A modelling framework for local thermal comfort assessment related to bicycle helmet use. (February 2023)

• Record Type: Journal Article
• Authors: Bröde, Peter; Aerts, Jean-Marie; De Bruyne, Guido; Mayor, Tiago Sotto; Annaheim, Simon; Fiala, Dusan; Kuklane, Kalev
• Is Part Of: Journal of thermal biology. Volume 112(2023)
• Publication Date: 2023-02
• Subjects: Thermal comfort -- Thermoregulation -- Headgear -- Sweating -- Model -- Local effects; Thermobiology -- Periodicals; Temperature -- Periodicals; Biology -- Periodicals; Thermobiologie -- Périodiques; Thermobiology; Periodicals; 571.46
• DOI: 10.1016/j.jtherbio.2022.103457
• Languages: English
• ISSNs: 0306-4565

Abstract

Abstract: Thermal discomfort due to accumulated sweat increasing head skin wettedness may contribute to low wearing rates of bicycle helmets. Using curated data on human head sweating and helmet thermal properties, a modelling framework for the thermal comfort assessment of bicycle helmet use is proposed. Local sweat rates ( LSR ) at the head were predicted as the ratio to the gross sweat rate ( GSR ) of the whole body or by sudomotor sensitivity ( SUD ), the change in LSR per change in body core temperature ( Δt re ). Combining those local models with Δt re and GSR output from thermoregulation models, we simulated head sweating depending on the characteristics of the thermal environment, clothing, activity, and exposure duration. Local thermal comfort thresholds for head skin wettedness were derived in relation to thermal properties of bicycle helmets. The modelling framework was supplemented by regression equations predicting the wind-related reductions in thermal insulation and evaporative resistance of the headgear and boundary air layer, respectively. Comparing the predictions of local models coupled with different thermoregulation models to LSR measured at the frontal, lateral and medial head under bicycle helmet use revealed a large spread in LSR predictions predominantly determined by the local models and the considered head region. SUD tended to overestimate frontal LSR but performed better for lateral and medial head regions, whereas predictions by LSR / GSR ratios were lower and agreed better with measured frontal LSR . However, even for the best models root mean squared prediction errors exceeded experimental SD by 18–30%. From the high correlation (R > 0.9) of skin wettedness comfort thresholds with local sweating sensitivity reported for different body regions, we derived a threshold value of 0.37 for head skin wettedness. We illustrate the application of the modelling framework using a commuter-cycling scenario, and discuss its potential as well as the needs for further research. Highlights: A framework for modelling local thermal comfort of bicycle helmet use is proposed. Predictions are tested against empirical head sweating data for bicycle helmet use. Equations are presented to predict wind effects on headgear thermal properties. Local sweating sensitivity highly correlates with skin wettedness comfort thresholds. Datasets on head sweating and headgear thermal properties are provided.