A Trauma-Informed Intervention for Positive Youth Development and Teacher Wellness in Rural Montana
Phase: NA
Conditions: Depression, Anxiety, Stress
Sponsor: Montana State University

A Trauma-Informed Intervention for Positive Youth Development and 
Teacher Wellness in Rural Montana
Dr. Lauren Davis
September 1, 2021 
Clinical Trial # NCT05085392

Statistical Design and Power 
Study design 
This study is a non-randomized, Phase I clinical trial. Participants will be high school students 
and teachers in southwest Montana. In the first year, 30 teachers from the same school district 
will be recruited to participate in a virtually -delivered trauma-informed yoga intervention (heart 
rate variability data will be gathered from a group of high school students to assess utility for the 
measure in year 2). In the second year, we will expand the intervention to high school students 
in Livingston city and rural Park County schools, recruiting 30 students in each area to 
participate in a synchronous, virtually-delivered, trauma-informed yoga intervention during their 
health enhancement classes.  In addition, 30 teachers from the  rural Park County schools will 
be recruited to participate in the same virtually-delivered, trauma-informed yoga intervention 
offered to Livingston teachers concurrently. 
In each year, participant assessments (surveys, salivary cortisol, and heart rate 
variability(HRV)) will be administered prior to intervention and immediately after the 
intervention at 6 weeks. Follow-up assessments will be conducted in the fall semester 
following each intervention to measure persistence of the treatment. Thus, Livingston 
teachers may have up to six time points of data collection, while Park County students and 
teachers and Livingston students will have up to three. In addition, salivary cortisol and HRV 
will be collected at the midpoint of the intervention (3 weeks) each year. Measures collected 
are detailed in Table 1. 
Table 1. Measures recorded for students and teachers, with collection timeline and validity measures. 
Description of Measure 
Source (if 
applicable) 
Timeline for 
Collection 
Cronbach’s 
Alpha/Validity & 
Reliability 
Center for Youth Wellness 
ACE-Q Self-Reporting 
Screener for Teens 
(Adolescents Only) 
Burke-Harris 
& Renschler, 
2015 
Pre-intervention (in order to 
determine ACE scores of 
participants) 
Longitudinal testing 
currently underway to 
measure content and 
construct 
validity/reliability 
Generalized Anxiety Disorder 
Scale (GAD-7) 
(Adolescents and Teachers) 
Spitzer et al., 
2006 
Pre- and post-intervention 
Cronbach's alpha: .79- 
.91 
Reliability = .85 
Validity = 73.3% 
Patient Health Questionnaire 
for Depressive Symptomology 
(PHQ-A for adolescents, 
PHQ-9 for Teachers) 
Johnson, 
2002 
Pre- and post-intervention 
Chronbach’s alpha = 
.835 
Reliability = .875 
Validity = 89.5% 
Connor-Davidson Resilience 
Scale (CD-RISC) 
(Adolescents and Teachers) 
Connor & 
Davidson, 
2003 
Pre- and post-intervention 
Chronbach’s alpha = 
.94 
Reliability = .96 
Columbia Suicidality Screener 
(C-SSRS) 
(Adolescents Only) 
Greist et al., 
2014 
Pre- and post-intervention 
Cronbach's alpha: .81- 
.95 
Reliability = .97 
Validity = 99% 
Professional Quality of Life 
Index (Pro-QOL) 
Hudnall 
Stamm, 2009 
Pre- and post-intervention 
Cronbach's alpha: .90 
Reliability = .80-.90 

(Teachers Only) 
Teachers’ Sense of Self- 
Efficacy (Short Form) 
(Teachers Only) 
Tschannen- 
Moran & 
Woolfolk-Hoy, 
2001 
Pre- and post-intervention 
Cronbach's alpha: .90 
Reliability = .74 
Cortisol salivary assays 
(Adolescents and Teachers) 
Salimetrics 
Laboratories 
Pre (beginning of week 1)-, 
mid- (week 3) and post- 
intervention (end of week 6) 
Mean accuracy of 
salivary cortisol testing 
> 90% 
Heart rate variability data 
(Adolescents and Teachers) 
HeartMath 
Institute 
Pre- (1 week prior to 
intervention to establish a 
baseline), mid- (week 3) and 
post-intervention (at 
conclusion of week 6) 
Pearson correlation between 
electrocardiogram (ECG) and 
ear clip pulse plethysmograph 
(PPG) device (Em Wave Pro 
Plus) mean resting baseline 
= .997*, p <0.01; RMSSD 
Pearson correlation = .958*, p 
< 0.01 
Collection of secondary data, 
including attendance and 
academic data (MAPS 
benchmark/progress 
monitoring standardized 
assessment scores, 
attendance, and office 
referrals) 
(Adolescents Only) 
N/A 
Office referrals/behavioral 
data: Collected 8 weeks 
prior to study and during 8 
weeks of intervention for 
comparison 
Attendance data: Collected 
8 weeks prior to study and 
during 8 weeks of 
intervention for comparison 
MAPS: Pre- and post- 
intervention 
MAPS Data: Pearson 
correlation coefficients 
range from .76-.82 in 
reading and .82-.86 in 
mathematics 
Other measures: N/A 
 
Power considerations 
This is an early-phase trial, the results of which will be used to inform full power analyses for 
future clinical trials. We do not expect to be powered to detect small intervention effects. 
However, as a preliminary assessment of power, we consider the power of a paired t-test 
comparing pre- and post- intervention scores on the various response measures, including 
survey scales, cortisol, and HRV. We compute power in terms of Cohen’s d, a standardized 
effect size measure that describes the impact of the intervention in terms of the number of 
standard deviations that the effect is equivalent to (so an effect of 5 points on a scale with SD 
20 would have a Cohen’s d of 0.25). On this scale, an effect size of 0.2 is typically considered 
“small”, 0.5 “moderate”, and 0.75 “large”. This may not align with clinically meaningful 
differences in outcome measures, however. 
For comparisons within intervention groups in their first (and sometimes only) year of 
intervention, we expect to have 30 participants in each group (Livingston students, Livingston 
teachers, rural students, rural teachers). Assuming a Type I error rate of α = 0.05, we achieve 
80% power to detect a Cohen’s d of 0.53, and 90% power to detect a Cohen’s d of 0.61. Thus, 
the study is adequately powered to detect moderate-to-large effects of the intervention. The 
study design will allow us to estimate the magnitude of effects, intra-class correlations by school 
and classroom, and the variability of intervention impacts—all the inputs needed to perform a 
formal power analysis for a future Phase III trial. Full power curves for n = 30 and n = 15 
participants are given in Figure 1. 

 
 
Figure 1. Power 
curves for sample 
sizes of 15 and 30. 
Solid black line 
indicates 80% power 
and dashed line 
indicates 90% power. 
 
 
 
 
 
 
 
 
 
 
 
 
Statistical Analysis Plan 
For each outcome, we will compute the change in the scale score or biomarker value from 
baseline to post-intervention (post – pre) and compute unadjusted averages and standard 
deviations. These computations will be done within participant group (Livingston students, 
Livingston teachers, rural students, rural teachers). 
We will also obtain adjusted estimates using mixed effects linear regression models of 
post-test score on pre-test score with demographic factors and random effects for school and 
intervention group nested within school (Laird & Ware 1982). This will allow us to estimate intra- 
class correlations for intervention group and school, which will be required for sample size 
estimation in a full efficacy trial. 
We propose the following model for the individual response (anxiety, depressive 
symptoms, etc.), denoted Yijk, where i = 1,…, I indexes school, j = 1,…, J indexes intervention 
group (only needed if there are multiple health education classes receiving the intervention in a 
school), and k = 1,…, N indexes participants within groups. 
𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖 = 𝑍𝑍𝑖𝑖𝑖𝑖𝑖𝑖 𝜃𝜃 + 𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖𝛿𝛿 + 𝛼𝛼𝑖𝑖 + 𝛽𝛽𝑗𝑗(𝑖𝑖) + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖 
𝛼𝛼𝑖𝑖 ~𝑁𝑁(0, 𝜏𝜏2) 
𝛽𝛽𝑗𝑗(𝑖𝑖)~𝑁𝑁(0, 𝜔𝜔2) 
𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖 ~𝑁𝑁(0, 𝜎𝜎2) 
Thus, αi represents a random effect for school i, with variance τ2β ,j represents a random effect 
corresponding to group j in school i, with variance 𝜔𝜔2. We model this as a function of Zijk, the 
pre-intervention score for participant k in group j in school i, and Xijk, the set of other modeled 
covariates, centered at their means. This means that the intercept parameter θ gives the 
expected effect of the program for a student or teacher with “average” demographics and other 
covariate values after controlling for within-school and within-group correlation. We can use the 
estimates of the random-effects variances and the residual variance to produce estimates of the 
intra-class correlations for school and for intervention group. 
This is a Linear Mixed Effects (LME) model. As all primary and secondary response 
variables are continuous and may reasonably be expected to be normally distributed we can 
implement the model using restricted maximum likelihood (Laird & Ware 1982) using the lme4 
package in R (Bates et. al., 2015). 

 
Biomarker analyses. Salivary cortisol and HRV will also be collected mid-intervention. 
To fully characterize temporal patterns in these outcomes, we will use a different LME model 
that captures the full trajectory over time. In this case, we propose the following model for the 
individual response, denoted Yijkt, where i = 1,…, I indexes school, j = 1,…, J indexes 
intervention group (only needed if there are multiple health education classes receiving the 
intervention in a school), k = 1,…, N indexes participants within groups, and t = 1,…3 indexes 
the time point of the observation. 
𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝜃𝜃𝜃𝜃𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 + 𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖 𝛿𝛿 + 𝛼𝛼𝑖𝑖 + 𝛽𝛽𝑗𝑗(𝑖𝑖) + 𝛾𝛾𝑘𝑘(𝑖𝑖𝑖𝑖) + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 
𝛼𝛼𝑖𝑖~𝑁𝑁(0, 𝜏𝜏2) 
𝛽𝛽𝑗𝑗(𝑖𝑖)~𝑁𝑁(0, 𝜔𝜔2) 
𝛾𝛾𝑘𝑘(𝑖𝑖𝑖𝑖)~𝑁𝑁(0, 𝜗𝜗2) 
𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖 ~𝑁𝑁(0, 𝜎𝜎2) 
Thus, αi represents a random effect for school i, with variance τ2, βj(i) represents a random effect 
corresponding to group j in school i, with variance 𝜔𝜔2, and γk(ij) represents a random effect 
corresponding to student k in group j and school i, with variance 𝜗𝜗2. We model this as a function 
of Tijkt, the time of the tth cortisol or HRV measure for participant k in group j in school i, and Xijk, 
the set of other baseline covariates included in the model, centered at their means. We can 
also consider a random slope model with varying slopes for each student over time if model 
diagnostics suggest this more complex model will be a better fit to the data. 
 
References: 
1. 
Nan M. Laird and James H. Ware (1982) Random-effects models for longitudinal data. 
Biometrics 38:963-974 
2. 
Douglas Bates, Martin Maechler, Ben Bolker, and Steve Walker (2015). Fitting Linear 
Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48