Accepted for/Published in: JMIR Research Protocols
Date Submitted: Aug 15, 2025
Date Accepted: Apr 17, 2026
Demographic profiles and methodology in the generation and validation of resting metabolic rate prediction equations: A systematic review protocol
ABSTRACT
Background:
Resting metabolic rate (RMR) prediction equations used today often rely on the consideration of binary sex. Significant intrasex variability and a lack of data on diverse populations raise concerns about these equations’ validity and generalizability. Existing systematic reviews have focused on specific populations like individuals with obesity or athletes, but none have systematically examined the demographic characteristics of participants used to derive these equations.
Objective:
Our central hypothesis is that the accuracy of RMR prediction is influenced by the demographic alignment between the equation's derivation population and the individual.
Methods:
We present a systematic review protocol to critically evaluate the literature and participant demographic profiles that underpin current RMR prediction equations. Our objectives are to: 1) determine the characteristics of participant populations, including reporting on gender and sex diversity, used in RMR equation research; 2) critically appraise the methodologies, findings, and reporting practices of studies that developed or validated RMR equations for binary populations; and 3) assess the generalizability of these equations for non-binary populations, such as intersex and transgender individuals. Following a PROSPERO-registered protocol (CRD420251084400), we will conduct a comprehensive search across multiple databases including Academic Search Premier, PubMed, and Web of Science. We will include peer-reviewed, English-language articles reporting on interventional or observational studies that generated RMR prediction equations and reported human participant demographic characteristics. Exclusion criteria include studies not generating prediction equations, lacking desired demographic data, or involving animals. Data extraction will include participant demographics (e.g., sex, gender, race/ethnicity, age, body composition), RMR test protocols, and reported reliability or validity metrics. Risk of bias will be assessed using the PROBAST tool.
Results:
Funded in June 2025 by a University of Nevada, Las Vegas Sports Innovation Initiative Catalyst Grant Funding Program, as well as a National Association for Kinesiology in Higher Education Hellison Interdisciplinary Research Grant.
Conclusions:
Findings will be disseminated through a narrative synthesis submitted for publication, adhering to PRISMA reporting guidelines. This review will identify gaps in the inclusivity and generalizability of current RMR prediction equations, informing future research and clinical applications. Clinical Trial: PROSPERO; CRD420251084400; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251084400
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