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Stroke-Specific FIM Models in an Urban Population.

Abstract: Data from the Functional Independence Measures (FIM) are important outcome measures in the stroke population undergoing rehabilitation. The FIM consists of 18 scales scored from 1 to 7; higher numbers mean greater ability. Several different scores can be obtained from the FIM: a total score (sum of all the scales), individual scale scores, and a physical and cognitive score (a two-dimensional interpretation). Research has shown that FIM data can have multidimensional components.[8] That implies different groupings of scales may exist and with this the possibility of developing more sophisticated and robust measures within stroke populations. This study examined the multidimensional aspects of FIM using an urban sample of ischemic and hemorrhagic stroke patients. We found a three-dimensional FIM solution for both stroke groups that included self-care, cognitive function, and toileting as the major grouping of scales. These findings confirm a multidimensional nature of FIM scores and show a condition-specific pattern of FIM scores in stroke patients. This knowledge will help nurses, clinicians, and researchers develop more sensitive ways of recording care outcomes; improved prediction of stroke outcomes may also emerge from this knowledge.

Current Knowledge

A number of instruments have been developed to assess functional performance and improvement following rehabilitation. One of those most commonly used in the United States is the Functional Independence Measure (FIM). The purpose of this study was to examine data from the FIM instrument to determine whether there were different groupings of scales specific to ischemic and hemorrhagic stroke patients. This information is important to know because it could lead to grouping FIM subscales in ways that could lead to better prediction of stroke outcomes. The study is also important because it includes data on hemorrhagic stroke patients, a group that has not been investigated to the same extent as ischemic patients.

The FIM consists of 18 ordinal subscales designed to assess an individual's physical functioning and behavior. The scales are eating, grooming, bathing, dressing upper body, dressing lower body, toileting, bladder management, bowel management, transfer bed/chair/wheelchair, transfer toilet, transfer tub/shower, mobility walk/chair, mobility stairs, communication, expression, social interaction, problem solving, and memory. Each scale is given a score on a 7-item ordinal scale, ranging from 1 (total assistance required--less than 25% independent activity) to 7 (complete independence). In common with similar instruments, the FIM requires consistent and reliable administration if it is to be useful in making comparisons within and across patients with similar diseases, as well as across different conditions where a measure of improved outcome is required.[1] The reliability and validity of the FIM have been established. An important aspect of using the FIM is that it is highly dependent upon the individual conducting the assessment. Considerable effort is expended in educating and training those who use this instrument. In general, good interrater reliability has been found when the FIM is used.[2,3,4,5]

The FIM scores can be used in a variety of different ways, depending on clinical or research imperatives. The total FIM score (the sum of all the subscales) can be used as an overall measure of a person's functional ability, while each of the individual 18 subscales can be used to assess a specific aspect of function or behavior. Between these extremes, other groupings of variables are known to exist. Most commonly seen is the two-dimensional model consisting of a physical and cognitive grouping of FIM scales.[6,9] This can be a useful way of looking at an individual's response to rehabilitation taking a global physical and psychological perspective. There is also conceptual support for a two-dimensional model: the World Health Organization uses the concepts of physical and mental well-being in its definition of health.[10]

For the stroke population, it has been established that there are at least two major subgroups (physical and cognitive) within the FIM. Other researchers have examined the FIM to determine whether there are consistent patterns of scales grouped together to create a multidimensional scale.[8] These authors suggest the FIM can be viewed from a number of different perspectives based upon the clinical or research demand and suggest more sophisticated and disease-specific dimensions of FIM data. They describe at least three dimensions to the FIM data applicable to stroke patients: activities of daily living (ADL), mobility, and cognitive functioning. While their study has helped researchers to understand the possible dimensions of a disease-specific interpretation of FIM data, more work is needed that looks at both ischemic and hemorrhagic stroke and, in relation to this study, a predominantly urban population. The purpose of this study was to examine the multidimensional nature of FIM scores for an urban stroke population.

Ischemic Stroke Patients


Data from a total of 2,303 patients with a diagnosis of ischemic stroke from an urban rehabilitation setting were analyzed. All patients were administered the FIM as part of the routine assessment process. The FIM scores were obtained at admission and discharge. In addition to the FIM, demographic data such as age, gender, ethnicity, marital status, and disposition were collected. Approval for this study was obtained from the appropriate ethics review board. The sample consisted of 1,273 (55.3%) women and 1,029 (44.7%) men. Of the whole sample, 1,366 (59.3%) were over 65 years of age; only 335 (15.1%) were married. The sample was predominantly African American (66.1%), with 13.5% Caucasian and 2.2% Asian. Eighteen percent had unknown ethnic origin.


To identify the structural relationships between variables comprising the FIM, principal components and varimax factor analysis were performed. Factor analysis allows for the examination of interrelationships between variables and permits the grouping of variables into factors. The extent to which a variable is associated with a factor is given by a factor loading score; the greater the factor loading (range of 0 to 1), the more associated that variable is to a factor (Tables 1 and 2). Factors are a grouping of variables closely related to one another in some way. This technique is very useful for reducing large numbers of variables into smaller ones. In our example, the 18 FIM variables were reduced to a smaller number of factors. Factor analysis can also be used to identify and confirm theoretical constructs.

Table 1

Varimax Rotation for Ischemic Stroke Patients (Factor Loadings)
Subscale Factor 1 Factor 2 Factor 3

Dressing lower body 0.841
Transfer bed/chair/wheelchair 0.818
Baffling 0.807 0.352
Dressing upper body 0.807 0.360
Transfer toilet 0.781
Transfer tub/shower 0.728
Grooming 0.642 0.489
Mobility walk/chair 0.544
Mobility stairs 0.529
Eating 0.528 0.507
Comprehension 0.895
Expression 0.877
Memory 0.877
Problem-solving 0.870
Social interaction 0.837
Bladder management 0.311 0.854
Toileting 0.385 0.827
Bowel management 0.305 0.319 0.812

Table 2

Factor Scores for Ischemic Stroke Patients (Factor Loadings)
Subscale Factor 1 Factor 2 Factor 3

Comprehension 0.886
Problem-solving 0.883
Memory 0.878
Expression 0.844
Social interaction 0.828
Eating 0.509 0.433 0.334
Transfer toilet 0.761 0.327
Dressing lower body 0.328 0.754 0.352
Transfer tub/shower 0.724
Dressing upper body 0.396 0.703
Bathing 0.374 0.701 0.349
Mobility stairs 0.667
Transfer bed/chair 0.636 0.461
Mobility walk/chair 0.563
Grooming 0.503 0.551 0.340
Bowel management 0.319 0.847
Bladder management 0.844
Toileting 0.838

A series of two-, three-, and four-factor models was examined. As expected, when forcing a two-factor model, variables loaded on physical (explaining 55.1% of variance) and cognitive (explaining 11.8% of variance). The total variance explained by a two-factor model was 66.9% (Table 1). The three-factor model produced the following grouping of variables:

* Factor 1 (self-care): dressing lower body, transfer bed/chair, bathing, dressing upper body, transfer toilet, transfer tub/shower, grooming, mobility walk/chair, and mobility stairs

* Factor 2 (cognitive): comprehension, expression, memory, problem-solving and social interaction

* Factor 3 (elimination): bladder management, toileting, and bowel management.

Factor 1 (self-care) accounted for 31.6% of variance, Factor 2 (cognitive), 27.7%, and Factor 3 (toileting), 14.8%. The total variance accounted for with the three-factor model was 74.2%. Reliability estimates (coefficient alpha) were determined for each of the three factors: self-care (0.94), cognitive (0.96), and elimination (0.93). The three-factor model explained more variance than the two-factor model.

An attempt was made to extract a fourth factor from the FIM data using varimax rotation. A factor called mobility (mobility walk/chair and mobility stairs) was identified that accounted for less than 5% of variance and had a coefficient alpha of 0.48. Because of the relatively small contribution of explained variance and poor internal consistency, we decided not to include this factor in the overall modeling of FIM data for ischemic stroke patients.

Hemorrhagic Stroke Patients


A similar analysis was performed on a sample of 217 patients who experienced a hemorrhagic stroke. This sample contained 112 (51.6%) women and 105 (48.8%) men. The ethnic background of the patients was as follows: 153 (70%) African American, 20 (9.2%) Caucasian, 4 (1.8%) Asian, and 40 (18.4%) with undisclosed backgrounds. Of the whole sample, 146 (67.3%) were 65 years and older, and 71 (32.7%) were under 65 years of age.


A two-factor model was examined with a varimax orthogonal rotation (Table 2). Factor 1 (physical) comprised 34.9% of variance, and Factor 2 (cognitive), 29.8%. The total variance explained by the two-factor model was 63.7%. (The ischemic stroke two-factor model explained 66.9% of variance.) A three-factor solution gave the following results: Factor 1 (cognitive) accounted for 28% of variance, Factor 2 (selfcare), 26%, and Factor 3 (toileting), 18.2%. The total variance accounted for by this model was 72.3%; it was 74.2% for ischemic patients. Reliability estimates (coefficient alpha) were determined for the three factors. Results were as follows: cognitive (0.95), self-care (0.92) and toileting (0.90). A four-factor model was examined, but as in the ischemic group of patients, relatively low alpha results were obtained (less than 0.05). This model was not developed further. As with the ischemic data, the three-factor model accounted for more explained variance than the two-factor model. In addition, the ischemic models explained more variance than their hemorrhagic counterparts.


The finding that a simple two-factor model may be insufficient to explain the nature of disability following hemorrhagic and ischemic stroke was borne out in this study. It is clear a multifaceted structure of outcomes is measured by the FIM. These findings are in keeping with the work of previous authors who also determined a three-factor model for stroke.[8] While scales used in the construction of cognitive factors (comprehension, expression, social interaction, problem solving, memory) were consistent in both sets of findings, what differed between the studies was the grouping of the other FIM scales into factors.[8] Stineman et al. identified their factors as ADL (eating, grooming, bathing, dressing upper body, dressing lower body, toileting, bladder management, bowel management), mobility (transfer bed/chair, transfer toilet, transfer tub/shower, mobility walk/chair, mobility stairs), and cognitive. Our study found a self-care factor (eating, grooming, bathing, dressing upper body, dressing lower body, transfer bed/chair, transfer toilet, transfer tub/shower, mobility walk/chair, mobility stairs), a cognitive and a toileting factor (toileting, bladder management, bowel management). It is difficult to determine the reason for these differences. The Stineman study had a huge sample (nearly 100,000), yet ours, with more than 2,000 cases, is large in itself. Other threats to the validity and reliability of our FIM data could be from the way the instrument was administered. However, data for our study were collected by staff members who were trained and experienced in collecting data with this tool. It is possible that differences could be accounted for by the inherent nature of an urban stroke population; perhaps this group has a distinctive set of subscales that are important when considering functional ability.

These findings have several implications for practitioners and researchers. FIM data can be used in a number of different ways depending on the clinical or research issues. The total score (one dimension) can provide an overall measure of independence, while scores on the 18 subscales (multidimension) can provide detailed information about a patient's current ability and changes over time. Use of the two-dimensional FIM can provide the clinician or researcher with detailed information about physical and cognitive functioning.

The three-dimensional FIM scores identified for these ischemic and hemorrhagic patients provide a detailed condition-specific set of scales that can be a sensitive indicator of a patient's improvement. Monitoring these scales (self-care, cognitive, toileting) may provide more useful, timely, and sensitive measures of change than relying on just one- or two-dimensional FIM scales. Exploring disease-specific dimensions of the FIM may also prove valuable by grouping scales in ways that make them better predictors of a patient's outcome following interventions. Future research should examine disease-specific FIM measures to assess outcomes from nursing interventions with stroke patients.


This study demonstrated that simple interpretations of FIM data collected on ischemic and hemorrhagic patients in an urban setting may not be adequate in explaining responses to nursing and health interventions. The two-factor model inherent in the FIM instrument, while providing an analysis of motor and cognitive function, may not adequately explain or measure changes in patient ability. Additional analysis strategies and interpretation may need to be employed and more sophisticated models developed to understand rehabilitation outcome data. Research has shown a variety of disease-specific groupings of FIM items beyond the basic two-factor model.[7] Understanding relevant substructures to the FIM model may enable a more effective targeting of interventions and a more sensitive analysis of rehabilitation and functional outcomes for urban populations. Knowing that diseases have different rehabilitation foci will enable more effective comparison between people with the same condition, rather than at present having all diseases judged by the same standards. Greater understanding of rehabilitation measures may lead to the development of more robust benchmarks for stroke outcomes, something becoming increasingly important in resource allocation. Determining the relative importance of condition-specific dimensions of the FIM, targeting interventions, and assessing outcomes by nurses and other professionals will be a challenge for the future.


Funding for this research was provided by the Center for Health Research, Wayne State University.


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[5.] Ottenbacher KJ, Msall ME, Lyon NR et al: Interrater agreement and stability of the Functional Independence Measure for Children (WeeFIM): Use in children with developmental disabilities. Arch Phys Med Rehabil 1997; 78(12): 1309-1315.

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[8.] Stineman MG, Jette A, Fiedler R, Granger C: Impairment-specific dimensions within the Functional Independence Measure. Arch Phys Med Rehabil 1997; 78(6): 636-643.

[9.] Williams BC, Li Y, Fries BE, Warren RL: Predicting patient scores between the functional independence measure and the minimum data set: development and performance of a FIM-MDS "crosswalk." Arch Phys Med Rehabil 1997; 78(1): 48-54. [published erratum appears in Arch Phys Med Rehabil 1998; 79(2): 231]

[10.] World Health Organization: International Classification of Impairments, Disabilities, and Handicaps. WHO, 1980.

Questions or comments about this article may be directed to: Stephen J. Cavanagh, PhD RN, at the College of Nursing, Wayne State University, 5557 Cass Avenue, Detroit, MI 48201 or by e-mail at He is an associate professor at Wayne State University.

Kevin Hogan, PhD CPsych, is principal lecturer at Wolverhampton University in the United Kingdom.

Vickie Gordon, MSN NP, is coordinator of Neurosurgical Nursing Services at the Detroit Medical Center.

Janecia Fairfax, MSN RN, is a nurse researcher at Wayne State University.
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Author:Cavanagh, Stephen J.; Hogan, Kevin; Gordon, Vickie; Fairfax, Janecia
Publication:Journal of Neuroscience Nursing
Geographic Code:1USA
Date:Feb 1, 2000
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