A Mobile App to Promote Alcohol and Drug SBIRT Skill Translation among Multi-Disciplinary Health Care Trainees: Results of a Randomized Controlled Trial

Abstract

Background: Adherence to clinical practice guidelines for alcohol and drug screening, brief intervention, and referral to treatment (SBIRT) is often inadequate. Mobile apps developed as clinical translation tools could improve the delivery of high fidelity SBIRT. Methods: This study tested the effectiveness of an SBIRT mobile app conceptually aligned with the Theory of Planned Behavior (TPB) to support SBIRT delivery by health care trainees (nursing, social work, internal medicine, psychiatry, and psychology) working in clinical settings (N ¼ 101). Bivariate analyses examined the rate of SBIRT delivery between trainees assigned to the experimental (app) and control (no app) study conditions; as well as the relationship between TPB-based constructs, intention to deliver SBIRT, and screening rates. Results: No significant differences were identified between the study conditions in SBIRT delivery. Significant correlations were found between intent to screen and TPB variables including attitudes/behavioral beliefs concerning substance use treatment (r ¼ .49, p ¼ .01); confidence in clinical skills (r ¼ .36, p ¼ .01); subjective norms (r ¼ .54, p ¼ .01) and perceived behavioral control over appointment time constraints (r ¼ .42, p ¼ .01). Also significant were correlations between percent of patients screened and confidence (r ¼ .24, p ¼ .05); subjective norms (r ¼ .22, p ¼ .05) and perceived behavioral control (r ¼ .28, p ¼ .01). Conclusions: The negative results of the study condition comparisons indicate the need for further investigation of strategies to optimize mobile app utilization, engagement, and effectiveness as a clinical translation tool. Findings of significant correlations between substance use screening rates and both norms and confidence support the potential value of the TPB model in explaining behavior of health care learners in SBIRT delivery.

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