Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?


Currently submitted to: JMIR Formative Research

Date Submitted: Nov 16, 2020
Open Peer Review Period: Nov 16, 2020 - Jan 11, 2021
(currently open for review)

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Automated Reinforcement Management System (ARMS): Phase I Methods and Development

  • Andre Miguel; 
  • Crystal Smith; 
  • Nicole Perea; 
  • Kim Johnson; 
  • Michael McDonell; 
  • Sterling McPherson



Alcohol use is directly related to over 3 million deaths worldwide every year. Contingency management (CM) is a cost-effective treatment for substance use disorders; however, few studies have examined the efficacy of CM for alcohol use disorder (AUD). Recent technological advances have enabled the combined use of mobile applications (apps) and low-cost electronic breathalyzer devices to remotely monitor alcohol use. Leveraging this type of technology, our group has recently developed an integrated CM system that would enable community treatment programs to deliver CM remotely to anyone who owns a smartphone.


In this study, we present a full description of our integrated CM system, Automated Reinforcement Management System (ARMS), and describe the protocol that will evaluate its feasibility and acceptability.


Initially, six clinicians will participate in a one-hour focus group where study staff will navigate through ARMS as it would be used by clinicians and patients. Clinicians will then provide feedback on the intervention in general. This information will be used to modify ARMS to make it more user friendly, time-saving, and relevant to treatment. A second focus group will summarize the changes made following the initial clinician feedback and will provide additional input regarding the potential utilization of ARMS. At the end of the second focus group, the clinicians’ acceptability of ARMS will be evaluated using the System Usability Scale (SUS). Following the clinician assessments of ARMS and after the final modifications are made, the system will be evaluated in terms of feasibility and patient acceptability using an A-B-A within-subject experimental design where 20 treatment-seeking individuals with AUD will be recruited. The two A phases will each last two weeks and the B phase will last four weeks. During all phases, participants will be asked to use the ARMS app to submit three breathalyzer samples per day (at 10am, 2pm, and 8pm). Participants will be prompted by their ARMS app at these pre-determined times to record and submit their breathalyzer samples. During the A phases (control conditions), participants will earn vouchers for every breathalyzer sample submitted, independent of sample results. During the B phase (CM condition), vouchers will be provided contingent upon the submission of alcohol-negative breathalyzer samples (BAC = 0.00). At the end of the A-B-A experiment trial, patients’ acceptability of ARMS will be evaluated with the SUS. Feasibility will be measured by whether or not ARMS could significantly increase alcohol abstinence.


This study will begin recruitment in January 2021 and is expected to be completed by December 2021.


This study will provide the baseline capability for the implementation of a remotely monitored contingency management platform. If successful, ARMS has the potential to provide effective treatment for AUDs to those living in remote rural areas.


Please cite as:

Miguel A, Smith C, Perea N, Johnson K, McDonell M, McPherson S

Automated Reinforcement Management System (ARMS): Phase I Methods and Development

JMIR Preprints. 16/11/2020:25796

DOI: 10.2196/preprints.25796


Download PDF

Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.