Building resilience through daily smartphone app use: results of a pilot study of the JoyPop app with social work students
Bibliographic record
Abstract
Background The JoyPop™ smartphone app is a digital intervention designed to enhance day-to-day resilience in youth, particularly those exposed to traumatogenic events [adverse childhood experiences (ACEs)]. Processes of adaptation that foster resilience in response to high stress include affect, cognitive, and behavioral regulation, and social interaction. Digital interventions have application for youth and those who provide them support, including social work trainees navigating the stressors of university studies concurrent with practice internships. Research on resilience-enhancing apps is needed to understand the underlying mechanisms by which change occurs and who is most likely to benefit from these interventions. Methods Social work student participants (N = 91) were invited to use the JoyPop app two times daily for 28 days. Baseline ACE exposure and change-over-time in affect regulation, stress responsivity, and social support were evaluated after 2 and 4 weeks of app use witht-tests and generalized estimating equation (GEE) modeling. Results Participants identified predominantly as cisgender women of European descent, mean age 26 years (SD = 6.78), 70% undergraduates, and reported consistent daily app use (Mean days = 26.9, SD = 1.90). Self-reported baseline ACE exposure was high (30% ≥ 5+). We tested change-over-time with generalized estimating equation and saw improvement in affect regulation in the Abbreviated Dysregulation Inventory scale (β = −3.38,p = <.001), and subscales of behavioral (β = −1.63,p = <.001), affect (β = −3.24,p = <.001), and cognitive regulation (β = 1.50,p = .009). Perceived stress decreased with app use (β = −2.65,p = <.001) and even more so for participants with reported exposure to more than 4 ACEs (β = −3.786,p = .030). Conclusions The exploratory findings from our pilot study suggest that consistent use of the app may enhance multidimensional resilience amongst university students who self-report higher than average levels of baseline traumatogenic exposures. Our findings support an approach modeling resilience as a complex, dynamic, multicomponent process supported by resources within and between individuals. Further testing of the mechanisms of adaptation in response to high stress that enhance resilience and identification of the JoyPop™ app features that influence this change is needed to validate that daily app use could help youth with experiences of past and current high stress to better regulate their affect, reduce stress reactivity, and increase resilience.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".