Effectiveness of Interpretation of Training to Reduce Anxiety: Evaluating Technology-based Delivery Models and Methods to Reduce Attrition
Phase: PHASE2
Conditions: Anxiety
Sponsor: University of Virginia

  
  
 
 
Interpretation Training to Reduce Anxiety: Evaluating Technology-based Delivery Models and Methods to 
Reduce Attrition 
 
Study Protocol and Statistical Analysis Plan 
 
NCT03498651 
 
May 24, 2023 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  

PREREGISTRATION 
 
1 
 
Web-Based Interpretation Bias Training to Reduce Anxiety: A Sequential, Multiple-
Assignment, Randomized Trial 
 
Metadata 
 
Contributors 
 
Katharine E. Daniel, Jeremy W. Eberle, and Bethany A. Teachman 
 
Description 
 
This is an initial pre-registration for a sequential, multiple assignment, randomized trial testing 
the target engagement and effectiveness of an online interpretation bias training intervention in a 
geographically diverse, anxious, treatment-seeking sample.  
 
Registration Type 
 
OSF Preregistration  
 
Date Registered 
 
December 2, 2020  
 
Date Created 
 
December 2, 2020  
 
Study Information 
 
Hypotheses 
 
This sequential, multiple assignment, randomized trial (SMART) was pre-registered at 
clinicaltrials.gov (https://clinicaltrials.gov/ct2/show/NCT03498651) and was designed to test the 
target engagement and effectiveness of an online interpretation bias training intervention in a 
geographically diverse, anxious, treatment-seeking sample. Further, this study aims to investigate 
whether adding minimal human contact for participants at high risk of attrition improves 
intervention retention and treatment outcomes. Here we pre-register the following hypotheses:  
 
Interpretation Bias and Anxiety Hypotheses 
 
A cognitive style characterized by rigid, negative interpretations of ambiguous situations has 
been associated with anxiety-related pathology (Beard, 2011). Cognitive bias modification for 
interpretations (CBM-I) is an online cognitive training intervention that aims to reduce 
symptoms of anxiety by training individuals to interpret ambiguous situations less negatively 
(Hallion & Ruscio, 2011). We will test the effectiveness of the CBM-I intervention on measures 
of target engagement (i.e., interpretation bias) and measures of anxiety severity. We expect that 

PREREGISTRATION 
 
2 
 
individuals assigned to all arms of the CBM-I treatment, as compared to those randomized to 
complete a series of psychoeducation tutorials, will show a greater increase in positive 
interpretation bias and a greater reduction in negative interpretation bias and anxiety symptoms 
from pre-treatment to post-treatment (where post-treatment is measured immediately following 
session 5). We also expect that treatment gains will be maintained for individuals assigned to 
CBM-I (vs. psychoeducation) at two-month follow-up. When comparing outcomes within the 
three CBM-I arms of the study, we expect the high-risk for attrition plus telecoaching group to 
show greater increases in positive interpretation bias and greater reductions in negative 
interpretation bias and anxiety symptoms compared to the high risk for attrition group who were 
not randomized to receive telecoaching. Comparisons between the low risk for attrition group 
and the high risk for attrition groups (with or without telecoaching) are of secondary interest and 
these hypotheses are non-directional. 
 
Treatment Dropout Hypotheses 
 
Online mental health interventions have long-been plagued by high user dropout (Eysenbach, 
2005). Therefore, it is important to investigate whether adding minimal human contact (i.e., 
providing telecoaching) to participants identified as being at high risk for treatment dropout 
improves retention in this online intervention trial. We expect that CBM-I participants identified 
as being at high risk of treatment dropout who were randomized to receive a supplemental 
telecoaching intervention will drop out at a later session than high risk CBM-I participants who 
did not get randomized to the telecoaching intervention. Further, we expect that the three CBM-I 
arms of the trial will be associated with less attrition than the psychoeducation comparison 
group. 
 
Notably, participants were not randomly assigned to their high- or low-risk status, so causal 
inference between the high and low risk CBM-I groups is unsupported by this SMART design. 
However, we expect that participants identified as being at high risk of treatment dropout but 
who did not receive the telecoaching intervention will show earlier treatment dropout than CBM-
I participants identified as being at low risk for attrition. As a secondary analysis, we will also 
compare treatment dropout between low risk CBM-I participants and those high risk CBM-I 
participants who received the additional telecoaching intervention to test whether or not the 
supplementary telecoaching intervention appears to buffer against the risk of early dropout. 
However, this hypothesis is non-directional.  
 
Design Plan 
 
Study Type 
 
Experiment - A researcher randomly assigns treatments to study subjects, this includes field or 
lab experiments. This is also known as an intervention experiment and includes randomized 
controlled trials. 
 
Blinding 
 

PREREGISTRATION 
 
3 
 
For studies that involve human subjects, they will not know the treatment group to which they 
have been assigned.  
 
Is there any additional blinding in this study? 
 
No response 
 
Study Design 
 
We recruited and enrolled 1746 anxious adults with access to a smartphone, computer, or tablet 
to participate in a “computer-based program to reduce anxiety symptoms.” Participants were 
eligible to participate if they scored at or above a 10 on the anxiety subscale of the Depression, 
Anxiety, and Stress Scale (DASS-AS; Lovibond & Lovibond, 1995), had access to an internet-
enabled device, and were at least 18 years of age. 
 
Enrolled participants who completed readiness rulers and demographics questions at 
pretreatment were randomly assigned to either complete five 15-minute cognitive bias 
modification for interpretations (CBM-I) training sessions with 40 scenarios per session or to a 
psychoeducation condition according to a 75:25 stratified randomization ratio schedule. 
Participants were instructed to complete one training session per week over the course of five 
weeks and were instructed to complete these online training sessions using their preferred 
internet-enabled device (i.e., participants could opt to use multiple devices or to use the same 
device throughout the five-week training). 
 
Immediately following the assessment administered after the first of five training sessions, 
participants initially randomized to the CBM-I condition were identified as being either at high 
or at low risk for treatment dropout using an algorithm based on theoretically- and empirically-
derived individual characteristics (e.g., age, symptom severity) and on qualities of their 
performance throughout the first training session (e.g., duration of web page views). Participants 
identified as being at high-risk for treatment dropout were randomly assigned according to a 
50:50 stratified ratio to either receive an additional, minimal human contact intervention 
(telecoaching) or to continue without change with the standard CBM-I condition. Participants 
identified as being at a lower risk for dropping out continued with the standard CBM-I 
intervention. 
 
Using this sequential, multiple assignment, randomized trial (SMART), we are able to compare 
the four treatment arms of this study on measures of anxiety severity, target engagement 
(interpretation bias), and attrition/adherence. The four treatment arms are: (1) CBM-I low risk 
for dropout, (2) CBM-I high risk for dropout with telecoaching added after session 1, (3) CBM-I 
high risk for dropout with no telecoaching added, and (4) psychoeducation throughout. Further, 
we will be able to test whether the trajectories of anxiety symptoms and attrition change 
differentially over time for study arms 3 and 4 after high-risk identification and subsequent 
randomization to either telecoaching or no change. 
 
• 
Study Flow.png  
• 
Detailed Study Flow.png  

PREREGISTRATION 
 
4 
 
• 
Detailed Study Flow Key.png  
 
Randomization 
 
The randomization scheme is detailed under the study design section of this pre-registration and 
is illustrated in the attached figures. Note that there are two points of randomization within this 
SMART design, the first according to a 75:25 ratio (where the majority of participants are 
randomized to the CBM-I intervention vs. the psychoeducation condition) and the second 
according to a 50:50 ratio (where this randomization is only applied to those participants in the 
CBM-I condition identified as being at high risk for dropout). Both randomization schemes were 
stratified based on self-reported gender and anxiety symptom severity at baseline. 
 
The above randomization scheme was specified to ultimately yield equal cell sizes for each of 
the four conditions (i.e., 25% of randomized subjects in each). However, as of 10/28/2019, the 
four cell sizes were imbalanced (ranging from 15.9% to 25%) due to a large number of CBM-I 
subjects not completing Session 1 after Stage 1 randomization to the CBM-I arm of the 
intervention. Importantly, completed Session 1 measures were needed for the attrition algorithm 
to be able to classify CBM-I subjects as high or low risk of dropout before Stage 2 
randomization could be applied to high risk subjects. Thus, to reduce the imbalance in the 
number of subjects randomly assigned to each of the four groups across both stages of 
randomization, we changed the allocation at Stage 1 randomization on 10/31/2019 from 25% 
Psychoeducation and 75% to CBM-I to 10% and 90%, respectively.  
 
Sampling Plan 
 
Existing Data 
 
Registration prior to analysis of the data 
 
Explanation of Existing Data 
 
We registered the overall project and identified primary outcomes at clinicaltrials.gov 
(https://clinicaltrials.gov/ct2/show/NCT03498651) prior to data collection. However, this pre-
registration through OSF is occurring after data collection but prior to any data analysis. This 
pre-registration represents the first set of substantive analyses proposed in this dataset. To date, 
these data have only been viewed by members of the overall data collection team to ensure 
accurate and representative enrollment of participants (i.e., that we are succeeding in recruiting a 
diverse sample). We also regularly reported attrition updates by blinded condition to an 
independent safety monitor throughout the data collection process and completed blind data 
integrity checks (i.e, to catch duplicate entries). Data cleaning is currently ongoing by members 
of the team who are not involved in hypothesis generation.  
 
Data Collection Procedures 
 
Intervention sessions were administered and data were collected through the established 
MindTrails website. Enrollment was open to an international sample of anxious adults (inclusion 

PREREGISTRATION 
 
5 
 
criteria listed in the study design section of this registration). Participants were recruited through 
postings on online newsletters, research websites, mental health forums, Craigslist, social media 
(e.g., Facebook, Instagram, Twitter, Reddit), and UVA student group listservs; flyers posted in 
the Charlottesville community and at UVA and other nearby universities and community 
colleges; news sources (e.g., articles, radio interviews, podcasts); and Google Ads. In addition, to 
improve recruitment of underrepresented groups, we sent emails to a market research firm's 
database of participants who self-identified with anxiety or depression and as Black/African 
American, Hispanic/Latinx, or Asian; we also posted in an online newsletter for multicultural 
student services at UVA. Note that participants may have presented to the Mindtrails website 
starting on 3/12/2020 due to concerns over the developing COVID-19 pandemic. Although 
future analyses may choose to test whether these participants interacted with the intervention 
differently than did pre-COVID participants, we elect to combine all participants in this initial 
investigation of the major aims of this clinical trial. 
 
Per protocol, after enrollment and informed consent was obtained, each participant completed a 
pre-treatment assessment battery, one training session per week over the course of five weeks, 
comprehensive assessment batteries immediately following training sessions 3 and 5, and a 
follow-up assessment battery two months after training ended. Participants also completed a 
shorter schedule of assessments after training sessions 1, 2, and 4 to reduce participant burden. 
 
Participants were compensated via e-gift cards according to the following payment schedule. 
Participants were awarded $5 per assessment battery completed at pre-treatment, after session 3, 
and after session 5. Participants were further awarded $10 for completing the 2-month follow-up 
assessment, amounting to a total possible compensation of $25 in e-gift cards distributed by 
Tango Cards. 
 
No files selected  
 
Sample Size 
 
Per protocol, we planned to recruit and enroll 1,000 highly anxious individuals and sequentially 
employ a series of weighted randomization rules to achieve four treatment arms (i.e., four study 
conditions) of approximately equal weight (approximately 250 participants per group). Data 
collection is now complete and we enrolled 1746 participants in total. Of these, 1238 started the 
first training session and are considered intent to treat (ITT). At Stage 1 randomization, 984 ITT 
participants were assigned to CBM-I, and 254 ITT participants were assigned to 
psychoeducation. 1075 ITT participants completed assessments immediately following the first 
training session (835 in CBM-I and 240 in psychoeducation) and are included in our primary 
analyses. The 835 CBM-I completers of the session 1 assessments were classified as higher (n = 
547) or lower (n = 288) risk of study dropout, whereas the 149 CBM-I participants who did not 
complete the session 1 assessments were not able to be classified as higher or lower risk of study 
dropout. Of the 547 CBM-I participants classified as higher risk of study dropout, at Stage 2 
randomization 282 were assigned to receive coaching and 265 were assigned to no change. In 
total, 559 of the 1238 ITT participants completed five training sessions and are deemed treatment 
completers. 
 

PREREGISTRATION 
 
6 
 
Sample Size Rationale 
 
Prior to data collection, we conducted an analysis of variance (ANOVA) to estimate the sample 
size needed to test main effects of delivery methods (i.e., phone vs. computer), human contact, 
and their interaction according to a 2 (phone vs. computer) x 3 (high risk: TeleCoach, high risk: 
continued CBM only, low risk: continued CBM only) design. While we ultimately decided to not 
randomize participants to CBM-I delivery platform (to prioritize participant preference to 
maximize engagement and minimize burden), our initial power analysis assumed small to 
medium effects for this 2x3 design. In this power analysis, we elected to be conservative due to 
the SMART design yielding unbalanced cell sizes. Further, given that the second stage of 
randomization is based on performance in the earlier stage, we were unable to determine what an 
effect size might be in advance. With our assumed effect size (d = .35, f = .175), to have 80% 
power, we would need n = 240 to detect a main effect. However, given that we expected high 
rates of attrition, we assumed a 66% dropout rate (based on previous attrition rates on our 
MindTrails cite), so we multiplied n = 240 by 3 [i.e., 1/1-.66)], resulting in N = 720. For the 
psychoeducation comparison condition, we elected to match the sample size to the expected final 
cell size for the three other conditions (i.e., n = 120). We did not scale up the condition sample 
size given the SMART design does not split the control condition in a second stage of 
randomization.  
 
Stopping Rule 
 
We stopped data collection based on a selected date. Data collection began on 3/18/2019. We 
analyzed data collected through 11/27/2020.  
 
Variables 
 
Manipulated Variables 
 
There were two stages of randomization throughout the SMART (described elsewhere in this 
pre-registration).  
 
No files selected  
 
Measured Variables 
 
Interpretation Bias (Target Engagement) 
 
1) Recognition Ratings for Measuring Change in Interpretation Bias (modified from 
Matthews & Mackintosh, 2000). To measure negative and positive interpretation bias, 
participants were asked to complete the recognition ratings measure (modified from Matthews & 
Mackintosh, 2000) at pre-intervention, after session 3, after session 5, and at the 2-month follow-
up. Participants were presented with nine ambiguous stories and were asked to imagine 
themselves in each of those nine situations. The final word of each story was presented as a word 
fragment and participants were asked to complete the final word to disambiguate the scenario. 
Participants were then asked to complete a comprehension question tied to each story’s 

PREREGISTRATION 
 
7 
 
resolution. After completing all nine stories, participants were presented with the title of each 
story, four alternative disambiguated interpretations per story, and were asked to rate how similar 
each of the four interpretations were to the original story’s resolution using a scale ranging from 
0 (“very different”) to 3 (“very similar”). Participants also had the option to select Prefer Not to 
Answer. Two of the disambiguated interpretations provided per story were threat-related (one 
negative, one positive/benign) and two were threat-unrelated (one negative, one positive/benign). 
Endorsements of threat-relevant negative disambiguated interpretations are averaged across all 
nine stories to compute the negative interpretation bias score, where higher scores reflect a 
greater negative interpretation style. Endorsements of threat-relevant positive/benign 
disambiguated interpretations are averaged across the nine stories to derive the positive 
interpretation bias score, where higher scores reflect a greater positive interpretation style. 
 
2) Brief Body Sensations Interpretations Questionnaire (BBSIQ; modified from 
Clark et al., 1997). To measure negative and positive interpretation bias using an approach less 
tied to the intervention’s procedure, participants were asked to complete the Brief Body 
Sensations Interpretations Questionnaire (BBSIQ; modified from Clark et al., 1997) at pre-
intervention, after session 3, after session 5, and at the 2-month follow-up. To complete the 
BBSIQ, participants were presented with 14 potentially threatening, ambiguous events and are 
asked to rate the extent to which they believed three alternative explanations for why the event 
might have occurred (where two explanations are benign and one explanation is anxiety-
congruent) using a 0 (“not at all likely”) to 4 (“extremely likely”) Likert scale. Participants also 
had the option to select Prefer Not to Answer. The negative interpretation bias score is computed 
by averaging the likelihood ratings for all anxiety-congruent explanations, such that higher 
scores reflect a greater negative interpretation style, whereas the positive interpretation bias score 
is computed by averaging the likelihood ratings for all benign explanations, such that higher 
scores reflect a greater positive interpretation style. 
 
Anxiety Symptoms 
 
1) Overall Anxiety Severity and Impairment Scale (OASIS; adapted from Norman, 
Cissell, Means-Christensen, & Stein, 2006). To measure subjective evaluations of the 
frequency and severity of anxiety, avoidance, and associated impairment, participants completed 
the five-item OASIS (Norman et al., 2006). All items are rated on a scale of 0 (lowest 
impairment/severity) to 4 (highest impairment/severity). Participants also had the option to select 
Prefer Not to Answer. The OASIS was assessed at all time points (pre-intervention, after all five 
sessions, and at two-month follow-up). 
 
2) Depression, Anxiety, Stress Scales-Short Form - Anxiety Subscale (DASS-AS; 
adapted from Lovibond & Lovibond, 1995). To assess eligibility to enroll in the study and to 
measure participants’ subjective evaluations of the frequency of their anxiety symptoms since 
their last training session, participants completed the seven-item DASS-AS using the response 
scale 0 (“not at all”) to 3 (“most of the time”), where higher scores indicate greater symptom 
severity. Participants also had the option to select Prefer Not to Answer. Participants completed 
this questionnaire at pre-screener, after session 3, after session 5, and at 2-month follow-up.  
 
No files selected  

PREREGISTRATION 
 
8 
 
 
Indices 
 
No response 
No files selected  
 
Analysis Plan 
 
Statistical Models 
 
Models Testing Interpretation Bias and Anxiety Symptom Hypotheses 
 
To assess the impact of different treatment arms on interpretation bias and anxiety symptom 
outcomes, we will run a series of multilevel models estimated in the Bayesian framework using 
the rjags package in R. Due to the repeated outcome monitoring structure of this clinical trial, 
assessment points will be nested within individual participants. We will control for 
socioeconomic status and age in all models given previous evidence that these demographic 
characteristics are associated with differential treatment responses to CBM-I. To our knowledge, 
this will be the first Bayesian test of CBM-I treatment effects and is also the first CBM-I 
SMART design. As such, we will use uninformative priors in these estimations. 
 
Due to the study’s SMART design, we will employ a piecewise linear function to model time. 
Doing so will allow us to minimize the bias that a simple linear mean trajectory would otherwise 
impose by not accounting for the possible deflection points at the time of re-randomization 
(Nahum-Shani et a., 2019). In a piecewise linear regression model, a separate line segment can 
be fit for different intervals over the course of the study, with the boundary for the time intervals 
forming a transition point, such as from one treatment stage to another. In the current study, we 
have three stages of interest: the first stage (S1) of the clinical trial (pre-treatment to re-
randomization point after session 1); the second stage (S2) of the clinical trial (session 2 to 
session 5); and the third, or follow-up, stage (S3) of the clinical trial (session 5 until two months 
later). With a piecewise regression model, the linear trend in the outcome during the first stage 
can be allowed to vary from the linear trend during the second and third stages, and to be 
impacted by different variables. We will assume the same intercept for all four treatment arms 
because, by design, treatment arms are not expected to differ at baseline. We will assume that the 
marginal mean trajectory between the 3 CBM-I treatment arms will be equivalent until re-
randomization occurs for the high-risk CBM-I participants following the first session. Given that 
longitudinal modeling of SMART data must accommodate features of the SMART design itself 
(Nahum-Shani et al., 2019), we will code time using three variables constructed in the manner 
outlined in Pre-registration Table 1. 
 

PREREGISTRATION 
 
9 
 
 
 
Further, to code treatment condition for the four treatment arms in line with methods described 
by Lu and colleagues (2015) and Nahum-Shani and colleagues (2019), we will create a label of 
contrast codes for each of the two stages of the intervention. We elect to use contrast coding (1/-
1) as opposed to dummy coding (0/1) so that we will be able to directly compare outcomes tests 
of regression coefficients to tests of main effects (see Collins, 2018; Myers, Well, & Lorch, 
2010). Because there are two intervention stages by design (i.e., two sequential randomization 
decision points), we will let (a1, a2) denote the different contrast codes we’ll test between 
treatment arms at both randomization stages. 
 
Let a1 = -1 for participants initially randomized to psychoeducation and let a1 = 1 for 
participants initially randomized to CBM-I. 
 
Given that there are multiple comparisons of interest following the second randomization point, 
we specify four different sets of a2 contrast values. These values are delineated in Pre-
registration Table 2 such that a2,1 compares the average of all three CBM-I arms to the 
psychoeducation arm, a2,2 compares the high risk CBM plus telecoaching arm to the high risk 
CBM with no telecoaching added arm, a2,3 compares the high risk CBM plus telecoaching arm 
to the low risk CBM arm, and a2,4 compares the high risk CBM with no telecoaching added arm 
to the low risk CBM arm. 
 

PREREGISTRATION 
 
10 
 
 
 
The proposed model equation is provided in Pre-Registration Equation Set 1. 
 
 
 
Estimands. We have identified three estimands of interest upon which to interpret our 
interpretation bias and anxiety hypotheses: Study arm differences in (1) average outcomes at the 
end of the session 5 and at 2-month follow-up (i.e., time-specific outcomes); (3) rates of change 

PREREGISTRATION 
 
11 
 
in outcomes between pre-intervention and session 1, between sessions 1 and 5, and between 
session 5 and 2-month follow up (i.e., stage-specific slopes); and (4) highest outcomes averaged 
across the entire course of the study (i.e., area under the curve from pre-intervention to follow-
up). 
 
Models Testing the Probability of Dropping Out of Treatment 
 
To assess whether the likelihood for participants to drop out of treatment at different stages of 
the study varies based on treatment arm, we will run a logistic multilevel model with a logit link 
function to predict at each of the five training sessions 2-5 whether completion (0) or 
noncompletion (1) of the training session (without regard for whether participants completed the 
assessments that immediately followed the last training session they completed) varies based on 
treatment arm group membership. Given that no treatment occurred at the follow-up assessment, 
we will include only stages S1 and S2, coded as follows (Pre-Registration Table 3) from pre-
intervention to Session 5. All other parameters will remain consistent with the primary outcomes 
model above. Treatment dropout analyses will only be run for the participants who completed at 
least the first training and associated assessment battery given that, by definition, treatment 
completers will have completed all five training sessions. 
 
 
 
The proposed model equation is provided in Pre-Registration Equation Set 2. 
 

PREREGISTRATION 
 
12 
 
 
 
Estimands. We have identified four estimands of interest upon which to interpret our 
treatment dropout hypotheses: Study arm differences in (1) average likelihood to have dropped 
out during or before session 5 training (i.e., time-specific outcome of primary interest), (2) 
average likelihood to have dropped out during or before session 2 training (i.e., time-specific 
outcome of secondary interest), (3) average likelihood to have dropped out during or before 
session 3 training (i.e., time-specific outcome of secondary interest); and (4) average likelihood 
to have dropped out during or before session 4 training (i.e., time-specific outcome of secondary 
interest).  
• 
Pre-Registration Table 1.png  
• 
Pre-Registration Table 2.png  
• 
Pre-Registration Equation Set 1.png  
• 
Pre-Registration Table 3.png  
• 
Pre-Registration Equation Set 2.png  
 
Transformations 
 
As stated above, we will center time (S1) at zero for all longitudinal models such that the pre-
intervention assessment points for all participants, regardless of the date of their initial 
assessment, will be set to 0. 
 
We will mean center any continuous baseline covariates (e.g., SES).  
 
Inference Criteria 
 
We will make statistical inferences according to obtained HPD 95% Credible Intervals.  
 

PREREGISTRATION 
 
13 
 
Data Exclusion 
 
We will exclude participants who completed the DASS-A screener multiple times until they met 
eligibility criteria. We will also exclude participants who did not complete the first training 
session and subsequent assessment battery because we could not apply the attrition algorithm to 
these participants. As such, we are unable to know whether these individuals would have been 
identified by our algorithm as being at lower or at higher risk for dropping out, and, if they were 
identified as being at higher risk for dropping out, whether they would have been randomized to 
receive telecoaching or not. While excluding these participants interferes with being able to 
make causal claims (i.e., this is not an intent-to-treat sample), it allows for us to reduce 
uncertainty and error in group assignment. Since we aim to compare outcomes between groups, 
we have decided to prioritize accurate group assignment over an ITT sample with group 
classification uncertainty. As such, our primary analyses will be conducted on the 1075 session 1 
assessment compeleters and our secondary completer analyses will be conducted on the 
participants who completed all five training sessions (whether those sessions were composed of 
psychoeducation or CBM-I). To be considered a completer within the high-risk plus telecoaching 
group, those participants must also have engaged with a telecoach at least once.  
 
Missing Data 
 
The present data likely exhibit two missing data patterns. At the item level, participants had the 
option to select Prefer Not to Answer when completing RR, the BBSIQ, the OASIS, and the 
DASS-AS, likely resulting in a general missing data pattern when they did so. In such cases, 
which we expect to be rare, we will analyze the mean of the available items. 
 
At the scale level, we expect attrition to yield a monotone missing data pattern. To find any 
measured variables that may relate to this pattern of missing data and that are not already 
included in the analysis models, we will test whether demographics, training confidence, change 
importance, and device use correlate with binary indicators of scale-level missingness at each 
time point given that analyses of missingness in prior MindTrails studies have found that these 
variables predict missingness (e.g., Eberle et al., 2020; Hohensee et al., 2020). Following an 
inclusive analysis strategy (Collins et al., 2001), we will include any variables that correlate with 
missingness at greater than |.40| (which Enders, 2010, p. 133, states is the correlation yielding the 
“most useful” auxiliary variables) as auxiliary variables in our Bayesian analyses to correct for 
any systematic bias resulting from these variables’ relationships with missingness. 
 
Exploratory Analysis 
 
No response 
 
Other 
 
References 
 
Beard, C. (2011). Cognitive bias modification for anxiety: current evidence and future directions. 
Expert Rev Neurother, 11(2), 299–311. https://doi.org/10.1586/ern.10.194 

PREREGISTRATION 
 
14 
 
 
Clark, D. M., Salkovskis, P. M., Ost, L. G., Breitholtz, E., Koehler, K. A., Westling, B. E., & 
Gelder, M. (1997). Misinterpretation of body sensations in panic disorder. Journal of 
Consulting and Clinical Psychology, 65, 203-213. 
 
Collins, L. M. (2018). Optimization of behavioral, biobehavioral, and biomedical interventions: 
The multiphase optimization strategy (MOST). Cham, Switzerland: Springer. 
 
Collins, L. M., Schafer, J. L., & Kam, C-M. (2001). A comparison of inclusive and restrictive 
strategies in modern missing data procedures. Psychological Methods, 6, 330-351. 
https://doi.org/10.1037/1082-989X.6.4.330 
 
Eberle, J. W., Boukhechba, M. O., Sun, J., Zhang, D., Funk, D. H., Barnes, L. E., & Teachman, 
B. A. (2020). Shifting episodic prediction with online cognitive bias modification: A 
randomized controlled trial. Preprint under review. PsyArXiv. http://doi.org/d54p 
 
Enders, C. K. (2010). Applied missing data analysis. Guilford press. 
 
Eysenbach G. (2005). The law of attrition. Journal of medical Internet research, 7(1), e11. 
https://doi.org/10.2196/jmir.7.1.e11 
 
Hallion, L. S., & Ruscio, A. M. (2011). A Meta-Analysis of the Effect of Cognitive Bias 
Modification on Anxiety and Depression. Psychological Bulletin. 
https://doi.org/10.1037/a0024355 
 
Hohensee, N., Meyer, M. J., & Teachman, B. A. (2020). The effect of confidence on dropout rate 
and outcomes in online cognitive bias modification. Journal of Technology in Behavioral 
Science. https://doi.org/10.1007/s41347-020-00129-8 
 
Lovibond, P. F., & Lovibond, S. H. (1995). The structure of negative emotional states: 
Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck 
Depressionand Anxiety Inventories. Behaviour Research and Therapy, 33(3), 335–
343.https://doi.org/10.1016/0005-7967(94)00075-U 
 
Lu, X., Nahum-Shani, I., Kasari, C., Lynch, K. G., Oslin, D. W., Pelham, W. E., … Almirall, D. 
(2016). Comparing dynamic treatment regimes using repeated-measures outcomes: 
Modeling considerations in SMART studies. Statistics in Medicine, 35(10), 1595–1615. 
https://doi.org/10.1002/sim.6819 
 
Mathews, A & Mackintosh, B. (2000). Induced emotional interpretation bias and anxiety. J 
Abnorm Psychol; 109(4):602-15. 
 
Myers, J. L., Well, A., & Lorch, R. F. (2010). Research design and statistical analysis (3rd ed.). 
New York, NY: Routledge. 
 

PREREGISTRATION 
 
15 
 
Nahum-Shani, I., Almirall, D., Yap, J. R. T., McKay, J. R., Lynch, K. G., Freiheit, E. A., & 
Dziak, J. J. (2019). SMART longitudinal analysis: A tutorial for using repeated outcome 
measures from SMART studies to compare adaptive interventions. Psychological 
Methods, 25(1), 1–29. https://doi.org/10.1037/met0000219 
 
Norman, S.B., Cissell, S.H., Means-Christensen, A.J., & Stein, M.B. (2006). Development and 
validation of an Overall Anxiety Severity and Impairment Scale (OASIS). Depress & 
Anxiety; 23(4): 245-9. https://doi.org/10.1002/da.20182. PMID: 16688739.