Total Worker Health® (TWH) is defined as policies, programs, and practices that integrate protection from work-related safety and health hazards with promotion of injury and illness prevention efforts to advance worker well-being. Sleep deficiency is a cross-cutting factor for TWH that not only impacts workplace safety, but also generates excess risk for obesity, chronic disease, and early mortality. Long-haul truck drivers average less sleep per night on the road than they do when sleeping at home due to long, irregular work hours and unfavorable sleeping conditions in truck sleeper berths (e.g., low quality mattresses, vibrations, noise, temperature). Sleep deficiency in trucking is a likely contributor to the 69% prevalence of obesity among US drivers, which increases the risk of obstructive sleep apnea and deadly crashes. Despite the severity of these interacting problems, research on engineering controls in commercial truck cabs to improve sleep and reduce fatigue is limited. Behavioral interventions to improve sleep among truck drivers are also limited. We must address these gaps and evaluate the economic cost-utility of interventions to stimulate industry investment in factors that substantially improve drivers' TWH.
The primary goal of this proposal is to evaluate the effects of an enhanced cab intervention on long-haul truck drivers' sleep and TWH with a randomized controlled design. A secondary goal is to evaluate the additive effects of a behavioral sleep intervention. We focus on truck driver teams (pairs), where one driver sleeps in a moving vehicle while the other partner drives, who experience twice as many awakenings as solo drivers. Our enhanced cab intervention will alter whole body vibrations during driving and sleep periods, and includes a therapeutic mattress system with anti-vibration characteristics (Thevorest) and an active suspension seat (BoseRide III). The enhanced cab will be evaluated alone and in combination with a behavioral sleep intervention adapted from our effective SHIFT program. Our preliminary studies show that the therapeutic mattress system alters vibrations and is strongly preferred by drivers, the active suspension seat reduces vibration exposure and fatigue, and that SHIFT produces robust health behavior changes. Our primary hypotheses are that relative to a control group, the enhanced cab intervention will improve objective measures of (a) sleep duration and quality, (b) fatigue, and (c) driver performance. We will also measure impacts on musculoskeletal pain, well-being, and health behaviors (diet, physical activity). We also hypothesize that intervention effects will be larger when combined with a behavioral sleep intervention. Our propensity for success is bolstered by our unique prior accomplishments and strong trucking industry support. To accomplish our goals and test our hypotheses we propose a 5-year project to accomplish 3 specific aims:
1. Pilot test intervention and experimental procedures. We will conduct formative research with dispatchers and driving teams and adapt our SHIFT intervention to focus explicitly on improving sleep. We will then pilot data collection and intervention procedures (enhanced cab and behavioral interventions) with team truck drivers. This preliminary work will guide adjustments prior to our randomized controlled trial.
2. Determine the effectiveness of an enhanced cab intervention alone and in combination with a behavioral sleep intervention for improving truck drivers' sleep, fatigue, and performance. Teams will be randomized into intervention and control groups. Intervention teams will complete 3 phases: baseline, enhanced cab intervention, and enhanced cab intervention + behavioral sleep intervention. Control teams will be measured at the same time points. Primary outcomes will include sleep duration and quality (actigraphy), fatigue (psychomotor vigilance task), and driver performance (fuel efficiency and hard braking events). Secondary outcomes will include musculoskeletal symptoms, well-being, diet, and exercise.
3. Conduct cost-utility analyses for interventions. We will gather historical data from trucking companies and model the cost-utility of intervention components. Model variables will include intervention costs and estimated returns or savings based on intervention effects, such as improvements in driver performance (e.g., fuel efficiency), reduced lost workdays, and reduced probability of fatigue-related crashes.
