Estimation bias and agreement limits between two common self-report methods of habitual sleep duration in epidemiological surveys
Bibliographic record
Abstract
Abstract Accurate measurement of habitual sleep duration (HSD) is crucial for understanding the relationship between sleep and health. This study aimed to assess the bias and agreement limits between two commonly used short HSD self-report methods, considering sleep quality (SQ) and social jetlag (SJL) as potential predictors of bias. Data from 10,268 participants in the International COVID Sleep Study-II (ICOSS-II) were used. Method-Self and Method-MCTQ were compared. Method-Self involved a single question about average nightly sleep duration (HSD self ), while Method-MCTQ estimated HSD from reported sleep times on workdays (HSD MCTQwork ) and free days (HSD MCTQfree ). Sleep quality was evaluated using a Likert scale and the Insomnia Severity Index (ISI) to explore its influence on estimation bias. HSD self was on average 42.41 ± 67.42 min lower than HSD MCTQweek , with an agreement range within ± 133 min. The bias and agreement range between methods increased with poorer SQ. HSD MCTQwork showed less bias and better agreement with HSD self compared to HSD MCTQfree . Sleep duration irregularity was − 43.35 ± 78.26 min on average. Subjective sleep quality predicted a significant proportion of variance in HSD self and estimation bias. The two methods showed very poor agreement and a significant systematic bias, both worsening with poorer SQ. Method-MCTQ considered sleep intervals without adjusting for SQ issues such as wakefulness after sleep onset but accounted for sleep irregularity and sleeping in on free days, while Method-Self reflected respondents’ interpretation of their sleep, focusing on their sleep on workdays. Including an SQ-related question in surveys may help bidirectionally adjust the possible bias and enhance the accuracy of sleep-health 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 | 0.175 | 0.255 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".