Trends and Developments in Fitness Test Research: A Scopus Bibliometric Analysis - Muhammad Dzikry Abdullah Al Ghazaly; Agung Hilmi Wahdi; Rifki Rosad; Mochamad Derry Prasaja; Nadila Maesara (ACTIVE: Journal of Physical Education, Sport, Health and Recreation, 2026)
The evaluation of physical fitness is a core element of sport science, clinical exercise physiology and population health surveillance but a comprehensive bibliometric synthesis of the recent global trend is missing. The present study aims to describe the productivity, intellectual structure, and thematic evolution of the research on fitness tests published between 2015 and 2026. A total of 5,766 peer-reviewed articles indexed in the Scopus database (Elsevier) were retrieved with the search string TITLE-ABS-KEY ("fitness test" OR "physical fitness test" OR "exercise test"). The articles were analysed through performance analysis and science mapping with Bibliometrix R-package (Biblioshiny), VOSviewer 1.6.20, and Microsoft Excel, following the protocol of (Aria & Cuccurullo, 2017; Donthu et al., 2021). The dataset included 92,894 citations (mean = 16.11; median = 7.00) from 1,134 source journals. The annual scientific production increased from 380 documents in 2015 to a maximum of 691 documents in 2024, with a 3-year moving average that indicates a sustained acceleration after 2020. The top contributing countries were: United States (n = 1,168), United Kingdom (n = 782), and Brazil (n = 580). Lotka’s law was confirmed (n ≈ 3.24), with a very skewed distribution of productivity; Bradford’s law identified 15 core journals, accounting for 34.1% of all output. Co-occurrence analysis of author keywords resulted in five thematic clusters: General Exercise & Fitness, Cardiorespiratory & Performance, Athletic Performance & Training, Health Aging & Body, and Methodological. Thematic evolution over three sub-periods (2015–2018, 2019–2022, 2023–2026) demonstrated a clear migration of the field from cardiorespiratory and laboratory-based assessment to wearable sensors, machine-learning-enabled monitoring and CRF risk stratification. These findings offer an empirical guide for researchers, clinicians, educators, and policymakers interested in advancing valid, scalable, and technology-integrated fitness assessment.