Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT01465555 |
| Other study ID # |
R01DA026469 |
| Secondary ID |
R01DA026469 |
| Status |
Completed |
| Phase |
Phase 1/Phase 2
|
| First received |
|
| Last updated |
|
| Start date |
January 2010 |
| Est. completion date |
December 2013 |
Study information
| Verified date |
March 2023 |
| Source |
Public Health Management Corporation |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
The specific aims of the project are to conduct a three-phase study to develop a data-driven
Clinical Alert feature to the RecoveryTrack™ Concurrent Recovery Monitoring (CRM) system and
test its efficacy, as follows:
Phase I - Analyze RecoveryTrack and outcomes data to create a clinical algorithm that
predicts early treatment attrition; adapt elements of a cognitive behavioral intervention
(CBI) for use in addressing Clinical Alerts, as well as adapting training and adherence
measures; reprogram RecoveryTrack with a Clinical Alert feature for each of the first three
monitoring assessments to inform counselors when a client is at High Risk to leave treatment.
Phase II -Conduct a feasibility trial to refine Clinical Alerts + CBI intervention and the
study measures/procedures.
Phase III - Conduct a pilot randomized clinical trial comparing outcomes of clients whose
counselors were randomized to Clinical Alerts + CBI to those of clients whose counselors were
assigned to TAU (control condition). The primary hypothesis is that clients who evidence a
High Risk for attrition will have longer lengths of stay in the Clinical Alerts + CBI
condition than High Risk clients in the control condition. Secondary client hypotheses are
that High Risk clients in the Clinical Alert + CBI condition will attend more treatment
sessions, have more drug-free urine results, and receive more ancillary services than High
Risk clients in the control condition.
Description:
Phase 1:Our study team completed Phase I of the project in June, 2011. Our initial plans
called for our team to analyze RT data that was collected by Delaware treatment programs to
determine specific predictors for early attrition from treatment. Eleven counselors were
trained to use RT as part of their standard practice in May, 2011. In an effort to generate a
predictive algorithm for Phase II, we analyzed data from three datasets available from IRB
approved research projects. However, the results were inconclusive, possibly due to small
sample sizes and differences in the recruitment and definition of drop out across samples. In
order to proceed with Phase II, we created an algorithm based largely on severity of
substance use, a consistent predictor of drop-out across the alcohol and illicit substance
literature; the algorithm also addresses when clients are not using but their Risk factors
greatly outweigh the influence of Protective factors. We determined that the algorithm did
work to identify clients who self-reported using substances or high levels of risk. However,
the identification rate of high-risk cases was too low, and so we modified the algorithm to
identify: 1) clients with high use at baseline, 2) clients with positive urine samples, 3)
clients with any use at subsequent [post-baseline] interviews, and 4) clients whose risk
factors outweighed their protective factors. This increased our rate of identification of
risk cases by about 32%.
As part of Phase I, we also completed the programming changes to RT to add a Clinical Alert
feature that informs counselors when a client is at high risk based on the algorithm. The CBI
intervention and training materials for Phase II was created. We developed the CBI into a
brief clinical "toolkit" that enabled counselors to respond to clinical risk based on their
judgment. This Toolkit allows counselors to exercise clinical judgment to determine whether
clients are generating a Clinical Alert because 1) they are using drugs or alcohol, or are at
risk to start using, 2) they have unmet psychosocial needs [i.e., need for psychiatric
consultation], or 3) they have a poor alliance with the counselor / treatment provider and
need some help building on the relationship. The Clinical Alert Toolkit included a series of
exercises / interventions that counselors could deploy once they had determined which of the
client's needs were most pressing (either based on their own judgment or in agreement with
the client).
Phase 2:Three counselors consented to participate in this study and were trained to use RT in
May, 2011 and were trained on the CBI intervention in June, 2011. Recruitment of client
participants began in July, 2011. The training and intervention materials were well received
by the participating counselors. One counselor ended their employment prior to the research
staff beginning client recruitment. 30 clients out of 35 who were approached participated in
the feasibility trial; 28 clients completed baseline (93%), 23 completed the one month follow
up (77%), 21 completed the two month follow up (70%), and 21 completed the three month follow
up (70%). Out of these clients, two clients were incarcerated for their 1 month follow up
window, 2 clients were incarcerated for their 2 month follow up window, and three clients
were incarcerated for their three month follow up window therefore they were not approached
to complete the follow-up interview. We used our experiences during Phase II to make several
changes in our training and clinical protocol for counselors to follow. We revised several of
our Toolkit components (specifically the worksheets to make them more user-friendly and to
reduce training burden). We also simplified our feedback strategy which took place during the
clinical supervision sessions to focus much less on general therapeutic skills and more
specifically on actual compliance with the trained Clinical Alert CBI. Additionally, we found
that while our Phase II training counselors verbalized motivation to try to learn to use the
CBI techniques, that the learning curve was slower than we desired, and we decided to employ
a contingency management feedback strategy to increase their incentive to acquire the basic
elements of the CBI more quickly. This revision required IRB approval; in the revised
approach, counselors could earn bonuses when a rated audio recorded session showed that they
had been at least minimally adherent in delivering the CBI. Finally, during Phase II we
completed the Attention Control training (focused on treatment planning) for use in the TAU
condition.
Phase 3: Because we needed to move the study from Delaware Site 1 (our original clinical site
and partner), our team recruited Alternate Site 1 in Philadelphia and Alternate Site 2 in
central New Jersey to conduct the Phase III clinical pilot trial. We enrolled 20 counselors
who were trained to use RT on a monthly basis with their clients, starting at the first
scheduled individual session and monthly thereafter. We randomly assigned these counselors to
receive either TAU treatment planning training or to receive Clinical Alert (CA) training.
After training, recruitment began. During the trial, three counselors in the CA condition
either were noncompliant with the study protocol (N = 2), or never had any clients report any
high risk (N = 1). We worked with all three of these counselors to encourage improved
engagement with the intervention and study procedures, but this was not successful.
Consequently, we decided that we would over-recruit additional counselors into the CA
condition, to create additional opportunities to determine whether the Clinical Alerts +
Intervention training would impact outcomes. We recruited an additional three counselors, but
did not randomize them; rather, we assigned them directly to the CA condition.
We enrolled 336 clients in Phase 3. 142 self-reported clinical RT data which would result in
a Clinical Alert profile (TAU: N = 78, CA: N = 64). Because of a low recruitment rate, the
study was severely underpowered for our subanalyses to examine specific effects of the
intervention training on dealing with High Risk cases; this is where we believed our
strongest effects would present themselves.
