The Role of Practitioner- and User-Set Goals in Engagement and Psychological Distress Among Kooth Digital Health Users: Retrospective Analysis
Bibliographic record
Abstract
BACKGROUND: Youth and young adult mental health concerns are rising globally, with digital mental health platforms offering a promising solution for accessible support. Among the various features these platforms provide, goal setting and achievement have been shown to positively influence behavior change and mental health outcomes. However, there is limited understanding of how user-set goals compare to those set collaboratively with a practitioner regarding their impact on user engagement and mental health outcomes in digital mental health platforms. OBJECTIVE: The purpose of this study was to examine the relationship between various goal-related variables (eg, the number of goals created and progress in user-set and practitioner-set goals) and user engagement as well as mental health (ie, psychological distress) on a free digital mental health platform. A secondary exploratory aim was to assess how different user-presenting issues were associated with platform engagement. METHODS: We leveraged secondary data from a free, web-based mental health platform for youth aged 10 to 25 years in the United Kingdom that offers goal-setting features, emotional journaling, peer support, asynchronous chat with practitioners, and various self-guided well-being activities. Data included in the analyses were from youth and young adults (mean age 15.84 years, SD 2.88; 522/691, 75.5% female) who engaged with the goal-setting feature and completed both pre- and postengagement psychological distress measures between January 2020 and December 2023. We examined the relationship between user-set goals and practitioner-set goals on user engagement and psychological distress via linear regressions. The impact of different user-presenting issues on engagement was also explored via linear regression. RESULTS: The number of practitioner-set goals created was positively associated with platform engagement (β=.16; P<.001), whereas the number of self-set goals and goal progress, whether self or practitioner set, were not. Progress on practitioner-set goals was significantly associated with reduced psychological distress (β=-.27; P<.001), while progress on self-set goals showed no significant association (P=.16). Physical health-related and school-related presenting issues were the strongest predictors of increased platform engagement (β=.21; P<.001 and β=.17; P<.001, respectively). CONCLUSIONS: These findings underscore the importance of collaborative goal setting in improving mental health outcomes for youth and young adults on digital mental health platforms. By highlighting the role of guided support and goal progression, this study enhances our understanding of how digital mental health platforms can better support young people's mental health and well-being. This paper also highlights how digital mental health platforms can serve as a valuable resource for addressing a wide range of mental health needs.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".