T99. HARNESSING DIGITAL TECHNOLOGIES TO ASSESS AND TREAT COGNITIVE SYMPTOMS IN SCHIZOPHRENIA
Bibliographic record
Abstract
Abstract Background Cognitive impairments are a core feature of schizophrenia. Although cognitive impairments have consistently shown to have negative impacts on functional outcomes among individuals with schizophrenia, assessing and treating these symptoms in clinical settings remains a difficult challenge. Interestingly, the growing potential of new digital technologies, such as smartphone applications and virtual reality, hold great promise in alleviating these impairments. Methods This presentation will introduce results from two recent studies using digital technologies to assess and treat cognitive symptoms in schizophrenia. In the first study, smartphone versions of the pen-and-paper Trail Making Tests A and B were developed. These cognitive tests measure speed of processing and cognitive flexibility. We assessed the validity of the smartphone versions of both Trail Making Tests in measuring these cognitive domains in in 37 healthy controls and 26 individuals with schizophrenia. Following the initial assessment, participants were asked to complete the two smartphone cognitive tests once a week for three months. This served as a measure of cognitive performance over time Results Results showed that it was feasible to measure cognition using a smartphone application in schizophrenia. Performances on both smartphone cognitive tests were significantly and positively correlated with the pen-and-paper versions (Parts A: r = 0.65, p < .001; Parts B: r= 0.44, p= .01). Additionally, significant differences were observed between controls and individuals with schizophrenia on both smartphone tests (Part A: t = -3.88, p = .004; Part B: t = -3.29, p = .002). Moreover, longitudinal results showed no significant effect of practice over time on the smartphone cognitive tests. Discussion Digital technologies have the potential to optimize cognitive assessments, monitoring, and care in schizophrenia. Our findings support the feasibility and efficacy of using digital technologies to measure and treat cognitive impairments in schizophrenia. Our research also highlights the importance of including scientists, clinicians, and content experts with schizophrenia in the development of these tools to ensure their validity and facilitate clinical implementation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".