Development of the Niggle App for Supporting Young People on Their Dynamic Journey to Well-being: Co-design and Qualitative Research Study
Bibliographic record
Abstract
BACKGROUND: Adolescence is a life stage characterized by intense development and increased vulnerability. Yet, young people rarely seek help for mental health, often due to stigma and embarrassment. Alarmingly, even those who do seek help may not be able to receive it. Interventions focused on well-being offer a protective factor against adversity. Highly effective, innovative, theoretically sound, accessible, and engaging mobile health (mHealth) interventions that can be used to look beyond mental ill-health and toward mental well-being are urgently needed. OBJECTIVE: We aimed to explore how young Australians conceptualize and construct recovery journeys from feeling unwell to being well in order to inform the conceptual design of a youth-led information-, resource-, and support-focused mHealth intervention. METHODS: A sample of young people, grouped by age (12-15 years, 16-19 years, and 20-25 years), took part in 3 in-person participatory design workshops (per group). Young people's understanding and representation of well-being, feeling unwell, and the recovery journey were investigated using visual and linguistic data collection methods: photo elicitation and journey mapping. A social constructionist perspective was used for thematic analysis to produce a conceptual model of the recovery journey. A mobile app was co-designed and all app functions were mapped through iterative development and testing by young people and a team of psychology, research, design and information technology experts. RESULTS: Young people (n=25) described a 6-stage journey with specific barriers and coping strategies. The findings, when situated within the personal recovery framework in mental health, emphasize the cyclic and iterative model of change. Through co-design, the new app-Niggle-was conceptualized as a visual representation of an amorphous problem, which can be addressed through app functions corresponding to the most helpful strategies that young people used to progress through the stages of their recovery journey. CONCLUSIONS: Niggle is available to offer support to young people for a range of problems and provides a hot link to counseling services in Australia. This paper elaborates on the process of in-depth qualitative data collection through visual, linguistic, and co-design methods. The findings of this study give insight into young people's understanding of well-being and recovery. This paper could aid the development of high-quality personalized mHealth interventions and support resources.
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 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 | 0.024 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".