Rasch Analysis of the Wrist and Hand Fugl-Meyer: Dimensionality and Item-Level Characteristics

Abstract

Upper extremity (UE) hemiparesis remains one of the most common impairments exhibited among the expanding stroke survivor population,1 and frequently undermines performance of valued activities. Indeed, despite weeks of rehabilitation, 50% of stroke survivors retain some degree of UE weakness 2 and up to seventy percent remain unable to functionally use their paretic UEs 3 in the months after stroke. Developed in 1975, the UE section of the Fugl-Meyer (UE FM) 4 remains one of the most established and widely-used 5 assessment of UE impairment in stroke. Moreover, the UE FM is recommended for use in stroke rehabilitative trials 6 and, unlike other measures of paretic UE dysfunction 7-9 only requires a few household items to administer, making it especially conducive for clinical use. Using classical test theory techniques, the psychometric reliability 10-13 and validity 14-18 of the UE FM have been shown, and support its integrated, clinical use for assessing UE impairment after stroke. Recent investigations that employ Rasch analysis also demonstrate the strength of the UE FM items themselves. For example, it is now well established that the majority of UE FM items represent the unidimensional construct of UE motor ability 19 and that the UE FM constitutes a useful tool for classifying post-stroke UE motor impairment as mild, moderate, or severe. 5 The proliferation of UE therapies targeting stroke survivors exhibiting minimal UE impairment 20-23 has necessitated the continued development and evaluation of assessment tools providing high reliability, validity, and clinical utility. Such tools are necessary because (a) clinical time is valuable and (b) recent evidence19,24 demonstrates that the traditional understanding of UE motor recovery (i.e., proximal to distal, reflexive then synergistic then isolated) is not absolute. Further, existing measures of wrist and hand motor impairment25-27 may require specialized materials, training, and may take an excessive amount of time. To address the need for a quickly administered, rigorous, bedside measure of active UE motor ability the wrist stability, wrist mobility, and hand items of the UE FM (W/H UE FM) were administered in a standardized manner to subjects with minimal28 and moderate29 UE impairment. This 12-item subset of the UE FM was recently shown to have high intrarater reliability (ICCs = 0.95), internal consistency (Cronbach α > 0.80; ordinal α > 0.80), and concurrent validity (Action Research Arm Test correlation > 0.70) in samples of mildly impaired28 and moderately impaired29 stroke survivors. These findings provide strong evidence that the W/H UE FM may prove a viable tool for efficient, reliable, and valid assessment of UE motor ability in persons with stroke who have mild and moderate impairment. Despite these promising results, it remains unclear how individual W/H UE FM items function in the population of stroke survivors exhibiting minimal UE impairment. Because individuals experiencing mild, and even moderate, UE impairment may be at risk for early supported discharge from rehabilitative services30-32 it is critical that the psychometric properties of these items be ascertained. Our overall goal was to examine the item-level psychometrics of W/H UE FM items in a population of mildly impaired stroke survivors using Rasch analysis. To accomplish this goal, the specific aims of this study were to: (a) determine whether W/H UE FM items represent a unidimensional construct, wrist and hand motor ability and (b) determine the Rasch modeled item-structure of the W/H UE FM. Rasch analysis provides a measurement model for evaluation of categorical data based on the tenet that a total score results from the interaction of (1) person ability and (2) item difficulty.33 The first step in a Rasch analysis is to construct a scalogram. The scalogram is an ordered table of data resulting from measurement of any single attribute (e.g. wrist and hand motor ability). People are ordered from least able to most able based on their ability. Thus, person ability is given by the number of items answered correctly or points earned out of the total possible. Similarly, test items are ordered from least difficult to most difficult, with item difficulty given as the number of items endorsed out of the total possible. These data are transformed from the ordinal (i.e., categorical) level of measurement to an interval scale by means of a log-odds transformation. The resulting logits enable direct, linear, comparison of person abilities and item difficulties, a primary advantage of the Rasch model. Applied to wrist and hand motor ability, as is the case here, the probability of a person's “success for any given item depends on the difference between the ability of the person and the difficulty of the item.”33 Rasch analysis also enables evaluation of individual items using fit statistics, which reflect the alignment of each item to the construct being measured.34 Researchers use these statistics to refine new and existing instruments, examine reliability and validity, and optimize clinical utility for specific populations.