The Effects of a Smartphone App (Feelee) to Enhance Adolescents’ Emotion Regulation Skills in a Forensic Outpatient Setting: Protocol for a Multiple Single-Case Experimental Design
Bibliographic record
Abstract
BACKGROUND: Difficulties in emotion regulation are a significant contributing factor to delinquent behavior in adolescence. These adolescents struggle with recognizing, comprehending, and controlling emotions, which impedes the effectiveness of current forensic treatments. In addition, forensic care often faces challenges regarding treatment engagement due to a lack of motivation and difficulties building an alliance between clients and caregivers. The use of Feelee, an app that collects and displays active and passive data, is promising to support adolescents in obtaining more insight into their emotion regulation abilities. Furthermore, the integration of smartphone apps, like Feelee, offers new perspectives to increase adolescents' engagement and adherence to treatment. OBJECTIVE: This study presents the research protocol for evaluating the initial effects of the Feelee app on emotion regulation among adolescents in the forensic outpatient setting. The Feelee app integrates with treatment as usual, and the multiple single-case experimental design methodology is discussed in detail. METHODS: A multiple single-case experimental ABA design was applied to examine the initial effectiveness of Feelee. A total of 24 participants from 2 forensic outpatient care centers completed a 2-week baseline (phase A1), 4-week intervention (phase B), and a 2-week follow-up (phase A2). The primary outcome, emotional regulation, is measured daily using self-reports via the smartphone. Secondary outcomes, including emotional differentiation, insight and self-reflection, emotional awareness, and treatment-related factors such as motivation and therapeutic alliance, are assessed through questionnaires administered at preintervention, postintervention, and follow-up points. Quantitative analyses follow single-case experimental design methods, including visual analysis of individual trajectories, standardized mean difference permutation distance tests, and Cohen d at the group level. A 95% CI is calculated per participant to assess change reliability. Secondary outcomes are analyzed using the Reliable Change Index. Qualitative follow-up interviews are analyzed using thematic analysis at both the individual and group levels. RESULTS: Data collection started in June 2023 and was completed in January 2025. By the time of final manuscript submission, 89 participants had been recruited and 24 had enrolled in the study. Study results will be published in peer-reviewed journals and presented at national and international conferences throughout 2025. CONCLUSIONS: This study aims to evaluate the effectiveness of the Feelee app in enhancing emotion regulation skills. By using a multiple single-case experimental ABA design, we will get a first insight into the addition of Feelee to treatment as usual in the forensic outpatient setting. Study strengths include the low-threshold addition, ecological validity, and the use of both quantitative and qualitative research methods. Further implications for clinical practice are discussed. TRIAL REGISTRATION: Central Committee on Research Involving Human Subjects NL-OMON54390; https://onderzoekmetmensen.nl/en/trial/54390 and ClinicalTrials.gov NCT06509360; https://clinicaltrials.gov/study/NCT06509360. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/64756.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".