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Perceived baseline and commuting stress for the 188 Austrian commuters (active commuters: n = 124, passive commuters: n = 64). Mean values for perceived stress, derived from up to three days of commuting, are shown together with 95% confidence intervals (CI). Active commuters: walking, cycling. Passive commuters: car, motorbike, public transport. Asterisks (*) indicate significant differences (p < .05).
Source publication
Objective
Little is known about the acute psychological stress responses caused by commuting. Evidence for the benefits of active commuting (e.g., walking, cycling) is usually based on studies without measurements in free-living environments and without consideration of daily variations in stress. This study investigated the association between com...
Context in source publication
Context 1
... active and 64 passive commuters (Table 1). Participants were between 18 and 64 years old (M = 28.0 ± 10.0). Active and passive commuters showed similar levels of baseline stress (M active = 16.9 ± 8.0, M passive = 16.9 ± 8.0, p = .988) but active commuters reported less commuting stress (M active = 13.9 ± 7.1, M passive = 17.3 ± 9.2, p = .007, Fig. 1). The results for the comparison between included (n = 186) and non-included (n = 27) participants with respect to the confounding variables of the main model are shown in Supplementary Table S1. Compared to included participants, non-included participants reported greater commuting time. Bivariate intercorrelations among all variables ...
Citations
... Regular participation in physical activity is known to reduce symptoms of depression and anxiety, improve mood, and enhance self-esteem. Active commuting contributes to these benefits by providing regular, unplanned physical activities that can reduce stress and increase overall life satisfaction (35). Guell et al. 's (36) research emphasized that the routine nature of active commuting can help establish a daily routine that fosters a positive psychological state. ...
... This study explored how behavioral preference, and perceived built environments act as moderators in the health-commuting relationship, confirming their general benefits but revealing complex dynamics. Regular physical activities and active commuting methods such as walking and cycling generally promote health (35). However, while regular physical activity can positively influence the dynamics between commuting and health, its direct impact on specific health metrics often becomes statistically insignificant when analyzed in isolation. ...
Introduction
Long-duration commuting is widely recognized for its significant influence on health. However, while research has traditionally focused on direct impacts, there remains a critical need to explore the nonlinear dynamics of this relationship. This study aims to deepen our understanding of how behavioral preferences and built environments contribute to these complex interactions.
Methods
This study was conducted in Jinan, China’s most congested city, using data from the “Jinan Residents Commuting Survey” of 1,755 participants aged 19 to 59. We applied Generalized Propensity Score Matching (GPSM) to explore the nonlinear effects of commuting time on self-rated health, adjusting for participants’ sociodemographic characteristics. Variables related to active commuting, physical activity, and perceived built environment were also examined for their potential moderating effects.
Results
Commuting for less than 21 minutes enhances health, but negative effects intensify and peak at 60 minutes. Heterogeneity analysis reveals that women and older adults, especially those with higher incomes, are more susceptible to long commutes, experiencing a delayed onset of adverse effects. While active commuting offers health benefits, it may exacerbate health issues if prolonged. Conversely, regular physical activity consistently improves health outcomes related to commuting. Additionally, factors like residential greenery and walkability help alleviate commuting-related stress, improving the overall commuting-health dynamic.
Discussion
This study clarifies the commuting-health relationship by identifying key time thresholds and the positive effects of active commuting and physical activity on mitigating longer commute impacts. The findings inform healthier commuting behaviors and offer practical guidelines for urban planning and policy-making to enhance commuter well-being.
... For instance, it was noted that in low-income countries, people might be forced to walk or cycle as a means of transport because of their poverty conditions and associated feelings of discomfort (Pucci et al., 2012;Lira and Paez, 2021). Nevertheless, the moderating role of these factors remains unclear and only potential since the results of their influence have not been widely confirmed (Sattler et al., 2020). ...
Research has proven that engaging in active mobility (AM), namely walking and cycling for transportation, significantly enhances physical activity levels, leading to better physical health. It is still unclear whether AM could also offer any mental health benefits. This scoping review aims to provide a comprehensive understanding of the current knowledge on the relationship between AM and mental health, given its crucial role in public health. The authors searched online databases to isolate primary studies written in English involving an adult sample (16 or over). AM was the exposure factor. Many mental health elements were included as outcomes (depression, anxiety, self-esteem, self-efficacy, stress, psychological and subjective well-being, resilience, loneliness and social support, quality of life, mood, life satisfaction and sleep). The results were organised in a narrative summary per each outcome selected, graphical syntheses and an overview of gaps to be further examined. The authors identified a total of 55 papers as relevant. The results show inconsistency in study designs, definition and operationalisation of the variables, approach and methodologies used. A cross-sectional design was the dominant choice, primarily examining data from national public health surveys. Nonetheless, there has been improvement in outcomes of interests, initially mainly the quality of life and affect. Lately, authors have focused on a broader range of mental health-related factors (such as travel satisfaction). The experimental studies showed promising mental health improvements in those who used active modes more than those who used motorised vehicles. It creates a rationale for further research towards implementing a unified theoretical and methodological framework to study the link between AM and mental health. The ultimate goal is to generate solid conclusions that could support building societies and cities through public health promotion and sustainable strategies, like walking and cycling as a means of transport.
... This report is confirmed by Schäfer et al. [69], who carried out a literature review on active commuting in Austrian populations and reported that actively commuting had a positive impact on cholesterol, lipid profile, waist circumference, and other weightrelated variables. Considering that active commuting is spread in Austria, we might argue that this type of leisure-time PA may have contributed to our findings [70]. In addition, dietary intake was associated with leisure-time PA in the present sample with those stating engagement in leisure-time PA reporting a significantly higher intake of fruit and vegetables, which increased even more for those exercising at least 4 days per week, i.e., matching the recommendations for healthy PA. ...
Lifestyle behaviors are key contributors to sustainable health and well-being over the lifespan. The analysis of health-related behaviors is crucial for understanding the state of health in different populations, especially teachers who play a critical role in establishing the lifelong health behaviors of their pupils. This multidisciplinary, nationwide study aimed to assess and compare lifestyle patterns of Austrian teachers and school principals at secondary levels I and II with a specific focus on physical activity and diet. A total number of 1350 teachers (1.5% of the eligible Austrian sample; 69.7% females; 37.7% from urban areas; mean age: 45.8 ± 11.4 years; mean BMI: 24.2 ± 4.0) completed a standardized online survey following an epidemiological approach. Across the total sample, 34.4% were overweight/obese with a greater prevalence of overweight/obesity in males than females (49.5% vs. 29.2%, p < 0.01) and rural vs. urban environments (35.9% vs. 31.3%). Most participants (89.3%) reported a mixed diet, while 7.9% and 2.9% were vegetarians and vegans, respectively. The average BMI of teachers with mixed diets (24.4 ± 4.0 kg/m2) was significantly higher than vegetarians (23.1 ± 3.2 kg/m2) and vegans (22.7 ± 4.3 kg/m2). Vegans reported a lower level of alcohol intake (p < 0.05) among dietary groups. There was no between-group difference in smoking (p > 0.05). The prevalence of engagement in regular physical activity was 88.7% for leisure-time sports/exercises and 29.2% for club sports. Compared with the previous reports on general populations, the present data suggest an acceptable overall health status among Austrian teachers.
... Die gesundheitsfördernden Effekte überwiegen mögliche Gefahren durch Verkehrsunfälle oder Exposition verschmutzter Luft dabei bei Weitem (Cepeda et al., 2017;Rojas-Rueda, de Nazelle, Tainio, & Nieuwenhuijsen, 2011;Sun et al., 2019). Auch auf Stress (Sattler et al., 2020) (Marques et al., 2020). ...
Zur Analyse von Zusammenhängen zwischen Radverkehr und Infrastruktur kommt eine breite Kombination unterschiedlicher Methoden in einem integrierten Gesamtansatz zum Einsatz. An die Herleitung der radfahrtauglichen Umgebung (Bikeability) über eine Literaturanalyse und einen interaktiven Expertenprozess schließen sich die Operationalisierung dieser Definition mittels offener Geodaten sowie die Bewertung der Einflüsse auf die Verkehrsmittelwahl in einem multinomialen Verkehrsmittelwahlmodell an. Auf der Ebene der Routenwahl werden dann die Einflussgrößen in einem diskreten Entscheidungsexperiment differenziert. Dabei kommen logistische Regressionsmodelle zum Einsatz. Des Weiteren werden Daten aus der Fahrradnavigation in einem Clusterverfahren genutzt. Im Ergebnis zeigt sich ein konsensuales Verständnis von Bikeability unter Abbildung des Zusammenspiels der fünf wichtigsten infrastrukturellen Parameter. Durch Nutzung offener Geodaten ist der entwickelte Ansatz uneingeschränkt räumlich übertragbar und thematisch adaptierbar. Das Verkehrsmittelwahlmodell belegt den stark positiven Einfluss der Bikeability auf die Wahl des Fahrrades als Verkehrsmittel. Auf der differenzierten Ebene der Routenwahl bestätigt sich der besondere Einfluss der Radinfrastruktur an Hauptverkehrsstraßen. Die Ergebnisse zeigen dabei eine Abstufung im Nutzen für den Radverkehr, die dem Ausmaß der baulichen Trennung vom motorisierten Individualverkehr entspricht, sowie spezifische individuelle und strukturelle Implikationen. Neben Infrastrukturen an Hauptstraßen wird durch die angewandten Methoden auch die generelle Bedeutung von Nebenstraßen verdeutlicht und weiter differenziert. Die Ergebnisse zeigen dabei den enormen Nutzen von Fahrradstraßen aus Sicht der Nutzenden. Die Erkenntnisse bieten spezifische Anknüpfungspunkte, sowohl für weitere Forschung als auch für Planung und Praxis, die in der Arbeit diskutiert werden.
... The term strain is distinctly described by previous scholars yet most are referring to the same concept of adverse effect or outcome of stressors (Lazarus & Folkman, 1987;Beehr, 1995;Van Dyne et al., 2002;Bhagat et al., 2010). Past study recognized that psychological strain occurs when environmental demands are perceived to exceed the adaptive capacity of the person (Sattler et al., 2020). This idea is in line with Panatik (2012), who suggested that strain has resulted from the mismatch between the person and the environment. ...
... Die gesundheitsfördernden Effekte überwiegen mögliche Gefahren durch Verkehrsunfälle oder Exposition verschmutzter Luft dabei bei Weitem (Cepeda et al., 2017;Rojas-Rueda, de Nazelle, Tainio, & Nieuwenhuijsen, 2011;Sun et al., 2019). Auch auf Stress (Sattler et al., 2020) (Marques et al., 2020). ...
A broad combination of different methods is used in an integrated approach to evaluate interrelations between infrastructure and bicycle transport. First, the bike-friendliness of the urban environment (bikeability) is defined via a literature analysis in combination with an interactive expert survey. This definition of bikeability is then operationalized using open geodata, ensuring transferability. In addition, the effects of bikeability on mode choice are evaluated using a multinomial logit model. On the detailed level of route choice, the influencing parameters are further differentiated in a graphical online stated preferences survey. Mixed logit discrete choice models are then developed to quantify the trade-offs of interest. Furthermore, extensive data retrieved from a bike routing engine are clustered and analysed to reveal underlying route preferences, without the potential effects of an overt survey situation. Results show a consensus in understanding of bikeability, as provided by experts. This is defined by a stable interaction of the components composing bikeability. The mode choice model proves the strong positive effect of high bikeability on choosing the bike as a mode of transport. On the detailed level of route choice, the particular influence of cycling infrastructure along main streets is confirmed, and differentiated according to the specific design. Aside from specific individual and structural implications, a greater separation from motorized transport generally corresponds with a higher utility for cyclists. Regarding side streets, the results reveal the general importance of minor roads and the enormous benefit of cycle streets prioritizing cyclists. The presented findings may be used for further research and deliver recommendations for planning, which are discussed in the present study. Zur Analyse von Zusammenhängen zwischen Radverkehr und Infrastruktur kommt eine breite Kombination unterschiedlicher Methoden in einem integrierten Gesamtansatz zum Einsatz. An die Herleitung der radfahrtauglichen Umgebung (Bikeability) über eine Literaturanalyse und einen interaktiven Expertenprozess schließen sich die Operationalisierung dieser Definition mittels offener Geodaten sowie die Bewertung der Einflüsse auf die Verkehrsmittelwahl in einem multinomialen Verkehrsmittelwahlmodell an. Auf der Ebene der Routenwahl werden dann die Einflussgrößen in einem diskreten Entscheidungsexperiment differenziert. Dabei kommen logistische Regressionsmodelle zum Einsatz. Des Weiteren werden Daten aus der Fahrradnavigation in einem Clusterverfahren genutzt. Im Ergebnis zeigt sich ein konsensuales Verständnis von Bikeability unter Abbildung des Zusammenspiels der fünf wichtigsten infrastrukturellen Parameter. Durch Nutzung offener Geodaten ist der entwickelte Ansatz uneingeschränkt räumlich übertragbar und thematisch adaptierbar. Das Verkehrsmittelwahlmodell belegt den stark positiven Einfluss der Bikeability auf die Wahl des Fahrrades als Verkehrsmittel. Auf der differenzierten Ebene der Routenwahl bestätigt sich der besondere Einfluss der Radinfrastruktur an Hauptverkehrsstraßen. Die Ergebnisse zeigen dabei eine Abstufung im Nutzen für den Radverkehr, die dem Ausmaß der baulichen Trennung vom motorisierten Individualverkehr entspricht, sowie spezifische individuelle und strukturelle Implikationen. Neben Infrastrukturen an Hauptstraßen wird durch die angewandten Methoden auch die generelle Bedeutung von Nebenstraßen verdeutlicht und weiter differenziert. Die Ergebnisse zeigen dabei den enormen Nutzen von Fahrradstraßen aus Sicht der Nutzenden. Die Erkenntnisse bieten spezifische Anknüpfungspunkte, sowohl für weitere Forschung als auch für Planung und Praxis, die in der Arbeit diskutiert werden.
This study explores the multifaceted relationship between travel patterns and mental health (MH) in China, offering a novel integrative approach that synthesizes various factors such as mode of transportation, cultural distance, financial implications, and trip planning. Utilizing a descriptive research design, 622 tourists were surveyed using a pen‐and‐paper questionnaire at designated tourist destinations in China. Findings reveal that travel positively influences MH by providing new experiences, socialization, and relaxation, leading to reduced stress and improved well‐being. Duration, frequency, and active travel modes are associated with better MH outcomes. Solo travel fosters personal growth, while group travel enhances social support. Natural environments offer greater MH benefits than urban settings, and leisure travel surpasses work‐related trips in promoting MH. Greater cultural distance, poor trip planning, and financial burdens negatively impact MH. This comprehensive framework offers insights into public health and tourism policies, advancing the understanding of how travel elements collectively influence MH.
Background
Community indicators may predict and influence individuals` mental health, and support or impede mental health management. However, there is no consensus on which indicators should be included in predictions, prognostic algorithms, or management strategies for community-based mental health promotion and prevention approaches. Therefore, this scoping review provides an overview of relevant community-level indicators for mental health in the general as well as risk populations in a European context.
Methods
We conducted a scoping review in the following electronic databases: PubMed, Embase, and PsycInfo. Eligible studies focused on context factors such as either the physical or social environment, reporting at least one mental health outcome and referring to a European population. Publications between 2012 and March 8, 2022 are considered.
Results
In total, the search yielded 12,200 identified records. After the removal of duplicates, 10,059 records were screened against the eligibility criteria. In total, 169 studies were included in the final analysis. Out of these included studies, 6% focused on pan-European datasets and 94% on a specific European country. Populations were either general or high-risk populations (56 vs. 44%, respectively) with depressive disorder as the main reported outcome (49%), followed by general mental health (33%) and anxiety (23%). Study designs were cross-sectional studies (59%), longitudinal (27%), and others (14%). The final set of indicators consisted of 53 indicators, which were grouped conceptually into 13 superordinate categories of community indicators. These were divided into the domains of the physical and social environment. The most commonly measured and reported categories of community indicators associated with mental health outcomes were social networks (n = 87), attitudinal factors toward vulnerable groups (n = 76), and the characteristics of the built environment (n = 56).
Conclusion
This review provides an evidence base of existing and novel community-level indicators that are associated with mental health. Community factors related to the physical and social environment should be routinely recorded and considered as influencing factors or potentially underestimated confounders. The relevance should be analyzed and included in clinical outcomes, data, monitoring and surveillance as they may reveal new trends and targets for public mental health interventions.
