Helping the Helpers: Adaptation and Evaluation of Stress First Aid for Healthcare Workers in the Veterans Health Administration During the COVID-19 Pandemic.

BACKGROUND: Early interventions are needed to support the behavioral health of healthcare staff in the context of the COVID-19 pandemic. Stress First Aid (SFA) is a self-care and peer support model for reducing burnout and stress that is designed for use in high-stress occupations. METHODS: We conducted a mixed-methods evaluation of an SFA program in the Veterans Health Administration (VHA). This brief, multi-session, didactic program was adapted for hospital workgroups. Program attendees completed a survey assessing implementation outcomes, burnout, stress, mood, and SFA skills at the beginning (N = 246) and end (n = 94) of the SFA program and a subgroup (n = 11) completed qualitative feedback interviews. FINDINGS: Program acceptability, appropriateness, and feasibility were rated highly. From pre- to post-SFA, the impact of the pandemic on stress and anxiety, as well as proficiency in supporting peers increased. Qualitative findings suggest the program provided a shared language to discuss stress, normalized stress reactions, met a need for stress management tools, and helped staff feel valued, empowered, connected with each other. Staff reported being more aware of their stress, but SFA was insufficient to address many of the systemic sources of burnout and stress. CONCLUSIONS AND APPLICATIONS TO PRACTICE: While the SFA program was well received, the impact of brief programs is likely to be modest when implemented in the middle of an ongoing pandemic and when burnout arises from chiefly from systemic sources. Lessons learned during the program implementation that may guide future efforts are discussed.

Dangerous intersections? A review of studies of fatigue and distraction in the automated vehicle.

The impacts of fatigue on the vehicle driver may change with technological advancements including automation and the increasing prevalence of potentially distracting in-car systems. This article reviews the authors' simulation studies of how fatigue, automation, and distraction may intersect as threats to safety. Distinguishing between states of active and passive fatigue supports understanding of fatigue and the development of countermeasures. Active fatigue is a stress-like state driven by overload of cognitive capabilities. Passive fatigue is produced by underload and monotony, and is associated with loss of task engagement and alertness. Our studies show that automated driving reliably elicits subjective symptoms of passive fatigue and also loss of alertness that persists following manual takeover. Passive fatigue also impairs attention and automation use in operators of Remotely Piloted Vehicles (RPVs). Use of in-vehicle media has been proposed as a countermeasure to fatigue, but such media may also be distracting. Studies tested whether various forms of phone-based media interacted with automation-induced fatigue, but effects were complex and dependent on task configuration. Selection of fatigue countermeasures should be guided by an understanding of the form of fatigue confronting the operator. System design, regulation of level of automation, managing distraction, and selection of fatigue-resilient personnel are all possible interventions for passive fatigue, but careful evaluation of interventions is necessary prior to deployment.

The relationship between cell phone use and management of driver fatigue: It's complicated.

INTRODUCTION: Voice communication may enhance performance during monotonous, potentially fatiguing driving conditions (Atchley & Chan, 2011); however, it is unclear whether safety benefits of conversation are outweighed by costs. The present study tested whether personalized conversations intended to simulate hands-free cell phone conversation may counter objective and subjective fatigue effects elicited by vehicle automation. METHOD: A passive fatigue state (Desmond & Hancock, 2001), characterized by disengagement from the task, was induced using full vehicle automation prior to drivers resuming full control over the driving simulator. A conversation was initiated shortly after reversion to manual control. During the conversation an emergency event occurred. RESULTS: The fatigue manipulation produced greater task disengagement and slower response to the emergency event, relative to a control condition. Conversation did not mitigate passive fatigue effects; rather, it added worry about matters unrelated to the driving task. Conversation moderately improved vehicle control, as measured by SDLP, but it failed to counter fatigue-induced slowing of braking in response to an emergency event. Finally, conversation appeared to have a hidden danger in that it reduced drivers' insights into performance impairments when in a state of passive fatigue. CONCLUSIONS: Automation induced passive fatigue, indicated by loss of task engagement; yet, simulated cell phone conversation did not counter the subjective automation-induced fatigue. Conversation also failed to counter objective loss of performance (slower braking speed) resulting from automation. Cell phone conversation in passive fatigue states may impair drivers' awareness of their performance deficits. Practical applications: Results suggest that conversation, even using a hands-free device, may not be a safe way to reduce fatigue and increase alertness during transitions from automated to manual vehicle control.

Active and passive fatigue in simulated driving: discriminating styles of workload regulation and their safety impacts.

Despite the known dangers of driver fatigue, it is a difficult construct to study empirically. Different forms of task-induced fatigue may differ in their effects on driver performance and safety. Desmond and Hancock (2001) defined active and passive fatigue states that reflect different styles of workload regulation. In 2 driving simulator studies we investigated the multidimensional subjective states and safety outcomes associated with active and passive fatigue. Wind gusts were used to induce active fatigue, and full vehicle automation to induce passive fatigue. Drive duration was independently manipulated to track the development of fatigue states over time. Participants were undergraduate students. Study 1 (N = 108) focused on subjective response and associated cognitive stress processes, while Study 2 (N = 168) tested fatigue effects on vehicle control and alertness. In both studies the 2 fatigue manipulations produced different patterns of subjective response reflecting different styles of workload regulation, appraisal, and coping. Active fatigue was associated with distress, overload, and heightened coping efforts, whereas passive fatigue corresponded to large-magnitude declines in task engagement, cognitive underload, and reduced challenge appraisal. Study 2 showed that only passive fatigue reduced alertness, operationalized as speed of braking and steering responses to an emergency event. Passive fatigue also increased crash probability, but did not affect a measure of vehicle control. Findings support theories that see fatigue as an outcome of strategies for managing workload. The distinction between active and passive fatigue is important for assessment of fatigue and for evaluating automated driving systems which may induce dangerous levels of passive fatigue.

Impact of distraction on the driving performance of adolescents with and without attention-deficit/hyperactivity disorder.

IMPORTANCE: This study extends the literature regarding attention-deficit/hyperactivity disorder (ADHD)-related driving impairments to a newly licensed, adolescent population. OBJECTIVE: To investigate the combined risks of adolescence, ADHD, and distracted driving (cell phone conversation and text messaging) on driving performance. DESIGN, SETTING, AND PARTICIPANTS: Adolescents aged 16 to 17 years with (n = 28) and without (n = 33) ADHD engaged in a simulated drive under 3 conditions (no distraction, cell phone conversation, and texting). During each condition, one unexpected event (eg, another car suddenly merging into driver's lane) was introduced. INTERVENTIONS: Cell phone conversation, texting, and no distraction while driving. MAIN OUTCOMES AND MEASURES: Self-report of driving history, average speed, standard deviation of speed, standard deviation of lateral position, and braking reaction time during driving simulation. RESULTS: Adolescents with ADHD reported fewer months of driving experience and a higher proportion of driving violations than control subjects. After controlling for months of driving history, adolescents with ADHD demonstrated more variability in speed and lane position than control subjects. There were no group differences for braking reaction time. Furthermore, texting negatively impacted the driving performance of all participants as evidenced by increased variability in speed and lane position. CONCLUSIONS: To our knowledge, this study is one of the first to investigate distracted driving in adolescents with ADHD and adds to a growing body of literature documenting that individuals with ADHD are at increased risk for negative driving outcomes. Furthermore, texting significantly impairs the driving performance of all adolescents and increases existing driving-related impairment in adolescents with ADHD, highlighting the need for education and enforcement of regulations against texting for this age group.

Fatigue and voluntary utilization of automation in simulated driving.

OBJECTIVE: A driving simulator was used to assess the impact on fatigue, stress, and workload of full vehicle automation that was initiated by the driver. BACKGROUND: Previous studies have shown that mandatory use of full automation induces a state of "passive fatigue" associated with loss of alertness. By contrast, voluntary use of automation may enhance the driver's perceptions of control and ability to manage fatigue. METHOD: Participants were assigned to one of two experimental conditions, automation optional (AO) and nonautomation (NA), and then performed a 35 min, monotonous simulated drive. In the last 5 min, automation was unavailable and drivers were required to respond to an emergency event. Subjective state and workload were evaluated before and after the drive. RESULTS: Making automation available to the driver failed to alleviate fatigue and stress states induced by driving in monotonous conditions. Drivers who were fatigued prior to the drive were more likely to choose to use automation, but automation use increased distress, especially in fatigue-prone drivers. Drivers in the AO condition were slower to initiate steering responses to the emergency event, suggesting optional automation may be distracting. CONCLUSION: Optional, driver-controlled automation appears to pose the same dangers to task engagement and alertness as externally initiated automation. APPLICATION: Drivers of automated vehicles may be vulnerable to fatigue that persists when normal vehicle control is restored. It is important to evaluate automated systems' impact on driver fatigue, to seek design solutions to the issue of maintaining driver engagement, and to address the vulnerabilities of fatigue-prone drivers.