Initial Development of a Multidimensional Computerized Adaptive Test for Intensive Longitudinal Assessment of Suicide Risk: Development and Usability Study
Bibliographic record
Abstract
Background: Intensive longitudinal designs support temporally granular study of processes, making methods like ecological momentary assessment (EMA) increasingly common in medical and behavioral science. However, the repetitive and intensive measurement strategies associated with these designs increase participant burden, which limits the breadth and precision of EMA surveys. This is particularly problematic for complex clinical phenomena, such as suicide risk, which research has shown is multidimensional and fluctuates over narrow time intervals (eg, hours). To overcome this limitation, we proposed the Computerized Adaptive Test for Suicide Risk Pathways (CAT-SRP), which supports the simultaneous assessment of multiple empirically informed risk domains and facilitates personalized survey content. Objective: The objective of this study is to develop, calibrate, and pilot the first multidimensional computerized adaptive test for suicidal thoughts and related psychosocial risk factors in intensive longitudinal designs like EMA. Methods: A web-based assessment platform was developed to adaptively administer the CAT-SRP. CAT-SRP items were modified from existing validated instruments to support administration in intensive longitudinal designs. The item bank was developed in line with major ideation-to-action theories of suicide and consultation with experts outside the study team. Exploratory item factor analysis was used to identify dimensionality of the item bank. Item parameters were calibrated using a multidimensional graded response model in a large cross-sectional community sample (n=1759, 36.33% with a history of suicidal thoughts). Following calibration, the CAT-SRP was evaluated in an EMA study of participants with a past month history of suicidal thoughts (n=29 across 2134 observations). Adaptive testing used D-optimal item selection, a dual variable-length stopping criterion, and Maximum a Posteriori (MAP) scoring. Descriptive statistics and mixed effects models were used to examine CAT-SRP performance (eg, efficiency and survey overlap) and relationships among CAT-SRP domain scores. Results: The calibration study identified 2 suicidal thought domains (active and passive thoughts) and 12 risk factor domains: humiliation, loneliness, anger, pain, defeat, impulsivity (ie, negative urgency), entrapment, distress tolerance, perceived burdensomeness, thwarted belongingness, aggression, and a positively valenced method factor. Domain information was the highest between average to high levels of domain scores. Study 2 showed that the CAT-SRP (1) administered surveys with low to moderate item overlap, (2) incurred low participant burden, and (3) may improve near-term prediction of suicidal thoughts relative to traditional EMA measurement. Most EMA surveys reached the maximum length, 50 questions, highlighting a need to refine selection and stopping rules. Conclusions: The CAT-SRP effectively personalized EMA survey content to respondents, which reduces the repetitiveness and perceived burden of intensive longitudinal research designs. Continuous domain scores from multidimensional computerized adaptive testing (MCAT) also provided more nuanced measurement compared to traditional approaches that struggle with zero-inflation in EMA and appeared to produce stronger predictive relationships. Overall, the CAT-SRP demonstrated strong methodological advantages to use CAT for intensive longitudinal data collection.
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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 | 0.026 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".