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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:

  • Daily steps of residents in Changsha dropped rapidly and substantially during the COVID-19 epidemic period. Source: Image created by the Authors; Copyright: Yilun Wang; URL:; License: Creative Commons Attribution (CC-BY).

    Physical Distancing Measures and Walking Activity in Middle-aged and Older Residents in Changsha, China, During the COVID-19 Epidemic Period: Longitudinal...


    Background: Physical distancing measures taken to contain COVID-19 transmission may substantially reduce physical activity levels and cause individuals to adopt a more sedentary lifestyle. Objective: The objective of this study is to determine if there was any change in daily steps, an important component of daily physical activity, and examine risk factors for frequent low daily steps during the COVID-19 epidemic. Methods: We used data collected from the Step Study, a population-based longitudinal study of walking activity among residents aged ≥40 years in Changsha, China. Daily steps were collected via a smartphone linked to WeChat, a social networking platform. We plotted mean daily steps and the prevalence of low daily steps (≤1500 steps/day) 30 days before (reference period) and 30 days after (epidemic period) January 21, 2020 (date of the first COVID-19 case diagnosed in Changsha), and compared it with the same corresponding period from 2019. We examined the association of risk factors with the prevalence of frequent low daily steps (≤1500 steps/day for ≥14 days) using logistic regression. Results: Among 3544 participants (mean age 51.6 years; n=1226 females, 34.6%), mean daily steps dropped from 8097 to 5440 and the prevalence of low daily steps increased from 3% (2287/76,136 person-day) to 18.5% (12,951/70,183 person-day) during the reference and epidemic periods, respectively. No such phenomenon was observed during the corresponding period in 2019. Older age (P for interaction=.001) and female sex (P for interaction<.001) were both associated with a higher prevalence of frequent low daily steps and were more pronounced during the epidemic period. More education was associated with a lower prevalence of frequent low daily steps during the reference period but not the epidemic period (P for interaction=.34). Body mass index or comorbidity were not associated with frequent low daily steps during either period. Conclusions: Daily steps of Changsha residents aged ≥40 years dropped significantly during the COVID-19 period, especially among older adults and females. Although successful physical distancing, measured by the rapid downward trend in daily step counts of residents, played a critical role in the containment of the COVID-19 epidemic, our findings of an increase in the prevalence of frequent low daily steps raise concerns about unintended effects on physical activity.

  • Source: Unsplash; Copyright: National Cancer Institute; URL:; License: Licensed by JMIR.

    The Role of Social Media in Enhancing Clinical Trial Recruitment: Scoping Review


    Background: Recruiting participants into clinical trials continues to be a challenge, which can result in study delay or termination. Recent studies have used social media to enhance recruitment outcomes. An assessment of the literature on the use of social media for this purpose is required. Objective: This study aims to answer the following questions: (1) How is the use of social media, in combination with traditional approaches to enhance clinical trial recruitment and enrollment, represented in the literature? and (2) Do the data on recruitment and enrollment outcomes presented in the literature allow for comparison across studies? Methods: We conducted a comprehensive literature search across 7 platforms to identify clinical trials that combined social media and traditional methods to recruit patients. Study and participant characteristics, recruitment methods, and recruitment outcomes were evaluated and compared. Results: We identified 2371 titles and abstracts through our systematic search. Of these, we assessed 95 full papers and determined that 33 studies met the inclusion criteria. A total of 17 studies reported enrollment outcomes, of which 9 achieved or exceeded their enrollment target. The proportion of participants enrolled from social media in these studies ranged from 0% to 49%. Across all 33 studies, the proportion of participants recruited and enrolled from social media varied greatly. A total of 9 studies reported higher enrollment rates from social media than any other methods, and 4 studies reported the lowest cost per enrolled participant from social media. Conclusions: While the assessment of the use of social media to improve clinical trial participation is hindered by reporting inconsistencies, preliminary data suggest that social media can increase participation and reduce per-participant cost. The adoption of consistent standards for reporting recruitment and enrollment outcomes is required to advance our understanding and use of social media to support clinical trial success.

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

    Efficacy of Electronic Acupuncture Shoes for Chronic Low Back Pain: Double-Blinded Randomized Controlled Trial


    Background: Chronic low back pain is a common problem and is associated with high costs, including those related to health care and indirect costs due to absence at work or reduced productivity. Previous studies have demonstrated that acupuncture or electroacupuncture can relieve low back pain. Electronic acupuncture shoes (EAS) are a novel device designed in this study. This device combines the properties of acupuncture and transcutaneous electrical nerve stimulation for clinical use. Objective: The aim of this study was to evaluate the efficacy of EAS in patients with chronic low back pain. Methods: In this prospective double-blinded randomized controlled study, the data of 83 patients who experienced chronic low back pain were analyzed. Patients came to our clinic for 20 visits and underwent assessment and treatment. Patients were randomly allocated to receive either EAS plus placebo nonsteroidal anti-inflammatory drugs (NSAIDs) (EAS group, n=42) or sham EAS plus NSAIDs (NSAID group, n=41). The visual analog scale (VAS) score and range of motion were assessed at baseline, before and after each EAS treatment, and 2 weeks after the last treatment. The time for achieving pain remission was recorded. Quality of life was assessed at the 2nd, 14th, and 20th visits. Results: After 6 weeks of treatment, the treatment success rate in each visit in the EAS group was higher than that in the NSAID group, as revealed by the intention-to-treat (ITT) and per-protocol (PP) analyses, but significant differences were observed only during the 16th visit in the ITT analysis (EAS group: 31/37, 84% and NSAID group: 21/34, 62%; P=.04). The change in the VAS score from baseline in each visit in the EAS group was greater than that in the NSAID group, as revealed by the ITT and PP analyses, and significant differences were observed in the 5th visit and 9th visit in the ITT analysis (P=.048 and P=.048, respectively). Significant differences were observed in the left rotation in the 2nd visit and 4th visit (P=.049 and P=.03, respectively). No significant differences were observed in the VAS score before and after treatment in each visit and in the quality of life in both groups. Conclusions: EAS might serve as a reliable alternative therapeutic tool for patients with chronic low back pain who are contraindicated for oral NSAIDs. Clinical Trial: NCT02468297

  • Source: Image created by the Authors/iStock; Copyright: The Authors/fad1986; URL:; License: Licensed by the authors.

    Digital Pain Mapping and Tracking in Patients With Chronic Pain: Longitudinal Study


    Background: Digital pain mapping allows for remote and ecological momentary assessment in patients over multiple time points spanning days to months. Frequent ecological assessments may reveal tendencies and fluctuations more clearly and provide insights into the trajectory of a patient’s pain. Objective: The primary aim of this study is to remotely map and track the intensity and distribution of pain and discomfort (eg, burning, aching, and tingling) in patients with nonmalignant spinal referred pain over 12 weeks using a web-based app for digital pain mapping. The secondary aim is to explore the barriers of use by determining the differences in clinical and user characteristics between patients with good (regular users) and poor (nonregular users) reporting compliance. Methods: Patients (N=91; n=53 women) with spinal referred pain were recruited using web-based and traditional in-house strategies. Patients were asked to submit weekly digital pain reports for 12 weeks. Each pain report consisted of digital pain drawings on a pseudo–three-dimensional body chart and pain intensity ratings. The pain drawings captured the distribution of pain and discomfort (pain quality descriptors) expressed as the total extent and location. Differences in weekly pain reports were explored using the total extent (pixels), current and usual pain intensity ratings, frequency of quality descriptor selection, and Jaccard similarity index. Validated e-questionnaires were completed at baseline to determine the patients’ characteristics (adapted Danish National Spine Register), disability (Oswestry Disability Index and Neck Disability Index), and pain catastrophizing (Pain Catastrophizing Scale) profiles. Barriers of use were assessed at 6 weeks using a health care–related usability and acceptance e-questionnaire and a self-developed technology-specific e-questionnaire to assess the accessibility and ease of access of the pain mapping app. Associations between total extent, pain intensity, disability, and catastrophizing were explored to further understand pain. Differences between regular and nonregular users were assessed to understand the pain mapping app reporting compliance. Results: Fluctuations were identified in pain reports for total extent and pain intensity ratings (P<.001). However, quality descriptor selection (P=.99) and pain drawing (P=.49), compared using the Jaccard index, were similar over time. Interestingly, current pain intensity was greater than usual pain intensity (P<.001), suggesting that the timing of pain reporting coincided with a more intense pain experience than usual. Usability and acceptance were similar between regular and nonregular users. Regular users were younger (P<.001) and reported a larger total extent of pain than nonregular users (P<.001). Conclusions: This is the first study to examine digital reports of pain intensity and distribution in patients with nonmalignant spinal referred pain remotely for a sustained period and barriers of use and compliance using a digital pain mapping app. Differences in age, pain distribution, and current pain intensity may influence reporting behavior and compliance.

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

    The Abortion Web Ecosystem: Cross-Sectional Analysis of Trustworthiness and Bias


    Background: People use the internet as a primary source for learning about medical procedures and their associated safety profiles and risks. Although abortion is one of the most common procedures worldwide among women in their reproductive years, it is controversial and highly politicized. Substantial scientific evidence demonstrates that abortion is safe and does not increase a woman’s future risk for depressive disorders or infertility. The extent to which information found on the internet reflects these medical facts in a trustworthy and unbiased manner is not known. Objective: The purpose of this study was to collate and describe the trustworthiness and political slant or bias of web-based information about abortion safety and risks of depression and infertility following abortion. Methods: We performed a cross-sectional study of internet websites using 3 search topics: (1) is abortion safe?, (2) does abortion cause depression?, and (3) does abortion cause infertility? We used the Google Adwords tool to identify the search terms most associated with those topics and Google’s search engine to generate databases of websites related to each topic. We then classified and rated each website in terms of content slant (pro-choice, neutral, anti-choice), clarity of slant (obvious, in-between, or difficult/can’t tell), trustworthiness (rating scale of 1-5, 5=most trustworthy), type (forum, feature, scholarly article, resource page, news article, blog, or video), and top-level domain (.com, .net, .org, .edu, .gov, or international domain). We compared website characteristics by search topic (safety, depression, or infertility) using bivariate tests. We summarized trustworthiness using the median and IQR, and we used box-and-whisker plots to visually compare trustworthiness by slant and domain type. Results: Our search methods yielded a total of 111, 120, and 85 unique sites for safety, depression, and infertility, respectively. Of all the sites (n=316), 57.3% (181/316) were neutral, 35.4% (112/316) were anti-choice, and 7.3% (23/316) were pro-choice. The median trustworthiness score was 2.7 (IQR 1.7-3.7), which did not differ significantly across topics (P=.409). Anti-choice sites were less trustworthy (median score 1.3, IQR 1.0-1.7) than neutral (median score 3.3, IQR 2.7-4.0) and pro-choice (median score 3.7, IQR 3.3-4.3) sites. Anti-choice sites were also more likely to have slant clarity that was “difficult to tell” (41/112, 36.6%) compared with neutral (25/181, 13.8%) or pro-choice (4/23, 17.4%; P<.001) sites. A negative search term used for the topic of safety (eg, “risks”) produced sites with lower trustworthiness scores than search terms with the word “safety” (median score 1.7 versus 3.7, respectively; P<.001). Conclusions: People seeking information about the safety and potential risks of abortion are likely to encounter a substantial amount of untrustworthy and slanted/biased abortion information. Anti-choice sites are prevalent, often difficult to identify as anti-choice, and less trustworthy than neutral or pro-choice sites. Web searches may lead the public to believe abortion is riskier than it is.

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

    Public Interest in Acne on the Internet: Comparison of Search Information From Google Trends and Naver


    Background: Acne vulgaris is a common skin disease primarily affecting young adults. Given that the internet has become a major source of health information, especially among the young, the internet is a powerful tool of communication and has a significant influence on patients. Objective: This study aimed to clarify the features of patients’ interest in and evaluate the quality of information about acne vulgaris on the internet. Methods: We compared the search volumes on acne vulgaris with those of other dermatological diseases using Google Trends from January 2004 to August 2019. We also determined the search volumes for relevant keywords of acne vulgaris on Google and Naver and evaluated the quality of answers to the queries in KnowledgeiN. Results: The regression analysis of Google Trends data demonstrated that the patients’ interest in acne vulgaris was higher than that for other dermatological diseases, such as atopic dermatitis (β=−20.33, 95% CI –22.27 to –18.39, P<.001) and urticaria (β=−27.09, 95% CI –29.03 to –25.15, P<.001) and has increased yearly (β=2.38, 95% CI 2.05 to 2.71, P<.001). The search volume for acne vulgaris was significantly higher in the summer than in the spring (β=–5.04, 95% CI –9.21 to –0.88, P=.018) and on weekends than on weekdays (β=–6.68, 95% CI –13.18 to –0.18, P=.044). The most frequently searched relevant keywords with “acne vulgaris” and “cause” were “stress,” “food,” and “cosmetics.” Among food, the 2 highest acne vulgaris–related keywords were milk and wheat in Naver and coffee and ramen in Google. The queries in Naver KnowledgeiN were mostly answered by a Korean traditional medicine doctor (53.4%) or the public (33.6%), but only 12.0% by dermatologists. Conclusions: Physicians should be aware of patients’ interest in and beliefs about acne vulgaris to provide the best patient education and care, both online and in the clinic.

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

    Development and Evaluation of a Digital Intervention for Fulfilling the Needs of Older Migrant Patients With Cancer: User-Centered Design Approach


    Background: Older migrant patients with cancer face many language- and culture-related barriers to patient participation during medical consultations. To bridge these barriers, an eHealth tool called Health Communicator was developed in the Netherlands. Essentially used as a digital translator that can collect medical history information from patients, the Health Communicator did not include an oncological module so far, despite the fact that the prevalence of Dutch migrant patients with cancer is rising. Objective: This study aims to systematically develop, implement, and conduct a pilot evaluation of an oncological module that can be integrated into the Health Communicator to stimulate patient participation among older Turkish-Dutch and Moroccan-Dutch patients with cancer. Methods: The Spiral Technology Action Research model, which incorporates 5 cycles that engage key stakeholders in intervention development, was used as a framework. The listen phase consisted of a needs assessment. The plan phase consisted of developing the content of the oncological module, namely the question prompt lists (QPLs) and scripts for patient education videos. On the basis of pretests in the do phase, 6 audiovisual QPLs on patient rights, treatment, psychosocial support, lifestyle and access to health care services, patient preferences, and clinical trials were created. Additionally, 5 patient education videos were created about patient rights, psychosocial support, clinical trials, and patient-professional communication. In the study phase, the oncological module was pilot-tested among 27 older Turkish-Dutch and Moroccan-Dutch patients with cancer during their consultations. In the act phase, the oncological model was disseminated to practice. Results: The patient rights QPL was chosen most often during the pilot testing in the study phase. Patients and health care professionals perceived the QPLs as easy to understand and useful. There was a negative correlation between the tool’s ease of use and patient age. Patients reported that using the module impacted the consultations positively and thought they were more active compared with previous consultations. Health care professionals also found patients to be more active than usual. Health care professionals asked significantly more questions than patients during consultations. Patients requested to see the patients’ rights video most often. Patients rated the videos as easy to understand, useful, and informative. Most of the patients wanted to use the tool in the future. Conclusions: Older migrant patients with cancer, survivors, and health care professionals found the oncological module to be a useful tool and have shown intentions to incorporate it into future consultation sessions. Both QPLs and videos were evaluated positively, the latter indicating that the use of narratives to inform older, low-literate migrant patients with cancer about health-related topics in their mother tongue is a viable approach to increase the effectiveness of health care communication with this target group.

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

    Authors’ Reply to: Is a Ratio Scale Assumption for Physician Ratings Justified? Comment on “What Patients Value in Physicians: Analyzing Drivers of...


  • Source: Unsplash; Copyright: Irwan iwe; URL:; License: Licensed by JMIR.

    Federated Learning on Clinical Benchmark Data: Performance Assessment


    Background: Federated learning (FL) is a newly proposed machine-learning method that uses a decentralized dataset. Since data transfer is not necessary for the learning process in FL, there is a significant advantage in protecting personal privacy. Therefore, many studies are being actively conducted in the applications of FL for diverse areas. Objective: The aim of this study was to evaluate the reliability and performance of FL using three benchmark datasets, including a clinical benchmark dataset. Methods: To evaluate FL in a realistic setting, we implemented FL using a client-server architecture with Python. The implemented client-server version of the FL software was deployed to Amazon Web Services. Modified National Institute of Standards and Technology (MNIST), Medical Information Mart for Intensive Care-III (MIMIC-III), and electrocardiogram (ECG) datasets were used to evaluate the performance of FL. To test FL in a realistic setting, the MNIST dataset was split into 10 different clients, with one digit for each client. In addition, we conducted four different experiments according to basic, imbalanced, skewed, and a combination of imbalanced and skewed data distributions. We also compared the performance of FL to that of the state-of-the-art method with respect to in-hospital mortality using the MIMIC-III dataset. Likewise, we conducted experiments comparing basic and imbalanced data distributions using MIMIC-III and ECG data. Results: FL on the basic MNIST dataset with 10 clients achieved an area under the receiver operating characteristic curve (AUROC) of 0.997 and an F1-score of 0.946. The experiment with the imbalanced MNIST dataset achieved an AUROC of 0.995 and an F1-score of 0.921. The experiment with the skewed MNIST dataset achieved an AUROC of 0.992 and an F1-score of 0.905. Finally, the combined imbalanced and skewed experiment achieved an AUROC of 0.990 and an F1-score of 0.891. The basic FL on in-hospital mortality using MIMIC-III data achieved an AUROC of 0.850 and an F1-score of 0.944, while the experiment with the imbalanced MIMIC-III dataset achieved an AUROC of 0.850 and an F1-score of 0.943. For ECG classification, the basic FL achieved an AUROC of 0.938 and an F1-score of 0.807, and the imbalanced ECG dataset achieved an AUROC of 0.943 and an F1-score of 0.807. Conclusions: FL demonstrated comparative performance on different benchmark datasets. In addition, FL demonstrated reliable performance in cases where the distribution was imbalanced, skewed, and extreme, reflecting the real-life scenario in which data distributions from various hospitals are different. FL can achieve high performance while maintaining privacy protection because there is no requirement to centralize the data.

  • Source: Freepik; Copyright: kues1; URL:; License: Licensed by JMIR.

    Is a Ratio Scale Assumption for Physician Ratings Justified? Comment on “What Patients Value in Physicians: Analyzing Drivers of Patient Satisfaction Using...

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  • Source: FlickR; Copyright: Trinity Care Foundation; URL:; License: Creative Commons Attribution + Noncommercial + NoDerivatives (CC-BY-NC-ND).

    Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study


    Background: With restrictions on movement and stay-at-home orders in place due to the COVID-19 pandemic, social media platforms such as Twitter have become an outlet for users to express their concerns, opinions, and feelings about the pandemic. Individuals, health agencies, and governments are using Twitter to communicate about COVID-19. Objective: The aims of this study were to examine key themes and topics of English-language COVID-19–related tweets posted by individuals and to explore the trends and variations in how the COVID-19–related tweets, key topics, and associated sentiments changed over a period of time from before to after the disease was declared a pandemic. Methods: Building on the emergent stream of studies examining COVID-19–related tweets in English, we performed a temporal assessment covering the time period from January 1 to May 9, 2020, and examined variations in tweet topics and sentiment scores to uncover key trends. Combining data from two publicly available COVID-19 tweet data sets with those obtained in our own search, we compiled a data set of 13.9 million English-language COVID-19–related tweets posted by individuals. We use guided latent Dirichlet allocation (LDA) to infer themes and topics underlying the tweets, and we used VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis to compute sentiment scores and examine weekly trends for 17 weeks. Results: Topic modeling yielded 26 topics, which were grouped into 10 broader themes underlying the COVID-19–related tweets. Of the 13,937,906 examined tweets, 2,858,316 (20.51%) were about the impact of COVID-19 on the economy and markets, followed by spread and growth in cases (2,154,065, 15.45%), treatment and recovery (1,831,339, 13.14%), impact on the health care sector (1,588,499, 11.40%), and governments response (1,559,591, 11.19%). Average compound sentiment scores were found to be negative throughout the examined time period for the topics of spread and growth of cases, symptoms, racism, source of the outbreak, and political impact of COVID-19. In contrast, we saw a reversal of sentiments from negative to positive for prevention, impact on the economy and markets, government response, impact on the health care industry, and treatment and recovery. Conclusions: Identification of dominant themes, topics, sentiments, and changing trends in tweets about the COVID-19 pandemic can help governments, health care agencies, and policy makers frame appropriate responses to prevent and control the spread of the pandemic.

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

    Proposed Implementation of Blockchain in British Columbia’s Health Care Data Management


    Background: There are several challenges such as information silos and lack of interoperability with the current electronic medical record (EMR) infrastructure in the Canadian health care system. These challenges can be alleviated by implementing a blockchain-based health care data management solution. Objective: This study aims to provide a detailed overview of the current health data management infrastructure in British Columbia for identifying some of the gaps and inefficiencies in the Canadian health care data management system. We explored whether blockchain is a viable option for bridging the existing gaps in EMR solutions in British Columbia’s health care system. Methods: We constructed the British Columbia health care data infrastructure and health information flow based on publicly available information and in partnership with an industry expert familiar with the health systems information technology network of British Columbia’s Provincial Health Services Authorities. Information flow gaps, inconsistencies, and inefficiencies were the target of our analyses. Results: We found that hospitals and clinics have several choices for managing electronic records of health care information, such as different EMR software or cloud-based data management, and that the system development, implementation, and operations for EMRs are carried out by the private sector. As of 2013, EMR adoption in British Columbia was at 80% across all hospitals and the process of entering medical information into EMR systems in British Columbia could have a lag of up to 1 month. During this lag period, disease progression updates are continually written on physical paper charts and not immediately updated in the system, creating a continuous lag period and increasing the probability of errors and disjointed notes. The current major stumbling block for health care data management is interoperability resulting from the use of a wide range of unique information systems by different health care facilities. Conclusions: Our analysis of British Columbia’s health care data management revealed several challenges, including information silos, the potential for medical errors, the general unwillingness of parties within the health care system to trust and share data, and the potential for security breaches and operational issues in the current EMR infrastructure. A blockchain-based solution has the highest potential in solving most of the challenges in managing health care data in British Columbia and other Canadian provinces.

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  • Interoperable HL7 FHIR platform to report PCR SARS-CoV-2 tests from laboratories to the Chilean government

    Date Submitted: Oct 22, 2020

    Open Peer Review Period: Oct 19, 2020 - Oct 29, 2020

    Background: Testing, traceability, and the isolation (TTI strategy) actions are a central strategy defined by WHO to contain the COVID-19 pandemic. In this sense, countries have had difficulties in co...

    Background: Testing, traceability, and the isolation (TTI strategy) actions are a central strategy defined by WHO to contain the COVID-19 pandemic. In this sense, countries have had difficulties in counting the number of people infected with SARS-CoV-2. Errors in reporting results are a common factor as well as the lack of interoperability between laboratories and governments. Approaches aimed at sending spreadsheets via email expose patients' privacy and have increased the probability of errors due to re-typing and generate a delay in the notification of results. Objective: Design and develop an interoperable platform to report PCR SARS-CoV-2 tests from laboratories to the Chilean government. Methods: The methodology to design and develop the interoperable platform was comprised of six well-structured stages: 1) Creation of a minimum dataset to PCR SARS-CoV-2 tests, 2) Modeling process and endpoints where institutions interchange information, 3) Standards and interoperability design, 4) Software development, 5) Quality assurance and 6) Software implementation. Results: The main result was the interoperable FHIR platform to report PCR SARS-CoV-2 tests from laboratories to the Chilean government. The platform was designed, developed, tested, and implemented following a structured methodology. The platform's performance to 1,000 requests resulted in a response time of 240 milliseconds, throughput was 28.3 requests per second, and the process management time was 131 milliseconds. The platform has availability of 99.9 %. The security was implemented with JSON Web Token (JWT) to ensure confidentiality, authorization, and authentication. All the PCR SARS-CoV-2 tests were accessible through an Application Programming Interface (API) gateway with valid credentials and the right access control list. Conclusions: The platform was implemented and is currently being used by UC Christus Laboratory. The platform is secure. It was tested adequately for confidentiality, secure authorization, authentication, and message integrity. This platform simplifies the reporting of PCR SARS-CoV-2 tests and reduces the time and probability of mistakes in counting positive cases. The interoperable solution with FHIR is working successfully and is open for the community, laboratories, and any institution that needs to report PCR SARS-CoV-2 tests.

  • Novel Machine-Learned Approach for COVID-19 Resource Allocation: A Tool for Evaluating Community Susceptibility

    Date Submitted: Oct 19, 2020

    Open Peer Review Period: Oct 19, 2020 - Dec 14, 2020

    Background: Despite worldwide efforts to develop an effective COVID vaccine, it is quite evident that initial supplies will be limited. Therefore, it is important to develop methods that will ensure t...

    Background: Despite worldwide efforts to develop an effective COVID vaccine, it is quite evident that initial supplies will be limited. Therefore, it is important to develop methods that will ensure that the COVID vaccine is allocated to the people who are at major risk until there is a sufficient global supply. Objective: The purpose of this study was to develop a machine-learning tool that could be applied to assess the risk in Massachusetts towns based on community-wide social, medical, and lifestyle risk factors. Methods: I compiled Massachusetts town data for 29 potential risk factors, such as the prevalence of preexisting comorbid conditions like COPD and social factors such as racial composition, and implemented logistic regression to predict the amount of COVID cases in each town. Results: Of the 29 factors, 14 were found to be significant (p < 0.1) indicators: poverty, food insecurity, lack of high school education, lack of health insurance coverage, premature mortality, population, population density, recent population growth, Asian percentage, high-occupancy housing, and preexisting prevalence of cancer, COPD, overweightness, and heart attacks. The machine-learning approach is 80% accurate in the state of Massachusetts and finds the 9 highest risk communities: Lynn, Brockton, Revere, Randolph, Lowell, New Bedford, Everett, Waltham, and Fitchburg. The 5 most at-risk counties are Suffolk, Middlesex, Bristol, Norfolk, and Plymouth. Conclusions: With appropriate data, the tool could evaluate risk in other communities, or even enumerate individual patient susceptibility. A ranking of communities by risk may help policymakers ensure equitable allocation of limited doses of the COVID vaccine.

  • Enhancing obstructive sleep apnea diagnosis with screening through disease phenotypes: a diagnostic research design

    Date Submitted: Oct 20, 2020

    Open Peer Review Period: Oct 19, 2020 - Dec 14, 2020

    Background: American Academy of Sleep Medicine guidelines suggests that clinical prediction algorithms can be used in obstructive sleep apnea (OSA) patients without replacing polysomnography (PSG) –...

    Background: American Academy of Sleep Medicine guidelines suggests that clinical prediction algorithms can be used in obstructive sleep apnea (OSA) patients without replacing polysomnography (PSG) – the gold standard. Objective: We aim to develop a clinical decision support system for OSA diagnosis, according to its standard definition (AHI plus symptoms), identifying high pre-test probability individuals based on risk and diagnostic factors. Methods: Forty-seven predictive variables were extracted from a cohort of patients who performed PSG. Fourteen variables found univariately significant were then used to compute the distance between OSA patients, defining a hierarchical clustering structure from which patient phenotypes were derived and described. Affinity from individuals at risk to OSA phenotypes was later computed and cluster membership used as an additional predictor in a Bayesian network classifier. Results: A total of 318 patients at risk were included, from which 207 individuals were diagnosed with OSA (mild=54%, moderate=24%, severe=22%). Based on predictive variables, three phenotypes were defined (Low=36%, Medium=50%, High=14%), with an increasing prevalence of symptoms and co-morbidities, the latter describing older and obese patients, and a substantive increase in some co-morbidities, suggesting their beneficial use as combined predictors (median AHI of 10, 14 and 31, respectively). Crossed-validation results demonstrate that the inclusion of OSA phenotypes as an adjusting predictor in a Bayesian classifier improved screening specificity (26% [24%-29%] to 38% [35%-40%]) while keeping sensitivity high (93% [91%-95%]), with model B doubling the diagnostic model effectiveness (diagnostic odds ratio of 8.14). Conclusions: Defined OSA phenotypes are a sensitive tool, enhancing our understanding of the disease, and allowing the derivation of a predictive algorithm which can clearly outperform symptom-based guideline recommendations as a rule-out approach for screening.

  • Pain recognition with ECG features in postoperative patients: A method validation study

    Date Submitted: Oct 19, 2020

    Open Peer Review Period: Oct 16, 2020 - Dec 11, 2020

    Background: There is a strong demand for an accurate and objective means for assessing acute pain among hospitalized patients to help clinicians provide a proper dosage of pain medications and in a ti...

    Background: There is a strong demand for an accurate and objective means for assessing acute pain among hospitalized patients to help clinicians provide a proper dosage of pain medications and in a timely manner. Heart rate variability (HRV) comprises changes in the time intervals between consecutive heartbeats, which can be measured through acquisition and interpretation of electrocardiogram (ECG) captured from bedside monitors or wearable devices. As increased sympathetic activity affects the HRV, an index of autonomic regulation of heart rate, ultra-short-term HRV analysis can provide a reliable source of information for acute pain monitoring. In this study, widely used HRV time- and frequency-domain measurements are used in acute pain assessments among postoperative patients. The existing approaches have only focused on stimulated pain on healthy subjects, whereas, to the best of our knowledge, there is no work in the literature building models using real pain data and on postoperative patients. Objective: To develop and evaluate an automatic and adaptable pain assessment algorithm based on ECG features for assessing acute pain in postoperative patients likely experiencing mild to moderate pain. Methods: The study used a prospective observational design. The sample consisted of 25 patient participants aged 18 to 65 years. In part 1 of the study, a Transcutaneous Electrical Nerve Stimulation unit was employed to obtain baseline discomfort threshold for the patients. In part 2, a multichannel biosignal acquisition device was used as patients were engaging in non-noxious activities. At all times, pain intensity was measured using patient self-reports based on the Numerical Rating Scale (NRS). A weak supervision framework was inherited for rapid training data creation. The collected labels were then transformed from 11 intensity levels to 5 intensity levels. Prediction models were developed using 5 different machine-learning methods. Mean prediction accuracy was calculated using Leave-One-Subject-Out cross-validation. We compared the performance of these models with the results from a previously published research study. Results: Five different machine-learning algorithms were applied to perform binary classification of no pain (NP) vs. 4 distinct pain levels (PL1 through PL4). Highest validation accuracy using 3 time-domain HRV features of BioVid research paper for no pain vs. any other pain level was achieved by SVM 62.72% (NP vs. PL4) to 84.14% (NP vs. PL2). Similar results were achieved for the top 8 features based on the Gini Index using the SVM method; with an accuracy ranging from 63.86% (NP vs. PL4) to 84.79% (NP vs. PL2). Conclusions: We propose a novel pain assessment method for postoperative patients using the ECG signal. Weak supervision applied for labeling and feature extraction improves the robustness of the approach. Our results show the viability of using a machine-learning algorithm to accurately and objectively assess acute pain among hospitalized patients.

  • Exploratory Feasibility Study of Quokka: A Local Community-Based Social Network for Wellbeing

    Date Submitted: Oct 12, 2020

    Open Peer Review Period: Oct 12, 2020 - Dec 7, 2020

    Background: Developing healthy habits and maintaining prolonged behavior change is often a difficult task. Mental health is one of the largest health concerns globally, including for people in college...

    Background: Developing healthy habits and maintaining prolonged behavior change is often a difficult task. Mental health is one of the largest health concerns globally, including for people in college. Objective: We conduct an exploratory feasibility study of local community-based interventions, like Quokka, and evaluate the intervention’s potential for promotion of local, social, and unfamiliar activities as they pertain to healthy habits. Methods: To evaluate this framework’s potential for increased participation in healthy habits, we conducted a 6 to 8 week feasibility study via a ‘challenge’ across 4 university campuses with a total of 277 participants. A different wellbeing theme was chosen for each week. We conducted weekly surveys to gauge factors that motivated users to complete or not complete the weekly challenge, identified participation trends, and evaluated the effectiveness of the intervention. We tested the hypotheses that Quokka participants will self-report participation in more local activities over remote activities for all challenges, more social activities than individual activities, and new over familiar activities. Results: After Bonferroni correction using a Clopper-Pearson Binomial proportion confidence interval for one test, we reject the hypothesis that similar proportion of users would participate in local and remote activities during the challenges (p < 0.001 for all challenge themes). Instead, there was a strong preference for local activities for all challenge themes. Similarly, users significantly preferred group activities over individual activities (p < 0.001 for most challenge themes). For most challenge themes, there were not enough data to significantly distinguish preference towards familiar or new activities (p < 0.001 for a subset of challenge themes in some schools). Conclusions: We find that local community-based wellbeing interventions like Quokka can facilitate positive behavior change. We discuss these findings and their implications for the research and design of location-based digital communities for wellbeing promotion.

  • Short-range forecasting of coronavirus disease 2019 (COVID-19) during early onset at county, health district, and state geographic levels: Comparative forecasting approach using seven forecasting methods

    Date Submitted: Oct 10, 2020

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

    Background: Modeling approaches have utilized variations on susceptible, infected, and recovered (SIR), susceptible, exposed, infected, and recovered (SEIR), and machine learning models to estimate th...

    Background: Modeling approaches have utilized variations on susceptible, infected, and recovered (SIR), susceptible, exposed, infected, and recovered (SEIR), and machine learning models to estimate the spread of coronavirus disease 2019 (COVID-19) based on the identified virus characteristics. Forecasting methods rely on real-time numbers of confirmed case and death counts to create forecasts based on the characteristics of the trends and averages of prior data. COVID-19 forecasting studies have varied in geographic scales from global, country, and state levels. These studies support the need to implement mitigation strategies to slow the spread, flatten the peak, inform policy, and indicate medical capacity burdens. Objective: Due to time requirements for developing a COVID-19 vaccine, evidence is needed to inform short-term forecasting method selection at county, health district, and state levels. Using publicly available real-time data provided online, we evaluate the performance of seven forecasting methods utilized to forecast cumulative COVID-19 case counts. Forecasts are evaluated based on how well they can forecast one-. three-, and seven-days forward when utilizing one-, three-, seven-, or all-prior days’ cumulative case counts during early onset of case spreading. This study provides an objective evaluation of the forecasting methods to identify forecasting model assumptions that contribute to lower error in forecasting COVID-19 cumulative case growth. This information benefits professionals, decision makers, and the public relying on the data provided by short-term case count estimates at varied geographic levels. Methods: One-, three-, and seven-days forecasts are created at the county, health district, and state levels using: (1) a naïve approach; (2) Holt-Winters exponential smoothing (HW); (3) growth rate (Growth); (4) moving average (MA); (5) autoregressive (AR); (6) autoregressive moving average (ARMA); and (7) autoregressive integrated moving average (ARIMA). Forecasts rely on 3,463 observations from Virginia’s county-level cumulative case counts as reported by The New York Times. 95% confidence of Median Absolute Error (MdAE) and Median Absolute Percentage Error (MdAPE) metrics of the resulting 216,698 forecasts are used to identify statistically significant differences. Results: Single-day MA forecast with three-day lookback obtained the lowest MdAE and statistically significantly differs from 39 (66.1%) to 53 (89.8%) of alternatives at each geographic level using P value equal to 0.05. Methods assuming stationary means of prior days’ counts outperform methods with assumptions of weak- or non-stationarity means. MdAPE results reveal statistically significant differences across geographic levels. Conclusions: For short-range COVID-19 cumulative case count forecasting at the county, health district, and state levels during early onset: (1) MA is effective for forecasting one-, three-, and seven-days’ cumulative case counts; (2) assumptions of stationarity of means in prior-observations are more effective than assumptions of weak- or non-stationarity means; and (3) geographic resolution is a factor in forecasting method selection. (This work received no external funding.)