Evidence map›Paper›PMID 41660072›Full record

ArticlePeerJ2026

Human-centered AI to promote youth mental health: a serendipitous natural experiment enabled by a digital health platform.

Tarun Reddy Katapally, Nadine Elsahli, Sheriff Tolulope Ibrahim, Jasmin Bhawra

Abstract read
In one paragraph

Article in PeerJ, 2026. 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. Article
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

4 authors.

Tarun Reddy KatapallyDEPtH Lab, School of Health Studies, Faculty of Health Sciences, University of Western Ontario, London, Ontario, Canada.ORCID 0000-0001-5765-1435
Nadine ElsahliDEPtH Lab, School of Health Studies, Faculty of Health Sciences, University of Western Ontario, London, Ontario, Canada.ORCID 0009-0004-8039-6179
Sheriff Tolulope IbrahimDEPtH Lab, School of Health Studies, Faculty of Health Sciences, University of Western Ontario, London, Ontario, Canada.ORCID 0009-0001-5956-0972
Jasmin BhawraCHANGE Research Lab, School of Occupational and Public Health, Ryerson Polytechnic University, Toronto, Ontario, Canada.ORCID 0000-0001-9926-8442

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Health systems are struggling to deliver timely preventive care, particularly for marginalized populations, necessitating integration across health, education, and social services. For Indigenous youth in rural communities, fragmented services, isolation, and limited culturally safe options worsen mental health disparities. Interactive technologies, particularly human-centered artificial intelligence (AI)-enabled digital health platforms grounded in human-computer interaction (HCI), can enable remote interaction with citizens and decision-makers. This study investigated a serendipitous natural experiment to assess varying levels of platform nudging on Indigenous youth compliance in a longitudinal intervention. Method: This study emerged from the final year of a 5-year initiative embedding a culturally appropriate digital health intervention into school curricula in rural Indigenous communities. While the broader aim was to assess long-term mental health outcomes, an unexpected system disruption assessment of digital nudging on compliance. The platform featured two interfaces: a citizen-facing mobile app for ecological assessments and nudges, and a scientist dashboard for monitoring engagement and triggering nudges. Youth received three nudges: (1) daily system-triggered reminders to complete assessments, (2) weekly non-personalized messages ( Results: Compliance, measured by completed mobile ecological prospective assessments (mEPAs), varied significantly across most phases. Comprehensive nudging (Phase 1) yielded the highest completion rates and fastest response times, which declined following the removal of personalized scientist-triggered nudges. Loss of personalized scientist-triggered nudges had the most substantial impact on compliance. Conclusions: Consistent system-triggered reminders and personalized "Best Picture" nudges were most effective in sustaining compliance. Findings highlight the importance of integrating personalized, two-way communication features into digital health platforms to strengthen engagement in rural Indigenous communities. By enabling real-time interaction between youth and scientists, the platform supported integration across health, education, and research sectors. Its human-controlled backend and customizable citizen-facing interface reflect principles of human-centered AI, emphasizing trust and autonomy. This approach offers a scalable model for ethical, effective digital interventions that balance technological precision and participant agency.

Indexed as

Artificial IntelligenceHealth PromotionMental HealthAdolescentDigital HealthDigital MediaFemaleHumansMaleMobile ApplicationsRural PopulationDigital citizen scienceDigital transformation of health systemsHuman-computer interactionIndigenous youth healthSystems integration

Identifiers

PMID41660072
PMCPMC12875249

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.