Energy-Efficient Aquafitness Equipment Adoption in Green Urban Health Development

The eco-digital fitness transition has been and is currently still affecting urban health infrastructures of all sizes and in many rehabilitation environments, and scholarship still lacks profound insights into the techno- ecological implications of this adoption. The purpose of this article is to use sensor-integrated aquafitness related technology to analyze the characteristics of biometric outputs and user feedback data for the different types of sustainability adoption data and different levels of rehabilitation policy needs of participants in the entire digital fitness transformation process. This study addresses this gap by drawing on a rich body of mixed- method evidence collected from wearable aquatic devices and participant interviews in a multi-site case study of urban rehabilitation centers. Biometric signals of all user groups in the aquatic environment get aggregated in a number of ways into a single biofeedback index for policy makers whose decision-making is critical for green health planning. The study designs the data correlation relationship of the adoption model elements, define the relationship between the aquafitness performance types, and implement the TOPSIS-based method. At the same time, according to the classification of usability features, clustering of behavioral–biometric relationships, filtering sensor information, and dashboard visualization are conducted. By providing a comprehensive understanding of how energy- efficient aquafitness adoption affects urban health sustainability, the insights from our analysis contribute to researchers, policy makers, and fitness practitioners alike. The paper concludes by identifying several promising areas for future eco-digital health innovation.

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