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Journal of Medical Internet Research

The leading peer-reviewed journal for digital medicine, and health & healthcare in the Internet age


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 2016: 5.175, 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: Freepik; Copyright: yanalya; URL:; License: Licensed by JMIR.

    A Deep Learning Method to Automatically Identify Reports of Scientifically Rigorous Clinical Research from the Biomedical Literature: Comparative Analytic Study


    Background: A major barrier to the practice of evidence-based medicine is efficiently finding scientifically sound studies on a given clinical topic. Objective: To investigate a deep learning approach to retrieve scientifically sound treatment studies from the biomedical literature. Methods: We trained a Convolutional Neural Network using a noisy dataset of 403,216 PubMed citations with title and abstract as features. The deep learning model was compared with state-of-the-art search filters, such as PubMed’s Clinical Query Broad treatment filter, McMaster’s textword search strategy (no Medical Subject Heading, MeSH, terms), and Clinical Query Balanced treatment filter. A previously annotated dataset (Clinical Hedges) was used as the gold standard. Results: The deep learning model obtained significantly lower recall than the Clinical Queries Broad treatment filter (96.9% vs 98.4%; P<.001); and equivalent recall to McMaster’s textword search (96.9% vs 97.1%; P=.57) and Clinical Queries Balanced filter (96.9% vs 97.0%; P=.63). Deep learning obtained significantly higher precision than the Clinical Queries Broad filter (34.6% vs 22.4%; P<.001) and McMaster’s textword search (34.6% vs 11.8%; P<.001), but was significantly lower than the Clinical Queries Balanced filter (34.6% vs 40.9%; P<.001). Conclusions: Deep learning performed well compared to state-of-the-art search filters, especially when citations were not indexed. Unlike previous machine learning approaches, the proposed deep learning model does not require feature engineering, or time-sensitive or proprietary features, such as MeSH terms and bibliometrics. Deep learning is a promising approach to identifying reports of scientifically rigorous clinical research. Further work is needed to optimize the deep learning model and to assess generalizability to other areas, such as diagnosis, etiology, and prognosis.

  • A group of people laughing, drinking, and posting photos (ie, alcohol posts) on social media. Source: Shutterstock; Copyright: Alessandro Biascioli; URL:; License: Licensed by the authors.

    Social Drinking on Social Media: Content Analysis of the Social Aspects of Alcohol-Related Posts on Facebook and Instagram


    Background: Alcohol is often consumed in social contexts. An emerging social context in which alcohol is becoming increasingly apparent is social media. More and more young people display alcohol-related posts on social networking sites such as Facebook and Instagram. Objective: Considering the importance of the social aspects of alcohol consumption and social media use, this study investigated the social content of alcohol posts (ie, the evaluative social context and presence of people) and social processes (ie, the posting of and reactions to posts) involved with alcohol posts on social networking sites. Methods: Participants (N=192; mean age 20.64, SD 4.68 years, 132 women and 54 men) gave researchers access to their Facebook and/or Instagram profiles, and an extensive content analysis of these profiles was conducted. Coders were trained and then coded all screenshotted timelines in terms of evaluative social context, presence of people, and reactions to post. Results: Alcohol posts of youth frequently depict alcohol in a positive social context (425/438, 97.0%) and display people holding drinks (277/412, 67.2%). In addition, alcohol posts were more often placed on participants’ timelines by others (tagging; 238/439, 54.2%) than posted by participants themselves (201/439, 45.8%). Furthermore, it was revealed that such social posts received more likes (mean 35.50, SD 26.39) and comments than nonsocial posts (no people visible; mean 10.34, SD 13.19, P<.001). Conclusions: In terms of content and processes, alcohol posts on social media are social in nature and a part of young people’s everyday social lives. Interventions aiming to decrease alcohol posts should therefore focus on the broad social context of individuals in which posting about alcohol takes place. Potential intervention strategies could involve making young people aware that when they post about social gatherings in which alcohol is visible and tag others, it may have unintended negative consequences and should be avoided.

  • Participant with cardiovascular disease uploading training data from home. Source: Image created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Home-Based Rehabilitation With Telemonitoring Guidance for Patients With Coronary Artery Disease (Short-Term Results of the TRiCH Study): Randomized...


    Background: Cardiac rehabilitation (CR) is an essential part of contemporary coronary heart disease management. However, patients exiting a center-based CR program have difficulty retaining its benefits. Objective: We aimed to evaluate the added benefit of a home-based CR program with telemonitoring guidance on physical fitness in patients with coronary artery disease (CAD) completing a phase II ambulatory CR program and to compare the effectiveness of this program in a prolonged center-based CR intervention by means of a randomized controlled trial. Methods: Between February 2014 and August 2016, 90 CAD patients (unblinded, mean age 61.2 years, SD 7.6; 80/90, 89.0% males; mean height 1.73 m, SD 0.7; mean weight 82.9 kg, SD 13; mean body mass index 27.5 kg/m2, SD 3.4) who successfully completed a 3-month ambulatory CR program were randomly allocated to one of three groups: home-based (30), center-based (30), or control group (30) on a 1:1:1 basis. Home-based patients received a home-based exercise intervention with telemonitoring guidance consisting of weekly emails or phone calls; center-based patients continued the standard in-hospital CR, and control group patients received the usual care including the advice to remain physically active. All the patients underwent cardiopulmonary exercise testing for assessment of their peak oxygen uptake (VO2 P) at baseline and after a 12-week intervention period. Secondary outcomes included physical activity behavior, anthropometric characteristics, traditional cardiovascular risk factors, and quality of life. Results: Following 12 weeks of intervention, the increase in VO2 P was larger in the center-based (P=.03) and home-based (P=.04) groups than in the control group. In addition, oxygen uptake at the first (P-interaction=.03) and second (P-interaction=.03) ventilatory thresholds increased significantly more in the home-based group than in the center-based group. No significant changes were observed in the secondary outcomes. Conclusions: Adding a home-based exercise program with telemonitoring guidance following completion of a phase II ambulatory CR program results in further improvement of physical fitness and is equally as effective as prolonging a center-based CR in patients with CAD. Trial Registration: NCT02047942; (Archived by WebCite at

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

    Evaluation of a Diabetes Self-Management Program: Claims Analysis on Comorbid Illnesses, Health Care Utilization, and Cost


    Background: An estimated 30.3 million Americans have diabetes mellitus. The US Department of Health and Human Services created national objectives via its Healthy People 2020 initiative to improve the quality of life for people who either have or are at risk for diabetes mellitus, and hence, lower the personal and national economic burden of this debilitating chronic disease. Diabetes self-management education interventions are a primary focus of this initiative. Objective: The aim of this study was to evaluate the impact of the Better Choices Better Health Diabetes (BCBH-D) self-management program on comorbid illness related to diabetes mellitus, health care utilization, and cost. Methods: A propensity score matched two-group, pre-post design was used for this study. Retrospective administrative medical and pharmacy claims data from the HealthCore Integrated Research Environment were used for outcome variables. The intervention cohort included diabetes mellitus patients who were recruited to a diabetes self-management program. Control cohort subjects were identified from the HealthCore Integrated Research Environment by at least two diabetes-associated claims (International Classification of Diseases-Ninth Revision, ICD-9 250.xx) within 2 years before the program launch date (October 1, 2011-September 30, 2013) but did not participate in BCBH-D. Controls were matched to cases in a 3:1 propensity score match. Outcome measures included pre- and postintervention all-cause and diabetes-related utilization and costs. Cost outcomes are reported as least squares means. Repeated measures analyses (generalized estimating equation approach) were conducted for utilization, comorbid conditions, and costs. Results: The program participants who were identified in HealthCore Integrated Research Environment claims (N=558) were matched to a control cohort of 1669 patients. Following the intervention, the self-management cohort experienced significant reductions for diabetes mellitus–associated comorbid conditions, with the postintervention disease burden being significantly lower (mean 1.6 [SD 1.6]) compared with the control cohort (mean 2.1 [SD 1.7]; P=.001). Postintervention all-cause utilization was decreased in the intervention cohort compared with controls with −40/1000 emergency department visits vs +70/1000; P=.004 and −5780 outpatient visits per 1000 vs −290/1000; P=.001. Unadjusted total all-cause medical cost was decreased by US $2207 in the intervention cohort compared with a US $338 decrease in the controls; P=.001. After adjustment for other variables through structural equation analysis, the direct effect of the BCBH-D was –US $815 (P=.049). Conclusions: Patients in the BCBH-D program experienced reduced all-cause health care utilization and costs. Direct cost savings were US $815. Although encouraging, given the complexity of the patient population, further study is needed to cross-validate the results.

  • Sharing suicidal ideation online. Source: iStock by Getty Images; Copyright: Sinicakover; URL:; License: Licensed by the authors.

    Detecting Suicidal Ideation on Forums: Proof-of-Concept Study


    Background: In 2016, 44,965 people in the United States died by suicide. It is common to see people with suicidal ideation seek help or leave suicide notes on social media before attempting suicide. Many prefer to express their feelings with longer passages on forums such as Reddit and blogs. Because these expressive posts follow regular language patterns, potential suicide attempts can be prevented by detecting suicidal posts as they are written. Objective: This study aims to build a classifier that differentiates suicidal and nonsuicidal forum posts via text mining methods applied on post titles and bodies. Methods: A total of 508,398 Reddit posts longer than 100 characters and posted between 2008 and 2016 on SuicideWatch, Depression, Anxiety, and ShowerThoughts subreddits were downloaded from the publicly available Reddit dataset. Of these, 10,785 posts were randomly selected and 785 were manually annotated as suicidal or nonsuicidal. Features were extracted using term frequency-inverse document frequency, linguistic inquiry and word count, and sentiment analysis on post titles and bodies. Logistic regression, random forest, and support vector machine (SVM) classification algorithms were applied on resulting corpus and prediction performance is evaluated. Results: The logistic regression and SVM classifiers correctly identified suicidality of posts with 80% to 92% accuracy and F1 score, respectively, depending on different data compositions closely followed by random forest, compared to baseline ZeroR algorithm achieving 50% accuracy and 66% F1 score. Conclusions: This study demonstrated that it is possible to detect people with suicidal ideation on online forums with high accuracy. The logistic regression classifier in this study can potentially be embedded on blogs and forums to make the decision to offer real-time online counseling in case a suicidal post is being written.

  • Health care provider using Google Glass face-mounted technology. Source: Image created by the Authors; Copyright: Sandra Odenheimer; URL:; License: Creative Commons Attribution (CC-BY).

    Patient Acceptance of Remote Scribing Powered by Google Glass in Outpatient Dermatology: Cross-Sectional Study


    Background: The ubiquitous use of electronic health records (EHRs) during medical office visits using a computer monitor and keyboard can be distracting and can disrupt patient-health care provider (HCP) nonverbal eye contact cues, which are integral to effective communication. Provider use of a remote medical scribe with face-mounted technology (FMT), such as Google Glass, may preserve patient-HCP communication dynamics in health care settings by allowing providers to maintain direct eye contact with their patients while still having access to the patient’s relevant EHR information. The medical scribe is able to chart patient encounters in real-time working in an offsite location, document the visit directly into EHR, and free HCP to focus only on the patient. Objective: The purpose of this study was to examine patient perceptions of their interactions with an HCP who used FMT with a remote medical scribe during office visits. This includes an examination of any association between patient privacy and trust in their HCP when FMT is used in the medical office setting. Methods: For this descriptive, cross-sectional study, a convenience sample of patients was recruited from an outpatient dermatology clinic in Northern California. Participants provided demographic data and completed a 12-item questionnaire to assess their familiarity, comfort, privacy, and perceptions following routine office visits with an HCP where FMT was used to document the clinical encounter. Data were analyzed using appropriate descriptive and inferential statistics. Results: Over half of the 170 study participants were female (102/170, 59.4%), 60.0% were Caucasian (102/170), 24.1% were Asian (41/170), and 88.8% were college-educated (151/170). Age ranged between 18 and 90 years (mean 50.5, SD 17.4). The majority of participants (118/170, 69.4%) were familiar with FMT, not concerned with privacy issues (132/170, 77.6%), and stated that the use of FMT did not affect their trust in their HCP (139/170, 81.8%). Moreover, participants comfortable with the use of FMT were less likely to be concerned about privacy (P<.001) and participants who trusted their HCP were less likely to be concerned about their HCP using Google Glass (P<.009). Almost one-third of them self-identified as early technology adopters (49/170, 28.8%) and 87% (148/170) preferred their HCP using FMT if it delivered better care. Conclusions: Our study findings support the patient acceptance of Google Glass use for outpatient dermatology visits. Future research should explore the use of FMT in other areas of health care and strive to include a socioeconomically diverse patient population in study samples.

  • Woman using a Web-based tailored smoking cessation intervention. Source: Maxpixel; Copyright: Maxpixel; URL:; License: Public Domain (CC0).

    The Effectiveness of Web-Based Tailored Smoking Cessation Interventions on the Quitting Process (Project Quit): Secondary Analysis of a Randomized Controlled...


    Background: Project Quit was a randomized Web-based smoking cessation trial designed and conducted by researchers from the University of Michigan, where its primary outcome was the 7-day point prevalence. One drawback of such an outcome is that it only focuses on smoking behavior over a very short duration, rather than the quitting process over the entire study period. Objective: The aim of this study was to consider the number of quit attempts during the 6-month study period as an alternative outcome, which would better reflect the quitting process. We aimed to find out whether tailored interventions (high vs low) are better in reducing the number of quit attempts for specific subgroups of smokers. Methods: To identify interactions between intervention components of smoking cessation and individual smoker characteristics, we employed Poisson regression to analyze the number of quit attempts. This approach allowed us to construct data-driven, personalized interventions. Results: A negative effect of the number of cigarettes smoked per day (P=.03) and a positive effect of education (P=.03) on the number of quit attempts were detected from the baseline covariates (n=792). Thus, for every 10 extra cigarettes smoked per day, there was a 5.84% decrease in the expected number of quit attempts. Highly educated participants had a 15.49% increase in their expected number of quit attempts compared with their low-educated counterparts. A negative interaction between intervention component story and smoker’s education was also detected (P=.03), suggesting that a high-tailored story given to highly educated people results in 13.50% decrease in the number of quit attempts compared with a low-tailored story. Conclusions: A highly individually tailored story is significantly more effective for smokers with a low level of education. This is consistent with prior findings from Project Quit based on the 7-day point prevalence.

  • Source: / Pixabay; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    A Teledermatology Scale-Up Framework and Roadmap for Sustainable Scaling: Evidence-Based Development


    Background: The objectives of South Africa’s electronic health (eHealth) strategy recognize the value proposition that telemedicine practices hold for rural and urban referrals, but a lack of accepted and formalized scale-up has impeded realization of benefits. While both synchronous and asynchronous teledermatology exist, these remain localized and not scaled-up. Skin pathology is often the first sign of an HIV/AIDS infection, which remains a major cause of morbidity and mortality in South Africa. It is essential to replace the current inefficient dermatology referral process with a swift, organized, and efficacious one. Objective: The objective of this study is to present an evidenced-based teledermatology scale-up framework (TDSF) and implementation roadmap (TDSF-IR). Methods: A qualitative method with a design science research process model was used which consisted of 5 phases: (1) Awareness, which confirmed the need for an evidence-based TDSF and supporting TDSF-IR; (2) Suggestion, where a proposal was delivered on how to develop a TDSF and TDSF-IR; (3) Development, where we identified recommended design requirements and used these to identify and critique existing teledermatology or related scale-up frameworks; (4) Evaluation and validation, where we assessed outputs of the development phase against the design requirements and validated by confirming the veracity of the TDSF and TDSF-IR (validation involved 4 key senior teledermatology stakeholders using a questionnaire with a 5-point Likert scale); and (5) Conclusion, where validation results were used to finalize and communicate the TDSF and TDSF-IR to users. Results: The study identified 5 TDSF components: eHealth building blocks, eHealth strategic objectives and budget, scale-up continuum periods, scale-up drivers, and scale-up phases. In addition, 36 subcomponents were identified. Each was further characterized and described to enable design of the final evidence-based TDSF. An implementation roadmap (TDSF-IR) was also prepared as a guide for an implementer with step-by-step instructions for application of the TDSF. For the validation study of the TDSF and supporting TDSF-IR, 4 purposively selected key senior teledermatology management stakeholders were asked if they found it useful as a guide to assist the South African public health system with teledermatology scale-up. The mean (SD) of Likert-scale rating was 4.0 (0.53) where 4=Agree and 33 of 36 responses were either agree or strongly agree. Conclusions: This study developed a TDSF and supporting roadmap (TDSF-IR) that are evidence-based. The proposed approach and described tools could be adapted to assist with ensuring scale-up and sustainability for other eHealth practices in other locations.

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

    Dissemination of a Web-Based Tool for Supporting Health Insurance Plan Decisions (Show Me Health Plans): Cross-Sectional Observational Study


    Background: The rate of uninsured people has decreased dramatically since the Affordable Care Act was passed. To make an informed decision, consumers need assistance to understand the advantages and disadvantages of health insurance plans. The Show Me Health Plans Web-based decision support tool was developed to improve the quality of health insurance selection. In response to the promising effectiveness of Show Me Health Plans in a randomized controlled trial (RCT) and the growing need for Web-based health insurance decision support, the study team used expert recommendations for dissemination and implementation, engaged external stakeholders, and made the Show Me Health Plans tool available to the public. Objective: The purpose of this study was to implement the public dissemination of the Show Me Health Plans tool in the state of Missouri and to evaluate its impact compared to the RCT. Methods: This study used a cross-sectional observational design. Dissemination phase users were compared with users in the RCT study across the same outcome measures. Time spent using the Show Me Health Plans tool, knowledge, importance rating of 9 health insurance features, and intended plan choice match with algorithm predictions were examined. Results: During the dissemination phase (November 2016 to January 2017), 10,180 individuals visited the SMHP website, and the 1069 users who stayed on the tool for more than one second were included in our analyses. Dissemination phase users were more likely to live outside St. Louis City or County (P<.001), were less likely to be below the federal poverty level (P<.001), and had a higher income (P=.03). Overall, Show Me Health Plans users from St. Louis City or County spent more time on the Show Me Health Plans tool than those from other Missouri counties (P=.04); this association was not observed in the RCT. Total time spent on the tool was not correlated with knowledge scores, which were associated with lower poverty levels (P=.009). The users from the RCT phase were more likely to select an insurance plan that matched the tool’s recommendations (P<.001) compared with the dissemination phase users. Conclusions: The study suggests that a higher income population may be more likely to seek information and online help when making a health insurance plan decision. We found that Show Me Health Plans users in the dissemination phase were more selective in the information they reviewed. This study illustrates one way of disseminating and implementing an empirically tested Web-based decision aid tool. Distributing Web-based tools is feasible and may attract a large number of potential users, educate them on basic health insurance information, and make recommendations based on personal information and preference. However, using Web-based tools may differ according to the demographics of the general public compared to research study participants.

  • Source: US Air Force; Copyright: Stacey Geiger; URL:; License: Public Domain (CC0).

    The Burden of a Remote Trial in a Nursing Home Setting: Qualitative Study


    Background: Despite an aging population, older adults are typically underrecruited in clinical trials, often because of the perceived burden associated with participation, particularly travel associated with clinic visits. Conducting a clinical trial remotely presents an opportunity to leverage mobile and wearable technologies to bring the research to the patient. However, the burden associated with shifting clinical research to a remote site requires exploration. While a remote trial may reduce patient burden, the extent to which this shifts burden on the other stakeholders needs to be investigated. Objective: The aim of this study was to explore the burden associated with a remote trial in a nursing home setting on both staff and residents. Methods: Using results from a grounded analysis of qualitative data, this study explored and characterized the burden associated with a remote trial conducted in a nursing home in Dublin, Ireland. A total of 11 residents were recruited to participate in this trial (mean age: 80 years; age range: 67-93 years). To support research activities, we also recruited 10 nursing home staff members, including health care assistants, an activities co-ordinator, and senior nurses. This study captured the lived experience of this remote trial among staff and residents and explored the burden associated with participation. At the end of the trial, a total of 6 residents and 8 members of staff participated in semistructured interviews (n=14). They reviewed clinical data generated by mobile and wearable devices and reflected upon their trial-related experiences. Results: Staff reported extensive burden in fulfilling their roles and responsibilities to support activities of the trial. Among staff, we found eight key characteristics of burden: (1) comprehension, (2) time, (3) communication, (4) emotional load, (5) cognitive load, (6) research engagement, (7) logistical burden, and (8) product accountability. Residents reported comparatively less burden. Among residents, we found only four key characteristics of burden: (1) comprehension, (2) adherence, (3) emotional load, and (4) personal space. Conclusions: A remote trial in a nursing home setting can minimize the burden on residents and enable inclusive participation. However, it arguably creates additional burden on staff, particularly where they have a role to play in locally supporting and maintaining technology as part of data collection. Future research should examine how to measure and minimize the burden associated with data collection in remote trials.

  • Source: The Authors /; Copyright: The Authors; URL:; License: Licensed by the authors.

    Comparing Approaches to Mobile Depression Assessment for Measurement-Based Care: Prospective Study


    Background: To inform measurement-based care, practice guidelines suggest routine symptom monitoring, often on a weekly or monthly basis. Increasingly, patient-provider contacts occur remotely (eg, by telephone and Web-based portals), and mobile health tools can now monitor depressed mood daily or more frequently. However, the reliability and utility of daily ratings are unclear. Objective: This study aimed to examine the association between a daily depressive symptom measure and the Patient Health Questionnaire-9 (PHQ-9), the most widely adopted depression self-report measure, and compare how well these 2 assessment methods predict patient outcomes. Methods: A total of 547 individuals completed smartphone-based measures, including the Patient Health Questionnaire-2 (PHQ-2) modified for daily administration, the PHQ-9, and the Sheehan Disability Scale. Multilevel factor analyses evaluated the reliability of latent depression based on the PHQ-2 (for repeated measures) between weeks 2 and 4 and its correlation with the PHQ-9 at week 4. Regression models predicted week 8 depressive symptoms and disability ratings with daily PHQ-2 and PHQ-9. Results: The daily PHQ-2 and PHQ-9 are highly reliable (range: 0.80-0.88) and highly correlated (r=.80). Findings were robust across demographic groups (age, gender, and ethnic minority status). Daily PHQ-2 and PHQ-9 were comparable in predicting week 8 disability and were independent predictors of week 8 depressive symptoms and disability, though the unique contribution of the PHQ-2 was small in magnitude. Conclusions: Daily completion of the PHQ-2 is a reasonable proxy for the PHQ-9 and is comparable to the PHQ-9 in predicting future outcomes. Mobile assessment methods offer researchers and clinicians reliable and valid new methods for depression assessment that may be leveraged for measurement-based depression care.

  • Source: Placeit; Copyright: The Authors; URL:; License: Licensed by the authors.

    Multicomponent mHealth Intervention for Large, Sustained Change in Multiple Diet and Activity Risk Behaviors: The Make Better Choices 2 Randomized Controlled...


    Background: Prevalent co-occurring poor diet and physical inactivity convey chronic disease risk to the population. Large magnitude behavior change can improve behaviors to recommended levels, but multiple behavior change interventions produce small, poorly maintained effects. Objective: The Make Better Choices 2 trial tested whether a multicomponent intervention integrating mHealth, modest incentives, and remote coaching could sustainably improve diet and activity. Methods: Between 2012 and 2014, the 9-month randomized controlled trial enrolled 212 Chicago area adults with low fruit and vegetable and high saturated fat intakes, low moderate to vigorous physical activity (MVPA) and high sedentary leisure screen time. Participants were recruited by advertisements to an open-access website, screened, and randomly assigned to either of two active interventions targeting MVPA simultaneously with, or sequentially after other diet and activity targets (N=84 per intervention) or a stress and sleep contact control intervention (N=44). They used a smartphone app and accelerometer to track targeted behaviors and received personalized remote coaching from trained paraprofessionals. Perfect behavioral adherence was rewarded with an incentive of US $5 per week for 12 weeks. Diet and activity behaviors were measured at baseline, 3, 6, and 9 months; primary outcome was 9-month diet and activity composite improvement. Results: Both simultaneous and sequential interventions produced large, sustained improvements exceeding control (P<.001), and brought all diet and activity behaviors to guideline levels. At 9 months, the interventions increased fruits and vegetables by 6.5 servings per day (95% CI 6.1-6.8), increased MVPA by 24.7 minutes per day (95% CI 20.0-29.5), decreased sedentary leisure by 170.5 minutes per day (95% CI –183.5 to –157.5), and decreased saturated fat intake by 3.6% (95% CI –4.1 to –3.1). Retention through 9-month follow-up was 82.1%. Self-monitoring decreased from 96.3% of days at baseline to 72.3% at 3 months, 63.5% at 6 months, and 54.6% at 9 months (P<.001). Neither attrition nor decline in self-monitoring differed across intervention groups. Conclusions: Multicomponent mHealth diet and activity intervention involving connected coaching and modest initial performance incentives holds potential to reduce chronic disease risk. Trial Registration: NCT01249989; (Archived by WebCite at

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  • Hospital big data to monitor influenza-like illness in real time

    Date Submitted: Jun 21, 2018

    Open Peer Review Period: Jun 25, 2018 - Aug 20, 2018

    Background: Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with one- to three-week delay. Accurate real-time monitoring systems of influenza ou...

    Background: Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with one- to three-week delay. Accurate real-time monitoring systems of influenza outbreaks could be useful for public health decisions. Several works have investigated the possibility to use internet-users’ activity data and different statistical models to predict influenza epidemics in near real-time. However, very few studies have investigated hospital big data. Objective: Here, we compared internet and electronic health records (EHR) data and different statistical models to identify the best approach (data type and statistical model) for ILI estimates in real time. Methods: We used Google Data for internet data and the clinical data warehouse eHOP, that includes all EHR from Rennes University Hospital (France), for hospital data. We compared three statistical models, Random Forest (RF), Elastic Net, and Support Vector Machine (SVM). Results: For national ILI incidence rate, the best correlation was 0.98 and the mean squared error (MSE) was 866 obtained with hospital data and the SVM model. For the Brittany region, the best correlation was 0.923 and MSE was 2364 with hospital data and the SVM model. Conclusions: For national ILI incidence rate, the best correlation was 0.98 and the mean squared error (MSE) was 866 obtained with hospital data and the SVM model. For the Brittany region, the best correlation was 0.923 and MSE was 2364 with hospital data and the SVM model.

  • The use of smart technology in an online community of patients suffering from degenerative cervical myelopathy.

    Date Submitted: Jun 21, 2018

    Open Peer Review Period: Jun 25, 2018 - Aug 20, 2018

    Background: Degenerative Cervical Myelopathy (DCM) is a prevalent and progressively disabling condition. Treatment is currently limited to surgery, the timing of which is not without controversy. New...

    Background: Degenerative Cervical Myelopathy (DCM) is a prevalent and progressively disabling condition. Treatment is currently limited to surgery, the timing of which is not without controversy. New international guidelines advise all patients should undergo lifelong surveillance, with moderate to severe or progressive disease offered surgery. Long-term surveillance will place substantial burden on health services. Moreover, clinic assessments may risk misrepresenting disease severity. The use of smart technology to monitor disease progression could provide an invaluable opportunity to lessen this burden and improve patient care. However, given the older demographic of DCM the feasibility of smart technology use is unclear. Objective: The aim of this study is to investigate current usage of smart technology in patients with self-reported DCM to inform design of smart technology applications targeted at monitoring DCM disease progression. Methods: Google Analytics from the patient section of, an international DCM charity with a large online patient community, were analysed over a one-year period. A total of 15,761 sessions were analysed. Results: In total, 39.6% of visitors accessed the website using desktop computer, 35.1% mobile and 25.3% tablet. Of the mobile visitors, 98.2% utilised a touchscreen device. A total of 53.7% of mobile visitors used iOS and 43.2% Android operating systems. Apple and Samsung were the most popular devices, utilised by 53.7% and 25.8% of visitors, respectively. Overall visitor age was representative of DCM trials. Smart technology was widely used by older visitors: 24.0% of mobile visitors and 60.5% of tablet visitors were 55 years or older. Conclusions: Smart technology is commonly used by DCM patients. DCM applications need to be iOS and Android compatible to be available to all patients.

  • How To Categorize Occupation And Disease History in Medicine? A Free-Text Online Survey On Six Clinics and 18 University Hospitals in Turkey

    Date Submitted: Jun 21, 2018

    Open Peer Review Period: Jun 25, 2018 - Aug 20, 2018

    The medical interview is one of the most challenging topics in medicine. The ways in which the interview is implemented varies greatly depending on the clinician. The ways in which a.) the interview i...

    The medical interview is one of the most challenging topics in medicine. The ways in which the interview is implemented varies greatly depending on the clinician. The ways in which a.) the interview is evaluated and, b.) the outcomes are determined, are also dependent on the bias of the clinician. For those reasons, the process of the medical interview is thought of as an art, rather than a science. In this study, clinicians were asked "how do you categorize the outcomes of the medical interview?” in relation to: a patient’s own disease history, a patients maternal and paternal disease history, and a patient’s current occupation. To obtain a sample representative of all clinicians in Turkey, we invited participants from 18 university hospitals dispersed through 14 providences. 1,270 clinicians representing specializations of otology, general surgery, internal medicine, cardiology, pulmonology and psychiatry were invited to participate in the study. Of those 1,270 clinicians, 77 of them responded to the survey. We obtained participation from clinicians in six of the 18 clinics. Our representative sample size was approximately 6% of the intended population.

  • RESPOND (REducing Stress and Preventing Depression): web-based rumination-focused cognitive behavioural therapy (i-RFCBT) for high ruminating university students at risk for depression: a randomised controlled trial

    Date Submitted: Jun 22, 2018

    Open Peer Review Period: Jun 25, 2018 - Aug 20, 2018

    Background: Prevention of depression is a priority to reduce its global disease burden. Targeting specific risk factors, such as rumination, may increase the efficacy of preventive interventions. Rumi...

    Background: Prevention of depression is a priority to reduce its global disease burden. Targeting specific risk factors, such as rumination, may increase the efficacy of preventive interventions. Rumination-focused CBT (RFCBT) was developed to specifically target depressive rumination. A prior randomised controlled trial (RCT) in Dutch 15-22-year-olds at risk because of elevated worry and rumination found that both guided web-based RFBCT (i-RFCBT) and group-delivered RFCBT equally reduced depressive symptoms and onset of depressive cases over 1-year follow-up, relative to usual care control. Objective: The primary objective was to test whether guided i-RFCBT would prevent the incidence of major depression relative to usual care when extended to UK university students and using diagnostic interviews to improve the assessment of incidence of depression. The secondary objective was to test the feasibility and estimated effect sizes of unguided i-RFCBT. Methods: To address the primary objective, a Phase III RCT was designed and powered to compare high risk university students (N = 235), selected with elevated worry/rumination, recruited via an open access website in response to circulars within universities and internet advertisement, randomised to receive either guided i-RFCBT (an interactive web-based version of RFCBT, supported by asynchronous written web-based support from qualified and specially trained therapists), or usual care control. To address the secondary objective, participants were also randomised to an adjunct arm of unguided (self-administered) i-RFCBT. Primary outcome was onset of a major depressive episode, assessed with structured diagnostic interview at 3 (post-intervention), 6 and 15 months post-randomisation, conducted by telephone, blind to condition. Secondary outcomes of symptoms of depression and anxiety and levels of worry and rumination were self-assessed through questionnaires at baseline and the same follow-up intervals. Results: A total of 235 participants were randomised to guided i-RFCBT (N = 82), unguided i-RFCBT (N = 76) or usual care (N = 77). For the primary comparison, guided i-RFCBT reduced risk of depression by 34% relative to usual care. Participants with higher levels of baseline stress benefited most from the intervention (HR: 0.43, 95% CI [0.21, 0.87], P = .02). Significant improvements in rumination, worry and depressive symptoms were found in the short to medium term. Of six modules, guided participants completed a mean of 3.46 modules (SD = 2.25), with 46.34% (38/82) compliant (completing ≥4 modules). Similar effect sizes and compliance rates were found for unguided i-RFCBT. Conclusions: These results confirm that guided i-RFCBT can reduce the onset of depression in high-risk young people reporting high levels of worry/rumination and stress. The feasibility study argues for formally testing unguided i-RFCBT as a preventive intervention: as a scalable intervention, if the observed effect sizes are robustly replicated in a Phase III trial, it has potential to significantly address the burden of depression. Clinical Trial: Current Controlled Trials ISRCTN12683436. Date of registration: 27/10/2014

  • Determining Period of Measurement in Time-Series Predictions of Disease Counts using Auto-Correlation and Change Point Analysis: Evidence from 2007-2017 in Northern Nevada

    Date Submitted: Jun 20, 2018

    Open Peer Review Period: Jun 24, 2018 - Aug 19, 2018

    For time-series analysis of disease counts, this paper proposes a method that identifies the shortest period of measurement, while not significantly decreasing prediction performance. There is a body...

    For time-series analysis of disease counts, this paper proposes a method that identifies the shortest period of measurement, while not significantly decreasing prediction performance. There is a body of literature in statistics that shows how auto-correlation can identify the best period of measurement in order to improve the performance of a time-series prediction; therefore, period of measurement plays an important role in improving the performance of disease-count predictions. However, from the operational perspective in public health surveillance, there is a length limitation in which the period of measurements can offer meaningful and valuable predictions. The proposed method is an attempt to shorten the period of prediction but not significantly decreasing the prediction performance. Our study applies change-point analysis on auto-correlations of different periods of measurement in order to identify the shortest period that has a similar time-series prediction performance to the best prediction. Our method uses Q-Score as performance indicator. The evaluation is conducted against artificial neural networks and autoregressive, integrated-moving average as time-series analysis methods. The data used in this evaluation contains disease counts from 2007 to 2017 in northern Nevada. The disease counts, including: Chlamydia, Salmonella, Respiratory syncytial virus (RSV), Gonorrhea, Meningitis Viral and Influenza A, were predicted. Auto-correlation cannot guarantee the best performance for prediction of disease counts. However, the proposed method adopting change-point analysis suggests a period of measurement that ensures an operationally acceptable prediction period and a performance not significantly different than the best prediction.

  • Wearable device-based walking programs in rural older adults can improve physical activity and health outcome: a feasibility study

    Date Submitted: Jun 20, 2018

    Open Peer Review Period: Jun 24, 2018 - Aug 19, 2018

    Background: Community-dwelling older adults living in rural areas are in a very unfavorable environment for health care compared to urban older adults. We thought that intermittent coaching through we...

    Background: Community-dwelling older adults living in rural areas are in a very unfavorable environment for health care compared to urban older adults. We thought that intermittent coaching through wearable devices would help to optimize health care for older adults in medically limited environments. Objective: We aimed to evaluate whether a wearable device and mobile-based intermittent coaching or self-management can increase physical activity and/or health outcome of small groups of older adults in rural areas. Methods: To address the above evaluation goal, we carried out the "Smart walk" program, which is a health care model using a wearable device to promote self-exercise focused on community-dwelling older adults managed by a community health center. We randomly selected older adults who had enrolled in a population-based, prospective cohort study of aging, the Aging Study of Pyeongchang Rural Area. The "Smart Walk" program was a 13-month program from March 2017 to March 2018, consisting of six months of coaching, one month of rest, and six months of self-management. We evaluated (1) differences in physical activity and health outcome according to frailty status and (2) pre- and post-analyses of the Smart walk program. We also performed intergroup analysis according to adherence of wearable devices. Results: We recruited 22 participants (11 robust and 11 pre-frail older adults). The two groups were similar in most variables, except age, frailty index, and SPPB score associated with frailty criteria. After a six-month coaching program, the pre-frail group showed significant improvement in usual gait speed (0.73±0.11 vs. 0.96±0.27, P=0.02), IPAQ Kcal (2790.36±2224.62 vs. 7589.72±4452.52, P = 0.01), and Euroqol-5D score (0.84±0.07 vs. 0.90±0.07, P=0.02) variables, although there was no significant improvement in the robust group. The average total step count was significantly different, and that of the coaching period was approximately four times higher than that of the self-management period (5,584,295.83 vs. 1,289,084.66, P <0.001). We found that the ‘long-self’ group who used the wearable device for the longest time increased body weight and BMI by 0.65 ± 1.317 and 0.097 ± 0.513, respectively, compared to other groups. Conclusions: Our Smart walking program improved physical fitness, anthropometric measurements, and geriatric assessment categories in a small group of older adults in rural areas with limited resources for monitoring. Further validation through various rural public health centers and a large number of rural older adults is required. Clinical Trial: This study was approved by the Asan Medical Center institution’s ethics committee (IRB File No: 2015-0673)