Engagement and Acceptability of Acceptance and Commitment Therapy in Daily Life in Early Psychosis: Secondary Findings From a Multicenter Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Acceptance and commitment therapy (ACT) is promising in the treatment of early psychosis. Augmenting face-to-face ACT with mobile health ecological momentary interventions may increase its treatment effects and empower clients to take treatment into their own hands. OBJECTIVE: This study aimed to investigate and predict treatment engagement with and acceptability of acceptance and commitment therapy in daily life (ACT-DL), a novel ecological momentary intervention for people with an ultrahigh risk state and a first episode of psychosis. METHODS: In the multicenter randomized controlled trial, 148 individuals with ultrahigh risk or first-episode psychosis aged 15-65 years were randomized to treatment as usual only (control) or to ACT-DL combined with treatment as usual (experimental), consisting of 8 face-to-face sessions augmented with an ACT-based smartphone app, delivering ACT skills and techniques in daily life. For individuals in the intervention arm, we collected data on treatment engagement with and acceptability of ACT-DL during and after the intervention. Predictors of treatment engagement and acceptability included baseline demographic, clinical, and functional outcomes. RESULTS: Participants who received ACT-DL in addition to treatment as usual (n=71) completed a mean of 6 (SD 3) sessions, with 59% (n=42) of participants completing all sessions. App engagement data (n=58) shows that, on a weekly basis, participants used the app 13 times and were compliant with 6 of 24 (25%) notifications. Distribution plots of debriefing scores (n=46) show that 85%-96% of participants reported usefulness on all acceptability items to at least some extent (scores ≥2; 1=no usefulness) and that 91% (n=42) of participants reported perceived burden by number and length of notifications (scores ≥2; 1=no burden). Multiple linear regression models were fitted to predict treatment engagement and acceptability. Ethnic minority backgrounds predicted lower notification response compliance (B=-4.37; P=.01), yet higher app usefulness (B=1.25; P=.049). Negative (B=-0.26; P=.01) and affective (B=0.14; P=.04) symptom severity predicted lower and higher ACT training usefulness, respectively. Being female (B=-1.03; P=.005) predicted lower usefulness of the ACT metaphor images on the app. CONCLUSIONS: Our results corroborate good treatment engagement with and acceptability of ACT-DL in early psychosis. We provide recommendations for future intervention optimization. TRIAL REGISTRATION: OMON NL46439.068.13; https://onderzoekmetmensen.nl/en/trial/24803.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".