Palabras clave
Fitness Analytics
Predictive Modeling
Wearable Data
Data-Driven Decision Making
Cómo citar
Resumen
Purpose: This article creates a competitive intelligence framework to determine the results of physical fitness on data analytics. The paper discusses a practical issue that is common to sport organizations, fitness platforms, public-health programs, universities, and clinical exercise services: huge amounts of physical activity and health data are being produced, yet most institutions do not have a system that allows them to convert that data into actionable foresight regarding performance, adherence, and recovery and fitness risk.
Methodology/approach: Study is a synthesis of integrative literature analysis, followed by a simulation-based analysis illustration. A literature review of the recent literature on competitive intelligence, sport analytics, wearables, machine learning, physical activity monitoring, and ethical AI published between 2021 and 2025 was viewed to define the framework, and a literature informed synthetic panel of repeated fitness observations was operationalized in such a way that feature engineering, model benchmarking, calibration assessment, decision translation, and post-deployment drift monitoring could be applied.
Originality/Relevance: The originality of the article is the ability to combine the logic of competitive intelligence with the predictive fitness analytics. The previous research has typically investigated the wearable monitoring, exercise prediction, or sport analytics separately. This paper re-positions physical fitness prediction as an intelligence that can be used to make anticipatory decisions, prioritize resources, intervene at a personal level, and maintain competitive advantage.
Key findings: The model shows that useful fitness prediction is found when fusing physiology, training load, behaviour, recovery, and contextual signals over rolling time windows and assessing them using properties of nested validation. Both boosted-tree and temporal models were the best on the discrimination in the analytical illustration, but deployment quality was as well founded on the criterion of calibration, explainability, and drift and intervention prioritization governance.
Theoretical/methodological contributions: The article uses the theory of competitive intelligence in the context of the physical fitness analytics industry and offers a valid model that could be utilized by scholars and managers. It adds a process perspective of how organizations may transform raw data into prospective intelligence, defines critical variables and model options to predict physical fitness, and has a research agenda in future on validation, fairness, interoperability, and human-AI interaction.
Citas
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