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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:

  • Using online inquiry services provided by internet hospitals. Source: Image created by the authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Determinants of Patients’ Intention to Use the Online Inquiry Services Provided by Internet Hospitals: Empirical Evidence From China


    Background: Internet hospitals show great potential for adequately fulfilling people’s demands for high-quality outpatient services, and with the normalization of the epidemic prevention and control of COVID-19, internet hospitals play an increasingly important role in delivering health services to the public. However, the factors that influence patients’ intention to use the online inquiry services provided by internet hospitals remain unclear. Understanding the patients’ behavioral intention is necessary to support the development of internet hospitals in China and promote patients’ intention to use online inquiry services provided by internet hospitals during the prevention and control of the COVID-19 epidemic. Objective: The purpose of this study is to identify the determinants of patients’ intention to use the online inquiry services provided by internet hospitals based on the theory of planned behavior (TPB). Methods: The hypotheses of our research model were developed based on the TPB. A questionnaire was developed through patient interviews, verified using a presurvey, and used for data collection for this study. The cluster sampling technique was used to include respondents with chronic diseases. Structural equation modeling was used to test the research hypotheses. Results: A total of 638 valid responses were received from patients with chronic diseases. The goodness-of-fit indexes corroborated that the research model was a good fit for the collected data. The model explained 45.9% of the variance in attitude toward the behavior and 60.5% of the variance in behavioral intention. Perceived behavioral control and perceived severity of disease had the strongest total effects on behavioral intention (β=.624, P=.004 and β=.544, P=.003, respectively). Moreover, perceived convenience, perceived information risk, emotional preference, and health consciousness had indirect effects on behavioral intention, and these effects were mediated by attitude toward the behavior. Among the four constructs, perceived convenience had the highest indirect effect on behavioral intention (β=.207; P=.001). Conclusions: Perceived behavioral control and perceived severity of disease are the most important determinants of patients’ intention to use the online inquiry services provided by internet hospitals. Therefore, internet hospitals should further optimize the design of online service delivery and ensure a reasonable assembly of high-quality experts, which will benefit the promotion of patients’ adoption intention toward online inquiry services for health purposes. Perceived convenience, emotional preference, and perceived risks also have effects on behavioral intention. Therefore, the relevant quality control standards and regulations for internet hospitals should be further developed and improved, and the measures to protect personal information should be strengthened to ensure the patient safety. Our study supports the use of the TPB in explaining patients’ intention to use online inquiry services provided by internet hospitals.

  • Source:; Copyright: bongkarn; URL:; License: Licensed by JMIR.

    Undergraduate Medical Competencies in Digital Health and Curricular Module Development: Mixed Methods Study


    Background: Owing to an increase in digital technologies in health care, recently leveraged by the COVID-19 pandemic, physicians are required to use these technologies appropriately and to be familiar with their implications on patient care, the health system, and society. Therefore, medical students should be confronted with digital health during their medical education. However, corresponding teaching formats and concepts are still largely lacking in the medical curricula. Objective: This study aims to introduce digital health as a curricular module at a German medical school and to identify undergraduate medical competencies in digital health and their suitable teaching methods. Methods: We developed a 3-week curricular module on digital health for third-year medical students at a large German medical school, taking place for the first time in January 2020. Semistructured interviews with 5 digital health experts were recorded, transcribed, and analyzed using an abductive approach. We obtained feedback from the participating students and lecturers of the module through a 17-item survey questionnaire. Results: The module received overall positive feedback from both students and lecturers who expressed the need for further digital health education and stated that the field is very important for clinical care and is underrepresented in the current medical curriculum. We extracted a detailed overview of digital health competencies, skills, and knowledge to teach the students from the expert interviews. They also contained suggestions for teaching methods and statements supporting the urgency of the implementation of digital health education in the mandatory curriculum. Conclusions: An elective class seems to be a suitable format for the timely introduction of digital health education. However, a longitudinal implementation in the mandatory curriculum should be the goal. Beyond training future physicians in digital skills and teaching them digital health’s ethical, legal, and social implications, the experience-based development of a critical digital health mindset with openness to innovation and the ability to assess ever-changing health technologies through a broad transdisciplinary approach to translate research into clinical routine seem more important. Therefore, the teaching of digital health should be as practice-based as possible and involve the educational cooperation of different institutions and academic disciplines.

  • A photo showing a smartphone with an app for activity tracking. Source: Pxhere; Copyright: Pxhere; URL:; License: Creative Commons Attribution + ShareAlike (CC-BY-SA).

    Primary Prevention of Cardiovascular Disease and Type 2 Diabetes Mellitus Using Mobile Health Technology: Systematic Review of the Literature


    Background: Digital technology is an opportunity for public health interventions to reach a large part of the population. Objective: This systematic literature review aimed to assess the effectiveness of mobile health–based interventions in reducing the risk of cardiovascular disease and type 2 diabetes mellitus. Methods: We conducted the systematic search in 7 electronic databases using a predefined search strategy. We included articles published between inception of the databases and March 2019 if they reported on the effectiveness of an intervention for prevention of cardiovascular disease or type 2 diabetes via mobile technology. One researcher performed the search, study selection, data extraction, and methodological quality assessment. The steps were validated by the other members of the research team Results: The search yielded 941 articles for cardiovascular disease, of which 3 met the inclusion criteria, and 732 for type 2 diabetes, of which 6 met the inclusion criteria. The methodological quality of the studies was low, with the main issue being nonblinding of participants. Of the selected studies, 4 used SMS text messaging, 1 used WhatsApp, and the remaining ones used specific smartphone apps. Weight loss and reduction in BMI were the most reported successful outcomes (reported in 4 studies). Conclusions: Evidence on the effectiveness of mobile health-based interventions in reducing the risk for cardiovascular disease and type 2 diabetes is low due to the quality of the studies and the small effects that were measured. This highlights the need for further high-quality research to investigate the potential of mobile health interventions. Trial Registration: International Prospective Register of Systematic Reviews (PROSPERO) CRD42019135405;

  • Source: 123RF / The Authors; Copyright: Dolgachov / The Authors; URL:|; License: Licensed by the authors.

    Influenza Screening via Deep Learning Using a Combination of Epidemiological and Patient-Generated Health Data: Development and Validation Study


    Background: Screening for influenza in primary care is challenging due to the low sensitivity of rapid antigen tests and the lack of proper screening tests. Objective: The aim of this study was to develop a machine learning–based screening tool using patient-generated health data (PGHD) obtained from a mobile health (mHealth) app. Methods: We trained a deep learning model based on a gated recurrent unit to screen influenza using PGHD, including each patient’s fever pattern and drug administration records. We used meteorological data and app-based surveillance of the weekly number of patients with influenza. We defined a single episode as the set of consecutive days, including the day the user was diagnosed with influenza or another disease. Any record a user entered 24 hours after his or her last record was considered to be the start of a new episode. Each episode contained data on the user’s age, gender, weight, and at least one body temperature record. The total number of episodes was 6657. Of these, there were 3326 episodes within which influenza was diagnosed. We divided these episodes into 80% training sets (2664/3330) and 20% test sets (666/3330). A 5-fold cross-validation was used on the training set. Results: We achieved reliable performance with an accuracy of 82%, a sensitivity of 84%, and a specificity of 80% in the test set. After the effect of each input variable was evaluated, app-based surveillance was observed to be the most influential variable. The correlation between the duration of input data and performance was not statistically significant (P=.09). Conclusions: These findings suggest that PGHD from an mHealth app could be a complementary tool for influenza screening. In addition, PGHD, along with traditional clinical data, could be used to improve health conditions. Trial Registration:

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

    Maximizing the Potential of Patient-Reported Assessments by Using the Open-Source Concerto Platform With Computerized Adaptive Testing and Machine Learning


    Patient-reported assessments are transforming many facets of health care, but there is scope to modernize their delivery. Contemporary assessment techniques like computerized adaptive testing (CAT) and machine learning can be applied to patient-reported assessments to reduce burden on both patients and health care professionals; improve test accuracy; and provide individualized, actionable feedback. The Concerto platform is a highly adaptable, secure, and easy-to-use console that can harness the power of CAT and machine learning for developing and administering advanced patient-reported assessments. This paper introduces readers to contemporary assessment techniques and the Concerto platform. It reviews advances in the field of patient-reported assessment that have been driven by the Concerto platform and explains how to create an advanced, adaptive assessment, for free, with minimal prior experience with CAT or programming.

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

    Digital Micro Interventions for Behavioral and Mental Health Gains: Core Components and Conceptualization of Digital Micro Intervention Care


    Although many people access publicly available digital behavioral and mental health interventions, most do not invest as much effort in these interventions as hoped or intended by intervention developers, and ongoing engagement is often low. Thus, the impact of such interventions is minimized by a misalignment between intervention design and user behavior. Digital micro interventions are highly focused interventions delivered in the context of a person’s daily life with little burden on the individual. We propose that these interventions have the potential to disruptively expand the reach of beneficial therapeutics by lowering the bar for entry to an intervention and the effort needed for purposeful engagement. This paper provides a conceptualization of digital micro interventions, their component parts, and principles guiding their use as building blocks of a larger therapeutic process (ie, digital micro intervention care). The model represented provides a structure that could improve the design, delivery, and research on digital micro interventions and ultimately improve behavioral and mental health care and care delivery.

  • Source: CC Search; Copyright: Rallyroo; URL:; License: Creative Commons Attribution (CC-BY).

    Information Access and Use by Patients With Cancer and Their Friends and Family: Development of a Grounded Theory


    Background: Information has been identified as a commonly unmet supportive care need for those living with cancer (ie, patients and their friends and family). The information needed to help individuals plan their lives around the consequences of cancer, such as the receipt of health care, is an example of an important informational need. A suitable theory to guide the development of interventions designed to meet this informational need has not been identified by the authors. Objective: The aim of this study is to generate a grounded theory capable of guiding the development of interventions designed to assist those living with cancer in meeting their informational needs. Methods: Classic grounded theory was used to analyze data collected through digitally recorded one-on-one audio interviews with 31 patients with cancer and 29 friends and family members. These interviews focused on how the participants had accessed and used information to plan their lives and what barriers they faced in obtaining and using this information. Results: The theory that emerged consisted of 4 variables: personal projects, cancer as a source of disruption to personal projects, information as the process of accessing and interpreting cancer-related data (CRD) to inform action, and CRD quality as defined by accessibility, credibility, applicability, and framing. CRD quality as a moderator of personal project disruption by cancer is the core concept of this theory. Conclusions: Informational resources providing accessible, credible, applicable, and positively framed CRD are likely key to meeting the information needs of those affected by cancer. Web-based informational resources delivering high-quality CRD focused on assisting individuals living with cancer in maintaining and planning their personal projects are predicted to improve quality of life. Research is needed to develop and integrate resources informed by this theoretical framework into clinical practice.

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

    Web-Based Patient-Reported Outcome Measures for Personalized Treatment and Care (PROMPT-Care): Multicenter Pragmatic Nonrandomized Trial


    Background: Despite the acceptability and efficacy of e–patient-reported outcome (ePRO) systems, implementation in routine clinical care remains challenging. Objective: This pragmatic trial implemented the PROMPT-Care (Patient Reported Outcome Measures for Personalized Treatment and Care) web-based system into existing clinical workflows and evaluated its effectiveness among a diverse population of patients with cancer. Methods: Adult patients with solid tumors receiving active treatment or follow-up care in four cancer centers were enrolled. The PROMPT-Care intervention supported patient management through (1) monthly off-site electronic PRO physical symptom and psychosocial well-being assessments, (2) automated electronic clinical alerts notifying the care team of unresolved clinical issues following two consecutive assessments, and (3) tailored online patient self-management resources. Propensity score matching was used to match controls with intervention patients in a 4:1 ratio for patient age, sex, and treatment status. The primary outcome was a reduction in emergency department presentations. Secondary outcomes were time spent on chemotherapy and the number of allied health service referrals. Results: From April 2016 to October 2018, 328 patients from four public hospitals received the intervention. Matched controls (n=1312) comprised the general population of patients with cancer, seen at the participating hospitals during the study period. Emergency department visits were significantly reduced by 33% (P=.02) among patients receiving the intervention compared with patients in the matched controls. No significant associations were found in allied health referrals or time to end of chemotherapy. At baseline, the most common patient reported outcomes (above-threshold) were fatigue (39%), tiredness (38.4%), worry (32.9%), general wellbeing (32.9%), and sleep (24.1%), aligning with the most frequently accessed self-management domain pages of physical well-being (36%) and emotional well-being (23%). The majority of clinical feedback reports were reviewed by nursing staff (729/893, 82%), largely in response to the automated clinical alerts (n=877). Conclusions: Algorithm-supported web-based systems utilizing patient reported outcomes in clinical practice reduced emergency department presentations among a diverse population of patients with cancer. This study also highlighted the importance of (1) automated triggers for reviewing above-threshold results in patient reports, rather than passive manual review of patient records; (2) the instrumental role nurses play in managing alerts; and (3) providing patients with resources to support guided self-management, where appropriate. Together, these factors will inform the integration of web-based PRO systems into future models of routine cancer care. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12616000615482;

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

    Integrating the Practical Robust Implementation and Sustainability Model With Best Practices in Clinical Decision Support Design: Implementation Science...


    Background: Clinical decision support (CDS) design best practices are intended to provide a narrative representation of factors that influence the success of CDS tools. However, they provide incomplete direction on evidence-based implementation principles. Objective: This study aims to describe an integrated approach toward applying an existing implementation science (IS) framework with CDS design best practices to improve the effectiveness, sustainability, and reproducibility of CDS implementations. Methods: We selected the Practical Robust Implementation and Sustainability Model (PRISM) IS framework. We identified areas where PRISM and CDS design best practices complemented each other and defined methods to address each. Lessons learned from applying these methods were then used to further refine the integrated approach. Results: Our integrated approach to applying PRISM with CDS design best practices consists of 5 key phases that iteratively interact and inform each other: multilevel stakeholder engagement, designing the CDS, design and usability testing, thoughtful deployment, and performance evaluation and maintenance. The approach is led by a dedicated implementation team that includes clinical informatics and analyst builder expertise. Conclusions: Integrating PRISM with CDS design best practices extends user-centered design and accounts for the multilevel, interacting, and dynamic factors that influence CDS implementation in health care. Integrating PRISM with CDS design best practices synthesizes the many known contextual factors that can influence the success of CDS tools, thereby enhancing the reproducibility and sustainability of CDS implementations. Others can adapt this approach to their situation to maximize and sustain CDS implementation success.

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

    Associations Between Patient Health Outcomes and Secure Message Content Exchanged Between Patients and Clinicians: Retrospective Cohort Study


    Background: The number of electronic messages securely exchanged between clinic staff and patients has risen dramatically over the last decade. A variety of studies explored whether the volume of messages sent by patients was associated with outcomes. None of these studies, however, examined whether message content itself was associated with outcomes. Because secure messaging is a significant form of communication between patients and clinic staff, it is critical to evaluate the context of the communication to best understand its impact on patient health outcomes. Objective: To examine associations between patients’ and clinicians’ message content and changes in patients’ health outcomes. Methods: We applied a taxonomy developed specifically for secure messages to 14,394 patient- and clinic staff–generated messages derived from patient-initiated message threads. Our study population included 1602 patients, 50.94% (n=816) of whom initiated message threads. We conducted linear regression analyses to determine whether message codes were associated with changes in glycemic (A1C) levels in patients with diabetes and changes in systolic (SBP) and diastolic (DBP) blood pressure in patients with hypertension. Results: Patients who initiated threads had larger declines in A1Cs (P=.01) compared to patients who did not initiate threads. Clinic nonresponse was associated with decreased SBP (β=–.30; 95% CI –0.56 to –0.04), as were staffs’ action responses (β=–30; 95% CI –0.58 to –0.02). Increased DBP, SBP, and A1C levels were associated with patient-generated appreciation and praise messages and staff encouragement with effect sizes ranging from 0.51 (A1C) to 5.80 (SBP). We found improvements in SBP associated with patients’ complaints (β=–4.03; 95% CI –7.94 to –0.12). Deferred information sharing by clinic staff was associated with increased SBP (β=1.29; 95% CI 0.4 to 2.19). Conclusions: This is the first research to find associations between message content and patients’ health outcomes. Our findings indicate mixed associations between patient message content and patient outcomes. Further research is needed to understand the implications of this work; in the meantime, health care providers should be aware that their message content may influence patient health outcomes.

  • 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...


    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:; License: Licensed by JMIR.

    Disaster eHealth: Scoping Review


    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.

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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.