Feasibility and accuracy of the ASERT digital questionnaire in mood tracking for a longitudinal research study on bipolar disorder
Bibliographic record
Abstract
Background: It is challenging for bipolar disorder (BD) studies to capture multiple mood states within a participant at in-person visits. Mood tracking could aid scheduling, but evaluation is usually done using clinical assessments inconvenient for participants to undergo often. However, frequent assessments are necessary to capture dynamic mood changes typical of BD. The Aktibipo Self-Rating Questionnaire (ASERT) is a simple, self-report mood survey. We examined the utility of collecting the ASERT weekly to assess mood changes and schedule follow-up visits. Methods: Sixty-one participants with BD completed the ASERT and were administered the Montgomery-Åsberg Depression Rating Scale (MADRS) and Young Mania Rating Scale (YMRS) during a baseline visit. Participants were then sent weekly text messages with an ASERT survey link. If participants exhibited at least a 5-point (later 8-point) change from baseline on either the mania or depression subscale, they were called and administered the MADRS or YMRS. A 10-point change on either phone-delivered clinical scale prompted a follow-up visit. Associations between ASERT subscales and clinical scales were evaluated using Spearman's correlation and robust regression. Results: Mean completion rate was 94.8 % and median completion time was 67 s. The ASERT depression and mania subscales correlated with the MADRS and YMRS at baseline and all follow-up time points. Our screening method aided scheduling, with 15 of 19 participants exhibiting a 10-point change or greater on the MADRS and/or YMRS at Visit 2. Conclusions: The ASERT can be feasibly deployed to track mood and can help schedule follow-up assessments in BD longitudinal studies.
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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 | 0.112 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".