Bosch IoT Lab, Institute of Technology Management, University of St Gallen, St Gallen, Switzerland.
J Med Internet Res. 2022 Aug 30;24(8):e36314. doi: 10.2196/36314.
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.
近年来，人们对研究如何改善日常生活中的幸福感以促进心理健康产生了浓厚的兴趣。对于许多人来说，每天开车上下班是一天中特别紧张的时刻，因此研究人员已经为改善通勤体验设计了各种车内幸福感干预措施。目前的研究已经验证了这些干预措施，但仅限于在一般不代表实际驾驶条件的对照实验中孤立地研究其效果。
本研究旨在确定驾驶行为与现实环境中幸福感之间的因果关系。这一知识应有助于更好地了解何时触发干预措施。
我们进行了一项现场研究，为 10 名通勤者提供了一辆汽车，让他们在 4 个月的时间内每天开车。在每次行程前后，司机都必须填写一份关于自己幸福感的问卷，幸福感的操作性定义是唤醒度和愉悦度。我们在车上安装了传感器，记录驾驶行为，如急刹车。我们还记录了与行程相关的因素，如驾驶时长，以及预定因素，如天气。我们进行了因果分析，基于有向无环图（DAG）来检查观测数据中的因果关系，并隔离检查变量之间的因果链。我们通过将后门准则应用于基于数据的图形模型来做到这一点。根据编译的调整集，在多元回归中估计了变量之间的因果效应。
因果分析表明，驾驶前较高的唤醒水平会影响驾驶行为。较高的唤醒水平降低了急事件的频率（P=.04）和平均速度（P=.001），同时促进了主动转向（P<.001）。反之，更频繁的刹车（P<.001）会增加驾驶后的唤醒水平，而较长的行程（P<.001）和较高的平均速度（P<.001）会降低唤醒水平。阳光的出现频率（P<.001）增加了驾驶前后的唤醒水平，而乘客的存在（P<.001）增加了愉悦度（P<.001）。
对因果关系的检查揭示了幸福感和驾驶之间的显著相互作用。低水平的预驾驶唤醒会损害驾驶行为，表现为更频繁的急事件和更少的预见性驾驶。驾驶对唤醒的影响大于对愉悦度的影响。特别是，在高速度下单调的驾驶情况，认知需求低，会增加驾驶员疲劳（唤醒水平低）的风险，从而损害驾驶行为。通过结合确定的因果链，可以推断出易损状态，这可能为及时提供改善驾驶幸福感的干预措施提供基础。
