Category: Clinical Practice (assessment, diagnosis, treatment, knowledge translation/EBP, implementation science, program development); Measurement
To co-calibrate the 30 Disabilities of the Arm, Shoulder and Hand (DASH) items, 37 Shoulder CAT items, and 18 Upper Extremity Functional Index (UEFI) items using item response theory (IRT), to compare measurement properties of the measures, and to explore the implications of these measurement properties on longitudinal studies of upper extremity functioning over time.
Design : Cross-sectional survey study.
Setting : Outpatient rehabilitation clinics.
Participants (or Animals, Specimens, Cadavers) : 34,809 patients took the Shoulder CAT questionnaire, 2724 patients completed the DASH, and 982 patients responded the UEFI. Among them 3,686 took two or more questionnaires at the admission.
Interventions : Not applicable.
Main Outcome Measure(s) : DASH, Shoulder CAT, UEFI
Results : Comparing to the mean (SD) of patient ability level of 0.37 (SD = 0.60) logits, the item difficulty estimates [mean (SD) = 0.0 (0.03) logits] matched well with the functional levels of the sample with no ceiling or floor effects. Item 'using your affected arm to touch an object on the back seat while sitting in the front seat of a car' appeared to be the most difficult item, followed by 'work overhead for more than 2 minutes', while tem 'flushing the toilet' and 'turn a key' were the easiest. We categorized items into high, moderate and low discrimination levels. Items 'place a can of soup on a shelf overhead' and 'reach the earlobe on the opposite side' were able to discriminate patients' upper extremity function better than other items, while 'tingling', 'stiffness', 'difficulty sleeping', and 'I feel less capable, less confident' had the lowest discrimination abilities.
Conclusions : Results may improve clinical understanding the activity domain of upper extremity functioning during clinical practice.
Jay Kapellusch– Associate Professor, Chair, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin
Xiaoyan Li– Assistant Professor, University of Texas Health Science Center at Houston, Houston, Texas
Sheng-Che Yen– Assistant Professor, Northeastern University, Boston, Massachusetts
Mohammed Habibur Rahman– Assistant Professor, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin
Chiung-ju Liu– Associate Professor, Indiana University - Purdue University Indianapolis, Indianapolis, Indiana
Inga Wang– Associate Professor, University of Wisconsin Milwaukee, Milwaukee, Wisconsin