Special Case Report
These are usually invited commentaries published alongside other articles. They may or may not be peer-reviewed.
Policy and Policy Proposals
Policy proposals should be based on a thorough review of the literature and stakeholder consultations, workshops or consensus building processes etc. If it is just the opinion of an individual (or small group of individuals), submit as viewpoint.
Viewpoints and Perspectives
Opinion articles or perspectives papers which would not otherwise qualify as "original papers", because they do not have much original data, but would also not qualify as reviews, because they are based on personal experiences, workshop results, system descriptions etc.
A"how-to" paper on an important practical or research issue. We recommend to contact the editor to discuss suitability of a topic before submitting it.Submission of slides or audio/video files as supplementary files is strongly recommended.
Web-based and Mobile Health Interventions
Mobile Health (mhealth)
Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine
e-Mental Health and Cyberpsychology
Infodemiology and Infoveillance
Infodemiology (Eysenbach 2002, Eysenbach 2006, Eysenbach 2009) has been defined as "the science of distribution and determinants of information in an electronic medium, specifically the Internet, or in a population, with the ultimate aim to inform public health and public policy. Infodemiology data can be collected and analyzed in near real time. Examples for infodemiology applications include: the analysis of queries from Internet search engines to predict disease outbreaks (eg. influenza); monitoring peoples' status updates on microblogs such as Twitter for syndromic surveillance; detecting and quantifying disparities in health information availability; identifying, defining, assessing and monitoring the "quality" of public health relevant publications on the Internet (eg. anti-vaccination sites, but also news articles or expert-curated outbreak reports); automated tools to measure information diffusion and knowledge translation, and tracking the effectiveness of health marketing campaigns e.g. by measuring how they resonate in social media or mass media outlets. Moreover, analyzing how people search and navigate the Internet for health-related information, as well as how they communicate and share this information, can provide valuable insights into health-related behavior of populations. " (Eysenbach 2009)
See also related E-Collections:
See also related Journals:
Participatory Medicine & E-Patients
Telehealth and Telemonitoring
Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology
Demographics of Users, Social & Digital Divide
Consumer & Patient Education and Shared-Decision Making
Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals
Research Instruments, Questionnaires, and Tools
e-Learning and Medical Education
Games and Gamification for Health
Peer-to-Peer Support and Online Communities
Clinical Information and Decision Making
Human Factors and Usability Case Studies
Recruitment of Research Participants
Quality/Credibility of eHealth Information and Trust Issues
Ethics, Privacy, and Legal Issues
We welcome high-impact original research, well-researched reviews, viewpoints and tutorials on emerging privacy and confidentiality issues in the age of personal health records, Google Health, Patient-Accessible Health Records, and Web-based behavior change interventions.
Knowledge Translation and Implementation Science
Engagement with and Adherence to Digital Health Interventions, Law of Attrition
Long-term adherence to digital health interventions is one of the fundamental problems in digital health - how can we make digital health interventions engaging to prevent people/participants to cease use or drop out from studies? This research priority and paradigm was first posited by Eysenbach in the classic highly cited paper "The Law of Attrition", and is also known as "Eysenbach's Law".
Virtual Reality and Virtual Worlds
eHealth Literacy / Digital Literacy
eHealth Literacy (nowadays also sometimes referred to as "digtial health literacy" was introduced and defined as "the ability to seek, find, understand, and appraise health information from electronic sources and apply the knowledge gained to addressing or solving a health problem" by Camron Norman and colleagues in their seminal 2006 paper "eHealth Literacy: Essential Skills for Consumer Health in a Networked World".
Physician and Health Services Rating by Consumers
Innovations and Technology for Physical Activity Education
Cost-Effectiveness and Economics
Theoretical Frameworks and Concepts
Crowdsourcing and Mechanical Turks
Patient-Reported Outcome Measures (PROMs)
A patient-reported outcome (PRO) is a health outcome directly reported by the patient who experienced it. It stands in contrast to an outcome reported by someone else, such as a physician-reported outcome, a nurse-reported outcome, and so on. PRO methods, such as questionnaires, are used in clinical trials or other clinical settings, to help better understand a treatment's efficacy. The use of digitized PROs, or electronic patient-reported outcomes (ePROs), is on the rise in today's health research industry and a frequent focus of JMIR papers.
Email Communication, Web-Based Communication, Secure Messaging
Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. More specifically, Kaplan and Haenlein define AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation”. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving" (Wikipedia).
This JMIR e-collection focuses on methods and approaches using artificial intelligence in health and medicine.
For clinical decision making see Decision Support for Health Professionals and Clinical Information and Decision Making; for decision support for consumers see also Consumer & Patient Education and Shared-Decision Making.
See also JMIR e-collections on Robotics in Rehabilitation, Chatbots and Conversational Agents, Robots in Healthcare, Machine Learning, and Natural Language Processing for related concepts and methods.
Chatbots and Conversational Agents
Chatbots are Artificial Intelligence programs (web-based or using smartphone app/messaging), which are increasingly used in particular for mental health applications (e.g. Depression and Mood Disorders), prevention and Behavior Change applications (such as Smoking Cessation or physical activity interventions). They are based on text-only exchanges between the client and a intelligent software which mimics a coach or therapist.
Innovations and Technology for Healthy Eating Education
Canada Health Infoway e-Collection Benefits Evaluation
Robots in Healthcare
Epublishing and Open Access
Commentaries, opinion pieces, and original research related to Open Access to the research literature.
Internet of Things
Blockchain, Distributed Ledger Apps for Health and Medicine
Blockchain technology and decentralized applications (DApps) have the potential to alleviate the traditionally high dependency on centralized, trusted parties for certification of information integrity and data ownership. These distributed ledger technologies (DLTs) mediate transactions and exchanges of digital assets in a decentralized and consensus-driven nature, which allows agreements (ie, smart contracts) to be directly made between interacting parties while guaranteeing their execution. Key properties of blockchain technology, including immutability, decentralization, distribution, replicated storage, and transparency, provide a unique position for this technology to serve as a potential infrastructure to address pressing issues in health care, such as incomplete records at point of care and difficult access to patients’ own health information.
Online Dating, Sexual Health Behavior
E-Health Policy and Health Systems Innovation
E-Health / Health Services Research and New Models of Care
Digital Health Reporting Standards, Quality and Transparency in e-Research
Virtual Communities and Communities of Practice for Healthcare Providers
Business and Entrepreneurship in eHealth
Scientometrics, Infometrics, and Altmetrics
Implantable Drug Delivery Systems, Ingestible Sensors and Digital Pills
eHealth Service, Product, Resource Reviews
Guidelines for Electronic Resources Reviews Revised December 2009 The purpose of the new electronic resources reviews section in the Journal of Medical Internet Research (JMIR) is to provide critical appraisals of electronic products and services that assist health care consumers and health professionals to select resources to manage or improve health. The focus is on consumer health informatics products, i.e. applications that have a direct interface to the consumer (although many of these products also have interfaces to health professionals, EMRs etc.). We have two different pathways of soliciting/getting reviews: developer/company-sponsored vs author sponsored. First, for "developer/company sponsored" reviews, the manufacturer, developer, or distributor of a service or product submits a description of a product/service (with access codes, if required) to JMIR, and the journal editor will try to find a reviewer. Whether or not a product/service/ressource is being reviewed is at the discretion of the editor. SUBMISSION AND (in case of review + publication) PUBLICATION FEES ARE THE RESPONSIBILITY OF THE SPONSORING DEVELOPER/COMPANY. The sponsor has no influence on the content of the review. Second, author- or journal-sponsored reviews, publication fees are the responsibility of the submitting author (or are subsidized by the journal). We require that the author has no conflicts of interests, in particular no relationships (financial or otherwise) to the developer, manufacturer, or distributor of the product/service. Reviewers will evaluate many types of resources such as websites, databases, research and reference tools, educational instruments, online behavior change programs, iphone/mobile phone applications, personal health record (PHR) systems and PHR platforms. Web-based and mobile phone resources are the primary focus of reviews, though innovative hardware and consumer devices are included as appropriate. To be reviewed, resources must be readily accessible for use by consumers, preferably in multiple locations and jurisdictions. We prefer to have reviews of products which have a potentially large impact, and are not only used locally, e.g. products which are launched by influential corporations. We also prefer to focus on new products (launched within the last 12 months or so). Some comparative information (competitor products and services) is useful and appreciated, although the focus should be on a single product or service. (Major comparative evaluation studies should be submitted as JMIR articles rather than reviews). The following guidelines are adapted from JMLA’s Electronic Resource Review guidelines http://www.webcitation.org/5mMZX6v7D. As with book reviews, electronic resources reviews should include a general description of the resource, the intended audience, and its good and bad points. The reviewer should include sufficient description to give others a clear idea of the purpose of the resource (include 1-2 screenshots), its major features, the accessibility and usability of the resource, the quality of the accompanying documentation and/or the effectiveness of the resource if tested in evaluation/research studies. Below is a list of some of the items to consider when writing reviews; not all items may be appropriate for all resources. • purpose • general description (also mention the reviewed version here) • contents (with screenshot) • intended audience • major features • accessibility (costs?) • usability • advantages / strong points • deficiencies and disadvantages, weak points • technological administration issues • review and critical appraisal of any effectiveness studies or other research published about the specific resource (if any); discuss possible impact on health services, health policy, public health, if any • timeliness • brief comparison to other similar products • rate the application on a Medicine 2.0 rating scale as defined below If the reviewed resource is web-accessible, then please create snapshots using webcitation.org. Reviews should be double-spaced and typed and normally should not exceed two to three pages in length (four pages is the maximum). Reviews exceeding four pages, double-spaced, eleven-point type may be edited for length. The number of photos, figures, illustrations, and tables is limited to one per review. The following elements (when available or applicable) should be included in the bibliographic information at the beginning of the review: * title (including version number) * date of publication * ISBN, ISSN, and/or URL * author or editor (last name, first name, and/or initials) * publisher with electronic and postal contact information * price (or pricing structure) * technical requirements Examples: Turning Research Into Practice (TRIP). Jon Brassey, TRIP Database, 12 Llansannor Drive, Cardiff, United Kingdom, CF10 4AW. email@example.com; http://www.tripdatabase.com/index.html; free Website. Evidence Matters. Evidence Matters, 78 St-Joseph West #209, Montreal, QC, H2T 2P4, Canada; 866.843.0756; ContactUs@EvidenceMatters.com; http://www.evidencematters.com; institutional subscriptions only, contact for pricing. At the end of the review, include: * reviewer's full name * abbreviations of any advanced academic degrees, professional degrees, or certifications (e.g., MLS, PhD, MD, RN, AHIP) * email address * institutional affiliation, city, and state (do not abbreviate) Examples: Richard Nollan, firstname.lastname@example.org, University of Tennessee, Memphis, Tennessee Carolyn M. Brown, email@example.com, Health Sciences Center Library, Emory University School of Medicine, Atlanta, Georgia Conflicts of Interest. State any actual or perceived conflicts of interest here. For electronic resource reviews we require that author add the following statement: “The author(s) of this review certify that they have no relationship (financial, spousal, or otherwise) to the developer or sales agents of the product/service reviewed.” References. For electronic resource reviews there is a strict reference limit of 5. References in electronic resources reviews including cited URLs (which should be webcited) should conform to JMIR/AMA style. See Reference Style in the Information for Authors and References on the JMIR site for further information. The editors reserve the right to make editorial changes if these changes do not affect the overall content of the review. Substantive changes will be discussed with reviewers. The editors reserve the right to reject reviews that are deemed unsatisfactory in quality. The JMIR requires authors to sign the copyright license agreement and disclosure form in case of acceptance; it is the responsibility of the first author to ensure that coauthors sign and submit the forms.
Digital Science, Open Science
New methods, frameworks, collaborations to conduct science and clinical trials in the digital age and age of open data
Registered reports adhere to the highest ethical standards in research and require a protocol to be published (e.g. in JMIR Research Protocols), ideally before data collection. Publishing a protocol (Registered Report Stage 1, or RR1) prevents bias and JMIR's "acceptance in principle" policy for projects with published protocol facilitates publication of study results even if they are negative.
This category/journal section/e-collection is for papers reporting the results (Registered Report Stage 2, or RR2). The IRRID in the abstract links back to the DOI of the protocol.
Precision medicine uses "big data" and data science to personalize diagnostic and therapeutic strategies for patients, based on their personal genetic and behavioral background.
An evidence-based critical appraisal of a paper published in another journal. Could be used to point out key papers in other journals, or to discuss methodological flaws. See separate guidelines on how to write a CATCH-IT paper.Will usually be forwarded to the original author for a rebuttal.
Theme issue 2019: Using Technology to Detect, Combat, and Prevent Research Misconduct
Theme Issue: Bayesian Methods In Medical Research
This is an ecollection of papers submitted as a result of our standing Call for Papers to reanalyze trials using a Bayesian Method. Submissions are still accepted!
Corrigenda and Addenda
This section lists all substantive corrections, additions or changes made to articles and reviews subsequent to their first publication in the journal. Corrigenda are usually submitted by the corresponding author of the original article, or the section editor. Published papers are considered "final", thus JMIR makes corrections to published papers only in exceptional circumstances.Note that while we do not charge to correct errata that are the responsibility of the publisher, we charge a $190 fee for discretionary corrigenda and addenda (please submit a correction under that section, if it is the authors' responsibility/decision to correct or add information to a already published article).
For corrigenda that are discretionary and a result of author-oversight (e.g. corrections in the affiliation etc) we charge a $190 processing fee to make changes in the original paper and publish an erratum. Please submit a correction statement (text similar to http://www.jmir.org/2015/3/e76/) at http://www.jmir.org/author/submit/1 under the section "Discretionary Corrigenda".
JMIR Theme Issue 2020/21: COVID-19 Special Issue
The Journal of Medical Internet Research is inviting submissions for a special issue of the journal dedicated to Covid-19 research.
All papers will be fast tracked and shared with the World Health Organization (WHO) immediately on submission. Please submit field reports, surveillance reports, technologies, apps, protocols and reports on isolation, suppression, treatment protocols, models, case studies, policy recommendations, rapid reviews, telework/telemedicine reports. etc.
JMH Theme Issue COVID-19 and Mental Health: Impact and Interventions (JMIR Mental Health)
Sensors at Home and Domotics
Innovations in Clinical Trials and Research Data Management
Drug Repurposing and Off-Label Use
Digital Pain Assessment and Management
Voice assistants are a subset of artificial intelligence powered chatbots/conversatinal agents that can understand natural human voice and which can respond with an artificial voice.
Examples for voice assistants are Amazon Alexa. Google Assistant. Microsoft Cortana. Samsung Bixby. Apple Siri. IBM Watson.
Symptom checkers (SCs) are tools developed to provide clinical decision support to laypersons.
See also/Related: Consumer & Patient Education and Shared-Decision Making