The Tech4Rest randomized controlled trial: Applying the hierarchy of controls to advance the sleep, health, and well-being of team truck drivers

Abstract

Objective:

To evaluate the effectiveness of interventions to improve sleep, reduce fatigue, and advance the well-being of team truck drivers.

Methods:

In a randomized controlled trial (k=24 teams; n=49 drivers; 61.3% of planned sample), intervention teams were exposed to baseline (3–4 weeks), cab enhancements (active suspension seat, therapeutic mattress; 3–4 weeks), and cab enhancements plus a behavioral sleep-health program (1–2 months). Control teams worked as usual during the same period.

Results:

Trends in sleep-related outcomes favored the intervention. Large and statistically significant intervention effects were observed for objectively measured physical activity (a behavioral program target). The discussion of results addresses effect sizes, statistical power, intervention exposure, and work organization.

Conclusion:

Trends, effect sizes, and significant findings in this rare trial provide valuable guidance for future efforts to improve working conditions and outcomes for team drivers.

Introduction

Occupational scientists have generated increasing evidence that employee safety, health, and well-being are interconnected. Working conditions, and the organization of work, create employees’ risk for experiencing injuries, safety incidents, and occupational illnesses. However, work exposures are not limited to physical hazards, and can have impacts well beyond physical safety and health. This includes psychosocial hazards, including exposure to job-related stressors. Occupational stress is associated with a range of negative outcomes, including insomnia ¹ and cardiovascular disease ^2,3. Further, the organization of work, especially long work hours and shiftwork, can impact employee physiology and health behaviors, including sleep, diet, and exercise ⁴. In response to scientific evidence, and as a strategy for shaping the future of occupational health research and practice, the National Institute for Occupational Safety and Health has developed the Total Worker Health^® approach. 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.” ⁵ (p. 1). The current project adopted a TWH approach to studying and advancing the safety, health, and well-being of truck driving teams.

When considering TWH approaches to advancing worker safety, health, and well-being, sleep is a factor that cuts strongly across all three domains. First, sufficient sleep (duration and quality) is essential for excellent and safe performance. Sleep deficiency is a demonstrated occupational and public safety and health hazard ^6–8 that is highly salient for commercial driving occupations. To illustrate, in a retrospective study with commercial truck drivers, excessive daytime sleepiness, sleep debt, and suspected sleep apnea were associated with increased odds for historical crashes and near crashes ⁹. Commercial truck drivers with obstructive sleep apnea who are compliant with their continuous positive airway pressure (CPAP) treatment have no elevated crash risk; however, drivers who are not compliant with their treatment have a five-fold increased risk for serious crashes ¹⁰. Second, there are tremendous additional “actions in the interactions” when considering sleep and health. Over thirty percent of employees exposed to job strain (high demands, low control) experience insomnia ¹. Sleep deficiency is associated with obesity, chronic diseases such as diabetes and high blood pressure ^11–13, and early death ^14,15. Potential ways that sleep deficiency may contribute to obesity and associated health conditions include impacts on metabolism ¹¹, food cravings and calorie consumption ¹⁶, and tiredness ¹⁷ potentially reducing motivation to exercise. Research with commercial drivers confirms, and further elucidates, many such interactions between the occupation, sleep challenges, and a range of health factors ^18,19. For example, in a study of 88,246 commercial drivers’ medical exams, obesity was associated with increased risk for sleep disorders, mental health disorders, high blood pressure, diabetes, and low back pain ²⁰. Sleep deficiency can have further psychosocial impacts for employees, as sleep deprived individuals experience worsened mood and cognitive performance ¹⁷, and are more likely to mistreat others at work ²¹.

Sleep deficiency is a special concern for shift workers who perform duties outside of a typical daytime shift (roughly 9am-5pm). Such work often requires employees to perform job duties, eat, and exercise during their biological night, and attempt to sleep during their biological day. Social events and schedules during home time can also compete with efforts to entrain the body clock for shift work and get sufficient sleep. The misalignment between the body’s biological clock time and living/sleeping periods can lead to a range of negative health experiences and outcomes for shift workers. This includes shift worker sleep disorder, which is characterized by difficulties going to sleep, staying asleep, sleeping when desired, and excessive sleepiness ²².

Sleep challenges are particularly acute for truck driving teams. Such teams are pairs of drivers who take turns driving in the same truck. While one partner is driving, the other is typically off duty in the sleeper berth attempting to sleep in a moving and vibrating vehicle. Companies that employ driving teams as a business strategy typically do so, in part, to transport high value, high priority, or perishable freight. Team driving keeps the truck moving almost continuously, reduces chances for theft during parked periods, and can transport freight to the customer roughly twice as fast as solo driving. The work arrangement requires night driving and day sleeping, and teams typically must negotiate and plan their overall driving and sleeping patterns. Work periods may last a week or longer. Teams may take turns driving night or day shifts for each work period, assume permanent night or day driving roles based on preferences, and/or experience planned or unplanned drift in driving shifts during a particular working period. To illustrate, teams are likely to work on a schedule that is slightly shorter than 24 hours. For example, the first driver could complete an 11-hour driving segment (lasting 11.5 hours with a required 30-minute break included), take 1-hour to eat with their partner and take care of other needs, and then start a 10-hour sleeper berth period as their partner assumes the on-duty driving role. With this swapping sequence the team would be running on a 22.5-hour day instead of a 24-hour day. If repeated consistently, the start of each driving shift and sleeping opportunity would drift 1.5 hours earlier each day.

Studies of team drivers’ sleep and health are extremely limited. However, one study that included driving teams ²³ found that team drivers were awakened four times more often in the sleeper berth than when they were at home, with evidence that truck vibrations were the probable cause of such awakenings. Due to required day sleeping and variable schedules, team drivers are at-risk for shiftwork sleep disorder and associated problems (e.g., difficulty staying asleep or getting sufficient sleep during main sleep periods). This is a realistic reason why companies have advocated for flexibility in hours of service rules for teams, and why the Federal Motor Carrier Safety Administration has supported pilot programs evaluating such flexible rules ^24,25. And finally, limited opportunities for physical activity and high levels of sedentary behaviors (e.g., sitting, laying down) are conditions of concern for all long-haul commercial truck drivers. These constraining conditions are exacerbated for driving teams, whose breaks may be briefer, occur at variable times during the day/night, and must meet the needs of two people.

To address the needs of driving teams and gaps in the literature, the Tech4Rest research program has employed the Hierarchy of Controls applied to TWH to guide the selection and evaluation of interventions ²⁶. This adjusted Hierarchy asserts that the first priority and most effective strategy for protecting workers is to Eliminate negative working conditions and barriers to safety, health, and well-being. This is followed by the strategies to Substitute safer and healthier workplace policies, processes, and practices; Redesign the work environment to enhance working conditions and improve safety, health, and well-being; Educate all employees and provide resources for improved knowledge; and Encourage or reinforce adoption of safe and healthy practices. Specifically, the Tech4Rest research has addressed hazard reduction through seat and mattress engineering controls (Redesign), and complimented changes to the physical environment with a tailored behavioral intervention (Educate, Encourage). The research program includes a published pilot study ²⁷ and a cluster-randomized controlled trial. The current paper is focused on reporting the results of the randomized controlled trial, but the pilot study and results provide important context and preliminary findings.

The Tech4 Rest pilot study involved 8 teams (n=16 drivers) from two trucking companies based on the west coast of the U.S. Driving teams at the first company primarily transported freight up and down the I-5 corridor with some work into Canada and experienced regular home time about once per week. Teams at the second company transported freight across the US and experienced home time about every two to three weeks. The pilot began by evaluating a standard coil spring mattress and a novel therapeutic mattress at the first company with 4 teams. At the second company an additional 4 teams completed the mattress conditions (in a counterbalanced order relative to the first company) and then completed an additional final phase that included the drivers’ preferred mattress (100% chose to keep the therapeutic mattress), the addition of an active suspension seat, and participation in a behavioral sleep-health program named Fit4Sleep. The active suspension seat (ClearMotion Active Suspension Seat, ClearMotion, Inc., Billerica, MA) reduces exposures to whole body vibration by up to 50% ^28,29 and also reduces indicators of end-of-shift fatigue ²⁹. The therapeutic mattress studied (ThevoRelief, Thomashilfen, Bremervörde, Germany) was recommended by an industry engineer to researchers due to favorable road testing with teams at an original equipment manufacturer. The Fit4Sleep behavioral program was informed by and adapted from prior effective interventions, including a physical activity-based intervention for insomnia ³⁰ and a multi-component mobile health intervention for truck drivers ³¹. Each mattress evaluation phase lasted for two to three weeks. The final phase with all intervention components lasted for three months. Self-reported outcomes focused on sleep, fatigue, physical activity, and sleep hygiene behaviors and were measured after each phase. Additional actigraphic measures of physical activity and sleep were measured during the first week of each phase. An additional actigraphy sample was collected toward the end of the final phase. Measures of whole body vibration (WBV) were collected to confirm performance of the active suspension seats and explore for possible vibration altering characteristics of the therapeutic mattress.

Results of the pilot study were very encouraging. For the mattress evaluation phase (n=16) the therapeutic mattress was universally and strongly preferred over drivers’ original mattresses and the new coil spring mattress, and produced larger improvements in sleep and fatigue outcomes relative to the new coil spring mattress. Effect sizes for the therapeutic mattress relative to the drivers’ original mattress for self-reported sleep and fatigue outcomes ranged in magnitude from d=0.19 (sleep duration) to d=0.82 (sleep quality). The increase in sleep duration was also observed in actigraphic measures (d=0.36). The final multi-component phase produced the largest effect sizes for sleep and fatigue outcomes (d range was 0.37 [sleep duration] to 1.49 [sleep quality]). This final phase also generated large improvements in targets of the behavioral intervention, including self-reported physical activity (d=1.83) and objectively measured weekly time in moderate intensity physical activity bouts (d=1.16 and 0.29) at the beginning and end of the program, respectively.

The current paper will summarize methods and results for the Tech4Rest cluster randomized controlled trial (clinicaltrials registration NCT03108599). This project evaluated cab enhancements (Redesign) alone, and then in combination with a behavioral program (Educate, Encourage) with team drivers. Primary outcomes were focused on sleep (duration, quality) and fatigue. Secondary intervention outcomes addressed targets of the behavioral intervention, including physical activity and sleep hygiene behaviors. A range of additional relevant measures were collected, including optional behavioral intervention targets (e.g., body composition, diet), factors that might be reduced by cab enhancements (e.g., pain), factors that could be affected indirectly by improved sleep and reduced fatigue (e.g., safety), and intervention process and manipulation check variables (e.g., intervention participation, WBV exposures).

Methods

Setting

The study was conducted in collaboration with a partner company that employed over 5,000 drivers, including a substantial number of driving teams. A Safety Manager and the Vice President of Safety were the study champions and secured corporate commitment to the project. The Safety Manager served as the company point person to work with researchers in implementing the project and helped form a planning team. A particular company division was identified as the focus of driver recruitment, and one specific terminal was identified as the location for completing driver study visits and seat and mattress installations. This company terminal included a shop where regular truck maintenance was performed. The planning team and local leaders from the participating terminal held regular meetings before and during implementation, with relevant leaders and staff from key operational units joining as needed (e.g., operations/driver managers, corporate and local maintenance personnel).

Participants

Inclusion criteria included currently working as a team truck driver, having a driving teammate who was both willing and eligible to participate, intention to drive with their current teammate for the next four months, and ability to visit the terminal involved with the study at least once per month. In addition, if diagnosed with Obstructive Sleep Apnea drivers had to be treatment compliant (based on self-report). Exclusion criteria were that drivers could not be a lead driver or trainer, or a new driver still being trained by a lead driver. This was due to trainer/trainee relationships typically lasting for 30 or fewer days. We considered a minimum job tenure eligibility requirement, but decided against it after deliberation with our organizational partner. In our experience, short job tenure (<one year) is associated with a higher risk for job turnover in the trucking industry. However, the mixture of team member transitions on top of typical industry job turnover suggested that even a relatively short tenure requirement (e.g., six months) could make too many teams ineligible. In other words, a tenure requirement might have reduced potential study attrition, but would have severely limited study enrollment.

Two waves of recruitment and implementation occurred. Wave one occurred between October, 2020 and March, 2021. Wave two occurred between April and August, 2021. A third recruitment wave was initiated, but ultimately cancelled due to a lower than anticipated response to recruitment messaging (only four teams expressed interest) and other logistic considerations. Wave three was not rescheduled due to the five-year grant period ending. Team drivers were recruited through standardized direct satellite text messages to their trucks and emails with the assistance of operations staff and driver managers. Driver managers were briefed on the study before recruitment messages were sent so they could answer questions and encourage participation. During the second wave of recruitment driver managers were paid $150 in compensation if they had one or more teams volunteer for the study, and an additional $50 if they participated in a one-on-one interview about their experiences managing driving teams. Interested drivers were invited in recruitment messages to call researchers to discuss the study. In general, one member of the team tended to call and serve as the point of communication for the team, but individual calls with each team member sometimes occurred. For teams where both members were interested after the phone call, researchers worked with driver managers to route those teams to the terminal during a targeted enrollment period.

The informed consent process took place at the company terminal in person with each team. The consent form and process contained limited information about interventions, which were addressed separately after data collection was complete for teams that were randomized to the intervention (see Design and Conditions below). Drivers in wave one of recruitment were paid $75 for their first study visit and then $100 for each of three primary follow-up visits. Drivers in wave two were paid $120 for completing each primary study visit with researchers.

The study was registered as a clinical trial prior to participant recruitment (NCT03108599). This registration specified the design and primary and secondary outcomes a priori. All study procedures were reviewed and approved by the Oregon Health & Science University Human Subjects Institutional Review Board. Additional COVID-19 related procedures (e.g., masking, distancing, sanitizing equipment, symptom monitoring and responses) were followed in order to conduct the study during the pandemic. Disease transmission prevention procedures for research in the community were reviewed and approved by an ad hoc research committee that operated during modified operations at the University. These procedures were further supported and documented through a letter of support from the Vice President of Safety at the partner company.

A Priori Power Analysis and Recruitment

A target sample size of 80 drivers was selected based on an a priori power analysis. With this sample size, the planned data collection time points, and study design, the project would possess 80% power to detect a moderate effect size (d=0.60) between groups in primary outcomes. This sample size target assumed 20% attrition by the final measurement time point (i.e., n=62) and an AR(1) correlation structure with rho=0.5 for repeated observations within each driver. The power analysis also took into account the additional potential correlations between truck drivers within a team by assuming a conservative (i.e., relatively high) intracluster correlation coefficient of 0.25.

Design and Conditions

The study employed a cluster randomized controlled design with randomization at the level of team. Unassigned study IDs for teams were randomized in blocks of four teams by the fourth and fifth authors in advance of each recruitment wave and sealed in envelopes. Half of each block was randomly assigned to the intervention condition. Envelopes were sent to researchers in the field in limited numbers, as needed. After drivers completed the informed consent process at their first study visit, they were assigned a study ID and the relevant envelope revealing their condition was opened by researchers. However, teams were not informed of their randomized condition until after completing baseline data collection. Intervention teams were then informed of their additional activities and given a supplementary schedule summary noting additional events associated with visits, such as active suspension seat installation.

There were four primary study measurement visits that occurred at baseline and then after each study phase. For self-reported outcomes, the first and second visits were both functionally baseline condition assessments. In the current paper the first visit was used as the source of baseline data unless otherwise specified. Study phases and visits varied in duration to a degree based on the teams’ schedules, and included baseline (three to four weeks), cab enhancements (three to four weeks), and cab enhancements plus the behavioral sleep-health intervention (two months for wave one, and one month for wave two). The duration of the behavioral program was shortened after wave one in response to experiencing higher than anticipated turnover/attrition. Control participants worked as usual during these periods, but were offered the mattress and part of the behavioral program after their fourth visit.

Control and Intervention Conditions

Standard seats at the company were Admiral air ride seats (National Seating, Franklin, TN) installed in Freightliner Cascadia 125 model trucks (2019–2022). Mattresses were model MFS (Designs International, Inc, Chatsworth, GA) coil spring mattresses with a foam layer. These are six inches thick and covered with a polypropylene cover. All team drivers at the company had access to fatigue prevention related training during onboarding and as continuing education. This training addressed topics such as sleep schedules, circadian rhythm management, sleep cycles, risk periods, light changes affecting sleep, and health risks after time changes. Most of these trainings were videos with post-tests.

Cab Enhancements

At the beginning of the cab enhancement period intervention teams’ trucks had the ClearMotion Active Suspension Seat (ClearMotion, Billerica, MA; formerly the Bose Ride^® System II) installed. This seat system includes smart sensors, a linear electromagnetic actuator, and a computer system that detects vibrations and actively dampens their transmission to the driver. The seat also offers three driver-selected vibration dampening options (low, medium, and high). Drivers’ standard company issued mattresses were replaced with the ThevoRelief “therapeutic” mattress (Thomashilfen, Bremervörde, Germany). This mattress includes a unique wing suspension system and a mattress with interlocking foams of different densities to support different body regions. The suspension system and mattress are held together in a cover that zips closed. Two mattresses were available for installation based on driver body weights. The ThevoRelief Model 100 (for body weights up to 100 kg) or ThevoRelief Model 135 (for body weights 100–135 kg; 19.1 × 99.1 × 190.5 cm [foam mattress component is 10.9 cm thick]). Both team members had to weigh over 100 kg to be assigned Model 135.

Cab Enhancements + Behavioral Program

The behavioral program was adapted from a prior effective mobile health intervention platform for truck drivers ³¹. This mobile adaptive web application was modified to deliver a sleep-health program that was further informed by an effective physical activity-based intervention for insomnia ³⁰. This program, named Fit4Sleep for implementation, focused on increasing physical activity and sleep hygiene behaviors in order to improve sleep duration and quality, increase energy levels, and reduce fatigue. The Fit4Sleep website facilitated an inter-team walking competition that included individual and team goal setting, behavioral self-monitoring, individual and inter-team feedback, online training topics, and multiple calls with a health coach. Incentives were also provided for individual participation and for the team that won the walking competition (see criteria and amounts below). After the three to four week cab enhancement phase, intervention teams completed an online Fit4Sleep training orientation, and were then set up with a website account and selected their goals with the help of researchers.

Goal Setting

Individual goal setting options for the walking competition included 50, 100, or 150 minutes of walking per week. The team goal was the sum of the two individual goals. Walking minutes were tracked and reported on an honor system and not with a step counter or accelerometer. Sleep hygiene behavioral goals included: 7 to 8 hours sleep, relaxing sleep routine, (all electronic) screens off 30 minutes before sleep, pleasant sleep environment (clean bedding, no light, noise control, 60–67 degrees Fahrenheit), avoid caffeine or energy drinks 4 hours before bed, avoid nicotine 2 hours before bed, avoid alcohol 4 hours before bed, and limit naps to 20–30 minutes. An optional weight loss goal could also be selected to lose 3, 5, or 7 lb (1.4, 2.3, or 3.2 kg) as well as optional dietary behavioral goals (e.g., eat 5 servings of fruits and vegetables a day, reduce portion sizes).

Weekly Behavioral Self-Monitoring with Feedback

Drivers were asked to login to the website and submit (track) their physical activity and sleep hygiene behaviors each week. Scores and feedback were updated with each submission. With each tracking submission a driver reported their total minutes of walking that week, as well as the number of days that week that they achieved their selected additional sleep hygiene goals. Individual and team walking feedback charts showed histograms of weekly walking minutes relative to a goal line. In the team histogram each team member’s contribution to the total minutes each week was shown in a different color. The inter-team competition rankings were shown by displaying the number of weeks each team in the competition met their goal using images of trucks with trailers filled in color for each week the team goal was met. Trucks for each team were rank ordered and identified with colors to distinguish a team from their competitors. To add interest to the feedback process, occasional pop-up messages with pictures were displayed when an individual had walked enough minutes to have traveled a particular distance, such as around the entire shoreline of Lake Tahoe. Sleep hygiene behavioral feedback was displayed as a data table with three columns - one for how many days in the last week they had performed the behavior, one for their best week, and one for the total days with the behavior.

Online Training

Drivers completed an online Fit4Sleep Orientation training topic with researchers during study visit three which outlined the purpose of the challenge, activities, and incentives. Other topics that could be completed during the Fit4Sleep challenge period included “Why Fit4Sleep?” and “Healthy Sleep.” Why Fit4Sleep addressed the sleep and health benefits of regular exercise, as well as interactions between health behaviors (e.g., sleep, eating, exercise). Healthy Sleep provided education on sleep physiology, circadian rhythms, and sleep hygiene practices. An additional topic was created and made available for friends and family members of the team drivers. Drivers were asked to invite people they lived with, especially spouses or partners they shared a bedroom with, to complete a topic titled “Fit4Sleep Family: Sleeping with a Shift Worker.” This topic provided a very brief overview of the Fit4Sleep program, highlights from the drivers’ longer training topics, and emphasized how couples and families could work together with their shift worker to maximize their sleep quantity and quality at home.

Health Coaching

Participants were offered up to four (wave one) and up to two (wave two) calls with a health coach. An experienced coach who was a member of the Motivational Interviewing Network of Trainers provided the majority of services. This person supervised two additional coaches who each had training in motivational interviewing. Each call followed a study specific protocol informed by motivational interviewing spirit, principles, and strategies ³² and were requested to take place with drivers during off duty/non-driving time. Coaches called drivers for appointments, and appointments were rescheduled as needed. The purpose of the calls was to help drivers personalize the program to align with their personal motives and priorities. This included discussing the physical activity and sleep hygiene goals they had selected, the reasons for selecting those goals and wanting to make changes in those areas, and helping drivers form specific plans and strategies for accomplishing their goals. Exploring and resolving ambivalence about goals or making changes, eliciting change talk, and helping drivers make commitments (if ready) were all aims of the process.

Incentives for the Team Competition and Individual Achievement

The winning team was the one that achieved their team walking goal on the most weeks during the challenge (eight weeks being the maximum for wave one and four weeks being the maximum for wave two). During wave one, the incentive for winning the inter-team competition was $100. Individual drivers could also earn Fit4Sleep Certification by logging their goals on the website at least six times, passing the three required trainings with a score of at least 80%, and completing four health coaching calls. The incentive for earning Certification in wave one was $100. During wave two, the incentive for winning the inter-team competition was a sleep- or health-related item chosen from a menu of options (e.g., weighted blanket, white noise machine). Individual drivers earned Fit4Sleep Certification in wave two by logging their goals on the website four times, passing the three required trainings with a score of at least 80%, and completing two health coaching calls. The incentive for earning Certification in wave two was $95.

Measures

Demographics

Measures collected to describe the sample addressed sex, race/ethnicity, marital status, relationship with driving partner, education, work hours/miles, work tenure, work-related stress, collision history, and health behaviors/conditions.

Primary Outcomes

Primary outcomes focused on self-reported sleep and fatigue. Sleep duration was measured with item four adapted from the Pittsburgh Sleep Quality Index (“When working and traveling in your truck, how many hours of actual sleep did you get during a typical night [or day, for shift workers]?”)³³. If item four was missing, we calculated total sleep time by subtracting items three (“When working and traveling in your truck, what time have you usually gotten up?”) from item one (“When working and traveling in your truck, what time have you usually gone to bed?”), accounting for item two (“When working and traveling in your truck, how long [in minutes] has it usually taken you to fall asleep?”). Sleep quality was measured with item six from the Pittsburgh Sleep Quality Index ³³, which was reversed scored so that higher values indicate better sleep quality, as well as with the eight-item short form version of the Patient-Reported Outcomes Measurement Information System (PROMIS) sleep disturbance scale ^34,35. PROMIS sleep disturbance items (e.g., “My sleep was restless”) were measured on a five-point Likert scale ranging from 1-“Not at all” to 5-“Very much” (with one item, “My sleep quality was,” measured on a scale from 1-“Very good” to 5-“Very poor”), and all items were summed for a total score (Control Baseline Cronbach’s α=0.94; Intervention Baseline α=0.82). Fatigue was measured with the eight-item short form version of the PROMIS sleep-related impairment questionnaire ^34,35, and the eight-item Swedish Occupational Fatigue Index (SOFI) ³⁶. Items on the PROMIS sleep-related impairment questionnaire (e.g., “I was sleepy during the daytime”) were measured on a five-point Likert scale ranging from 1-“Not at all” to 5-“Very much,” and all items were summed for a total score (Control Baseline α=0.86; Intervention Baseline α=0.83). Items on the SOFI were measured on a seven-point Likert scale ranging from 0-“Not at all” to 6-“To a very high degree,” and all items were summed for a total score (Control Baseline α=0.86; Intervention Baseline α=0.88). We asked participants to respond to all self-report items as they experienced them in the past month, or since their last visit with the research team, when working and traveling in their truck.

Self-reported sleep measures were supplemented with actigraphs worn on the wrist for one-week periods (Actigraph GT3x+ or GT3x-BT, Actigraph, Pensacola, Florida). The Actigraph ActiLife software algorithm includes downweighting of vibrations/movements outside the typical human cycles per second generated by walking or jogging ³⁷. In our preliminary studies, this feature also appeared to benefit the accuracy of software coding of sampled intervals as sleep or wake in vibrating sleeper berths. Electronic log books obtained from the partner company and sleep diaries were used to help identify sleeper berth periods and rest intervals (bed and wake times). A study specific sleep scoring protocol was created, informed by several past actigraphy projects, that involved inspecting a visualization of log book, sleep diary, and Actilife sleep/wake epochs together in order to manually set rest intervals for main sleep periods for each driver day. Resulting actigraphic outcomes included total sleep time and sleep efficiency during main sleep periods.

Secondary Outcomes

Secondary outcomes focused on factors targeted by the behavioral intervention and general health and well-being. Sleep hygiene practices were measured with the 13-item Sleep Hygiene Index ³⁸. Items (e.g., “I went to bed at different times from day to day”) were measured on a five-point Likert scale ranging from 0-“Never” to 4-“Always,” and items were summed for a total score (Control Baseline α=0.64; Intervention Baseline α=0.82). Note that one item (“I slept on an uncomfortable bed”) was omitted from this total score due to the inherent bias introduced by the intervention mattress. Self-reported physical activity was measured with two items from the International Physical Activity Questionnaire-Short Form.^39,40 Participants were asked to report on how many days in the past week they engaged in at least 10 minutes of moderate and vigorous physical activity, respectively.

Objectively measured physical activity was measured with hip worn accelerometers worn for one-week periods (Actigraph GT3x+ or GT3x-BT, Actigraph, Pensacola, Florida). As noted above, the Actigraph ActiLife software algorithm includes downweighting of vibrations/movements outside the typical cycles per second generated by human activity ³⁷. To further minimize contamination by false activity generated by vibrating trucks, physical activity actigraphic analyses focused on the occurrence of bouts of moderate or vigorous physical activity lasting 10 or more minutes ⁴¹ using settings available in ActiLife software. In unpublished preliminary studies ⁴² we found that moderate intensity physical activity bouts were never falsely generated by trucks during driving time as recorded in electronic driving logs. Days with excessive non-wear time (>240 continuous min) were excluded from analyses. Objective physical activity outcomes were average bouts per week and average minutes per week spent in activity bouts.

Self-assessed physical and mental health were measured with the PROMIS Global Health scale ^43,44. Although response anchor labels varied across items, all items were measured on a 1–5 scale. Total physical (Control Baseline α=0.60; Intervention Baseline α=0.33) and mental health (Control Baseline α=0.85; Intervention Baseline α=0.76) scores were computed by summing each subscale’s four items, respectively.

Other Outcomes

Additional outcomes in the study, which are included in this paper primarily for their potential descriptive value for future studies of team drivers, were comprised of self-reported collisions with and without truck or property damage, minor (first aid treatment only) and major (resulting in lost work time) injuries at work, pain intensity and interference with normal home and work activities, and health factors related to optional goals in the behavioral program (e.g., body composition, blood pressure, dietary behaviors). Collisions were measured with three items (“How many times have you struck a curb or object with no property damage?”, “While operating your work vehicle, did you have any safety incidents or collisions involving another vehicle that resulted in vehicle/property damage?”, and “While operating your work vehicle, did you have any safety incidents or collisions involving only your own vehicle that resulted in vehicle/property damage?”) measured on a scale from 0–5+ incidents, adapted from previously developed safety items for truck drivers ³¹. Minor (first aid only) and major (lost work time) injuries were measured with two respective items on a scale from 0–5+ injuries, adapted from a previously developed common set of occupational injury items ⁴⁵. The time anchor for collision and injury questions was in the past month or since we last saw you. Informed by the Nordic Musculoskeletal Questionnaire ^46,47,48, pain intensity was reported at its worst in the past 7 days, ranging from 0-“No pain” to 10-“Worst pain imaginable,” in the past week for 5 body areas, respectively: neck/shoulders, forearm/wrist, low back, upper back, and lower extremities. Pain intensity items were summed for a total score. Also informed by Nordic-style assessments, pain interference was measured on a five-point Likert scale, ranging from 1-“Not at all” to 5-“Extremely” for each of the five body areas, during the past month or since the last study visit. Items were summed for a total score. Body weight and percent body fat were measured with a Tanita TBF-310GS scale (Tanita Corporation, Tokyo, Japan). Body Mass index was calculated from measured body weight and measured height with a SECA 213 stadiometer, SECA, Hamburg, Germany). Blood pressure was measured with an Omron Intellisense Digital Blood Pressure Monitor HEM-907XL (Omron Healthcare Inc., Bannockburn, Illinois) as the average of three consecutive measurements. Dietary measures included the frequency (0-“Never” to 9-“5 or more times per day” in the past month or since the last study visit) of consumption of sugary snacks, sugary drinks, fast food meals, and meals brought from home ⁴⁹. Each item was treated as a separate outcome. A single item assessing the usual number of servings of fruit and vegetables consumed per day was also administered, with serving size examples informed by Thompson et al. ⁵⁰.

Process/Manipulation Checks

To confirm that the active suspension seats were turned on and reducing whole body vibration exposures (WBV) we collected triaxial accelerometer samples (AX3, Axivity Ltd., Newcastle, United Kingdom) of vibrations from the truck floor and seat top during baseline and cab enhancement periods. Additional exploratory samples were collected from the mattress top under the pillow, on the bed frame, and on the floor near the mattress. Axivity devices sampled at 400 Hz for approximately 4 days. GPS data (Model CR-Q1100P; Qstarz Co., Taipei, Taiwan) were collected to identify driving periods for analysis. Electronic log book data were also collected from our partner company.

For the WBV analysis, LabVIEW programs (Version 2016; National Instruments, Austin, TX) were used to align acceleration data with GPS data and combine them into one file for each team. Using the GPS data, a 1-hr segment of the aligned data was identified for analysis for each team where the truck was travelling at freeway speed (~105 km/hr). A LabVIEW program then applied weighting to the raw, unweighted acceleration data in each file segment as described in ISO 2631–1⁵¹. And finally, additional LabVIEW programs were used to calculate daily vibration exposure, or A(8) from the selected 1-hr data segments, which was the predominant, Z-axis average weighted vibration exposures standardized to an 8-hr day.

To evaluate intervention dose or exposure for the behavioral intervention we assessed number of tracking submissions completed, number of training topics completed with 80% correct, and whether or not individuals earned Fit4Sleep Certification.

Intervention Effectiveness Analysis Methods

Intervention effectiveness was evaluated with an intent-to-treat analysis using data from baseline, cab enhancement, and cab enhancement + behavioral program conditions. By definition, intent-to-treat analysis “…is a method for analyzing results in a prospective randomized study where all participants who are randomized are included in the statistical analysis and analyzed according to the group they were originally assigned, regardless of what treatment (if any) they received” ⁵², (p. 1075). This is a gold standard analysis approach for clinical trials that utilizes available data from all participants at all time points. Specifically, as we planned a priori in our research protocol, we modeled the outcomes using generalized linear mixed models (GLMM), which offer a method for handling missing data without requiring imputation. GLMMs extend the basic linear model through the addition of random effect terms, which account for the dependence between observations, as with repeated measures for the same individual over time. Mixed models are well-suited to dealing with missing data due to dropout, as they do not require the same number of observations per subject, so that inferences made about the sample can be based on all available measurements ⁵³. The intent-to-treat analysis is thus conservative and maximizes use of contributed data.

Results

Of the 49 participants who enrolled the study, 32 completed all 4 primary study visits (65.3% retention). The most common reason for leaving the study was job turnover. Other common reasons included the breakup of a driving team and one member not following up or continuing (see Figure 1 for the Consort diagram). This experienced sample size after attrition was half of the study’s target sample size after estimated attrition (32 vs. 64).

Figure 1.

Consort Diagram: Tech4Rest Randomized Controlled Trial. Note. In wave two, one of the co-drivers on an intervention team left the company and study after Baseline. The new co-driver of that team was recruited and enrolled in the study during the Cab Enhancement visit. This is the reason for an odd number of drivers in the intervention arm at baseline.

The sample averaged 41.0 yrs. (SD=12.8) and was predominantly male (81.2%), Caucasian (46.9%), and unmarried or not living with a partner (64.3%). Reported relationships with driving partners were predominantly company assigned (34.7%) or “other” (40.8%; presumed to be a self-selected or chosen partner from within the company). The remaining quarter of the sample worked with their spouse or boyfriend/girlfriend as their team member. Reported average tenure as a team truck driver was less than two years, and tenure with their current team driver partner was close to one year on average (although the average partnership tenure for intervention teams was less than half a year). Notable levels of health-related factors in the sample included 54.2% reporting being current smokers and 30.6% reporting chronic pain. The majority of drivers were categorized as being low or intermediate risk for obstructive sleep apnea based on the STOP-BANG assessment. See Table 1 to review additional sample demographics.

Table 1.

Sample Demographics: The Tech4Rest Randomized Controlled Trial.

	Intervention (N=24)	Control (N=25)	Total Sample (N=49)
Variable	Mean (SD) or N (%)	Mean (SD) or N (%)	Mean (SD) or N (%)
Age (years)	40.22 (11.05)	41.71 (14.55)	40.98 (12.84)
Gender (% Female)	4 (16.67)	5 (20.83)	9 (18.75)
Race
American Indian/Alaskan Native	1 (4.17)	2 (8.00)	3 (6.12)
Asian	0 (0.00)	1 (4.00)	1 (2.04)
Black/African American	4 (16.67)	6 (24.00)	10 (20.41)
Native Hawaiian/Pacific Islander	1 (4.17)	0 (0.00)	1 (2.04)
White/Caucasian	12 (50.00)	11 (44.00)	23 (46.94)
>1 race	4 (16.67)	3 (12.00)	7 (14.29)
Other	2 (8.33)	2 (8.00)	4 (8.16)
Ethnicity (% Hispanic)	6 (25.00)	4 (16.67)	10 (20.83)
Household
Married or Living with Partner	11 (45.83)	7 (28.00)	18 (36.73)
Dependent Children ≥1	8 (33.33)	7 (29.17)	15 (31.25)
Relationship with Driving Partner
Company Assigned	9 (37.50)	8 (32.00)	17 (34.69)
Spouse	2 (8.33)	1 (4.00)	3 (6.12)
Boyfriend, Girlfriend	3 (12.50)	6 (24.00)	9 (18.37)
Other	10 (41.67)	10 (40.00)	20 (40.82)
Education
Grades 1–8 (Elementary)	1 (4.17)	0 (0.00)	1 (2.04)
Grades 9–11 (Some high school)	1 (4.17)	1 (4.00)	2 (4.08)
Grade 12 or GED (graduate)	7 (29.17)	12 (48.00)	19 (38.78)
College 1–3 years (Some college/techn)	12 (50.00)	8 (32.00)	20 (40.82)
College 4 years or more (College grad)	3 (12.50)	3 (12.00)	6 (12.24)
Graduate school (Masters or doctoral)	0 (0.00)	1 (4.00)	1 (2.04)
Work Hours/Miles
Total Weekly Driving Hours	59.95 (12.86)	57.48 (20.12)	58.66 (16.90)
Total Weekly On-duty Hours	67.64 (11.04)	64.50 (18.23)	66.00 (15.14)
Work-Related Stress
(Job) Stress in General (7–35)a	20.34 (5.54)	19.88 (7.49)	20.11 (6.50)
Tension with Driving Partner (0–5)a	0.44 (0.61)	0.28 (0.60)	0.36 (0.60)
Tenure (years)
Industry	1.40 (1.75)	2.77 (7.38)	2.10 (5.40)
Current company	1.04 (1.64)	1.83 (7.03)	1.44 (5.11)
As a team truck driver	1.12 (1.66)	2.21 (6.02)	1.68 (4.44)
With current driving partner	0.44 (0.46)	1.80 (6.92)	1.14 (4.95)
Collisions and Injuries in Last 5 Years
≥1 Collision w/ property damage only	3 (12.50)	6 (24.00)	9 (18.37)
≥1 Collision w/ injury	0 (0.00)	1 (4.17)	1 (2.08)
Health Behaviors and Conditions
Smoked within past week	13 (54.17)	13 (54.17)	26 (54.17)
Used alcohol within past month	12 (50.00)	7 (29.17)	19 (39.58)
High blood pressure	1 (4.17)	4 (16.00)	5 (10.20)
High blood pressure medication	0 (0.00)	3 (75.00)	3 (60.00)
High cholesterol	1 (4.17)	1 (4.00)	2 (4.08)
High cholesterol medication	1 (100.00)	0 (0.00)	1 (50.00)
Osteoarthritis/Arthritis	4 (16.67)	1 (4.00)	5 (10.20)
Arthritis medication	1 (25.00)	0 (0.00)	1 (20.00)
Asthma	3 (12.50)	1 (4.00)	4 (8.16)
Asthma medication	1 (33.33)	0 (0.00)	1 (25.00)
Diabetes	0 (0.00)	1 (4.00)	1 (2.04)
Diabetes medication	NA	1 (100.00)	1 (100.00)
Anxiety	3 (12.50)	3 (12.50)	6 (12.50)
Anxiety medication	3 (100.00)	3 (100.00)	6 (100.00)
Depression	2 (8.33)	1 (4.00)	3 (6.12)
Depression medication	2 (100.00)	1 (100.00)	3 (100.00)
Chronic pain	8 (33.33)	7 (28.00)	15 (30.61)
Chronic pain medication	4 (50.00)	1 (14.29)	5 (33.33)
Obstructive Sleep Apnea Risk
Low Risk	17 (70.83)	15 (60.00)	32 (65.31)
Intermediate Risk	4 (16.67)	7 (28.00)	11 (22.45)
High Risk	3 (12.50)	3 (12.00)	6 (12.24)

Note. Sample sizes for some variables were less than the maximum possible due to occasional missing data.

^aHigher score = higher stress or tension. Job Stress was measured with the Stress in General Scale [47]. Tension was measured with a question created for a daily sleep diary, and was a single or average rating per person based on the question “How much tension was there with your driving partner yesterday?“

NA, not applicable.

Results for Primary and Secondary Outcomes

In the intent-to-treat analysis differences between groups in both primary and main secondary outcomes were predominantly non-significant, with the exception of significant differences between groups in objective physical activity during the final phase (see Table 2). Therefore, trends in results are discussed with an emphasis on descriptive effect sizes of d=0.20 or greater. Directional trends for four out of five self-reported primary outcomes favored the intervention group at the final visit (the sufficiency of data samples for actigraphic sleep outcomes were limited, and were therefore excluded from intent-to-treat analyses). Effect sizes greater than d=0.20 were observed in favor of the intervention group for sleep quality (d=0.39), sleep disturbance (d=−0.42), and sleep-related impairment (d=−0.41). Trends in secondary outcomes were more mixed. To illustrate, consider trends in the behavioral intervention targets. Changes in self-reported moderate physical activity and sleep hygiene behaviors favored the intervention group after the cab enhancement condition, but then changed to favor the control group at the final time point when the intervention would be expected to achieve its peak effects. This pattern was different for objectively measured moderate intensity physical activity bouts, with results favoring the control group after the cab enhancement phase, but then strongly favoring (at a statistically significant level) the intervention group during the final condition. General physical health favored the intervention group at the final time point (d=0.20), however, reported general mental health favored the control group at the same time point (d=−0.50).

Table 2.

Intent-to-Treat Analysis of Intervention Effects (n=49): The Tech4Rest Randomized Controlled Trial.

Outcomes	Intervention Mean (SE)	Control Mean (SE)	Diff. in Diff. (SE)	Effect Size1	p-value
Primary Outcomes
Sleep Duration (hours)
Baseline	6.58 (0.36)	7.27 (0.36)
Post-Cab Enhancement	7.11 (0.42)	7.46 (0.42)	0.34 (0.59)	0.19	0.565
Post-Fit4Sleep	7.44 (0.40)	7.87 (0.43)	0.27 (0.63)	0.14	0.671
Sleep Quality (0–3)
Baseline	1.76 (0.14)	1.63 (0.14)
Post-Cab Enhancement	1.87 (0.15)	1.77 (0.15)	−0.03 (0.18)	−0.03	0.867
Post-Fit4Sleep	2.06 (0.15)	1.64 (0.17)	0.28 (0.21)	0.39	0.199
Sleep Disturbance (8–40)
Baseline	18.99 (1.36)	23.13 (1.39)
Post-Cab Enhancement	17.32 (1.53)	20.69 (1.54)	0.77 (1.87)	0.02	0.685
Post-Fit4Sleep	15.67 (1.53)	21.99 (1.61)	−2.18 (1.92)	−0.42	0.264
Sleep-Related Impairment (8–40)
Baseline	14.92 (1.07)	17.38 (1.09)
Post-Cab Enhancement	13.72 (1.19)	15.89 (1.20)	0.28 (1.33)	0.01	0.834
Post-Fit4Sleep	12.56 (1.27)	16.96 (1.31)	−1.95 (1.56)	−0.41	0.220
Fatigue (0–48)
Baseline	11.76 (1.91)	16.23 (1.95)
Post-Cab Enhancement	10.47 (2.13)	14.09 (2.15)	0.86 (2.48)	0.10	0.732
Post-Fit4Sleep	11.22 (2.17)	12.74 (2.23)	2.95 (3.05)	0.34	0.340
Secondary Outcomes
Sleep Hygiene (0–48)
Baseline	17.32 (1.40)	20.00 (1.43)
Post-Cab Enhancement	16.41 (1.57)	17.62 (1.58)	1.47 (1.88)	0.25	0.439
Post-Fit4Sleep	16.62 (1.51)	20.32 (1.55)	−1.03 (1.81)	−0.15	0.574
Moderate Physical Activity (0–7 days)
Baseline	1.68 (0.40)	1.38 (0.42)
Post-Cab Enhancement	2.85 (0.48)	1.89 (0.48)	0.66 (0.78)	0.25	0.401
Post-Fit4Sleep	1.76 (0.51)	1.69 (0.53)	−0.16 (0.70)	−0.10	0.822
Vigorous Physical Activity (0–7 days)
Baseline	1.56 (0.44)	1.27 (0.45)
Post-Cab Enhancement	1.90 (0.50)	1.58 (0.50)	0.03 (0.69)	−0.01	0.970
Post-Fit4Sleep	1.77 (0.52)	1.62 (0.53)	−0.19 (0.70)	−0.11	0.782
Minutes per Week in Bouts
Baseline	2.38 (5.46)	10.33 (5.32)
Post-Cab Enhancement	1.01 (5.91)	15.39 (6.08)	−6.42 (10.68)	−0.46	0.550
Post-Fit4Sleep	19.43 (6.84)	2.64 (6.58)	24.82 (11.92)	3.24	0.041
Freedson Bouts per Week
Baseline	0.16 (0.19)	0.63 (0.19)
Post-Cab Enhancement	0.09 (0.45)	1.20 (0.46)	−0.66 (0.80)	−0.75	0.410
Post-Fit4Sleep	0.65 (0.26)	0.22 (0.25)	0.89 (0.45)	1.58	0.050
Physical Health (4–20)
Baseline	15.16 (0.43)	14.67 (0.44)
Post-Cab Enhancement	15.20 (0.49)	14.78 (0.49)	−0.07 (0.61)	−0.03	0.911
Post-Fit4Sleep	15.75 (0.48)	14.91 (0.49)	0.35 (0.63)	0.20	0.580
Mental Health (4–20)
Baseline	14.88 (0.63)	13.04 (0.64)
Post-Cab Enhancement	14.24 (0.71)	13.50 (0.71)	−1.10 (0.84)	−0.35	0.200
Post-Fit4Sleep	14.71 (0.69)	14.54 (0.71)	−1.67 (0.82)	−0.50	0.049

Note.

¹Effect size calculation: (Mean change in intervention group / standard deviation [raw] of pre-score in intervention group) - (Mean change in control group / standard deviation [raw] of pre-score in control group). Sleep quality was measured with a single item on a scale from 0-“Very Bad” to 3-“Very good,” such that higher scores indicate better sleep quality. Sleep disturbance and sleep-related impairment were computed as the sum score of 8 items, respectively, on a scale from 1-“Not at all” to 5-“Very much,” such that higher scores indicate greater sleep disturbance and impairment. Fatigue was computed as the sum score of 8 items on a scale from 0-“Not at all” to 6-“To a very high degree,” such that higher scores indicate greater fatigue. Sleep hygiene was computed as the sum score of 12 items on a scale from 0-“Never” to 4-“Always,” where higher scores indicate worse sleep hygiene (Note: the item, “I slept on an uncomfortable bed,” was omitted from the calculation due to the inherent bias of the introduction on the new mattress for intervention participants). Mental and physical health were computed as sum scores of 4 items, respectively, on a variety of scale anchors ranging from 1–5, such that higher scores indicate better health.

Given the observed study attrition rate of 34.7%, a descriptive analysis of study “completers” only (n=32) was conducted for primary and main secondary study outcomes (see Table S1). The rationale was to examine descriptive statistics for those who completed all study time points and compare results to the intent-to-treat GLMM analysis. The results of this analysis for self-reported primary outcomes roughly mirrored the GLMM analysis for sleep and fatigue outcomes (four out of five differences favored the intervention group at the final time point). Effect sizes in favor of the intervention group for self-reported sleep duration, sleep disturbance, and sleep-related impairment were all slightly larger for completers compared to the intent-to-treat analysis. A subsample (n=11) of drivers had sufficient sleep actigraphy, electronic log, and/or sleep diary data at the relevant three time points (minimum of 2 days of data at each time point) to be included in completers’ analyses. Intervention effects on actigraphic sleep duration were substantially larger in magnitude than those observed with self-reported outcomes for both post-cab enhancement (d=1.93) and post-behavioral program (d=1.87) time points. However, actigraphic sleep efficiency effects trended in the unexpected direction at post-cab enhancement (d=−0.37) and post-behavioral program (d=−1.03). For additional outcomes in the completers’ analyses, trends were similar but with very large effect sizes in favor of the intervention group for actigraphically measured minutes in moderate intensity activity bouts (d=14.59) and total bouts per week (d=5.33).

Results for Other Outcomes and Process Measures

The results for other outcomes relevant to driver safety, health, and well-being were predominantly non-significant (see Table S2). Factors related to optional goals in the behavioral program included body weight and dietary behaviors. Body weight was relatively stable during the intervention period across both groups. The majority of trends in dietary behavioral outcomes favored the control group at the final time point with small-to-medium effect sizes. Two statistically significant differences were observed among optional goal related factors, and included changes favoring the control group post-cab enhancement in percent body fat and fruit and vegetable intake. However, bioelectric impedance measurement of percent body fat is affected by hydration levels, and thus the more stable body weight outcomes should be considered the more reliable assessment of body composition. Trends in pain outcomes were also stable across groups with effect sizes smaller than 0.10. Collisions and injuries have low base rates, and within a three- to four-month study protocol it was not expected that we would see between groups differences in these outcomes. As expected, frequencies were very low and these outcomes are reported in Table S2 mainly for descriptive purposes.

A subsample of trucks with the most complete WBV data samples were selected for manipulation check analyses for truck seats (five trucks). Results confirmed that the active suspension seats were installed and functioning as designed, with A(8) WBV exposures extrapolated from the 1-hr of measurement being lower when compared the standard air ride seat. The mean (SD) exposures from the original seat were 0.26 (± 0.05) m/s² compared to the active suspension intervention seat 0.16 (± 0.03). In other words, the active suspension seat reduced WBV exposure by 38.5% relative to the original seat. Analyses of mattress tops relative to the floor were explored, but were not deemed reliable enough to report (large standard deviations, less objective methods for confirming occupancy compared to the seat).

Fit4Sleep participation varied across waves, with higher general participation in the smaller wave one. Intervention participation data analyses focused on the subsample of 17 intervention drivers who remained in the study at visit three, which is when the Fit4Sleep program was initiated. In wave one all four drivers completed at least one coaching call and averaged 3.0 (SD=0.8) out of 4 possible sessions. In wave two, 12 of 13 drivers completed at least one health coaching call, and the group average calls completed was 1.4 (SD=1.0) out of 2 expected sessions. Wave one drivers averaged 6.8 (SD=2.5) out of 8 possible weekly logs. Participation in weekly logging was limited to one driver in wave two who completed 2 out of 4 possible logs, producing a group average in wave two of 0.2 (SD=0.6) logs submitted.

Both intervention implementation waves could complete three possible trainings. All 17 drivers completed the orientation training and averaged 46.0% and 93.0% in pre- and post-tests, respectively. The average total number of training sessions completed was 1.4 (SD=0.7), with waves one and two averaging 2.0 (SD=1.2) and 1.2 (SD=0.4), respectively. In wave two only two drivers completed the Why Fit4Sleep training and no drivers completed the Healthy Sleep training.

Discussion

Although trends in primary sleep-related outcomes favored the intervention group, these differences were predominantly non-significant and effect sizes were generally smaller than those observed in the Tech4Rest pilot study. All results considered, the favorable trends and descriptive effect sizes for sleep-related outcomes should be viewed as encouraging. The study was underpowered, and thus, the risk for Type 2 errors was elevated (falsely retaining null hypotheses). Among secondary outcomes we observed large and statistically significant intervention effects on actigraphically measured physical activity levels (a target of the behavioral intervention) during the final intervention phase. Patterns in other outcomes were mixed, with some non-significant trends and one significant difference in a secondary outcome favoring the control group (e.g., mental health).

One possible explanation for the observed smaller or mixed effects is that the interventions are actually not effective, and that the previously observed pilot effects were due to random chance variation. We believe this explanation is unlikely to be true given the strength and consistency of pilot results, the established benefits of the active suspension seat alone, the evidence-based history of the behavioral intervention, the trends observed in the current randomized controlled trial for sleep-related outcomes, and large and significant effects on objective physical activity observed in the trial. Another explanation is that the interventions were less effective and produced smaller effect sizes than in the pilot due to such factors as shorter condition durations, the foam layer in the standard mattress at the participating company, and lower levels of participation in the behavioral intervention. These factors, in combination with the underpowered sample size due to recruitment and attrition challenges, are a more likely set of explanations for the results observed.

Qualitative observations and interviews with drivers provide some possible reasons why study attrition was higher than expected, and intervention participation was lower than expected. Teams were highly stable and participated through a longer study protocol without any turnover during the pilot study. In the current project during wave one higher within team and company turnover was observed, and thus adjustments were made to the duration of the behavioral program and driver manager incentives were added to protect against attrition in wave two. Corporate culture, organizational systems, and methods for matching team members together are possible explanations for the higher turnover. In addition, demands on team drivers were very high in general during the study period due to seasonal holiday demands, and also general high demand for transportation services during the COVID-19 pandemic. These demands appeared to suppress intervention participation beyond completing the online orientation and coaching calls. Wave one began during the holiday peak transportation season. Drivers reported working very long hours and corporate incentives were in place to work through the holidays. For drivers participating in wave two, many reported extreme weather as a barrier to participation (ranging from floods to temperatures reaching 110 degrees) in addition to stressful workloads (e.g., being given too many loads of freight to drop off and not enough time). Drivers shared that these demands impacted their ability and/or desire to spend time outside of work logging their behaviors on the Fit4sleep website or completing the online trainings. Additionally, some drivers cited not being very tech savvy and/or having difficulty accessing the website to log their behaviors as barriers.

Strengths, Limitations, and Future Directions

Strengths of the study include the randomized controlled design, interventions prioritized by the Hierarchy of Controls, evidence-based intervention components that included changes to physical working conditions, baseline measures collected before drivers were informed of randomization, objective measurement of physical activity, and measurement of intervention exposure or dose. Favorable trends in sleep-related outcomes, large and significant improvements to objective measures of physical activity, the effectiveness of the active suspension seat at reducing WBV, and drivers’ strong preferences for the therapeutic mattress are all encouraging of future replications and extensions of cab enhancements and behavioral sleep-health programs for truck driving teams.

Limitations suggest areas for future research, including issues to address in replications and extensions. First, the current project did not achieve its target sample size of 80 drivers, which was based on an a priori power analysis. There is a need for future research studies with fully powered samples. In this regard, effect sizes observed in the Tech4Rest pilot and randomized trial may inform future power analyses, study designs, and target sample sizes. While studies with larger samples are desirable, there is also scientific value in replication or repeatability of descriptive effect sizes. Smaller studies that reproduce effects are of value. Further, the project did not alter work schedules to be more consistent or stable, or measure circadian disruption. Circadian disruption due to work and sleep variability is highly probable in the team driving arrangement. Although very difficult to implement with a mobile workforce, future research would benefit from measuring biological clock time of team drivers at baseline using in-lab measures such as dim light melatonin onset from salivary samples ⁵⁴. This biological clock time could then be analyzed in relation to drivers experienced schedules and sleep and fatigue outcomes. We believe it is particularly critical for future research with team drivers to measure and investigate sleep regularity, which is emerging as an important correlate with negative health outcomes and poor performance ^55,56.

Although a study strength was that physical technology in the cab was enhanced, the project did not address other potentially disrupting factors for sleep, such as noise, temperature, and potential light pollution. Future research would benefit from measuring exposures to factors such as light from screens, or studying interventions to reduce factors such as noise through improved vehicle insulation. Further, while the therapeutic mattress appears to be preferred by driving teams, there remains great opportunity for innovation in this area. For example, it might be feasible to develop active suspension or partial active suspension bed platforms that would be more effective at reducing disturbing vibrations and jostling in the sleeper berth (analogous to the major advances in seat technology) than a therapeutic mattress alone. On this theme, future translational research and outreach is needed to increase adoption of active suspension seats, which have been demonstrated to substantially reduce WBV exposures²⁸ and reduce indicators of end-of-shift fatigue²⁹.

Further, the inclusion of objective actigraphic measures of bouts of moderate intensity physical activity was a strength of the study. This bout analysis approach increased confidence that large truck vibrations were not generating false physical activity. However, at the same time, the bout approach excluded lower intensity physical activity. Future researchers are encouraged to experiment with the integration of additional biometric measures through other types of wearable sensors in order to accurately measure lower intensity physical activity, as well as to more easily or accurately assess sleep duration and quality with commercial drivers. In the domain of sleep measurement and analysis, actigraphic measures of sleep were collected during the study, but sufficient data were available for only a small subset of drivers. Actigraphic sleep analyses are particularly challenging to complete for participants who sleep in a vibrating environment, and in the current project, electronic log book and sleep diary data sources were integrated into the study specific sleep scoring protocol. In future studies, complimenting sleep actigraphy with measures such as head position, respirations, or heart rate would all help increase the ease of scoring and accuracy of outputs. In preliminary studies a head-worn single channel electroencephelogram device (Sleep Profiler, Advanced Brain Monitoring, Carlsbad, CA) was evaluated, but it was polluted too severely by vehicle vibrations to use in the study. To our knowledge the now unavailable Nightcap ²³ which measured head position, eye movements, and eyelid activity is perhaps the most reliable method in the literature for assessing sleep among teams in a vibrating sleeper berth.

And finally, the strong Tech4Rest pilot results considered in combination with encouraging findings from the randomized trial (gold standard design, yet underpowered), indicate a need for research that adapts, replicates, or extends evaluation of these various intervention components. This could include research to further delineate if and how the therapeutic mattress alters vibration exposures, and specific reasons why it has been so well received by team drivers. The behavioral intervention would also benefit from further study to understand whether low participation was due to transient or unique factors present during the pandemic, particular transportation seasons, or persistent characteristics of the team driving occupation. Given the relatively strong levels of participation in the pilot, it is possible that the behavioral program is a relatively good fit with teams that are highly stable and long lived, but not such a good fit for teams that are shorter lived or at companies with higher turnover rates. Given the safety-sensitive nature of the work, future research and practice to maximize participation in tailored sleep and health interventions for teams is desirable.

Conclusions

The safety, health, and well-being of commercial truck drivers are socially important topics. The commercial truck driving workforce is essential and integral to global economies, yet these people experience a range of challenging working conditions that increase their risk for many negative health outcomes. We believe TWH approaches are called for when addressing commercial driver health, including the application of the Hierarchy of Controls to selecting and evaluating interventions. Interventions to improve sleep sufficiency are perhaps central for commercial drivers, as it is a factor that cuts strongly across safety, health, and well-being. Sleep is particularly salient for truck driving teams who sleep in moving and vibrating vehicles and also experience highly variable shiftwork schedules. Trends observed in the Tech4Rest pilot and randomized controlled trial encourage future replications and extensions of both cab enhancements and behavioral sleep-health programs for team drivers. Evidence is strong for the benefits of active suspension seats for reducing WBV and improving other outcomes among commercial drivers. Future research is encouraged to study seats, mattresses, and additional cab enhancements, as well as studying and altering work schedules and job design. Research and practice related to tailored behavioral programs is also needed to compliment changes to working conditions as they are likely to provide further benefits for workers. Results observed in the current study supported the effectiveness of the Fit4Sleep intervention for increasing physical activity. Trends in sleep-related outcomes were encouraging. Employers can use such behavioral programs to fulfill their obligations to inform employees about known hazards in their jobs - especially those hazards that are poorly controlled or difficult to control - and also educate and encourage employees to work in ways that minimize their exposures and maximize their safety, health, and well-being.

Learning Objectives:

After reading this article, the learner will be better able to:

• Discuss the public health importance of addressing working conditions for truck driving teams to improve their sleep, reduce fatigue, and advance their well-being

• Understand how whole body vibration exposures and shiftwork schedules relate to team drivers’ sleep and fatigue

• Describe trends, effect sizes, and significant results for sleep, fatigue, and physical activity outcomes for enhanced cab interventions alone, and in combination with a behavioral sleep-health program

• Identify several specific opportunities to replicate, extend, and advance this area of research and practice