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Journal Description

The Journal of Medical Internet Research (JMIR), now in its 21st year, is the pioneer open access eHealth journal and is the flagship journal of JMIR Publications. It is the leading digital health journal globally in terms of quality/visibility (Impact Factor 2019: 5.03), ranking Q1 in the medical informatics category, and is also the largest journal in the field. The journal focuses on emerging technologies, medical devices, apps, engineering, telehealth and informatics applications for patient education, prevention, population health and clinical care. As a leading high-impact journal in its disciplines (health informatics and health services research), it is selective, but it is now complemented by almost 30 specialty JMIR sister journals, which have a broader scope, and which together receive over 6.000 submissions a year. Peer-review reports are portable across JMIR journals and papers can be transferred, so authors save time by not having to resubmit a paper to different journal but can simply transfer it between journals. 

As an open access journal, we are read by clinicians, allied health professionals, informal caregivers, and patients alike, and have (as with all JMIR journals) a focus on readable and applied science reporting the design and evaluation of health innovations and emerging technologies. We publish original research, viewpoints, and reviews (both literature reviews and medical device/technology/app reviews).

We are also a leader in participatory and open science approaches, and offer the option to publish new submissions immediately as preprints, which receive DOIs for immediate citation (eg, in grant proposals), and for open peer-review purposes. We also invite patients to participate (eg, as peer-reviewers) and have patient representatives on editorial boards.

Be a widely cited leader in the digitial health revolution and submit your paper today!

 

Recent Articles:

  • Untitled. Source: Image created by the authors; Copyright: The Authors; License: Licensed by the authors.

    Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and...

    Abstract:

    Background: Many factors involved in the onset and clinical course of the ongoing COVID-19 pandemic are still unknown. Although big data analytics and artificial intelligence are widely used in the realms of health and medicine, researchers are only beginning to use these tools to explore the clinical characteristics and predictive factors of patients with COVID-19. Objective: Our primary objectives are to describe the clinical characteristics and determine the factors that predict intensive care unit (ICU) admission of patients with COVID-19. Determining these factors using a well-defined population can increase our understanding of the real-world epidemiology of the disease. Methods: We used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modeling) to analyze the electronic health records (EHRs) of patients with COVID-19. We explored the unstructured free text in the EHRs within the Servicio de Salud de Castilla-La Mancha (SESCAM) Health Care Network (Castilla-La Mancha, Spain) from the entire population with available EHRs (1,364,924 patients) from January 1 to March 29, 2020. We extracted related clinical information regarding diagnosis, progression, and outcome for all COVID-19 cases. Results: A total of 10,504 patients with a clinical or polymerase chain reaction–confirmed diagnosis of COVID-19 were identified; 5519 (52.5%) were male, with a mean age of 58.2 years (SD 19.7). Upon admission, the most common symptoms were cough, fever, and dyspnea; however, all three symptoms occurred in fewer than half of the cases. Overall, 6.1% (83/1353) of hospitalized patients required ICU admission. Using a machine-learning, data-driven algorithm, we identified that a combination of age, fever, and tachypnea was the most parsimonious predictor of ICU admission; patients younger than 56 years, without tachypnea, and temperature <39 degrees Celsius (or >39 ºC without respiratory crackles) were not admitted to the ICU. In contrast, patients with COVID-19 aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnea and delayed their visit to the emergency department after being seen in primary care. Conclusions: Our results show that a combination of easily obtainable clinical variables (age, fever, and tachypnea with or without respiratory crackles) predicts whether patients with COVID-19 will require ICU admission.

  • Source: Burst; Copyright: Matthew Henry; URL: https://burst.shopify.com/photos/laptop-from-side?q=laptop; License: Licensed by JMIR.

    Disaster eHealth: Scoping Review

    Abstract:

    Background: Although both disaster management and disaster medicine have been used for decades, their efficiency and effectiveness have been far from perfect. One reason could be the lack of systematic utilization of modern technologies, such as eHealth, in their operations. To address this issue, researchers’ efforts have led to the emergence of the disaster eHealth (DEH) field. DEH’s main objective is to systematically integrate eHealth technologies for health care purposes within the disaster management cycle (DMC). Objective: This study aims to identify, map, and define the scope of DEH as a new area of research at the intersection of disaster management, emergency medicine, and eHealth. Methods: An extensive scoping review using published materials was carried out in the areas of disaster management, disaster medicine, and eHealth to identify the scope of DEH. This review procedure was iterative and conducted in multiple scientific databases in 2 rounds, one using controlled indexed terms and the other using similar uncontrolled terms. In both rounds, the publications ranged from 1990 to 2016, and all the appropriate research studies discovered were considered, regardless of their research design, methodology, and quality. Information extracted from both rounds was thematically analyzed to define the DEH scope, and the results were evaluated by the field experts through a Delphi method. Results: In both rounds of the research, searching for eHealth applications within DMC yielded 404 relevant studies that showed eHealth applications in different disaster types and disaster phases. These applications varied with respect to the eHealth technology types, functions, services, and stakeholders. The results led to the identification of the scope of DEH, including eHealth technologies and their applications, services, and future developments that are applicable to disasters as well as to related stakeholders. Reference to the elements of the DEH scope indicates what, when, and how current eHealth technologies can be used in the DMC. Conclusions: Comprehensive data gathering from multiple databases offered a grounded method to define the DEH scope. This scope comprises concepts related to DEH and the boundaries that define it. The scope identifies the eHealth technologies relevant to DEH and the functions and services that can be provided by these technologies. In addition, the scope tells us which groups can use the provided services and functions and in which disaster types or phases. DEH approaches could potentially improve the response to health care demands before, during, and after disasters. DEH takes advantage of eHealth technologies to facilitate DMC tasks and activities, enhance their efficiency and effectiveness, and enhance health care delivery and provide more quality health care services to the wider population regardless of their geographical location or even disaster types and phases.

  • TOC image. Source: Flickr; Copyright: Mike MacKenzie; URL: https://www.flickr.com/photos/152824664@N07/30212411048; License: Creative Commons Attribution (CC-BY).

    Developing a Process for the Analysis of User Journeys and the Prediction of Dropout in Digital Health Interventions: Machine Learning Approach

    Abstract:

    Background: User dropout is a widespread concern in the delivery and evaluation of digital (ie, web and mobile apps) health interventions. Researchers have yet to fully realize the potential of the large amount of data generated by these technology-based programs. Of particular interest is the ability to predict who will drop out of an intervention. This may be possible through the analysis of user journey data—self-reported as well as system-generated data—produced by the path (or journey) an individual takes to navigate through a digital health intervention. Objective: The purpose of this study is to provide a step-by-step process for the analysis of user journey data and eventually to predict dropout in the context of digital health interventions. The process is applied to data from an internet-based intervention for insomnia as a way to illustrate its use. The completion of the program is contingent upon completing 7 sequential cores, which include an initial tutorial core. Dropout is defined as not completing the seventh core. Methods: Steps of user journey analysis, including data transformation, feature engineering, and statistical model analysis and evaluation, are presented. Dropouts were predicted based on data from 151 participants from a fully automated web-based program (Sleep Healthy Using the Internet) that delivers cognitive behavioral therapy for insomnia. Logistic regression with L1 and L2 regularization, support vector machines, and boosted decision trees were used and evaluated based on their predictive performance. Relevant features from the data are reported that predict user dropout. Results: Accuracy of predicting dropout (area under the curve [AUC] values) varied depending on the program core and the machine learning technique. After model evaluation, boosted decision trees achieved AUC values ranging between 0.6 and 0.9. Additional handcrafted features, including time to complete certain steps of the intervention, time to get out of bed, and days since the last interaction with the system, contributed to the prediction performance. Conclusions: The results support the feasibility and potential of analyzing user journey data to predict dropout. Theory-driven handcrafted features increased the prediction performance. The ability to predict dropout at an individual level could be used to enhance decision making for researchers and clinicians as well as inform dynamic intervention regimens.

  • Twitter hashtag #DoctorsAreDickheads: patient and doctor image. Source: Unsplash/ Pexels; Copyright: Anthony Tran/ Karolina Grabowska; URL: http://www.jmir.org/2020/10/e17595/; License: Licensed by JMIR.

    Recommendations From the Twitter Hashtag #DoctorsAreDickheads: Qualitative Analysis

    Abstract:

    Background: The social media site Twitter has 145 million daily active users worldwide and has become a popular forum for users to communicate their health care concerns and experiences as patients. In the fall of 2018, a hashtag titled #DoctorsAreDickheads emerged, with almost 40,000 posts calling attention to health care experiences. Objective: This study aims to identify common health care conditions and conceptual themes represented within the phenomenon of this viral Twitter hashtag. Methods: We analyzed a random sample of 5.67% (500/8818) available tweets for qualitative analysis between October 15 and December 31, 2018, when the hashtag was the most active. Team coders reviewed the same 20.0% (100/500) tweets and the remainder individually. We abstracted the user’s health care role and clinical conditions from the tweet and user profile, and used phenomenological content analysis to identify prevalent conceptual themes through sequential open coding, memoing, and discussion of concepts until an agreement was reached. Results: Our final sample comprised 491 tweets and unique Twitter users. Of this sample, 50.5% (248/491) were from patients or patient advocates, 9.6% (47/491) from health care professionals, 4.3% (21/491) from caregivers, 3.7% (18/491) from academics or researchers, 1.0% (5/491) from journalists or media, and 31.6% (155/491) from non–health care individuals or other. The most commonly mentioned clinical conditions were chronic pain, mental health, and musculoskeletal conditions (mainly Ehlers-Danlos syndrome). We identified 3 major themes: disbelief in patients’ experience and knowledge that contributes to medical errors and harm, the power inequity between patients and providers, and metacommentary on the meaning and impact of the #DoctorsAreDickheads hashtag. Conclusions: People publicly disclose personal and often troubling health care experiences on Twitter. This adds new accountability for the patient-provider interaction, highlights how harmful communication affects diagnostic safety, and shapes the public’s viewpoint of how clinicians behave. Hashtags such as this offer valuable opportunities to learn from patient experiences. Recommendations include developing best practices for providers to improve communication, supporting patients through challenging diagnoses, and promoting patient engagement.

  • Source: Pexels; Copyright: bongkarn thanyakij; URL: https://www.pexels.com/photo/crop-doctor-working-on-laptop-in-doctors-office-3758741/; License: Licensed by JMIR.

    Elaborating Models of eHealth Governance: Qualitative Systematic Review

    Abstract:

    Background: Large-scale national eHealth policy programs have gained attention not only for benefits but also for several unintended consequences and failed expectations. Given the complex and mixed accounts of the results, questions have been raised on how large-scale digitalization programs are governed to reach health policy goals of quality improvement and equal access along with necessary digital transformations. In this qualitative systematic review, we investigate the following question: How is governance implemented and considered in the studies included in the qualitative review? Objective: The aim of this study is to arrive at informed and recognizable conceptualizations and considerations of models of governance connected to eHealth, as presented and discussed in the scientific literature. In turn, we hope our results will help inform the discussion of how to govern such processes to obtain collectively negotiated objectives. Methods: A qualitative systematic review is a method for integrating or comparing with the findings from qualitative studies. It looks for “themes” or “constructs” that lie in or across individual qualitative studies. This type of review produces a narrative synthesis with thematic analysis and includes interpretive conceptual models. The goal is an interpretation and broadens the understanding of a particular phenomenon. We searched the PubMed database using predefined search terms and selected papers published in 2010. We specified the criteria for selection and quality assessment. Results: The search returned 220 papers. We selected 44 abstracts for full-text reading, and 11 papers were included for full-text synthesis. On the basis of the 11 papers, we constructed four governance models to categorize and conceptualize the findings. The models are political governance, normally depicting top-down processes; medical governance, which normally depicts bottom-up processes; the internet and global model, emphasizing international business strategies coupled with the internet; self-governance, which builds upon the development of the internet and Internet of Things, which has paved the way for personal governance and communication of one’s own health data. Conclusions: Collective negotiations between the nation-state and global policy actors, medical and self-governance actors, and global business and industry actors are essential. Technological affordances represent both positive and negative opportunities concerning the realization of health policy goals, and future studies should scrutinize this dynamic.

  • Mobile technologies. Source: istockphoto.com; Copyright: AndreyPopov; URL: https://www.istockphoto.com/photo/man-with-smartwatch-and-cellphone-showing-heartbeat-rate-gm1062905516-284161295; License: Licensed by the authors.

    Selecting Mobile Health Technologies for Electronic Health Record Integration: Case Study

    Abstract:

    Mobile health (mHealth) technologies, such as wearable devices and sensors that can be placed in the home, allow for the capture of physiologic, behavioral, and environmental data from patients between clinic visits. The inclusion of these data in the medical record may benefit patients and providers. Most health systems now have electronic health records (EHRs), and the ability to pull and send data to and from mobile devices via smartphones and other methods is increasing; however, many challenges exist in the evaluation and selection of devices to integrate to meet the needs of diverse patients with a range of clinical needs. We present a case report that describes a method that our health system uses, guided by a telehealth model to evaluate the selection of devices for EHR integration.

  • Source: Pexels; Copyright: Negative Space; URL: https://www.pexels.com/photo/computer-desk-laptop-stethoscope-48604/; License: Licensed by JMIR.

    Adoption of a Personal Health Record in the Digital Age: Cross-Sectional Study

    Abstract:

    Background: As health care organizations strive to improve health care access, quality, and costs, they have implemented patient-facing eHealth technologies such as personal health records to better engage patients in the management of their health. In the Kingdom of Saudi Arabia, eHealth is also growing in accordance with Vision 2030 and its National Transformation Program framework, creating a roadmap for increased quality and efficiency of the health care system and supporting the goal of patient-centered care. Objective: The aim of this study was to investigate the adoption of the personal health record of the Ministry of National Guard Health Affairs (MNGHA Care). Methods: A cross-sectional survey was conducted in adults visiting outpatient clinics in hospitals at the Ministry of National Guard Health Affairs hospitals in Riyadh, Jeddah, Dammam, Madinah, and Al Ahsa, and primary health care clinics in Riyadh and Qassim. The main outcome measure was self-reported use of MNGHA Care. Results: In the sample of 546 adult patients, 383 (70.1%) reported being users of MNGHA Care. MNGHA Care users were more likely to be younger (P<.001), high school or university educated (P<.001), employed (P<.001), have a chronic condition (P=.046), use the internet to search for health-related information (P<.001), and use health apps on their mobile phones (P<.001). Conclusions: The results of this study show that there is substantial interest for the use of MNGHA Care personal health record with 70% of participants self-reporting use. To confirm these findings, objective data from the portal usage logs are needed. Maximizing the potential of MNGHA Care supports patient engagement and is aligned with the national eHealth initiative to encourage the use of technology for high-quality, accessible patient-centered care. Future research should include health care provider perspectives, incorporate objective data, employ a mixed-methods approach, and use a theoretical framework.

  • Source: freepik; Copyright: freepik; URL: https://www.freepik.com/free-photo/doctor-using-his-phone_5480778.htm#page=1&query=doctor%20using%20phone&position=38; License: Licensed by JMIR.

    Reducing Alert Fatigue by Sharing Low-Level Alerts With Patients and Enhancing Collaborative Decision Making Using Blockchain Technology: Scoping Review and...

    Abstract:

    Background: Clinical decision support (CDS) is a tool that helps clinicians in decision making by generating clinical alerts to supplement their previous knowledge and experience. However, CDS generates a high volume of irrelevant alerts, resulting in alert fatigue among clinicians. Alert fatigue is the mental state of alerts consuming too much time and mental energy, which often results in relevant alerts being overridden unjustifiably, along with clinically irrelevant ones. Consequently, clinicians become less responsive to important alerts, which opens the door to medication errors. Objective: This study aims to explore how a blockchain-based solution can reduce alert fatigue through collaborative alert sharing in the health sector, thus improving overall health care quality for both patients and clinicians. Methods: We have designed a 4-step approach to answer this research question. First, we identified five potential challenges based on the published literature through a scoping review. Second, a framework is designed to reduce alert fatigue by addressing the identified challenges with different digital components. Third, an evaluation is made by comparing MedAlert with other proposed solutions. Finally, the limitations and future work are also discussed. Results: Of the 341 academic papers collected, 8 were selected and analyzed. MedAlert securely distributes low-level (nonlife-threatening) clinical alerts to patients, enabling a collaborative clinical decision. Among the solutions in our framework, Hyperledger (private permissioned blockchain) and BankID (federated digital identity management) have been selected to overcome challenges such as data integrity, user identity, and privacy issues. Conclusions: MedAlert can reduce alert fatigue by attracting the attention of patients and clinicians, instead of solely reducing the total number of alerts. MedAlert offers other advantages, such as ensuring a higher degree of patient privacy and faster transaction times compared with other frameworks. This framework may not be suitable for elderly patients who are not technology savvy or in-patients. Future work in validating this framework based on real health care scenarios is needed to provide the performance evaluations of MedAlert and thus gain support for the better development of this idea. Trial Registration:

  • Source: Pexels; Copyright: Anna Shvets; URL: https://www.pexels.com/photo/woman-with-hands-on-her-face-in-front-of-a-laptop-4226215/; License: Licensed by JMIR.

    Acceptability and Effectiveness of NHS-Recommended e-Therapies for Depression, Anxiety, and Stress: Meta-Analysis

    Abstract:

    Background: There is a disconnect between the ability to swiftly develop e-therapies for the treatment of depression, anxiety, and stress, and the scrupulous evaluation of their clinical utility. This creates a risk that the e-therapies routinely provided within publicly funded psychological health care have evaded appropriate rigorous evaluation in their development. Objective: This study aims to conduct a meta-analytic review of the gold standard evidence of the acceptability and clinical effectiveness of e-therapies recommended for use in the National Health Service (NHS) in the United Kingdom. Methods: Systematic searches identified appropriate randomized controlled trials (RCTs). Depression, anxiety, and stress outcomes at the end of treatment and follow-up were synthesized using a random-effects meta-analysis. The grading of recommendations assessment, development, and evaluation approach was used to assess the quality of each meta-analytic comparison. Moderators of treatment effect were examined using subgroup and meta-regression analysis. Dropout rates for e-therapies (as a proxy for acceptability) were compared against controls. Results: A total of 24 studies evaluating 7 of 48 NHS-recommended e-therapies were qualitatively and quantitatively synthesized. Depression, anxiety, and stress outcomes for e-therapies were superior to controls (depression: standardized mean difference [SMD] 0.38, 95% CI 0.24 to 0.52, N=7075; anxiety and stress: SMD 0.43, 95% CI 0.24 to 0.63, n=4863), and these small effects were maintained at follow-up. Average dropout rates for e-therapies (31%, SD 17.35) were significantly higher than those of controls (17%, SD 13.31). Limited moderators of the treatment effect were found. Conclusions: Many NHS-recommended e-therapies have not been through an RCT-style evaluation. The e-therapies that have been appropriately evaluated generate small but significant, durable, beneficial treatment effects. Trial Registration: International Prospective Register of Systematic Reviews (PROSPERO) registration CRD42019130184; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=130184

  • Source: rawpixel.com; Copyright: rawpixel.com; URL: https://www.rawpixel.com/image/65844/free-photo-image-senior-seniors-asian; License: Licensed by JMIR.

    Toward a Digital Platform for the Self-Management of Noncommunicable Disease: Systematic Review of Platform-Like Interventions

    Abstract:

    Background: Digital interventions are effective for health behavior change, as they enable the self-management of chronic, noncommunicable diseases (NCDs). However, they often fail to facilitate the specific or current needs and preferences of the individual. A proposed alternative is a digital platform that hosts a suite of discrete, already existing digital health interventions. A platform architecture would allow users to explore a range of evidence-based solutions over time to optimize their self-management and health behavior change. Objective: This review aims to identify digital platform-like interventions and examine their potential for supporting self-management of NCDs and health behavior change. Methods: A literature search was conducted in January 2020 using EBSCOhost, PubMed, Scopus, and EMBASE. No digital platforms were identified, so criteria were broadened to include digital platform-like interventions. Eligible platform-like interventions offered a suite of discrete, evidence-based health behavior change features to optimize self-management of NCDs in an adult population and provided digitally supported guidance for the user toward the features best suited to their needs and preferences. Data collected on interventions were guided by the CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) checklist, including evaluation data on effectiveness and process outcomes. The quality of the included literature was assessed using the Mixed Methods Appraisal Tool. Results: A total of 7 studies were included for review. Targeted NCDs included cardiovascular diseases (CVD; n=3), diabetes (n=3), and chronic obstructive pulmonary disease (n=1). The mean adherence (based on the number of follow-up responders) was 69% (SD 20%). Of the 7 studies, 4 with the highest adherence rates (80%) were also guided by behavior change theories and took an iterative, user-centered approach to development, optimizing intervention relevance. All 7 interventions presented algorithm-supported user guidance tools, including electronic decision support, smart features that interact with patterns of use, and behavior change stage-matching tools. Of the 7 studies, 6 assessed changes in behavior. Significant effects in moderate-to-vigorous physical activity were reported, but for no other specific health behaviors. However, positive behavior change was observed in studies that focused on comprehensive behavior change measures, such as self-care and self-management, each of which addresses several key lifestyle risk factors (eg, medication adherence). No significant difference was found for psychosocial outcomes (eg, quality of life). Significant changes in clinical outcomes were predominately related to disease-specific, multifaceted measures such as clinical disease control and cardiovascular risk score. Conclusions: Iterative, user-centered development of digital platform structures could optimize user engagement with self-management support through existing, evidence-based digital interventions. Offering a palette of interventions with an appropriate degree of guidance has the potential to facilitate disease-specific health behavior change and effective self-management among a myriad of users, conditions, or stages of care.

  • Source: The Authors/Placeit; Copyright: The Authors/Placeit; URL: https://www.jmir.org/2020/9/e14783; License: Licensed by JMIR.

    Effects of Interactivity on Recall of Health Information: Experimental Study

    Abstract:

    Background: Information provided in an interactive way is believed to be engaging because users can actively explore the information. Yet empirical findings often contradict this assumption. Consequently, there is still little known about whether and how interactivity affects communication outcomes such as recall. Objective: The aim of this study was to investigate mechanisms through which interactivity affects recall of online health information. We tested whether and how cognitive involvement, perceived active control, and cognitive load mediate the effects of interactivity on recall. In addition, we examined need for cognition and health literacy as potential moderators of the mediation effects. Given the increasing popularity of dietary supplement use, our health website focused on this topic. Methods: In an online between-subjects experiment (n=983), participants were randomly assigned to control condition (no interactive features), moderate interactivity (dropdown menus), and high interactivity (dropdown menus and responsive infographics). Two weeks before the experiment, background characteristics and moderating variables were measured. During website visit, data on users’ online behavior were collected. Recall was measured postexposure. Results: Participants recalled significantly less information in the moderate (mean 3.48 [SD 2.71]) and high (mean 3.52 [SD 2.64]) interactivity conditions compared with the control condition (mean 5.63 [SD 2.18]). In the mediation analysis, we found direct, negative effects of moderate (b=–2.25, 95% CI –2.59 to –1.90) and high (b=–2.16, 95% CI –2.51 to –1.81) levels of interactivity on recall as well. In the relationship between interactivity and recall, cognitive involvement had a partial negative mediation effect (moderate interactivity: b=–.20; 95% CI –0.31 to –0.10; high interactivity: b=–.21, 95% CI –0.33 to –0.10) and perceived active control had a partial positive mediation effect (moderate interactivity: b=.28, 95% CI 0.18 to 0.40; high interactivity: b=.27, 95% CI 0.16 to 0.40). Conclusions: Interactivity decreased recall. In addition, through interactivity participants were less involved with the content of the information, yet they felt they had more control over the information. These effects were stronger in the high need for cognition and high health literate groups compared with their counterparts.

  • Source: freepik; Copyright: jcomp; URL: https://www.freepik.com/free-photo/freelance-work-from-home-teen-ager-is-working-home_7955632.htm#page=1&query=adolescent%20using%20computer&position=43; License: Licensed by JMIR.

    24-Month Outcomes of Primary Care Web-Based Depression Prevention Intervention in Adolescents: Randomized Clinical Trial

    Abstract:

    Background: Adolescent depression carries a high burden of disease worldwide, but access to care for this population is limited. Prevention is one solution to curtail the negative consequences of adolescent depression. Internet interventions to prevent adolescent depression can overcome barriers to access, but few studies examine long-term outcomes. Objective: This study compares CATCH-IT (Competent Adulthood Transition with Cognitive Behavioral Humanistic and Interpersonal Training), an internet-based intervention, to a general health education active control for depression onset at 12 and 24 months in adolescents presenting to primary care settings. Methods: A 2-site randomized trial, blinded to the principal investigators and assessors, was conducted comparing Competent Adulthood Transition with Cognitive Behavioral Humanistic and Interpersonal Training to health education to prevent depressive episodes in 369 adolescents (193 youths were randomly assigned to Competent Adulthood Transition with Cognitive Behavioral Humanistic and Interpersonal Training and 176 to health education) with subthreshold depressive symptoms or prior depressive episodes. Participants were recruited from primary care settings in the United States. The primary outcome was the occurrence of a depressive episode, determined by the Depression Symptom Rating. The secondary outcome was functioning, measured by the Global Assessment Scale. Results: In intention-to-treat analyses, the adjusted hazard ratio favoring Competent Adulthood Transition with Cognitive Behavioral Humanistic and Interpersonal Training for first depressive episode was not statistically significant at 12 months (hazard ratio 0.77, 95% CI 0.42-1.40, P=.39) and 24 months (hazard ratio 0.87, 95% CI 0.52-1.47, P=.61). Competent Adulthood Transition with Cognitive Behavioral Humanistic and Interpersonal Training provided preventive benefit for first depressive episode for those with mild hopelessness or at least moderate paternal monitoring at baseline. Global Assessment Scale scores improved comparably in both groups (intention-to-treat). Conclusions: A technology-based intervention for adolescent depression prevention implemented in primary care did not have additional benefit at 12 or 24 months. Further research is necessary to determine whether internet interventions have long-term benefit. Trial Registration: ClinicalTrials.gov NCT01893749; http://clinicaltrials.gov/ct2/show/NCT01893749.

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  • Telemedicine-guided point-of-care ultrasound can be feasible and effective in a life-threatening situation: The case of a field hospital during the COVID-19 pandemic

    Date Submitted: Oct 6, 2020

    Open Peer Review Period: Oct 27, 2020 - Dec 27, 2020

    Background: Lightweight portable ultrasound is widely available, especially in inaccessible geographical areas. It demonstrates effectiveness and diagnosis improvement even in field conditions but no...

    Background: Lightweight portable ultrasound is widely available, especially in inaccessible geographical areas. It demonstrates effectiveness and diagnosis improvement even in field conditions but no precise information about protocols, acquisition time, image interpretation, and the relevance in changing medical conduct exists. The COVID-19 pandemic implied many severe cases and the rapid construction of field hospitals with massive general practitioner (GP) recruitment. Objective: This prospective and descriptive study aimed to evaluate the feasibility of telemedicine guidance using a standardized multi-organ sonographic assessment protocol in untrained GPs during a COVID-19 emergency in a field hospital. Methods: Eleven COVID-19 in-patients presenting life-threatening complications, attended by local staff who spontaneously requested on-time teleconsultation, were enrolled. All untrained doctors successfully positioned the transducer and obtained key images guided by a remote doctor via telemedicine, with remote interpretation of the findings. Results: Only four (36%) general practitioners obtained the appropriate key heart image on the left parasternal long axis window, and three (27%) had an image interpreted remotely on-time. The evaluation time ranged from seven to 42 minutes, with a mean of 22.7 + 12. Conclusions: Telemedicine is effective in guiding GPs to perform portable ultrasound in life-threatening situations, showing effectiveness in conducting decisions.

  • Use of Structural Topic Modeling to explore impacts of personal characteristics on successful engagement with a technology application; health 360x; Clinical Implementation Study

    Date Submitted: Aug 16, 2020

    Open Peer Review Period: Oct 26, 2020 - Dec 26, 2020

    Background: Although technology promises to solve the challenge of targeting and tailoring interventions to the individual; engagement with technology is low among minority communities. Health coaches...

    Background: Although technology promises to solve the challenge of targeting and tailoring interventions to the individual; engagement with technology is low among minority communities. Health coaches enhance engagement with technology but results vary Objective: We explored the role of coach and participant characteristics and their interactions on successful engagement with technology for self-management skills acquisition in high risk diabetics Methods: This was a clinical implementation study. Longitudinal data and transcripts of participant-coach interactions were taken from a study evaluating the impact of Health 360x and coaching on self-management skills acquisition;as part of care coordination in the Morehouse Choice Accountable Care Organization (MCACO). Topic modeling, a natural language processing method that reliably uncovers conversation topics was used. Structural topic modeling allowed us to include metadata into our analysis. We validated the output by identifying topics based on high characteristic words, manually verifying highest scoring talk turns and labeling topics, logging example conversations. We used mixed effects logistic regression to quantify participant and coach characteristics and interactions Results: We identified 17,000 talk turns; 7196 in the ‘achieved’ group and 9,644 in the ‘not achieved’ group. There were important differences in the content of highest scoring topics depending on whether the coach-participant dyad achieved their goals or did not achieve their goal. The conversations in the coach-participant dyads who achieved their health goals were balanced versus in the ‘not achieved’ where the coaches tended to dominate the conversation. Female participants with female coaches were significantly more likely to achieve their health goals. Goal setting alone had a negative impact on attaining desired outcomes. Conclusions: Among diabetic patients who received the health 360x coach facilitated technology intervention for self management behavior change; i)Goal setting requires additional interventions in order to lead to improved outcomes ii)coach participant dyads who achieved behavioral goals, engaged in balanced conversational exchanges iii) better performance among female-female participant coach dyads may indicate cultural expectations that can be further explored in a society with growing diversity among patients and the healthcare workforce. Our use of topic modeling in this application is novel and it creates an opportunity to introduce this technique into every day patient provider encounters. The opportunity to create outputs that guide further physician action and patient action could drive better patient engagement and overall patient health outcomes. Clinical Trial: Not applicable

  • Usability of Telemedicine in Physical Therapy Rehabilitation

    Date Submitted: Oct 25, 2020

    Open Peer Review Period: Oct 25, 2020 - Dec 25, 2020

    The term ‘Telemedicine’ was coined in the 1970s to literally imply ‘healing at a distance’. Physical therapy rehabilitation (PTR) focuses on the re-institution of function in bodily strength a...

    The term ‘Telemedicine’ was coined in the 1970s to literally imply ‘healing at a distance’. Physical therapy rehabilitation (PTR) focuses on the re-institution of function in bodily strength and movement. Covid-19 has created a challenge in one-on-one PTR session due to social distancing, which requires the minimization of all non-essential physical contact. Most outpatient services in PTR have had to be staggered and minimized to increase adherence to social distancing requirements and flatten the pandemic’s curve. Telemedicine is applicable in PTR in a number of ways, including guided therapy sessions, and remote monitoring of patient progress through videoconferencing. Telemedicine allows patients to access PTR from the comfort of their homes, which minimizes travel costs and general strain on the body. Although it has been encumbered by various challenges, telemedicine could revolutionize the delivery of PTR wile also increasing access to the essential healthcare service.

  • Data visualization in chronic neurological and mental health condition self-management: a systematic review of user perspectives

    Date Submitted: Oct 25, 2020

    Open Peer Review Period: Oct 25, 2020 - Dec 20, 2020

    Background: Remote measurement technology (RMT) such as mobile health devices and applications, are increasingly used by those living with chronic neurological and mental health conditions. RMT enable...

    Background: Remote measurement technology (RMT) such as mobile health devices and applications, are increasingly used by those living with chronic neurological and mental health conditions. RMT enables real-world data collection and regular feedback, providing users with insights about their own conditions. Data visualizations are an integral part of RMT, though little is known about visualization design preferences from the perspectives of those living with chronic conditions. Objective: Explore the experiences and preferences of individuals with chronic neurological and mental health conditions on data visualizations derived from RMT to manage health. Methods: In this systematic review, we searched peer-reviewed literature and conference proceedings (PubMed, IEEE Xplore, EMBASE, Web of Science, ACM Computer-Human Interface proceedings, and the Cochrane Library) for original articles published between January 2007 and February 2020 that reported perspectives on data visualization of people living with chronic neurological and mental health conditions. Two reviewers independently screened each abstract and full-text article, with disagreements resolved through discussion. Studies were critically appraised and extracted data underwent thematic synthesis. Results: We identified 28 eligible publications from 24 studies representing 11 conditions. Coded data coalesced into four themes: desire for data visualization, the impact of visualizations on condition management, visualizations as data reporting tools, and visualization design considerations. Data visualizations were viewed an integral part of users’ experiences with RMT, impacting satisfaction and engagement. However, user preferences were diverse and often conflicting, both between and within conditions. Conclusions: When used effectively, data visualizations are valuable, engaging components of RMT. They can provide structure and insight, allowing individuals to manage their own health more effectively. However, visualizations are not “one-size-fits-all,” and it is important to engage with potential user during visualization design to understand when, how, and with whom the visualizations will be used to manage health.

  • “A question of trust” and “a leap of faith”: A qualitative study of participants’ perspectives on consent, privacy and trust in smart home research

    Date Submitted: Oct 23, 2020

    Open Peer Review Period: Oct 23, 2020 - Dec 18, 2020

    Background: ‘Ubiquitous’, ‘smart’ computing technology has the potential to assist humans in numerous ways, including health and social care. Covid-19 has notably hastened the move to remote d...

    Background: ‘Ubiquitous’, ‘smart’ computing technology has the potential to assist humans in numerous ways, including health and social care. Covid-19 has notably hastened the move to remote delivery of many health services, such as Primary Care. Development of technology involves a variety of stakeholders in the process of testing, refinement, and evaluation. Where stakeholders are research participants, this poses both practical and ethical challenges, particularly if the research is situated in people’s homes. Researchers must observe prima facie ethical obligations linked to participants’ interests in having their autonomy and privacy respected. Objective: This research explores ethical considerations around consent, privacy, anonymisation and data-sharing with participants involved in SPHERE, a project developing smart technology for monitoring people’s health behaviours in their homes. Their unique insights from being part of this unusual experiment offers a valuable perspective on how to properly approach informed consent for future research. Methods: Semi-structured qualitative interviews with whole households (adults and children) were conducted with 7 households/16 participants recruited from SPHERE. Purposive sampling was used to invite participants from a range of household types and ages. Interviews were conducted in participants’ homes or on-site at the University of Bristol. Interviews were digitally recorded, transcribed verbatim and then thematically analysed. Results: Four themes were identified: (1) motivations for participating; (2) transparency, understanding and consent; (3) privacy, anonymity and data use; and (4) trust in research. Motivations to participate in SPHERE stemmed from an altruistic desire to support research directed towards the public good. Participants were satisfied with the SPHERE consent process despite reporting some difficulties: recalling and understanding information received; the timing and amount of information provision; and sometimes finding the information to be abstract. Participants were also satisfied that privacy was assured and judged that reasons for conducting the research compensated for threats to privacy. Participants trusted the project and the team. Factors relevant to developing and maintaining this trust were the trustworthiness of the research team, provision of necessary information, the control participants had over participation, and positive prior experiences of research involvement. Conclusions: This small study offers valuable insights into the perspectives of participants in smart home research on important ethical considerations around consent and privacy. The findings might have practical implications for future research regarding the types of information researchers should convey, the extent to which anonymity can be assured, and the long-term duty of care owed to participants who place trust in researchers not only on the basis of this information, but also because of their institutional affiliation. This study highlights important ethical implications: although autonomy matters, trust appears to matter most. Researchers should therefore be alert to the need to foster and maintain trust, particularly as failing to do so might have deleterious effects on future research.

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

    Date Submitted: Oct 19, 2020

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

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

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

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