BACKGROUND: Although insomnia is a prevalent complaint with significant morbidity, it often remains unrecognized and untreated. Brief and valid instruments are needed both for screening and outcome assessment. This study examined psychometric indices of the Insomnia Severity Index (ISI) to detect cases of insomnia in a population-based sample and to evaluate treatment response in a clinical sample. METHODS: Participants were 959 individuals selected from the community for an epidemiological study of insomnia (Community sample) and 183 individuals evaluated for insomnia treatment and 62 controls without insomnia (Clinical sample). They completed the ISI and several measures of sleep quality, fatigue, psychological symptoms, and quality of life; those in the Clinical sample also completed sleep diaries, polysomnography, and interviews to validate their insomnia/good sleep status and assess treatment response. In addition to standard psychometric indices of reliability and validity, item response theory analyses were computed to examine ISI item response patterns. Receiver operating curves were used to derive optimal cutoff scores for case identification and to quantify the minimally important changes in relation to global improvement ratings obtained by an independent assessor. RESULTS: ISI internal consistency was excellent for both samples (Cronbach α of 0.90 and 0.91). Item response analyses revealed adequate discriminatory capacity for 5 of the 7 items. Convergent validity was supported by significant correlations between total ISI score and measures of fatigue, quality of life, anxiety, and depression. A cutoff score of 10 was optimal (86.1% sensitivity and 87.7% specificity) for detecting insomnia cases in the community sample. In the clinical sample, a change score of -8.4 points (95% CI: -7.1, -9.4) was associated with moderate improvement as rated by an independent assessor after treatment. CONCLUSION: These findings provide further evidence that the ISI is a reliable and valid instrument to detect cases of insomnia in the population and is sensitive to treatment response in clinical patients.
BACKGROUND: Although insomnia is a prevalent complaint with significant morbidity, it often remains unrecognized and untreated. Brief and valid instruments are needed both for screening and outcome assessment. This study examined psychometric indices of the Insomnia Severity Index (ISI) to detect cases of insomnia in a population-based sample and to evaluate treatment response in a clinical sample. METHODS:Participants were 959 individuals selected from the community for an epidemiological study of insomnia (Community sample) and 183 individuals evaluated for insomnia treatment and 62 controls without insomnia (Clinical sample). They completed the ISI and several measures of sleep quality, fatigue, psychological symptoms, and quality of life; those in the Clinical sample also completed sleep diaries, polysomnography, and interviews to validate their insomnia/good sleep status and assess treatment response. In addition to standard psychometric indices of reliability and validity, item response theory analyses were computed to examine ISI item response patterns. Receiver operating curves were used to derive optimal cutoff scores for case identification and to quantify the minimally important changes in relation to global improvement ratings obtained by an independent assessor. RESULTS: ISI internal consistency was excellent for both samples (Cronbach α of 0.90 and 0.91). Item response analyses revealed adequate discriminatory capacity for 5 of the 7 items. Convergent validity was supported by significant correlations between total ISI score and measures of fatigue, quality of life, anxiety, and depression. A cutoff score of 10 was optimal (86.1% sensitivity and 87.7% specificity) for detecting insomnia cases in the community sample. In the clinical sample, a change score of -8.4 points (95% CI: -7.1, -9.4) was associated with moderate improvement as rated by an independent assessor after treatment. CONCLUSION: These findings provide further evidence that the ISI is a reliable and valid instrument to detect cases of insomnia in the population and is sensitive to treatment response in clinical patients.
Authors: Eileen R Chasens; Charles W Atwood; Lora E Burke; Mary Korytkowski; Robert Stansbury; Patrick J Strollo; Susan M Sereika Journal: Contemp Clin Trials Date: 2018-12-02 Impact factor: 2.226
Authors: David A Kalmbach; Philip Cheng; Louise M O'Brien; Leslie M Swanson; Roopina Sangha; Srijan Sen; Constance Guille; Andrea Cuamatzi-Castelan; Alasdair L Henry; Thomas Roth; Christopher L Drake Journal: Sleep Med Date: 2020-03-21 Impact factor: 3.492
Authors: Carmela Alcántara; Linda C Gallo; Jia Wen; Katherine A Dudley; Douglas M Wallace; Yasmin Mossavar-Rahmani; Daniela Sotres-Alvarez; Phyllis C Zee; Alberto R Ramos; Megan E Petrov; Melynda D Casement; Martica H Hall; Susan Redline; Sanjay R Patel Journal: Sleep Date: 2019-04-01 Impact factor: 5.849
Authors: Christopher L Drake; David A Kalmbach; J Todd Arnedt; Philip Cheng; Christine V Tonnu; Andrea Cuamatzi-Castelan; Cynthia Fellman-Couture Journal: Sleep Date: 2019-02-01 Impact factor: 5.849
Authors: David A Kalmbach; Daniel J Buysse; Philip Cheng; Thomas Roth; Alexander Yang; Christopher L Drake Journal: Sleep Med Date: 2019-11-14 Impact factor: 3.492