Actionable companion · Motivation & engagement

Learning with agency: Exploring the Perceptions of mHealth Interventions for the Prevention of Common Mental Disorders in University Students in Singapore: Qualitative Study

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Background Mental health interventions delivered through mobile health (mHealth) technologies can increase the access to mental health services, especially among university students. The development of mHealth intervention is complex and needs to be context sensitive. There is currently limited evidence on the perceptions, needs, and barriers related to these interventions in the Southeast Asian context. Objective This qualitative study aimed to explore the perception of university students and mental health supporters in Singapore about mental health services, campaigns, and mHealth interventions with a focus on conversational agent interventions for the prevention of common mental disorders such as anxiety and depression. Methods We conducted 6 web-based focus group discussions with 30 university students and one-to-one web-based interviews with 11 mental health supporters consisting of faculty members tasked with student pastoral care, a mental health first aider, counselors, psychologists, a clinical psychologist, and a psychiatrist. The qualitative analysis followed a reflexive thematic analysis framework. Results The following 6 main themes were identified: a healthy lifestyle as students, access to mental health services, the role of mental health promotion campaigns, preferred mHealth engagement features, factors that influence the adoption of mHealth interventions, and cultural relevance of mHealth interventions. The interpretation of our findings shows that students were reluctant to use mental health services because of the fear of stigma and a possible lack of confidentiality. Conclusions Study participants viewed mHealth interventions for mental health as part of a blended intervention. They also felt that future mental health mHealth interventions should be more personalized and capable of managing adverse events such as suicidal ideation.

What kind of evidence is this?

Conversational & pedagogical agents · Evidence synthesis

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

This study comprised 6 focus group discussions (FGDs) with university students and 11 interviews with mental health supporters via the videoconferencing platform, Zoom (Zoom Video Communications, Inc). The advertisements for the study were distributed via a social media group for students at a university in Singapore. For the interviews, email invitations to close contacts of the research team and snowball sampling were used. All participants gave their informed consent.

Limitations and uncertainty · source passage

This study involved various stakeholders from potential users to mental health supporters from various professions, including faculty members, counselors, psychologists, a clinical psychologist, and a psychiatrist. Although the study used purposeful sampling to gather views and opinions from various mental health professionals, these perspectives were valuable to understanding the various unique systems and how the students engaged these systems for mental health support. The convergence of opinions among mental health professionals suggested agreement between the various professions on the needs and barriers to help-seeking behavior among university students. Opinions on the mHealth intervention between students and mental health supporters also largely converged with more detailed therapeutic concepts used by supporters compared with those of students. Having external facilitators also helped reduce the confirmation bias by not emphasizing specific features that the research team was currently exploring and instead allowing open discussion between the facilitators and FGD participants. However, because both facilitators and FGD participants were not experts in the field of mHealth intervention design, there was also a lack of depth in the discussion of the pros and cons of specific features mentioned during the discussion. However, current findings are limited to the context of mental health support among university students and generalizability is limited owing to the qualitative nature of the study.

Design the system before the pilot

Define whether the agent asks questions, gives hints, explains or assesses. Test hallucinated feedback, excessive praise, misleading confidence and responses when the learner is confused. A conversational interface alone does not establish autonomous agency.

Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

A 90-day district pilot

Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Alicia Salamanca-Sanabria; Ahmad Ishqi Jabir; Xiaowen Lin; Aishah Alattas; A Baki Kocaballi; Jimmy Lee; Tobias Kowatsch; Lorainne Tudor Car. Exploring the Perceptions of mHealth Interventions for the Prevention of Common Mental Disorders in University Students in Singapore: Qualitative Study. Journal of Medical Internet Research (2023-03-20). https://doi.org/10.2196/44542

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.