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Background: Background: COVID-19 has been an unprecedented challenge to the global healthcare system. Tools that can improve the focus of surveillance efforts and clinical decision support are of paramount importance. Objective: Objective: New medical informatics technologies are needed to enable effective control of the pandemic. Methods: Methods: The Honghu Hybrid System (HHS) for COVID-19 collected, integrated, standardized and analyzed data from multiple sources, including the case reporting system, diagnostic labs, electronic medical records and social media on mobile devices. Results: Results: HHS was developed and successfully deployed within 72 hours in the city of Honghu in Hubei Province, China. Syndromic surveillance component in HHS covered over 95% of the population of over 900,000 people and provided near real-time evidence for the control of epidemic emergencies. Clinical decision support component in HHS was also provided to improve patient care and prioritize the limited medical resources. Conclusions: Conclusions: The facilitating factors and challenges are discussed to provide useful insights to other cities to build up suitable solutions based on big-data technologies. The HHS for COVID-19 proved to be feasible, sustainable and effective and can be migrated.
PREreview + JMIR Publications Virtual COVID-19 Preprint Journal Club
(Toronto/Portland, 27 March 2020) PREreview and JMIR Publications are joining forces to bring together scientists from across the globe for a virtual discussion and collaborative review of a recent preprint covering new research related to coronavirus SARS-COV-2 leading to the disease referred to as COVID-19.
Help us select the preprint for the virtual journal club discussion!
The candidate preprint should:
have medical or public health implications related to coronavirus SARS-COV-2;
preferably, it should not be currently submitted undertaking journal-organized peer review (submissions to JMIR are exempt).
Selected/Discussed Preprints may be offered rapid publication in a JMIR journal, but this is optional for the author.
This call for nominations will close on Friday, April 3rd. The event will take place via the remote conferencing service Zoom on April 14, 2020. More details about the event will be posted here in the next two weeks.
For questions or concerns, please email us at contact@prereview.org or ed-support@jmir.org.
NOMINATE A PREPRINT FOR VIRTUAL JOURNAL CLUB DISCUSSION VIA THIS FORM OR BY DIRECTLY REPLYING TO OUR TWEET
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 2018: 4.945, ranked #1 out of 26 journals in the medical informatics category) and in terms of size (number of papers published). The journal focuses on emerging technologies, medical devices, apps, engineering, 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. 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 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.
Background: Active video games (AVGs) can increase physical activity (PA) and help produce higher physiological expenditure. Animated narrative videos (NVs) possess unique immersive and motivational properties. When added to AVGs, they have been found to increase moderate-to-vigorous physical activity (MVPA) as opposed to the original no video condition. However, there is no evidence of whether that was due to the NV or the addition of an animated video to an AVG.
Objective: This study aimed to investigate the differential effect of adding an NV versus a nonnarrative video (NNV) to an AVG on PA parameters and physiological responses and to explore the mediating role of immersion.
Methods: A total of 22 children aged 8 to 12 years were randomly assigned to the NV or NNV condition. They were instructed to play an AVG (on Xbox Kinect) for as long as they wanted. We used accelerometers to estimate the time spent (in minutes) in MVPA. Heart rate (HR) and rate of perceived exertion (RPE) were measured before, during, and after the AVG play session. The participants then reported their experience of narrative immersion via a questionnaire.
Results: The NV group had significantly higher narrative immersion (mean 3.50, SD 0.55 vs mean 2.91, SD 0.59; P=.03) and MVPA than the NNV group (mean 20.11, SD 13.75 vs mean 7.85, SD 5.83; P=.02). Narrative immersion was positively correlated with MVPA (r=0.52; P=.01) and average HR during AVG (r=0.43; P=.05). Mediation analysis indicated that narrative immersion mediated the effect of NV (NV vs NNV) on MVPA (direct effect: beta=7.51; P=.01). The indirect effect was that NV was positively correlated with the mediator variable narrative immersion (beta=.59; P=.03), which was itself marginally associated with MVPA (beta=6.95; P=.09); when narrative immersion was included in the model, the regression coefficient was attenuated.
Conclusions: AVG with added narratives elicits more narrative immersion, resulting in more minutes in MVPA. Narrative immersion served as a mediator between NV and MVPA via its elicitation of an elevated HR without increasing RPE. The inclusion of immersive narratives in AVG could be helpful for inducing MVPA, to enhance AVG engagement without additional exertion.
Background: Emotional eating (EE) is one of the most significant symptoms of various eating disorders. It has been difficult to collect a large amount of behavioral data on EE; therefore, only partial studies of this symptom have been conducted. To provide adequate support for online social media users with symptoms of EE, we must understand their behavior patterns to design a sophisticated personalized support system (PSS).
Objective: This study aimed to analyze the behavior patterns of emotional eaters as the first step to designing a personalized intervention system.
Methods: The machine learning (ML) framework and Latent Dirichlet Allocation (LDA) topic modeling tool were used to collect and analyze behavioral data on EE. Data from a subcommunity of Reddit, /r/loseit, were analyzed. This dataset included all posts and feedback from July 2014 to May 2018, comprising 185,950 posts and 3,528,107 comments. In addition, deleted and improperly collected data were eliminated. Stochastic gradient descent–based ML classifier with an accuracy of 90.64% was developed to collect refined behavioral data of online users with EE behaviors. The expert group that labeled the dataset to train the ML classifiers included a medical doctor specializing in EE diagnosis and a nutritionist with profound knowledge of EE behavior. The experts labeled 5126 posts as EE (coded as 1) or others (coded as 0). Finally, the topic modeling process was conducted with LDA.
Results: The following 4 macroperspective topics of online EE behaviors were identified through linguistic evidence regarding each topic: addressing feelings, sharing physical changes, sharing and asking for dietary information, and sharing dietary strategies. The 5 main topics of feedback were dietary information, compliments, consolation, automatic bot feedback, and health information. The feedback topic distribution significantly differed depending on the type of EE behavior (overall P<.001).
Conclusions: This study introduces a data-driven approach for analyzing behavior patterns of social website users with EE behaviors. We discovered the possibility of the LDA topic model as an exploratory user study method for abnormal behaviors in medical research. We also investigated the possibilities of ML- and topic modeling–based classifiers to automatically categorize text-based behavioral data, which could be applied to personalized medicine in future research.
Background: Individuals in stressful work environments often experience mental health issues, such as depression. Reducing depression rates is difficult because of persistently stressful work environments and inadequate time or resources to access traditional mental health care services. Mobile health (mHealth) interventions provide an opportunity to deliver real-time interventions in the real world. In addition, the delivery times of interventions can be based on real-time data collected with a mobile device. To date, data and analyses informing the timing of delivery of mHealth interventions are generally lacking.
Objective: This study aimed to investigate when to provide mHealth interventions to individuals in stressful work environments to improve their behavior and mental health. The mHealth interventions targeted 3 categories of behavior: mood, activity, and sleep. The interventions aimed to improve 3 different outcomes: weekly mood (assessed through a daily survey), weekly step count, and weekly sleep time. We explored when these interventions were most effective, based on previous mood, step, and sleep scores.
Methods: We conducted a 6-month micro-randomized trial on 1565 medical interns. Medical internship, during the first year of physician residency training, is highly stressful, resulting in depression rates several folds higher than those of the general population. Every week, interns were randomly assigned to receive push notifications related to a particular category (mood, activity, sleep, or no notifications). Every day, we collected interns’ daily mood valence, sleep, and step data. We assessed the causal effect moderation by the previous week’s mood, steps, and sleep. Specifically, we examined changes in the effect of notifications containing mood, activity, and sleep messages based on the previous week’s mood, step, and sleep scores. Moderation was assessed with a weighted and centered least-squares estimator.
Results: We found that the previous week’s mood negatively moderated the effect of notifications on the current week’s mood with an estimated moderation of −0.052 (P=.001). That is, notifications had a better impact on mood when the studied interns had a low mood in the previous week. Similarly, we found that the previous week’s step count negatively moderated the effect of activity notifications on the current week’s step count, with an estimated moderation of −0.039 (P=.01) and that the previous week’s sleep negatively moderated the effect of sleep notifications on the current week’s sleep with an estimated moderation of −0.075 (P<.001). For all three of these moderators, we estimated that the treatment effect was positive (beneficial) when the moderator was low, and negative (harmful) when the moderator was high.
Conclusions: These findings suggest that an individual’s current state meaningfully influences their receptivity to mHealth interventions for mental health. Timing interventions to match an individual’s state may be critical to maximizing the efficacy of interventions.
Trial Registration: ClinicalTrials.gov NCT03972293; http://clinicaltrials.gov/ct2/show/NCT03972293
Background: Adolescent depression is one of the largest health issues in the world and there is a pressing need for effective and accessible treatments.
Objective: This trial examines whether affect-focused internet-based psychodynamic therapy (IPDT) with therapist support is more effective than an internet-based supportive control condition on reducing depression in adolescents.
Methods: The trial included 76 adolescents (61/76, 80% female; mean age 16.6 years), self-referred via an open access website and fulfilling criteria for major depressive disorder. Adolescents were randomized to 8 weeks of IPDT (38/76, 50%) or supportive control (38/76, 50%). The primary outcome was self-reported depressive symptoms, measured with the Quick Inventory of Depressive Symptomatology for Adolescents (QIDS-A17-SR). Secondary outcomes were anxiety severity, emotion regulation, self-compassion, and an additional depression measure. Assessments were made at baseline, postassessment, and at 6 months follow-up, in addition to weekly assessments of the primary outcome measure as well as emotion regulation during treatment.
Results: IPDT was significantly more effective than the control condition in reducing depression (d=0.82, P=.01), the result of which was corroborated by the second depression measure (d=0.80, P<.001). IPDT was also significantly more effective in reducing anxiety (d=0.78, P<.001) and increasing emotion regulation (d=0.97, P<.001) and self-compassion (d=0.65, P=.003). Significantly more patients in the IPDT group compared to the control group met criteria for response (56% vs 21%, respectively) and remission (35% vs 8%, respectively). Results on depression and anxiety symptoms were stable at 6 months follow-up. On average, participants completed 5.8 (SD 2.4) of the 8 modules.
Conclusions: IPDT may be an effective intervention to reduce adolescent depression. Further research is needed, including comparisons with other treatments.
Trial Registration: International Standard Randomised Controlled Trial Number (ISRCTN) 16206254; http://www.isrctn.com/ISRCTN16206254
Background: Connected medical technology is increasingly prevalent and offers both a host of new therapeutic potentials and cybersecurity-related considerations. Current practice largely does not include discussions of cybersecurity issues when clinicians obtain informed consent.
Objective: This paper aims to raise awareness about cybersecurity considerations for connected medical technology as they relate to informed consent discussions between patients and clinicians.
Methods: Clinicians, health care cybersecurity researchers, and informed consent experts propose the concept of a cybersecurity informed consent for connected medical technology.
Results: This viewpoint discusses concepts designed to facilitate further discussion on the need, development, and execution of cybersecurity informed consent.
Conclusions: Cybersecurity informed consent may be a necessary component of informed consent practices, as connected medical technology proliferates in the health care environment.
Background: Individuals with newly diagnosed cancer may experience impaired health in several aspects and often have a large need for information and support. About 30% will experience symptoms of anxiety and depression, with varying needs of knowledge and support. Despite this, many of these patients lack appropriate support. Internet-based support programs may offer a supplement to standard care services, but must be carefully explored from a user perspective.
Objective: The purpose of this study was to explore the participants’ perceptions of the relevance and benefits of an internet-based stepped care program (iCAN-DO) targeting individuals with cancer and concurrent symptoms of anxiety and depression.
Methods: We performed a qualitative study with an inductive approach, in which we used semistructured questions to interview 15 individuals using iCAN-DO. We analyzed the interviews using content analysis.
Results: The analysis found 17 subcategories regarding the stepped care intervention, resulting in 4 categories. Participants described the need for information as large and looked upon finding information almost as a survival strategy when receiving the cancer diagnosis. iCAN-DO was seen as a useful, reliable source of information and support. It was used as a complement to standard care and as a means to inform next of kin. Increased knowledge was a foundation for continued processing of participants’ own feelings. The optimal time to gain access to iCAN-DO would have been when being informed of the diagnosis. The most common denominator was feeling acknowledged and supported, but with a desire for further adaptation of the system to each individual’s own situation and needs.
Conclusions: Users saw the internet-based stepped care program as safe and reliable and used it as a complement to standard care. Similar interventions may gain from more personalized contents, being integrated into standard care, or using symptom tracking to adjust the contents. Offering this type of program close to diagnosis may provide benefits to users.
Trial Registration: ClincalTrials.gov NCT-01630681; https://clinicaltrials.gov/ct2/show/NCT01630681
Background: Sexually transmitted infections (STIs) are on the rise in the United States, and adolescent girls (15-19 years old) are more susceptible to acquiring STIs than their male peers. The co-occurrence of alcohol use and sexual risk taking contribute significantly to STI acquisition. Mobile health (mHealth) interventions are ideally suited for our target population and have demonstrated increases in STI testing in young people, as well as reductions in alcohol use.
Objective: This pilot study used both qualitative and quantitative methods to explore the views of adolescent girls (age range 15-19 years old; 74.6%, 279/374 white) on the desired qualities and content of an mHealth app for sexual health.
Methods: We conducted nine 60-min in-depth interviews (IDIs) to gather information and identify themes of sexual health and alcohol use, and we tested the feasibility of using a two-week social media campaign to collect survey information regarding sexual health risk in adolescent girls.
Results: We iteratively coded IDIs and identified major themes around pressure of alcohol use, lack of STI knowledge, male pressure to not use condoms, and pregnancy as a worse outcome than STIs. Results from the web-based survey on risky health behaviors, which was completed by 367 participants, support the use of a sexual health app designed for girls.
Conclusions: Future work will integrate these themes to inform the development of a culturally sensitive mHealth app to prevent STIs among adolescent girls.
Background: Patients with secondary pain due to mucositis after chemotherapy require treatment with morphine. Use of electronic video games (EVGs) has been shown to be an effective method of analgesia in other clinical settings.
Objective: The main objective of this study was to assess the association between the use of EVGs and the intensity of pain caused by chemotherapy-induced mucositis in pediatric patients with cancer. The secondary objective was to assess the association between changes in pain intensity and sympathetic-parasympathetic balance in this sample of pediatric patients.
Methods: Clinical records were compared between the day prior to the use of EVGs and the day after the use of EVGs. The variables were variations in pupil size measured using the AlgiScan video pupilometer (IDMed, Marseille, France), heart rate variability measured using the Analgesia Nociception Index (ANI) monitor (Mdoloris Medical Systems, Loos, France), intensity of pain measured using the Numerical Rating Scale (score 0-10), and self-administered morphine pump parameters.
Results: Twenty patients (11 girls and nine boys; mean age 11.5 years, SD 4.5 years; mean weight 41.5 kg, SD 20.7 kg) who met all the inclusion criteria were recruited. EVGs were played for a mean of 2.3 (SD 1.3) hours per day, resulting in statistically significant changes. After playing EVGs, there was significantly lower daily morphine use (before vs after playing EVGs: 35.9 vs 28.6 µg/kg/day, P=.003), lower demand for additional pain relief medication (17 vs 9.6 boluses in 24 hours, P=.001), lower scores of incidental pain intensity (7.7 vs 5.4, P=.001), lower scores of resting pain (4.8 vs 3.2, P=.01), and higher basal parasympathetic tone as measured using the ANI monitor (61.8 vs 71.9, P=.009). No variation in pupil size was observed with the use of EVGs.
Conclusions: The use of EVGs in pediatric patients with chemotherapy-induced mucositis has a considerable analgesic effect, which is associated physiologically with an increase in parasympathetic vagal tone despite lower consumption of morphine.
Background: Technological surrogate nursing (TSN) derives from the idea that nurse-caregiver substitutes can be created by technology to support chronic disease self-care.
Objective: This paper begins by arguing that TSN is a useful and viable approach to chronic disease self-care. The analysis then focuses on the empirical research question of testing and demonstrating the effectiveness and safety of prototype TSN supplied to patients with the typical complex chronic disease of coexisting type 2 diabetes and hypertension. At the policy level, it is shown that the data allow for a calibration of TSN technology augmentation, which can be readily applied to health care management.
Methods: A 24-week, parallel-group, randomized controlled trial (RCT) was designed and implemented among diabetic and hypertensive outpatients in two Hong Kong public hospitals. Participants were randomly assigned to an intervention group, supplied with a tablet-based TSN app prototype, or to a conventional self-managing control group. Primary indices—hemoglobin A1c, systolic blood pressure, and diastolic blood pressure—and secondary indices were measured at baseline and at 8, 12, 16, and 24 weeks after initiation, after which the data were applied to test TSN effectiveness and safety.
Results: A total of 299 participating patients were randomized to the intervention group (n=151) or the control group (n=148). Statistically significant outcomes that directly indicated TSN effectiveness in terms of hemoglobin 1c were found in both groups but not with regard to systolic and diastolic blood pressure. These findings also offered indirect empirical support for TSN safety. Statistically significant comparative changes in these primary indices were not observed between the groups but were suggestive of an operational calibration of TSN technology augmentation. Statistically significant changes in secondary indices were obtained in one or both groups, but not between the groups.
Conclusions: The RCT’s strong behavioral basis, as well as the importance of safety and effectiveness when complex chronic illness is proximately self-managed by layperson patients, prompted the formulation of the empirical joint hypothesis that TSN would improve patient self-care while satisfying the condition of patient self-safety. Statistical and decision analysis applied to the experimental outcomes offered support for this hypothesis. Policy relevance of the research is demonstrated by the derivation of a data-grounded operational calibration of TSN technology augmentation with ready application to health care management.
Trial Registration: ClinicalTrials.gov NCT02799953; https://clinicaltrials.gov/ct2/show/NCT02799953
Background: Although gun violence has been identified as a major public health concern, the scope and significance of internet gun advertising is not known.
Objective: This study aimed to quantify the characteristics of gun advertising on social media and to compare the reach of posts by manufacturers with those of influencers.
Methods: Using a systematic search, we created a database of recent and popular Twitter and YouTube posts made public by major firearm manufacturers and influencers. From our sample of social media posts, we reviewed the content of the posts on the basis of 19 different characteristics, such as type of gun, presence of women, and military or police references. Our content analysis summarized statistical differences in the information conveyed in posts to compare advertising approaches across social media platforms.
Results: Sample posts revealed that firearm manufacturers use social media to attract audiences to websites that sell firearms: 14.1% (131/928; ±2.9) of Twitter posts, 53.6% (228/425; ±6.2) of YouTube videos, and 89.5% (214/239; ±5.1) of YouTube influencer videos link to websites that facilitate sales. Advertisements included women in efforts to market handguns and pistols for the purpose of protection: videos with women included protection themes 2.5 times more often than videos without women. Top manufacturers of domestic firearms received 98 million channel views, compared with 6.1 billion channel views received by the top 12 YouTube influencers.
Conclusions: Firearm companies use social media as an advertising platform to connect viewers to websites that sell guns. Gun manufacturers appropriate YouTube servers, video streaming services, and the work of YouTube influencers to reach large audiences to promote the widespread sale of consumer firearms. YouTube and Twitter subsidize gun advertising by offering server and streaming services at no cost to gun manufacturers, to the commercial benefit of Google and Twitter’s corporate ownership.
Background: Smartphone apps promoting physical activity (PA) are abundant, but few produce substantial and sustained behavior change. Although many PA apps purport to induce users to compare themselves with others (by invoking social comparison processes), improvements in PA and other health behaviors are inconsistent. Existing literature suggests that social comparison may motivate PA for some people under some circumstances. However, 2 aspects of work that apply social comparison theory to PA apps remain unclear: (1) how comparison processes have been operationalized or harnessed in existing PA apps and (2) whether incorporating sources of variability in response to comparison have been used to tailor comparison features of apps, which could improve their effectiveness for promoting PA.
Objective: The aim of this meta-review was to summarize existing systematic, quantitative, and narrative reviews of behavior change techniques in PA apps, with an emphasis on social comparison features, to examine how social comparison is operationalized and implemented.
Methods: We searched PubMed, Web of Science, and PsycINFO for reviews of PA smartphone apps. Of the 3743 initial articles returned, 26 reviews met the inclusion criteria. Two independent raters extracted the data from these reviews, including the definition of social comparison used to categorize app features, the percentage of apps categorized as inducing comparison, specific features intended to induce comparison, and any mention of tailoring comparison features. For reference, these data were also extracted for related processes (such as behavioral modeling, norm referencing, and social networking).
Results: Of the included review articles, 31% (8/26) categorized app features as prompting social comparison. The majority of these employed Abraham and Michie’s earliest definition of comparison, which differs from versions in later iterations of the same taxonomy. Very few reviews specified what dimension users were expected to compare (eg, steps, physical fitness) or which features of the apps were used to induce comparison (eg, leaderboards, message boards). No review referenced tailoring of comparison features. In contrast, 54% (14/26) reviews categorized features for prompting behavioral modeling and 31% (8/26) referenced tailoring app features for users’ personal goals or preferences.
Conclusions: The heterogeneity across reviews of PA apps and the absence of relevant information (eg, about dimensions or features relevant for comparison) create confusion about how to best harness social comparison to increase PA and its effectiveness in future research. No evidence was found that important findings from the broader social comparison literature (eg, that people have differing preferences for and responses to social comparison information) have been incorporated in the design of existing PA apps. Greater integration of the mobile health (mHealth) and social comparison literatures may improve the effectiveness of PA apps, thereby increasing the public health impact of these mHealth tools.
Background: Our study addresses a gap in the modern information systems (IS) use literature by investigating factors that explain patient portal satisfaction (SWP) and perceptions about health-seeking behavior (HSB). A novel feature of our study is the incorporation of actual portal use data rather than the perceptions of use intention, which prevails in the modern IS literature.
Objective: This study aimed to empirically validate factors that influence SWP as an influencing agent on portal use and HSB. Our population segment was comprised of college students with active patient portal accounts.
Methods: Using web-based survey data from a population of portal users (n=1142) in a university health center, we proposed a theoretical model that adapts constructs from the Technology Acceptance Model by Davis, the revised Technology Adoption Model by Venkatesh, the Unified Theory of the Acceptance and Use of Technology 2, and the Health Belief Model by Rosenstock et al. We validated our model using structural equation modeling techniques.
Results: Our model explained nearly 65% of the variance in SWP (R2=0.6499), nearly 33% of the variance in portal use (R2=0.3250), and 29% of the variance in HSB (R2=0.2900). Statistically significant antecedents of SWP included social influence (beta=.160, t499=6.145), habit (beta=.114, t499=4.89), facilitating conditions (beta=.062, t499=2.401), effort expectancy (beta=.311, t499=11.149), and performance expectancy (beta=.359, t499=11.588). SWP influenced HSB (beta=.505, t499=19.705) and portal use (beta=.050, t499=2.031). We did not find a statistically significant association between portal use and HSB (beta=.015, t499=0.513). Perceived severity significantly influenced HSB (beta=.129, t499=4.675) but not portal use (beta=.012, t499=.488).
Conclusions: Understanding the importance of SWP and the role it plays in influencing HSB may point to future technology design considerations for information technology developers and health care providers. We extend current Expectancy Confirmation Theory research by finding a positive association between SWP and portal use.
Background: Background: COVID-19 has been an unprecedented challenge to the global healthcare system. Tools that can improve the focus of surveillance efforts and clinical decision support are of para...
Background: Background: COVID-19 has been an unprecedented challenge to the global healthcare system. Tools that can improve the focus of surveillance efforts and clinical decision support are of paramount importance. Objective: Objective: New medical informatics technologies are needed to enable effective control of the pandemic. Methods: Methods: The Honghu Hybrid System (HHS) for COVID-19 collected, integrated, standardized and analyzed data from multiple sources, including the case reporting system, diagnostic labs, electronic medical records and social media on mobile devices. Results: Results: HHS was developed and successfully deployed within 72 hours in the city of Honghu in Hubei Province, China. Syndromic surveillance component in HHS covered over 95% of the population of over 900,000 people and provided near real-time evidence for the control of epidemic emergencies. Clinical decision support component in HHS was also provided to improve patient care and prioritize the limited medical resources. Conclusions: Conclusions: The facilitating factors and challenges are discussed to provide useful insights to other cities to build up suitable solutions based on big-data technologies. The HHS for COVID-19 proved to be feasible, sustainable and effective and can be migrated.
Background: During outbreaks of diseases a great amount of health threatening misinformation is produced and released. In the web-2 era much of this misinformation is disseminated via social media whe...
Background: During outbreaks of diseases a great amount of health threatening misinformation is produced and released. In the web-2 era much of this misinformation is disseminated via social media where information could spread easily and quickly. Monitoring social media content provides crucial insights for health managers to manage the crisis. Objective: Given the misinformation surrounding COVID-19 outbreak, this study was aimed to analyze contents of the most commonly used social networks in Iran that is among the affected countries. Methods: A social media monitoring conducted through a qualitative design to analyze the discussions of social media users about the content related to COVID-19 transferred via Iranian medical faculty members` groups in Telegram and Whats App during Feb 20 to March 20, 2020 emphasizing the misinformation. Discourse analysis was applied and the written dialogues and discussions regarding misinformation about different aspects of the outbreak between medical faculty members all over the country were analyzed. Results: Cultural factors, demand pressure for information during the crisis, the easiness of information dissemination via social networks, marketing incentives and the poor legal supervision of online contents are the main reasons of misinformation dissemination. Disease statistics; treatments, vaccines and medicines; prevention and protection methods; dietary recommendations and disease transmission ways are the main subjective categories of releasing misinformation regarding novel coronavirus outbreak. Consequences of misinformation dissemination regarding disease include psychosocial; economic; health status; health system and ethical ones. Active and effective presence of health professionals and authorities on social media during the crisis and the improvement of public health literacy in the long term are the most recommended strategies for dealing with issues related to misinformation. Conclusions: This study contributes the management of COVID-19 outbreak trough providing applicable insights for health managers to manage public information in this challenging time. Clinical Trial: Not applicable
Background: Along with the spread of novel coronavirus disease (COVID-19), internet hospitals in China were engaged in the epidemic prevention and control, offering epidemic-related online services an...
Background: Along with the spread of novel coronavirus disease (COVID-19), internet hospitals in China were engaged in the epidemic prevention and control, offering epidemic-related online services and medical supports to the public. Objective: To explore the role of internet hospitals during the prevention and control of COVID-19 in China. Methods: Online epidemic-related consultations from multicenter internet hospitals in China during the epidemic of COVID-19 were collected. The counselees were described and classified into seven type groups. Symptoms were recorded and compared with reported COVID-19 patients. Hypochondriacal suspicion and offline-visit motivation were detected within each counselees’ group to evaluate the social panic of the epidemic along with the consequent medical seeking behaviors. The counselees’ motivation and the doctors’ recommendation for offline visit were compared. Risk factors affecting the counselees’ tendency of hypochondriacal suspicion and offline visit were explored by logistic regression models. The epidemic prevention and control measures based on internet hospitals were listed and the corresponding effects were discussed. Results: 4913 consultations were enrolled for analysis with the median age of the counselees 28 years (inter-quartile range: 22-33 years). There were 104(2.1%) healthy counselees, 147(3.0%) hypochondriacal counselees, 34(0.7%) exposed counselees, 853(17.4%) mildly suspicious counselees, 42(0.9%) moderately suspicious counselees, 3550(72.3%) highly suspicious counselees and 183(3.7%) severely suspicious counselees. 94.2% counselees had epidemic-related symptoms with a distribution similar to those of COVID-19. The hypochondriacal suspicion mode (44.1%) was common. The counselees’ motivation and the doctors’ recommendation for offline visit were inconsistent (P<0.001) with Cohen Kappa score 0.039, indicating irrational medical-seeking behaviors. Adult counselees (OR=1.816, P<0.001) with epidemiological exposure (OR= 7.568, P<0.001), shortness of breath (OR=1.440, P=0.001), diarrhea (OR=1.272, P=0.04) and unrelated symptoms (OR=1.509, P<0.001) were more likely to have hypochondriacal suspicion. Counselees with severe illnesses (OR= 2.303, P<0.001), fever (OR= 1.660, P<0.001), epidemiological exposure history (OR=1.440, P=0.012) and hypochondriacal suspicion (OR= 4.826, P<0.001) were more likely to attempt for offline visit. Re-attended counselees (OR=0.545, P=0.002) were less motivated to go to the offline clinic. Conclusions: Internet hospitals can serve different types of epidemic counselees, offer essential medical supports to the public during COVID-19, further release the social panic, promote social distancing, enhance the public’s ability of self-protection, correct irrational medical seeking behaviors, reduce the chance of nosocomial cross infection, facilitate epidemiological screening, thus play an important role on preventing and controlling COVID-19.
Background: Infections incidence in people with type 1 diabetes often makes self-management problematic, i.e. difficulties in controlling blood glucose (BG) levels. During the course of infections, th...
Background: Infections incidence in people with type 1 diabetes often makes self-management problematic, i.e. difficulties in controlling blood glucose (BG) levels. During the course of infections, the body demands more energy in order to supply the active tissues in the immune response. Thus, alteration in carbohydrate metabolism is expected to keep up the body’s demand by enhancing glucose uptake and utilization, increasing glucose production, increasing insulin resistance and others. Consequently, despite consuming regular meals, any ingested carbohydrate might cause significant increase in BG levels and often takes longer time to settle down as compared to the regular/normal day. It is common to observe prolonged hyperglycemia episodes, and frequent insulin injections. Patients have to struggle with enhanced and frequent insulin injections so as to lower the abnormal BG episode. This kind of event (BG anomalies) presents an enormous opportunity for automatically detecting infection incidence using self-recorded data, and thereby detecting infectious disease outbreak if properly detected with a dedicated algorithm. Moreover, it can also enable to provide a personalized decision support and learning platform for individuals, family and caregivers. During the course of infection, information regarding BG evolution, alterations in insulin sensitivity, shift incurred in ratio of insulin to carbohydrate, which is a change in amount of insulin needed for every gram of carbohydrate consumed, could be vital. Despite these potential, there has been very limited study that focused on detecting infection incidences in an individual with type 1 diabetes using a dedicated personalized algorithm. Objective: The study aims to develop an algorithm, i.e. a personalized health model, which can automatically detect the incidence of infection in people with type 1 diabetes using self-recorded BG levels, diet intake (carbohydrate in grams) and insulin information as indicator variables. The model is expected to detect deviations from the norm due to infections incidences considering elevated BG level (hyperglycemia incidences), coupled with unusual change in insulin to carbohydrate ratio (frequent insulin injections and unusual reduction in carbohydrate intakes). Methods: Method: Semi-supervised models, i.e. one-class classifiers, were trained and tested to detect incidence of infection in people with type 1 diabetes. Three group of one-class classifiers were trained on regular/normal day measurements (target datasets) and tested on dataset containing both the target (regular days) and non-target (infection days); boundary and domain-based, density-based, and reconstruction-based method. The boundary and domain-based method includes one-class support vector machine (v-SVM), minimum spanning tree (MST), support vector data description (SVDD), nearest neighbor (NN), and incremental svm (incSVM). Density-based method includes Parzen, Naïve Parzen, normal Gaussian, mixture of Gaussian (MOG), minimum covariance Gaussian (MCG), k-nearest neighbor (KNN), and local outlier factor (LOF). The reconstruction-based method includes Auto-encoder network, self-organizing map (SOM), K-means, and principal component analysis (PCA). For comparison purposes, two unsupervised models were also tested; local outlier factor (LOF) and connectivity-based outlier factor (COF). The one-class classifiers were evaluated based on twenty times 5-fold stratified cross validation. Area under the ROC curve (AUC), sensitivity, and F1-score were taken into consideration for measuring the models performance. The models were compared on two groups of data; raw data and filtered data (with a moving average filter of 2-days). Generally, the models were compared based on their detection performance, complexity, computational time, and number of samples required.
Materials: A high precision self-recorded data of ten patient years collected from 3 real subjects (2 males and 1 females with average age of 34 (13.2) years) with type 1 diabetes were used. The datasets consist of BG measurement and continuous glucose monitor (CGM), injected insulin (basal and bolus), diet (carbohydrate in grams), and self-reported events of acute infection. It is costly and time consuming to collect such a rich and large dataset from a lot of participants, if not impossible. The patients have used different diabetes self-management technologies to gather these datasets including Diabetes Diary, Spike, Dexcom CGM, insulin Pens and pumps. The datasets are consisted of regular/normal years without infection incidences and years with at least one or more acute infection incidences. The regular/normal patient years are used, as baseline data, to compare the effect of all patient controllable parameters and patient uncontrollable parameters during the incidence of infection. The self-reported incidences of acute infections are a case of influenza (flu), and mild and light common cold without fever. All the experiments were conducted using MATLAB® 2018b (Mathworks, Inc, Natwick, MA). Results: The analysis of self-recorded data of ten patient years reveals that BG levels and insulin to carbohydrate ratio are highly affected by the incidence of infection as compared to the regular/normal days. Semi-supervised and unsupervised models trained and tested using bivariate input, BG levels and insulin to carbohydrate ratio, achieved an excellent performance in describing the dataset, i.e. detecting the infection days from the regular/normal days. However, the unsupervised methods suffer in performance degradation as compared to the one-class classifier mainly because of the atypical nature of the data, not distributed uniformly, where some regions contain high density and other are sparse. In regard to the one-class classifiers, the boundary and domain-based method produced better description of the data as compared to the density and reconstruction-based methods mainly because of the atypicality of the data. Regarding the computational time, NN, SVDD, and SOM took considerable training time, which typically grows as the samples size increases. As for the models testing time, only LOF and COF took considerable time. Conclusions: We demonstrated the applicability of semi-supervised and unsupervised models for the detection of infection incidences in people with type 1 diabetes. Detecting the incidence of infection in these patient group can provide an opportunity to devise tailored services, i.e. a personalized decision support and a learning platform for the individuals, and simultaneously can be used for detecting potential public health threats, i.e. infectious disease outbreak, on a large scale through a spatio-temporal cluster detection. In general, the proposed approaches achieved excellent performance, and in particular the boundary and domain-based method performed better. In contrast to the particular models, v-SVM, K-NN, and K-means achieved better performance in all the infection cases. Altogether, we foresee that the presented result could encourage researchers to examine beyond the presented features into other additional features of the self-recorded data, e.g. various CGM feature and physical activity data, on a large scale basis.
Background: Type 1 diabetes mellitus is a blood glucose (BG) metabolic disorder, which is caused by deficiencies of insulin secretion from pancreatic cells. The relationship between infection incident...
Background: Type 1 diabetes mellitus is a blood glucose (BG) metabolic disorder, which is caused by deficiencies of insulin secretion from pancreatic cells. The relationship between infection incidents and elevated BG levels has been known for a long time. People with diabetes often experience prolonged episodes of elevated BG levels as a result of infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings on how to use such self-recorded data as a secondary source of information for other purposes, such as self-management related decision support during infection incidences and digital infectious disease detection system. Objective: The aim of the study is to demonstrate how people with type 1 diabetes can assist in detecting infectious diseases outbreak. Furthermore, to shade light upon the possibility of assisting the individual during such an incident. Specifically, we aim to retrospectively analyze the effect of infection incidences, such as influenza (flu), and light and mild common cold without fever, in order to identify key parameters that can effectively be used as potential indicators (events) of infection incidences. Moreover, the paper presents a general framework of a proposed digital infectious disease detection system based on self-recorded data from people with type 1 diabetes. Methods: We retrospectively analyzed high precision self-recorded data of 10 patient years captured within the longitudinal records of 3 people with type 1 diabetes. Getting such a rich and big dataset from large number of participants are extremely expensive and difficult to acquire, if not impossible. The participants were 2 males and 1 female with an average age of 34 (13.2) years. The dataset incorporates BG levels (Self-monitoring of blood glucose (SMBG) and continuous glucose measurement (CGM)), insulin (bolus and basal), diet (carbohydrate in grams) and self-reported events of illness. All the participants were free from any other health complications and other diseases during these years. Five normal patient years without any infection incidences and five patient years each with at least one or more cases of self-reported acute-infection incidences were analyzed and compared. We investigated the temporal evolution and probability distribution of BG levels, injected insulin, carbohydrate intake, and insulin to carbohydrate ratio within a specified timeframe (weekly, daily and hourly). For the daily and hourly timeframes, a moving average filter and non-parametric density estimation techniques, kernel density estimator, were used to analyze the data trend and distribution respectively, before, during, and after the infection incidences. The pre-infection, infection, and post-infection week analysis were carried out on raw dataset based on the week’s daily mean and standard deviation of BG levels, and daily sum and standard deviation of insulin and carbohydrate. A statistical boxplot was used to depict the comparison during pre-infection, infection, and post-infection week. All the experiments were carried out using Matlab 2018a. Results: Our analysis demonstrated that upon infection incidences, there is a dramatic shift in the operating point of the individual BG dynamics in all the timeframes (weekly, daily and hourly), which clearly violate the usual norm of BG dynamics. During regular/normal situations, BG levels usually lower when there is a significant increase in insulin injection and reduction in carbohydrate consumption. However, in all of the individual’s infection cases as opposed to the regular/normal days, there were prolonged period with elevated BG levels despite injecting higher amounts of insulin and reduced amount of carbohydrate consumption. For instance, in all the infection week on average, BG levels were elevated by 6.1% and 16%, insulin (bolus) were increased by 42% and 39.3%, carbohydrate consumption were reduced by 19% and 28.1%, and insulin to carbohydrate ratio were raised by 108.7% as compared to pre-infection and post-infection week respectively. Conclusions: We presented a novel approach on how to use self-recorded data from people with type 1 diabetes to develop an infection detection system. The analysis revealed that despite tight BG management regimes, BG levels were still elevated during the infection period, demonstrating the significant effect of infection on BG dynamics. Throughout the infection period, BG levels were elevated despite injecting higher amount of insulin and consuming lower amount of carbohydrate. The changes might be subjected to hormonal changes in the body as a result of infection incidences. However, the magnitude of the impact on BG dynamics could be correlated with different factors such as degree and severity of infection, type of pathogens, associated hormones involved and others. These changes are quite significant anomalies as compared to the regular/normal days, where BG levels lower with increased insulin injection and reduced carbohydrate consumption, and therefore, can be detected with appropriate individualized computational models, i.e., algorithms that span from prediction models to anomalies detection algorithms. Generally, we foresee that these findings can benefit the efforts towards building the next generation digital infectious disease detection systems and provoke further thoughts in this challenging field.
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...
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 anti-fungal therapies, with multiple studies reporting only a few cases over several years. However, manual retrospective record review to detect cases is time-consuming. Objective: 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 a twelve-year period (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 (presence of indwelling catheters, recent major surgery, diabetes, immunosuppressed status, etc.) 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 documentation of any fungal ocular involvement. Results: 265 patients had culture-proven fungemia, with Candida albicans (43%) and Candida glabrata (28%) being the most common fungal species in blood culture. The in-hospital mortality rate was 41%. Seven 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. This supports prior studies reporting low rates of ocular involvement in fungemia. Additionally, it demonstrates an application of natural language processing to expedite review of narrative notes. This approach is highly relevant for ophthalmology, where diagnoses are often based on physical exam findings that are documented within clinical notes.
