Adverse Childhood Experiences and Building Resilience With the JoyPop App: Evaluation Study (Preprint)
Bibliographic record
Abstract
BACKGROUND The effects of adverse childhood experiences (ACEs) on mental health, self-regulatory capacities, and overall resilience are well-known. Given such effects, ACEs may play a role in how individuals adjust to challenges later in life. Of interest in this study is the transition to university, a time of heightened stress when adapting to circumstances is required and when those with ACEs may need additional in-the-moment support to exercise resilience. A smartphone app may provide a worthwhile and readily accessible medium for a resilience intervention, provided behavioral outcomes are adequately evaluated. OBJECTIVE This study evaluates the impact of an innovative, smartphone app–based resilience intervention. The JoyPop app was designed to promote resilience through the use of self-regulatory skills such as emotion regulation and executive functioning. Among a sample of first-year undergraduate students, we explored whether use of the app would be associated with positive changes in resilience and related outcomes, and whether these benefits were influenced by level of childhood adversity. METHODS Participants (N=156) were requested to use the JoyPop app for 4 weeks, at least twice daily. Changes in resilience, emotion regulation, executive functioning, and depression were assessed after 2 and 4 weeks of app usage using multilevel modeling. RESULTS The sample of 156 participants included 123 females and 33 males, with a mean age of 19.02 years (SD 2.90). On average participants used the app on 20.43 of the possible 28 days (SD 7.14). App usage was associated with improvements in emotion regulation (χ21=44.46; P<.001), such that it improved by 0.25 points on the 18-point scale for each additional day of app usage, and symptoms of depression (χ21=25.12; P<.001), such that depression symptoms were reduced by .08 points on the 9-point scale with each additional day of app usage. An interaction between ACEs and days of app usage existed for emotion regulation, such that participants with more adversity evidenced a faster rate of change in emotion regulation (P=.02). CONCLUSIONS Results highlight that daily incorporation of an app-based resilience intervention can help youth who have experienced adversity to improve emotion regulation skills and experience reductions in depression. The JoyPop app represents an important step forward in the integration of resilience intervention research with a technology-based medium that provides in-the-moment support.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".