The Autonomic Progress Bar Motivates Treatment Completion for Patients of Stimulant Use Disorder and Cannabis Use Disorder

Abstract

Background: The intrinsic motivation behind the "need to complete" is more influential than external incentives. We introduced a novel progress-bar tool to motivate the completion of programs designed to treat stimulant and cannabis use disorders. We further examined the effectiveness of the progress bar's scoring approach in forecasting consistently negative urine tests.Methods: This study's participants included 568 patients with stimulant, amphetaminetype, and cannabis use disorders who were undergoing 12-month mandatory treatment programs at Taichung Veterans General Hospital in Taiwan. Patients were given scores of 1, -1, or 0 depending on whether they received negative, positive, or missing urinalysis reports, respectively. The autonomic progress bar generated weekly score totals. At the group level, score i donated scores from all patients for a given week (i denoted the week). Score i was standardized to adjusted score i . We then conducted Autoregressive Integrated Moving Average (ARIMA) Model of time-series analyses for the adjusted score i .Results: A total of 312 patients maintained treatment progress over the 12-month program. The autonomic score calculator totaled the shared achievements of these patients. The coefficients of the lag variables for mean (p), lag variables for residual error term (q), and number of orders for ensuring stationary (d) were estimated at p = 3, d = 4, and q = 7 for the first half of the treatment program, and were estimated at p = 2, d = 2, and q = 3 for the second half. Both models were stationary and tested as fit for prediction (p < 0.05). Sharply raised adjusted scores were predicted during the high-demand treatment phase.Discussion: This study's novel progress-bar tool effectively motivated treatment completion. It was also effective in forecasting continually negative urine tests. The tool's free open-source code makes it easy to implement among many substancetreatment services.

Time series analysis in preventive intervention research: A step‐by‐step guide

“…Chen et al. (2020) exemplify ARIMA modeling to predict the effectiveness of an intervention for drug use through urine tests among hospital patients. Based on the study findings, the ARIMA models significantly predicted future test outcomes after treatment, demonstrating the forecasting efficacy of ARIMA models in this intervention research.…”