The distracted mind on the wheel: overall propensity to mind wandering is associated with road crash responsibility.

Responsibility for the crash We determined responsibility levels in the crash using a standardized method adapted from the Robertson and Drummer crash responsibility tool (11). The adapted method takes into account mitigating factors likely to reduce driver responsibility: road environment, vehicle related factors, traffic conditions, type of accident, traffic rule obedience, and difficulty of the driving task. Each factor scores from 1 (not mitigating, i.e. favorable to driving) to 3 or 4 (mitigating, i.e. not favorable to driving). All six scores are summed to provide a responsibility score (multiplied by 8/6 to be comparable with the eight factor score proposed by Robertson and Drummer). This method has been previously validated in the French context (5,12–15). Indeed, two factors such as “level of fatigue” and “witness observation” are unavailable in French Police records. The higher the score, the lower the responsibility. Responsibility scores are classified into three categories: 8- 12=responsible; 13-15=contributory; >15=not responsible. Drivers displaying any degree of responsibility for the crash were classified as cases (score ≤15); drivers who were judged not responsible (score >15) served as controls. The interviewer was unaware of the responsibility status while interviewing the participants since responsibility scores were computed during the analysis. Risk factors Participants were asked to describe their thoughts just before the crash and the question was coupled with a numeric scale from 0 to 10 that captured the self-estimated level of perturbation. In order to reduce memory bias and halo effect, two opportunities were offered during the interview to report thoughts which were subsequently classified as being related or not to driving. The Mind Wandering State was defined as the report of any thought unrelated to driving. A Disturbing Thought (DT) corresponded to a Mind Wandering State with a perturbation rating higher than 4. Perturbation level was indeed the answer to “How disturbed / distracted was this thought?”. Mind Wandering Trait was built from a scale comprising four items selected based on their clinical significance. Two items are part of the Day Dreaming Frequency Scale (DDFS): Daydreams and fantasies make up X % of the day, and Recalling things from the past, thinking of the future, or imagining unusual kinds of event occupies X% of my day (16). Two items were developed from literature data: In general, when you drive, how often do you happen to think about something else? And In general, when you read, how often do you happen to think about something else. For each question, the related time spent each day was measured from 0 to 100 percent. If the frequency was higher than 50% for at least one item, the patient was defined as in the high category of the boolean MWT variable. The analysis also included well-known risk factors for road crash and potential confounders such as patient characteristics (age, sex, socioeconomic category), alcohol consumption during the 6 hours before the crash and self-reported psychotropic drug use the day before accident. Characteristics of the crash were also reported (location, vehicle type). The variable Distractive Activity was obtained by asking participants about their activities just before the crash (this included use of a mobile phone, listening to radio/television, talking with or listening to a passenger, manipulation of electronic devices, manipulation of objects, grooming, smoking, eating, drinking, reading). Patients were also asked to evaluate their pain at the time of the interview with a numeric scale; A painful participants was defined as with a self-rated pain value strictly superior to 3. Participants were also asked whether they had been distracted by a distracting event that occurred inside or outside the vehicle. Sleep Deprivation was evaluated with The Epworth Sleepiness Scale (ESS) (17). Statistical AnalysisUnivariate analysis was conducted to investigate the link between crash responsibility and risk factors using Student t-test for continuous variable and Chi-square test for categorical variable. Multivariate analysis was then performed with a step by step backwards selection procedure keeping all significant variables (p < 0.05) and all confounders (variation of β > 20%). We then tested interactions between independent variables kept in the final model. Finally, we performed sensitivity analyses to assess the robustness of the results: 1. by stratifying on pain; 2. by changing the cut-off for responsibility score to 14 and 16; 3. by stratifying on the existence of chronic disease.事故责任判定 本研究采用改编自Robertson与Drummer事故责任判定工具（11）的标准化方法，确定交通事故中的责任等级。改编后的方法纳入了可降低驾驶员责任的减轻因素：道路环境、车辆相关因素、交通状况、事故类型、交通规则遵守情况，以及驾驶任务难度。各因素评分范围为1（无减轻作用，即对驾驶有利）至3或4（有减轻作用，即对驾驶不利）。将六项评分求和得到责任得分（乘以8/6以匹配Robertson与Drummer提出的八因子评分体系）。该方法此前已在法国情境中得到验证（5,12–15）。由于法国警方记录中缺失“疲劳程度”与“证人观察”两项因素，因此未纳入考量。得分越高，责任越低。责任得分被划分为三类：8~12分=有责；13~15分=次要责任；>15分=无责。对事故负有任何程度责任的驾驶员被划为病例组（得分≤15）；被判定无责的驾驶员（得分>15）作为对照组。由于责任得分在分析阶段才会计算，访谈者在与参与者面谈时并不知晓其责任状态。 危险因素 研究人员要求参与者描述事故发生前一刻的想法，并搭配0至10的数值量表以记录其自我评估的扰动程度。为减少记忆偏差与晕轮效应（halo effect），访谈中设置了两次报告想法的机会，后续将这些想法划分为与驾驶相关或无关。心智游移状态（Mind Wandering State）被定义为任何与驾驶无关的想法报告。干扰性思维（Disturbing Thought，DT）指扰动评分高于4分的心智游移状态。扰动程度即对“该想法令你有多烦躁/分心？”这一问题的回答。心智游移特质（Mind Wandering Trait，MWT）基于四项具有临床意义的条目构建而成。其中两项条目来自白日梦频率量表（Day Dreaming Frequency Scale，DDFS）：“白日梦与幻想占据每日X%的时间”，以及“回忆过往、畅想未来或想象非常规事件占据每日X%的时间”（16）。另外两项条目基于文献数据制定：“总体而言，你驾驶时多久会一次走神？”与“总体而言，你阅读时多久会一次走神？”。针对每个问题，每日花费在该类思维上的时间占比以0至100%的量表进行测量。若至少有一个条目的占比高于50%，则将该参与者归类为布尔型变量心智游移特质（MWT）的高分组。 分析还纳入了已知的道路交通事故危险因素与潜在混杂因素，如参与者特征（年龄、性别、社会经济类别）、事故前6小时内的饮酒情况，以及事故前一日自我报告的精神类药物使用情况。同时记录了事故特征（事发地点、车辆类型）。分心活动（Distractive Activity）变量通过询问参与者事故前一刻的活动获得，内容包括使用移动电话、收听广播/电视、与乘客交谈或倾听乘客讲话、操作电子设备、摆弄物品、整理仪容、吸烟、进食、饮水、阅读等。此外，研究人员还要求参与者以数值量表评估访谈当下的疼痛程度：自我评估疼痛值严格高于3分的参与者被定义为疼痛组。参与者还被问及是否受到车内或车外发生的干扰事件分散注意力。睡眠剥夺（Sleep Deprivation）情况通过爱泼沃斯嗜睡量表（Epworth Sleepiness Scale，ESS）（17）进行评估。 统计分析 首先采用单变量分析，探究事故责任与危险因素之间的关联：连续变量采用学生t检验（Student t-test），分类变量采用卡方检验（Chi-square test）。随后采用逐步后退选择法进行多变量分析，保留所有具有统计学显著性的变量（p<0.05）以及所有混杂因素（β值变化>20%）。接着检验纳入最终模型的自变量之间的交互作用。最后开展敏感性分析以评估结果的稳健性：1. 按疼痛状态分层；2. 将责任得分的截断值调整为14与16；3. 按慢性疾病存在与否分层。