The Use of Mobile Apps in Adolescent Psychotherapy: Assessment of Psychotherapists’ Perspectives
Bibliographic record
Abstract
BACKGROUND: Therapy-accompanying mental health apps can play an important role in the psychotherapeutic treatment of adolescents. They can enhance adolescents' engagement and autonomy, provide immediate support in critical situations, and positively influence the therapeutic working alliance. Nevertheless, mental health apps are rarely used by psychotherapists. Furthermore, due to the limited or nonexistent use of apps in psychotherapy, little is known about the actual barriers and drivers affecting their integration into psychotherapists' daily routines. To better understand how mental health apps should be designed for practical use, it is essential to explore psychotherapists' perspectives on key app features and characteristics, as well as the factors influencing their integration into clinical practice. OBJECTIVE: This study aims to analyze which app features and characteristics are essential for psychotherapists to use a mobile app in psychotherapy with adolescents and to identify the key drivers and barriers influencing the integration of a psychotherapeutic app from the psychotherapists' perspectives. METHODS: We conducted 3 feasibility studies using Steps, a transdiagnostic, therapy-accompanying app for adolescents, across 3 different psychotherapeutic treatment contexts: inpatient treatment, treatment in psychiatric outpatient clinics, and outpatient treatment with psychotherapists in private practice. All studies followed a qualitative quasi-experimental design. Participants provided information on their age, occupation, years of work experience, media affinity, attitudes toward psychotherapeutic apps, perceived app quality and feasibility, and the implementation process of the therapy-accompanying app. Qualitative data were analyzed using deductive qualitative content analysis. A total of 40 mental health professionals participated across the 3 studies (study 1: n=18; study 2: n=13; study 3: n=9). RESULTS: Study participation and app usage rates were low across all studies. Six core features for a transdiagnostic, therapy-accompanying app were identified: mood checks, library, reminders, goals and tasks, emergency kit, and questionnaires. Additionally, the integration of mental health apps into daily routines was influenced by various drivers and barriers. The most significant barriers included technological issues and practical constraints, such as limited time and resources. The most important driver was the perceived improvement in treatment quality. CONCLUSIONS: Overall, psychotherapists were generally open to using a therapy-accompanying mental health app. However, study participation and app usage remained low. As psychotherapists act as gatekeepers for patients' use of mental health apps, their needs should be prioritized in the development and implementation of such apps. TRIAL REGISTRATION: German Clinical Trials Register DRKS00031258; https://drks.de/search/en/trial/DRKS00031258/details.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".