JMIR Publications

JMIR Data

A multidisciplinary journal to publish open datasets for analysis and re-analysis

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Journal Description


"The future is data" (Patty Brennan, Incoming NLM Director, 2016)

 

Do you have a dataset that has already been analyzed and led to a number of publications, but which could also be valuable for other researchers? Do you want to get credit for generating a potentially interesting dataset even if you are not the one who wish to analyze them? Do you wish to launch a challenge / competition (you have a dataset, and want others to solve a problem or answer a question with it - can be combined with publishing a competition document in JMIR Challenges).

 

JMIR Data is a new unique journal focusing on the publication and curation of datasets, small and large, in the field of medicine and health.

 

This can include - but is not limited to - molecular and genomic data, patient/participant data from trials and experiments (properly anonymized), patient-generated data (e.g. from accelerometers/mobile apps) or patient-reported outcomes, unstructured data such as interview/focus group transscripts, "big data" from a variety of data sources, or unusual data such as Internet log files or app usage files.

 

Data can be from already published studies (which should be cited or even included), or unpublished data which has not yet been analyzed but may be useful for others.  

 

Data files can be provided as excel files, SQL files, or other file formats.

 

All data files need to be properly anonymized and de-identified to protect the privacy of participants.

 

Data will be lightly peer-reviewed, with a focus on privacy (can individuals be re-identified?) and on the brief paper accompaniying the dataset.

 

Each submission consists of a brief paper and the dataset(s) as Mutlimedia Appendix. We also recommend to include other relevant documents such as IRB approvals etc.

 

For the paper we recommend the following structure:

 

Introduction: What is the history/background/motivation for the dataset / datacollection and the issues to be addressed with the dataset? Possible research questions to be answered with the dataset? Have the research questions been answered already completely or partially or not at all?

 

Methods: How were the data collected?

 

Results: Briefly state all results and cite the papers known 

 

Discussion: For whom is the dataset useful and what remains to be done/analyzed?

 

Conflict of Interest

 

Acknowledgments (optional)

 

Author contributions (optional) 

 

Multimedia Appendix: All data files, as well as IRB approval / informed consent forms etc., if applicable, and PDFs of publications (if published under a Creative Commons license)

 

In addition to publishing brief papers with datasets, we will also publish viewpoint papers, tutorials and reviews related to collection of datasets, ethics and privacy (e.g. de-identification methods), file formats and curation of datasets.

 

Latest Submissions Open for Peer-Review:

View All Open Peer Review Articles
  • User Participation and Engagement with the See Me Smoke-Free mHealth App: Results of a Prospective Feasibility Trial

    Date Submitted: Apr 21, 2017

    Open Peer Review Period: May 31, 2017 - Jul 14, 2017

    Background: The See Me Smoke-Free (SMSF) mobile health (mHealth) application (app) was developed to help women quit smoking by targeting concerns about body weight, body image, and self-efficacy throu...

    Background: The See Me Smoke-Free (SMSF) mobile health (mHealth) application (app) was developed to help women quit smoking by targeting concerns about body weight, body image, and self-efficacy through cognitive behavioral techniques and guided imagery audio files addressing smoking, diet, and physical activity. A feasibility trial found associations between SMSF usage and positive treatment outcomes. This paper reports a detailed exploration of program use among those who downloaded the app, and the relationship between program use and treatment outcomes. Objective: To determine whether: 1) participants were more likely to set quit dates, be current smokers, and report higher levels of smoking at baseline than non-participants; 2) participants opened the app and listened to audio files more frequently than non-participants; and 3) participants with more app usage had a higher likelihood of smoking abstinence at follow-up. Methods: The SMSF feasibility trial was a single arm, within-subjects, prospective cohort study with assessments at baseline, 30- and 90-days post-enrollment. The SMSF app was deployed on the Google Play store for download, and basic profile characteristics were obtained for all app installers. Additional variables were assessed for study participants. Participants were prompted to use the app daily during study participation. Crude differences in baseline characteristics between trial participants and non-participants were evaluated using t-tests (continuous variables) and Fisher’s exact tests (categorical variables). Exact Poisson tests were used to assess group-level differences in mean usage rates over the full study period, using aggregate Google Analytics data on participation and usage. Negative binomial regression models were used to estimate associations of app usage with participant baseline characteristics, after adjustment for putative confounders. Associations between app usage and smoking abstinence were assessed using separate logistic regression models for each outcome measure. Results: Participants (n=151) were more likely than non-participants (n=96) to report female gender (P < 0.02) and smoking in the 30 days prior to enrollment (P < 0.0001). Participants and non-participants opened the app and updated quit dates at the same average rate (Rate ratio (RR) 0.98; 95% CI: 0.92, 1.04; P = 0.43), but participants started audio files (RR 1.07; 95% CI: 1.00, 1.13; P < 0.04) and completed audio files (RR 1.11; 95% CI: 1.03, 1.18; P < 0.003) at significantly higher rates than non-participants. Higher app usage among participants was generally associated with increased smoking cessation, and most effect sizes suggested strong associations, though generally without statistical significance. Conclusions: The current study suggests potential efficacy of the SMSF app, as increased usage was generally associated with higher smoking abstinence. A planned randomized controlled trial will assess the SMSF app’s efficacy as an intervention tool to help women quit smoking. Clinical Trial: ClinicalTrials.gov NCT02972515

  • Low- and No-Cost Strategies to Recruit Women to a Mobile Health Smoking Cessation Trial

    Date Submitted: Jan 19, 2017

    Open Peer Review Period: May 31, 2017 - Jul 14, 2017

    Background: Successful recruitment and retention of adequate numbers of participants to mobile health (mHealth) studies remains a challenge. Given that researchers must decide how to invest limited re...

    Background: Successful recruitment and retention of adequate numbers of participants to mobile health (mHealth) studies remains a challenge. Given that researchers must decide how to invest limited recruitment resources, it is important to identify the most effective recruitment strategies, defined as those that incur low costs relative to participant yield. Objective: The objective of this manuscript is to describe the development and implementation process for the recruitment phase of an mHealth intervention designed to increase smoking cessation among weight-concerned women smokers. These recruitment methods could be applicable across a range of mHealth studies. Methods: Study information was released to the media in multiple phases. First, local city and state media were contacted, followed by national women’s health media, and finally outlets in states with high smoking rates. Stories and mentions resulting from the press releases (earned media) were disseminated via existing department and new study-specific social media accounts. Strategic hashtags were used in Facebook and Twitter posts to connect with broader smoking cessation campaigns. Posts were also made to third-party Facebook smoking cessation communities and Internet classifieds sites. Results: Media coverage was documented across 75 publications and radio/television broadcasts, 35 of which were local, 39 national, and 1 international. Between March 30th and July 31st, 2015, 151 participants were successfully recruited to the study. Conclusions: Leveraging social media, and coordinating with university public affairs offices were effective and low-cost strategies to earn media coverage, and reach potential participants. Clinical Trial: Not Applicable

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