Digital twin applications for indoor environmental quality in healthy building research: a systematic review of indicators, methods and computational paradigms

Digital Twin (DT) technologies are increasingly adopted in Healthy Buildings research to monitor and optimise Indoor Environmental Quality (IEQ). However, their implementation remains heterogeneous, with substantial variability in indicator selection, methodological configurations and computational architectures. This study presents a systematic review of 62 peer-reviewed contributions to analyse how DTs are operationalised across IEQ-domains and to identify structural integration patterns within the field. Through combined bibliometric mapping and qualitative content analysis, the review classifies DT-based IEQ applications according to three interconnected dimensions: (i) indicator typologies (dose-related, building-related and occupant-related), (ii) methodological approaches (quantitative, qualitative and hybrid), and (iii) computational paradigms, including scripting-based workflows, visual programming environments, machine-learning models, simulation-driven frameworks and semantic graph-based systems. Rather than ranking individual studies, the analysis examines dominant configurations and emerging trajectories of integration across these dimensions. The results reveal a strong prevalence of dose-related environmental indicators and quantitative sensor-driven methods, typically associated with monitoring-oriented and predictive architectures. In contrast, building semantics and occupant-centred variables remain comparatively under-integrated, limiting the representation of behavioural variability and long-term exposure dynamics. By systematically mapping how indicators, methods and computational architectures intersect, this review clarifies the current structural orientation of DT-research for IEQ and outlines research directions toward more integrated, interoperable and health-aware digital building ecosystems.