A population‐based psychometric analysis of the insomnia severity index in black women with and without a history of cancer. (14th June 2021) - Yusufov, Miryam; Recklitis, Christopher; Zhou, Eric S.; Bethea, Traci N.; Rosenberg, Lynn
Abstract: Black women are under‐represented in insomnia research. Further, cancer treatments increase the risk of late effects, thus affecting the sleep of psychologically and medically vulnerable cancer survivors. The Insomnia Severity Index (ISI) is widely used, but has not been researched in black women, and research in cancer survivors is limited. Prior studies demonstrate that psychometric properties of the ISI are not consistent across samples. This study examined the internal consistency and factor structure of the ISI in 29, 500 participants from the Black Women's Health Study, an epidemiological study of black women in the United States. This cohort included 28, 214 women without a cancer history and 1, 286 cancer survivors. Exploratory, confirmatory and multigroup analyses were conducted to determine the psychometric properties of the ISI in these groups. The mean ISI score was 7.18 (standard deviation [ SD ] = 6.82). Findings supported the internal consistency reliability of the ISI in black women with (Ω = 0.896) and without (Ω = 0.892) a cancer history. Exploratory factor analyses supported a one‐factor structure. Confirmatory factor analyses indicated that fit of this one‐factor model was not robust in survivors (Satorra‐Bentler chi‐square [ χ SB 2 (14)] = 197.78, comparative fit index [CFI] = 0.928, root mean‐square error of approximation [RMSEA] = 0.143) or in women with no cancer history ( χ SB 2 (14) = 2, 887.93, CFI = 0.945, RMSEA = 0.121), but the alternative models we examined were not superior. Although factor structures in previous studies have varied considerably, we found a one‐factor structure. Although internal consistency reliability was strong, factor analytic results did not further support the ISI. Inconsistencies in ISI measurement properties across studies may reflect differences in sample sizes and populations.