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Understanding the Interactions Between Driving Behavior and Well-being in Daily Driving: Causal Analysis of a Field Study published in JMIR (IF 7.08)

We are delighted that our paper was published in the Journal of Medical Internet Research (JMIR). JMIR is the pioneer open access eHealth journal and is a leading digital health journal globally in terms of quality/visibility (with an Impact Factor of 7.08).

In our work, we take a step toward understanding people’s well-being in an everyday situation, the daily car trip. Understanding the well-being of people is a cornerstone in detecting ways to improve well-being in everyday situations as a means to promote mental health, which gained substiantial interest in recent years. We contribute to these efforts by using robust causal analysis to analyze the data collected from our project ARNE. The examination of the cause-effect relationships between driving behavior and the daily well-being of drivers unveiled significant interactions. These insights can be used to infer states of vulnerability that may form the basis for timely delivered interventions to improve well-being while driving. For more details, please refer to the publication:

Paul Stephan, Felix Wortmann, and Kevin Koch. “Understanding the Interactions Between Driving Behavior and Well-Being in Daily Driving: Causal Analysis of a Field Study”. Journal of Medical Internet Research. 10.2196/36314 [PDF]

Investigating 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.

The 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.

We 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.

The 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.

The 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.

2022-08-31T13:18:01+02:00August 31st, 2022|