Method. Participants were 4,340 students (M age=22.3, SD age=2.3; 64.5% Non-white; 64.3% female) from 24 Texas colleges (12 2-year and 12 4-year schools). Students completed an online survey in fall 2015 and five subsequent surveys six months apart until spring 2017. The data for this abstract come from waves 4-6. Items assessed energy drink consumption, past month alcohol use, and binge drinking, all coded as any use=1 versus no use =0. A multi-level cross-lagged path model, using Mplus 7.4, was used to examine the bi-directional associations between energy drink consumption and alcohol use (past month use and binge drinking in two separate models) across the three waves, controlling for socio-demographics, impulsivity, sensation seeking, and school type (2 year vs. 4 year school) with school as the random effect.
Results. The cross-lagged model fit the data well for both models: although the chi-square values were significant (p<.05). For the past month use model the CFI was .99, the TLI was .98, and the RMSEA was .01. Stability paths between each wave for both past month alcohol use (beta=.82 and .80) and energy drink consumption (beta =.52 and .47) were significant. Over and above the stability paths and covariates, past month alcohol use at wave 4 predicted energy drink consumption at wave 5 (p<0.001). Further, energy drink consumption at wave 5 predicted past month alcohol use at wave 6 (p<0.001). For the binge drinking model the CFI was .98, the TLI was .96, and the RMSEA was .01. Stability paths between each wave for both binge drinking (beta=.77 and .75) and energy drink consumption (beta =.52 and .45) were significant. Over and above the stability paths and covariates, binge drinking at waves 4 and 5 predicted energy drink consumption at waves 5 and 6 (p<0.05), respectively. Further, energy drink consumption at wave 4 predicted binge drinking at wave 5 (p<0.001).
Conclusion. Findings extend existing research by showing that energy drink consumption and alcohol use, both past month as well as binge drinking, are bi-directionally associated. Interventions with young adults should recognize that these two substances are linked with each other over time, and target both in programs and messaging.