Category: Measurement; Clinical Practice (assessment, diagnosis, treatment, knowledge translation/EBP, implementation science, program development)
Our purposes were to: (1) examine the psychometric properties of 30 Disabilities of the Arm, Shoulder and Hand (DASH) items using the one-parameter Rasch (partial credit model) and two-parameter graded response model, and (2) present the keyform to improve clinical interpretation of the patient's improvement and goal setting.
Design : Cross-sectional survey study.
Setting : Outpatient rehabilitation clinics.
Participants (or Animals, Specimens, Cadavers) :
Clinical data of 2,726 patients, requested from the Focus on Therapeutic Outcomes, Inc. with orthopedic shoulder impairments seeking outpatient rehabilitation therapy were analyzed.
Main Outcome Measure(s) : DASH
Internal consistency of items was 0.94. With a separation index of 4.0, the 30 items can separate patient sample into 5.7 statistically distinct strata. Comparing to the mean (SD) of patient ability level of 0.62 (SD = 0.43) 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 'recreational activities in which you move your arm freely' appeared to be the most difficult item, followed by 'do heavy household jobs', while tem 'turn a key' was the easiest. Items 'do heavy household jobs' and 'garden or outdoor property work' were able to discriminate patients' upper extremity function better than other items, while 'tingling in your arm, shoulder or hand', 'stiffness', 'difficulty sleeping', and 'I feel less capable, less confident' had the lowest discrimination abilities. We presented the keyform with a clinical example for the application of clinical interpretation and goal setting.
Conclusions : Results may improve clinical interpretation of the DASH measure and assist clinicians using patient-reported outcomes during clinical practice.
Stephen Hou– Clinical Assistant Professor, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin
Chiung-ju Liu– Associate Professor, Indiana University - Purdue University Indianapolis, Indianapolis, Indiana
Mohammed Habibur Rahman– Assistant Professor, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin
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
Inga Wang– Associate Professor, University of Wisconsin Milwaukee, Milwaukee, Wisconsin