Validation of a one-item acute stress scale for driving tasks
Authors: Sjörs Dahlman Anna , Karlsson Kåre , Candefjord Stefan , Anund Anna 
Published: June 2024

Emergency personnel, such as ambulance crews and firefighters, must perform well both mentally and physically during emergency responses around the clock. The opportunity for recovery and rest is often limited during their shifts. This can lead to an increased risk of fatigue and perceived stress during emergency responses, which also increases the risk of traffic accidents. Stress has been identified as a contributing factor to road crashes due to its negative impact on driving performance [1-3]. Stress increases the crash risk by affecting cognitive abilities, resulting in inadequate information processing and imperfect perception which may, in turn, lead to deterioration of driver performance [4]. Physiological measurements can be used to detect driver stress but there is also a need for subjective ratings scales that are easy to use in a driving setting. Driver sleepiness is often measured with the 9-point one-item Karolinska Sleepiness Scale [5] but there is no equivalent measure of acute driver stress. In this study we investigated if a one-item stress scale could be used to measure acute driver stress in an ambulance simulator. The VTI acute stress scale (VSS) was developed to quantify perceived stress. The 9 verbal anchors are designed to match the verbal anchors of the Karolinska Sleepiness Scale (KSS). The VSS anchors are: 1 completely relaxed (feeling entirely calm and relaxed), 2 very relaxed, 3 relaxed, 4 rather relaxed, 5 neither relaxed nor stressed, 6 slightly stressed, 7 stressed (feeling some tension and pressure), 8 very stressed, 9 extremely stressed (feeling very tense and under high pressure, on the verge of what I can handle).A simulator trial was conducted with ambulance driving scenarios designed to induce various stress levels in emergency response personnel. Each participant performed three simulator tasks: task A was a low-stress routine drive, task B was a medium-stress urgent callout, task C was a high-stress emergency response. The tasks were counterbalanced between participants. The participants completed a questionnaire with questions about task load (NASA-TLX), stress (9-point VSS scale), and sleepiness (9-point KSS scale) after each drive.Participants (33 men and 16 women) were recruited among ambulance personnel and emergency response personnel in western Sweden. Data collections took place at two different ambulance stations on five different occasions in 2022. The study protocol was approved by the Swedish Ethical Review Authority (ref 2021-04352). Forty-eight drivers had VSS ratings from all three drives. The mean ratings were A=3.17 (SD 1.58, range 1-7), B=3.77 (SD 1.59, range 1-6), and C=4.65 (SD 1.77, range 1-7). An ANOVA with task (A, B, C) as a fixed factor and participant as a random factor showed that there was a significant difference in VSS ratings between tasks (F=22.9, p<0.001, η2=0.330). Post-hoc tests (TukeyHSD) showed that task A had significantly lower rating than task B (p=0.021) and task C (p<0.001) and task B had significantly lower ratings than task C (p<0.001). The stress ratings were moderate throughout the trials, with no ratings above 7 on the 9-point scale. This could be due to simulator scenarios not being perceived as equally stressful as real-life emergency driving situations. The VSS was significantly correlated with NASA-TLX subscales mental demand (r=0.606), physical demand (r=0.419), temporal demand (r=0.605), performance (r=0.313), effort (r=0.541), and frustration (r=0.553). These medium correlations show that the VSS acute stress score is related to but not identical to workload. In conclusion, the VSS can be used to measure acute driver stress in moderately stressful driving conditions.This study was funded by a grant from the strategic vehicle research and innovation (FFI) program at Sweden’s Innovation Agency (VINNOVA), grant number 2020-05157, and through the SUAB project financed by the European Social Fund, grant number 2020/00110.1.Mou, L., et al., Driver stress detection via multimodal fusion using attention-based CNN-LSTM. Expert Systems with Applications, 2021. 173: p. 114693.2.Rastgoo, M.N., et al., A critical review of proactive detection of driver stress levels based on multimodal measurements. ACM Computing Surveys (CSUR), 2018. 51(5): p. 1-35.3.Beanland, V., et al., Driver inattention and driver distraction in serious casualty crashes: Data from the Australian National Crash In-depth Study. Accident Analysis & Prevention, 2013. 54: p. 99-107.4.Wiberg, H., et al., Physiological responses related to moderate mental load during car driving in field conditions. Biological psychology, 2015. 108: p. 115-125.5.Åkerstedt, T. and M. Gillberg, Subjective and Objective Sleepiness in the Active Individual International Journal of Neuroscience, 1990. 52(1-2): p. 29-37.