Multisensory Interaction and Predictive Modelling of Indoor Environmental Comfort for Pregnant Women in Obstetrics Departments in Chinese Hospitals

Description

Indoor environmental comfort in healthcare buildings has traditionally been evaluated using general population models that insufficiently account for the physiological and psychological particularities of vulnerable groups. Pregnant women, due to gestational physiological adaptations and emotional sensitivity, exhibit distinctive thermoregulatory and perceptual responses that remain underexplored in hospital environments. This research addresses the lack of an integrated framework for understanding and predicting indoor environmental comfort for pregnant women in outpatient healthcare settings.
This study establishes a multidimensional comfort framework integrating physical environmental parameters, physiological responses, psychological states, and adaptive behaviours. Field investigations were conducted in hospital outpatient departments, combining objective environmental measurements, physiological monitoring, and structured questionnaires. Statistical analyses were first employed to examine reliability, distributional patterns, and group differences across pregnancy stages and accompanying individuals. Subsequently, structural equation modelling (PLS-SEM) was applied to identify path relationships, mediation and moderation mechanisms, and structural variations across early, mid, and late pregnancy. Findings demonstrate that thermal factors exert the strongest direct and indirect influence on overall environmental comfort, with psychophysiological variables, particularly emotional state and pulse rate, serving as significant mediators. Gestational-stage-dependent structural differences were observed, indicating progressive strengthening of psychophysiological mediation in later pregnancy. Comparative analysis further revealed perceptual and structural divergences between pregnant women and accompanying individuals.
Building upon the identified dominant role of thermal factors, a stage-sensitive thermal comfort model was developed. Neutral and acceptable temperature ranges were derived, metabolic rates were recalibrated based on individual basal metabolic rate estimation, and revised PMV and adaptive models were validated. The proposed model demonstrates improved predictive performance compared to existing standards.
To advance personalised comfort prediction, multiple machine learning algorithms were developed and benchmarked. Model interpretability analysis using SHAP revealed nonlinear interactions among environmental, physiological, and behavioural variables, as well as seasonal and gestational dependencies. The integration of mechanism-based modelling and data-driven prediction enabled the construction of a personalised comfort prediction framework. Finally, the implications of comfort optimisation on hospital energy performance were assessed through simulation, demonstrating the feasibility of balancing thermal comfort improvement with energy efficiency.
Overall, this thesis contributes a gestational-stage-sensitive, psychophysiology-informed, and data-driven framework for evaluating and predicting indoor environmental comfort for pregnant women in healthcare settings. The findings provide theoretical advancement in multisensory comfort modelling and practical guidance for evidence-based hospital environmental design and operation.

Period	30 Mar 2026 → 1 Jun 2026
Examinee	Rui Guan
Examination held at	University of Nottingham Ningbo China
Degree of Recognition	International