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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.

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Recent Articles:

  • Source: Image created by the Authors; Copyright: The Authors; URL: http://www.jmir.org/2020/8/e21486/; License: Creative Commons Attribution + Noncommercial (CC-BY-NC).

    Use of the HoloLens2 Mixed Reality Headset for Protecting Health Care Workers During the COVID-19 Pandemic: Prospective, Observational Evaluation

    Abstract:

    Background: The coronavirus disease (COVID-19) pandemic has led to rapid acceleration in the deployment of new digital technologies to improve both accessibility to and quality of care, and to protect staff. Mixed-reality (MR) technology is the latest iteration of telemedicine innovation; it is a logical next step in the move toward the provision of digitally supported clinical care and medical education. This technology has the potential to revolutionize care both during and after the COVID-19 pandemic. Objective: This pilot project sought to deploy the HoloLens2 MR device to support the delivery of remote care in COVID-19 hospital environments. Methods: A prospective, observational, nested cohort evaluation of the HoloLens2 was undertaken across three distinct clinical clusters in a teaching hospital in the United Kingdom. Data pertaining to staff exposure to high-risk COVID-19 environments and personal protective equipment (PPE) use by clinical staff (N=28) were collected, and assessments of acceptability and feasibility were conducted. Results: The deployment of the HoloLens2 led to a 51.5% reduction in time exposed to harm for staff looking after COVID-19 patients (3.32 vs 1.63 hours/day/staff member; P=.002), and an 83.1% reduction in the amount of PPE used (178 vs 30 items/round/day; P=.02). This represents 222.98 hours of reduced staff exposure to COVID-19, and 3100 fewer PPE items used each week across the three clusters evaluated. The majority of staff using the device agreed it was easy to set up and comfortable to wear, improved the quality of care and decision making, and led to better teamwork and communication. In total, 89.3% (25/28) of users felt that their clinical team was safer when using the HoloLens2. Conclusions: New technologies have a role in minimizing exposure to nosocomial infection, optimizing the use of PPE, and enhancing aspects of care. Deploying such technologies at pace requires context-specific information security, infection control, user experience, and workflow integration to be addressed at the outset and led by clinical end-users. The deployment of new telemedicine technology must be supported with objective evidence for its safety and effectiveness to ensure maximum impact.

  • Source: Unsplash.com; Copyright: Macau Photo Agency; URL: https://unsplash.com/photos/4I6VHLP5Ws4; License: Licensed by JMIR.

    The Infection Rate of COVID-19 in Wuhan, China: Combined Analysis of Population Samples

    Abstract:

    Background: The coronavirus disease (COVID-19) pandemic began in Wuhan, China, in December 2019. Wuhan had a much higher mortality rate than the rest of China. However, a large number of asymptomatic infections in Wuhan may have never been diagnosed, contributing to an overestimated mortality rate. Objective: This study aims to obtain an accurate estimate of infections in Wuhan using internet data. Methods: In this study, we performed a combined analysis of the infection rate among evacuated foreign citizens to estimate the infection rate in Wuhan in late January and early February. Results: Based on our analysis, the combined infection rate of the foreign evacuees was 0.013 (95% CI 0.008-0.022). Therefore, we estimate the number of infected people in Wuhan to be 143,000 (range 88,000-242,000), which is significantly higher than previous estimates. Our study indicates that a large number of infections in Wuhan were not diagnosed, which has resulted in an overestimated case fatality rate. Conclusions: Increased awareness of the original infection rate of Wuhan is critical for proper public health measures at all levels, as well as to eliminate panic caused by overestimated mortality rates that may bias health policy actions by the authorities.

  • Source: FlickR; Copyright: watts_photos; URL: https://www.flickr.com/photos/126288307@N05/31551639132; License: Creative Commons Attribution (CC-BY).

    Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic

    Abstract:

    Background: A novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting knowledge models. In an emergent epidemic, language processing can enable rapid conversion of unstructured text to a novel knowledge model. However, although this idea has often been suggested, no opportunity has arisen to actually test it in real time. The current coronavirus disease (COVID-19) pandemic presents such an opportunity. Objective: The aim of this study was to evaluate the added value of information from clinical text in response to emergent diseases using natural language processing (NLP). Methods: We explored the effects of long-term treatment by calcium channel blockers on the outcomes of COVID-19 infection in patients with high blood pressure during in-patient hospital stays using two sources of information: data available strictly from structured electronic health records (EHRs) and data available through structured EHRs and text mining. Results: In this multicenter study involving 39 hospitals, text mining increased the statistical power sufficiently to change a negative result for an adjusted hazard ratio to a positive one. Compared to the baseline structured data, the number of patients available for inclusion in the study increased by 2.95 times, the amount of available information on medications increased by 7.2 times, and the amount of additional phenotypic information increased by 11.9 times. Conclusions: In our study, use of calcium channel blockers was associated with decreased in-hospital mortality in patients with COVID-19 infection. This finding was obtained by quickly adapting an NLP pipeline to the domain of the novel disease; the adapted pipeline still performed sufficiently to extract useful information. When that information was used to supplement existing structured data, the sample size could be increased sufficiently to see treatment effects that were not previously statistically detectable.

  • Source: Pexels; Copyright: Anna Shvets; URL: https://www.pexels.com/photo/man-wearing-red-ribbon-3900469/; License: Licensed by JMIR.

    Social, Behavioral, and Cultural factors of HIV in Malawi: Semi-Automated Systematic Review

    Abstract:

    Background: Demographic and sociobehavioral factors are strong drivers of HIV infection rates in sub-Saharan Africa. These factors are often studied in qualitative research but ignored in quantitative analyses. However, they provide in-depth insight into the local behavior and may help to improve HIV prevention. Objective: To obtain a comprehensive overview of the sociobehavioral factors influencing HIV prevalence and incidence in Malawi, we systematically reviewed the literature using a newly programmed tool for automatizing part of the systematic review process. Methods: Due to the choice of broad search terms (“HIV AND Malawi”), our preliminary search revealed many thousands of articles. We, therefore, developed a Python tool to automatically extract, process, and categorize open-access articles published from January 1, 1987 to October 1, 2019 in the PubMed, PubMed Central, JSTOR, Paperity, and arXiV databases. We then used a topic modelling algorithm to classify and identify publications of interest. Results: Our tool extracted 22,709 unique articles; 16,942 could be further processed. After topic modelling, 519 of these were clustered into relevant topics, of which 20 were kept after manual screening. We retrieved 7 more publications after examining the references so that 27 publications were finally included in the review. Reducing the 16,942 articles to 519 potentially relevant articles using the software took 5 days. Several factors contributing to the risk of HIV infection were identified, including religion, gender and relationship dynamics, beliefs, and sociobehavioral attitudes. Conclusions: Our software does not replace traditional systematic reviews, but it returns useful results to broad queries of open-access literature in under a week, without a priori knowledge. This produces a “seed dataset” of relevance that could be further developed. It identified known factors and factors that may be specific to Malawi. In the future, we aim to expand the tool by adding more social science databases and applying it to other sub-Saharan African countries.

  • A smombie walking on a zebra crossing. Source: image created by the authors; Copyright: The Authors; URL: http://www.jmir.org/2020/8/e19350; License: Creative Commons Attribution (CC-BY).

    Factors Influencing the Smartphone Usage Behavior of Pedestrians: Observational Study on “Spanish Smombies”

    Abstract:

    Background: Smartphone addiction has become a reality accepted by all. Some previous studies have shown that the use of smartphones on public roads while walking is very common among the young population. The term “smombie” or smartphone zombie has been coined for this behavior. Such behavior causes a reduction in the attention given to other pedestrians and drivers and may result in accidents or collisions. However, there are no precise data about how many people use the phone while they are walking on the street. Smartphone usage habits are evolving rapidly, and more in-depth information is required, particularly about how users interact with their devices while walking: traditional phone conversations (phone close to the ear), voice chats (phone in front of the head), waiting for notifications (phone in hand), text chats (user touching the screen), etc. This in-depth information may be useful for carrying out specific preventive actions in both the education field (raising awareness about the risks) and in the infrastructure field (redesigning the cities to increase safety). Objective: This study aimed to gather information about pedestrians’ smartphone usage and to identify population groups wherein interventions should be focused to prevent accidents. The main hypothesis was that gender, age, and city area can significantly influence the smartphone usage of the pedestrians while walking. Methods: An observational study of pedestrians in the street was carried out in Elche, a medium-sized Spanish city of 230,000 inhabitants. The following data were gathered: gender, age group, location, and type of smartphone interaction. A specific smartphone app was developed to acquire data with high reliability. The statistical significance of each variable was evaluated using chi-squared tests, and Cramér’s V statistic was used to measure the effect sizes. Observer agreement was checked by the Cohen kappa analysis. Results: The behavior of 3301 pedestrians was analyzed, of which 1770 (53.6%) were females. As expected, the effect of the main variables studied was statistically significant, although with a small effect size: gender (P<.001, V=0.12), age (P<.001, V=0.18), and city area (P<.001, V=0.16). The phone in hand or “holding” behavior was particularly dependent on gender for all age groups (P<.001, V=0.09) and to a greater extent in young people (P<.001, V=0.16). Approximately 39.7% (222/559) of the young women observed showed “holding” or “smombie” behavior, and they comprised the highest proportion among all age and gender groups. Conclusions: An in-depth analysis of smartphone usage while walking revealed that certain population groups (especially young women) have a high risk of being involved in accidents due to smartphone usage. Interventions aimed at reducing the risk of falls and collisions should be focused in these groups.

  • Source: Image created by the authors; Copyright: The Authors; URL: https://www.jmir.org/2020/8/e19006; License: Licensed by JMIR.

    Mobile Health App for Japanese Adult Patients With Asthma: Clinical Observational Study

    Abstract:

    Background: Inappropriate asthma control reduces quality of life and causes increased exacerbations. Mobile health (mHealth) employs information and communication technology for surveying health-related issues. Objective: This noninterventional, observational study assessed current real-world asthma control levels among Japanese patients with asthma and cough variant asthma (CVA) using the Zensoku-Log app. Methods: We developed the app using the ResearchKit platform and conducted a mobile-based, self-reporting, observational survey among patients with asthma and CVA. The app was downloaded 7855 times between February 2016 and February 2018, and enabled collection of data on symptoms, comorbidities, quality of life, medications, asthma control, and adherence. Results: Of the 1744 eligible participants (median age 33 years; range 20-74 years; male-to-female ratio 38.7:61.3), 50.97% (889/1744) reported unscheduled visits, 62.84% (1096/1744) reported regularly scheduled visits, 23.14% (402/1737) smoked, and 40.75% (705/1730) had pets. In addition, 91.89% (1598/1739) of participants had atopic predisposition, including allergic rhinitis and atopic dermatitis. Daily inhaled corticosteroid and oral corticosteroid treatment had been prescribed for 89.45% (1552/1735) and 22.07% (383/1735) of participants, respectively. Although an asthma control questionnaire demonstrated poor asthma control in 58.48% (1010/1727), a leukotriene receptor antagonist, theophylline, and a long-acting muscarinic antagonist had been prescribed for only 30.66% (532/1735), 15.91% (276/1735), and 4.38% (76/1735), respectively. The Adherence Starts with Knowledge 12 total score was 29. In the 421 participants who repeated the questionnaire, asthma control increased significantly between the initial and last rounds (P=.002). Conclusions: Users of this mHealth app in Japan had poorly controlled asthma and may need more treatment for asthma and their comorbidities. Repeated app users demonstrated improved asthma control. Trial Registration: UMIN Clinical Trial Registry UMIN000021043; https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000023913.

  • Source: freepik; Copyright: Racool_studio; URL: https://www.freepik.com/free-photo/vaping-device_7782316.htm#page=3&query=vape&position=28; License: Licensed by JMIR.

    Tactics for Drawing Youth to Vaping: Content Analysis of Electronic Cigarette Advertisements

    Abstract:

    Background: The use of electronic cigarettes (e-cigarettes), also known as vaping, has risen exponentially among North American youth in recent years and has become a critical public health concern. The marketing strategies used by e-cigarette companies have been associated with the uptick in use among youth, with video advertisements on television and other electronic platforms being the most pervasive strategy. It is unknown how these advertisements may be tapping into youth needs and preferences. Objective: The aim of this 2-phase study was to examine the marketing strategies that underpin e-cigarette advertisements, specifically in the context of television. Methods: In phase 1, a scoping review was conducted to identify various influences on e-cigarette uptake among youth. Results of this scoping review informed the development of a coding framework. In phase 2, this framework was used to analyze the content of e-cigarette advertisements as seen on 2 popular television channels (Discovery and AMC). Results: In phase 1, a total of 20 articles met the inclusion criteria. The resultant framework consisted of 16 key influences on e-cigarette uptake among youth, which were categorized under 4 headings: personal, relational, environmental, and product-related. In phase 2, 38 e-cigarette advertisements were collected from iSpot.tv and represented 11 popular e-cigarette brands. All of the advertisements tapped into the cited influences of youth e-cigarette uptake, with the most commonly cited influences (product and relational) tapping into the most, at 97% (37/38) and 53% (20/38), respectively. Conclusions: The findings highlight the multidimensional influences on youth uptake of e-cigarettes, which has important implications for developing effective antivaping messages, and assist public health professionals in providing more comprehensive prevention and cessation support as it relates to e-cigarette use. The findings also bring forward tangible strategies employed by e-cigarette companies to recruit youth into vaping. Understanding this is vital to the development of cohesive strategies that combat these provaping messages.

  • Source: Image created by the Authors; Copyright: The Authors (Sally Baxter); URL: http://www.jmir.org/2020/8/e18855/; License: Creative Commons Attribution (CC-BY).

    Text Processing for Detection of Fungal Ocular Involvement in Critical Care Patients: Cross-Sectional Study

    Abstract:

    Background: Fungal ocular involvement can develop in patients with fungal bloodstream infections and can be vision-threatening. Ocular involvement has become less common in the current era of improved antifungal therapies. Retrospectively determining the prevalence of fungal ocular involvement is important for informing clinical guidelines, such as the need for routine ophthalmologic consultations. However, manual retrospective record review to detect cases is time-consuming. Objective: This study aimed to determine the prevalence of fungal ocular involvement in a critical care database using both structured and unstructured electronic health record (EHR) data. Methods: We queried microbiology data from 46,467 critical care patients over 12 years (2000-2012) from the Medical Information Mart for Intensive Care III (MIMIC-III) to identify 265 patients with culture-proven fungemia. For each fungemic patient, demographic data, fungal species present in blood culture, and risk factors for fungemia (eg, presence of indwelling catheters, recent major surgery, diabetes, immunosuppressed status) were ascertained. All structured diagnosis codes and free-text narrative notes associated with each patient’s hospitalization were also extracted. Screening for fungal endophthalmitis was performed using two approaches: (1) by querying a wide array of eye- and vision-related diagnosis codes, and (2) by utilizing a custom regular expression pipeline to identify and collate relevant text matches pertaining to fungal ocular involvement. Both approaches were validated using manual record review. The main outcome measure was the documentation of any fungal ocular involvement. Results: In total, 265 patients had culture-proven fungemia, with Candida albicans (n=114, 43%) and Candida glabrata (n=74, 28%) being the most common fungal species in blood culture. The in-hospital mortality rate was 121 (46%). In total, 7 patients were identified as having eye- or vision-related diagnosis codes, none of whom had fungal endophthalmitis based on record review. There were 26,830 free-text narrative notes associated with these 265 patients. A regular expression pipeline based on relevant terms yielded possible matches in 683 notes from 108 patients. Subsequent manual record review again demonstrated that no patients had fungal ocular involvement. Therefore, the prevalence of fungal ocular involvement in this cohort was 0%. Conclusions: MIMIC-III contained no cases of ocular involvement among fungemic patients, consistent with prior studies reporting low rates of ocular involvement in fungemia. This study demonstrates an application of natural language processing to expedite the review of narrative notes. This approach is highly relevant for ophthalmology, where diagnoses are often based on physical examination findings that are documented within clinical notes. Trial Registration:

  • Source: Image created by the Authors; Copyright: The Authors; URL: http://www.jmir.org/2020/8/e19657/; License: Creative Commons Attribution + Noncommercial (CC-BY-NC).

    Developing a Mobile App for Monitoring Medical Record Changes Using Blockchain: Development and Usability Study

    Abstract:

    Background: Although we are living in an era of transparency, medical documents are often still difficult to access. Blockchain technology allows records to be both immutable and transparent. Objective: Using blockchain technology, the aim of this study was to develop a medical document monitoring system that informs patients of changes to their medical documents. We then examined whether patients can effectively verify the monitoring of their primary care clinical medical records in a system based on blockchain technology. Methods: We enrolled participants who visited two primary care clinics in Korea. Three substudies were performed: (1) a survey of the recognition of blockchain medical records changes and the digital literacy of participants; (2) an observational study on participants using the blockchain-based mobile alert app; and (3) a usability survey study. The participants’ medical documents were profiled with HL7 Fast Healthcare Interoperability Resources, hashed, and transacted to the blockchain. The app checked the changes in the documents by querying the blockchain. Results: A total of 70 participants were enrolled in this study. Considering their recognition of changes to their medical records, participants tended to not allow these changes. Participants also generally expressed a desire for a medical record monitoring system. Concerning digital literacy, most questions were answered with “good,” indicating fair digital literacy. In the second survey, only 44 participants—those who logged into the app more than once and used the app for more than 28 days—were included in the analysis to determine whether they exhibited usage patterns. The app was accessed a mean of 5.1 (SD 2.6) times for 33.6 (SD 10.0) days. The mean System Usability Scale score was 63.21 (SD 25.06), which indicated satisfactory usability. Conclusions: Patients showed great interest in a blockchain-based system to monitor changes in their medical records. The blockchain system is useful for informing patients of changes in their records via the app without uploading the medical record itself to the network. This ensures the transparency of medical records as well as patient empowerment.

  • Source: Unsplash; Copyright: Daniel Sone; URL: https://unsplash.com/photos/cQ8FfVNvbew; License: Licensed by the authors.

    Adherence of Female Health Care Workers to the Use a Web-Based Tool for Improving and Modifying Lifestyle: Prospective Target Group Pilot Study

    Abstract:

    Background: Health care professionals are exposed to the psychological and physiological effects of stress, which is a well-known risk factor for various mental and physical health problems. Objective: The aims of this study were to assess the adherence of female health care workers to use a web-based tool for improving and modifying lifestyle and to identify the potential factors influencing their adherence. Methods: A prospective, observational study was performed. A total of 80 female health care workers (physicians and gradated nurses) from 2 university medical centers and female members of a family medicine society participated. Participants completed a questionnaire that inquired about their basic demographic data and physical fitness. Physical fitness was assessed by the Rockport Fitness Walking Test. Adherence to a web-based application (24@life) was followed for 3 months and the number of log-ins into the application was counted. Results: The study was conducted from March to October 2019. Significantly high workload has been detected in all groups (P<.05), except in the general practitioner with normal workload group. The graduated nurse working in the surgery room group showed chronic stress with elevated S-cortisol levels (>690 nmol/L); activated cellular immune system with elevated concentrations of lymphocytes (reference 1.1-2.5 × 109 cells/L), CD3 cells (reference 0.7-1.9 × 109 cells/L), CD8 cells (reference 0.2-0.7 × 109 cells/L), and HLA-DR/CD3 cells (reference 0.04-0.2 × 109 cells/L); and the worst quality of sleep (mean 2.8 [SD 1.2]). Only 32 of 80 participants (40%) were adherent to the web-based application. Participants most frequently viewed web pages on areas of physical activity (497 times) and nutrition (332 times). No factors or participant’s characteristics such as weight (odds ratio [OR] 1.026, 95% CI 0.977-1.078), BMI (OR 0.993, 95% CI 0.834-1.184), age (OR 0.970, 95% CI 0.910-1.034), or stress level (OR 0.997, 95% CI 0.995-1.000) were identified to affect the adherence rates. Conclusions: Female health care workers exposed to high workload did not find the web-based application useful for improving and modifying their lifestyle. Therefore, other strategies that might help health care workers facing stress and improve their lifestyle should be identified.

  • Untitled. Source: Pixabay; Copyright: lindsayfox; URL: https://pixabay.com/photos/cigarette-marlboro-tobacco-smoke-1301661/; License: Licensed by JMIR.

    Comparing Methods of Recruiting Spanish-Preferring Smokers in the United States: Findings from a Randomized Controlled Trial

    Abstract:

    Background: There is a pressing need to address the unacceptable disparities and underrepresentation of racial and ethnic minority groups, including Hispanics or Latinxs, in smoking cessation trials. Objective: Given the lack of research on recruitment strategies for this population, this study aims to assess effective recruitment methods based on enrollment and cost. Methods: Recruitment and enrollment data were collected from a nationwide randomized controlled trial (RCT) of a Spanish-language smoking cessation intervention (N=1417). The effectiveness of each recruitment strategy was evaluated by computing the cost per participant (CPP), which is the ratio of direct cost over the number enrolled. More effective strategies yielded lower CPPs. Demographic and smoking-related characteristics of participants recruited via the two most effective strategies were also compared (n=1307). Results: Facebook was the most effective method (CPP=US $74.12), followed by TV advertisements (CPP=US $191.31), whereas public bus interior card advertising was the least effective method (CPP=US $642.50). Participants recruited via Facebook had lower average age (P=.008) and had spent fewer years in the United States (P<.001). Among the participants recruited via Facebook, a greater percentage of individuals had at least a high school education (P<.001) and an annual income above US $10,000 (P<.001). In addition, a greater percentage of individuals were employed (P<.001) and foreign born (P=.003). In terms of subethnicity, among the subjects recruited via Facebook, a lower percentage of individuals were of Mexican origin (P<.001) and a greater percentage of individuals were of Central American (P=.02), South American (P=.01), and Cuban (P<.001) origin. Conclusions: Facebook was the most effective method for recruiting Hispanic or Latinx smokers in the United States for this RCT. However, using multiple methods was necessary to recruit a more diverse sample of Spanish-preferring Hispanic or Latinx smokers.

  • INEBRIA Special Interest Group on Digital Interventions. Source: iStock; Copyright: yacobchuk; URL: https://www.istockphoto.com/photo/unhappy-young-woman-using-her-modern-smartphone-gm1046511028-279990262; License: Licensed by the authors.

    e-INEBRIA Special Interest Group Roadmap for Best Practices for Research on Brief Digital Interventions for Problematic Alcohol and Illicit Drug Use

    Abstract:

    There is great potential for scaling up the delivery of brief interventions for alcohol and illicit drug use, given the increasing coverage of mobile devices and technologies for digital interventions, including apps for smartphones and tablets. However, while the number of digital interventions is increasing rapidly, the involvement of brief-intervention researchers and the development of good practices has just begun. In 2018, the Special Interest Group on digital interventions of the International Network on Brief Interventions for Alcohol & Other Drugs (e-INEBRIA SIG) initiated a conversation regarding possible avenues of future research, which subsequently became a roadmap for digital interventions. This roadmap consists of points considered relevant for future research, ongoing technological developments, and their implementation across a continuum of prevention and care. Moreover, it outlines starting points for the diversification of brief digital interventions, as well as next steps for quality improvement and implementation in public health and clinical practice. The roadmap of the e-INEBRIA SIG on digital interventions is a starting point that indicates relevant next steps and provides orientation for researchers and interested practitioners with regard to the ambiguous literature and the complexity of current digital interventions.

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  • Epidemiology of Reopening in the COVID-19 Pandemic in the United States, Europe and Asia: an Interrupted Time Series Analysis

    Date Submitted: Aug 14, 2020

    Open Peer Review Period: Aug 14, 2020 - Oct 9, 2020

    Background: Since the discovery of the novel coronavirus (SARS-CoV-2), COVID-19 has become a global healthcare and economic crisis. The United States (US) and Europe exhibited wide impacts from the vi...

    Background: Since the discovery of the novel coronavirus (SARS-CoV-2), COVID-19 has become a global healthcare and economic crisis. The United States (US) and Europe exhibited wide impacts from the virus with more than six million cases by the time of our analysis. To inhibit spread, stay-at-home orders and other non-pharmaceutical interventions (NPIs) were instituted. Beginning late April 2020, some US states, European, and Asian countries lifted restrictions and started the reopening phases. Objective: In this study, the changes of confirmed cases, hospitalizations, and deaths were analyzed after reopening for 11 countries and 40 US states. Additionally, the distribution of these categories was further analyzed by age due to the known increased risk in elderly patients. Methods: Time series data for cases, hospitalizations, and deaths were collected from various public data sources. An indicator of changes following reopening was defined as significant slope changes (P<0.05) by interrupted time series analysis or significant mean differences (P<0.05) by two-tailed Student t-test. A positive slope coefficient after reopening indicates increase trend, and a negative slope coefficient indicates inversely. Results: Reopening had varied effects on COVID-19 cases depending on the region. Eight countries had increased cases after reopening while only two countries showed the same trend in deaths. In the US, 30 states had observed increases in cases while only seven observed increased deaths. In addition, we found that states with later reopening dates were more likely to have significant decreases in cases, hospitalizations, and deaths. Furthermore, age distributions through time were analyzed in relation to COVID-19 in the US. Younger age groups typically had an increased share of cases after reopening. Conclusions: Recent increases in cases and hospitalizations tended to a younger population and did not fully translate into increased deaths. US states that reopened later were more likely to have better results.

  • MOSAICO, a web platform to design, perform, and assess collaborative clinical scenarios for medical students.

    Date Submitted: Aug 13, 2020

    Open Peer Review Period: Aug 13, 2020 - Aug 20, 2020

    Background: The Collaborative Clinical Simulation (CCS) model is a structured method for the development and assessment of clinical competencies through small groups working collaboratively on simulat...

    Background: The Collaborative Clinical Simulation (CCS) model is a structured method for the development and assessment of clinical competencies through small groups working collaboratively on simulated environments. From 2016 to date, the CCS model has been applied successfully in undergraduate and graduate medical students from the University of Talca (Chile), the University of Barcelona (Spain), and in the University VIC-Manresa (Spain). All the templates for building the clinical cases and the assessment instruments with CCS were printed on paper. Considering several CCS sessions and the number of participating students that are required throughout the medical degree curriculum, it is impossible to keep an organized record when the instruments are printed on paper. Moreover, with the COVID-19 pandemic, the web-platforms take importance as a safe training environment for students and medical faculties, where the new educational environment should include the consolidation and adaptation of didactic sessions creating and using available virtual cases and using different web-platforms. Objective: Design and develop a web-platform to strengthen the Collaborative Clinical Simulation model. Methods: The design of the web platform was aimed to support each phase of the CCS by incorporating functional and non-functional requirements needed to run collaborative sessions. The software was developed under the Model-View-Controller architecture to separate the views from the data model and the business logic. Results: MOSAICO, a web platform to design, perform, and assess collaborative clinical scenarios for medical students. MOSAICO has four modules: educational design, students’ collaborative design, collaborative simulation, and collaborative debriefing. Three different user profiles: academic simulation unit, teacher, and student. These users interact under different roles in collaborative simulations. MOSAICO enables a collaborative environment (connected by the Internet) to design clinical scenarios guided by the teacher, and use all data generated for discussing in the debriefing session with the teacher as a guide. Conclusions: MOSAICO was implemented and is used frequently in different simulation sessions at the University of Talca, where medical students can work collaboratively connected by the Internet. The web platform supports all the stages of the CCS model, and the teachers use MOSAICO as technological infrastructure to schedule, design, and execute the simulation activities.

  • COVID-19 Mobile Apps for Contact Tracing: A Review on Technology and User Opinions

    Date Submitted: Aug 13, 2020

    Open Peer Review Period: Aug 13, 2020 - Oct 8, 2020

    Background: Contact tracing has been a key part of the worldwide measure in response to the COVID-19 pandemic. Many countries across the globe have released their contact tracing application. This has...

    Background: Contact tracing has been a key part of the worldwide measure in response to the COVID-19 pandemic. Many countries across the globe have released their contact tracing application. This has resulted in the proliferation of several contact tracing applications that used a variety of technologies. Objective: This study analyses most of the COVID-19 Contact tracing apps in use today. Beyond investigating the privacy features, design, and implications of these apps, this research examines the underlying technologies used in contact tracing applications. It also attempts to provide some insights into their level of penetration and gauge their public reception. Methods: The research sampled 13 applications corresponding to 10 countries based on the underlying technology used. The selected applications were all free to download. The inclusion criteria also ensured that most COVID-19 declared epicentre (countries) were included in the sample, such as Italy. The sampled apps included also countries that relatively did well in controlling the outbreak of COVID-19 such as Singapore. Informational apps or un-official contact tracing apps were excluded from this study except for the South Korean app as this was amongst the first app launching globally. A brute force keyword search technique was used to scrap the reviews of each of the 13 apps under reviews. Results: The study identified seven distinct technologies used by or incorporated in COVID-19 tracing applications. In total 13 distinct applications were selected for this study. Conclusions: Contact tracing applications come with their own set of challenges as well. Key amongst these challenges is privacy. Of course, this is anticipated as you can’t expect to trace and track peoples’ movement by a government authority without addressing the privacy issues.

  • Machine learning-based PRediction of ICU DElirium (PRIDE) Model for Delirium Prediction in Intensive Care Units: A Retrospective Study

    Date Submitted: Aug 12, 2020

    Open Peer Review Period: Aug 12, 2020 - Aug 19, 2020

    Background: Delirium occurs frequently among patients admitted to intensive care unit (ICU). There is only limited evidence to support interventions to treat or resolve delirium in patients who have a...

    Background: Delirium occurs frequently among patients admitted to intensive care unit (ICU). There is only limited evidence to support interventions to treat or resolve delirium in patients who have already developed delirium. Therefore, the early recognition and prevention of delirium is important in the management of critically ill patients. Objective: This study aimed to develop and validate the PRIDE (PRediction of ICU DElirium) model with machine learning using electronic health record data for delirium prediction within 24 hours from ICU admission. Methods: This is a retrospective cohort study performed at a tertiary referral hospital with 120 ICU beds. Machine learning-based PRIDE (PRediction of ICU DElirium) models were developed using patient data from the first 2 years of the study period and validated using patient data from the last 6 months. eXtreme Gradient Boosting (XGBoost), random forest (RF), deep neural network (DNN), and logistic regression (LR) were used. The PRIDE model was externally validated using MIMIC-III data. Results: We only included patients who were 18 years or older at the time of admission and who stayed in the medical or surgical ICU. A total of 37,543 cases were collected. After patient exclusion, 12,409 remained as our study population, of which 3,816 (30.8%) patients experienced delirium incidents during the study period. The MIMIC-3 dataset, based on the exclusion criteria, out of the 96,016 ICU admission cases, 2,061 cases were included, and 272 (13.2%) delirium incidents occurred. In the internal validation, the area under the receiver operating characteristics (AUROC) for XGBoost, RF, DNN, and LR was 0.919 (95% CI 0.919–0.919), 0.916 (95% CI 0.916–0.916), 0.881 (95% CI 0.878–0.884), and 0.875 (95% CI 0.875–0.875), respectively. Regarding the external validation, the best AUROC was 0.721 (95% CI 0.72–0.721), 0.697 (95% CI 0.695–0.699), 0.655 (95% CI 0.654–0.657), and 0.631 (95% CI 0.631–0.631) for RF, XGBoost, DNN, and LR, respectively. The Brier score of the XGBoost model is 0.094, indicating that it is well calibrated. Conclusions: A machine learning approach based on electronic health record data can be used to predict delirium within 24 hours of ICU admission. Clinical Trial: N/A

  • Visualising patterns of engagement with a behaviour change app for alcohol reduction.

    Date Submitted: Aug 10, 2020

    Open Peer Review Period: Aug 10, 2020 - Oct 5, 2020

    Background: The development of behaviour change apps often follows an iterative process, where the app evolves into a more complex, dynamic or personalised intervention through cycles of research, dev...

    Background: The development of behaviour change apps often follows an iterative process, where the app evolves into a more complex, dynamic or personalised intervention through cycles of research, development and implementation. Understanding how existing users engage with an app (e.g. frequency, amount, depth and duration of use) can help guide further incremental improvements. Visualisations provide a good understanding of patterns of engagement, as usage data are often longitudinal and rich. Objective: To visualise behavioural engagement with Drink Less, a behaviour change app which aims to reduce hazardous and harmful alcohol consumption in the general adult UK population. Methods: We explored behavioural engagement among 19,233 existing users of Drink Less. Users were included in the analytic sample if they were: from the UK; aged 18 years or over; interested in drinking less alcohol; had a baseline Alcohol Use Disorders Identification Test (AUDIT) score of 8 or above indicative of excessive drinking; and downloaded the app between 17th May 2017 to 22nd January 2019 (615 days). Measures of when sessions begin, length of sessions, time to disengagement, and patterns of use were visualised with heat maps, time-line plots, k-modes clustering analyses and Kaplan-Meier plots. Results: The visualisations gave us five important insights: (i) the existing daily notification, delivered at 11am, appeared to have a very strong impact on engagement in the following hour; (ii) behavioural engagement decreased over time, with 50% of users disengaging (defined as no use for seven or more consecutive days) 22 days after download; (iii) three distinct trajectories of use were identified: Engagers (24% users), Slow Disengagers (19% users) and Fast Disengagers (57% users); (iv) the depth of engagement was limited, with 85% of sessions occurring within the ‘Self-monitoring and Feedback’ module; and (v) outside the hour after the daily notification is sent, a peak of both frequency and amount of time spent per session was observed in the evenings. Conclusions: Visualisations of behavioural engagement with the Drink Less app suggest that the current daily notification substantially impacts engagement. Our next research aim is to further optimise the notification policy by tailoring to contextual circumstances of individuals over time. This will be achieved by a Micro-Randomised Trial (MRT), and these visualisations were helpful in both gaining a better understanding of engagement and informing the design of the MRT.

  • BlockMed: Blockchain-based Electronic Consent Management for Secure and Privacy-Protected Healthcare Interoperability

    Date Submitted: Aug 10, 2020

    Open Peer Review Period: Aug 9, 2020 - Oct 4, 2020

    Background: An electronic consent management system can improve the care service significantly by balancing the risks to patient privacy with the benefits of health information exchange and interopera...

    Background: An electronic consent management system can improve the care service significantly by balancing the risks to patient privacy with the benefits of health information exchange and interoperability. Patients leave their health information on multiple providers’ silo. A holistic report and privacy-preserved analysis can help to expedite several medical services including both personal- and community-care. Furthermore, access to consent-based, anonymous health records can accelerate innovation in health services and researches. Objective: We propose BlockMed, a proof-of-concept (POC) for a novel, cost-effective e-consent management system. Given proper consent, BlockMed can query the patient’s information fractured over multiple healthcare-providers’ silo make it available to the patient. At the same time, BlockMed also enables privacy-preserved data analysis by third-party service providers in the same system. Leveraging the unique and anonymous Ethereum id, BlockMed masks out the patient’s original identification in the provider’s secured local silo and abstract away any complication caused by changes in the identification information on multiple silos. Methods: The core functionalities of BlockMed are developed with a set of smart contracts on Ethereum blockchain. To develop those smart contracts, we have divided the potential users into three different groups such as, patient, provider, and third-party analyzer. Users are identified by their anonymous Ethereum id and need to sign the consent to access healthcare data. After signing, BlockMed can automatically initiate the queries to fetch data from providers’ data warehouse, enables analysis on any third-party service provider’s infrastructure if required, and finally, presents a report to the intended users including the patient. The signed consents stay on Ethereum forever leaving a permanent audit trail to uphold the integrity of the system. Results: We evaluated our system in terms of its functionality and cost. Our decentralized application (DApp) can not only query the data from multiple providers' silo but also enable third-party report generation with proper consent and privacy. Masking out the actual patient identification information under anonymous Ethereum ID our DApp can operate irrespective of geographical boundaries. Our cost analysis shows that the adoption of decentralized blockchain-based technology can avoid huge amount of capital investment required for similar services using centralized infrastructure such as AWS cloud. Conclusions: Many prior studies have already confirmed that blockchain can improve and expedite data sharing among different providers. Our POC, BlockMed takes it to one step ahead where the data analysis is also integrated. We prove the efficacy of BlockMed by evaluating its functionalities qualitatively as well as its comparing its cost with an alternative cloud-based architecture.

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