BACKGROUND: The public health importance of daytime sleepiness as a risk factor for accidents, interpersonal problems, and decreased productivity has been recognized. However, epidemiologic research on this topic has been limited by the reliance on laboratory measures (i.e., the Multiple Sleep Latency Test-MSLT). Two scales, daytime sleepiness and nocturnal sleep onset, have been identified from the self-report Sleep-Wake Activity Inventory (SWAI) in a clinic sample and validated against the MSLT. This study evaluates the replicability of the two scales in a population sample and assesses potential thresholds in scale scores that distinguish normal from pathologic levels of daytime sleepiness and difficulty falling asleep. METHODS: The sample consisted of 2181 subjects 18-45 years old in the Detroit metropolitan area. All sleep characteristic information covered the 2 weeks prior to interview. Split-half sample factor analyses were conducted to assess replicability of the results. Distribution of scale scores and their relation to construct validity variables were used to evaluate possible thresholds. RESULTS: A two-factor model appeared to best account for the variation among the 12 items from the SWAI. The two factors accounted for 50% of the variance in both split-half sample analyses. The revised eight-item daytime sleepiness and two-item nocturnal sleep onset scales showed good and fair internal consistency respectively across both split-half samples. There appeared to be a "natural break" in daytime sleepiness scale scores that was associated with a substantial and consistent change in number of hours slept. No breaks appeared in nocturnal sleep onset scores. CONCLUSIONS: This study replicated the results of the clinic-based study and suggested a potentially useful diagnostic threshold for self-report excessive daytime sleepiness. Epidemiology of sleep depends on the ability to move from the laboratory to population surveys in reliable and valid ways. Development of self-report is a step in that direction.
BACKGROUND: The public health importance of daytime sleepiness as a risk factor for accidents, interpersonal problems, and decreased productivity has been recognized. However, epidemiologic research on this topic has been limited by the reliance on laboratory measures (i.e., the Multiple Sleep Latency Test-MSLT). Two scales, daytime sleepiness and nocturnal sleep onset, have been identified from the self-report Sleep-Wake Activity Inventory (SWAI) in a clinic sample and validated against the MSLT. This study evaluates the replicability of the two scales in a population sample and assesses potential thresholds in scale scores that distinguish normal from pathologic levels of daytime sleepiness and difficulty falling asleep. METHODS: The sample consisted of 2181 subjects 18-45 years old in the Detroit metropolitan area. All sleep characteristic information covered the 2 weeks prior to interview. Split-half sample factor analyses were conducted to assess replicability of the results. Distribution of scale scores and their relation to construct validity variables were used to evaluate possible thresholds. RESULTS: A two-factor model appeared to best account for the variation among the 12 items from the SWAI. The two factors accounted for 50% of the variance in both split-half sample analyses. The revised eight-item daytime sleepiness and two-item nocturnal sleep onset scales showed good and fair internal consistency respectively across both split-half samples. There appeared to be a "natural break" in daytime sleepiness scale scores that was associated with a substantial and consistent change in number of hours slept. No breaks appeared in nocturnal sleep onset scores. CONCLUSIONS: This study replicated the results of the clinic-based study and suggested a potentially useful diagnostic threshold for self-report excessive daytime sleepiness. Epidemiology of sleep depends on the ability to move from the laboratory to population surveys in reliable and valid ways. Development of self-report is a step in that direction.
Authors: Martina Minnerop; Bernd Weber; Jan-Christoph Schoene-Bake; Sandra Roeske; Sandra Mirbach; Christian Anspach; Christiane Schneider-Gold; Regina C Betz; Christoph Helmstaedter; Marc Tittgemeyer; Thomas Klockgether; Cornelia Kornblum Journal: Brain Date: 2011-11-29 Impact factor: 13.501
Authors: Carla Gliem; Martina Minnerop; Sandra Roeske; Hanna Gärtner; Jan-Christoph Schoene-Bake; Sandra Adler; Juri-Alexander Witt; Felix Hoffstaedter; Christiane Schneider-Gold; Regina C Betz; Christoph Helmstaedter; Marc Tittgemeyer; Katrin Amunts; Thomas Klockgether; Bernd Weber; Cornelia Kornblum Journal: PLoS One Date: 2019-03-07 Impact factor: 3.240