Publishing Policies

(We are phasing out sections marked with *, replace with topical section)

Literature reviews, preferably systematic reviews and meta-analyses (authors are asked to follow the QUORUM checklist when submitting reviews), on the impact of digital health, mHealth, wearables, and apps for medicine on health outcomes and health policy.

Note that app and product reviews, where authors systematically searched app stores and review apps, are collected in the section Quality Evaluation and Descriptive Analysis/Reviews of Multiple Existing Mobile Apps. 

Related: 
Wearables and MHealth Reviews

Quality Evaluation and Descriptive Analysis/Reviews of Multiple Existing Mobile Apps

These are usually invited commentaries published alongside other articles. They may or may not be peer-reviewed.

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.

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 contacting the editor to discuss the suitability of a topic before submitting it. Submission of slides or audio/video files as supplementary files is strongly recommended.

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)

More recently, the World Health Organization (WHO) has declared an “infodemic” on top of the COVID-19 pandemic, and recognized the importance of infodemiology as a key method in fighting the infodemic. The seminal WHO papers on this topic were published in JMIR journals (e.g. Tangcharoensathien et al. 2020; Calleja et al. 2021).

See also related E-Collections: 

Infoveillance, Infodemiology and Digital Disease Surveillance  

Infoveillance and Social Listening

Pharmacovigilance

See also related Journals:

JMIR Infodemiology

JMIR Public Health & Surveillance 

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.

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".

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". 

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.

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.

For applications in medical education see also ChatGPT and Generative Language Models in Medical Education, 
Artificial Intelligence (AI) in Medical Education, Virtual Patients

See also JMIR e-collections on Robotics in Rehabilitation, Chatbots and Conversational Agents, Theme Issue 2022: Chatbots and COVID-19, Robots in Healthcare, Machine Learning, and Natural Language Processing 

Related Journal (preferred journal for machine-learning/AI applications that are not in clinical routine use): 
JMIR AI

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 an intelligent software which mimics a coach or therapist.

Related Themes/E-Collections:
ChatGPT and Generative Language Models in Medical Education 
Artificial Intelligence
Natural Language Processing

See also: Mobile Health in Psychiatry, mHealth for Wellness, Behavior Change and Prevention, Text-messaging (SMS)-Based Interventions. 

JMIR has a partnership with Infoway in which the APCs of Infoway-funded authors can be waived. For further information, please see KB article.

See also JMIR e-collections on Robotics in Rehabilitation, Chatbots and Conversational Agents, and Artificial Intelligence in Health.

Commentaries, opinion pieces, and original research related to Open Access to the research literature.

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. 

See also special 2019 theme issue on Blockchain and the call for papers.

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science is a similar concept as data mining and big data.

Related E-Collections:

Big Data

Machine Learning

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. jon@tripdatabase.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, email@ut.edu, University of Tennessee, Memphis, Tennessee Carolyn M. Brown, brown@emory.edu, 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.

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. 

Related E-Collections:

Big Data

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.

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!

Letters to the Editor are exempt from Article Processing Fees. While this journal does publish "Research Letters" (short articles containing original data), JMIR also publishes letters responding to a previously published article. Short articles containing original data should be submitted as general article, not as a letter. A letter to the editor must cite and critique or substantially comment on a recent paper published in JMIR. The letter will be forwarded to the authors of the cited or critiqued article or other reviewers, and original authors will get a chance to respond. While some new data in a letter are allowed, a letter is NOT a short research report.

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".

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, vaccinations, models, case studies, policy recommendations, rapid reviews, telework/telemedicine reports. etc.

See also: 

• Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports - see JMIR Public Health & Surveillance COVID-19 Theme Issue Call for Papers.

• JMIR Theme Issue: Covid-19 Special Issue (Articles submitted in response to the J Med Internet Res Call for Papers)

• JMH Theme Issue COVID-19 and Mental Health: Impact and Interventions (JMIR Mental Health)

• JPP Theme Issue: Digital Approaches for Pediatric Healthcare Delivery during the Coronavirus (Covid-19) Pandemic (see JMIR Pediatrics & Parenting Call for Papers)

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. 

See also: 

Artificial Intelligence [Section Id: 797]Chatbots and Conversational Agents [Section Id: 763]

Symptom checkers (SCs) are tools developed to provide clinical decision support to laypersons.
See also/Related: Consumer & Patient Education and Shared-Decision Making

Papers sponsored by or related to projects from DiMe, which is a nonprofit professional society dedicated to advancing digital medicine to optimize health.

This is a new article category introduced on 2022. APF $1500 / max 750 words. An abstract is not required. 

Theme issue (e-collection) related to role of health information technologies (HIT) in promoting and protecting reproductive health decisions. 

Closed theme issue (sponsored) invited papers on the present and future of pandemic technologies 

Guest Editors:

1. Dennis Krämer, dennis.kraemer@uni-goettingen.de
2. Joschka Haltaufderheide, joschka.haltaufderheide@uni-potsdam.de 
3. Jochen Vollmann, jochen.vollmann@rub.de 

The theme issue was funded by the German Federal Ministry for Education and Research and carried out as part of the international project "Digital Technologies of the Covid 19 Pandemic - A transnational Dialogue between Germany and Japan".

Invited papers only 

Contributions of telehealth, virtual consulting or other health care innovations to the net zero agenda; development and implementation of medical or health care programs to support increased environmental sustainability; projects and initiatives to combat or respond to climate change

See also: 
Environmental Health