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Journal Description

The Journal of Medical Internet Research (JMIR), now in its 20th 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 2017: 4.671, ranked #1 out of 22 journals) and in terms of size (number of papers published). The journal focuses on emerging technologies, medical devices, apps, engineering, and informatics applications for patient education, prevention, population health and clinical care. As leading high-impact journal in its' disciplines (health informatics and health services research), it is selective, but it is now complemented by almost 30 specialty JMIR sister journals, which have a broader scope. Peer-review reports are portable across JMIR journals and papers can be transferred, so authors save time by not having to resubmit a paper to different journals. 

As open access journal, we are read by clinicians, allied health professionals, informal caregivers, and patients alike, and have (as 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:

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

    Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study


    Background: Mood disorders are common and associated with significant morbidity and mortality. Better tools are needed for their diagnosis and treatment. Deeper phenotypic understanding of these disorders is integral to the development of such tools. This study is the first effort to use passively collected mobile phone keyboard activity to build deep digital phenotypes of depression and mania. Objective: The objective of our study was to investigate the relationship between mobile phone keyboard activity and mood disturbance in subjects with bipolar disorders and to demonstrate the feasibility of using passively collected mobile phone keyboard metadata features to predict manic and depressive signs and symptoms as measured via clinician-administered rating scales. Methods: Using a within-subject design of 8 weeks, subjects were provided a mobile phone loaded with a customized keyboard that passively collected keystroke metadata. Subjects were administered the Hamilton Depression Rating Scale (HDRS) and Young Mania Rating Scale (YMRS) weekly. Linear mixed-effects models were created to predict HDRS and YMRS scores. The total number of keystrokes was 626,641, with a weekly average of 9791 (7861), and that of accelerometer readings was 6,660,890, with a weekly average 104,076 (68,912). Results: A statistically significant mixed-effects regression model for the prediction of HDRS-17 item scores was created: conditional R2=.63, P=.01. A mixed-effects regression model for YMRS scores showed the variance accounted for by random effect was zero, and so an ordinary least squares linear regression model was created: R2=.34, P=.001. Multiple significant variables were demonstrated for each measure. Conclusions: Mood states in bipolar disorder appear to correlate with specific changes in mobile phone usage. The creation of these models provides evidence for the feasibility of using passively collected keyboard metadata to detect and monitor mood disturbances.

  • Source: iStock by Getty Images; Copyright: cnythzl; URL:; License: Licensed by the authors.

    Accuracy of Internet-Based Patient Self-Report of Postdischarge Health Care Utilization and Complications Following Orthopedic Procedures: Observational...


    Background: The accuracy of patient self-report of health care utilization and complications has yet to be determined. If patients are accurate and engaged self-reporters, collecting this information in a manner that is temporally proximate to the health care utilization events themselves may prove valuable to health care organizations undertaking quality improvement initiatives for which such data are often unavailable. Objective: The objective of this study was to measure the accuracy of patient self-report of health care utilization and complications in the 90 days following orthopedic procedures using an automated digital patient engagement platform. Methods: We conducted a multicenter real-world observational cohort study across 10 orthopedic practices in California and Nevada. A total of 371 Anthem members with claims data meeting inclusion criteria who had undergone orthopedic procedures between March 1, 2015, and July 1, 2016, at participating practices already routinely using an automated digital patient engagement platform for asynchronous remote guidance and telemonitoring were sent surveys through the platform (in addition to the other materials being provided to them through the platform) regarding 90-day postencounter health care utilization and complications. Their self-reports to structured survey questions of health care utilization and complications were compared to claims data as a reference. Results: The mean age of the 371 survey recipients was 56.5 (SD 15.7) years, 48.8% (181/371) of whom were female; 285 individuals who responded to 1 or more survey questions had a mean age of 56.9 (SD 15.4) years and a 49.5% (141/285) female distribution. There were no significant differences in demographics or event prevalence rates between responders and nonresponders. With an overall survey completion rate of 76.8% (285/371), patients were found to have accuracy of self-report characterized by a kappa of 0.80 and agreement of 0.99 and a kappa of 1.00 and agreement of 1.00 for 90-day hospital admissions and pulmonary embolism, respectively. Accuracy of self-report of 90-day emergency room/urgent care visits and of surgical site infection were characterized by a kappa of 0.45 and agreement of 0.96 and a kappa of 0.53 and agreement of 0.97, respectively. Accuracy for other complications such as deep vein thrombosis, hemorrhage, severe constipation, and fracture/dislocation was lower, influenced by low event prevalence rates within our sample. Conclusions: In this multicenter observational cohort study using an automated internet-based digital patient engagement platform, we found that patients were most accurate self-reporters of 90-day hospital admissions and pulmonary embolism, followed by 90-day surgical site infection and emergency room/urgent care visits. They were less accurate for deep vein thrombosis and least accurate for hemorrhage, severe constipation, and fracture/dislocation. A total of 76.8% (285/371) of patients completed surveys without the need for clinical staff to collect responses, suggesting the acceptability to patients of internet-based survey dissemination from and collection by clinical teams. While our methods enabled detection of events outside of index institutions, assessment of accuracy of self-report for presence and absence of events and nonresponse bias analysis, low event prevalence rates, particularly for several of the complications, limit the conclusions that may be drawn for some of the findings. Nevertheless, this investigation suggests the potential that engaging patients in self-report through such survey modalities may offer for the timely and accurate measurement of matters germane to health care organizations engaged in quality improvement efforts post discharge.

  • Source: Pixabay; Copyright: skeeze; URL:; License: Public Domain (CC0).

    Patient-Centered eHealth Interventions for Children, Adolescents, and Adults With Sickle Cell Disease: Systematic Review


    Background: Sickle cell disease is an inherited blood disorder that affects over 100,000 Americans. Sickle cell disease–related complications lead to significant morbidity and early death. Evidence supporting the feasibility, acceptability, and efficacy of self-management electronic health (eHealth) interventions in chronic diseases is growing; however, the evidence is unclear in sickle cell disease. Objective: We systematically evaluated the most recent evidence in the literature to (1) review the different types of technological tools used for self-management of sickle cell disease, (2) discover and describe what self-management activities these tools were used for, and (3) assess the efficacy of these technologies in self-management. Methods: We reviewed literature published between 1995 and 2016 with no language limits. We searched MEDLINE, EMBASE, CINAHL, PsycINFO, and other sources. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Two independent reviewers screened titles and abstracts, assessed full-text articles, and extracted data from articles that met inclusion criteria. Eligible studies were original research articles that included texting, mobile phone–based apps, or other eHealth interventions designed to improve self-management in pediatric and adult patients with sickle cell disease. Results: Of 1680 citations, 16 articles met all predefined criteria with a total of 747 study participants. Interventions were text messaging (4/16, 25%), native mobile apps (3/16, 19%), Web-based apps (5/16, 31%), mobile directly observed therapy (2/16, 13%), internet-delivered cognitive behavioral therapy (2/16, 13%), electronic pill bottle (1/16, 6%), or interactive gamification (2/16, 13%). Interventions targeted monitoring or improvement of medication adherence (5/16, 31%); self-management, pain reporting, and symptom reporting (7/16, 44%); stress, coping, sleep, and daily activities reporting (4/16, 25%); cognitive training for memory (1/16, 6%); sickle cell disease and reproductive health knowledge (5/16, 31%); cognitive behavioral therapy (2/16, 13%); and guided relaxation interventions (1/16, 6%). Most studies (11/16, 69%) included older children or adolescents (mean or median age 10-17 years; 11/16, 69%) and 5 included young adults (≥18 years old) (5/16, 31%). Sample size ranged from 11 to 236, with a median of 21 per study: <20 in 6 (38%), ≥20 to <50 in 6 (38%), and >50 participants in 4 studies (25%). Most reported improvement in self-management–related outcomes (15/16, 94%), as well as high satisfaction and acceptability of different study interventions (10/16, 63%). Conclusions: Our systematic review identified eHealth interventions measuring a variety of outcomes, which showed improvement in multiple components of self-management of sickle cell disease. Despite the promising feasibility and acceptability of eHealth interventions in improving self-management of sickle cell disease, the evidence overall is modest. Future eHealth intervention studies are needed to evaluate their efficacy, effectiveness, and cost effectiveness in promoting self-management in patients with sickle cell disease using rigorous methods and theoretical frameworks with clearly defined clinical outcomes.

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

    Evaluating Doctor Performance: Ordinal Regression-Based Approach


    Background: Doctor’s performance evaluation is an important task in mobile health (mHealth), which aims to evaluate the overall quality of online diagnosis and patient outcomes so that customer satisfaction and loyalty can be attained. However, most patients tend not to rate doctors’ performance, therefore, it is imperative to develop a model to make doctor’s performance evaluation automatic. When evaluating doctors’ performance, we rate it into a score label that is as close as possible to the true one. Objective: This study aims to perform automatic doctor’s performance evaluation from online textual consultations between doctors and patients by way of a novel machine learning method. Methods: We propose a solution that models doctor’s performance evaluation as an ordinal regression problem. In doing so, a support vector machine combined with an ordinal partitioning model (SVMOP), along with an innovative predictive function will be developed to capture the hidden preferences of the ordering labels over doctor’s performance evaluation. When engineering the basic text features, eight customized features (extracted from over 70,000 medical entries) were added and further boosted by the Gradient Boosting Decision Tree algorithm. Results: Real data sets from one of the largest mobile doctor/patient communication platforms in China are used in our study. Statistically, 64% of data on mHealth platforms lack the evaluation labels from patients. Experimental results reveal that our approach can support an automatic doctor performance evaluation. Compared with other auto-evaluation models, SVMOP improves mean absolute error (MAE) by 0.1, mean square error (MSE) by 0.5, pairwise accuracy (PAcc) by 5%; the suggested customized features improve MAE by 0.1, MSE by 0.2, PAcc by 3%. After boosting, performance is further improved. Based on SVMOP, predictive features like politeness and sentiment words can be mined, which can be further applied to guide the development of mHealth platforms. Conclusions: The initial modelling of doctor performance evaluation is an ordinal regression problem. Experiments show that the performance of our proposed model with revised prediction function is better than many other machine learning methods on MAE, MSE, as well as PAcc. With this model, the mHealth platform could not only make an online auto-evaluation of physician performance, but also obtain the most effective features, thereby guiding physician performance and the development of mHealth platforms.

  • homepage (montage). Source: The Authors /; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    The Association Between Increased Levels of Patient Engagement With an Internet Support Group and Improved Mental Health Outcomes at 6-Month Follow-Up:...


    Background: We recently reported that depressed and anxious primary care patients randomized to a moderated internet support group (ISG) plus computerized cognitive behavioral therapy (cCBT) did not experience improvements in depression and anxiety over cCBT alone at 6-month follow-up. Objective: The 1% rule posits that 1% of participants in online communities generate approximately 90% of new user-created content. The aims of this study were to apply the 1% rule to categorize patient engagement with the ISG and identify whether any patient subgroups benefitted from ISG use. Methods: We categorized the 302 patients randomized to the ISG as: superusers (3/302, 1.0%), top contributors (30/302, 9.9%), contributors (108/302, 35.8%), observers (87/302, 28.8%) and those who never logged in (74/302, 24.5%). We then applied linear mixed models to examine associations between engagement and 6-month changes in health-related quality of life (HRQoL; Short Form Health Survey Mental Health Component, SF-12 MCS) and depression and anxiety symptoms (Patient-Reported Outcomes Measurement Information System, PROMIS). Results: At baseline, participant mean age was 42.6 years, 81.1% (245/302) were female, and mean Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder scale (GAD-7), and SF-12 MCS scores were 13.4, 12.6, and 31.7, respectively. Of the 75.5% (228/302) who logged in, 61.8 % (141/228) created ≥1 post (median 1, interquartile range, IQR 0-5); superusers created 42.3 % (630/1488) of posts (median 246, IQR 78-306), top contributors created 34.6% (515/1488; median 11, IQR 10-18), and contributors created 23.1 % (343/1488; median 3, IQR 1-5). Compared to participants who never logged in, the combined superuser + top contributor subgroup (n=33) reported 6-month improvements in anxiety (PROMIS: –11.6 vs –7.8; P=.04) and HRQoL (SF-12 MCS: 16.1 vs 10.1; P=.01) but not in depression. No other subgroup reported significant symptom improvements. Conclusions: Patient engagement with the ISG was more broadly distributed than predicted by the 1% rule. The 11% of participants with the highest engagement levels reported significant improvements in anxiety and HRQoL. Trial Registration: NCT01482806; (Archived by WebCite at

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

    Exploring User Needs for a Mobile Behavioral-Sensing Technology for Depression Management: Qualitative Study


    Background: Today, college students are dealing with depression at some of the highest rates in decades. As the primary mental health service provider, university counseling centers are limited in their capacity and efficiency to provide mental health care due to time constraints and reliance on students’ self-reports. A mobile behavioral-sensing platform may serve as a solution to enhance the efficiency and accessibility of university counseling services. Objective: The main objectives of this study are to (1) understand the usefulness of a mobile sensing platform (ie, iSee) in improving counseling services and assisting students’ self-management of their depression conditions, and (2) explore what types of behavioral targets (ie, meaningful information extracted from raw sensor data) and feedback to deliver from both clinician and students’ perspectives. Methods: We conducted semistructured interviews with 9 clinicians and 12 students with depression recruited from a counseling center at a large Midwestern university. The interviews were 40-50 minutes long and were audio recorded and transcribed. The interview data were analyzed using thematic analysis with an inductive approach. Clinician and student interviews were analyzed separately for comparison. The process of extracting themes involved iterative coding, memo writing, theme revisits, and refinement. Results: From the clinician perspective, the mobile sensing platform helps to improve counseling service by providing objective evidence for clinicians and filling gaps in clinician-patient communication. Clinicians suggested providing students with their sensed behavioral targets organized around personalized goals. Clinicians also recommended delivering therapeutic feedback to students based on their sensed behavioral targets, including positive reinforcement, reflection reminders, and challenging negative thoughts. From the student perspective, the mobile sensing platform helps to ease continued self-tracking practices. Students expressed their need for integrated behavioral targets to understand correlations between behaviors and depression. They also pointed out that they would prefer to avoid seeing negative feedback. Conclusions: Although clinician and student participants shared views on the advantages of iSee in supporting university counseling, they had divergent opinions on the types of behavioral targets and feedback to be provided via iSee. This exploratory work gained initial insights into the design of a mobile sensing platform for depression management and informed a more conclusive research project for the future.

  • Source: Pxhere; Copyright: Pxhere; URL:; License: Public Domain (CC0).

    Web-Based Intervention Using Behavioral Activation and Physical Activity for Adults With Depression (The eMotion Study): Pilot Randomized Controlled Trial


    Background: Physical activity is a potentially effective treatment for depression and depressive relapse. However, promoting physical activity in people with depression is challenging. Interventions informed by theory and evidence are therefore needed to support people with depression to become more physically active. eMotion is a Web-based intervention combining behavioral activation and physical activity promotion for people in the community with symptoms of depression. Objective: The objectives were to assess the feasibility and acceptability of delivering eMotion to people in the community with symptoms of depression and to explore outcomes. Methods: Participants with elevated depressive symptoms were recruited from the community through various methods (eg, social media) and randomized to eMotion or a waiting list control group for 8 weeks. eMotion is an administratively supported weekly modular program that helps people use key behavior change techniques (eg, graded tasks, action planning, and self-monitoring) to re-engage in routine, pleasurable, and necessary activities, with a focus on physical activities. Feasibility data were collected that included the following: recruitment and trial retention rates; fidelity of intervention delivery, receipt, and enactment; and acceptability of the intervention and data collection procedures. Data were collected for the primary (depression) and secondary outcomes (eg, anxiety, physical activity, fidelity, and client satisfaction) at baseline and 2 months postrandomization using self-reported Web-based questionnaires and accelerometers. Delivery fidelity (logins, modules accessed, time spent) was tracked using Web usage statistics. Exploratory analyses were conducted on the primary and secondary outcomes. Results: Of the 183 people who contacted the research team, 62 were recruited and randomized. The mean baseline score was 14.6 (SD 3.2) on the 8-item Patient Health Questionnaire depression scale (PHQ-8). Of those randomized, 52 participants provided accelerometer-recorded physical activity data at baseline that showed a median of 35.8 (interquartile range [IQR] 0.0-98.6) minutes of moderate-to-vigorous physical activity (MVPA) recorded in at least 10-minute bouts per week, with only 13% (7/52) people achieving guideline levels (150 minutes of MVPA per week). In total, 81% (50/62) of participants provided follow-up data for the primary outcome (PHQ-8), but only 39% (24/62) provided follow-up accelerometer data. Within the intervention group, the median number of logins, modules accessed, and total minutes spent on eMotion was 3 (IQR 2.0-8.0), 3 (IQR 2.0-5.0), and 41.3 (IQR 18.9-90.4), respectively. Acceptability was mixed. Exploratory data analysis showed that PHQ-8 levels were lower for the intervention group than for the control group at 2 months postrandomization (adjusted mean difference −3.6, 95% CI −6.1 to −1.1). Conclusions: It was feasible to deliver eMotion in UK communities to inactive populations. eMotion has the potential to be effective and is ready for testing in a full-scale trial. Further work is needed to improve engagement with both the intervention and data collection procedures. Trial Registration: NCT03084055; (Archived by WebCite at

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

    Creating Low-Cost 360-Degree Virtual Reality Videos for Hospitals: A Technical Paper on the Dos and Don’ts


    This article will provide a framework for producing immersive 360-degree videos for pediatric and adult patients in hospitals. This information may be useful to hospitals across the globe who may wish to produce similar videos for their patients. Advancements in immersive 360-degree technologies have allowed us to produce our own “virtual experience” where our children can prepare for anesthesia by “experiencing” all the sights and sounds of receiving and recovering from an anesthetic. We have shown that health care professionals, children, and their parents find this form of preparation valid, acceptable and fun. Perhaps more importantly, children and parents have self-reported that undertaking our virtual experience has led to a reduction in their anxiety when they go to the operating room. We provide definitions, and technical aspects to assist other health care professionals in the development of low-cost 360-degree videos.

  • The online messaging portal of the Partner in Balance program (montage). Source: The Authors /; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    Effectiveness of a Blended Care Self-Management Program for Caregivers of People With Early-Stage Dementia (Partner in Balance): Randomized Controlled Trial


    Background: The benefits of electronic health support for dementia caregivers are increasingly recognized. Reaching caregivers of people with early-stage dementia could prevent high levels of burden and psychological problems in the later stages. Objective: The current study evaluates the effectiveness of the blended care self-management program, Partner in Balance, compared to a control group. Methods: A single-blind randomized controlled trial with 81 family caregivers of community-dwelling people with mild dementia was conducted. Participants were randomly assigned to either the 8-week, blended care self-management Partner in Balance program (N=41) or a waiting-list control group (N=40) receiving usual care (low-frequent counseling). The program combines face-to-face coaching with tailored Web-based modules. Data were collected at baseline and after 8 weeks in writing by an independent research assistant who was blinded to the treatment. The primary proximal outcome was self-efficacy (Caregiver Self-Efficacy Scale) and the primary distal outcome was symptoms of depression (Center for Epidemiological Studies Depression Scale). Secondary outcomes included mastery (Pearlin Mastery Scale), quality of life (Investigation Choice Experiments for the Preferences of Older People), and psychological complaints (Hospital Anxiety and Depression Scale-Anxiety and Perceived Stress Scale). Results: A significant increase in favor of the intervention group was demonstrated for self-efficacy (care management, P=.002; service use P=.001), mastery (P=.001), and quality of life (P=.032). Effect sizes were medium for quality of life (d=0.58) and high for self-efficacy care management and service use (d=0.85 and d=0.93, respectively) and mastery (d=0.94). No significant differences between the groups were found on depressive symptoms, anxiety, and perceived stress. Conclusions: This study evaluated the first blended-care intervention for caregivers of people with early-stage dementia and demonstrated a significant improvement in self-efficacy, mastery, and quality of life after receiving the Partner in Balance intervention, compared to a waiting-list control group receiving care as usual. Contrary to our expectations, the intervention did not decrease symptoms of depression, anxiety, or perceived stress. However, the levels of psychological complaints were relatively low in the study sample. Future studies including long-term follow up could clarify if an increase in self-efficacy results in a decrease or prevention of increased stress and depression. To conclude, the program can provide accessible preventative care to future generations of caregivers of people with early-stage dementia. Trial Registration: Netherlands Trial Register NTR4748; (Archived by WebCite at

  • A physician at the Nashik Kumbh Mela, a mass gathering in western India, uses a 3G enabled tablet computer, cloud computing, and remote analytics for real-time tracking of disease outbreaks. Thousands of healthcare providers in India are already using mobile devices for logging patient health information. Source: Image created by the authors; Copyright: The Authors; License: Licensed by JMIR.

    Reimagining Health Data Exchange: An Application Programming Interface–Enabled Roadmap for India


    In February 2018, the Government of India announced a massive public health insurance scheme extending coverage to 500 million citizens, in effect making it the world’s largest insurance program. To meet this target, the government will rely on technology to effectively scale services, monitor quality, and ensure accountability. While India has seen great strides in informational technology development and outsourcing, cellular phone penetration, cloud computing, and financial technology, the digital health ecosystem is in its nascent stages and has been waiting for a catalyst to seed the system. This National Health Protection Scheme is expected to provide just this impetus for widespread adoption. However, health data in India are mostly not digitized. In the few instances that they are, the data are not standardized, not interoperable, and not readily accessible to clinicians, researchers, or policymakers. While such barriers to easy health information exchange are hardly unique to India, the greenfield nature of India’s digital health infrastructure presents an excellent opportunity to avoid the pitfalls of complex, restrictive, digital health systems that have evolved elsewhere. We propose here a federated, patient-centric, application programming interface (API)–enabled health information ecosystem that leverages India’s near-universal mobile phone penetration, universal availability of unique ID systems, and evolving privacy and data protection laws. It builds on global best practices and promotes the adoption of human-centered design principles, data minimization, and open standard APIs. The recommendations are the result of 18 months of deliberations with multiple stakeholders in India and the United States, including from academia, industry, and government.

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

    Barriers to and Facilitators of Engagement With Remote Measurement Technology for Managing Health: Systematic Review and Content Analysis of Findings


    Background: Remote measurement technology refers to the use of mobile health technology to track and measure change in health status in real time as part of a person’s everyday life. With accurate measurement, remote measurement technology offers the opportunity to augment health care by providing personalized, precise, and preemptive interventions that support insight into patterns of health-related behavior and self-management. However, for successful implementation, users need to be engaged in its use. Objective: Our objective was to systematically review the literature to update and extend the understanding of the key barriers to and facilitators of engagement with and use of remote measurement technology, to guide the development of future remote measurement technology resources. Methods: We conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines involving original studies dating back to the last systematic review published in 2014. We included studies if they met the following entry criteria: population (people using remote measurement technology approaches to aid management of health), intervention (remote measurement technology system), comparison group (no comparison group specified), outcomes (qualitative or quantitative evaluation of the barriers to and facilitators of engagement with this system), and study design (randomized controlled trials, feasibility studies, and observational studies). We searched 5 databases (MEDLINE, IEEE Xplore, EMBASE, Web of Science, and the Cochrane Library) for articles published from January 2014 to May 2017. Articles were independently screened by 2 researchers. We extracted study characteristics and conducted a content analysis to define emerging themes to synthesize findings. Formal quality assessments were performed to address risk of bias. Results: A total of 33 studies met inclusion criteria, employing quantitative, qualitative, or mixed-methods designs. Studies were conducted in 10 countries, included male and female participants, with ages ranging from 8 to 95 years, and included both active and passive remote monitoring systems for a diverse range of physical and mental health conditions. However, they were relatively short and had small sample sizes, and reporting of usage statistics was inconsistent. Acceptability of remote measurement technology according to the average percentage of time used (64%-86.5%) and dropout rates (0%-44%) was variable. The barriers and facilitators from the content analysis related to health status, perceived utility and value, motivation, convenience and accessibility, and usability. Conclusions: The results of this review highlight gaps in the design of studies trialing remote measurement technology, including the use of quantitative assessment of usage and acceptability. Several processes that could facilitate engagement with this technology have been identified and may drive the development of more person-focused remote measurement technology. However, these factors need further testing through carefully designed experimental studies. Trial Registration: International Prospective Register of Systematic Reviews (PROSPERO) CRD42017060644; (Archived by WebCite at

  • Cloud computing in health care. Source: Flickr; Copyright: NEC Corporation of America; URL:; License: Creative Commons Attribution (CC-BY).

    Rethinking the Meaning of Cloud Computing for Health Care: A Taxonomic Perspective and Future Research Directions


    Background: Cloud computing is an innovative paradigm that provides users with on-demand access to a shared pool of configurable computing resources such as servers, storage, and applications. Researchers claim that information technology (IT) services delivered via the cloud computing paradigm (ie, cloud computing services) provide major benefits for health care. However, due to a mismatch between our conceptual understanding of cloud computing for health care and the actual phenomenon in practice, the meaningful use of it for the health care industry cannot always be ensured. Although some studies have tried to conceptualize cloud computing or interpret this phenomenon for health care settings, they have mainly relied on its interpretation in a common context or have been heavily based on a general understanding of traditional health IT artifacts, leading to an insufficient or unspecific conceptual understanding of cloud computing for health care. Objective: We aim to generate insights into the concept of cloud computing for health IT research. We propose a taxonomy that can serve as a fundamental mechanism for organizing knowledge about cloud computing services in health care organizations to gain a deepened, specific understanding of cloud computing in health care. With the taxonomy, we focus on conceptualizing the relevant properties of cloud computing for service delivery to health care organizations and highlighting their specific meanings for health care. Methods: We employed a 2-stage approach in developing a taxonomy of cloud computing services for health care organizations. We conducted a structured literature review and 24 semistructured expert interviews in stage 1, drawing on data from theory and practice. In stage 2, we applied a systematic approach and relied on data from stage 1 to develop and evaluate the taxonomy using 14 iterations. Results: Our taxonomy is composed of 8 dimensions and 28 characteristics that are relevant for cloud computing services in health care organizations. By applying the taxonomy to classify existing cloud computing services identified from the literature and expert interviews, which also serves as a part of the taxonomy, we identified 7 specificities of cloud computing in health care. These specificities challenge what we have learned about cloud computing in general contexts or in traditional health IT from the previous literature. The summarized specificities suggest research opportunities and exemplary research questions for future health IT research on cloud computing. Conclusions: By relying on perspectives from a taxonomy for cloud computing services for health care organizations, this study provides a solid conceptual cornerstone for cloud computing in health care. Moreover, the identified specificities of cloud computing and the related future research opportunities will serve as a valuable roadmap to facilitate more research into cloud computing in health care.

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  • The cultural adaptation of a scalable WHO eMental Health program for overseas Filipino workers

    Date Submitted: Jul 16, 2018

    Open Peer Review Period: Jul 20, 2018 - Sep 14, 2018

    Background: eMental Health interventions can address the mental health needs of different populations. Cultural adaptation of these interventions is crucial to establish better fit with the cultural g...

    Background: eMental Health interventions can address the mental health needs of different populations. Cultural adaptation of these interventions is crucial to establish better fit with the cultural group and to achieve better treatment outcomes. Objective: The aim of this study is to describe the cultural adaptation of the World Health Organization’s eMental Health program, Step-by-Step, for overseas Filipino workers (OFWs). We used a framework which posits that cultural adaptation should enhance: (a) relevance, in that the cultural group can relate with the content; (b) acceptability, where the cultural group will not find any element offensive; (c) comprehensibility, in that the program is understandable, and; (d) completeness, wherein the adapted version covers the same concepts and constructs as the original program. We aimed to have English and Filipino, and male and female versions. Methods: Three experienced Filipino psychologists provided their perspectives on the program and how it might be adapted for OFWs. We then adapted the program and obtained further feedback and suggestions from 28 OFWs working in diverse industries through focus group discussions (FGDs). We conducted seven FGDs with all-male participants and nine FGDs with all-female participants. In each FGD, cognitive interviewing was used to probe for relevance, acceptability, comprehensibility, and completeness of illustrations and text. Participant feedback guided a further round of iterative program adaptations, which were again shown to participants to seek additional feedback for validation and improvement. Results: We made a number of key adaptations to the Step-by-Step program. To enhance relevance, we adapted the program narrative to match OFW experiences, incorporated Filipino values, and illustrated familiar problems and activities. To increase acceptability, our main characters were changed to wise elders rather than health professionals (reducing mental health and help-seeking stigma), potentially political or unacceptable content was removed, and the program was made suitable for OFWs working in a variety of sectors. To increase comprehension, we used English and Filipino languages, simplified the text to ease interpretation of abstract terms or ideas, and ensured that text and illustrations matched. We also used Taglish (i.e., merged English and Filipino) when participants deemed pure Filipino translations sounded odd or were difficult to understand. Lastly, we retained the core elements and concepts included in the original Step-by-Step program to maintain completeness. Conclusions: This study showed the utility of using the four-point framework that focuses on acceptance, relevance, comprehensibility, and completeness in cultural adaptation. In the end, we achieved a culturally-appropriate adapted version of the Step-by-Step program for OFWs. We discuss lessons we learned in the process to guide future cultural adaptation projects of eMental Health interventions.

  • Evaluation of E-Learning for Medical Education in Low- and Middle-Income Countries: A Systematic Review

    Date Submitted: Jul 20, 2018

    Open Peer Review Period: Jul 20, 2018 - Jul 28, 2018

    Background: E-learning in medical education can contribute to alleviating the severe shortages of health workers in many low- and middle-income countries. In the past few decades, the rapid developmen...

    Background: E-learning in medical education can contribute to alleviating the severe shortages of health workers in many low- and middle-income countries. In the past few decades, the rapid development of technologies resulted in an abundance of new resources, including personal computers, smartphones, handheld devices, software and the Internet – at constantly decreasing costs. Consequently, educational interventions increasingly integrate e-learning to tackle the challenges of health workforce development and training. However, evaluations of e-learning interventions still lack clear methodology to assess the effectiveness and the success of e-learning for medical education, especially in those countries where they are most needed. Objective: Our specific research aim was to systematically describe currently used evaluation methods and definitions for the success of medical e-learning interventions for medical doctors and medical students in low- and middle-income countries. Our long-term objective is to contribute to generating effective and robust e-learning interventions to address critical health worker shortages in low- and middle-income countries. Methods: Seven databases were searched for e-learning interventions for medical education in low- and middle-income countries, covering publications ranging from January 2007 to June 2017. We derived search terms following a preliminary review of relevant literature and included studies published in English which implemented e-learning asynchronously for medical doctors and/or medical students in a low- or middle-income country. Three reviewers screened the references, assessed their study quality, and synthesized extracted information from the literature. Results: We included 52 studies representing a total of 12294 participants. Most of the e-learning evaluations were assessed summatively (83%) and within pilot studies (73%), relying mainly on quantitative evaluation methods using questionnaire (45%) and/or knowledge testing (36%). We identified a lack of evaluation standards for medical e-learning interventions, as methods varied considerably in the evaluation of their medical e-learning interventions with a high variation in study quality (general low study quality, based on study quality scales MERSQI, NOS and NOS-E), study period (ranging from 5 days up to 6 years), assessment methods (6 different main methods) and outcome measures (a total of 52 different outcomes), as well as in the interpretation of intervention success. The majority of studies relied on subjective measures and self-made evaluation frameworks, resulting in low comparability and validity of evidence. Most of the included studies reported success in their e-learning intervention. Conclusions: The evaluation of e-learning interventions needs to produce meaningful and comparable results. Currently, a majority of evaluations of e-learning approaches to educate medical doctors and medical students is based on self-reported measures that lack adherence to a standard evaluation framework. While the majority of studies report success of e-learning interventions – suggesting the potential benefits of the e-learning – the overall low quality of the evidence makes it difficult to draw firm conclusions. Methods development, study design guidance, and standardization of evaluation outcomes and approaches for e-learning interventions will be important for this field of education research to prosper. Methodological strength and standardization are particularly important, because the majority of the existing studies evaluate pilot interventions. Rigorous evidence on pilot success can improve the chances of scaling and sustaining e-learning approaches for health workers.

  • Digital health - Hope, Hype, and Halt

    Date Submitted: Jul 16, 2018

    Open Peer Review Period: Jul 20, 2018 - Sep 14, 2018

    Over the past 40 years, the healthcare community has been repeatedly excited by the hope of providing better care through the effective adoption of the technology. In the hope that digital health is g...

    Over the past 40 years, the healthcare community has been repeatedly excited by the hope of providing better care through the effective adoption of the technology. In the hope that digital health is going to be the game changer, an aura of hype has been created amongst the stakeholders of healthcare industry. However, digital health is yet to witness a large-scale adoption that could match the hope created about its utility. There does not exist an example where digital health has successfully transformed the health system of a geography and has demonstrated a net positive return on the initial investment. Owing to the lack of a positive business case, the initiatives pertaining to digital health are losing steam. Corporates are shutting down digital health labs, staunching investments in digital health, digital health conferences are consolidating, and governments are re-evaluating the funding regimes for such initiatives. For the technology to be able to create desired impact in this sector, the principle stakeholders namely governments, hospitals, insurers, tech developers, medical professionals, and patients need to participate equitably. The resources need to be focused on high impact areas like epidemiology surveys, legal and regulatory frameworks, geriatric care, and human resources training. For a new technology to thrive, the industry competitors and governments must work in unison to develop solutions that are pragmatic, solves the problems, reduce the cost of care delivery, and are sustainable in the long-term. Digital health is not dead, but it is in a stage where its revival will be an up-hill task.

  • Effectiveness of Smartphone-Based Self-Management Interventions on Self-care and Health Relevant Outcomes in Patients with Type 2 Diabetes: A Systematic Review and Meta-analysis

    Date Submitted: Jul 15, 2018

    Open Peer Review Period: Jul 18, 2018 - Sep 12, 2018

    Background: Type 2 Diabetes Mellitus (T2DM) is a major health problem worldwide. Proper self-management can improve health outcomes and reduces risk of diabetic complications. Recently, smartphone-bas...

    Background: Type 2 Diabetes Mellitus (T2DM) is a major health problem worldwide. Proper self-management can improve health outcomes and reduces risk of diabetic complications. Recently, smartphone-based technology has been used for self-management programs but their effectiveness in improving self-efficacy, self-care activities, health-related quality of life (HRQoL) and clinical outcomes for patients with T2DM is not well understood. Objective: To review the evidence and determine the effectiveness of smartphone-based self-management interventions on self-efficacy, self-care activities, HRQoL, glycated haemoglobin (HbA1c), body mass index (BMI), blood pressure (BP) levels of adults with T2DM. Methods: A systematic search of five databases (PubMed, Embase, Cochrane, CINAHL and Scopus) was conducted. Study published in English, from January 2007 to January 2018, were considered. Only randomised controlled trials (RCTs) of smartphone-based self-management interventions for patients with T2DM that reported any of the study outcomes were included. Two reviewers independently screened the studies, extracted data and assessed the quality of the studies. Meta-analyses were conducted for the different study outcomes. Results: A total of 26 articles, consisting of 22 studies with 2645 participants were included in the review. A meta-analysis conducted on self-efficacy revealed a large improvement of 0.98 (95% confidence interval [CI] 0.42 to 1.55; P < 0.001) with smartphone-based self-management interventions. The effect size on self-care activities was also large (d = 0.90; 95% CI 0.24 to 1.57; P < 0.001). Significant heterogeneity was present among studies pooled for both outcomes and subgroup analyses were conducted for self-efficacy. Smartphone-based self-management interventions also gave a small improvement on HRQoL (d = 0.26; 95% CI 0.06 to 0.47; P = .01) and a significant reduction in HbA1c (pooled MD = -0.55; 95% CI -0.60 to -0.40; P < 0.001). The effects on BMI and BP were not statistically significant. Conclusions: Smartphone-based self-management interventions appear to have beneficial effects on self-efficacy, self-care activities and health-relevant outcomes for patients with T2DM. However, more research with good study designs is needed to evaluate the effectiveness of smartphone-based self-care interventions for T2DM. Clinical Trial: NA

  • Development and long-term usability and acceptability of ExPRESS, a smartphone app to monitor basic symptoms and early signs of psychosis relapse

    Date Submitted: Jul 15, 2018

    Open Peer Review Period: Jul 18, 2018 - Sep 12, 2018

    Background: Relapse of schizophrenia is common, has profound, adverse consequences for patients and is costly to health services. Early signs interventions aim to use warning signs of deterioration to...

    Background: Relapse of schizophrenia is common, has profound, adverse consequences for patients and is costly to health services. Early signs interventions aim to use warning signs of deterioration to prevent full relapse. Such interventions show promise but could be further developed. The current paper addresses two developments: adding basic symptoms to checklists of conventional early signs; using a smartphone app (ExPRESS) to aid early signs monitoring. Objective: 1. Design a pool of self-report items assessing basic symptoms (Basic Symptoms Checklist, BSC); 2. Develop and beta test a smartphone app (ExPRESS) monitoring early signs, basic symptoms and psychotic symptoms; 3. Test the long-term usability of ExPRESS by gathering qualitative feedback from participants asked to use it weekly for six months. Methods: The BSC items and ExPRESS app were developed by a multidisciplinary team and adjusted following feedback from beta testers (n=5) with a schizophrenia diagnosis. Individuals (n=18) who had experienced a relapse of schizophrenia within the past year were then asked to use ExPRESS once a week for 6 months to answer questions on their experience of early signs, basic symptoms and psychotic symptoms. Face-to-face qualitative interviews (n=16) were conducted at the end of follow-up to explore participants’ experiences of using the phone app. The topic guide sought participants’ views on the following a priori themes: item content, layout and wording; the way the app looked; length and frequency of assessments; worries about using the app; how app use fitted with participants’ routines; the app’s extra features. Interview transcripts were analyzed using the framework method which allows both a priori and a posteriori themes to be identified and examined. Results: Participants had a mean age of 38 (range 22-57). Participants’ responses to a priori topics indicated that long-term use of the ExPRESS app was acceptable; they suggested small changes that could be made for future versions of ExPRESS. A posteriori themes gave further insight into individuals’ experiences of using ExPRESS. Some participants reported finding it more accessible than visits from a clinician, since assessments were more frequent, more anonymous and did not require the individual to explain their feelings in their own words. Nevertheless, barriers to app use were also reported. Despite the app containing no overtly therapeutic components, some participants found that answering weekly questions on the app prompted self-reflection which had therapeutic value for them. Conclusions: This study suggests that apps are an acceptable means of long-term symptom monitoring for service users with schizophrenia diagnosis across a wide age range. As long as the potential benefits are understood, patients are generally willing and motivated to use a weekly symptom-monitoring app; virtually all participants in the current study were prepared to do so for more than six months. Clinical Trial: NCT03558529

  • The Times They Are a-Changin’ – Healthcare 4.0 is Coming!

    Date Submitted: Jul 14, 2018

    Open Peer Review Period: Jul 18, 2018 - Sep 12, 2018

    The Industrial Revolution brought new economics and new epidemic patterns to the people, which formed the healthcare 1.0 that focused on public health solutions. The emergence of large production conc...

    The Industrial Revolution brought new economics and new epidemic patterns to the people, which formed the healthcare 1.0 that focused on public health solutions. The emergence of large production concept and technology brought healthcare to 2.0. Bigger hospitals and better medical education were established, and doctors were trained for specialty for better treatment quality. The size of computer shrunk. This allowed fast development of computer-based devices and information technology, leading the healthcare to 3.0. The initiation of smart medicine nowadays announces the arrival of healthcare 4.0 with new brain and new hands. It is an era of big revision of previous technologies, one of which is artificial intelligence which will lead humans to a new world that emphasizes more on advanced and continuous learnings.