Mi Jung Lee1,2, Sergio Romero3,4, Craig A Velozo5, Ann L Gruber-Baldini6, Lisa M Shulman7. 1. Department of Occupational Therapy, University of Florida, PO BOX 100164, Gainesville, FL, 32610, USA. 2. Center of Innovation on Disability and Rehabilitation Research (CINDRR), Department of Veterans Affairs, 101 SE 2nd Place Ste 104, Gainesville, FL, 32601, USA. 3. Department of Occupational Therapy, University of Florida, PO BOX 100164, Gainesville, FL, 32610, USA. sromero@phhp.ufl.edu. 4. Center of Innovation on Disability and Rehabilitation Research (CINDRR), Department of Veterans Affairs, 101 SE 2nd Place Ste 104, Gainesville, FL, 32601, USA. sromero@phhp.ufl.edu. 5. Division of Occupational Therapy, Medical University of South Carolina, 151-B Rutledge Avenue, MSC 962, Charleston, SC, 29425, USA. 6. Division of Gerontology, Department of Epidemiology & Public Health, University of Maryland School of Medicine, Rm 213, Howard Hall, 660 west redwood, Baltimore, MD, 21201, USA. 7. Parkinson's Disease and Movement Disorders, Department of Neurology, University of Maryland School of Medicine, 110 S. Paca Street, Rm 3-S-127, Baltimore, MD, 21201, USA.
Abstract
PURPOSE: This study investigated the PROMIS Self-Efficacy Measure for Managing Chronic Conditions (PROMIS-SE) domain distributions and examined the factor structure of the PROMIS-SE. METHODS: A total of 1087 individuals with chronic conditions participated in this study. PROMIS-SE's item banks and two short forms (eight-item and four-item) measuring five behavioral domains (daily activities(DA), Emotions(EM), medications and treatments(MT), social interactions(SS), and Symptoms(SX)) were examined. PROMIS-SE's T-score ranges and distributions were examined to identify domain metric distributions and confirmatory factor analysis (CFA) was conducted to test a multidimensional model fit to the PROMIS-SE. RESULTS: PROMIS-SE domains showed different T-score ranges and distributions for item banks and two short forms across all five domains. While PROMIS-SE EM demonstrated the highest T-scores (least negatively skewed), MT had the lowest T-scores (most negatively skewed) for all three forms. In general, respondents were more likely to achieve highest self-efficacy ratings (very confident) for domains DA, MT, and SS as compared to domains EM and SX. CFA confirmed that a multidimensional model adequately fit all three PROMIS-SE forms. CONCLUSION: Our results indicate that self-efficacy T-distributions are not consistent across domains (i.e., managing medications and treatments domain was more negatively skewed difficult than other domains), which is a requirement for making inter-domain comparisons. A multidimensional model could be used to enhance the PROMIS-SE's estimate accuracy and clinical utility.
PURPOSE: This study investigated the PROMIS Self-Efficacy Measure for Managing Chronic Conditions (PROMIS-SE) domain distributions and examined the factor structure of the PROMIS-SE. METHODS: A total of 1087 individuals with chronic conditions participated in this study. PROMIS-SE's item banks and two short forms (eight-item and four-item) measuring five behavioral domains (daily activities(DA), Emotions(EM), medications and treatments(MT), social interactions(SS), and Symptoms(SX)) were examined. PROMIS-SE's T-score ranges and distributions were examined to identify domain metric distributions and confirmatory factor analysis (CFA) was conducted to test a multidimensional model fit to the PROMIS-SE. RESULTS: PROMIS-SE domains showed different T-score ranges and distributions for item banks and two short forms across all five domains. While PROMIS-SE EM demonstrated the highest T-scores (least negatively skewed), MT had the lowest T-scores (most negatively skewed) for all three forms. In general, respondents were more likely to achieve highest self-efficacy ratings (very confident) for domains DA, MT, and SS as compared to domains EM and SX. CFA confirmed that a multidimensional model adequately fit all three PROMIS-SE forms. CONCLUSION: Our results indicate that self-efficacy T-distributions are not consistent across domains (i.e., managing medications and treatments domain was more negatively skewed difficult than other domains), which is a requirement for making inter-domain comparisons. A multidimensional model could be used to enhance the PROMIS-SE's estimate accuracy and clinical utility.
Authors: Morten Aa Petersen; Mogens Groenvold; Neil Aaronson; Peter Fayers; Mirjam Sprangers; Jakob B Bjorner Journal: Qual Life Res Date: 2006-04 Impact factor: 4.147
Authors: Stephen M Haley; Pengsheng Ni; Helene M Dumas; Maria A Fragala-Pinkham; Ronald K Hambleton; Kathleen Montpetit; Nathalie Bilodeau; George E Gorton; Kyle Watson; Carole A Tucker Journal: Qual Life Res Date: 2009-02-17 Impact factor: 4.147