INTRODUCTION: Truck driving is the most common vocation among males internationally with a high proportion overweight/obese due to a combination of work and lifestyle factors leading to health complications. With limited studies in this area, this systematic review aimed to identify and describe interventions addressing weight reduction in truck drivers. METHODS: Five electronic databases were searched, January 2000 to June 2020 (CINAHL, Cochrane Library, Embase, Ovid Medline, Scopus). Inclusion criteria: experimental primary studies, long-distance (>= 500 kms) truck drivers, peer reviewed publications in English. Weight loss interventions included physical activity, diet, behavioral therapy, or health promotion/education programs. Exclusions: non-interventional studies, medications or surgical interventions. Two independent researchers completed screening, risk of bias (RoB) and data extraction with discrepancies managed by a third. Study descriptors, intervention details and outcomes were extracted. RESULTS: Seven studies (two RCTs, five non-RCTs,) from three countries were included. Six provided either counselling/coaching or motivational interviewing in combination with other components eg, written resources, online training, provision of exercise equipment. Four studies demonstrated significant effects with a combined approach, however, three had small sample sizes (< 29). The effect sizes for 5/7 studies were medium to large size (5/7 studies), indicating likely clinical significance. RoB assessment revealed some concerns (RCTs), and for non-RCTs; one moderate, two serious and two with critical concerns. Based on the small number of RCTs and the biases they contain, the overall level of evidence in this topic is weak. CONCLUSION: Interventions that include a combination of coaching and other resources may provide successful weight reduction for truck drivers and holds clinical significance in guiding the development of future interventions in this industry. However, additional trials across varied contexts with larger sample populations are needed.

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