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Advanced psychometric testing on a clinical screening tool to evaluate insomnia: sleep condition indicator in patients with advanced cancer
Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
Pediatric Health Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
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2020 (English)In: Sleep and Biological Rhythms, ISSN 1446-9235, E-ISSN 1479-8425, Vol. 18, p. 343-349Article in journal (Refereed) Published
Abstract [en]

Purpose: To examine the psychometric properties of the Sleep Condition Indicator (SCI) using different psychometric approaches [including classical test theory, Rasch models, and receiver operating characteristics (ROC) curve] among patients with advanced cancer.

Methods: Through convenience sampling, patients with cancer at stage III or IV (n = 859; 511 males; mean ± SD age = 67.4 ± 7.5 years) were recruited from several oncology units of university hospitals in Iran. All the participants completed the SCI, Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), Hospital Anxiety and Depression Scale (HADS), General Health Questionnaire (GHQ), and Edmonton Symptom Assessment Scale (ESAS). In addition, 491 participants wore an actigraph device to capture objective sleep.

Results: Classical test theory [factor loadings from confirmatory factor analysis = 0.76–0.89; test–retest reliability = 0.80–0.93] and Rasch analysis [infit mean square (MnSq) = 0.63–1.31; outfit MnSq = 0.61–1.23] both support the construct validity of the SCI. The SCI had significant associations with ISI, PSQI, ESS, HADS, GHQ, and ESAS. In addition, the SCI has satisfactory area under ROC curve (0.92) when comparing a gold standard of insomnia diagnosis. Significant differences in the actigraphy measure were found between insomniacs and non-insomniacs based on the SCI score defined by ROC.

Conclusion: With the promising psychometric properties shown in the SCI, healthcare providers can use this simple assessment tool to target the patients with advanced cancer who are at risk of insomnia and subsequently provide personalized care efficiently.

Place, publisher, year, edition, pages
Springer, 2020. Vol. 18, p. 343-349
Keywords [en]
Advanced cancer, Insomnia, Oncology, Psychometric properties, Sleep
National Category
Nursing Neurology
Identifiers
URN: urn:nbn:se:hj:diva-50284DOI: 10.1007/s41105-020-00279-5ISI: 000549371100001Scopus ID: 2-s2.0-85087945151Local ID: HOA HHJ 2020;HHJÖvrigtISOAI: oai:DiVA.org:hj-50284DiVA, id: diva2:1458991
Available from: 2020-08-18 Created: 2020-08-18 Last updated: 2020-12-30Bibliographically approved

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Pakpour, Amir H.

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