Describing Physical Activity Patterns of Truck Drivers Using Actigraphy

Background@#Truck driving is a highly sedentary occupation that places workers at risk for chronic health conditions, such as obesity and high blood pressure. The primary purpose of this study was to objectively describe truck drivers' typical physical activity (PA) patterns. @*Methods@#We used ∼7–10-day baseline PA actigraphy data samples from drivers in the Safety & Health Involvement For Truckers (SHIFT) study (n = 394). Driver PA patterns (e.g., average number of ≥10 minute Freedson bouts per week, time in bouts, and common days/times for PA) were summarized with descriptive analyses. We also compared objective accelerometer data to self-reports. @*Results@#Drivers' weekly PA averaged 14.4 minutes (SD = 37.0), and most PA occurred between 5–6 pm on Tuesdays and Wednesdays. Drivers overestimated self-reported weekly exercise by over 60 min/week compared to accelerometer data. @*Conclusion@#Our results suggest that objective PA assessment may be warranted over self-report when possible, and timing may be key in future PA intervention work with truck drivers.

Process Evaluation of a Mobile Weight Loss Intervention for Truck Drivers

BACKGROUND: In a cluster-randomized trial, the Safety and Health Involvement For Truck drivers intervention produced statistically significant and medically meaningful weight loss at 6 months (−3.31 kg between-group difference). The current manuscript evaluates the relative impact of intervention components on study outcomes among participants in the intervention condition who reported for a postintervention health assessment (n = 134) to encourage the adoption of effective tactics and inform future replications, tailoring, and enhancements. METHODS: The Safety and Health Involvement For Truck drivers intervention was implemented in a Web-based computer and smartphone-accessible format and included a group weight loss competition and body weight and behavioral self-monitoring with feedback, computer-based training, and motivational interviewing. Indices were calculated to reflect engagement patterns for these components, and generalized linear models quantified predictive relationships between participation in intervention components and outcomes. RESULTS: Participants who completed the full program-defined dose of the intervention had significantly greater weight loss than those who did not. Behavioral self-monitoring, computer-based training, and health coaching were significant predictors of dietary changes, whereas behavioral and body weight self-monitoring was the only significant predictor of changes in physical activity. Behavioral and body weight self-monitoring was the strongest predictor of weight loss. CONCLUSION: Web-based self-monitoring of body weight and health behaviors was a particularly impactful tactic in our mobile health intervention. Findings advance the science of behavior change in mobile health intervention delivery and inform the development of health programs for dispersed populations.