Development of Automated Reinforcement Management System (ARMS): Protocol for a Phase I Feasibility and Usability Study
Bibliographic record
Abstract
BACKGROUND: Alcohol use is directly related to over 3 million deaths worldwide every year. Contingency management is a cost-effective treatment for substance use disorders; however, few studies have examined its efficacy for alcohol use disorder. Recent technological advances have enabled the combined use of mobile apps and low-cost electronic breathalyzer devices to remotely monitor alcohol use. Leveraging this type of technology, our study group has recently developed an integrated contingency management system that would enable community treatment programs to remotely deliver contingency management to anyone who owns a smartphone. OBJECTIVE: In this paper, we present a full description of our integrated contingency management system, Automated Reinforcement Management System (ARMS), and describe a protocol that will evaluate its feasibility and usability. METHODS: Initially, 6 clinicians will participate in a 1-hour focus group where the study staff will navigate through ARMS as it would be used by clinicians and patients. Clinicians will provide feedback on the intervention in general, which 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. Thereafter, the clinicians' acceptability of ARMS will be evaluated using the System Usability Scale. Following the clinicians' assessments of ARMS and final modifications, the system will be evaluated in terms of feasibility and patient usability by using an A-B-A within-subject experimental design wherein 20 treatment-seeking individuals with alcohol use disorder will be recruited. The two A phases (control conditions) will each last 2 weeks, and the B phase (contingency management condition) will last 4 weeks. During all phases, participants will be asked to use the ARMS app to submit three breathalyzer samples per day (at 10 AM, 2 PM, and 8 PM). Participants will be prompted by the ARMS app at these predetermined times to record and submit their breathalyzer samples. During the A phases, participants will earn vouchers for every breathalyzer sample submitted, independent of their sample results. During the B phase, vouchers will be provided contingent upon the submission of alcohol-negative breathalyzer samples (breath alcohol content = 0.00). At the end of the A-B-A experiment trial, patients' usability of the ARMS app will be evaluated using the System Usability Scale. Feasibility will be measured based on whether the ARMS app helped significantly increase alcohol abstinence. RESULTS: Recruitment for this study began in January 2021 and is expected to be completed by December 2021. CONCLUSIONS: 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 alcohol use disorders to individuals living in remote rural areas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.036 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.059 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".