Journal of Multidisciplinary Healthcare
School of Nursing and Midwifery / School of Science
Edith Cowan University - Open Access Support Scheme 2020
Background: Treatment satisfaction is an important indicator for treatment compliance and glycemic control. Although psychometric properties of the Diabetes Treatment Satisfaction Questionnaire have been confirmed in several languages, it remains unclear the extent to which the factorial structure of this tool is valid for Arabic speaking populations.
Purpose: This study set out to confirm the construct validity of the Arabic version of the Diabetes Treatment Satisfaction Questionnaire (DTSQ) by investigating the fit of published factor structures and the reliability of responses in patients diagnosed with type 2 diabetes.
Methods: Data were from a large cross-sectional study of 1002 patients with diabetes in Jordan. Confirmatory factor analysis was used to compare three different models of the 8-item questionnaire (one factor, two factors, three factors) across patients treated with insulin and patients treated with oral hypoglycaemic medications.
Results: Statistics covered the factorial validity and omega reliability coefficient (Ωw) of the DTSQ. We were able to replicate the three different models of the 8-item Diabetes Treatment Satisfaction Questionnaire reported in previous studies, yet a two-factor model provided the best fit to the data in our sample with omega reliability coefficient (Ωw) of the subscales above 0.70.
Conclusion: The finding suggests a cross-cultural invariance of the factor structure of the Arabic version of the Diabetes Treatment Satisfaction Questionnaire, as we were able to replicate the same factor structure using the Arabic translated version of the tool and using non-English speaking participants. Within known limitations and gaps in the literature, healthcare professionals working with Arabic speaking patients may find this tool useful for identification of high-risk patients and those in need for interventions to promote glycemic control.
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Multidisciplinary biological approaches to personalised disease diagnosis, prognosis and management