JMIR Formative Research

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

Editor-in-Chief:

Gunther Eysenbach, MD, MPH, FACMI


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.

This journal 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 in JMIR Research Protocols). 

JMIR Formative Research has been accepted for indexing in PubMed and PubMed Central.

Recent Articles

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Early Results from COVID-19 Studies

Early in the pandemic, in 2020, Koehlmoos et al completed a framework synthesis of currently available self-reported symptom tracking programs for COVID-19. This framework described relevant programs, partners and affiliates, funding, responses, platform, and intended audience, among other considerations.

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Formative Evaluation of Digital Health Interventions

Computer tools based on artificial intelligence could aid clinicians in memory clinics in several ways, such as by supporting diagnostic decision-making, web-based cognitive testing, and the communication of diagnosis and prognosis.

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Formative Evaluation of Digital Health Interventions

Symptoms related to endometriosis have a significant impact on the quality of life, and symptoms often recur. The experience sampling method (ESM), a digital questioning method characterized by randomly repeated momentary assessments, has several advantages over traditionally used measurements, including the ability to assess the temporal relationship between variables such as physical, mental, and social factors.

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Early Results from COVID-19 Studies

The 2020-2021 COVID-19 pandemic has added to the mental health strain on individuals and groups across the world in a variety of ways. Viral mitigation protocols and viral spread affect people on all continents every day, but at widely different degrees. To understand more about the mental health consequences of the pandemic, it is important to investigate whether or how people gather pandemic-related information and how obtaining this information differentially affects individuals.

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Formative Evaluation of Digital Health Interventions

The rising incidence of chronic diseases is a growing concern, especially in Singapore, which is one of the high-income countries with the highest prevalence of diabetes. Interventions that promote healthy lifestyle behavior changes have been proven to be effective in reducing the progression of prediabetes to diabetes, but their in-person delivery may not be feasible on a large scale. Novel technologies such as conversational agents are a potential alternative for delivering behavioral interventions that promote healthy lifestyle behavior changes to the public.

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Early Results from COVID-19 Studies

The COVID-19 pandemic has resulted in significant changes to adolescents’ daily lives and, potentially, to their mental health. The pandemic has also disproportionately affected historically marginalized and at-risk communities, including people of color, socioeconomically disadvantaged people, people identifying as female, and youth.

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Formative Evaluation of Digital Health Interventions

Fetal alcohol spectrum disorders (FASD) are prevalent neurodevelopmental conditions. Significant barriers prevent family access to FASD-informed care. To improve accessibility, a scalable mobile health intervention for caregivers of children with FASD is under development. The app, called Families Moving Forward (FMF) Connect, is derived from the FMF Program, a parenting intervention tailored for FASD. FMF Connect has 5 components: Learning Modules, Family Forum, Library, Notebook, and Dashboard.

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Formative Evaluation of Digital Health Interventions

Mobile health (mHealth) technologies, such as wearable sensors, smart health devices, and mobile apps, that are capable of supporting pregnancy care are emerging. Although mHealth could be used to facilitate the tracking of health changes during pregnancy, challenges remain in data collection compliance and technology engagement among pregnant women. Understanding the interests, preferences, and requirements of pregnant women and those of clinicians is needed when designing and introducing mHealth solutions for supporting pregnant women’s monitoring of health and risk factors throughout their pregnancy journey.

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Formative Evaluation of Digital Health Interventions

As poor diet quality is a significant risk factor for multiple noncommunicable diseases prevalent in the United States, it is important that methods be developed to accurately capture eating behavior data. There is growing interest in the use of ecological momentary assessments to collect data on health behaviors and their predictors on a micro timescale (at different points within or across days); however, documenting eating behaviors remains a challenge.

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Formative Evaluation of Digital Health Interventions

Junior physicians report higher levels of psychological distress than senior doctors and report several barriers to seeking professional mental health support, including concerns about confidentiality and career progression. Mobile health (mHealth) apps may be utilized to help overcome these barriers to assist the emotional well-being of this population and encourage help-seeking.

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Formative Evaluation of Digital Health Interventions

Health and well-being promotions are key points of educational programs for adolescents within schools. There are several health education programs mainly based on lifestyle habit changes; however, social and emotional dimensions should be considered within these educational strategies.

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Development and Evaluation of Research Methods, Instruments and Tools

Stroke, a cerebrovascular disease, is one of the major causes of death. It causes significant health and financial burdens for both patients and health care systems. One of the important risk factors for stroke is health-related behavior, which is becoming an increasingly important focus of prevention. Many machine learning models have been built to predict the risk of stroke or to automatically diagnose stroke, using predictors such as lifestyle factors or radiological imaging. However, there have been no models built using data from lab tests.

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Preprints Open for Peer-Review

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