Evidence map›Paper›PMID 36040791›Full record

ArticleJournal of medical Internet research2022

Understanding the Interactions Between Driving Behavior and Well-being in Daily Driving: Causal Analysis of a Field Study.

Paul Stephan, Felix Wortmann, Kevin Koch

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Paul StephanBosch IoT Lab, Institute of Technology Management, University of St Gallen, St Gallen, Switzerland.ORCID 0000-0002-9197-7567
Felix WortmannBosch IoT Lab, Institute of Technology Management, University of St Gallen, St Gallen, Switzerland.ORCID 0000-0001-5034-2023
Kevin KochBosch IoT Lab, Institute of Technology Management, University of St Gallen, St Gallen, Switzerland.ORCID 0000-0003-4523-2668

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInvestigating ways to improve well-being in everyday situations as a means of fostering mental health has gained substantial interest in recent years. For many people, the daily commute by car is a particularly straining situation of the day, and thus researchers have already designed various in-vehicle well-being interventions for a better commuting experience. Current research has validated such interventions but is limited to isolating effects in controlled experiments that are generally not representative of real-world driving conditions.

objectiveThe aim of the study is to identify cause-effect relationships between driving behavior and well-being in a real-world setting. This knowledge should contribute to a better understanding of when to trigger interventions.

methodsWe conducted a field study in which we provided a demographically diverse sample of 10 commuters with a car for daily driving over a period of 4 months. Before and after each trip, the drivers had to fill out a questionnaire about their state of well-being, which was operationalized as arousal and valence. We equipped the cars with sensors that recorded driving behavior, such as sudden braking. We also captured trip-dependent factors, such as the length of the drive, and predetermined factors, such as the weather. We conducted a causal analysis based on a causal directed acyclic graph (DAG) to examine cause-effect relationships from the observational data and to isolate the causal chains between the examined variables. We did so by applying the backdoor criterion to the data-based graphical model. The hereby compiled adjustment set was used in a multiple regression to estimate the causal effects between the variables.

resultsThe causal analysis showed that a higher level of arousal before driving influences driving behavior. Higher arousal reduced the frequency of sudden events (P=.04) as well as the average speed (P=.001), while fostering active steering (P<.001). In turn, more frequent braking (P<.001) increased arousal after the drive, while a longer trip (P<.001) with a higher average speed (P<.001) reduced arousal. The prevalence of sunshine (P<.001) increased arousal and of occupants (P<.001) increased valence (P<.001) before and after driving.

conclusionsThe examination of cause-effect relationships unveiled significant interactions between well-being and driving. A low level of predriving arousal impairs driving behavior, which manifests itself in more frequent sudden events and less anticipatory driving. Driving has a stronger effect on arousal than on valence. In particular, monotonous driving situations at high speeds with low cognitive demand increase the risk of the driver becoming tired (low arousal), thus impairing driving behavior. By combining the identified causal chains, states of vulnerability can be inferred that may form the basis for timely delivered interventions to improve well-being while driving.

Indexed as

Automobile DrivingAccidents, TrafficHumansSurveys and QuestionnairesTransportationcausal inferencecommutedaily drivingdirected acyclic graphfield studyjust-in-time interventionsmental healthmental well-beingstresswell-being

Identifiers

PMID36040791
PMCPMC9472037

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.