The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
The Alongside Digital Wellness Program for Youth: Longitudinal Pre-Post Outcomes Study
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Abstract
Background: Youth are increasingly experiencing psychological distress. Schools are ideal settings for disseminating mental health support, but they are often insufficiently resourced to do so. Digital mental health tools represent a unique avenue to address this gap. The Alongside digital program is one such tool, intended as a universal prevention and early intervention. The platform includes social-emotional learning and self-help wellness features as well as an artificial intelligence–powered chatbot designed to build coping skills. Objective: This evaluation aimed to examine the near-term impact of Alongside app use on students’ self-reported mental health outcomes. Methods: We conducted a nonrandomized pilot pragmatic evaluation leveraging anonymized user data. All data came from current Alongside users attending public middle and high schools in Texas and New Mexico, between 10 and 18 years old. Schools were actively engaged in partnership with Alongside and approved all procedures. Users were asked to complete mental health questionnaires upon app registration and at 1 and 3 months post registration. We conducted preregistered analyses as well as exploratory analyses to determine how symptoms changed over time and what factors (eg, demographic and app use) predicted changes in distress. Results: Analyses revealed statistically significant within-person decreases in overall distress (Young Person’s CORE; primary outcome) from baseline to 1 month with a small effect size (t42=2.21, P=.03, r=0.34); however, there was no evidence that scores significantly decreased from baseline to 3 months (W=1821, n=85, P=.16). We found that at 3 months, identifying as part of the lesbian, gay, bisexual, transgender, queer, and questioning community predicted greater decreases in distress; otherwise, no demographic factors were significant predictors. In a nonregistered exploratory analysis of a subsample of users who reported elevated distress at baseline, decreases in distress were seen at both 1 month (W=128, n=20, P=.02, r=0.52) and 3 months (W=682, n=42, P=.004, r=0.45). Conclusions: There may be short-term benefits associated with using the Alongside digital program. Further studies are required to determine potential preventative effects.
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Alongside is a multi-component platform combining social-emotional learning content with an AI chatbot, so the effect cannot be attributed to the conversational agent specifically.
“Alongside is also a multi-component platform combining social-emotional learning content with an AI chatbot, so the effect cannot be attributed to the conversational agent specifically.”
From Artificial intelligence and immersive technologies for LGBTQ+ youth: a narrative review (2026) · cites this paper as Cohen et al., 2025
The Alongside finding is suggestive rather than conclusive because the design was a single-arm pre-post evaluation with no control condition, the LGBTQ subgroup comprised 11 of 61 users, and the one-month test had six degrees of freedom.
“The finding is suggestive rather than conclusive: the design was a single-arm pre-post evaluation with no control condition, the LGBTQ subgroup comprised 11 of 61 users, and the one-month test had six degrees of freedom.”
From Artificial intelligence and immersive technologies for LGBTQ+ youth: a narrative review (2026) · cites this paper as Cohen et al., 2025
In an exploratory, non-preregistered subgroup analysis, LGBTQ identification predicted greater reductions in distress at one and three months among users with clinically elevated baseline distress in the Alongside program.
“In an exploratory, non-preregistered subgroup analysis, LGBTQ identification predicted greater reductions in distress at one and three months among users with clinically elevated baseline distress.”
From Artificial intelligence and immersive technologies for LGBTQ+ youth: a narrative review (2026) · cites this paper as Cohen et al., 2025
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Cites
Colizzi, M., Lasalvia, A., & Ruggeri, M. (2020) · International Journal of Mental Health Systems · doi:10.1186/s13033-020-00356-9 · Cited by 727
Gruber, J., Prinstein, M. J., Clark, L. A., Rottenberg, J., Abramowitz, J. S., Albano, A. M., Aldao, A., Borelli, J. L., Chung, T., Davila, J., et al. (2021) · American Psychologist · doi:10.1037/amp0000707 · Cited by 697
Self-citationToussaint, A., Hüsing, P., Gumz, A., Wingenfeld, K., Härter, M., Schramm, E., & Löwe, B. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.01.032 · Cited by 641 · cited 1×
“Scores are summed and range from 0 to 6, with higher scores indicating greater levels of anxiety [23].”
Duong, M. T., Bruns, E. J., Lee, K., Cox, S., Coifman, J., Mayworm, A., & Lyon, A. R. (2021) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-020-01080-9 · Cited by 444
A dual-factor model of mental health: Toward a more comprehensive understanding of youth functioning
Antaramian, S. P., Huebner, E. S., Hills, K. J., & Valois, R. F. (2010) · American Journal of Orthopsychiatry · doi:10.1111/j.1939-0025.2010.01049.x · Cited by 405
Evaluation of the PHQ-2 as a Brief Screen for Detecting Major Depression Among Adolescents
Richardson, L. P., Rockhill, C., Russo, J. E., Grossman, D. C., Richards, J., McCarty, C., McCauley, E., & Katon, W. (2010) · Pediatrics · doi:10.1542/peds.2009-2712 · Cited by 377
Use of Digital Mental Health for Marginalized and Underserved Populations
Schueller, S. M., Hunter, J. F., Figueroa, C., & Aguilera, A. (2019) · Current Treatment Options in Psychiatry · doi:10.1007/s40501-019-00181-z · Cited by 268
Young People Seeking Help Online for Mental Health: Cross-Sectional Survey Study
Pretorius, C., Chambers, D., Cowan, B., & Coyle, D. (2019) · JMIR Mental Health · doi:10.2196/13524 · Cited by 143
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Research works printed but not linked: 21
- 10.1016/j.chc.2023.02.002 · DOI
- 10.1186/1471-244X-10-113 · DOI · Paper page
- 10.1146/annurev-publhealth-040119-094247 · DOI
- 10.1007/s10567-024-00487-2 · DOI
- 10.1086/704958 · DOI
- 10.1016/S2215-0366(14)70312-8 · DOI · Paper page
- 10.1016/j.childyouth.2023.107023 · DOI
- Panchal, N, 2022, Encyclopedia of Health Services Research, 6
- Hoover, S, 2023, Scanlan Center for School Mental Health Practice Briefs, 1
- 10.2196/13050 · DOI
- 10.1016/j.jadohealth.2020.06.008 · DOI · Paper page
- 10.1055/s-0042-108647 · DOI
- 10.1080/15374416.2019.1683852 · DOI · Paper page
- 10.1080/14733140902979722 · DOI
- 10.1007/s10597-013-9619-1 · DOI
- 10.1177/0164027504268574 · DOI · Paper page
- 10.1080/15374416.2017.1405353 · DOI
- 10.1111/camh.12128 · DOI
- 10.1037/ccp0000505.supp · DOI
- 10.1007/s12310-023-09598-7 · DOI
- 10.1001/jamapediatrics.2023.1473 · DOI
Cited by
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Artificial intelligence and immersive technologies for LGBTQ+ youth: a narrative review
Shekarchi, R., Eleuteri, S., Saladino, V., Calaresi, D., Giordano, F., & Prunas, A. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.2002054
Metadata
- Authors
- Cohen, K., Rapoport, A., Friis, E., Hill, S., Feldman, S., & Schleider, J.
- Venue
- JMIR Formative Research
- Publisher
- JMIR Publications Inc.
- Published
- 2025-10-08
- Volume
- 9
- Pages
- e73180
- ISSN
- 2561-326X
- PMC
- PMC12547339
- Cited by
- 9
- References
- 8
- Language
- en
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