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Background: The increasing frequency and diversity of human disease outbreaks due to a plethora of ecological, environmental, and socio-economic factors thereby stressing healthcare systems worldwide. The most recent of these outbreaks is the novel coronavirus (COVID-19). For public health systems, understanding the temporal and spatial distribution of COVID-19 is a top priority. The wild spread of COVID-19 was also mirrored with intense attention on social media platforms, including Twitter. Information shared by individuals on social media concerning the virus can help decision-makers and healthcare professionals identify main issues involving COVID-19 and interventions to address them. Objective: This study aimed to analyze posts on Twitter to identify the main thoughts, attitudes, feelings, and topics that are discussed concerning the COVID-19. Methods: Leveraging a set of tools (Twitter’s search Application Programming Interface, Tweepy Python library, and Postgress database) and using a set of pre-defined search terms (coronavirus, 2019-nCov, and COVID-19), we extracted the text and metadata (number of likes and retweets, and user profile information including the number of followers) for public tweets in the English language for the period from 2 February 2020 and 3 March 2020. Tweets were analyzed using word frequencies of single words and double-word combinations. We leveraged Latent Dirichlet Allocation for topic modelling to identify the topics in the tweets. Additionally, sentiment analysis, extracting the mean number of retweets, likes, and followers for each topic and calculated interaction rate for each topic. Results: Out of approximately 2.8 million tweets for the study period, 167,073 unique tweets from 160,829 unique users met the inclusion criteria and were analyzed. Twelve topics grouped into four main themes: (i) Origin of the virus, (ii) its sources, (iii) its impact on people, countries, and the economy, as well as (iv) ways of mitigating the risk of infection. The mean of sentiment was positive in all topics except two topics: deaths caused by COVID-19 and increased racism. The mean followers of the account posting tweet topics ranged from 2,722 (increased racisms) to 13,413 (economic losses). The highest mean of likes for the tweets was 15.4 (economic losses) while the lowest was 3.94 (travel bans and warnings). Conclusions: Public health crisis response activities on the ground and online are becoming increasingly ‘simultaneous and intertwined’ and social media provides a lucrative opportunity to spread public health knowledge directly to the public. Health systems should work on building national and international detection and surveillance systems of “digital” diseases listening and monitoring social media. However, there is also a need for a more proactive and agile public health presence on social media to combat the spread of “fake news”.
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: Given the extensive time needed to conduct a nationally representative household survey and the commonly low response rate of phone surveys, rapid online surveys may be a promising method to assess and track knowledge and perceptions among the general public during fast-moving infectious disease outbreaks.
Objective: This study aimed to apply rapid online surveying to determine knowledge and perceptions of coronavirus disease 2019 (COVID-19) among the general public in the United States and the United Kingdom.
Methods: An online questionnaire was administered to 3000 adults residing in the United States and 3000 adults residing in the United Kingdom who had registered with Prolific Academic to participate in online research. Prolific Academic established strata by age (18-27, 28-37, 38-47, 48-57, or ≥58 years), sex (male or female), and ethnicity (white, black or African American, Asian or Asian Indian, mixed, or “other”), as well as all permutations of these strata. The number of participants who could enroll in each of these strata was calculated to reflect the distribution in the US and UK general population. Enrollment into the survey within each stratum was on a first-come, first-served basis. Participants completed the questionnaire between February 23 and March 2, 2020.
Results: A total of 2986 and 2988 adults residing in the United States and the United Kingdom, respectively, completed the questionnaire. Of those, 64.4% (1924/2986) of US participants and 51.5% (1540/2988) of UK participants had a tertiary education degree, 67.5% (2015/2986) of US participants had a total household income between US $20,000 and US $99,999, and 74.4% (2223/2988) of UK participants had a total household income between £15,000 and £74,999. US and UK participants’ median estimate for the probability of a fatal disease course among those infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was 5.0% (IQR 2.0%-15.0%) and 3.0% (IQR 2.0%-10.0%), respectively. Participants generally had good knowledge of the main mode of disease transmission and common symptoms of COVID-19. However, a substantial proportion of participants had misconceptions about how to prevent an infection and the recommended care-seeking behavior. For instance, 37.8% (95% CI 36.1%-39.6%) of US participants and 29.7% (95% CI 28.1%-31.4%) of UK participants thought that wearing a common surgical mask was “highly effective” in protecting them from acquiring COVID-19, and 25.6% (95% CI 24.1%-27.2%) of US participants and 29.6% (95% CI 28.0%-31.3%) of UK participants thought it was prudent to refrain from eating at Chinese restaurants. Around half (53.8%, 95% CI 52.1%-55.6%) of US participants and 39.1% (95% CI 37.4%-40.9%) of UK participants thought that children were at an especially high risk of death when infected with SARS-CoV-2.
Conclusions: The distribution of participants by total household income and education followed approximately that of the US and UK general population. The findings from this online survey could guide information campaigns by public health authorities, clinicians, and the media. More broadly, rapid online surveys could be an important tool in tracking the public’s knowledge and misperceptions during rapidly moving infectious disease outbreaks.
Background: Smoking remains a leading cause of preventable death and illness. Internet interventions for smoking cessation have the potential to significantly impact public health, given their broad reach and proven effectiveness. Given the dose-response association between engagement and behavior change, identifying strategies to promote engagement is a priority across digital health interventions. Text messaging is a proven smoking cessation treatment modality and a powerful strategy to increase intervention engagement in other areas of health, but it has not been tested as an engagement strategy for a digital cessation intervention.
Objective: This study examined the impact of 4 experimental text message design factors on adult smokers’ engagement with an internet smoking cessation program.
Methods: We conducted a 2×2×2×2 full factorial screening experiment wherein 864 participants were randomized to 1 of 16 experimental conditions after registering with a free internet smoking cessation program and enrolling in its automated text message program. Experimental factors were personalization (on/off), integration between the web and text message platforms (on/off), dynamic tailoring of intervention content based on user engagement (on/off), and message intensity (tapered vs abrupt drop-off). Primary outcomes were 3-month measures of engagement (ie, page views, time on site, and return visits to the website) as well as use of 6 interactive features of the internet program. All metrics were automatically tracked; there were no missing data.
Results: Main effects were detected for integration and dynamic tailoring. Integration significantly increased interactive feature use by participants, whereas dynamic tailoring increased the number of features used and page views. No main effects were found for message intensity or personalization alone, although several synergistic interactions with other experimental features were observed. Synergistic effects, when all experimental factors were active, resulted in the highest rates of interactive feature use and the greatest proportion of participants at high levels of engagement. Measured in terms of standardized mean differences (SMDs), effects on interactive feature use were highest for Build Support System (SMD 0.56; 95% CI 0.27 to 0.81), Choose Quit Smoking Aid (SMD 0.38; 95% CI 0.10 to 0.66), and Track Smoking Triggers (SMD 0.33; 95% CI 0.05 to 0.61). Among the engagement metrics, the largest effects were on overall feature utilization (SMD 0.33; 95% CI 0.06 to 0.59) and time on site (SMD 0.29; 95% CI 0.01 to 0.57). As no SMD >0.30 was observed for main effects on any outcome, results suggest that for some outcomes, the combined intervention was stronger than individual factors alone.
Conclusions: This factorial experiment demonstrates the effectiveness of text messaging as a strategy to increase engagement with an internet smoking cessation intervention, resulting in greater overall intervention dose and greater exposure to the core components of tobacco dependence treatment that can promote abstinence.
Trial Registration: ClinicalTrials.gov NCT02585206; https://clinicaltrials.gov/ct2/show/NCT02585206.
Background: Electronic medical record (EMR) systems capture large amounts of data per patient and present that data to physicians with little prioritization. Without prioritization, physicians must mentally identify and collate relevant data, an activity that can lead to cognitive overload. To mitigate cognitive overload, a Learning EMR (LEMR) system prioritizes the display of relevant medical record data. Relevant data are those that are pertinent to a context—defined as the combination of the user, clinical task, and patient case. To determine which data are relevant in a specific context, a LEMR system uses supervised machine learning models of physician information-seeking behavior. Since obtaining information-seeking behavior data via manual annotation is slow and expensive, automatic methods for capturing such data are needed.
Objective: The goal of the research was to propose and evaluate eye tracking as a high-throughput method to automatically acquire physician information-seeking behavior useful for training models for a LEMR system.
Methods: Critical care medicine physicians reviewed intensive care unit patient cases in an EMR interface developed for the study. Participants manually identified patient data that were relevant in the context of a clinical task: preparing a patient summary to present at morning rounds. We used eye tracking to capture each physician’s gaze dwell time on each data item (eg, blood glucose measurements). Manual annotations and gaze dwell times were used to define target variables for developing supervised machine learning models of physician information-seeking behavior. We compared the performance of manual selection and gaze-derived models on an independent set of patient cases.
Results: A total of 68 pairs of manual selection and gaze-derived machine learning models were developed from training data and evaluated on an independent evaluation data set. A paired Wilcoxon signed-rank test showed similar performance of manual selection and gaze-derived models on area under the receiver operating characteristic curve (P=.40).
Conclusions: We used eye tracking to automatically capture physician information-seeking behavior and used it to train models for a LEMR system. The models that were trained using eye tracking performed like models that were trained using manual annotations. These results support further development of eye tracking as a high-throughput method for training clinical decision support systems that prioritize the display of relevant medical record data.
Background: Rapid human papillomavirus (HPV) DNA testing is an emerging cervical cancer screening strategy in resource-limited countries, yet it requires follow-up of women who test HPV positive.
Objective: This study aimed to determine if one-way text messages improved attendance to a 14-month follow-up cervical cancer screening among HPV-positive women.
Methods: This multicenter, parallel-group randomized controlled trial was conducted at 3 hospitals in Tanzania. Eligible participants were aged between 25 and 60 years, had tested positive to a rapid HPV test during a patient-initiated screening, had been informed of their HPV result, and had a private mobile phone with a valid number. Participants were randomly assigned in a 1:1 ratio to the intervention or control group through an incorporated algorithm in the text message system. The intervention group received one-way text messages, and the control group received no text messages. The primary outcome was attendance at a 14-month health provider-initiated follow-up screening. Participants were not blinded, but outcome assessors were. The analysis was based on intention to treat.
Results: Between August 2015 and July 2017, 4080 women were screened for cervical cancer, of which 705 were included in this trial—358 women were allocated to the intervention group, and 347 women were allocated to the control group. Moreover, 16 women were excluded before the analysis because they developed cervical cancer or died (8 from each group). In the intervention group, 24.0% (84/350) women attended their follow-up screening, and in the control group, 23.8% (80/335) women attended their follow-up screening (risk ratio 1.02, 95% CI 0.79-1.33).
Conclusions: Attendance to a health provider-initiated follow-up cervical cancer screening among HPV-positive women was strikingly low, and one-way text messages did not improve the attendance rate. Implementation of rapid HPV testing as a primary screening method at the clinic level entails the challenge of ensuring a proper follow-up of women.
Trial Registration: ClinicalTrials.gov NCT02509702; https://clinicaltrials.gov/ct2/show/NCT02509702.
Background: Variations in patient demand increase the challenge of balancing high-quality nursing skill mixes against budgetary constraints. Developing staffing guidelines that allow high-quality care at minimal cost requires first exploring the dynamic changes in nursing workload over the course of a day.
Objective: Accordingly, this longitudinal study analyzed nursing care supply and demand in 30-minute increments over a period of 3 years. We assessed 5 care factors: patient count (care demand), nurse count (care supply), the patient-to-nurse ratio for each nurse group, extreme supply-demand mismatches, and patient turnover (ie, number of admissions, discharges, and transfers).
Methods: Our retrospective analysis of data from the Inselspital University Hospital Bern, Switzerland included all inpatients and nurses working in their units from January 1, 2015 to December 31, 2017. Two data sources were used. The nurse staffing system (tacs) provided information about nurses and all the care they provided to patients, their working time, and admission, discharge, and transfer dates and times. The medical discharge data included patient demographics, further admission and discharge details, and diagnoses. Based on several identifiers, these two data sources were linked.
Results: Our final dataset included more than 58 million data points for 128,484 patients and 4633 nurses across 70 units. Compared with patient turnover, fluctuations in the number of nurses were less pronounced. The differences mainly coincided with shifts (night, morning, evening). While the percentage of shifts with extreme staffing fluctuations ranged from fewer than 3% (mornings) to 30% (evenings and nights), the percentage within “normal” ranges ranged from fewer than 50% to more than 80%. Patient turnover occurred throughout the measurement period but was lowest at night.
Conclusions: Based on measurements of patient-to-nurse ratio and patient turnover at 30-minute intervals, our findings indicate that the patient count, which varies considerably throughout the day, is the key driver of changes in the patient-to-nurse ratio. This demand-side variability challenges the supply-side mandate to provide safe and reliable care. Detecting and describing patterns in variability such as these are key to appropriate staffing planning. This descriptive analysis was a first step towards identifying time-related variables to be considered for a predictive nurse staffing model.
Background: Sleep hygiene is important for maintaining good sleep and reducing insomnia.
Objective: This study examined the long-term efficacy of a theory-based app (including cognitive behavioral therapy [CBT], theory of planned behavior [TPB], health action process approach [HAPA], and control theory [CT]) on sleep hygiene among insomnia patients.
Methods: The study was a 2-arm single-blind parallel-group randomized controlled trial (RCT). Insomnia patients were randomly assigned to a treatment group that used an app for 6 weeks (ie, CBT for insomnia [CBT-I], n=156) or a control group that received only patient education (PE, n=156) through the app. Outcomes were assessed at baseline and 1 month, 3 months, and 6 months postintervention. Primary outcomes were sleep hygiene, insomnia, and sleep quality. Secondary outcomes included attitudes toward sleep hygiene behavior, perceived behavioral control, behavioral intention, action and coping planning, self-monitoring, behavioral automaticity, and anxiety and depression. Linear mixed models were used to evaluate the magnitude of changes in outcomes between the two groups and across time.
Results: Sleep hygiene was improved in the CBT-I group compared with the PE group (P=.02 at 1 month, P=.04 at 3 months, and P=.02 at 6 months) as were sleep quality and severity of insomnia. Mediation analyses suggested that perceived behavioral control on sleep hygiene as specified by TPB along with self-regulatory processes from HAPA and CT mediated the effect of the intervention on outcomes.
Conclusions: Health care providers might consider using a CBT-I app to improve sleep among insomnia patients.
Trial Registration: ClinicalTrials.gov NCT03605732; https://clinicaltrials.gov/ct2/show/NCT03605732
Background: Engaging socioeconomically disadvantaged populations in health research is vital to understanding and, ultimately, eliminating health-related disparities. Digital communication channels are increasingly used to recruit study participants, and recent trends indicate a growing need to partner with the social service sector to improve population health. However, few studies have recruited participants from social service settings using multiple digital channels.
Objective: This study aimed to recruit and survey 3791 adult clients of a social service organization via telephone and digital channels. This paper aimed to describe recruitment outcomes across five channels and compare participant characteristics by recruitment channel type.
Methods: The Cancer Communication Channels in Context Study recruited and surveyed adult clients of 2-1-1, a social service–focused information and referral system, using five channels: telephone, website, text message, web-based live chat, and email. Participants completed surveys administered either by phone (if recruited by phone) or on the web (if recruited from digital channels, ie, website, text message, Web-based live chat, or email). Measures for the current analysis included demographic and health characteristics.
Results: A total of 3293 participants were recruited, with 1907 recruited by phone and 1386 recruited from digital channels. Those recruited by phone had a moderate study eligibility rate (42.23%) and the highest survey completion rate (91.24%) of all channels. Individuals recruited by text message had a high study eligibility rate (94.14%) yet the lowest survey completion rate (74.0%) of all channels. Sample accrual goals were achieved for phone, text message, and website recruitment. Multivariable analyses found differences in participant characteristics by recruitment channel type. Compared with participants recruited by phone, those recruited from digital channels were younger (adjusted odds ratio [aOR] 0.96, 95% CI 0.96-0.97) and more likely to be female (aOR 1.52, 95% CI 1.23-1.88), married (aOR 1.52, 95% CI 1.22-1.89), and other than non-Hispanic black (aOR 1.48, 95% CI 1.22-1.79). Those recruited via phone also were more likely to have more than a high school education (aOR 2.17, 95% CI 1.67-2.82), have a household income ≥US $25,000 a year (aOR 2.02, 95% CI 1.56-2.61), and have children living in the home (aOR 1.26, 95% CI 1.06-1.51). Additionally, participants recruited from digital channels were less likely than those recruited by phone to have public health insurance (aOR 0.75, 95% CI 0.62-0.90) and more likely to report better overall health (aOR 1.52, 95% CI 1.27-1.83 for good-to-excellent health).
Conclusions: Findings indicate the feasibility and utility of recruiting socioeconomically disadvantaged adults from the social service sector using multiple communication channels, including digital channels. As social service–based health research evolves, strategic recruitment using a combination of traditional and digital channels may be warranted to avoid underrepresentation of highly medically vulnerable individuals, which could exacerbate disparities in health.
Background: Online patient communities provide new channels for users to access and share medical information. In-depth study of users’ willingness to share information in online patient communities is of great significance for improving the level of information sharing among the patient community and the long-term development of communities.
Objective: The aim of this study was to build a model of factors affecting patients’ willingness to share medical information from the perspective of both positive and negative utilities. Specifically, we aimed to determine the influence of online information support and privacy concerns, as well as the moderating effect of disease severity and information sensitivity of different patients on their willingness to share.
Methods: Data from 490 users with experience in online patient communities were collected through a questionnaire survey, and structural equations were applied to empirically verify the model hypotheses.
Results: Privacy concerns negatively affected the patients’ willingness to share information (P<.001), whereas online information support positively affected patients’ willingness to share information (P<.001), and information sensitivity negatively moderated the impact of online information support on sharing willingness (P=.01). Disease severity positively moderated the impact of privacy concerns on sharing willingness (P=.05). However, the hypotheses that information sensitivity is a negative moderator and disease severity is a positive moderator of the impact of privacy concerns on sharing willingness could not be supported.
Conclusions: To improve the level of user information sharing, the online patient community should design a safe user registration process, ensure the confidentiality of information, reduce the privacy concerns of users, and accurately identify the information needs of patients to provide personalized support services.
Background: Physical activity (PA) is important for children with a chronic disease. Serious games may be useful to promote PA levels among these children. Objective: The primary purpose of this systematic review was to evaluate the effectiveness of serious games on PA levels in children with a chronic disease. Methods: PubMed, EMBASE, PsycINFO, ERIC, Cochrane Library, and CINAHL were systematically searched for articles published from January 1990 to May 2018. Both randomized controlled trials and controlled clinical trials were included to examine the effects of serious games on PA levels in children with a chronic disease. Two investigators independently assessed the intervention, methods, and methodological quality in all articles using the Cochrane risk of bias tool. Both qualitative and quantitative analyses were performed. Results: This systematic review included 9 randomized controlled trials (886 participants). In 2 of the studies, significant between-group differences in PA levels in favor of the intervention group were reported. The meta-analysis on PA levels showed a nonsignificant effect on moderate to vigorous PA (measured in minutes per day) between the intervention and control groups (standardized mean difference 0.30, 95% CI –0.15 to 0.75, P=.19). The analysis of body composition resulted in significantly greater reductions in BMI in the intervention group (standardized mean difference –0.24, 95% CI –0.45 to 0.04, P=.02). Conclusions: This review does not support the hypothesis that serious games improve PA levels in children with a chronic disease. The meta-analysis on body composition showed positive intervention effects with significantly greater reductions in BMI in favor of the intervention group. A high percentage of nonuse was identified in the study of serious games, and little attention was paid to behavior change theories and specific theoretical approaches to enhance PA in serious games. Small sample sizes, large variability between intervention designs, and limited details about the interventions were the main limitations. Future research should determine which strategies enhance the effectiveness of serious games, possibly by incorporating behavior change techniques.
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: The increasing frequency and diversity of human disease outbreaks due to a plethora of ecological, environmental, and socio-economic factors thereby stressing healthcare systems worldwide....
Background: The increasing frequency and diversity of human disease outbreaks due to a plethora of ecological, environmental, and socio-economic factors thereby stressing healthcare systems worldwide. The most recent of these outbreaks is the novel coronavirus (COVID-19). For public health systems, understanding the temporal and spatial distribution of COVID-19 is a top priority. The wild spread of COVID-19 was also mirrored with intense attention on social media platforms, including Twitter. Information shared by individuals on social media concerning the virus can help decision-makers and healthcare professionals identify main issues involving COVID-19 and interventions to address them. Objective: This study aimed to analyze posts on Twitter to identify the main thoughts, attitudes, feelings, and topics that are discussed concerning the COVID-19. Methods: Leveraging a set of tools (Twitter’s search Application Programming Interface, Tweepy Python library, and Postgress database) and using a set of pre-defined search terms (coronavirus, 2019-nCov, and COVID-19), we extracted the text and metadata (number of likes and retweets, and user profile information including the number of followers) for public tweets in the English language for the period from 2 February 2020 and 3 March 2020. Tweets were analyzed using word frequencies of single words and double-word combinations. We leveraged Latent Dirichlet Allocation for topic modelling to identify the topics in the tweets. Additionally, sentiment analysis, extracting the mean number of retweets, likes, and followers for each topic and calculated interaction rate for each topic. Results: Out of approximately 2.8 million tweets for the study period, 167,073 unique tweets from 160,829 unique users met the inclusion criteria and were analyzed. Twelve topics grouped into four main themes: (i) Origin of the virus, (ii) its sources, (iii) its impact on people, countries, and the economy, as well as (iv) ways of mitigating the risk of infection. The mean of sentiment was positive in all topics except two topics: deaths caused by COVID-19 and increased racism. The mean followers of the account posting tweet topics ranged from 2,722 (increased racisms) to 13,413 (economic losses). The highest mean of likes for the tweets was 15.4 (economic losses) while the lowest was 3.94 (travel bans and warnings). Conclusions: Public health crisis response activities on the ground and online are becoming increasingly ‘simultaneous and intertwined’ and social media provides a lucrative opportunity to spread public health knowledge directly to the public. Health systems should work on building national and international detection and surveillance systems of “digital” diseases listening and monitoring social media. However, there is also a need for a more proactive and agile public health presence on social media to combat the spread of “fake news”.
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: 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.
Modern forms of technology-augmented healthcare are focusing on personalization of the delivery of medical services. This trend is driven in part by the growing rhetoric around patient diversity, empo...
Modern forms of technology-augmented healthcare are focusing on personalization of the delivery of medical services. This trend is driven in part by the growing rhetoric around patient diversity, empowerment, and choice as factors that impact the success of care. In parallel, there is a push for applying the latest advances in AI-based systems, especially intelligent agents (IA) or artificial agents (AA), as a way of autonomously carrying out and/or supporting interaction within healthcare service and personal health contexts. Robots, conversational agents, voice assistants, virtual characters—do these disparate forms of AI-based agents applied in care contexts have something in common? When and under what circumstances? Here we describe how they can manifest as “socially embodied AI,” which we define as the state an AI-based agent takes on when embedded within social and technologically nonpartisan “bodies” and contexts: a social form of human-AI interaction (HAII). We argue that this state is constructed by and dependent on human perception, arising when an embodied AI is perceived as having social characteristics and being socially interactive. Moreover, as a “circumstantial” category, we argue that if certain criteria are met, then any embodied AI can become socially embodied; however, this may not be true for all people at all times and in all situations. As a first step towards dealing with this complexity, we present an ontology for demarcating when embodied AI transition into socially embodied AI. We draw from theory and practice in human-machine communication (HMC), human-computer interaction (HCI), human-robot interaction (HRI), human-agent interaction (HAI), and social psychology. We reinforce our theoretical work with expert insights from a card sort workshop. We then propose an ontological heuristic for describing the dynamic threshold through which an AI-based agent becomes socially embodied: the Tepper line. We explore two case studies to illustrate the dynamic and contextual nature of this heuristic in healthcare contexts. We end by discussing possible implications of “crossing the Tepper line” from both AI- and human-centered viewpoints in person-centered care.
