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Development and Validation of the Depression Inventory for Type 1 Diabetes (DID-1)
Mónica Carreira1,2, María Soledad Ruiz de Adana2,3,4, Marta Domínguez2,3,4
1Department of Personality, Assessment and Psychological Treatment, Faculty of Psychology, University of Málaga, Campus de Teatinos, 29071 Málaga, Spain.
International Journal of Environmental Research and Public Health
|December 10, 2021
Summary
People with type 1 diabetes (T1D) experience higher depression rates. A new tool, the Depression Inventory for type 1 Diabetes (DID-1), accurately assesses depressive symptoms in T1D patients, improving detection.
Area of Science:
- Psychiatry and Endocrinology
- Psychometrics
- Clinical Psychology
Background:
- Individuals with type 1 diabetes (T1D) have a higher prevalence of depression compared to the general population.
- Depression negatively impacts T1D prognosis and management.
- Existing depression screening tools may not adequately address the unique characteristics of T1D patients.
Purpose of the Study:
- To develop a novel assessment instrument for depressive symptoms specifically tailored for individuals with type 1 diabetes.
- To rigorously evaluate the psychometric properties of this new scale.
Main Methods:
- Development of the 45-item Depression Inventory for type 1 Diabetes (DID-1) using a 7-point Likert scale.
- Assessment of reliability using Cronbach's alpha and Spearman-Brown split-half coefficient.
- Evaluation of validity (concurrent, convergent, discriminant) and factor analysis to identify underlying dimensions.
Main Results:
- The DID-1 demonstrated high internal consistency and temporal stability.
- The scale exhibited adequate concurrent, convergent, and discriminant validity.
- Factor analysis revealed 7 distinct factors contributing to depressive symptoms in T1D, explaining 61.612% of the variance. A cut-off score of 155 was established.
Conclusions:
- The DID-1 is a reliable, valid, and practical instrument for assessing depressive symptoms in type 1 diabetes.
- This specialized scale effectively mitigates biases present in non-specific depression assessment tools for diabetes patients.
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