Evaluating an Online, Single-Session Intervention Targeting Self-Injurious Behavior in Adolescents
Phase: NA
Conditions: Nonsuicidal Self Injury, Self Hatred, Suicidal Thoughts, Suicide Attempts
Sponsor: University of Denver

1 
Title: Evaluating an Online, Single-Session Intervention Targeting Self-Injurious Behavior in 
Adolescents 
 
NCT: NCT04498143 
 
Unique Protocol ID: University of Denver Institutional Review Board #1505797-3 
 
Registered via Open Science Framework: 8-13-2020, https://osf.io/x5cd9 
 
 

2 
Evaluating an Online, Single-Session Intervention Targeting Self-
Injurious Behavior in Adolescents 
 
Data Collection Status: Have any data been collected for this study already? Note: 'Yes' 
is a discouraged answer for this pre-registration form.  
 
No data has been collected already for this study. 
  
What's the main question being asked or hypothesis being tested in this study? 
 
To determine whether participants randomized to an online single session intervention (SSI) 
targeting non-suicidal self-injurious behavior (“Project SAVE”) endorse: 
 
1. Lower self-reported likelihood of future non-suicidal self injury immediately post-
intervention (secondary outcome) 
2. Lower self-reported frequency of suicidal ideation (in the past 3 months) at 3 month 
follow-up (secondary outcome) 
3. Lower self-reported frequency of non-suicidal self-injury (in the past 3 months) at 3 
month follow-up (primary outcome) 
 
relative to those randomized to an active control condition (i.e. supportive therapy SSI; 
Schleider & Weisz, 2018) in a large sample of cisgender heterosexual & sexual and gender 
minority teenagers ages 13-16 years old.  
 
Describe the key dependent variable(s) specifying how they will be measured. 
  
Our primary and secondary dependent variables will be measured using the self-report version 
of the Self-Injurious Thoughts and Behaviors Interview-R (SITBI-R; Fox, Harris, Wang, Millner, 
Deming, & Nock, 2020). Items in this measure ask participants about their 
attitudes/beliefs/history concerning various self-injurious thoughts and behaviors, including 
suicidal ideation (SI) and non-suicidal self-injury (NSSI). While our participants will complete all 
questions from the SITBI-R, the present study evaluates items that measure past 3-month 
frequency of NSSI, past 3-month frequency of SI, likelihood of future NSSI (see below section).  
 
https://shirleywang.rbind.io/papers/Fox_psychassess_2020.pdf 
 
Primary Dependent Variable 
 
NSSI Frequency in the Past 3 Months. Our primary outcome variable will compare past 3-
month NSSI frequency at 3-month follow-up—for participants assigned to the Project SAVE SSI 
versus supportive therapy SSI (i.e. control condition). At pre-intervention and 3-month follow-up 
time points, participants will indicate “how many times they have purposely hurt themselves 
without wanting to die” in the past 3 months in an open-response text box. Notably, in the 
present study’s survey flow, this question is displayed following display logic—such that 

3 
participants endorsing zero NSSI in the past 3-months (via a previous question) skip to the end 
of the question block and are not prompted to answer our main frequency outcome question. 
For these individuals, we will impute a past 3-month frequency value equal to 0 (as they have 
endorsed zero NSSI in the previous 3 months in the earlier question). More information about 
how we will handle very large numbers (i.e. outliers) for this outcome is reported below (see 
“Outliers and Exclusions” section; “Winsorizing Outliers” subsection). 
 
Secondary Dependent Variables 
 
Likelihood of Future NSSI. Likelihood of future NSSI will be measured immediately pre- and 
post-intervention. We will evaluate self-reported likelihood of future non-suicidal self-injury 
immediately post-intervention—comparing participants in the Project SAVE SSI versus 
supportive therapy SSI control conditions, and controlling for pre-intervention likelihood score. 
An item from the Self-Injurious Thoughts and Behaviors Interview-R (Fox et al., 2020) will 
assess participants’ beliefs about the likelihood of future non-suicidal self injury on a 5-point 
Likert Scale (range = 0 to 4; 0 = “not at all”; 4 = “extremely”). 
 
SI Frequency in the Past 3 Months. We will compare past 3-month SI frequency at 3-month 
follow-up—for participants assigned to the Project SAVE SSI versus supportive therapy SSI (i.e. 
control condition). At pre-intervention and 3-month follow-up time points, participants will 
indicate “how many days did they have thoughts about killing themselves for more than a few 
minutes” in the past 3 months in an open-response text box. Notably, in the present study’s 
survey flow, this question is displayed following display logic—such that participants endorsing 
zero history of SI in the past 3 months (via a previous question) skip to the end of the question 
block and are not prompted to answer our frequency outcome question. For these individuals, 
we will impute a past 3-month frequency value equal to 0 (as they have endorsed zero SI in the 
previous 3 months in the earlier question). More information about how we will handle very large 
numbers (i.e. outliers) for this outcome is reported below (see “Outliers and Exclusions” section; 
“Winsorizing Outliers” subsection). 
 
All Other Variables 
 
Demographics. Immediately pre-intervention, participants will be asked to report on the 
following demographic variables: age, sex assigned at birth, gender identity, race/ethnicity, 
sexual/romantic attraction and orientation, mental health treatment history, and zip code.   
 
Perceived Socioeconomic and Social Status. Immediately pre-intervention, participants will 
be asked to rate their perceived socioeconomic and social status using the two items from the 
MacArthur Scale of Subjective Social Status-Youth Version (Goodman et al., 2001). 
Respondents indicate where they see themselves on a ladder with 10 rungs (range: 1 to 10 for 
both items, where 1 = families with most money/education/jobs and youth with highest 
respect/grades/social standing; 10 = families with least money/education/jobs and youth with 
lowest respect/grades/social standing). 
 

4 
https://doi.org/10.1542/peds.108.2.e31  
 
Pubertal Development. Pubertal development will be measured pre-intervention via the the 
Pubertal Development Scale (Carskadon & Acebo, 1993).  This scale asks participants to rate 5 
items relating to their physical development milestones (e.g. “body hair growth”) on a 5-point 
Likert scale (1 = “not yet started”; 2 = “barely started”; 3 = “definitely started”; 4 = “seems 
complete”; 5 = “I don’t know/Not applicable”). Excluding “5” responses, higher scores generally 
indicate being further along in pubertal development.  
 
https://www.jahonline.org/article/1054-139X(93)90004-9/pdf   
 
Discrimination. The Expanded Everyday Discrimination Scale will be measured pre-
intervention to gauge the levels of relatively minor, every-day, chronic discrimination 
experienced by participants.  The original scale (Williams et al., 1997) asks participants, “In your 
day-to-day life, how often do any of the following things happen to you?” for 9 items (e.g. “you 
are treated with less courtesy than other people are”) on a 5-point Likert scale (1= “never”; 6 = 
“almost every day”).  These items assess the participant’s observations about how others treat 
and act around them on a daily basis.  The expanded version of this scale includes a 10th item, 
“You are followed around in stores.”  Scores on the Expanded Everyday Discrimination Scale 
range from 10-60, with higher scores indicating higher levels of chronic discrimination 
experienced by participants.  Respondents are also asked to identify what they believe to be the 
main reason(s) for these experiences (e.g. gender, race, age, etc.). 
 
https://scholar.harvard.edu/files/davidrwilliams/files/measuring_discrimination_resource_june_2
016.pdf 
 
Self Hate. Self-hate will be assessed immediately pre-intervention, post-intervention, and 
follow-up using the Self Hate Scale (Turnell, Fassnacht, Batterham, Calear, & Kyrios, 2019), 
which is measured on a 7- point Likert scale.  The Self Hate Scale asks participants to “Rate 
how true each of the following statements are for you right now, in this moment” for 7 items (e.g. 
“I hate myself”; 1 = “not at all true for me”; 7 = “very true for me”).  Scores on the Self Hate 
Scale range from 7-49, with higher scores indicating higher levels of self hate.   
 
https://www.sciencedirect.com/science/article/abs/pii/S0165032718313144 
 
Disordered Eating. Disordered eating behaviors will be measured at pre-intervention and 3-
month follow-up using The Dietary Restriction Screener (DRS-2; Haynos & Fruzetti, 2015). The 
DRS-2 is a 9-item measure evaluating restrictive eating, bingeing, and purging behaviors in 
participants. 6 items ask participants whether or not they have engaged in restrictive eating, 
bingeing, or purging behaviors in the past year or in the past 3 months (0 = no; 1 = yes). The 
other 3 items assess the frequency of these behaviors over the past 28 days.   
 
https://link.springer.com/article/10.1007/s40519-014-0161-0 
 

5 
SSI Feedback. Acceptability and participant perception of each SSI will be assessed using the 
Program Feedback Scale (PFS).  This scale was adapted for web-based SSIs (Schleider, 
Mullarkey, & Weisz, 2019) and is based on validated acceptability measures used for digital 
interventions.  The PFS asks participants to “Please tell us how much you agree with each 
statement below” for 7 statements (e.g. “the activity was easy to use”) using a 5-point Likert 
scale (1 = “really disagree”; 5= “really agree”).  For these 7 items, scores can range from 7 to 
35, with higher scores indicating greater acceptability of the SSI (how much participants 
enjoyed/ understood/ would recommend/ could easily use the SSI).  The PFS also assesses 
qualitative participant feedback with 3 open-ended questions (“What did you like about the 
program?”, “What would you change about the program?”, and “Is there anything else you’d like 
to share with us about the program?”). In keeping with other web-based intervention research 
among youth, we will define a mean score of 3.5 or higher on the PFS (across all 7 items, 1-5 
scale) as “acceptable”. We will also report means and standard deviations for each individual 
item of the PFS (see https://psyarxiv.com/tdrpc/). 
 
https://www.researchprotocols.org/2019/7/e13368/#box1 
 
How many and which conditions will participants be assigned to? (optional) 
  
Participants will be randomly assigned to 1 of 2 possible conditions, per a 1:1 allocation ratio: 
(1) an online, active control group program encouraging feelings disclosure (i.e. supportive 
therapy SSI), or (2) an online program targeting behaviors and thoughts relating to self-injury 
(i.e. Project “SAVE”—Stop Adolescent Violence Everywhere—SSI). Random assignment to 
condition will be conducted in a triple-masked manner. Therefore, participants will be masked to 
whether they received active treatment and investigators will be masked to which condition the 
participant is randomized to—as the randomization occurs automatically within the survey. 
Additionally, as the primary and secondary outcomes are self-report measures, we will not be 
utilizing an outcomes assessor (e.g. a clinical interviewer).  Participants will be informed of their 
condition assignment following their completion of the full study protocol by debriefing form. 
Investigators will be informed of participant condition after data collection is complete (i.e. the 
data analysis phase).  
 
Supportive Therapy (“Share Your Feelings”) SSI (Schleider & Weisz, 2019; see 
https://osf.io/u4axs/) is a ~30-minute, self-administered, web-based program that uses 
components of supportive therapy to encourage feelings sharing.  Like Project SAVE, this SSI 
takes approximately 30 minutes to complete and is self-administered online.  The supportive 
therapy SSI encourages participants in the control group to identify and express their feelings by 
(1) explaining why sharing feelings is natural, important, and helpful and (2) including 
testimonials from teens who have shared their feelings with close others. 
 
Project SAVE (“Stop Adolescent Violence Everywhere”) SSI is a ~30-minute, self-
administered, web-based program that uses components of cognitive behavior therapy and 
dialectical behavior therapy designed to decrease self-injurious behaviors in youth. The SSI’s 
content is designed to maximize relevance for youth engaging in self-injurious behaviors by 

6 
directly addressing two commonly reported functions of self-injury in youth: using self-injury for 
self-punishment and/or for emotion regulation. The Project SAVE SSI has 4 general content 
sections: (1) explaining the science behind how changing your actions (i.e. decreasing self-
injurious behaviors) can positively impact your thoughts and emotions over time; (2) providing 
scientific evidence and testimonials from other teens that have successfully decreased their 
self-injurious behaviors and noticed positive change as a result; (3) evidence-based tips for 
overcoming common obstacles to decreasing self-injurious behaviors in day to day life; and (4) 
offering an opportunity for youth to share their own thoughts and advice on what they have 
learned with other teenagers who are facing similar challenges. These content areas are 
reinforced by intervention design features intended to maximize persuasiveness and 
memorability, in line with other efficacious, web-based SSIs for adolescents.  
 
Specify exactly which analyses you will conduct to examine the main 
question/hypothesis. (optional) 
 
Testing for Differential Dropout 
 
First, we will test for differential dropout by study condition. To do this, we will use Z-tests of 
differential proportions where we compare the proportion of people who drop out before 
completing the study (Y or N) as a function of treatment condition (0 = control group SSI, 1 = 
Project SAVE SSI). We will run two tests evaluating differential dropout: (1) measuring dropout 
prior to finishing our post-intervention questions, and (2) measuring dropout prior to finishing our 
3-month follow-up questions. To be considered a study “completer” for the post-intervention 
questions, a participant must have made it through all post-intervention survey sections (i.e. all 
sections but “final block” section that presents information for study logistics). To be considered 
a study “completer” for the 3-month follow-up questions, a participant must have made it 
through all follow-up survey sections (i.e. all sections but “final block” section that presents 
information for study logistics).  
 
If the p value is greater than .05 we will assume dropout was not dependent on condition 
assignment and can proceed with interpreting the effects of intervention assignment (i.e. 
proceed with the between-group analyses exactly as described below, starting in the section 
labeled “Hypothesis 1”). 
 
If the p value for this test is less than .05 we will conclude that dropout was dependent on 
condition assignment, and we will take the following steps to improve the interpretability of our 
results. Unequal dropout between treatment groups can introduce bias in trial results; however, 
this is not always the case (Bell, Kenward, Fairclough, & Horton, 2013; 
https://doi.org/10.1136/bmj.e8668). One major concern is that, if data are not missing at 
random, systematic differences may exist between “dropouts” and “completers” that cannot be 
corrected for, limiting the interpretability of our results. For example, if more control condition 
participants experienced a worsening of clinical symptoms and dropped out before completing 
follow-up measures, this worsening of symptoms would not be captured by completers’ data (or 
by data imputed based on the available completers’ data). Thus, should unequal dropout exist 

7 
between conditions in the present study, we will report the results of our main analyses below, 
highlighting (1) limitations to interpretation, and (2) ways in which this will inform future 
intervention development. 
 
Further, if unequal dropout is present, we will run a sensitivity analysis to determine a range of 
possible effect sizes for our outcomes of interest, varying how much “dropout participants” may 
have changed on each outcome. To establish lower- and upper-end estimates of effect size, we 
will: (1) calculate residual change for each of our 3 outcomes, (2) identify the 25th and 75th 
percentile values for residual change in each outcome, (3) run analyses for each outcome 
where we impute the lower residual change value (25th percentile) for all dropouts/missing data, 
as well as the higher residual change value (75th percentile) for all dropouts/missing data. This 
approach explicitly assumes data are not missing at random, providing a more unbiased 
estimate of the overall treatment effect in the presence of unequal dropout.  
 
Testing Hypothesis 1: Do teenagers in the Project SAVE SSI condition report lower self-
reported likelihood of future NSSI immediately post-intervention—relative to teenagers in the 
active control program?  
 
We will first test if the assumptions necessary to interpret a multiple linear regression are met. If 
not, we will follow the procedures below. We will document any of these changes when we 
report our results. 
  
- 
If assumptions for interpreting a multiple linear regression are not met, we will perform a 
two-way ordinal regression (as this outcome will be Likert data), with pre-intervention 
likelihood of future NSSI and treatment condition entered as our independent variables, 
and post-intervention likelihood of future NSSI as the dependent variable. We will 
consider a p value of less than .05 for treatment condition (in favor of the Project SAVE 
SSI) as a significant effect of the Project SAVE treatment on likelihood of future NSSI.  
  
- 
If the assumptions for interpreting a multiple linear regression are met, we will enter pre-
intervention likelihood of future NSSI as a covariate and treatment condition as the 
predictor of post-intervention likelihood of future NSSI in a multiple linear regression. We 
will consider a p value of less than .05 for the treatment condition coefficient (in favor of 
the Project SAVE SSI) as a significant effect of the Project SAVE treatment on likelihood 
of future NSSI.  
 
Testing Hypothesis 2: Do teenagers in the Project SAVE SSI condition endorse lower 
frequency of SI in the past 3 months—relative to teenagers in the active control program? 
 
First, we create our secondary outcome variable (i.e. frequency of SI in the past 3 months) by 
imputing values equal to 0 for participants who endorsed zero SI in the previous 3 months in an 
earlier question. See “SI Frequency in the Past 3 Months” under the key dependent variables 
section for more details.  
 

8 
Next, we will determine whether a Poisson regression, a negative binomial regression, a zero-
inflated Poisson regression, or a zero-inflated negative binomial regression is most appropriate 
for analyzing our past 3-month frequency outcome (count variable). To do this, we will run all 
four models, conducting model fit tests to compare AIC values, BIC values, and raw scores for 
each regression. If all three indices agree (i.e. select the same “best” model of the four 
possible), we will interpret the results of that regression. If they do not agree (i.e. select different 
“best” models from the four possible), we will report the results from each of the selected 
models. 
 
Regardless of model type, we will enter pre-intervention past 3-month frequency of SI as a 
covariate, and treatment condition as a predictor, of past 3-month frequency of SI at follow-up in 
the chosen regression(s). We will consider a p value of less than .05 for the treatment condition 
coefficient (in favor of the Project SAVE SSI) as a significant effect of the Project SAVE 
treatment on frequency of SI in the past 3-months. 
 
Testing Hypothesis 3: Do teenagers in the Project SAVE SSI condition endorse lower 
frequency of NSSI in the past 3 months—relative to teenagers in the active control program?  
 
We will follow the same procedures for hypothesis 2 but replacing “SI frequency in the past 3 
months” with “NSSI frequency in the past 3 months.” 
 
Correcting for Multiple Comparisons  
 
We will use a false discovery rate approach to correct for multiple comparisons among our main 
confirmatory tests of hypotheses 1 - 3. As such, in all places where a significant effect is defined 
by a p value less than .05, this is referring to the post-correction p value (i.e. the p value after 
correction using the false discovery rate method).  
 
Outliers and Exclusions. Describe exactly how outliers will be defined and handled, and 
your precise rule(s) for excluding observations. (optional) 
 
Type 1 Exclusions 
 
Type 1 exclusions are exclusions that can be automated and do not require consensus among 
investigators. A research team member who will not be involved with conducting the primary 
study analyses will use type 1 exclusions to determine when participant recruitment is complete. 
Recruitment for the present study will end once 500 participants have been randomized who 
pass all type 1 exclusions tests (i.e. are still eligible for inclusion after excluding for type 1 
criteria; see our section on sample size for more details).  
 
 Here, we will exclude participants that fail to meet the following study inclusion criteria: 
- 
Must be 13 - 16 years old at the time of the baseline survey 
- 
Must report comfort reading and writing in English 

9 
- 
Must endorse no learning disability, visual impairment, or other difficulty that makes it 
difficult to answer questions on a computer 
- 
Must have access to a laptop or smartphone with internet access 
- 
Must endorse purposefully hurting self without wanting to die within the past month 
- 
Must endorse either “I do not like myself” or “I hate myself” on CDI-II item 7 in a screener 
survey 
Here, we will exclude the following participants based on failure to meet the following quality 
check criteria: 
- 
Participants who exit the study prior to condition randomization for our listed analyses 
(note: the present study will use an intention-to-treat approach, where every participant 
who was randomized to a study condition—and who is not excluded for one of the 
specific reasons outlined in this section—will be included our our analyses)  
 
Type 2 Exclusions 
 
Type 2 exclusions are exclusions that cannot be automated and require consensus among 
investigators. Participants excluded for type 2 reasons will be subtracted from the N = 500 total 
individuals who were randomized and met criteria for inclusion following type 1 exclusions. 
 
Here, we will exclude the following participants based on failure to meet the following quality 
check criteria: 
- 
Participants who respond with either copy/pasted responses from text earlier in the 
intervention (e.g. copy and pasting only text from a previous testimonial slide) to any of 
free response questions 
- 
Participants demonstrating an obvious lack of English fluency in open response 
questions; these participants will be identified by consensus from all study team 
members  
- 
Participants responding with random text in open response questions; these participants 
will be identified by consensus from all study team members  
- 
Duplicate responses from the same individual in baseline or follow-up surveys (i.e. more 
than one response with an identical IP address). Where duplicate responses for the 
same individual are present in the same survey (e.g. more than 1 response for a single 
individual at follow-up), we will exclude the response that is less complete, retaining the 
more complete of the two responses. If both responses are 100% complete, we will 
retain the first of the two responses for that survey. Notably, if an individual completes 
the baseline survey more than once—and happens to be randomized to both 
conditions—we will exclude this individual’s responses from our analysis altogether.  
- 
We will also exclude for primary analyses (but may run sensitivity analyses including 
them) any participants who provide responses of 3 words or fewer to writing prompts 
that ask for at least 2 sentences or more. 
 
These exclusions are based on previous single session intervention research conducted online 
 

10 
(for one example, see Schleider & Weisz, 2018: 
https://doi.org/10.1080/15374416.2017.1405353) 
 
Imputing Data 
 
After excluding individuals based on the above criteria, we will impute any missing data in our 
variables of interest (pre- and post-intervention likelihood of future NSSI; pre- and post-
intervention desire for future NSSI; past 3-month frequency of NSSI at pre-intervention and 
follow-up). To do this, we will execute an expectation-maximization and bootstrapping algorithm 
via the Amelia II package in R (Honaker, King, & Blackwell, 2011; https://cran.r-
project.org/web/packages/Amelia/Amelia.pdf). All variables from the corresponding statistical 
model (i.e. hypothesis tests 1 - 3) will be included in the algorithm used to create the imputed 
data for that test: (a) baseline values for the outcome—likelihood of NSSI, desire for NSSI, or 
past 3-month frequency of NSSI, (b) follow-up values for the outcome, and (c) condition 
assignment, as well as (d) other baseline variables that are likely associated with each outcome 
variable (for past 3-month frequency of NSSI at follow-up: lifetime history of NSSI (y/n), past-
month frequency of NSSI, past-year frequency of NSSI, and lifetime frequency of NSSI at 
baseline; for likelihood of NSSI post-intervention: all of these same variables plus past 3-
month frequency of NSSI at baseline; for past 3-month frequency of SI at follow-up: lifetime 
history of SI (y/n), past-month frequency of SI, past 3-month frequency of SI, past-year 
frequency of SI, and lifetime frequency of SI at baseline). Together, these variables (a - d) will 
be used to create the imputed datasets for each of our three hypotheses. 
 
The number of imputed datasets we create will equal the percentage of missing data for each of 
our three outcome variables (post-intervention likelihood of NSSI, post-intervention desire for 
future NSSI, past 3-month frequency at follow-up), rounded up to the next integer value. For 
example, we will create 7 imputed datasets to test hypothesis 1 if 6.2% of the data for the post-
intervention likelihood of NSSI outcome variable is missing. We will create 8 imputed datasets to 
test hypothesis 2 if 7.6% of the data for the post-intervention desire for future NSSI outcome 
variable is missing. 
 
Lastly, we will run all of our confirmatory tests for hypothesis 1 - 3 as outlined in this pre-
registration, within the original study data (i.e. removing/disregarding missing data in the original 
dataset and ultimately using non-imputed data). We will report the results in our supplementary 
materials for the present study. If main results/interpretations differ between analyses run within 
imputed vs. non-imputed data, we will report and discuss our paper’s discussion. 
 
Winsorizing Outliers 
 
We will winsorize outliers in our two count variables of interest (past 3-month frequency of NSSI 
at pre-intervention and follow-up) before running tests for hypothesis 2 and 3. Outliers will be 
defined as values above the 95th percentile for the variable in question, calculated using the 
Winsorize() function in the DescTools package in R (Singnorell et al., 2020, https://cran.r-

11 
project.org/web/packages/DescTools/DescTools.pdf); flagged outliers will be replaced with the 
95th percentile value. 
 
How many observations will be collected or what will determine the sample size? No 
need to justify decision, but be precise about exactly how the number will be determined. 
(optional) 
  
The final sample size for collected data will be N = 500 randomized participants (250 per 
condition) who meet our type 1 inclusion criteria (i.e. who are not excluded by type 1 exclusion 
criteria outlined in the above section). The total number of participants who provide usable data 
will then be determined by the number of participants who meet our type 2 inclusion criteria (i.e. 
who are not excluded by type 2 exclusion criteria outlined above). Therefore, the present study 
will have a maximum of 500 participants, with a final sample size determined by the number of 
participants meeting our type 2 inclusion criteria specified above. 
 
Other. Anything else you would like to pre-register? (e.g., secondary analyses, variables 
collected for exploratory purposes, unusual analyses planned?) (optional) 
 
Anticipated Sample Demographics 
 
The present study will recruit research participants via online social media advertisements. 
Based on samples recruited for other studies by members of this research team (using similar 
recruitment procedures; Fox et al., 2020, https://doi.org/10.1037/ccp0000486; Smith, Fox, 
Carter, Thoma, & Hooley, in press), we anticipate the following demographic representation 
among the present study’s research participants: 
 
65-75% White 
5-15% Asian 
5-15% Latinx 
5-15% Black 
 
The present investigators will recruit participants with the explicit goal of increasing racial 
diversity and representation among our research participants (see Roberts et al., 2020; 
https://doi.org/10.1177%2F1745691620927709). If we fail to meet the anticipated percentages 
for Asian, Latinx, or Black participants outlined above, we will specifically address lack of racial 
diversity as a potential limitation of generalizability within the text of the treatment results paper.   
 
Additionally, we plan to recruit approximately 50% sexual or gender minority (SGM)-identifying 
participants via online advertisements that target LGBTQ+ interests/groups as well as 
advertisements targeted toward the general teen population. 
 
Treatment Effects Among SSI Completers 
 

12 
An exploratory analysis will examine whether or not treatment assignment is a significant 
predictor of our primary outcome (i.e. NSSI frequency in the past 3 months at follow-up in SAVE 
vs. control conditions), among participants who have completed an SSI. To do this, we will run 
the same procedures as above for testing hypothesis 3—among the subsample of participants 
who completed a full online program (either Project SAVE SSI or the active control group SSI).   
 
Treatment Effects Among SGM-Identifying Youth 
 
An additional exploratory analysis of treatment effects within the specific subsample of SGM-
identifying youth will be conducted in future research. Additionally, PFS total mean and item-
level means will be reported specifically for these youth. These analyses will not be conducted 
nor reported in the present paper, and a more detailed pre-registration will be submitted at a 
later date. 
 
Additional Exploratory Analyses 
 
Additional secondary analyses will: 
 
(1) employ predictive modeling techniques to predict which individuals benefited from, vs. did 
not benefit from, the Project SAVE intervention program.  
(2) evaluate cross-sectional relationships between other variables collected within the SITBI-R 
and Self-Hate Scale (e.g. desire to stop future NSSI; likelihood of future NSSI; self-hatred). 
 
These analyses will not be conducted nor reported in the present paper, and a more detailed 
pre-registration will be submitted at a later date.