A Feasibility Trial of Power Up: Smartphone App to Support Patient Activation and Shared Decision Making for Mental Health in Young People
Bibliographic record
Abstract
BACKGROUND: Digital tools have the potential to support patient activation and shared decision making in the face of increasing levels of mental health problems in young people. There is a need for feasibility trials of digital interventions to determine the usage and acceptability of interventions. In addition, there is a need to determine the ability to recruit and retain research participants to plan rigorous effectiveness trials and, therefore, develop evidence-based recommendations for practice. OBJECTIVE: This study aimed to determine the feasibility of undertaking a cluster randomized controlled trial to test the effectiveness of a smartphone app, Power Up, co-designed with young people to support patient activation and shared decision making for mental health. METHODS: Overall, 270 young people were screened for participation and 52.5% (142/270) were recruited and completed baseline measures across 8 specialist child mental health services (n=62, mean age 14.66 (SD 1.99) year; 52% [32/62] female) and 2 mainstream secondary schools (n=80; mean age 16.88 [SD 0.68] years; 46% [37/80] female). Young people received Power Up in addition to management as usual or received management as usual only. Posttrial interviews were conducted with 11 young people from the intervention arms (specialist services n=6; schools n=5). RESULTS: Usage data showed that there were an estimated 50 (out of 64) users of Power Up in the intervention arms. Findings from the interviews indicated that young people found Power Up to be acceptable. Young people reported (1) their motivation for use of Power Up, (2) the impact of use, and (3) barriers to use. Out of the 142 recruited participants, 45.0% (64/142) completed follow-up measures, and the approaches to increase retention agreed by the steering group are discussed. CONCLUSIONS: The findings of this study indicate that the app is acceptable, and it is feasible to examine the effectiveness of Power Up in a prospective cluster randomized controlled trial. TRIAL REGISTRATION: ClinicalTrials.gov NCT02552797; https://clinicaltrials.gov/ct2/show/NCT02552797 (Archived by WebCite at http://www.webcitation.org/6td6MINP0).
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".