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Record W4409205733 · doi:10.2196/66558

Co-Designing, Developing, and Testing a Mental Health Platform for Young People Using a Participatory Design Methodology in Colombia: Mixed Methods Study

2025· article· en· W4409205733 on OpenAlexvenueno aff
Laura Ospina‐Pinillos, Débora L. Shambo-Rodríguez, Mónica Natalí Sánchez-Nítola, Laura C Gallego-Sanchez, María Isabel Riaño-Fonseca, Andrea Carolina Bello-Tocancipá, Álvaro Andrés Navarro-Mancilla, Jaime A. Pavlich‐Mariscal, Alexandra Pomares Quimbaya, Carlos Gómez–Restrepo, Ian B. Hickie, Jo‐An Occhipinti

Bibliographic record

VenueJMIR Human Factors · 2025
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintMental healthCitizen journalismPsychologyComputer scienceEngineeringWorld Wide WebPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Globally, mental health (MH) problems increasingly affect young people, contributing significantly to disability and disease. In low- and middle-income countries, such as Colombia, barriers to accessing care exacerbate the treatment gap. In addition, the lack of widespread digital interventions further deepens the digital health divide between the Global North and Global South, limiting equitable access to innovative MH solutions. OBJECTIVE: This study aims to co-design and develop an MH platform using participatory design methodologies and conduct a 15-month naturalistic observational trial to assess its feasibility among Colombian youth. METHODS: This study used a mixed methods approach within a structured research and development cycle. To ensure a user-centered design, we began with a series of co-design workshops, where stakeholders collaboratively identified key user needs. Following this, usability testing was conducted in 2 stages, alpha and beta, using the System Usability Scale (SUS) to assess functionality and user experience. To capture real-world interactions, a naturalistic observational trial ran from July 2022 to October 2023, collecting data on user engagement and system performance. This study integrated quantitative and qualitative analyses. RESULTS: A total of 146 individuals participated in the co-design process, with 110 (75.3%) contributing to the development of platform components and 36 (24.7%) participating in usability testing. The co-designed platform integrated several key features, including social media and advertising, an MH screening tool, registration, targeted psychoeducational resources, automated tailored recommendations, and a "track-as-you-go" feature for continuous MH monitoring. Additional elements included user-friendly follow-up graphs, telecounseling integration, customizable well-being nudges, an emergency button, and gamification components to enhance engagement. During usability testing, the beta prototype received a median SUS score of 85.0 (IQR 80-92.5), indicating high usability. In the subsequent observational trial, which ran from July 2022 to October 2023, a total of 435 users interacted with the platform-314 (72.2%) as registered users and 121 (27.8%) anonymously. Emotional distress was prevalent, with 63.7% (200/314) of the registered users and 61.2% (74/121) of the anonymous users reporting distress, as measured by the 6-item Kessler Psychological Distress Scale. Despite 102 users requesting telecounseling, only 26.5% (27/102) completed a session. While usability scores remained high, engagement challenges emerged, with only 18.8% (59/314) of the users continuing platform use beyond the first day. CONCLUSIONS: This study explored the development and user experience of a youth MH platform in Colombia, demonstrating that a cocreation approach is both feasible and effective. By actively involving users throughout the design process, the platform achieved high usability and incorporated features that resonated with its target audience. However, sustaining long-term engagement remains a challenge, as does addressing privacy concerns, particularly for younger users. These findings highlight the importance of continuous user-centered refinement to enhance both accessibility and retention in digital MH interventions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.534
GPT teacher head0.589
Teacher spread0.055 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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