Six multidimensional sleep health facets in older adults identified with factor analysis of actigraphy: Results from the Einstein Aging Study
Bibliographic record
Abstract
OBJECTIVES: The concept of multi-dimensional sleep health, originally based on self-report, was recently extended to actigraphy in older adults, yielding five components, but without a hypothesized rhythmicity factor. The current study extends prior work using a sample of older adults with a longer period of actigraphy follow-up, which may facilitate observation of the rhythmicity factor. METHODS: = 77.2 years, 67% females; 47% White, 40% Black, 13% Hispanic/Others) over 2 weeks were used in exploratory factor analysis to determine factor structures, followed by confirmatory factor analysis on a different subsample. The utility of this approach was demonstrated by associations with global cognitive performance (Montreal Cognitive Assessment). RESULTS: Exploratory factor analysis identified six factors: Regularity: standard deviations of four sleep measures: midpoint, sleep onset time, night total sleep time (TST), and 24-hour TST; Alertness/Sleepiness (daytime): amplitude, napping (mins and #/day); Timing: sleep onset, midpoint, wake-time (of nighttime sleep); up-mesor, acrophase, down-mesor; Efficiency: sleep maintenance efficiency, wake after sleep onset; Duration: night rest interval(s), night TST, 24-hour rest interval(s), 24-hour TST; Rhythmicity (pattern across days): mesor, alpha, and minimum. Greater sleep efficiency was associated with better Montreal Cognitive Assessment performance (β [95% confidence interval] = 0.63 [0.19, 1.08]). CONCLUSIONS: Actigraphic records over 2 weeks revealed that Rhythmicity may be an independent factor in sleep health. Facets of sleep health can facilitate dimension reduction, be considered predictors of health outcomes, and be potential targets for sleep interventions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".