A Mindfulness and Peer Mentoring Program to Improve Adherence to Medication Assisted Treatment for Opioid Use Disorders- R61
Phase: NA
Conditions: Opioid-use Disorder, Medication Assisted Treatment, Substance Use Disorders
Sponsor: University of Alabama, Tuscaloosa

COVER PAGE 
NCT ID: NCT04233671 
Grant Number: R61AT010802-01 
Official Title: A Mindfulness and Peer Mentoring Program to Improve Adherence to 
Medication Assisted Treatment for Opioid Use Disorders 
Document Date: July 19, 2021

MiMP R61 ANALYTIC PLAN 
AIM 1 [R61]: To establish the feasibility and acceptability of the Minds and Mentors Program (MiMP) for 
individuals on medications for OUD (MOUD; n = 20). 
Objective 1: Successfully train counselors and peer mentors, recruit and randomize 20 individuals on MOUD 
for OUD into the group based MiMP or TSF, and successfully implement the programs within the first two years.  
Hypothesis 1: Successfully train two counselors and two peer facilitators; the MiMP will demonstrate enrollment, 
randomization, and retention rates ≥ 50%; and ≥ 50% of participants will positively endorse MiMP as acceptable.  
 
Objective 2: Prior to the R33 comparing between-group differences in adherence (primary outcome), we will 
examine pre- to post-intervention (within-subject) changes in self-reported craving, relapse, stress, anxiety, 
depression, and social support (secondary outcomes) to determine whether there is preliminary evidence that 
these outcomes can be improved in those receiving the MiMP. 
Hypothesis 2: The MiMP will produce reductions in one or more of the following: self-reported stress, cravings, 
depression, or anxiety, or improvements in perceived social support.  
 
 
The primary outcome variable is adherence to MOUD, quantified as the number of days MOUD was received 
as indicated during the last 4 weeks of the intervention period.  We expect that this can be treated as a continuous 
variable in regression models. However, prior to modeling we will examine its distribution, to determine if this is 
appropriate. If, for example, we observe large ceiling effects (many participants report very high or perfect 
adherence) then we may model non-adherence as the number of days when MOUD was not received. In this 
case we expect it would have something similar to a Poisson distribution, possibly with over-dispersion which 
would suggest the use of negative binomial regression.  The secondary outcome of relapse will be treated in 
two ways, as a binary indicator if any relapse during the last 4 weeks of the intervention period as determined 
by UDS or self-report, and the total number of self-reported days of use during the last four weeks of the 
intervention period. The first dichotomous outcome will require logistic regression. The second approach will 
depend on the observed distribution and may be treated as either negative binomial or continuous. The other 
secondary outcome, cravings, can be treated as continuous, which will also be true for the ancillary outcomes 
including depression, anxiety, and stress, are continuous variables.  
 
The table below shows all other secondary measures and the instruments used to measures each of the 
variables. These instruments are also consistent with data harmonization efforts by the NIH HEAL Initiative. Data 
collection will happen in accordance the timeframes stipulated above, T0 - T4. To examine within-subject and 
between-subject differences from pre to post intervention using repeated measures analysis of covariance 
(ANCOVA), including covariates, as necessary. Study outcomes will be also be examined using longitudinal 
regression models, including covariates, as necessary. The full model will include fixed within-subject effects to 
predict outcomes (stress, depression, anxiety, cravings, self-efficacy, baseline cortisol, cortisol reactivity).  
 
Instrument  
Construct 
Instrument  
Construct  
Opioid Craving Scale 
Craving  
PROMIS Pain Intensity 
Pain Intensity  
Perceived Stress Scale 
Stress  
PROMIS Pain Interference 
Pain Interference  
Generalized Anxiety Disorder 7 
Anxiety  
PROMIS Pain Catastrophizing 
Pain Catastrophizing 
Patient Health Questionnaire-9 
Depression  
Sleep Scale from the Medical 
Outcomes Study 
Sleep  
Community Assessment Index 
Community 
Integration  
Patient Global Impression of 
Change 
Treatment efficacy  
PROMIS Quality of Life  
Quality of life  
BPI Pain Severity 
Pain Severity  
 
Cortisol Data Analyses: This type of analysis will also be used in assessment of the cortisol data (i.e., stress 
reactivity) to examine the effects of treatment on baseline (resting) cortisol levels and cortisol reactivity. Cortisol 
data is often skewed,100 so transformations may be performed. The cortisol response will be quantified by 
calculating the area under the curve (AUC).101 The trapezoidal formula is given by:  
]
2
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(
[
1
5
1
+
=
+

i
i
i
i
m
m
t
, where ti is the interval between sample i and sample i+1 and mi is the level of cortisol for 
sample i. In addition, the influence of pre-treatment cortisol (resting and reactivity) on responsiveness to 
treatment (as measured by our primary outcome variables) will be examined.