Implementing Digital Tools for Mental Health Support in Young Individuals in Colombia: Mixed Methods Feasibility Study
Bibliographic record
Abstract
Background: The growing prevalence of mental health disorders among young people is a pressing global concern, particularly in low- and middle-income countries where access to care is limited. Digital tools, leveraging Information and Communication Technologies, offer promising approaches to bridge these gaps. Objective: This study evaluated the feasibility of 2 digital mental health tools-Youth Collective Minds (YMC), a web-based platform, and Mental Beat (MB; Avicenna Research), a smartphone app-targeted at young individuals aged 18-25 years in Bogotá, Colombia. Methods: Participants (N=35) engaged with both platforms over 3 weeks in this mixed methods feasibility study, which incorporated thematic analysis with a deductive framework for qualitative data. Univariate analyses were performed to examine baseline patterns and data distributions, while bivariate analyses were conducted to investigate relationships and associations between variables, providing a comprehensive evaluation of the platforms' feasibility in the acceptability, demand, implementation, and practicality domains. Results: Participants were primarily women (22/35, 63%) with a median age of 23 (IQR 21-24) years. A total of 1308 annotations were coded: acceptability (annotations=707), demand (annotations=116), implementation (annotations=276), and practicality (annotations=209). Participants highlighted YMC's psychoeducational resources and MB's ease of use as strengths. However, technical issues, including server malfunctions and insufficient feedback, impacted engagement. Quantitatively, 83% (29/35) expressed willingness to reuse YMC and 83% (29/35) MB. Sensor data from MB indicated significant associations between psychological distress and smartphone usage. Participants with higher psychological distress showed greater median battery charging of 585 (IQR 321-615) compared to those without distress, 188 (IQR 42-309; P=.04). Poor sleep quality was also associated with increased median battery discharge of 2867 (IQR 1697.5-3935.5) compared to participants who reported sufficient sleep, 556 (IQR 200-2968; P=.003). GPS data showed that participants who visited more unique locations had lower psychological distress scores, with a negative correlation (r=-0.424; P=.05). In terms of platform usage, in YMC, surveys on emotions (30/35, 86%) and stress (28/35, 80%) were the most frequently completed, while telecounseling services were underused, with only 8.6% (3/35) of participants accessing mental health telecounseling. In MB, surveys of positive emotions (97.1%) and relationships (97.1%) were answered by more than 90% (32/35) of participants. Conclusions: This study demonstrated the feasibility and acceptability of digital tools for mental health support among Colombian youth, suggesting that these tools promote self-awareness and mental health management but require technical refinements to enhance engagement. The study's limitations, including a small sample size and short duration, underscore the need for broader research. Implementing participant feedback, strengthening cybersecurity, and scaling these tools could address mental health challenges in low- and middle-income countries, where such interventions are critically needed. These digital platforms represent promising steps toward bridging gaps in mental health care access.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".