Abstract
Introduction
Good sleep is not merely the absence of sleep disorder symptoms, yet this criterion is commonly applied in research studies. We developed the Good Sleeper Scale-13 (GSS-13) to standardise identification of good sleepers.
Methods
We conducted a secondary analysis of the 2019 Sleep Health Foundation online survey of adult Australians (N = 2,044, aged 18–90 years). Possible GSS-13 items were chosen collaboratively with co-authors. Exploratory factor analysis (EFA) was conducted on 10% of the dataset chosen at random (N = 191) for factor identification and item reduction. Confirmatory factor analysis (CFA) on the remaining 90% (N = 1,853) tested model fit. Associations with sleep concerns, health, and daytime functioning tested validity of the final version.
Results
From EFA, six factors were identified: Adequate Sleep; Insomnia; Regularity; Timing; Sleep Duration; Perceived Sleep Problem. On CFA, model fit was comparable to other sleep instruments, X² (67) = 387.34, p < .001, CFI = .95, TLI = .92, RMSEA = .05. Cronbach’s alpha was largely acceptable (≥.7) across subscales. Consistent correlations were found between GSS-13 global scores and outcomes, including “a good night’s sleep” (r = .65, p < .001), feeling un-refreshed (r = -.53, p < .001), and general health rating (r = .44, p < .001). Classification accuracy for insomnia symptoms was also high (AUC = .84).
Conclusions
The GSS-13 is psychometrically sound, correlated well with sleep, health, and daytime functioning, and can be used to identify good sleepers for research. Future work will test relationships with other sleep measures.
Good sleep is not merely the absence of sleep disorder symptoms, yet this criterion is commonly applied in research studies. We developed the Good Sleeper Scale-13 (GSS-13) to standardise identification of good sleepers.
Methods
We conducted a secondary analysis of the 2019 Sleep Health Foundation online survey of adult Australians (N = 2,044, aged 18–90 years). Possible GSS-13 items were chosen collaboratively with co-authors. Exploratory factor analysis (EFA) was conducted on 10% of the dataset chosen at random (N = 191) for factor identification and item reduction. Confirmatory factor analysis (CFA) on the remaining 90% (N = 1,853) tested model fit. Associations with sleep concerns, health, and daytime functioning tested validity of the final version.
Results
From EFA, six factors were identified: Adequate Sleep; Insomnia; Regularity; Timing; Sleep Duration; Perceived Sleep Problem. On CFA, model fit was comparable to other sleep instruments, X² (67) = 387.34, p < .001, CFI = .95, TLI = .92, RMSEA = .05. Cronbach’s alpha was largely acceptable (≥.7) across subscales. Consistent correlations were found between GSS-13 global scores and outcomes, including “a good night’s sleep” (r = .65, p < .001), feeling un-refreshed (r = -.53, p < .001), and general health rating (r = .44, p < .001). Classification accuracy for insomnia symptoms was also high (AUC = .84).
Conclusions
The GSS-13 is psychometrically sound, correlated well with sleep, health, and daytime functioning, and can be used to identify good sleepers for research. Future work will test relationships with other sleep measures.
| Original language | English |
|---|---|
| Article number | O009 |
| Pages (from-to) | A4-A5 |
| Number of pages | 2 |
| Journal | Sleep Advances |
| Volume | 2 |
| Issue number | Suppl 1 |
| DOIs | |
| Publication status | Published - Oct 2021 |
| Event | 32nd Annual Scientific Meeting of the Australia and New Zealand Sleep Science Association: Sleep Downunder 2021 - Online Duration: 10 Oct 2021 → 13 Oct 2021 Conference number: 32nd http://32nd Annual Scientific Meeting of the Australia and New Zealand Sleep Science Association |
Keywords
- gerstmann-straussler-scheinker disease
- sleep disorders
- insomnia
- sleep duration