Exploring current smartphone-based cognitive assessments in schizophrenia and bipolar disorder
Bibliographic record
Abstract
Schizophrenia and bipolar disorder are associated with cognitive deficits that contribute significantly to disability. However, traditional in-lab cognitive assessments are time-consuming and not optimized for remote administration. Recent advancements in smartphone technology enable momentary cognitive assessments in a real-world context. This brief report reviews recent research in momentary cognitive assessments in individuals with schizophrenia and bipolar disorder through reviewing mobile platforms and cognitive assessments studied. A total of 14 experimental articles were reviewed, focusing on cognitive domains including visual working memory, processing speed, executive function, verbal fluency, verbal memory, social cognition, and typing patterns. The review highlights the feasibility of remote cognitive assessment with smartphones, and provides a layout of domains studied in this context, but illustrates a low volume of current research, the need for additional studies, and the potential for innovations like digital phenotyping.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".