Latent class analysis of actigraphy within the depression early warning (DEW) longitudinal clinical youth cohort
Bibliographic record
Abstract
BACKGROUND: Wearable-generated data yield objective information on physical activity and sleep variables, which, are in turn, related to the phenomenology of depression. There is a dearth of wearable-generated data regarding physical activity and sleep variables among youth with clinical depression. METHODS: Longitudinal (up to 24 months) quarterly collections of wearable-generated variables among adolescents diagnosed with current/past major depression. Latent class analysis was employed to classify participants on the basis of wearable-generated: Activity, Sleep Duration, and Sleep efficiency. The Patient Health Questionnaire adapted for adolescents (PHQ-9-A), and the Ruminative Response Scale (RRS) at study intake were employed to predict class membership. RESULTS: Seventy-two adolescents (72.5% girls) were recruited over 31 months. Activity, Sleep Duration, and Sleep efficiency were reciprocally correlated, and wearable-generated data were reducible into a finite number (3 to 4) of classes of individuals. A PHQ-A score in the clinical range (14 and above) at study intake predicted a class of low physical activity (Acceleration) and a class of shorter Sleep Duration. LIMITATIONS: Limited power related to the sample size and the interim nature of this study. CONCLUSIONS: This study of wearable-generated variables among adolescents diagnosed with clinical depression shows that a large amount of longitudinal data is amenable to reduction into a finite number of classes of individuals. Interfacing wearable-generated data with clinical measures can yield insights on the relationships between objective psychobiological measures and symptoms of adolescent depression, and may improve clinical management of depression.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".