0343 Actigraphy-derived Behavioral Activity Rhythm In Individuals With Insomnia
Bibliographic record
Abstract
Activity, as recorded by actigraphy, can be used to extrapolate objective measures of sleep and behavioral sleep/wake rhythmicity. While actigraphy has been widely used in insomnia research, knowledge regarding circadian abnormalities as measured by activity variability across the 24-hour period in individuals with insomnia remains limited. The present study aimed to examine behavioral activity rhythm in insomnia using hour-by-hour aggregation approach. Seventeen participants with insomnia (INS; 24.94 ± 5.44 years, 64.7% female) and 44 age- and sex-matched good sleepers (GS; 25.23 ± 4.85 years, 70.5% female) participated. Wrist actigraphy recorded daily activity levels in 30-s epochs over a two-week period. Epoch-by-epoch activity counts were aggregated into hour-by-hour bins, and hourly mean activity levels were calculated. Factorial (group x time) mixed models were conducted to examine whether behavioral activity rhythms differed between the INS and the GS participants over the 24-hour period. No significant between-group differences were observed for mean activity throughout the day (INS: 171.28 ± 66.77, GS: 147.65 ± 31.47, p = .169). Mixed models assessing hour-by-hour daily activity revealed a significant group x time interaction (p = .017) with a significant main effect of time (p < .001). The INS group showed significantly lower activity from 7 to 9 PM (all ps < .05). Individuals with insomnia showed reduced diurnal activity, particularly during late evening. This activity difference was only detected when evaluating diurnal pattern of activity as average daytime activity did not differ significantly between groups. A more sedentary behavioral pattern associated with insomnia may have significant clinical implications. Future studies are warranted to evaluate the combined impact of interventions targeting activity rhythm and sleep to improve symptoms in patients. Research supported by the Canadian Institutes of Health Research (MOP42504).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".