away from seeking help. These individuals may instead use web-based means to articulate their concerns. Objective: This study aimed to extract posts of suicidality among opioid users on Reddit using machine learning methods. The performance of the models is derivative of the data purity, and the results will help us to better understand the rationale of these users, providing new insights into individuals who are part of the opioid epidemic. Methods: Reddit posts between June 2017 and June 2018 were collected from r/suicidewatch, r/depression, a set of opioid-related subreddits, and a control subreddit set. We first classified suicidal versus nonsuicidal languages and then classified users with opioid usage versus those without opioid usage. Several traditional baselines and neural network (NN) text classifiers were trained using subreddit names as the labels and combinations of semantic inputs. We then attempted to extract out-of-sample data belonging to the intersection of suicide ideation and opioid abuse. Amazon Mechanical Turk was used to provide labels for the out-of-sample data. Results: Classification results were at least 90% across all models for at least one combination of input; the best classifier was convolutional neural network, which obtained an F1 score of 96.6%. When predicting out-of-sample data for posts containing both suicidal ideation and signs of opioid addiction, NN classifiers produced more false positives and traditional methods produced more false negatives, which is less desirable for predicting suicidal sentiments. Conclusions: Opioid abuse is linked to the risk of unintentional overdose and suicide risk. Social media platforms such as Reddit contain metadata that can aid machine learning and provide information at a personal level that cannot be obtained elsewhere. We demonstrate that it is possible to use NNs as a tool to predict an out-of-sample target with a model built from data sets labeled by characteristics we wish to distinguish in the out-of-sample target. Trial Registration:

  • Adaptive Internet-Delivered Psychological Treatment (IDPT) Systems. Source: Image created by the authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Adaptive Elements in Internet-Delivered Psychological Treatment Systems: Systematic Review


    Background: Internet-delivered psychological treatments (IDPTs) are built on evidence-based psychological treatment models, such as cognitive behavioral therapy, and are adjusted for internet use. The use of internet technologies has the potential to increase access to evidence-based mental health services for a larger proportion of the population with the use of fewer resources. However, despite extensive evidence that internet interventions can be effective in the treatment of mental health disorders, user adherence to such internet intervention is suboptimal. Objective: This review aimed to (1) inspect and identify the adaptive elements of IDPT for mental health disorders, (2) examine how system adaptation influences the efficacy of IDPT on mental health treatments, (3) identify the information architecture, adaptive dimensions, and strategies for implementing these interventions for mental illness, and (4) use the findings to create a conceptual framework that provides better user adherence and adaptiveness in IDPT for mental health issues. Methods: The review followed the guidelines from Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The research databases Medline (PubMed), ACM Digital Library, PsycINFO, CINAHL, and Cochrane were searched for studies dating from January 2000 to January 2020. Based on predetermined selection criteria, data from eligible studies were analyzed. Results: A total of 3341 studies were initially identified based on the inclusion criteria. Following a review of the title, abstract, and full text, 31 studies that fulfilled the inclusion criteria were selected, most of which described attempts to tailor interventions for mental health disorders. The most common adaptive elements were feedback messages to patients from therapists and intervention content. However, how these elements contribute to the efficacy of IDPT in mental health were not reported. The most common information architecture used by studies was tunnel-based, although a number of studies did not report the choice of information architecture used. Rule-based strategies were the most common adaptive strategies used by these studies. All of the studies were broadly grouped into two adaptive dimensions based on user preferences or using performance measures, such as psychometric tests. Conclusions:  Several studies suggest that adaptive IDPT has the potential to enhance intervention outcomes and increase user adherence. There is a lack of studies reporting design elements, adaptive elements, and adaptive strategies in IDPT systems. Hence, focused research on adaptive IDPT systems and clinical trials to assess their effectiveness are needed.

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

    Searching PubMed to Retrieve Publications on the COVID-19 Pandemic: Comparative Analysis of Search Strings


    Background: Since it was declared a pandemic on March 11, 2020, COVID-19 has dominated headlines around the world and researchers have generated thousands of scientific articles about the disease. The fast speed of publication has challenged researchers and other stakeholders to keep up with the volume of published articles. To search the literature effectively, researchers use databases such as PubMed. Objective: The aim of this study is to evaluate the performance of different searches for COVID-19 records in PubMed and to assess the complexity of searches required. Methods: We tested PubMed searches for COVID-19 to identify which search string performed best according to standard metrics (sensitivity, precision, and F-score). We evaluated the performance of 8 different searches in PubMed during the first 10 weeks of the COVID-19 pandemic to investigate how complex a search string is needed. We also tested omitting hyphens and space characters as well as applying quotation marks. Results: The two most comprehensive search strings combining several free-text and indexed search terms performed best in terms of sensitivity (98.4%/98.7%) and F-score (96.5%/95.7%), but the single-term search COVID-19 performed best in terms of precision (95.3%) and well in terms of sensitivity (94.4%) and F-score (94.8%). The term Wuhan virus performed the worst: 7.7% for sensitivity, 78.1% for precision, and 14.0% for F-score. We found that deleting a hyphen or space character could omit a substantial number of records, especially when searching with SARS-CoV-2 as a single term. Conclusions: Comprehensive search strings combining free-text and indexed search terms performed better than single-term searches in PubMed, but not by a large margin compared to the single term COVID-19. For everyday searches, certain single-term searches that are entered correctly are probably sufficient, whereas more comprehensive searches should be used for systematic reviews. Still, we suggest additional measures that the US National Library of Medicine could take to support all PubMed users in searching the COVID-19 literature.

  • Microblogging behavior in the face of Covid-19. Source: Image created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data


    Background: The COVID-19 pandemic has created a global health crisis that is affecting economies and societies worldwide. During times of uncertainty and unexpected change, people have turned to social media platforms as communication tools and primary information sources. Platforms such as Twitter and Sina Weibo have allowed communities to share discussion and emotional support; they also play important roles for individuals, governments, and organizations in exchanging information and expressing opinions. However, research that studies the main concerns expressed by social media users during the pandemic is limited. Objective: The aim of this study was to examine the main concerns raised and discussed by citizens on Sina Weibo, the largest social media platform in China, during the COVID-19 pandemic. Methods: We used a web crawler tool and a set of predefined search terms (New Coronavirus Pneumonia, New Coronavirus, and COVID-19) to investigate concerns raised by Sina Weibo users. Textual information and metadata (number of likes, comments, retweets, publishing time, and publishing location) of microblog posts published between December 1, 2019, and July 32, 2020, were collected. After segmenting the words of the collected text, we used a topic modeling technique, latent Dirichlet allocation (LDA), to identify the most common topics posted by users. We analyzed the emotional tendencies of the topics, calculated the proportional distribution of the topics, performed user behavior analysis on the topics using data collected from the number of likes, comments, and retweets, and studied the changes in user concerns and differences in participation between citizens living in different regions of mainland China. Results: Based on the 203,191 eligible microblog posts collected, we identified 17 topics and grouped them into 8 themes. These topics were pandemic statistics, domestic epidemic, epidemics in other countries worldwide, COVID-19 treatments, medical resources, economic shock, quarantine and investigation, patients’ outcry for help, work and production resumption, psychological influence, joint prevention and control, material donation, epidemics in neighboring countries, vaccine development, fueling and saluting antiepidemic action, detection, and study resumption. The mean sentiment was positive for 11 topics and negative for 6 topics. The topic with the highest mean of retweets was domestic epidemic, while the topic with the highest mean of likes was quarantine and investigation. Conclusions: Concerns expressed by social media users are highly correlated with the evolution of the global pandemic. During the COVID-19 pandemic, social media has provided a platform for Chinese government departments and organizations to better understand public concerns and demands. Similarly, social media has provided channels to disseminate information about epidemic prevention and has influenced public attitudes and behaviors. Government departments, especially those related to health, can create appropriate policies in a timely manner through monitoring social media platforms to guide public opinion and behavior during epidemics.

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

    The Public’s Perception of the Severity and Global Impact at the Start of the SARS-CoV-2 Pandemic: A Crowdsourcing-Based Cross-Sectional Analysis


    Background: COVID-19 is a rapidly developing threat to most people in the United States and abroad. The behaviors of the public are important to understand, as they may have a tremendous impact on the course of this novel coronavirus pandemic. Objective: This study intends to assess the US population’s perception and knowledge of the virus as a threat and the behaviors of the general population in response. Methods: A prospective cross-sectional study was conducted with random volunteers recruited through Amazon Mechanical Turk, an internet crowdsourcing service, on March 24, 2020. Results: A total of 969 participants met the inclusion criteria. It was found that the perceived severity of the COVID-19 pandemic significantly differed between age groups (P<.001) and men and women (P<.001). A majority of study participants were actively adhering to the Centers for Disease Control and Prevention guidelines. Conclusions: Though many participants identified COVID-19 as a threat, many failed to place themselves appropriately in the correct categories with respect to risk. This may indicate a need for additional public education for appropriately defining the risk of this novel pandemic.

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

    Investigating Patients’ Intention to Continue Using Teleconsultation to Anticipate Postcrisis Momentum: Survey Study


    Background: The COVID-19 crisis has drastically changed care delivery with teleconsultation platforms experiencing substantial spikes in demand, helping patients and care providers avoid infections and maintain health care services. Beyond the current pandemic, teleconsultation is considered a significant opportunity to address persistent health system challenges, including accessibility, continuity, and cost of care, while ensuring quality. Objective: This study aims at identifying the determinants of patients’ intention to continue using a teleconsultation platform. It extends prior research on information technology use continuance intention and teleconsultation services. Methods: Data was collected in November 2018 and May 2019 with Canadian patients who had access to a teleconsultation platform. Measures included patients’ intention to continue their use; teleconsultation usefulness; teleconsultation quality; patients’ trust toward the digital platform, its provider. and health care professionals; and confirmation of patients’ expectations toward teleconsultation. We used structural equation modeling employing the partial least squares component-based technique to test our research model and hypotheses. Results: We analyzed a sample of 178 participants who had used teleconsultation services. Our findings revealed that confirmation of expectations had the greatest influence on continuance intention (total effects=0.722; P<.001), followed by usefulness (total effects=0.587; P<.001) and quality (total effects=0.511; P<.001). Usefulness (β=.60; P<.001) and quality (β=.34; P=.01) had direct effects on the dependent variable. The confirmation of expectations had direct effects both on usefulness (β=.56; P<.001) and quality (β=.75; P<.001) in addition to having an indirect effect on usefulness (indirect effects=0.282; P<.001). Last, quality directly influenced usefulness (β=.34; P=.002) and trust (β=.88; P<.001). Trust does not play a role in the context under study. Conclusions: Teleconsultation is central to care going forward, and it represents a significant lever for an improved, digital delivery of health care in the future. We believe that our findings will help drive long-term teleconsultation adoption and use, including in the aftermath of the current COVID-19 crisis, so that general care improvement and greater preparedness for exceptional situations can be achieved.

  • Source: Creative Commons; Copyright: Ars Electronica; URL:; License: Licensed by JMIR.

    Proposal and Assessment of a De-Identification Strategy to Enhance Anonymity of the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM)...


    Background: De-identifying personal information is critical when using personal health data for secondary research. The Observational Medical Outcomes Partnership Common Data Model (CDM), defined by the nonprofit organization Observational Health Data Sciences and Informatics, has been gaining attention for its use in the analysis of patient-level clinical data obtained from various medical institutions. When analyzing such data in a public environment such as a cloud-computing system, an appropriate de-identification strategy is required to protect patient privacy. Objective: This study proposes and evaluates a de-identification strategy that is comprised of several rules along with privacy models such as k-anonymity, l-diversity, and t-closeness. The proposed strategy was evaluated using the actual CDM database. Methods: The CDM database used in this study was constructed by the Anam Hospital of Korea University. Analysis and evaluation were performed using the ARX anonymizing framework in combination with the k-anonymity, l-diversity, and t-closeness privacy models. Results: The CDM database, which was constructed according to the rules established by Observational Health Data Sciences and Informatics, exhibited a low risk of re-identification: The highest re-identifiable record rate (11.3%) in the dataset was exhibited by the DRUG_EXPOSURE table, with a re-identification success rate of 0.03%. However, because all tables include at least one “highest risk” value of 100%, suitable anonymizing techniques are required; moreover, the CDM database preserves the “source values” (raw data), a combination of which could increase the risk of re-identification. Therefore, this study proposes an enhanced strategy to de-identify the source values to significantly reduce not only the highest risk in the k-anonymity, l-diversity, and t-closeness privacy models but also the overall possibility of re-identification. Conclusions: Our proposed de-identification strategy effectively enhanced the privacy of the CDM database, thereby encouraging clinical research involving multiple centers.

  • Source: freepik; Copyright: jannoon028; URL:; License: Licensed by JMIR.

    Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning–Based Classification and Retrieval Framework With a Large Endoscopic...


    Background: The early diagnosis of various gastrointestinal diseases can lead to effective treatment and reduce the risk of many life-threatening conditions. Unfortunately, various small gastrointestinal lesions are undetectable during early-stage examination by medical experts. In previous studies, various deep learning–based computer-aided diagnosis tools have been used to make a significant contribution to the effective diagnosis and treatment of gastrointestinal diseases. However, most of these methods were designed to detect a limited number of gastrointestinal diseases, such as polyps, tumors, or cancers, in a specific part of the human gastrointestinal tract. Objective: This study aimed to develop a comprehensive computer-aided diagnosis tool to assist medical experts in diagnosing various types of gastrointestinal diseases. Methods: Our proposed framework comprises a deep learning–based classification network followed by a retrieval method. In the first step, the classification network predicts the disease type for the current medical condition. Then, the retrieval part of the framework shows the relevant cases (endoscopic images) from the previous database. These past cases help the medical expert validate the current computer prediction subjectively, which ultimately results in better diagnosis and treatment. Results: All the experiments were performed using 2 endoscopic data sets with a total of 52,471 frames and 37 different classes. The optimal performances obtained by our proposed method in accuracy, F1 score, mean average precision, and mean average recall were 96.19%, 96.99%, 98.18%, and 95.86%, respectively. The overall performance of our proposed diagnostic framework substantially outperformed state-of-the-art methods. Conclusions: This study provides a comprehensive computer-aided diagnosis framework for identifying various types of gastrointestinal diseases. The results show the superiority of our proposed method over various other recent methods and illustrate its potential for clinical diagnosis and treatment. Our proposed network can be applicable to other classification domains in medical imaging, such as computed tomography scans, magnetic resonance imaging, and ultrasound sequences.

  • Source: freepik; Copyright: 8photo; URL:; License: Licensed by JMIR.

    Public Emotions and Rumors Spread During the COVID-19 Epidemic in China: Web-Based Correlation Study


    Background: Various online rumors have led to inappropriate behaviors among the public in response to the COVID-19 epidemic in China. These rumors adversely affect people’s physical and mental health. Therefore, a better understanding of the relationship between public emotions and rumors during the epidemic may help generate useful strategies for guiding public emotions and dispelling rumors. Objective: This study aimed to explore whether public emotions are related to the dissemination of online rumors in the context of COVID-19. Methods: We used the web-crawling tool Scrapy to gather data published by People’s Daily on Sina Weibo, a popular social media platform in China, after January 8, 2020. Netizens’ comments under each Weibo post were collected. Nearly 1 million comments thus collected were divided into 5 categories: happiness, sadness, anger, fear, and neutral, based on the underlying emotional information identified and extracted from the comments by using a manual identification process. Data on rumors spread online were collected through Tencent’s Jiaozhen platform. Time-lagged cross-correlation analyses were performed to examine the relationship between public emotions and rumors. Results: Our results indicated that the angrier the public felt, the more rumors there would likely be (r=0.48, P<.001). Similar results were observed for the relationship between fear and rumors (r=0.51, P<.001) and between sadness and rumors (r=0.47, P<.001). Furthermore, we found a positive correlation between happiness and rumors, with happiness lagging the emergence of rumors by 1 day (r=0.56, P<.001). In addition, our data showed a significant positive correlation between fear and fearful rumors (r=0.34, P=.02). Conclusions: Our findings confirm that public emotions are related to the rumors spread online in the context of COVID-19 in China. Moreover, these findings provide several suggestions, such as the use of web-based monitoring methods, for relevant authorities and policy makers to guide public emotions and behavior during this public health emergency.

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  • Impact of Interventional Policies Including Vaccine on COVID-19 Propagation and Socio-Economic Factors: Predictive Model Enabling Simulations Using Machine Learning and Big Data

    Date Submitted: Nov 24, 2020

    Open Peer Review Period: Nov 24, 2020 - Dec 1, 2020

    Background: A novel coronavirus disease has emerged (later named COVID-19) and caused the world to enter a new reality, with many direct and indirect factors influencing it. Some are human-controllabl...

    Background: A novel coronavirus disease has emerged (later named COVID-19) and caused the world to enter a new reality, with many direct and indirect factors influencing it. Some are human-controllable (e.g. interventional policies, mobility and the vaccine); some are not (e.g. the weather). We have sought to test how a change in these human-controllable factors might influence two measures: the number of daily cases against economic impact. If applied at the right level and with up-to-date data to measure, policymakers would be able to make targeted interventions and measure their cost. Objective: The study aimed to provide a predictive analytics framework to model, predict and simulate COVID-19 propagation and the socio-economic impact of interventions intended to reduce the spread of the disease such as policy and/or vaccine. It allows policymakers, government representatives and business leaders to make better-informed decisions about the potential effect of various interventions with forward-looking views via scenario planning. Methods: We leveraged a recently launched opensource COVID-19 big data platform and used published research to find potentially relevant variables (features), completing feature selection and engineering via in-depth data quality checks and analytics. An advanced machine learning pipeline has been developed. It contains the ensemble models, auto/semi-auto hyperparameter tuning and customized interpretability functions. And It is self-evolving as always learned from the most recent data. The output predicts daily cases and economic factors (e.g. small business revenue) to allow simulation of interventions including a vaccine (proxied by an influenza vaccination efficacy model). This framework is built using an open-source technology stack and we make the source code being publicly available as well. Results: This model is self-evolving and deployed on modern machine learning architecture. It has high accuracy for trend prediction (back-tested with r-squared). We bring simulation and interpretability in the framework. It models not just daily-cases, but also socio-economic demographics. Conclusions: Human behaviour and extreme natural disasters are hard to measure with data points. No model can provide an answer that is correct 100% of the time; however, with high-quality model and big data, a forward-looking view can be inferred or at least noted. This predictive model can help the policymakers to test scenarios, plan proactive actions, optimize logistics, measure the cost and create an open dialogue with the general public.

  • Effects of Background Color, Flash and Exposure Value on the Accuracy of Smartphone-Based Pill Recognition System Using Deep Convolutional Neural Network

    Date Submitted: Nov 24, 2020

    Open Peer Review Period: Nov 24, 2020 - Jan 19, 2021

    Background: It is difficult to develop a drug image recognition system due to the difference of the pill color influenced by external environmental factors such as the illumination or presence of flas...

    Background: It is difficult to develop a drug image recognition system due to the difference of the pill color influenced by external environmental factors such as the illumination or presence of flash. Objective: In this study, we wanted to see how the difference in color between the reference image and the real-world image affects the accuracy in pill recognition under 12 real-world conditions according to the background colors, presence of flash, and exposure values (EV). Methods: We used 19 medications with different features of colors, shapes, and dosages. The average color difference was calculated based on the color distance between the reference image and the real-world image. Results: In the case of the black background, as the exposure value lowered, the accuracy of top-1 and top-5 increased independently of the presence of flash. The top-5 accuracy in black background increased from 26.8% to 72.6% with the flash on and from 29.5% to 76.8% with the flash off as EV decreased as well. On the other hand, top-5 accuracy was 62.1% to 78.4% in white background with the flash on. The best top-1 accuracy was 51.1 % in the white background, flash on, and EV+2.0. The best top-5 accuracy was 78.4% in the white background, flash on, and EV0. Conclusions: The accuracy generally increased as the color difference decreased except in the case of black background and EV-2.0. This study reveals that the background colors, presence of flash, and exposure values in real-world conditions are important factors affecting the performance of a pill recognition model.

  • Component Analysis of a Blended Care CBT intervention for Depression and Anxiety: Pragmatic Retrospective Study

    Date Submitted: Nov 23, 2020

    Open Peer Review Period: Nov 23, 2020 - Dec 1, 2020

    Background: Depression and anxiety are leading causes of disability worldwide. Though effective treatments exist, depression and anxiety remain undertreated. Blended care psychotherapy, combining the...

    Background: Depression and anxiety are leading causes of disability worldwide. Though effective treatments exist, depression and anxiety remain undertreated. Blended care psychotherapy, combining the scalability of online interventions with the personalization and engagement of a live therapist, is a promising approach for increasing access to evidence-based care. Objective: To evaluate the effectiveness and individual contribution of two components - i) digital tools and ii) video-based therapist-led sessions - in a blended care CBT-based intervention under real world conditions. Methods: A retrospective cohort design was used to analyze N=1374 US-based individuals who enrolled in blended care psychotherapy. Of these, at baseline, 763 participants had depression symptoms in the clinical range (based on PHQ-9), and 1255 had anxiety symptoms in the clinical range (based on GAD-7). Participants had access to the program as a mental health benefit offered by their employer. The CBT-based blended care psychotherapy program consisted of regular video sessions with therapists, complemented by digital lessons and digital exercises assigned by the clinician and completed in between sessions. Depression and anxiety levels and clients’ treatment engagement were tracked throughout treatment. A 3-level individual growth curve model incorporating time-varying covariates was utilized to examine symptom trajectories of PHQ-9 scores (for those with clinical range of depression at baseline) and GAD-7 scores (for those with clinical range of anxiety at baseline). Results: On average, individuals exhibited a significant decline in depression and anxiety symptoms during the initial weeks of treatment (p<.001), and a continued decline over subsequent weeks at a slower rate (p<.001). Engaging in a therapy session in a week was associated with lower GAD-7 (b=-0.76) and PHQ-9 (b=-0.95) scores in the same week, as well as lower GAD-7 (b=-0.41) and PHQ-9 (b=-0.36) scores the following week (all p<.01). Similarly, engaging with digital lessons was independently associated with lower GAD-7 (b=-0.20) and PHQ-9 (b=-0.19) scores during the same week, and lower GAD-7 (b=-0.28) and PHQ-9 (b=-0.30) the following week (all p<.01). Conclusions: Therapist-led video sessions and digital lessons had separate contributions to improvements in symptoms of depression and anxiety over the course of treatment. Future research should investigate whether clients’ characteristics are related to differential effects of therapist-led and digital components of care.

  • Interdisciplinary Online Hackathons as an Approach to Combat the COVID-19 Pandemic: Case Study.

    Date Submitted: Nov 23, 2020

    Open Peer Review Period: Nov 23, 2020 - Nov 30, 2020

    Background: The COVID-19 outbreak has affected the lives of millions of people by causing a dramatic impact on several healthcare systems and the global economy. This devastating pandemic has brought...

    Background: The COVID-19 outbreak has affected the lives of millions of people by causing a dramatic impact on several healthcare systems and the global economy. This devastating pandemic has brought communities across the globe to work on this issue in an unprecedented manner. Objective: This case study describes the steps and methods employed in the conduction of a remote online health hackathon centered on challenges the COVID-19 pandemic poses. It aims to deliver a clear implementation road map for other organizations to follow. Methods: This 4-day hackathon was conducted in April 2020, based on 6 COVID-19-related challenges defined by frontline clinicians and researchers from various disciplines. An online survey was structured to assess: (i) individual experience satisfaction, (ii) level of interprofessional skill exchange, (iii) maturity of the projects realized, and (iv) overall quality of the event. At the end of the event, participants were invited to participate in an online survey with 17 (+5 optional) items, including multiple-choice and open-ended questions which assessed their experience regarding the remote character of the event and their individual project, interprofessional skill exchange, and their confidence in working on a digital health project before and after the hackathon. Complementary, mentors, who guided the participants through the event also provided feedback to the organizers through an online survey. Results: A total of 48 participants and 52 mentors based in 8 different countries participated and developed 14 projects. A total of 75 mentorship video sessions were held. Participants reported increased confidence in starting a digital health venture or a research project after successfully participating in the hackathon, and are likely to continue working on their projects. Of the participants that provided feedback, 60% (n=18) would not have started their project without this particular hackathon, and indicated that the hackathon encouraged and enabled them to progress faster, eg by building interdisciplinary teams, gaining new insights and feedback provided by the mentors, and creating a functional prototype. Conclusions: This study provides insights into how online hackathons can contribute to solving the challenges and effects of a pandemic in several regions of the world. The online format fosters team diversity, increases cross-regional collaboration, and can be executed much faster and at lower costs compared to in-person events. Results on preparation, organization, and evaluation of this online hackathon are useful to other institutions and initiatives which are willing to introduce similar event formats in the fight against COVID-19.

  • Mixed Methods Evaluation of an Intra-Hospital Telemedicine Program for Patients Admitted with COVID-19

    Date Submitted: Nov 23, 2020

    Open Peer Review Period: Nov 23, 2020 - Jan 18, 2021

    Background: Increasing incidence of coronavirus 2019 (COVID-19) infection has challenged healthcare systems to increase capacity while needing to conserve personal protective equipment (PPE) supplies...

    Background: Increasing incidence of coronavirus 2019 (COVID-19) infection has challenged healthcare systems to increase capacity while needing to conserve personal protective equipment (PPE) supplies and minimize nosocomial spread. Telemedicine shows promise to address these challenges but lacks comprehensive evaluation in the inpatient environment. Objective: To evaluate an intra-hospital telemedicine program (virtual care), along with its impact on exposure risk and communication. Methods: We conducted a natural experiment of virtual care on patients admitted for COVID-19. The primary exposure variable was documented use of virtual care. Patient characteristics, PPE use rates and their association with virtual care use were assessed. In parallel, we conducted surveys with patients and clinicians to capture satisfaction with virtual care along the domains of communication, medical treatment, and exposure risk. Results: Of 137 total patients in our primary analysis, 43 patients used virtual care. In total, there were 82 inpatient days of use and 401 inpatient days without use. Hospital utilization and illness severity was similar in patients who opted-in vs opted-out. Virtual care was associated with a significant reduction in PPE use and physical exam rate. Surveys of 41 patients and clinicians showed high rates of recommendation for further use, and subjective improvements in communication. However, providers and patients expressed limitations in usability, medical assessment and empathetic communication. Conclusions: In this pilot natural experiment, only a subset of patients used inpatient virtual care. When used, virtual care was associated with reductions in PPE use, reductions in exposure risk, and patient and provider satisfaction.

  • The ENGAGED framework: a qualitative study of the current use of social media by NHS trusts

    Date Submitted: Nov 22, 2020

    Open Peer Review Period: Nov 22, 2020 - Jan 17, 2021

    Background: The number of social media users in the UK is rapidly rising. However, there is a lack on primary research as to how the National Health Service (NHS) is using social media to engage patie...

    Background: The number of social media users in the UK is rapidly rising. However, there is a lack on primary research as to how the National Health Service (NHS) is using social media to engage patients and the public. Objective: To understand the current methodology, implementation and strategy of social media use within NHS Trusts. Methods: A qualitative grounded theory approach was taken through semi-structured interviews with NHS Trusts. Selection was based on the Trusts quality ratings by the Care Quality Commission (CQC) in 2017, selecting the highest 15 and lowest 15 ranked trusts. Telephone interviews were conducted with a member of the communication teams and were audio recorded then transcribed. Three independent researchers thematically analysed the transcripts, to draw themes that emerged from the transcripts. Results: Following a pilot study, we conducted interviews with the communications team of 27 NHS trusts across the UK. Six main themes arose from the interviews: 1) The social media and communications teams; 2)The Trust; 3) The Trusts’ use of social media; 4)The Trusts’ management of their social media ; 5)The future of social media; 6)The use of social media within the NHS). These six higher themes consisted of a total of 26 subthemes. Conclusions: The themes allow us to understand how social media is currently used within the NHS, as well as its potential future scope. Recognising the main areas of importance to Trusts and current difficulties they are facing, allow us to explore ways of increasing social media use by NHS Trusts. We have proposed a set of guidelines, known as the ENGAGED framework, which trusts can use to enhance social media use and enagagement. Clinical Trial: Nil