Physical Activity Levels and Diurnal Patterns in COMISA Versus Age- and Sex-Matched Insomniacs and Good Sleepers
Bibliographic record
Abstract
Background: Over 30% of people with obstructive sleep apnea have co-morbid chronic insomnia. This combination (COMISA) worsens daytime functioning and health outcomes more than either condition alone. Its effects on physical activity (PA) are unknown. This study’s aims were to i) compare objectively-measured PA levels and diurnal patterns between people with COMISA and age- and sex-matched Insomniacs (INS) and Good Sleepers (GS); and ii) assess the impact of an 8-week exercise (Ex) or relaxation (Rel) intervention on PA in COMISA. \nMethods: This secondary analysis used activity data collected from 15 COMISA participants, 15 INS, and 15 GS matched on age (±5 years) and sex. Participants wore an accelerometer (Actiwatch-2) on their non-dominant wrist and recorded sleep/wake activities in a diary for 14 days. Mean daily PA levels and morning/afternoon/evening PA levels were compared between groups with a repeated-measures ANOVA, as were pre- to post-intervention changes in PA levels in COMISA participants randomly assigned to Ex (N=4) or Rel (N=4). \nResults: Mean daily PA was 278±40, 316±108, and 335±121 counts/minute (cpm) in COMISA, INS, and GS respectively. The group difference was not significant, but the effect size was moderate (η_p^( 2)=0.063). Evening PA level was significantly lower than morning or afternoon level across groups (p<0.001). Mean PA level did not change after Ex or Rel. \nConclusion: These findings confirm the presence of an evening drop in PA level across populations, signal a moderate negative effect of COMISA on PA levels, and call for interventions specifically targeting PA behavior in this population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".