Process Evaluation of a Mobile Weight Loss Intervention for Truck Drivers
10.1016/j.shaw.2018.08.002
- Author:
Brad WIPFLI
1
;
Ginger HANSON
;
Kent ANGER
;
Diane L ELLIOT
;
Todd BODNER
;
Victor STEVENS
;
Ryan OLSON
Author Information
1. School of Public Health, Oregon Health & Science University and Portland State University, Portland, USA. bwipfli@pdx.edu
- Publication Type:Original Article
- Keywords:
Intervention process evaluation;
Mobile health;
Occupational health;
Weight loss
- MeSH:
Body Weight;
Health Behavior;
Linear Models;
Motivational Interviewing;
Motor Activity;
Motor Vehicles;
Occupational Health;
Telemedicine;
Weight Loss
- From:Safety and Health at Work
2019;10(1):95-102
- CountryRepublic of Korea
- Language:English
-
Abstract:
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.
