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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 2.1 More information about Impact Factor CiteScore 3.5 More information about CiteScore

JMIR Formative Research (JFR, ISSN 2561-326X) 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 Journal Impact Factor of 2.1 according to the latest release of the Journal Citation Reports from Clarivate, 2025.

With a CiteScore of 3.5 (2024) JMIR Formative Research is a Q2 journal in the field of Medicine (miscellaneous), according to Scopus data.

Recent Articles

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Formative Evaluation of Digital Health Interventions

This study develops an open-source large language model–based chatbot tailored for Korean health consultations. The chatbot was implemented using the retrieval-augmented generation (RAG) technique alongside metadata filtering to enhance its performance.

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Formative Evaluation of Digital Health Interventions

Patient and public involvement is essential for developing patient-centered and acceptable eHealth interventions, yet little is known about how digital collaboration with patient representatives can best be implemented in psycho-oncological research.

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Formative Evaluation of Digital Health Interventions

Greater homework adherence in cognitive behavioral therapy (CBT) is associated with positive treatment outcomes. However, the problems emerging from CBT homework use are common and affect adherence. In recent years, gamification has been explored to increase intervention adherence, but not yet in relation specifically to homework assignments.

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Formative Evaluation of Digital Health Interventions

Accurately differentiating severe from nonsevere COVID-19 clinical types is critical for the health care system to optimize workflow. Current techniques lack the ability to accurately classify COVID-19 clinical types in patients, especially as SARS-CoV-2 continues to mutate.

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Formative Evaluation of Non-Ehealth Innovations

Nearly all youth use the internet daily, with many maintaining several social media accounts. As increasing numbers of young people engage online and the ways we communicate fundamentally change, it is increasingly important to ask: how do these shifts influence youth mental health? To better understand how social media may affect mental health, researchers require validated tools that capture young people’s heterogeneous experiences with social media. However, few available measures evaluate the full range of positive and negative behaviors associated with its use, limiting our ability to meaningfully advance interventions promoting online hygiene.

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Development and Evaluation of Research Methods, Instruments and Tools

Older adults managing chronic illnesses, such as cancer and Alzheimer disease and related dementias (ADRD), often experience significant physical or cognitive impairments that hinder daily activities and increase caregiver burden. Smart Internet of Things (IoT) technologies offer promising solutions by enabling passive monitoring, timely reminders, and personalized support at home. However, these technologies must be carefully tailored to accommodate users’ individualized needs and preferences.

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Development and Evaluation of Research Methods, Instruments and Tools

Personal digital health technologies (DHTs) enable real-time monitoring of physiological metrics and behavioral data, including heart rate variability (HRV), supporting analysis of pregnancy-related conditions and personalized care throughout the perinatal period. While recent studies demonstrate the utility of personal DHTs in tracking pregnancy-related symptoms, they often rely on aggregate statistical methods that overlook individual variability.

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Formative Evaluation of Digital Health Interventions

Bystander intervention is one of the most commonly used methods to curb the sexual violence crisis on college campuses. Most universities conduct training among their student bodies to ensure students are familiar with the procedure. However, it is necessary to remind and repeat messages to audiences to underscore their importance and solidify that knowledge among populations.

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Pilot studies (ehealth)

Digital self-control tools (DSCTs) have emerged as technological interventions to address excessive smartphone usage and promote digital well-being. However, these tools face persistent challenges with user attrition and sustained engagement, compromising their long-term effectiveness. Current literature lacks an understanding of how observable behavioral indicators, as opposed to self-reported measures, are associated with user engagement and readiness to change in DSCTs.

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Formative Evaluation of Digital Health Interventions

In October 2022, the Nutrition Now (NN) e-learning resource was implemented within Maternal and Child Healthcare centers and Early Childhood Education and Care centers of a southern Norwegian municipality. The e-learning resource targets expectant parents, parents of children aged 0‐2 years, and Early Childhood Education and Care staff, aiming to promote healthy dietary behaviors during the first 1000 days of life.

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Formative Evaluation of Digital Health Interventions

Approximately 1 in 3 pregnancies in the United States are complicated by one or more adverse pregnancy outcomes. This high prevalence contributes to the elevated rates of maternal and infant mortality in the United States. Modifiable prepregnancy or preconception lifestyle factors have been associated with adverse pregnancy outcomes in observational studies, which underscores the importance of preconception care.

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Formative Evaluation of Digital Health Interventions

Depression affects more than 300 million people worldwide and is a leading contributor to the global disease burden. Traditional diagnostic methods, such as structured clinical interviews, are reliable but impractical for frequent or large-scale screening. Self-report tools like the Patient Health Questionnaire-8 (PHQ-8) require disclosure and clinician oversight, limiting accessibility. Recent artificial intelligence–based approaches leverage multimodal behavioral cues (linguistic, acoustic, and visual) for automated depression detection but remain constrained by limited adaptability, scarce annotated data, weak emotional expression in real-world settings, and the high computational cost of deployment of socially assistive robots (SARs).

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