Perceptions and experiences of patients, caregivers, and clinicians on using digital phenotyping measures for outcome prediction in first episode of psychosis (FEP): A qualitative study
Bibliographic record
Abstract
Background : Predicting outcomes in first-episode psychosis (FEP) is critical for tailoring interventions. Digital phenotyping, the real-time collection of high-resolution data through mobile and wearable devices, has emerged as a promising method to improve mental health assessment and prognostic prediction. Objective : To explore the experiences and perspectives of patients, caregivers, and clinicians on using digital phenotyping for outcome prediction in FEP. Method : This qualitative study is part of a mixed-methods longitudinal project. Patients with FEP (aged 12–35) were recruited from the Early Psychosis Intervention Nova Scotia (EPINS) program (N = 40) and completed a six-day digital phenotyping assessment involving ecological momentary assessment (EMA) via smartphone and passive monitoring using wearable devices. Semi-structured interviews were then conducted with a random sample of 19 patients, 6 caregivers, and 6 clinicians and analyzed thematically following Braun and Clarke’s six-step framework. Results : Three themes emerged: (1) experiences and attitudes; (2) perceived value; and (3) barriers and concerns. Patients and caregivers generally reported positive experiences, highlighting benefits such as increased self-awareness and motivation for physical activities. Barriers included practical burden, psychological risks, technical and accessibility challenges, prediction validity, and data privacy. Conclusion : Digital phenotyping is generally acceptable and feasible in FEP, but concerns need to be considered in future applications.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".