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      What is the best measure for assessing diabetes distress? A comparison of the Problem Areas in Diabetes and Diabetes Distress Scale: results from Diabetes MILES–Australia

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          Abstract

          <p class="first" id="d3498046e154">This study used Rasch analysis to examine the psychometric validity of the Diabetes Distress Scale and the Problem Areas in Diabetes scale to assess diabetes distress in 3338 adults with diabetes (1609 completed the Problem Areas in Diabetes scale ( n = 675 type 1 diabetes; n = 934 type 2 diabetes) and 1705 completed the Diabetes Distress Scale ( n = 693 type 1 diabetes; n = 1012 type 2 diabetes)). While criterion and convergent validity were good, Rasch analysis revealed suboptimal precision and targeting, and item misfit. Unresolvable multidimensionality within the Diabetes Distress Scale suggests a total score should be avoided, while suboptimal precision suggests that the Physician-related and Interpersonal distress subscales should be used cautiously. </p>

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          Author and article information

          Journal
          Journal of Health Psychology
          J Health Psychol
          SAGE Publications
          1359-1053
          1461-7277
          April 22 2016
          April 2018
          April 22 2016
          April 2018
          : 23
          : 5
          : 667-680
          Affiliations
          [1 ]Centre for Eye Research Australia, University of Melbourne, the Royal Victorian Eye and Ear Hospital
          [2 ]Singapore Eye Research Institute, Singapore
          [3 ]Duke-NUS Medical School, Singapore
          [4 ]The Australian Centre for Behavioural Research in Diabetes, Australia
          [5 ]Centre for Social and Early Emotional Development, School of Psychology, Deakin University, Australia
          [6 ]Department of Medical and Clinical Psychology, Centre of Research on Psychology in Somatic diseases (CoRPS), Tilburg University, The Netherlands
          [7 ]AHP Research Ltd, UK
          Article
          10.1177/1359105316642006
          27106085
          7357a24a-0f6e-438c-9366-79dc1d645c88
          © 2018

          http://journals.sagepub.com/page/policies/text-and-data-mining-license

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