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. 2019 Oct 9;42(10):zsz151.
doi: 10.1093/sleep/zsz151.

Identification of subgroups of chemotherapy patients with distinct sleep disturbance profiles and associated co-occurring symptoms

Maria Tejada  1 , Carol Viele  1 , Kord M Kober  1 , Bruce A Cooper  1 , Steven M Paul  1 , Laura B Dunn  2 , Marilyn J Hammer  3 , Fay Wright  4 , Yvette P Conley  5 , Jon D Levine  6 , Christine Miaskowski  1
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Identification of subgroups of chemotherapy patients with distinct sleep disturbance profiles and associated co-occurring symptoms

Maria Tejada et al. Sleep. .

Abstract

Study objectives: Purposes of this study were to identify subgroups of patients with distinct sleep disturbance profiles and to evaluate for differences in demographic, clinical, and various sleep characteristics, as well for differences in the severity of co-occurring symptoms among these subgroups.

Methods: Outpatients with breast, gynecological, gastrointestinal, or lung cancer (n = 1331) completed questionnaires six times over two chemotherapy cycles. Self-reported sleep disturbance was evaluated using the General Sleep Disturbance Scale (GSDS). Latent profile analysis was used to identify distinct subgroups.

Results: Three latent classes with distinct sleep disturbance profiles were identified (Low [25.5%], High [50.8%], Very High [24.0%]) across the six assessments. Approximately 75% of the patients had a mean total GSDS score that was above the clinically meaningful cutoff score of at least 43 across all six assessments. Compared to the Low class, patients in High and Very High classes were significantly younger, had a lower functional status, had higher levels of comorbidity, and were more likely to be female, more likely to have childcare responsibilities, less likely to be employed, and less likely to have gastrointestinal cancer. For all of the GSDS subscale and total scores, significant differences among the latent classes followed the expected pattern (Low < High < Very High). For trait and state anxiety, depressive symptoms, morning and evening fatigue, decrements in attentional function, and decrements in morning and evening energy, significant differences among the latent classes followed the expected pattern (Low < High < Very High).

Conclusions: Clinicians need to perform in-depth assessments of sleep disturbance and co-occurring symptoms to identify high-risk patients and recommend appropriate interventions.

Keywords: anxiety; attentional function; cancer; chemotherapy; depression; fatigue; latent profile analysis; pain; sleep disturbance.

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Figures

Figure 1.
Figure 1.
Sleep disturbance trajectories for patients in each of the latent classes.
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