The effects of traveling in different transport modes on galvanic skin response (GSR) as a measure of stress: An observational study

Investigating heart rate responses of children to active travel: a mixed effects modeling analysis

BACKGROUND

The active travel concept has gained recognition as an effective method to enhance physical activity in children's everyday mobility. Yet, there is a notable gap of quantitative evidence on its direct impacts, especially regarding physiological outcomes that can concretely demonstrate its influences on physical intensity and overall health of children.

METHODS

This study investigates the effects of active travel on children, focusing on trip-level physiological responses, specifically heart rate. Through a mixed survey design, we obtained a repeated-measures dataset from a cohort of 73 children (average age 13) across three Austrian secondary schools, each observed for 7 days. By applying mixed effects modelling approach, both fixed and random effects to children's heart rates were explored.

RESULTS

According to results from developed mixed linear models (maximum R2 = 0.22), it revealed a significant positive association between active travel mode(s) engagement and heightened physiological responses (Low, Medium and High active level of trip compare to None: β = 1.81, 5.01 and 8.34, t-value = 4.70, 10.29 and 21.34), indicating that increased usage of active travel modes led to elevated heart rate values. Additionally, subjective perceptions also played an important role in influencing children's physiological responses. Feelings of exhausted, anxiety, and stress contributing to a higher heart rate (β = 14.79, 16.69 and 1.99, t-value = 20.42, 5.67 and 3.0), while unhappiness was associated with a lower heart rate (β = -5.96, t-value = -14.62). Effects of social interactions were also found during the trip. The presence of adults was associated with a calming effect (β = -1.92, t-value = -5.72), whereas solo travel resulted in lower heart rate compared to travel with peers (β = -1.95, t-value = -6.59).

CONCLUSION

Our article innovatively employs mixed survey methods and heart rate data to quantitatively demonstrate the positive association between engagement in active travel modes and heightened physiological responses in children, elucidating the intricate interplay of subjective perceptions and social interactions during trips, thus offering valuable insights into the health implications of active travel at a trip level. Future research will keep on leveraging survey and experimental methods.

Mobility Infrastructures and Health: Scoping Review of studies in Europe

Objectives

Movement-friendly environments with infrastructure favouring active mobility are important for promoting physical activity. This scoping literature review aims at identifying the current evidence for links between mobility infrastructures and (a) behaviour regarding active mobility, (b) health outcomes and (c) co-benefits.

Method

This review was conducted in accordance with the PRISMA scoping review guidelines using PubMed and EMBASE databases. Studies included in this review were conducted in Europe, and published between 2000 and March 2023.

Results

146 scientific articles and grey literature reports were identified. Connectivity of sidewalks, walkability, and accessibility of shops, services and work are associated with walking. Cycling is positively associated with cycle-paths, separation of cycling from traffic and proximity to greenspaces, and negatively associated with traffic danger. Increased active transportation has a protective effect on cardiovascular and respiratory health, obesity, fitness, and quality of life. Co-benefits result from the reduction of individual motorized transportation including reduced environmental pollution and projected healthcare expenditure.

Conclusion

Mobility infrastructure combined with social and educational incentives are effective in promoting active travel and reducing future healthcare expenses. A shift to active transportation would increase both individual and community health and decrease greenhouse gas emissions.

Enhancing thermal comfort prediction in high-speed trains through machine learning and physiological signals integration

Heating, Ventilation, and Air Conditioning (HVAC) systems in high-speed trains (HST) are responsible for consuming approximately 70% of non-operational energy sources, yet they frequently fail to ensure provide adequate thermal comfort for the majority of passengers. Recent advancements in portable wearable sensors have opened up new possibilities for real-time detection of occupant thermal comfort status and timely feedback to the HVAC system. However, since occupant thermal comfort is subjective and cannot be directly measured, it is generally inferred from thermal environment parameters or physiological signals of occupants within the HST compartment. This paper presents a field test conducted to assess the thermal comfort of occupants within HST compartments. Leveraging physiological signals, including skin temperature, galvanic skin reaction, heart rate, and ambient temperature, we propose a Predicted Thermal Comfort (PTC) model for HST cabin occupants and establish an intelligent regulation model for the HVAC system. Nine input factors, comprising physiological signals, individual physiological characteristics, compartment seating, and ambient temperature, were formulated for the PTS model. In order to obtain an efficient and accurate PTC prediction model for HST cabin occupants, we compared the accuracy of different subsets of features trained by Machine Learning (ML) models of Random Forest, Decision Tree, Vector Machine and K-neighbourhood. We divided all the predicted feature values into four subsets, and did hyperparameter optimisation for each ML model. The HST compartment occupant PTC prediction model trained by Random Forest model obtained 90.4% Accuracy (F1 macro = 0.889). Subsequent sensitivity analyses of the best predictive models were then performed using SHapley Additive explanation (SHAP) and data-based sensitivity analysis (DSA) methods. The development of a more accurate and operationally efficient thermal comfort prediction model for HST occupants allows for precise and detailed feedback to the HVAC system. Consequently, the HVAC system can make the most appropriate and effective air supply adjustments, leading to improved satisfaction rates for HST occupant thermal comfort and the avoidance of energy wastage caused by inaccurate and untimely predictive feedback.

The impact of black carbon (BC) on mode-specific galvanic skin response (GSR) as a measure of stress in urban environments

Previous research has shown that walking and cycling could help alleviate stress in cities, however there is poor knowledge on how specific microenvironmental conditions encountered during daily journeys may lead to varying degrees of stress experienced at that moment. We use objectively measured data and a robust causal inference framework to address this gap. Using a Bayesian Doubly Robust (BDR) approach, we find that black carbon exposure statistically significantly increases stress, as measured by Galvanic Skin Response (GSR), while cycling and while walking. Augmented Outcome Regression (AOR) models indicate that greenspace exposure and the presence of walking or cycling infrastructure could reduce stress. None of these effects are statistically significant for people in motorized transport. These findings add to a growing evidence-base on health benefits of policies aimed at decreasing air pollution, improving active travel infrastructure and increasing greenspace in cities.

Day-to-day intrapersonal variability in mobility patterns and association with perceived stress: A cross-sectional study using GPS from 122 individuals in three European cities

Many aspects of our life are related to our mobility patterns and individuals can exhibit strong tendencies towards routine in their daily lives. Intrapersonal day-to-day variability in mobility patterns has been associated with mental health outcomes. The study aims were: (a) calculate intrapersonal day-to-day variability in mobility metrics for three cities; (b) explore interpersonal variability in mobility metrics by sex, season and city, and (c) describe intrapersonal variability in mobility and their association with perceived stress. Data came from the Physical Activity through Sustainable Transport Approaches (PASTA) project, 122 eligible adults wore location measurement devices over 7-consecutive days, on three occasions during 2015 (Antwerp: 41, Barcelona: 41, London: 40). Participants completed the Short Form Perceived Stress Scale (PSS-4). Day-to-day variability in mobility was explored via six mobility metrics using distance of GPS point from home (meters:m), distance travelled between consecutive GPS points (m) and energy expenditure (metabolic equivalents:METs) of each GPS point collected (n = 3,372,919). A Kruskal-Wallis H test determined whether the median daily mobility metrics differed by city, sex and season. Variance in correlation quantified day-to-day intrapersonal variability in mobility. Levene's tests or Kruskal-Wallis tests were applied to assess intrapersonal variability in mobility and perceived stress. There were differences in daily distance travelled, maximum distance from home and METS between individuals by sex, season and, for proportion of time at home also, by city. Intrapersonal variability across all mobility metrics were highly correlated; individuals had daily routines and largely stuck to them. We did not observe any association between stress and mobility. Individuals are habitual in their daily mobility patterns. This is useful for estimating environmental exposures and in fuelling simulation studies.