Therapy Companion Mobile App for Acceptance and Commitment Therapy Exercises (ACTaide): Therapist and Client Co-Design Study
Bibliographic record
Abstract
BACKGROUND: Acceptance and commitment therapy (ACT) relies heavily on the between-session practice of therapeutic exercises to promote skill acquisition and improve psychological flexibility. However, adherence to this between-session practice remains a challenge. Mobile apps offer a promising solution to bridge this gap. However, few ACT apps focus exclusively on supporting clients in their between-session practice, and fewer apps involve stakeholders in their design. ACTaide, a therapy companion mobile app co-designed with stakeholders, addresses these barriers by guiding clients through ACT exercises and metaphors using annotated image sequences, supporting their between-session practice. OBJECTIVE: This study aimed to co-design ACTaide with therapists and clients, incorporating their feedback to ensure the app aligns with clinical goals and the needs of end users. The research explored stakeholder preferences and feedback on app functionality, design, and features to guide iterative design improvements. METHODS: Using a qualitative, user-centered design framework, we conducted 4 consecutive focus groups: 2 with 10 licensed ACT therapists and 2 with 14 psychotherapy clients. Each focus group included semistructured discussions and co-design activities. Data were collected through audio recordings and design artifacts (eg, sketches), which were analyzed using thematic content analysis. RESULTS: A total of 9 themes were identified, reflecting areas of convergence and divergence between therapists and clients. The therapists and clients expressed enthusiasm for ACTaide as a tool to support between-session practice. Both groups emphasized the importance of a user-friendly, intuitive, and aesthetically appealing interface, with a preference for high-quality visuals over text-heavy features. Personalization and customization were viewed as essential for enhancing app engagement. The therapists prioritized accessibility and clinical appropriateness, voicing concerns about features that may be inconsistent with ACT principles, such as symptom rating scales, and clarified their role in app delivery. By contrast, the clients emphasized wanting greater interactivity and elements of gamification to improve engagement. Slight discrepancies were noted between therapists' preferences for minimal designs and clients' preferences for more vibrant and engaging aesthetics. Overall, both groups recognized the app's potential to address barriers to homework adherence and to extend the benefits of therapy into clients' daily lives. CONCLUSIONS: The study illustrates the value of using a user-centered, co-design approach in the development of ACTaide, an adjunctive mental health app for the between-session practice of ACT exercises and metaphors tailored to therapist and client preferences. Through the integration of stakeholder feedback, the findings provide actionable insights for designing psychotherapy tools that balance clinical goals with user preferences. Future research will focus on testing high-fidelity prototypes to evaluate acceptability, usability, and engagement.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".