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JMIR Formative Research

Process evaluations, early results, and feasibility/pilot studies of digital and non-digital interventions

Editor-in-Chief:

Amaryllis Mavragani, PhD, Scientific Editor at JMIR Publications, Canada


Impact Factor 2.4 More information about Impact Factor CiteScore 4.2 More information about CiteScore

JMIR Formative Research publishes peer-reviewed, openly accessible papers containing results from process evaluations, feasibility/pilot studies and other kinds of formative research and preliminary results. While the original focus was on the design of medical- and health-related research and technology innovations, JMIR Formative Research publishes studies from all areas of medical and health research.

Formative research is research that occurs before a program is designed and implemented, or while a program is being conducted. Formative research can help

  • define and understand populations in need of an intervention or public health program
  • create programs that are specific to the needs of those populations
  • ensure programs are acceptable and feasible to users before launching
  • improve the relationship between users and agencies/research groups
  • demonstrate the feasibility, use, satisfaction with, or problems with a program before large-scale summative evaluation (looking at health outcomes)

Many funding agencies will expect some sort of pilot/feasibility/process evaluation before funding a larger study such as a Randomized Controlled Trial (RCT).

Formative research should be an integral part of developing or adapting programs and should be used while the program is ongoing to help refine and improve program activities. Thus, formative evaluation can and should also occur in the form of a process evaluation alongside a summative evaluation such as an RCT.

JMIR Formative Research fills an important gap in the academic journals landscape, as it publishes sound and peer-reviewed formative research that is critical for investigators to apply for further funding, but that is usually not published in outcomes-focused medical journals aiming for impact and generalizability.

Summative evaluations of programs and apps/software that have undergone a thorough formative evaluation before launch have a better chance to be published in high-impact flagship journals; thus, we encourage authors to submit - as a first step - their formative evaluations in JMIR Formative Research (and their evaluation protocols to JMIR Research Protocols). 

JMIR Formative Research is indexed in MEDLINEPubMed, PubMed CentralDOAJ, Scopus, Sherpa/Romeo, EBSCO/EBSCO Essentials, and the Emerging Sources Citation Index (ESCI).

JMIR Formative Research received a 2025 Impact Factor of 2.4, ranking Q2 in Health Care Sciences & Services (97/194).

JMIR Formative Research received a Scopus CiteScore of 4.2 (2025), placing it in the 68th percentile (149/466) as a second quartile (Q2) journal in the field of Medicine, and in the 52nd percentile (81/168) as a second quartile (Q2) journal in the field of Health Informatics. 


Recent Articles

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Formative Evaluation of Digital Health Interventions

Type 2 diabetes (T2D) is one of the most common noncommunicable diseases, requiring ongoing lifestyle changes and continuous glucose management through medication, diet, and physical activity. Traditional self-monitoring of blood glucose can be burdensome, especially with frequent finger pricks. As continuous glucose monitoring (CGM) becomes more affordable and accessible, it offers benefits such as increased glucose awareness, behavioral modifications, and reduced anxiety. However, challenges remain, including cost, discomfort, skin reactions, and privacy concerns. In the United Kingdom, perceptions of CGM among people with T2D, including both users and nonusers, are not well understood, limiting insight into factors influencing adoption and sustained use.

Woman in white shirt holding smartphone, illuminated by red light
Formative Evaluation of Digital Health Interventions

Generative artificial intelligence (GenAI) conversational agents are increasingly integrated within digital mental health interventions (DMHIs). However, empirical data on real-world engagement, usage patterns, and satisfaction with GenAI conversational agents remain limited.

Doctor analyzing medical scans of skeleton, brain, and heart on laptop
Formative Evaluation of Digital Health Interventions

The use of artificial intelligence and machine learning (ML) tools is now common in the advancement of health care services and clinical risk estimation. Legacy systems make use of highly informative feature sets developed from years of clinical expertise and research to estimate different outcomes, but only recently have they been tested against novel statistical approaches. One such system, the Johns Hopkins Adjusted Clinical Group (ACG) System, is a long-standing and widely used approach to categorizing clinical risk factors, and it is amenable to ML techniques.

Doctor pointing at medical scan on laptop showing human anatomy and brain
Case Report

Artificial intelligence (AI) has the potential to enhance resource efficiency, improve patient treatment, and increase safety in health care. Still, there is limited knowledge on how to implement and evaluate AI solutions in real-world clinical settings. To address this gap, we conducted a formative process evaluation of the first large-scale procurement and implementation of a commercial AI solution in Norwegian health care. F The Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework, was used for the formative process evaluation throughout the 4-year project to guide data collection, analysis, and real-time feedback.

Businesswoman in a black blazer smiles while typing on a smartphone.
Formative Evaluation of Digital Health Interventions

Young adults have high rates of mental health problems, such as mood or anxiety symptoms, and high rates of problematic drinking. Many young adults who undergo psychiatric hospitalization to address depression and anxiety symptoms also engage in risky drinking and tend to drink to cope with negative emotions. However, in many cases, treatment programs focusing on mood and anxiety symptoms often fail to adequately address problematic alcohol use in young adults.

Medical professional using tablet in clinic, patient in background
Development and Evaluation of Research Methods, Instruments and Tools

Large language models (LLMs) have gained increasing popularity in medical education, with evidence supporting their educational value when framed through the lens of cognitive load theory. Source-based LLMs, which explicitly ground responses in user-uploaded material via retrieval-augmented generation algorithms, may offer additional educational value by using student-developed materials to conceptualize new areas of learning within a familiar framework. This has applications for areas like medical education in dermatology, which could benefit from inclusive sources and enhanced education to alleviate health care gaps. However, no prior studies have examined whether the inclusion of student-authored notes alters the response characteristics of a source-based LLM when responding to medical questions.

Doctor and paramedics provide oxygen to a young boy in a hospital bed.
Formative Evaluation of Digital Health Interventions

Simulation-based training has established itself as integral to clinical education, particularly for high-stakes, low-frequency pediatric emergencies. Innovations incorporating virtual reality (VR) are rapidly gaining traction for offering scalable, repeatable, and immersive opportunities for scenario-based learning. Understanding its role and applicability in postgraduate pediatric training, however, remains limited, with further exploration required into how pediatric trainees perceive, conceptualize, and anticipate VR-based simulation within real-world training contexts.

Blockchain infographic showing a patient at the center, linked to hospital, electronic health record, research, privacy, security, and mobile app
Formative Evaluation of Digital Health Interventions

Fragmentation of electronic health records in oncology hinders coordinated care, delays diagnoses, and limits therapeutic personalization. Blockchains promise to promote secure, interoperable, and patient-centered data governance; however, patient perceptions of blockchains remain underexplored, particularly in middle-income countries such as Brazil.

Woman checks fitness app on phone as man runs on park path.
Development and Evaluation of Research Methods, Instruments and Tools

Motivational interviewing (MI) is an effective counseling approach for promoting health behavior change, but its scalability is constrained by the need for highly trained human counselors. Large language models (LLMs) may provide a scalable way to support MI counseling, but evidence remains limited, especially for Chinese MI resources and evaluations based on standardized MI fidelity frameworks.

Doctor using laptop showing echocardiogram of prolapsed valve in left ventricle
Formative Evaluation of Digital Health Interventions

Mitral valve prolapse (MVP) is a common valvulopathy associated, in a minority of cases, with heart failure, severe mitral regurgitation (MR), and sudden arrhythmic death. Digital tools hold promise for faster and more efficient recruitment of study participants into a large-scale MVP Registry.

Preprints Open for Peer Review

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