Associations between emotional symptoms and self-reported aberrant driving behaviors in older adults

Effects of insomnia on risky driving behavior among bus drivers: The mediating effect of mental health

Crashes caused by problems with bus drivers' physical and mental health have increased in frequency in recent years. Insomnia, a common type of sleep problem, has significant positive relationships with both crash risk and mental health problems, especially anxiety and depression, which are themselves associated with driving behavior. However, few studies have conducted analysis on sleep-related problems and mental health exclusively on bus drivers, nor on how these problems influence driving performance. Thus, this study explored the effect of insomnia and mental health on bus drivers' risky driving behavior and evaluated the interaction of four variables: insomnia, anxiety, depression, and risky driving behavior. The survey-based investigation was conducted in a bus company in Suzhou, China, with 1,295 bus drivers participating. Insomnia, anxiety, and depression were self-reported based on professional mental health scales and risky driving behaviors were measured by the Driver Behavior Questionnaire. Two mediation models and a chain mediation model were developed to examine relationships among the bus drivers' insomnia, anxiety, depression, and risky driving behavior. Results revealed that (a) bus drivers less than 31 years old, drivers with more than 11 years' experience driving buses, and those with crash and violation involvement within three years demonstrated more severe degrees of insomnia, anxiety, depression, and risky driving behavior; (b) there were significant positive correlations and interactions among the four variables. Results specifically related to the interaction among variables include findings that (a) anxiety mediated between insomnia and risky driving behavior; (b) depression mediated between insomnia and risky driving behavior; and (c) anxiety affected bus drivers' risky driving behavior primarily though depression. The findings in this study indicate the importance of regular physical and mental health examination of bus drivers and suggest that interventions focused on insomnia and mental health problems may be helpful to reduce risky driving behaviors of bus drivers both directly and indirectly.

Driving Behaviour in Depression Based on Subjective Evaluation and Data from a Driving Simulator

Road traffic collisions are a major issue for public health. Depression is characterized by mental, emotional and executive dysfunction, which may have an impact on driving behaviour. Patients with depression (N = 39) and healthy controls (N = 30) were asked to complete questionnaires and to drive on a driving simulator in different scenarios. Driving simulator data included speed, safety distance from the preceding vehicle and lateral position. Demographic and medical information, insomnia (Athens Insomnia Scale, AIS), sleepiness (Epworth Sleepiness Scale, ESS), fatigue (Fatigue Severity Scale, FSS), symptoms of sleep apnoea (StopBang Questionnaire) and driving (Driver Stress Inventory, DSI and Driver Behaviour Questionnaire, DBQ) were assessed. Gender and age influenced almost all variables. The group of patients with depression did not differ from controls regarding driving behaviour as assessed through questionnaires; on the driving simulator, patients kept a longer safety distance. Subjective fatigue was positively associated with aggression, dislike of driving, hazard monitoring and violations as assessed by questionnaires. ESS and AIS scores were positively associated with keeping a longer safety distance and with Lateral Position Standard Deviation (LPSD), denoting lower ability to keep a stable position. It seems that, although certain symptoms of depression (insomnia, fatigue and somnolence) may affect driving performance, patients drive more carefully eliminating, thus, their impact.

Association of Driving Fitness and Driving Efficacy in the Elderly with Generalized Anxiety Disorder

Purpose

The purpose of this study was to investigate the relationship between driving fitness and driving efficacy in elderly patients with generalized anxiety disorder and identify factors influencing anxiety.

Methods

The anxiety level, driving efficacy, and driving performance were assessed for 45 elderly drivers who had been diagnosed with or were suspected of having an anxiety disorder in the past. The Korean-Self-rating Anxiety Scale (K-SAS), Korean-Adelaide Driving Self-Efficacy Scale (K-ADSES), and Korean-Drivers 65 Plus (K-D65+) were used as test tools. Factors affecting anxiety and the differences between the groups with high anxiety symptoms and anxiety in the normal range were analyzed.

Results

There were differences in driving efficiency and driving performance between elderly drivers with anxiety scores in the normal range and those with mild-to-moderate anxiety (p < 0.05). Significant factors (p < 0.001) influencing driving anxiety in the high anxiety group were GAD duration (β = 0.170), driving difficulty (β = 10.648), drug use (β = 0.656), traffic sign/signal awareness (β = −0.870).

Conclusion

Our results suggest that a combination of a driving rehabilitation approach and interventions that can provide emotional support and reduce mental health anxiety, as well as exposure treatment for driving performance training, may be necessary for driving rehabilitation of the elderly with generalized anxiety disorder.

Cognitive, psychological and social factors associated with older adults' mobility: a scoping review of self-report and performance-based measures

Although many factors have been associated with mobility among older adults, there is paucity of research that explores the complexity of factors that influence mobility. This review aims to synthesise the available evidence for factors comprising the cognitive, psychological, and social mobility determinants and their associations with mobility self-reported and performance-based outcomes in older adults (60 years). We followed Arksey and O'Malley's five stages of a scoping review and searched PubMed, EMBASE, PsychINFO, Web of Science, AgeLine, Allied and Complementary Medicine Database, Cumulative Index to Nursing and Allied Health Literature and Sociological Abstract databases. Reviewers in pairs independently conducted title, abstract, full-text screening and data extraction. We reported associations by analyses rather than articles because articles reported multiple associations for factors and several mobility outcomes. Associations were categorised as significantly positive, negative, or not significant. We included 183 peer-reviewed articles published in 27 countries, most of which were cross-sectional studies and conducted among community-dwelling older adults. The 183 articles reported 630 analyses, of which 381 (60.5%) were significantly associated with mobility outcomes in the expected direction. For example, older adults with higher cognitive functioning such as better executive functioning had better mobility outcomes (e.g., faster gait speed), and those with poor psychological outcomes, such as depressive symptoms, or social outcomes such as reduced social network, had poorer mobility outcomes (e.g., slower gait speed) compared to their counterparts. Studies exploring the association between cognitive factors, personality (a psychological factor) and self-reported mobility outcomes (e.g., walking for transportation or driving), and social factors and performance-based mobility outcomes in older adults are limited. Understanding the additive relationships between cognitive, psychological, and social factors highlights the complexity of older adults' mobility across different forms of mobility, including independence, use of assistive devices, transportation, and driving.

Real-Time Driving Behavior Identification Based on Multi-Source Data Fusion

Real-time driving behavior identification has a wide range of applications in monitoring driver states and predicting driving risks. In contrast to the traditional approaches that were mostly based on a single data source with poor identification capabilities, this paper innovatively integrates driver expression into driving behavior identification. First, 12-day online car-hailing driving data were collected in a non-intrusive manner. Then, with vehicle kinematic data and driver expression data as inputs, a stacked Long Short-Term Memory (S-LSTM) network was constructed to identify five kinds of driving behaviors, namely, lane keeping, acceleration, deceleration, turning, and lane changing. The Artificial Neural Network (ANN) and XGBoost algorithms were also employed as a comparison. Additionally, ten sliding time windows of different lengths were introduced to generate driving behavior identification samples. The results show that, using all sources of data yields better results than using the kinematic data only, with the average F1 value improved by 0.041, while the S-LSTM algorithm is better than the ANN and XGBoost algorithms. Furthermore, the optimal time window length is 3.5 s, with an average F1 of 0.877. This study provides an effective method for real-time driving behavior identification, and thereby supports the driving pattern analysis and Advanced Driving Assistance System.

Use of multilevel modeling to examine variability of distracted driving behavior in naturalistic driving studies

Current methods of analyzing data from naturalistic driving studies provide important insights into real-world safety-related driving behaviors, but are limited in the depth of information they currently offer. Driving measures are frequently collapsed to summary levels across the study period, excluding more fine-grained differences such as changes that occur from trip to trip. By retaining trip-specific data, it is possible to quantify how much a driver differs from trip to trip (within-person variability) in addition to how he or she differs from other drivers (between-person variability). To the authors' knowledge, the current study is the first to use multilevel modeling to quantify variability in distracted driving behavior in a naturalistic dataset of older drivers. The current study demonstrates the utility of examining within-person variability in a naturalistic driving dataset of 68 older drivers across two weeks. First, multilevel models were conducted for three distracted driving behaviors to distinguish within-person variability from between-person variability in these behaviors. A high percentage of variation in distracted driving behaviors was attributable to within-person differences, indicating that drivers' behaviors varied more across their own driving trips than from other drivers (ICCs = .93). Then, to demonstrate the utility of personal characteristics in predicting daily driving behavior, a hypothetical model is presented using simulated daily sleep duration from the previous night to predict distracted driving behavior the following day. The current study demonstrates substantial variability in driving behaviors within an older adult sample and the promise of individual characteristics to provide better prediction of driving behaviors relevant to safety, which can be applied in investigations of current naturalistic driving datasets and in designing future studies.

Components of driving competency measurement in the elderly: A scoping review

Background: Iran will face the "aging Tsunami" phenomenon by the 2040s. Therefore, paying attention to the elderly's driving to maintain and promote their independence and quality of life on the one hand and paying attention to the dangers of driving by the elderly for road safety will be important. The purpose of this research was to determine the components of driving competency in the elderly.

Methods: The research has employed a scoping review. To this end, searches of scientific databases were conducted using keywords between 1990 and 2019. The process of selecting the documentation was-based on the PRISMA chart.

Results: In the first phase, 2769 records were found, and finally, 37 records met the inclusion criteria set for this study. The results indicated that 18 components were extracted that were classified into seven main categories including cognitive, sensory, motor, mental functions, and medications, diseases, and driving history.

Conclusion: Sensory, motor, and cognitive abilities are the most important components of elderly safe driving. Therefore, as age increases, chronic disease, multiple drug use, and subsequent problems increase. This can affect the ability to drive safely and can cause traffic injuries. Therefore, it is recommended to use the results of this research to design a suitable tool and model for assessing driving competency in the elderly.