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Updated: Sep 25, 2025

Human Circadian Phenotyping and Diurnal Performance Testing in the Real World
Published on: April 7, 2020
Cancer sleep symptom-related phenotypic clustering differs across three cancer specific patient cohorts
Kristina Kairaitis1,2,3, Ayey S Madut1, Harini Subramanian1
1Ludwig Engel Centre for Respiratory Research, The Westmead Institute for Medical Research, Westmead, New South Wales, Australia.
Abstract:
Specific sleep disorders have been linked to disease progression in different cancers. We hypothesised sleep symptom clusters would differ between cancer types. The aim of this study was to compare sleep symptom clusters in post-treatment melanoma, breast and endometrial cancer patients. Data were collected from 124 breast cancer patients (1 male, 60 ± 15 years, 28.1 ± 6.6 kg/m2 ), 82 endometrial cancer patients (64.0 ± 12.5 years, 33.5 ± 10.4 kg/m2 ) and 112 melanoma patients (59 male, 65.0 ± 18.0 years, 29.1 ± 6.6 kg/m2 ). All patients completed validated questionnaires to assess sleep symptoms, including the Epworth Sleepiness Scale (ESS), Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), and Functional Outcomes of Sleep Questionnaire-10 (FOSQ-10). Snoring, tiredness, observed apneas, age, BMI, and gender data were also collected. Binary values (PSQI, ISI, FOSQ), or continuous variables for sleepiness (ESS) and perceived sleep quality (PSQI), were created and sleep symptom clusters were identified and compared across cancer cohorts. Four distinct sleep symptom clusters were identified: minimally symptomatic (n = 152, 47.7%); insomnia-predominant (n = 87, 24.9%); very sleepy with upper airway symptoms (n = 51, 16.3%), and severely symptomatic with severe dysfunction (n = 34, 11.1%). Breast cancer patients were significantly more likely to be in the insomnia predominant or severely symptomatic with severe dysfunction clusters, whereas melanoma patients were more likely to be minimally symptomatic or sleepy with upper airway symptoms (p <0.0001). Endometrial cancer patients were equally distributed across symptom clusters. Sleep symptom clusters vary across cancer patients. A more personalised approach to the management of sleep-related symptoms in these patients may improve the long term quality of life and survival.
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