The Grow It! app—longitudinal changes in adolescent well-being during the COVID-19 pandemic: a proof-of-concept study
Bibliographic record
Abstract
Adolescent mental health and well-being have been adversely impacted by the COVID-19 pandemic. In this preregistered longitudinal study, we evaluated whether adolescents' well-being improved after playing the multiplayer serious game app Grow It! During the first lockdown (May-June 2020), 1282 Dutch adolescents played the Grow It! app (age = 16.67, SD = 3.07, 68% girls). During the second lockdown (December-May 2020 onwards), an independent cohort of 1871 adolescents participated (age = 18.66, SD = 3.70, 81% girls). Adolescents answered online questionnaires regarding affective and cognitive well-being, depressive symptoms, anxiety, and impact of COVID-19 at baseline. Three to six weeks later, the baseline questionnaire was repeated and user experience questions were asked (N = 462 and N = 733 for the first and second cohort). In both cohorts, affective and cognitive well-being increased after playing the Grow It! app (t = - 6.806, p < 0.001; t = - 6.77, p < 0.001; t = - 6.12, p < 0.001; t = - 5.93, p < 0.001; Cohen's d range 0.20-0.32). At the individual level, 41-53% of the adolescents increased in their affective or cognitive well-being. Adolescents with higher risk profiles (i.e., more depressive symptoms, lower atmosphere at home, and more COVID-19 impact) improved more strongly in their well-being. Positive user evaluations and app engagement were unrelated to changes in affective and cognitive well-being. This proof-of-concept study tentatively suggests that Grow It! supported adolescents during the pandemic.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".