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Latest Submissions Open for Peer Review

JMIR has been a leader in applying openness, participation, collaboration and other "2.0" ideas to scholarly publishing, and since December 2009 offers open peer review articles, allowing JMIR users to sign themselves up as peer reviewers for specific articles currently considered by the Journal (in addition to author- and editor-selected reviewers).

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JMIR Submissions under Open Peer Review

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Titles/Abstracts of Articles Currently Open for Review:

  • Background: The use of video consultations (VCs) in Norwegian general practice rapidly increased during the COVID-19 pandemic. During societal lockdowns, VCs were used for nearly all types of clinical problems, as physical consultations were kept to a minimum. Objective: To explore GPs’ experiences of potential and pitfalls associated with the use of VCs during the first pandemic lockdown. Methods: Between April 14th and May 3rd, 2020, all regular Norwegian GPs (N=4858) were invited to answer an online survey, which included open-ended questions about their experiences with advantages and pitfalls of VCs. A total of 2558 free text answers were provided by 657 of the 1237 GPs who participated in the survey. The material was subjected to reflexive thematic analysis. Results: Four main themes were identified. First, VCs appear most suitable when the GPs encounter known patients or previously presented health problems. Secondly, GPs describe new potentials: opportunities to tailor trajectories more seamlessly, to gain valuable insight into patients’ psychosocial life, and to get in contact with vulnerable patients who might otherwise not seek medical help. Thirdly, the communication style on video is scarce with loss of smalltalk and non-verbal hints. This was seen as effective but could also imply risks. Finally, VCs might lead to erosion of the therapeutic relationships with negative implications for patient safety in a longer perspective. Conclusions: During the pandemic societal lockdown, VCs were most suitable for consultations with previously known patients. The study revealed potentials including relationship-building with vulnerable patients who might otherwise be reluctant to seek help. Pitfalls of VCs were possible negative impact on quality of care and patient safety. The findings have relevance for future implementation of VCs and deserve further exploration under less stressful circumstances.

  • Analysis of public attitudes towards anxiety disorder in social media

    Date Submitted: Jan 16, 2023
    Open Peer Review Period: Jan 16, 2023 - Mar 13, 2023

    Background: Anxiety disorder has become a major clinical and public health problem, which causes a significant economic burden in worldwide. Public attitudes towards anxiety can impact the psychological state, help seeking and social activities of people with anxiety. Objective: The purpose of this study was to explore public attitudes towards anxiety disorders and the changing trends of these attitudes by analyzing the posts related to anxiety disorders in social media (e.g., Sina Weibo), as well as psycholinguistic and topical features of the text content of posts. Methods: A total of 325,807 Sina Weibo posts with the keyword term “anxiety disorder” from April 2018 to March 2022 were collected and analyzed. First, it analyzed the changing trends of the number and total length of posts every month. Second, a Chinese Linguistic Psychological Text Analysis System (TextMind) was used to analyze the changing trends of language features of posts. Third, we used a topic model for semantic content analysis to identify specific themes in Weibo users' attitudes toward anxiety. Results: The changing trends of the number and the total length of posts indicated that anxiety related posts significantly increased from April 2018 to March 2022, and were greatly impacted by the beginning of the new terms. The analysis of linguistic features showed that the frequency of cognitive process, affective process, biological process words and assent words increased significantly over time, while the frequency of social process words decreased significantly, and the public anxiety was greatly impacted by the COVID-19 pandemic. Feature correlation analysis showed that the frequencies of words related to work and family are almost negatively correlated with the ones of other psychological words. Semantic content analysis identified five common topics: discrimination and stigma, symptoms and physical health, treatment and support, work and social, and family and life. The results illustrated that the area of topic "family and life" decreased significantly over time, while the other four areas of topics all increased. Conclusions: The findings of this study indicate that pubic discrimination and stigma against anxiety disorder remain high, particularly in the aspects of self-denial and negative emotions. People with anxiety disorders should receive more social support to reduce the impact of discrimination and stigma.

  • Background: There remains to be significant uncertainty in the definition of the long COVID disease, its expected clinical course, and its impact on daily functioning. Social media platforms can generate valuable insights into patient-reported health outcomes as the content is produced at high resolution by patients and caregivers, representing experiences that may be unavailable to most clinicians. Objective: We aim to determine the validity and effectiveness of advanced NLP approaches built to derive insight into Long COVID-related patient-reported health outcomes from social media platforms. Methods: We use Transformer-based BERT models to extract and normalize long COVID Symptoms and Conditions (SyCo) from English posts on Twitter and Reddit. Furthermore, we estimate the occurrence and co-occurrence of SyCo terms at any point or across time and locations. Finally, we compare the extracted health outcomes with human annotations and highly utilized clinical outcomes grounded in the medical literature. Results: Based on our findings, the top three most commonly occurring groups of long COVID symptoms are systemic (such as “fatigue”), neuropsychiatric (such as “anxiety“ and “brain fog”), and respiratory (such as “shortness of breath”). Regarding the co-occurring symptoms, the pair of ‘fatigue & headaches’ is most common. In addition, we show that other conditions, such as infection, hair loss, and weight loss, as well as mentions of other diseases, such as flu, cancer, or Lyme disease, are among the top reported terms by social media users. Conclusions: The outcome of our social media-derived pipeline is comparable with the outcomes of peer-reviewed articles relevant to long COVID symptoms. Overall, this study provides unique insights into patient-reported health outcomes from long COVID and valuable information about the patient’s journey that can help healthcare providers anticipate future needs. Clinical Trial: N/A

  • Care as Basis for Participatory Design: A Digitally Mediated Study with Older Adults in COVID-19 Pandemic

    Date Submitted: Jan 15, 2023
    Open Peer Review Period: Jan 15, 2023 - Mar 12, 2023

    Background: COVID-19 has changed many people's lives and modes of participation. The pandemic and its different countermeasures put a new lense on interactions between professional researchers and community participants, both in terms of negotiating the positions between the parties and in terms of taking and giving care. Care here is a broad concept, encompassing medical and physical care acts, as well as assistance, support, and concern to promote mental and social health. Objective: Our research questions were: “How can digitally mediated participatory design (PD) work during COVID-19 and how can we understand digital PD as care?” The objective was to investigate whether and how caring participatory design is an appropriate approach during pandemics and whether and how this approach can contribute to the care of people who might be in need. Methods: We reflect on the PD process and examine how the actors enact care and what aspects of care are manifested. The project, “ACCESS”, took place in Siegen, Germany, and targeted the co-creation of a mobile demo kit aimed to improve the digital literacy and everyday appropriation of digital media with and for older people in their homes. Research experiences of the projects SFB 1187 and SNSF 74 CareComLabs contributed to the reflections on care and caring communities. Results: The research project included preparatory work for enabling older adults to become participants with a group of university researchers. The use of digital technology has allowed the participatory project to continue, as well as to draw attention to the digital skills of older adults and ways to improve their digital literacy as part of care. Through a series of workshops, a range of current IT products were explored by a group of 21 older adults. We provide empirically based concepts of older adults in accommodating themselves as well as sensitizing concepts for differentiating aspects of care according to Tronto and of participatory design using digital tools. The data suggests that it is not enough to focus solely on the technologies and how they are used; it is also necessary to focus on the social structures in which help is available and in which technologies in particular offer opportunities to do care. Conclusions: We examine how the actors, the research participants effectively enact care and demonstrate how such ‘care’ is a necessary basis for the genuinely participatory approach. We identify how different forms of care were carried out, and thereby provided support during COVID-19. We document how the co-creation of different digital media tools can be used to provide a community with mutual care and support.

  • Application of Patient-Reported Outcome measurements in clinical trials of tumor in mainland China

    Date Submitted: Jan 14, 2023
    Open Peer Review Period: Jan 13, 2023 - Mar 10, 2023

    Background: International health policy and researchers have emphasized the value of evaluating patient-reported outcomes (PROs) in clinical studies, while the characteristics of patient-reported outcomes in tumors in China are not well established. Objective: The purpose of this study was to assess the application and characteristics of PROs instruments as primary and/or secondary outcomes in randomized clinical trials of tumors in mainland China. Methods: This cross-sectional study used data from tumor-randomized clinical trials in mainland China from January 1, 2010, to June 30, 2022. The ClinicalTrials.gov databases and Chinese Clinical Trial Registry were selected as the databases. Trials were classified according to those that (1) listed PRO instruments as primary outcomes, (2) listed PRO instruments as secondary outcomes, (3) listed PRO instruments as coprimary outcomes, and (4) did not mention any PRO instruments. Data on study phase, setting, regions, center, participant age and gender, target diseases, and names of the PRO instruments were extracted from trials. The target diseases involved in the trials were grouped by the American Joint Committee on Cancer (AJCC) Cancer Staging Manual 8th edition. Results: Among a total of 6445 trials, 2429 (37.7%) trials used the PRO instruments in their outcomes. Among them, 27.0% (656/2429) trials listed PRO instruments as primary outcomes, 52.5% (1275/2429) trials used as secondary outcomes, and 20.5% (498/2429) trials used as coprimary outcome in their outcomes. From more than 2.1 million people included in the trials, data on 542 895 (25.2%) patients were collected by PRO instruments as primary or secondary outcomes, and coprimary outcomes were assessed for 75 275 (3.5%) patients. The most common conditions for which used explicitly specified PRO instruments were thorax tumor (18.7%), gastrointestinal tract tumor (13.0%), and lower gastrointestinal tract tumor (12.4%). The most common instruments for PRO measurements were the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30, visual analog scale, numeric rating scale, Traditional Chinese Medicine symptom scale, and Pittsburgh Sleep Quality Index. Conclusions: Over the past several years, the use of PROs increased in tumor-randomized clinical trials conducted in mainland China. Nonetheless, the patient's opinion still seems to be rarely measured. Specific PRO instruments should be widely used in different categories of tumor disease and there is room for improvement in standard PROs.

  • Building the business case for an inclusive approach to digital health measurement

    Date Submitted: Jan 13, 2023
    Open Peer Review Period: Jan 13, 2023 - Mar 10, 2023

    Background: The use of digital health measurement tools has grown substantially in recent years, and demand for these products is expected to grow further. However, there are concerns that the promised benefits from digital health products will not be shared equitably. Historically underserved populations such as those with lower education and income, the elderly, racial and ethnic minorities, individuals in rural areas, and those with disabilities may find such tools inaccessible or poorly suited for their needs. Because underserved populations shoulder a disproportionate share of the US disease burden, they also represent a substantial share of digital health measurement companies’ target markets. As such, inclusive principles should be core to the product development process to ensure that the resulting tools are broadly accessible and effective. In this context, inclusivity will not only maximize societal benefit, but can also lead to greater commercial success. Objective: A critical element in fostering inclusive product development is building the business case for why it is worthwhile. The DATAcc Market Opportunity Calculator was developed as an open access resource to enable digital health measurement product developers to build a business case for incorporating inclusive practices into their R&D processes. Methods: The DATAcc Market Opportunity calculator combines data on population demographics from the US Census, the American Community Survey, and the CDC Disability and Health Data System with data on disease prevalence and health status from the CDC National Health Information Survey and the CDC Disability and Health Data System. Together these data sources can be used to calculate the share of US adults with specific conditions (e.g., diabetes, hypertension) falling into various population segments along key “inclusion vectors” (e.g., race and ethnicity, disability status). Results: A free and open resource, the DATAcc Market Opportunity Calculator can be accessed from the DATAcc website. Users start by selecting the target health condition which their product addresses and an inclusion vector along which to segment the patient population. The calculator displays each segment as a share of the overall US adult population and its share specifically among adults with the target condition, quantifying the importance of underserved patient segments to the target market. For example, simplifying prompts on a hypertension-focused product to make it more accessible for adults with lower educational attainment is shown by the calculator to increase the target market by 2M people. The calculator also enables users to estimate the value of improvements to their product’s inclusivity by modeling the downstream impact on the size of the accessible market. For example, the calculator indicates that enabling compatibility of an arthritis-focused product with older phone models and low bandwidth connections to make it more accessible to low-income adults can increase potential revenue by 400M USD. Conclusions: Digital health measurement is still in its infancy. Now is the time to establish an early precedent for inclusive product development in order to maximize societal benefit and build sustainable commercial returns. The DATAcc Market Opportunity Calculator can help build the business case for the “why” – showing how inclusivity can translate directly to expanded market size and financial opportunity. Once the decision has been made to pursue inclusive design, other components of the broader DATAcc toolkit for inclusive product development can help support the “how”. Clinical Trial: N/A

  • A Scoping Review of Digital Interventions that Treat Post-/Long-COVID

    Date Submitted: Jan 13, 2023
    Open Peer Review Period: Jan 13, 2023 - Mar 10, 2023

    Background: Patients with Post-/Long-COVID need support, and health care professionals require clear evidence for their work with patients. Digital interventions can meet these requirements, especially if personal contact is limited. Objective: We answered the question of what the current evidence of digital interventions for patients with Post-/Long-COVID regarding physical- and mental health or mental well-being is. Methods: Scoping review in which original studies were summarized that examined the online treatment Post-/Long-COVID patients with the use of digital interventions. Following the PICO scheme, original studies were summarized in which patients with Post-/Long-COVID symptoms used digital interventions that aimed to help them to recover. Results: k = 8 original studies matching the inclusion criteria. Three were “pre-test” studies. Three describe the implementation of a telerehabilitation program, one is a Post-/Long-COVID program, and one study describes the results of qualitative interviews with patients who used an online peer support group. It was found that digital interventions can help patients with Post-/Long-COVID improve physiological health such as fatigue, breathlessness, and to sustain usual activities. However, in patients who were treated in the intensive care unit or had symptoms such as worsening short-term memory, and unpleasant dreams effects are less likely. Digital interventions can provide individualized monitoring and tailoring that meet the requirements of different patients and individual changes over time which is particularly important to overcome fatigue. Mental health (e.g., depression) was improved by digital intervention in most of the previous studies. Conclusions: More systematic research with larger sample sizes is required to overcome sampling bias and include the health care professionals’ perspective as well as help patients mobilize support by health care professionals and social network partners. The evidence so far suggests that patients should be provided with digital interventions to overcome their symptoms and reintegrate into everyday life, including work.

  • Harnessing electronic health records for real-world evidence

    Date Submitted: Jan 11, 2023
    Open Peer Review Period: Jan 11, 2023 - Mar 8, 2023

    While randomized controlled trials (RCTs) are the gold-standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data (RWD) has been vital in post-approval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of RWD is electronic health records (EHRs), which contain detailed information on patient care in both structured (e. g., diagnosis codes) and unstructured (e. g., clinical notes, images) form. Despite the granularity of the data available in EHRs, critical variables required to reliably assess the relationship between a treatment and clinical outcome can be challenging to extract. We provide an integrated data curation and modeling pipeline leveraging recent advances in natural language processing, computational phenotyping, modeling techniques with noisy data to address this fundamental challenge and accelerate the reliable use of EHRs for RWE, as well as the creation of digital twins. The proposed pipeline is highly automated for the task and includes guidance for deployment. Examples are also drawn from existing literature on EHR emulation of RCT and accompanied by our own studies with Mass General Brigham (MGB) EHR.

  • Deep learning-based prediction for significant coronary artery stenosis on coronary computed tomography angiography in asymptomatic populations

    Date Submitted: Jan 10, 2023
    Open Peer Review Period: Jan 10, 2023 - Mar 7, 2023

    Background: Although coronary computed tomography angiography (CCTA) is currently utilized as the frontline test to accurately diagnose coronary artery disease (CAD) in clinical practice, there are still debates as screening tool for asymptomatic population. Objective: Using deep learning (DL), we sought to develop a prediction model for significant coronary artery stenosis on CCTA and identify the beneficiaries of CCTA in apparently healthy asymptomatic adults. Methods: We retrospectively reviewed 11,180 individuals who underwent CCTA during routine health check-ups between 2012 and 2019. Main outcome was the presence of coronary artery stenosis of ≥70% on CCTA. The prediction model was developed using machine learning (ML), including DL. Its performance was compared with pretest probabilities, including the pooled cohort equation (PCE), CAD consortium, and updated Diamond-Forrester (UDF) scores. Results: In the cohort comprising 11,180 apparently healthy asymptomatic individuals (mean age 56.1 years; men 69.8%), 516 (4.6%) had significant coronary artery stenosis. Among the ML methods, a neural network with multi-task (19 selected features), one of the DL methods, was chosen because it produced the best performance (area under the curve (AUC), 0.782) with a high diagnostic accuracy of 71.6%. Our DL-based model demonstrated a better prediction than the PCE (AUC, 0.719), CAD consortium score (AUC, 0.696), and UDF score (AUC, 0.705). Age, sex, HbA1c, and HDL cholesterol were highly ranked features. Personal education and monthly income levels were included as important features of the model. Conclusions: A neural network with multi-task model was successfully developed for detecting CCTA-derived stenosis of ≥70% in asymptomatic populations. In clinical practice, we could provide more precise indications for CCTA as a screening tool to identify individuals at a higher risk even in asymptomatic populations.

  • Background: The use of triage systems such as the Manchester Triage System is a standard procedure to determine the sequence of treatment in emergency departments. When using the Manchester Triage System, time targets for treatment are determined. These are commonly displayed in the emergency department information system to emergency department staff. Using measurements as targets has been associated with a decline in meeting those targets. Objective: We investigated the impact of displaying time targets for treatment to physicians on processing times. Methods: We analyzed the effects of displaying time targets to emergency department staff on waiting times in a prospective cross-over study during the introduction of a new emergency department information system. The old information system version used a module that showed the time target determined by the Manchester Triage System, while the new system version used a priority list instead. Results: The average emergency department length of stay and waiting times increased when using an emergency department information system that did not display time targets (time from admission to treatment t ̅pre = 15 min., IQR = 6-39, t ̅post = 11 min., IQR = 5-23). However, severe cases with high acuity (as indicated by the triage score) benefited from lower waiting times (0.15 times as high as in pre-intervention for MTS1, only 0.49 as high for MTS2). Furthermore, patients with severe injuries were less likely to receive delayed treatment, and we observed reduced odds of late treatment when crowding occurred. Conclusions: Our results suggest that it is beneficial to use a priority list instead of displaying time targets to emergency department personnel. Time targets may lead to false incentives. Our work highlights that working better is not the same as working faster.

  • Using Machine Learning of Online Expression to Explain Recovery Trajectories

    Date Submitted: Jan 9, 2023
    Open Peer Review Period: Jan 8, 2023 - Mar 5, 2023

    Background: Smartphone-based digital health applications (“apps”) are increasingly used to support behavior change and prevent relapse among those with substance use disorders (SUDs). These systems also collect a wealth of data from participants, including the content of messages exchanged in peer-to-peer support forums. The ways individuals self-disclose and exchange social support in these forums may provide insight into their recovery course, but manual review of a large corpus of text by human coders is inefficient. Objective: The present study, first, seeks to evaluate the feasibility of applying supervised machine learning to perform large-scale automated content analysis of an online peer-to-peer discussion forum. Second, we use the machine-coded data to understand, at a large scale, how communication styles relate to writers’ substance use and wellbeing outcomes at six months. Methods: Data were collected from a smartphone app that connects patients with SUDs to online peer support via a discussion forum. Two-hundred and sixty-eight patients over 18 years old with SUDs diagnoses were recruited by primary care providers from three Federally Qualified Healthcare Centers in the United States beginning in 2014. Two waves of survey data were collected to measure demographic characteristics and study outcomes: one at baseline (before accessing the app) and one after six months of using the app. Messages were downloaded from the peer-to-peer forum and subject to manual content analysis, identifying forms of social support and self-disclosure on the forum. These data were used to train supervised machine learning algorithms to automatically identify seven types of expression (emotional support, informational support, negative affect, change talk, insightful disclosure, gratitude, and universality disclosure). Subsequently, regression analyses examined how each expression type, represented as a proportion of a user’s total messages, was associated with recovery outcomes at six months, while controlling for these outcomes at baseline. Results: Over six months, 231 participants posted on the app’s support forum, of whom 216 (94%) posted at least one message in the content categories of interest. These 216 participants generated 10,503 messages over six months. We found, first, that our supervised machine learning approach allowed for large-scale content coding while retaining a high level of accuracy (average F-score of 0.86 across the content categories). Second, individuals’ expression styles were associated with recovery outcomes. For social support, a greater proportion of messages giving emotional support to peers was related to reduced substance use (Odds ratio = 0.12, p = 0.032). For self-disclosure, a greater proportion of messages expressing universality—feelings of oneness of closeness to the support group—was related to improved quality of life (β = 11.83, p = 0.038), whereas a greater proportion of negative affect expressions was negatively related to quality of life (β = -11.00, p = 0.045) and mood (β = -1.49, p = 0.007). We also found that the proportion of messages expressing emotional support and universality increased over time. Conclusions: This study highlights a method of computer-assisted content analysis with potential to provide real-time insights into peer-to-peer communication dynamics in online discussion contexts. Expression of emotional support, universality, and negative affect were significantly related to recovery outcomes, and attending to these dynamics may be important for appropriate and timely intervention. The increasing proportion of emotional support and universality suggests potential benefits of sustaining engagement in peer-to-peer forums. Conclusions: Our findings show that the expression types linked to positive recovery outcomes (i.e., emotional support giving and universality expressions) increased over time as a proportion of messages sent, suggesting the potential importance of sustaining participation and motivating more participants to interact. With the prevalence of the Internet, online peer-to-peer forums represent a growing support venue for those in recovery. This study extracted seven types of messages exchanged on a smartphone-based forum and applied supervised ML to perform large-scale quantitative content analysis over six months. Analyses leveraging the machine-coded data suggest forms of peer-to-peer communication that distinguish individuals’ likely recovery course, notably emotional support, universality, and negative affect expressions. Attending to these forms of expression may help to develop interventions that better respond to participants’ recovery needs.

  • Global research on emerging trends of obstetrics during COVID-19 pandemic: a bibliometric analysis

    Date Submitted: Jan 7, 2023
    Open Peer Review Period: Jan 7, 2023 - Mar 4, 2023

    Background: Coronavirus disease-2019 (COVID-19) has caused continuous effects on the public globally. In recent years, COVID-19-related studies and publications have shown blowout development. It is challenging to identify development trends and hot areas by using traditional review methods for such massive data. Therefore, this study aimed to explore the global COVID-19 research in obstetrics using a comprehensive bibliometric analysis. Objective: This study aimed to systematically performed a bibliometric analysis to explore the current status and hotspots of COVID-19 in obstetrics. Methods: A comprehensive online search was conducted on COVID-19-related studies in obstetrics in the Web of Science Core Collection (WOSCC) database from 1 January 2020 to 30 November 2022. Microsoft Excel 2016, VOSviewer version, CiteSpace version, and two online platforms were used to conduct the bibliometric and visualization analyses. Results: A total of 9868 articles were included in further analysis, including authors, titles, number of citations, countries and author affiliations, and where the studies were conducted. The United States has contributed the most significant publications with the leading position. “Sahin, Dilek” has the largest output, and “Khalil, Asma” was the most influential author with the highest citations. Keywords of “Pregnancy,” “Sars Cov,” and “Service” with the highest frequency, and “Qualitative research,” “Public health,” and “Mental health” might be the new research hotspots and frontiers. The top three concerned genes included ACE2, CRP, and IL6; while, COVID-19, infectious, and death occupied the top three words for frequency of disease. Conclusions: The effects of COVID-19 on obstetrics have attracted considerable attention worldwide, resulting in numerous articles published in the past three years. Major publications were from the United States, England, and China, and the new research hotspot is gradually shifting from the COVID-19 mechanism and its related clinical research to studying its psychological and social effects on pregnant women. Our research classified the existing research better so that clinicians and researchers can summarize the overall point of view of the existing literature and obtain more accurate conclusions.

  • Background: Digital health technologies are becoming more available to children and young people (CYP) and their families. However, there are no scoping reviews that provide both an overview of the characteristics of digital interventions for CYP and potential challenges to be considered when developing and implementing them. Objective: This study aimed to systematically review scientific publications to identify the current characteristics and potential complications of digital interventions for CYP. Methods: We searched five databases (PubMed, Scopus, Embase, Medline, and CINAHL) and Google Scholar for eligible clinical trials published between January 1, 2018 and August 19, 2022. The initial search of the five databases yielded 3775 citations; duplicates and those not meeting the inclusion criteria were eliminated. Eventually, 34 articles, including 15 retrieved from Google Scholar, were included in the final review. Relevant information was then extracted from the 34 articles and the descriptive characteristics and potential challenges were classified. Results: Mental health (76.5%) was the most common target for digital intervention for CYP, exceeding physical health (23.5%) by more than three times. In addition, a substantial number of digital interventions were solely dedicated to CYP. Digital interventions for CYP were more likely to be delivered via computers (50%) rather than smartphones (38.2%). The duration of the digital intervention for CYP was more likely to vary depending on the target users (from a single session to 28 weeks) rather than the target disease (from 4 to 24 weeks). Intervention components were classified into five categories: guidance, tasks and activities, reminder and monitoring, supportive feedback, and reward systems. Potential challenges were subcategorized into ethical challenges, interpersonal challenges, and societal challenges. For ethical challenges, consent of CYP or caregivers to participate in research, potential risk of adverse events, and data privacy issues were considered. For interpersonal challenges, the engagement of CYP was affected by the preference or barrier of caregivers to participate in studies. For societal challenges, restricted ethnicity in recruitment, limited availability of digital technology, differences in internet use patterns and preferences between girls and boys, unified clinical settings, and language barrier were described. Conclusions: We identified potential challenges and provided suggestions about ethical, interpersonal, and societal aspects to consider when developing and deploying digital-based interventions for CYP. Our findings provide a thorough overview of the published literature and may serve as a comprehensive, informative foundation for any stakeholder responsible for the development and implementation of digital-based interventions for CYP.

  • How Augmenting Reality Changes the Reality of Simulation: An Ethnographic Analysis

    Date Submitted: Jan 5, 2023
    Open Peer Review Period: Jan 5, 2023 - Mar 2, 2023

    Background: Simulation-based medical education provides key medical training for high-risk events. Augmented reality enhanced simulation (AR) projects digital images of realistic exam findings into a participant’s field of view. It is unknown how AR-enhanced simulation compares to traditional mannequin-based simulation (TM) with regards to influencing participant behavior and attention. Objective: Ethnographically compare and categorize Traditional Mannequin-Based Simulation Based Medical Education (SBME) to Augmented Reality-Enhanced SBME and provide suggestions for educators looking to delineate these two modalities. Methods: Twenty recorded interprofessional simulations (10 TM, 10 AR) featuring a decompensating child were evaluated through video-based focused ethnography. A generative question was posed, “How do the experiences and behaviors of participants vary based on the simulation modality?” Iterative data collection, analysis, and pattern explanation were performed by a review team spanning critical care, simulation, and qualitative expertise. Results: Findings clustered into three core themes: 1) focus and attention, 2) suspension of disbelief, and 3) communication. AR facilitated a more comprehensive clinical assessment; however kinesthetic interventions were impeded. Participant focus during AR was on patient exam changes, whereas in TM focus was on the cardiorespiratory monitor. Finally, communication differed with calmer and clearer communication during TM while AR communication was more chaotic. Conclusions: Primary differences clustered to focus and attention, suspension of disbelief, and communication. TM may be superior for hands-on skill acquisition while AR may be superior for assessment-focused simulations. Next steps include applying these themes to virtual reality-based simulation and true patient encounters to inform best practices for use of the growing portfolio of simulation modalities to best align with targeted learning objectives.

  • Background: Despite the prevalent use of digital media, traditional reading approaches like reading on paper or employing oral reading are recommended when high levels of RP are required. However, little research on Reading Performance (RP) according to the reading medium has been conducted in current prevailing digital device environment. In addition, although the effectiveness of oral reading has been shown to help immature language speakers, the effect of oral reading has not been proven for mature language speakers. For the Reading Performance (RP), both objective indicators such as reading comprehension and perceived indicators such as enjoyment are considered important in today's diverse reading environment. Objective: The purpose of this study is to determine whether there are differences in objective and perceived RP according to two factors: reading media (paper versus screen), and methods (silent versus oral) in mature language speakers. Methods: We conducted a two-by-two between-subject mixed method experimental study. A total of 63 immature language speakers participated based on the smartphone ownership, and daily internet usage were recruited. For the reading medium variable, participants read on either paper or screen. For the reading method variable, participants followed either silent reading or oral reading. The reading materials and comprehension questions used in the study were those from XXX(blind-review) SAT. To measure the objective RP, we evaluated comprehension in two levels; low- and high-construal. For the perceived RP, we surveyed three variables; reading preference, reading convenience, and expected comprehension. Results: Our experimental study revealed that reading preference in perceived RP is higher when reading on paper than on screen (F(1, 61)=8.391, P=.005). Reading preference of perceived RP is also higher when oral reading than silent reading (F(1,61)=7.808, P =.007). However, the results showed no significant difference between groups in reading convenience and expected comprehension of perceived RP. In the objective RP, low- and high-construal level of comprehension did not differ according to the reading media and methods. Conclusions: As far as we know, this is the first study that measures the concept of perceived RP explicitly. Our study, in which readers preferred reading on paper rather than on a screen, even though participants reported no difference in objective RP, implies that measures should be taken to overcome the negative perception of digital reading. In addition, the findings that the perceived PR of oral reading was higher than that of silent reading suggests that oral reading can be used for mature language speakers in situations where perceived RP is the reading goal.

  • Background: Healthcare is considered one of the most stressful occupations because multiple stressors exist in the workplace. Mobile stress management intervention is currently widely used in mental health intervention as an affordable and accessible approach. However, one of the major challenges in applying mobile intervention is the low interaction and engagement of participants, which hinders the optimal effects of mobile intervention, especially in self-guided mobile interventions where employees receive no human support during the intervention. Objective: The present study aims to compare the effects of CIMI, a complex interactive multimodal intervention, on physiological and psychological stress measures in comparison to the self-guided mobile intervention. Methods: We conducted a non-randomized, controlled study in two Chinese general hospitals. This study enrolled 245 healthcare workers who met the inclusion criteria for at least one of the three dimensions of the Depression, Anxiety, and Stress scale. All eligible participants were required to complete a questionnaire and wear a 24-hour Holter device to assess the physiological indicators of stress as indexed by heart rate variability at both baseline and post-intervention. During the program, participants in the CIMI group received a 12-week online intervention with the following four components: mobile stress management education, a web-based WeChat social network, tailored feedback, and a nurse coach, while the control group only received a self-guided intervention. Results: After a 12-week intervention, the perceived stress, depression, anxiety, fatigue, sleepiness symptoms, and subjective happiness in the CIMI group improved more significantly than those in the self-guided group. Additionally, we found a reduction in HRV parameters in the control group rather than the CIMI group. Conclusions: CIMI was an effective intervention for improving both psychological and physiological indicators of stress among distressed HCWs. The findings provide objective evidence for developing an effective and economically viable mobile stress management intervention with a minimum of human support, but further research is needed. Clinical Trial: ClinicalTrial.gov., NCT05239065; https://clinicaltrials.gov/ct2/show/NCT05239065.

  • Background: Infodemiology is a growing public health concern aggravating the dissemination of scientific facts to a population. During the COVID-19 pandemic, the efficacy of Hydroxychloroquine as a therapeutic solution emerged as a case of misinfodemic. While the internet and social media were primary conduits for Hydroxychloroquine infodemiology, cable television (TV) networks served as vital sources. Claimed experts are allocated cable TV airtime to discuss and disseminate scientific facts about diseases and health in broadcasts that shape the subsequent infodemiology patterns. However, understanding how the information spread by credible experts influences the allocated airtime in cable TV remains a gap. Objective: This study investigates the experts’ credibility as a doctor (DOCTOR), being from the government (GOVTEXPERT), and the sentiments (SENTIMENT) expressed in the cable TV discussion broadcasts influence the allocated airtime (AIRTIME). While DOCTOR and GOVTEXPERT reflect on the expert’s credibility, SENTIMENT points to the information credibility around infodemiology. This study is contextualized to the controversial discussions in three major cable TV networks in the United States. Methods: We collected broadcast transcribes on relevant hydroxychloroquine broadcasts on cable TV between March and October 2020. We coded the experts as DOCTOR or GOVTEXPERT using publicly available data. Using a machine learning algorithm, we mined the transcriptions to code the sentiments of broadcasts into POSITIVE, NEGATIVE, NEUTRAL, and MIXED SENTIMENTS. Results: We find DOCTOR is associated with less AIRTIME (P=.01) as a counterintuitive finding, whereas GOVTEXPERT is associated with more airtime (P<.001) in a base model. The interaction effects show that government experts with a doctorate receive less airtime (P=.03). Sentiments in broadcasts play a role, more so for NEGATIVE (P<.001), NEUTRAL (p=.00,) and MIXED (p=.03) sentiments’ direct effects on airtime. However, only the government experts with POSITIVE sentiments during the broadcast receive more airtime (P<.001). Furthermore, NEGATIVE sentiments in broadcasts reflect less airtime both for DOCTOR (P<.001) and GOVTEXPERT (P<.001). Conclusions: Although source credibility is an essential aspect of monitoring and reflecting on infodemic issues, cable TV media seems to have different dynamics in creating an effect because of its appeal of characters used and the broadcasting process. Surprisingly, the findings point out that doctors do not have good appeal. In contrast, government experts as sources allure audiences to get more airtime on cable TV on the contentious topic of Hydroxychloroquine’s efficacy in treating COVID-19. Doctors presenting facts with negative sentiments is not helpful either. However, government experts have better airtime when they radiate positive sentiments during the broadcasting. The study findings have substantial implications on unraveling the puzzle around the source credibility issues for infoveillance.

  • Background: Nursing narratives comprise an intriguing feature in the prediction of short-term clinical outcomes. However, it is unclear which nursing narratives impact significantly the prediction of postoperative length of stay (LOS) in deep-learning models. Objective: Therefore, we applied the REverse Time AttentIoN (RETAIN) model to predict LOS, entering nursing narratives as the main input. Methods: A total of 354 patients who underwent ovarian cancer surgery at the Seoul National University Bundang Hospital during 2014–2020 were retrospectively enrolled. Nursing narratives collected within three postoperative days were used to predict prolonged LOS (≥ 10 days). Results: Nursing narratives entered on the first day were the most influential predictors in prolonged LOS. The likelihood of prolonged LOS increased if the physician had to check the patient often and if the patient received intravenous fluids or intravenous patient-controlled analgesia late. Conclusions: The use of RETAIN on nursing narratives predicted postoperative LOS effectively for patients who underwent ovarian cancer surgery. These findings suggest that accurate and interpretable deep-learning information obtained shortly after surgery may accurately predict prolonged LOS.

  • Background: Online communities and the posts they generate represent an unprecedented resource for studying subjective emotional experiences, capturing population types and sizes not typically available in the laboratory. Here we mined such a platform to explore a putative specificity of the emotional experience of substance cessation and its cross-substance overlap. Objective: An important motivation for this exploration was to investigate transdiagnostic clues that could ultimately be used for mental health outreach. Specifically, we aimed to characterize the emotions associated with cessation of three major substances and compare them to emotional experiences reported in non-substance cessation posts. Methods: Two million pseudonymous posts made respectively in the fall of 2020 (discovery dataset) and fall of 2019 (replication) were obtained from 394 forums at Reddit.com. We tracked emotion word frequencies in posts from three substance cessation forums (for alcohol, nicotine, and cannabis topic categories), contrasting them to general forums. Emotion word frequencies were further tracked on multiple distinct categories of emotions and represented as a multidimensional emotion vector for each forum. We quantified the degree of emotional resemblance between different forums by computing cosine similarity on these vectorized representations. For substance cessation posts with self-reported time since last use, we explored changes in the use of emotion words as a function of abstinence duration. Results: Compared to posts from general forums, substance cessation posts showed more expression of anxiety, disgust, pride, and gratitude words. ‘Anxious’ emotion words were attenuated for abstinence durations > 100 days compared to shorter durations (t12=3.08, two-tailed, P=.009). The cosine similarity analysis identified an emotion profile preferentially expressed in the cessation posts across substances, with lesser but still prominent similarities to posts about social anxiety and ADHD. These results were replicated in the 2019 (pre-COVID-19) data and were distinct from control analyses using non-emotion words. Conclusions: We identified a unique subjective experience phenotype of emotions associated with the cessation of three major substance types, replicable across two time periods. We noted changes to this experience as a function of duration of abstinence. Although to a lesser extent, this phenotype quantifiably resembled the emotion phenomenology of other relevant subjective experiences (social anxiety, ADHD). Taken together, these transdiagnostic results suggest a novel approach for future identification of at-risk populations, allowing for the development and deployment of specific and timely interventions.

  • Background: Open source Electronic Health Records (EHRs) can improve healthcare delivery in low and lower-middle income countries (LMICs). There is demand for open source EHR systems in LMICs as they are cost-effective and provide the flexibility to customise systems to meet context-specific needs. However, open source EHRs have not proliferated rapidly. Implementation barriers prevent successful adoption. Little is known about the roles of implementers in addressing these barriers. Existing research focuses predominantly on technical perspectives for open source EHR project implementation. In contrast, this scoping review identifies contextual barriers impacting the implementation of open source EHR systems for LMICs. Objective: This scoping review aims to provide an overview of the key contextual barriers impacting the implementation of open source EHR systems for LMIC settings, and identifies areas for future research. Methods: An interdisciplinary scoping literature review was undertaken, guided by a systematic methodological framework based on Arksey and O’Malley. Seven databases were selected from three disciplines: Medicine and health sciences, computing, and social sciences. The Mixed Methods Appraisal Tool (MMAT) and the Critical Appraisal Skills Programme (CASP) checklists were utilised to assess the quality of relevant studies. Data was collated, summarised, results were reported qualitatively adopting a narrative synthesis approach. Results: The 13 studies included in this review examined open source EHR implementation in LMICs from three distinct perspectives: socio-environmental barriers, technological barriers, and organisational barriers. Key issues that influenced the implementation were identified in the literature as: limited funding (n=13), sustainability (n=13), organisational and management (n=11), infrastructure (n=10), data privacy and protection (n=10), and ownership (n=5). Data protection and confidentiality, ownership and ethics emerged as important issues, often overshadowed by technical processes (n=11). Conclusions: While open source EHRs have the potential to facilitate enhanced healthcare and encourage sustainable development in LMICs, it is vital to take into consideration the specific context in which such technologies are to be implemented within. This study revealed the key perceived barriers that impact open source EHR implementation success. Research is required to better understand the implementation process and how socio-environmental, technical, and organisational barriers can be addressed, particularly in relation to ethics and management of data protection. We hope that the review results will inform areas for future research and enhance implementation.

  • Virtual Reality head-mounted displays to assess skills in Emergency Medicine- a validity study

    Date Submitted: Dec 21, 2022
    Open Peer Review Period: Dec 21, 2022 - Feb 15, 2023

    Background: Many junior doctors are unprepared to manage acutely ill patients in the emergency department. The setting is often stressful, and urgent treatment decisions are needed. Overlooking symptoms and making wrong choices may lead to significant patient morbidity or death, and it is essential to ensure that junior doctors are competent. Virtual Reality (VR) software can provide standardized and unbiased assessment, but solid validity evidence is necessary before implementation. Objective: To gather validity evidence for using 360-degree VR videos with integrated multiple-choice questions (MCQ) to assess emergency medicine skills. Methods: Five full-scale emergency medicine scenarios were recorded with a 360-degree video camera, and MCQs were integrated into the scenarios played in a head-mounted display. We invited three groups of medical students with different experience levels to participate: first-year medical students (novice group), last-year medical students without emergency medicine training (intermediate group), and last-year medical students with completed emergency medicine training (experienced group). Each participant's total test score was calculated based on the number of correct MCQ answers (maximum score of 28), and the groups’ mean scores were compared. The participants rated their experienced presence in emergency scenarios using the Igroup Presence Questionnaire (IPQ) and the cognitive workload with the NASA Task Load Index (NASA-TLX). Results: We included 61 medical students from December 2020 to December 2021. The experienced group had significantly higher mean scores than the intermediate group (23 vs. 20, P=0.038), and the intermediate group had significantly higher scores than the novice group (20 vs. 14, P<0.001). The contrasting groups’ standard-setting method established a pass/fail score of 19 points (68% of the maximum score possible). Inter-scenario reliability was high, with a Cronbach’s alpha of 0.82. The participants experienced the VR scenarios with a high degree of presence with an IPQ score of 5.83 (scale 1-7) and the task to be mentally demanding with a NASA-TLX score of 13.30 (scale 1-21). Conclusions: This study provides validity evidence to support using 360-degree VR scenarios to assess emergency medicine skills. The students evaluated the VR experience as mentally demanding with a high degree of presence, suggesting that VR is a promising new technology for emergency medicine skills assessment.

  • Use of digital health technologies in primary health care (PHC) in the Sub-Saharan Africa Region: a SWOT analysis

    Date Submitted: Dec 21, 2022
    Open Peer Review Period: Dec 21, 2022 - Feb 15, 2023

    Background: In many health systems globally, digital health technologies (DHT) have become increasingly commonplace as a means of delivering primary care. COVID-19 has further increased the pace of this trend. While DHTs have been postulated to reduce inequalities, increase access, and strengthen health systems, how DHT implementation has been realised in the sub-Saharan Africa (SSA) healthcare environment remains to be further explored. Objective: To capture the multidisciplinary experiences of SSA experts and primary care healthcare providers using DHTs to explore the strengths and weaknesses, as well as opportunities and threats regarding the implementation and use of DHTs in SSA primary care settings. Methods: A combination of qualitative approaches was adopted (i.e., online focus groups and semi-structured interviews), using an online platform. Participants were recruited through AfroPHC and researchers contact networks, using convenience sampling, and included if having experience with digital technologies in primary health care in SSA. Focus and interviews were conducted, respectively, in November 2021 and January-March 2022. Topic guides were used to cover relevant topics in the interviews and focus groups, using the Strengths, Weaknesses, Opportunities and Threats (SWOT) framework. Transcripts were compiled verbatim and systematically reviewed by two independent reviewers using thematic analysis to identify emerging themes. The Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist was used to ensure the study meets the recommended standards of qualitative data reporting. Results: Strengths of DHT use ranged from improving access to care, supporting the continuity of care, and increasing care satisfaction and trust, to greater collaboration, enabling safer decision-making, and hastening progress towards universal health coverage. Weaknesses included poor digital literacy, health inequalities, lack of human resources, inadequate training, lack of basic infrastructure and equipment, and poor coordination when implementing DHTs. DHTs were perceived as an opportunity to improve patient digital literacy, increase equity, promote more patient-centric design in upcoming DHTs, streamline healthcare resource expenditure, and provide a means to learn international best practices. Major threats identified include the lack of buy-in from both patients and providers, insufficient human resources and local capacity, inadequate governmental support, overly restrictive regulations, and a lack of focus on cybersecurity and means for patient data protection. Conclusions: The research highlights the complex challenges of implementing DHTs in the SSA context, as a fast-moving health delivery modality, as well as the need for multi-stakeholder involvement. Future research should explore the nuances of these findings across different technologies and settings in the SSA region, and its implications on health and health care equity, capitalising on mixed-methods research, including the use of real-world quantitative data to understand patient health needs. The promise of digital health will only be realised when informed by studies that incorporate patient perspective at every stage of the research cycle.

  • Exploring patient advisors’ perceptions of virtual care across Canada: A phenomenological study

    Date Submitted: Dec 21, 2022
    Open Peer Review Period: Dec 20, 2022 - Feb 14, 2023

    Background: While virtual care services existed prior to the emergence of COVID-19, the pandemic catalyzed a rapid transition from in-person to virtual care service delivery across the Canadian healthcare system. Virtual care includes synchronous or asynchronous delivery of healthcare services through online video visits, telephone visits, or secure messaging. Patient advisors are people with patient and caregiving experiences who collaborate within the healthcare system to share insights and experiences in order to improve healthcare . Objective: This study aimed to understand patient advisors’ perceptions related to virtual care and potential impacts on healthcare quality. Methods: We adopted a phenomenological approach whereby we interviewed 20 participants who were patient advisors across Canada using a semi-structured interview protocol. The protocol was developed by content experts and medical education researchers. The interviews were audio-recorded, transcribed verbatim, and analyzed thematically. Data collection stopped once thematic saturation was reached. The study was conducted at Queen’s University, Kingston, ON. We recruited 20 participants from five Canadian provinces (17 females and three males). Results: Six themes were identified: 1) Characteristics of effective healthcare, 2) Experiences with virtual care, 3) Modality preferences, 4) Involvement of others, 5) Risks associated with virtual care encounters, and 6) vulnerable populations. Participants reported that high-quality healthcare included building relationships and treating patients holistically. Generally, participants described positive experiences with virtual care during the pandemic, including greater efficiency, increased accessibility, and that virtual care was less stressful and more patient-centred. Participants comparing virtual care with in-person care reported that time, scheduling, and content of interactions were similar across modalities. However, participants also shared the perception that certain modalities were more appropriate for specific clinical encounters (e.g., prescription renewals and follow-up appointments). Perspectives related to the involvement of family members and medical trainees were positive. Potential risks included miscommunication, privacy concerns, and inaccurate patient assessments. All participants agreed that stakeholders should be proactive with applying strategies to support vulnerable patients. Participants also recommended education for patients and providers to improve virtual care delivery. Conclusions: Participants reported experiences of virtual care encounters were relatively positive. Future work could focus on delivering training and resources for providers and patients. While initial experiences are positive, there is a need for ongoing stakeholder engagement and evaluation to improve patient and caregiver experiences with virtual care.

  • Advancing Gun Violence Research with Social Media Data: A Computational Analysis of Gun Ownership

    Date Submitted: Dec 20, 2022
    Open Peer Review Period: Dec 20, 2022 - Feb 14, 2023

    Background: Social media data represent a potentially valuable source of information to support gun violence research. Strengthening the empirical and methodological foundations for using social media data in this context is important for advancing the future application of social media data to gun violence research. Objective: We assess the extent to which social media-based estimates are able to accurately capture geographic variability in firearms-related outcomes using firearm ownership as a test. Methods: We use Twitter data from 2019-2021 and state of the art computational methods to construct a machine learning model of firearm ownership. We create state-specific estimates of ownership and assess these estimates by comparing them to benchmark measures. Results: Methodologically, our study highlights the importance of large draws from social media data when location identification is paramount. Our analytic approach for modeling firearm ownership using machine learning and adjusting estimates using an inferred demographic provide examples of how these techniques can be used and expanded in future gun violence research. Empirically, we find a strong positive correlation between Twitter-based estimates of gun ownership and benchmark ownership estimates. For states meeting a threshold requirement of a minimum of 100 labeled Twitter users, the Pearson’s and Spearman’s correlations are 0.63 (p<0.001) and 0.64 (p <0.001), respectively. Conclusions: Our findings underscore the potential of social media data for providing new windows into firearm behavior and outcomes, especially when measures from traditional data sources are limited or unavailable. Social media data carry analytical challenges when used for research purposes. Careful attention to them, as well as to ethical standards for use, is essential as the frontiers of social media data’s use in research are explored.

  • A dyadic digital health module for chronic disease shared care: Design thinking

    Date Submitted: Dec 19, 2022
    Open Peer Review Period: Dec 19, 2022 - Feb 13, 2023

    Background: The Covid-19 pandemic forced the spread of digital health to address limited clinical resources for managing chronic health conditions. At the same time, it illuminated the population of older patients who could not access this care without an informal caregiver (IC) due to accessibility, technological literacy, or English proficiency concerns. For patients with heart failure, this rapid transition to digital health further exacerbated the demand on ICs and pushed Canadians towards a dyadic care model in the management of chronic diseases, where patients and ICs work to manage care together. Our previous work identified an opportunity to improve this dyadic HF experience through a shared model of dyadic digital health. We call this alternative model of care “Caretown,” which empowers ICs to concurrently expand the patient’s ability for self-care while acknowledging IC needs to facilitate a greater level of support. Objective: The aim of this viewpoint paper is to present the systematic design and development of the Caretown dyadic management module.While heart failure is the outlined use-case, we report on disease agnostic features. Methods: To build the Caretown model, we 1) leveraged the Knowledge to Action (KTA) framework for its ability to translate knowledge into action, and 2) borrowed the Google Sprint from industry titans to quickly “solve big problems and test new ideas” which has been effective in the medical and digital health space. Specifically, we blended these two concepts into a new framework called the “KTA Sprint”. Results: Six core disease-agnostic features were identified to support ICs in care dyads to provide more effective care and to capitalize on the synergistic benefits of dyadic care. These six features were designed to be customizable to suit the patient’s condition, were informed by stakeholder and task analysis, corroborated with literature, and vetted through user needs assessment interviews. These features include (1) Live Reports to enhance data sharing and facilitate appropriate IC support; (2) Care Cards to enhance guidance on the caregiving role; (3) Direct Messaging to dissolve the disconnect across the circle of care; (4) Medication Wallet to improve guidance on managing complex medication regimens; (5) Medical Events Timeline to improve and consolidate management and organization; and (6) Caregiver Resources to provide disease-specific education and support their self-care. Conclusions: These disease-agnostic features were designed to address informal caregiver needs in supporting their care partner. We anticipate the implementation of these features will empower a shared model of care for chronic disease management through digital health, and will improve outcomes for care dyads.

  • Online social networks of individuals with adverse childhood experiences

    Date Submitted: Dec 22, 2022
    Open Peer Review Period: Dec 19, 2022 - Feb 13, 2023

    Background: Adverse childhood experiences (ACEs), which include abuse and neglect and various household challenges like exposure to intimate partner violence and substance use in the home can have negative impacts on lifelong health of affected individuals. Among various strategies for mitigating the adverse effects of ACEs is to enhance connectedness and social support for those who have experienced ACEs. However, how social networks of those who experienced ACEs differ from those who did not is poorly understood. Objective: In the present study, we use Reddit and Twitter data to investigate and compare social networks among individuals with and without ACEs exposure. Methods: We first use a neural network classifier to identify the presence or absence of public ACEs disclosures in social media posts. We then analyze egocentric social networks comparing individuals with self-reported ACEs to those with no reported history. Results: We found that, although individuals reporting ACEs had fewer total followers in online social networks, they had higher reciprocity in following behavior (i.e., mutual following with other users), a higher tendency to follow and be followed by other individuals with ACEs, and a higher tendency to follow back individuals with ACEs rather than individuals without ACEs. Conclusions: These results imply that individuals with ACEs may try to actively connect to others having similar prior traumatic experiences as a positive connection and coping strategy. Supportive interpersonal connections online for individuals with ACEs appear to be a prevalent behavior and may be a way to enhance social connectedness and resilience in those who have experienced ACEs.

  • Background: Digital mental health interventions that incorporate adaptive elements have the potential to further our understanding of optimal intensity of therapist-assistance and inform stepped care models. Further evaluation is needed to assess various support intensities of digital mental health intervention programs that adapt to the evolving needs of consumers. Objective: The primary objective was to investigate and comparatively evaluate the efficacy of a transdiagnostic biopsychosocial digital mental health intervention program called Life Flex with or without therapist-assistance for adults with sub-threshold symptoms or a diagnosis of anxiety and/or depression. Methods: The study is a randomised adaptive clinical trial design. At step 1, all participants had access to the Life Flex program with eligibility to have their program augmented with therapist-assistance determined by program engagement and/or symptom severity. At step 2, participants who met stepped-care criteria were randomised to have their treatment program augmented with either low-intensity therapist-assistance (10 minutes per week of video-chat support for 7 weeks) or high-intensity therapist-assistance (50 minutes per week of video-chat support for 7 weeks). Participants (N = 103, Age M = 34.17, SD = 10.50) were assessed at pre-intervention (Week 0), during intervention (Week 3, Week 6), post-intervention (Week 9), and at 3-month follow-up (Week 21). Participants also completed diagnostic assessments at pre-intervention, post-intervention and 3-month follow-up. The primary outcomes of anxiety (GAD-7) and depression (PHQ-9) were tested in three conditions: Life Flex program only, Life Flex program + low-intensity therapist-assistance, and Life Flex program + high-intensity therapist-assistance. Results: There were no significant differences in the outcome measures between intervention conditions. However, there were significant time effect changes in most outcomes over timepoints. All three intervention conditions demonstrated strong and significant treatment effect change on the two primary outcomes (GAD-7 and PHQ-9) and one secondary outcome (mental health rating), with absolute values of Cohen’s d ranging from 0.82 to 1.79. Results showed that non-responders at week 3 who were stepped-up to therapist-assistance were able to increase program engagement and treatment response in step 2. At post-intervention, 67.7% of participants no longer met diagnostic criteria for anxiety and/or depression and at 3-month follow-up, 69.4% no longer met any diagnostic criteria. Conclusions: The findings highlight that early detection of engagement and non-treatment response presents an opportunity to increase consumer engagement within digital mental health intervention studies incorporating an adaptive design. While study findings indicate that therapist-assistance was no more effective than digital mental health intervention program only for reducing symptoms of anxiety and/or depression, the data highlight the potential influence of participant selection bias and participant preferences within stepped-care treatment models. The study provides evidence of the efficacy of a transdiagnostic biopsychosocial digital mental health intervention program however further research is required. Clinical Trial: ACTRN12620000422921

  • Background: Digital contact tracing algorithms (DCTAs)have emerged as ubiquitous phenomenon throughout the pandemic of SARS-CoV 2. The ethical impact of DCTAs has been discussed especially with reference to users’ autonomy privacy and as a potential threat to informational self-determination. An important part of ethical evaluations of DCTAs is to develop understanding of their information flow and their contextual situatedness to be able to ethically evaluate questions of privacy. Objective: This paper presents an exemplary empirical ethics analysis of contextual factors of two different DCTAs Secondly, implications for the ethical question of privacy are discussed. Methods: We conducted a comparative case study of the algorithm of the Google Apple Exposure notification framework (GAEFN) as exemplified in the German Corona Warn App (CWA) and the Japanese CIRCLE algorithm with the guiding questions as to How a social encounter is represented within the algorithms of DCTAs, and 2) how different representations from different cultural backgrounds relate to prevalent structures and concepts of their context of creation. Results: Our analysis shows that both algorithms use the idea of representing a social encounter of two subjects. These subjects are gain significance in terms of risk against the background of their temporal and spatial properties. However, the comparative analysis reveals at least two major differences. GAEFN clearly prioritizes temporality over spatiality. The representation of spatiality on the other hand is reduced to mere distance without any direction or orientation. The CIRCLE-framework on the other hand, prioritizes spatiality over temporality. To our understanding, these different concepts and prioritizations can be seen to align with important cultural differences in considering basic concepts like subject, time and space in eastern and western thought. Conclusions: The differences noted in this work essentially lead to two different normative questions of privacy that are raised against the respective backgrounds. Ethical evaluation has to be aware of these differences to avoid culturally insensitive approaches.

  • Background: Co-creation is increasingly seen as a way to democratize research and bridge the implementation gap between research and practice. Despite promises of increased effectiveness, relevance, and uptake of health interventions, progress is hindered by the lack of comprehensive and consolidated knowledge about co-creation. Assembling this knowledge into a database is a crucial step toward making co-creation a more robust, trustworthy, and evidence-based methodology. However, there are two considerable challenges to achieving this. First, there is a lack of clarity and standardization of the terminology about co-creation. Second, the rapid increase in scientific publishing means that obtaining comprehensive knowledge requires dealing with a vast body of literature, which is beyond human capacity. Objective: This study aimed to develop a curated database consolidating literature published about co-creation in diverse fields. The objectives were to pull together relevant literature for stakeholders interested in co-creation and its use, and to better understand the co-creation landscape and the potential causes of fragmentation. Methods: To comprehensively include relevant literature, this study developed a novel methodology by combining attributes of systematic review methodology (eg, Preferred Reporting Items for Systematic Reviews and Meta-Analyses) with artificial intelligence technology. We set a broad definition of co-creation that captured the essence of existing definitions, and was inclusive of fields beyond public health, while still accommodating the variation in terminology. We then relied on artificial intelligence to effectively filter out irrelevant information. We also implemented a bibliometric analysis and a quality control procedure to assess the content and accuracy of the database. Results: The final version of the database includes 13,501 papers, which are indexed in Zenodo and accessible in an open-access downloadable format. The quality assessment showed that 20.35% (140/688) of the database likely contains irrelevant material, and that it captured 90.62% (58/64) of the relevant literature. Participatory, and forms of the term co-creation, occurred most frequently in the title and abstracts of included literature. Furthermore, the analysis of authorship, citations, and the source landscape, indicates that there is little collaboration within and between fields using co-creation. Conclusions: This study produced a high-quality curated open-access database consolidating the literature about co-creation. In doing so, the study demonstrates that it is possible to consolidate knowledge about diffuse concepts using a combination of human and artificial intelligence. Through the bibliometric analysis, this study also visualizes the current co-creation landscape and the potential causes of fragmentation. The database lifts the main barrier that most researchers and practitioners will face in seeking evidence about co-creation, namely, the fragmentation of knowledge and the ensuing dilemma of having to deal with a vast amount of information. This database makes it possible to perform rapid literature reviews about co-creation.

  • A cost-effectiveness analysis of a web-based sexual health intervention to prevent sexually transmitted infections in China

    Date Submitted: Dec 14, 2022
    Open Peer Review Period: Dec 14, 2022 - Feb 8, 2023

    Background: The prevention of sexually transmitted infections (STIs) in China particularly among young adults, and Chlamydia trachomatis (CT) infections are the most common STIs in young women. One of the most effective ways to prevent STIs is the consistent use of condoms during sexual intercourse. There has been no economic evaluation for an interactive web-based sexual health program, Smart Girlfriend, in China. Objective: To evaluate the cost-effectiveness of the Smart Girlfriend (a web-based sexual health program) to prevent sexually transmitted infections (STIs) compared with the control (a one-page information sheet about condom use) in China. Methods: A decision-analytical model that included a decision tree followed by a Markov structure of Chlamydia trachomatis (CT) infections was developed since CT was the most prevalent of STIs in young women. The model represents the lifetime experience following receiving the intervention and the control. The one-way and probabilistic sensitivity analyses were conducted. The main outcomes were the number of CT infections, and the incremental cost per quality-adjusted life-year (QALY). Results: In the base-case analysis, the introduction of the Smart Girlfriend would avert 0.45% of CT infections, 0.3% of Pelvic Inflammatory Disease, and 0.04% Chronic Pelvic Pain, resulting in a gain of 70 discounted QALYs and cost savings of 4342 USD over a 4-year time horizon, compared with the control for a cohort of 10,000 sexually active nonpregnant young women. With more than 4,548 users, the intervention would be cost-effective, and with more than 8,315 users, the intervention would be cost-saving. A 99% probability of being cost-effective was detected with a willingness to pay 17,409 USD per QALY. Conclusions: The Smart Girlfriend is cost-effective and possibly cost-saving. This result was particularly sensitive to the number of websites users and launching the website would be cost-effective if more than 4,548 people used it. Further work is warranted to explore if the findings could be expanded in women who have sex with women and other STIs.

  • Background: Diabetes is the most expensive chronic condition in the United States. Several barriers of self-monitoring of blood glucose or continuous glucose monitoring have been reported. Remote patient monitoring with appropriate support has potential to give the solutions. Objective: To characterize Medicaid diabetic patient adherence to daily remote patient monitoring and investigate blood glucose changes associate with the monitoring service. Methods: This study targeted Texas Medicaid patients with diabetes. 180 days of blood glucose data from a remote patient monitoring company were analyzed to assess transmission rates and blood glucose changes. The first 30 days of data were excluded due to startup effects. Patients were separated into adherent and non-adherent cohorts, where adherent patients transmitted data on at least 80% of days. z- and t-tests were performed to compare transmission rates and blood glucose changes between two cohorts. Results: Mean patient age was 70.5 (SD 11.8), with 66.8% female, 91.9% urban, and 89% from south Texas (n=382). The adherent cohort (n=186, 48.7%) had a mean transmission rate of 82.8% before the adherence call and 91.1% after. The non-adherent cohort (n=196, 51.3%) had a mean transmission rate of 45.9% before and 60.2% after. The mean blood glucose levels of the adherent cohort decreased by an average of 9 mg/dL (P=.002) over 5 months. Conclusions: About half of the Medicaid patients were adherent to remote patient monitoring and they saw significant improvement in blood glucose values. The adherence call intervention was important in helping patients consistently follow daily monitoring protocols.

  • Background: Dementia is a progressive disease that gradually worsens over a long period of time, increases the caregiver burden of the family, and decreases the quality of life of dementia patients and their families. Objective: This study identified multiple risk factors for depression using machine learning by using main caregivers for dementia patients in a home care setting who participated in a national epidemiologic survey in South Korea. Methods: Subjects were 25,634 main caregivers (without depression = 21,369, and with depression = 4,265), who took care of dementia patients at home based on the 2019-2020 community health survey. This study developed a model for predicting the depression of dementia caregivers by using Bayesian nomogram and six variables with the highest feature importance identified in LightGBM to understand the relationship between predictive factors regarding the depression of caregivers in South Korea. Results: The results of this study showed that subjective stress level, subjective health status, cognitive impairment counseling in the past year, economic activity, gender, and dementia screening in the past year were key risk factors for predicting the depression of dementia caregivers. The results of 10-fold cross validation showed that the AUC, general accuracy, precision, recall, and F1-score of the developed nomogram were 0.82, 0.85, 0.83, 0.85, and 0.83, respectively. Conclusions: It is necessary to screen groups vulnerable to depression and develop customized emotional support services for them to prevent the depression of family main caregivers who are caring for older adults with dementia at home.

  • Background: Considering the impact that the pandemic had on healthcare-related communication systems, patients and citizens have been exposed to a plethora of information from different sources and different levels of accuracy and reliability. Proper communication during the emergency period has represented an ethical and public health issue. The Province of Trento (PAT), the local healthcare trust (APSS), the Bruno Kessler Foundation (FBK) with the support of TrentinoSalute4.0 jointly designed and implemented a comprehensive, technology-enabled communication strategy to properly inform citizens on the status and effects of the Covid-19 pandemic, basic health measures against infections and up-to-date regulations, all within the same platform, which could be reached either from the dedicated webpage or using the TreCovid19 mobile application. In addition, information was also available on social media and official websites of the public institutions involved. Objective: The aim of this communication initiative was to ensure proper and ethically-sound information to the citizens of the Province of Trento, by providing a set of easy-to-use tools supported by technologies to share information and respond to citizens’ questions focusing on Covid-19 pandemic and related impact in terms of epidemiological, societal, legal aspects Methods: We adopted a multi-level strategy with three different – albeit harmonized – communication tools: i) dedicated mobile application designed to provide useful information and recent updates about the epidemic numbers, video tutorials delivered by the Healthcare trust and presenting tips and advice on specific safety procedures, information on the regional and national decrees and regulations linked with the Covid-19 pandemic; ii) social media profiles (Twitter, Facebook, Instagram, YouTube, LinkedIn...); iii) advanced AI-based chatbot embedded in the healthcare institutions website to inform citizens and timely address their questions related to the COVID pandemic. Results: The coordinated work between the main stakeholders managed to release the platform TreCovid19 at the onset of the pandemic, on March 16, 2020. Content on the platform was synchronized with other institutional media that were kept up to date and used to answer citizens' main doubts and concerns about the emergency management and local and national regulations. All information provided was validated by the local healthcare and political authorities. In 2020-2022 the number of TreCovid19 app users was significantly associated with the number of positive cases, but significantly more correlated with rates of deaths and hospitalization (all P values <.001), suggesting it was mainly used during the emergency phase of the pandemic. Conclusions: To our knowledge, the initiative described in this paper represents the first experience in Italy of a multi-layer, public-driven strategy to ensure proper information on the status and effects of the Covid-19 pandemic. This initiative was possible thanks to the joint efforts of public health institutions in empowering healthcare staff and citizens and in developing and deploying technology-enabled services. Clinical Trial: NA

  • Rural Counties of the United States are Underrepresented in Measures of Consumer Broadband

    Date Submitted: Dec 12, 2022
    Open Peer Review Period: Dec 8, 2022 - Feb 2, 2023

    Background: Patients in rural and underserved communities lack access to specialty care, preventive care, and even emergency care. Telehealth has the potential to mitigate this lack of access; however, many telehealth services are only viable where broadband internet is available. Existing data sets measuring broadband access may underrepresent the state of broadband in rural and underserved communities Objective: We examined three consumer broadband data sets from two organizations to see if the number of user-generated internet speed tests per 1,000 residents varied across county-level rurality. Methods: We analyzed data at the county level from Measurement Labs and Ookla for Good (fixed and mobile) across calendar years 2020 and 2021. We used the number of tests conducted per 1,000 residents within United States counties as the outcome variable, and Rural-Urban Continuum Codes (RUCC) as the main independent variable of interest. Included covariates were year/quarter, percent non-Hispanic White, percent non-Hispanic Black or African-American, percent Hispanic, average household size, percent of adults with a high school diploma, unemployment rate, percent persons in poverty, percent population over 65 years of age, percent female, percent non-English-speaking household, and percent of households that have a computer and broadband connection. Results: Using OLS models with robust standard errors, we found the number of speed tests per 1,000 residents was smaller in counties with fewer than 20,000 residents relative to counties with over 1 million residents for all three data sources. However, patterns of association with other covariates emerged as significant in some models and not others, suggesting key differences among users generating speed tests in all three data sources. Conclusions: Our findings demonstrate a consistent underrepresentation of residents from very rural counties in three large, publicly available data sets of user-generated internet speed tests. Additional data collection is needed to inform broadband infrastructure investment to identify those communities most left behind by broadband expansion efforts.

  • Assessing Interventions on Crowdsourcing Platforms for Improving Patient Behaviors in Primary Care Settings

    Date Submitted: Jul 26, 2022
    Open Peer Review Period: Dec 8, 2022 - Feb 8, 2023

    Background: The principles of behavioral economics (BE) suggest that there are many potential ways to develop meaningful health care partnerships with patients. Crowdsourced experimental surveys may help efficiently assess the time and cost needed for different options. Objective: The goals of this study were (1) to assess the feasibility of using crowdsourced surveys to evaluate BE interventions for patient partnerships, and (2) to assess the impact of two BE-based intervention designs, psychological rewards and loss of framing, on simulated medication reconciliation behaviors in a simulated primary care setting. Methods: We conducted between-subject survey experiments on a crowdsourcing platform (Amazon Mechanical Turk) to assess the design of behavioral interventions. Interventions were aimed at improving a targeted behavior that pertains to bringing medicines to primary care office visits. The baseline and three simulated interventions were compared in simulated primary care office visit scenarios. Interventions were monetary compensation, status effect as a psychological reward, and loss frame as a modification of the status effect. Willingness to bring medicines was measured on a 5-point Likert scale. A reverse coding question was included to assess response intentionality. Results: A total of 569 study participants were recruited. There were 132 in the baseline group, 187 in the monetary compensation group, 149 in the psychological reward group, and 101 in the loss framing group. All three interventions increased participants’ willingness to bring medicines significantly when compared to the baseline scenario. The monetary compensation intervention caused an increase of 13.06% (P<.001), psychological rewards increased willingness by 6.53% (P=.025), and a loss frame on the psychological rewards increased willingness by 16.80% (P<.001). Responses to the reverse coding question were consistent with the willingness questions. Conclusions: In primary care, bringing medications to office visits is a frequently advocated patient partnership behavior that is nonetheless not widely adopted. Crowdsourcing platforms such as MTurk support efforts to efficiently and rapidly reach large groups of individuals to assess the efficacy of behavioral interventions. We found that crowdsourced survey-based experiments with simulated monetary compensation resulted in valid simulated behavioral responses. Simulated psychological status design, especially with a loss framing design, had a significant impact on the targeted behavior. It should thus be considered an effective behavioral intervention design to enhance patient engagement in primary care. These results support the use of crowdsourcing platforms to augment and complement traditional approaches to learning about behavioral economics for patient engagement.