Advancing Student Suicide Interventions With Scalable Technologies (ASSIST)
Phase: NA
Conditions: Suicidal Ideation, Suicide Attempt, Self-Harm
Sponsor: University of Massachusetts, Amherst

 
 
ASSIST: Advancing Student Suicide Interventions with 
Scalable Technologies  
MPIs: Katherine Dixon-Gordon, Ph.D., Brooke Ammerman 
Grant Number: P50MH129701 subaward 
ClinicalTrials.gov: NCT07211373, Protcol ID: UMassIRB6366 
Most recently renewed by UMass IRB #6366, expiration 3/3/2026 
Updated 10/9/2025 
 
 
 

 
Study Protocol and Data Analytic Plan 
 
1. Brief Description of Protocol 
 
1.1 Protocol Overview 
 
Suicide is a leading cause of death for college-aged individuals,1 yet student health centers 
struggle to keep pace with the rise in behavioral health issues.2 Mobile-based applications, such 
as JasprHealth, can deliver evidence-based skills intended to reduce imminent suicide risk (e.g., 
reducing means access), improve emotional states (e.g., via distraction and coaching to act 
opposite to emotions), and reduce feelings of social isolation (e.g., via shared stories). Although 
mobile-device-delivered interventions hold the potential to make interventions widely accessible, 
user engagement presents a substantial barrier to efficacy.3,4 The proposed study, ASSIST: 
Advancing Student Suicide Interventions with Scalable Technologies, aims to improve 
engagement with mobile-delivered suicide prevention applications, with the ultimate goal of 
reducing suicidal thoughts and behaviors in college students. Including human elements 
alongside JasprHealth has the potential to improve the uptake of this evidence-based, accessible 
mobile-device-delivered intervention.5,6  
 
The aim of this study was to examine the effects of the technological application resource Jaspr 
vs. Jaspr+ human augmentation (e.g., motivationally focused orientation plus prompts) on 
acceptability, preliminary effectiveness, and engagement among 50 college students who screen 
positive for suicide risk (n=25 per condition) over the course of 2 months. Candidate mechanisms 
(e.g., coping skills, self-stigma) will also be assessed. Participants were randomized via the 
Redcap randomization module7, stratified by site.  
 
1.2 Methods Summary 
 
Self-report measures will be assessed at baseline, week 4, week 8, along with weekly for some 
secondary measures.  
 
Table 2. Aim 2 and 3 Jaspr Implementation Outcomes 
Construct  
Definition 
Measure 
Time point 
Effectiveness 
Effectiveness of Jaspr+ vs. Jaspr 
1. Suicide Ideation Questionnaire (SIQ8) – past month 
2. C-SSRS95,96 self-report screener past month (lifetime 
at baseline) and past week (weekly for Weeks 0-4) 
✓ Baseline 
✓ Weekly 
✓ 4wk 
✓ 8wk 
1. Candidate mechanisms: 17-item suicide-related 
coping,9 ways of coping,10 16-item personal suicide 
stigma questionnaire11 (PSSQ), and self-reported 
engagement 
2. Acceptability of Implementation Measure (AIM)12 - 4-
item measure to determine whether Jaspr is liked, 
appealing, welcomed and approved (patient 109) 
3. Client Satisfaction Questionnaire – 8 (CSQ813–15) for 
patients 
 

2. Data Analytic Plan 
2.1 Preliminary Analyses 
Preliminary analyses will examine descriptive data to examine distributional properties of each 
variable. Transformations (e.g., log transformations) will be applied for variables not meeting 
normality assumptions. Alternative models (e.g., logistic, Poisson, negative binomial) will be used 
for variables that are not sufficiently normalized by these transformations and for count and 
dichotomous outcomes, as appropriate. Extreme outliers will be considered and adjusted if 
necessary. 
 
We will consider data missingness. If the data are in fact missing not completely at random, we 
may only include the ITT sample that also completed at least one of the timepoints. If data are 
MAR, we will use Full Information Maximum Likelihood Estimation (FIML),16 
 
Covariates will be considered per analysis. We will consider including covariates that show 
associations with the dependent variables (DVs) in analyses17 particularly if they differ across 
conditions, including demographic variables. Given the small sample size, nonsignificant 
variables in models may be trimmed from final model reporting. 
2.2 Primary Analyses 
Given that most of these outcomes will be assessed at Weeks 0 (baseline), 4, and 8, along with 
secondary measures weekly from Weeks 0-4, for most primary analyses we will plan to use mixed 
effects models. We plan to evaluate whether the data are appropriate for use of multilevel models 
which account for the interdependence of multiple repeated measures within individuals, using 
full maximum likelihood estimation. Consistent with recommended practices,18 we will consider a 
series of models to test how to consider time, levels, and which (if any) random effects to include. 
If such models do not accurately capture the data, we may resort to single-level regressions. In 
addition, it is important to consider variability in the group clusters, which is a key strength of using 
multilevel model analyses to model complex change over time. Alpha will be set to .05 for primary 
pre-specified outcomes without error correction given the small sample size. 
2.2.1 Primary Outcomes 
We will examine the effect of condition (Jaspr vs. Jaspr+) on effectiveness on the SIQ and 
CSSRS. We will also examine the effect of time overall in either condition on effectiveness 
outcomes. We may also examine change slopes within each condition and test how these slopes 
differ. 
2.2.2 Secondary Outcomes 
We will examine the effect of condition (Jaspr vs. Jaspr+) on effectiveness on coping, suicide-
related self-efficacy, and suicide stigma, as well as perceived engagement. We will also examine 
the effect of time overall in either condition on effectiveness outcomes. We may also examine 
change slopes within each condition and test how these slopes differ. 
 
As described above, where appropriate we will leverage the repeated measures nature of these 
data to increase power, but may collapse across levels of data if appropriate to best model 
variability (e.g., we may create difference scores for startle outcomes across categories; we may 
aggregate counts of self-injury or attempts post treatment as a single level outcome rather than 
modeling change via a latent growth curve model if insufficient fluctuation in these outcomes is 
found). Mixed effects models will test the change across time in self-reported affect, skin 
conductance, and heart rate variability in the paradigms over treatment, and will compare these 
time effects across conditions. 
 

2.3 Additional Analyses 
Additionally, we will consider sex (if adequately powered), and other demographic characteristics 
(ethnic, racial, gender, and sexual identities, age) and psychopathology as moderators of 
treatment response.  
 
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