Evidence map›Paper›PMID 41100172›Full record

ArticleJMIR pediatrics and parenting2025

Real-World Symptom Trajectories in Adolescents With and Without Suicide Risk Receiving Care from Rula Health: Retrospective Study.

Lara Baez, Kelsey L McAlister, Douglas Newton, Sam Seiniger, Allie Woodhouse, Jennifer Huberty

Abstract read
In one paragraph

Article in JMIR pediatrics and parenting, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Trial
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Lara BaezFit Minded Inc, Phoenix, AZ, United States.ORCID https://orcid.org/0000-0003-3342-2325
Kelsey L McAlisterFit Minded Inc, Phoenix, AZ, United States.ORCID https://orcid.org/0000-0003-1548-4936
Douglas NewtonRula Health, Santa Clara, CA, United States.ORCID https://orcid.org/0000-0002-7382-8488
Sam SeinigerRula Health, Santa Clara, CA, United States.ORCID https://orcid.org/0009-0009-3777-2948
Allie WoodhouseRula Health, Santa Clara, CA, United States.ORCID https://orcid.org/0009-0004-5127-0913
Jennifer HubertyFit Minded Inc, Phoenix, AZ, United States.ORCID https://orcid.org/0000-0002-0276-4640

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMore than 5 million US adolescents experience mental or behavioral health conditions, yet two-thirds remain untreated, and suicide is the second leading cause of death. These gaps highlight the urgent need for accessible care. Digital mental health interventions that integrate measurement-based care (MBC) and personalized mental health care provider matching offer a promising solution, but few studies have examined their real-world impact among adolescents at elevated suicide risk.

objectiveThis study aims to evaluate symptom improvements among adolescents with and without elevated suicide risk receiving care from Rula Health, an MBC-based digital mental health intervention with personalized intake through mental health care provider matching. We aimed to (1) compare baseline demographic and clinical characteristics between adolescents with and without elevated suicide risk at intake and (2) examine depression and anxiety symptom trajectories over the first 12 visits between adolescents with and without elevated suicide risk at intake.

methodsWe conducted a retrospective analysis of real-world clinical data from adolescents who received mental health services through Rula Health. Adolescents were classified as no suicide risk or elevated suicide risk based on the Columbia-Suicide Severity Rating Scale. Depression and anxiety symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder-7 (GAD-7) at baseline and before each session. Minimal clinically important differences were defined as a reduction of 5 more points for PHQ-9 and 4 or more points for GAD-7. Symptom changes were examined up to 12 visits. We used t tests and chi-square tests to compare baseline characteristics between suicide risk groups and linear mixed-effects models (adjusted for demographics and clinical factors) to assess symptom change and trajectory differences over time.

resultsThe sample included 3533 adolescents in the no suicide risk group and 2712 in the elevated suicide risk group. The elevated suicide risk group had a greater proportion of female adolescents, younger adolescents (P<.001), non-Hispanic individuals (P=.002), and those with a primary depressive diagnosis, comorbid conditions, psychiatric involvement, and higher baseline PHQ-9 and GAD-7 scores (P<.001). The no suicide risk group attended more sessions and stayed in care longer (P<.001). Depression and anxiety scores decreased over visits (PHQ-9: B=-0.39; P<.001; GAD-7: B=-0.35; P<.001), with average improvements exceeding minimal clinically important difference thresholds. The elevated suicide risk group's depression and anxiety symptoms decreased at a higher rate than those of the no suicide risk group (PHQ-9: B=-0.32; P<.001; GAD-7: B=-0.18; P<.001).

conclusionsAdolescents with elevated suicide risk showed greater and faster improvement in depression and anxiety symptoms, reaching similar levels as those without elevated suicide risk by 12 visits. Rula Health's model can support high-risk youth in real-world settings. Future research should assess the impact of MBC and mental health care provider matching, including study designs that isolate their specific effects on outcomes.

Indexed as

digital therapymeasurement-based caresuicidalityteenagerstelehealthyouth

Identifiers

PMID41100172
PMCPMC12572749

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.