STUDY OBJECTIVE: Recent efforts have been made to develop quantitative frequency, duration, and severity criteria for insomnia. The current study was conducted to test a range of frequency and severity criteria sets for discriminating primary insomnia sufferers from normal sleepers. PARTICIPANTS: Seventy-two adults with primary insomnia and 88 age-matched normal sleepers. METHODS: Participants completed 14 consecutive nights of sleep logs to monitor their home sleep patterns. Receiver-operator characteristic curve analyses were used to compare a range of severity and frequency criteria sets for discriminating the insomnia and normal-sleeper groups. In addition, sensitivity and specificity tests were conducted for a range of wake-time severity cutoffs based on 2-week mean sleep-log data. RESULTS: Receiver-operator characteristic curve analyses showed that no 1 combination of severity and frequency criteria maximized sensitivity and specificity. Rather, the optimal frequency cutoff decreased as the severity criterion increased. Analyses of mean sleep-log data showed that an average sleep-onset latency or middle-of-the-night wake time (ie, time awake between sleep onset and final morning awakening) cutoff of 20 minutes or longer over 2 weeks of sleep-log monitoring appeared to best maximize sensitivity (94.4%) and specificity (79.6%) for insomnia classification. CONCLUSIONS: The optimal quantitative insomnia criteria found herein differ from those previously proposed. Nonetheless, results suggest that quantitative criteria derived from sleep-log data may be useful for classification of primary insomnia.
STUDY OBJECTIVE: Recent efforts have been made to develop quantitative frequency, duration, and severity criteria for insomnia. The current study was conducted to test a range of frequency and severity criteria sets for discriminating primary insomnia sufferers from normal sleepers. PARTICIPANTS: Seventy-two adults with primary insomnia and 88 age-matched normal sleepers. METHODS:Participants completed 14 consecutive nights of sleep logs to monitor their home sleep patterns. Receiver-operator characteristic curve analyses were used to compare a range of severity and frequency criteria sets for discriminating the insomnia and normal-sleeper groups. In addition, sensitivity and specificity tests were conducted for a range of wake-time severity cutoffs based on 2-week mean sleep-log data. RESULTS: Receiver-operator characteristic curve analyses showed that no 1 combination of severity and frequency criteria maximized sensitivity and specificity. Rather, the optimal frequency cutoff decreased as the severity criterion increased. Analyses of mean sleep-log data showed that an average sleep-onset latency or middle-of-the-night wake time (ie, time awake between sleep onset and final morning awakening) cutoff of 20 minutes or longer over 2 weeks of sleep-log monitoring appeared to best maximize sensitivity (94.4%) and specificity (79.6%) for insomnia classification. CONCLUSIONS: The optimal quantitative insomnia criteria found herein differ from those previously proposed. Nonetheless, results suggest that quantitative criteria derived from sleep-log data may be useful for classification of primary insomnia.
Authors: Elaine M Boland; Jennifer R Goldschmied; Monica R Kelly; Suzanne Perkins; Philip R Gehrman; Patricia L Haynes Journal: Chronobiol Int Date: 2019-08-01 Impact factor: 2.877
Authors: Jessica C Levenson; Wendy M Troxel; Amy Begley; Martica Hall; Anne Germain; Timothy H Monk; Daniel J Buysse Journal: J Clin Sleep Med Date: 2013-02-01 Impact factor: 4.062
Authors: Daniel J Buysse; Yu Cheng; Anne Germain; Douglas E Moul; Peter L Franzen; Mary Fletcher; Timothy H Monk Journal: Sleep Med Date: 2009-12-04 Impact factor: 3.492
Authors: Hylton E Molzof; Sarah E Emert; Joshua Tutek; Mazheruddin M Mulla; Kenneth L Lichstein; Daniel J Taylor; Brant W Riedel Journal: Sleep Med Date: 2018-09-06 Impact factor: 3.492