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Relationships between initial motor assessment scale scores and length of stay, mobility at discharge and discharge destination after stroke.


Stroke is the second biggest cause of death among Australians annually and accounts for one quarter of chronic adult disability in Australia, making it one of the nation's highest causes of morbidity (Australian Institute of Health and Welfare 2008, National Stroke Foundation 2008). Every year approximately 60,000 strokes occur, with around 70% of these being first time events (Australian Institute of Health and Welfare 2008). Strokes have been estimated to cost the health system $2.14 billion annually (National Stroke Foundation 2008). Of the people suffering a stroke, one in five will die within 1 month, and one in three will die within one year (National Stroke Foundation 2008). Approximately 88% of stroke survivors live at home and most have a permanent disability. An increase in the overall number, as well as the proportion, of people over 65 years of age; in addition to advances in medical technology have resulted in an increase in the number of older people surviving stroke and requiring rehabilitation and ongoing care (Australian Institute of Health and Welfare 2008).

The average length of stay (LOS) for someone undergoing rehabilitation within an Australian public health service following a stroke is 71.3 days (McKenna et al 2002). As many as 17% of stroke survivors are unable to return home following discharge from rehabilitation (Tooth et al 2005). Inpatient rehabilitation following stroke and discharge to institutionalised care, such as nursing home accommodation, are associated with high social and economic costs (Australian Institute of Health and Welfare 2008). In 2000-2001, the breakdown of stroke care costs within the health system costs were as follows: inpatient rehabilitation ($150 million), nursing home care ($63 million) and provision of allied health services ($4.8 million) (Senes 2006). These costs place a considerable burden on health resources and are steadily increasing (Australian Institute of Health and Welfare 2008, Senes 2006).

The clinician's ability to measure the effectiveness of rehabilitation treatment underpins good practice. This is achieved using context appropriate, widely used, reliable, valid and responsive outcome measures (Barak and Duncan 2006, Salter et al 2005). If such tools are administered correctly, they can provide a guide to treatment planning and prioritisation, document progress made by the individual, indicate the need for modifying treatment regimes and assist in prognostication. In stroke rehabilitation settings, outcomes following rehabilitation have been measured in numerous ways (Salter et al 2005). Length of stay and discharge destination are commonly used measures of outcome, though both strongly depend on many factors making accurate prediction complex. To date no clear relationship has been established between location and severity of the stroke, and LOS or discharge destination. Indeed many studies examining predictive models of LOS have reported consistently low correlations between these factors (McKenna et al 2002, Tooth et al 2005, Wee et al 2003). Small sample sizes (McKenna et al 2002, Tooth et al 2005), retrospective study designs (Lutz 2004, McKenna et al 2002) and the multifactorial nature of contributors to LOS (McKenna et al 2002, Shah et al 1989, Wee et al 2003) have been cited as possible reasons for these findings.

Several studies analysing correlations between LOS and stroke category using the classification developed by Bamford et al (Table 1) have also reported conflicting results (Bamford et al 1991, Hakim and Bakheit 1998, Tooth et al 2005). Tooth et al (2005) found that those suffering a LACI had a significantly reduced LOS compared to someone suffering a POCI, whereas Hakim & Bakheit (1998) found there was no real difference between PACI, LACI and POCI classifications and that all these classifications had a shorter LOS than those surviving a TACI. These findings agree with clinical expectations as a TACI classified stroke generally results in the most severe disability; however it is clear that the relationships between LOS and stroke category are complex.

Length of hospital inpatient stay following stroke has been shown to have a moderate association with measures of function at admission such as the Barthel Index (Bohannon et al 2002) and the Berg Balance Scale (Wee et al 2003). Many factors such as motor function (Bohannon et al 2002, Hakim and Bakheit 1998, Tooth et al 2005), pre-morbid living status (Tooth et al 2005), LOS in the acute setting (Tooth et al 2005, Wee et al 2003) and the presence and extent of co-morbidities (Galski et al 1993, Lew et al 2002, Tooth et al 2005) have been reported to predict LOS. A negative correlation between initial motor function and discharge destination has been demonstrated previously (Bohannon et al 2002, Tooth et al 2005), and age has been shown to be a significant predictor of discharge destination, with younger individuals more likely to be discharged home (Bohannon et al 2002).

A commonly used tool to measure motor recovery and functional ability following stroke is the Motor Assessment Scale (MAS) (Carr et al 1985). The MAS is a criterion-based scale assessing 8 domains of functional motor activity with each item scored on a 7-point ordinal scale (ranging from 0= no motor function, to 6= optimal task performance or performance completed within the set time frame) (Carr and Shepherd 1998). The MAS has been shown to be reliable when used by qualified as well as undergraduate therapists (Carr et al 1985, Poole and Whitney 1988) and validity has been confirmed in 2 separate studies comparing total MAS scores with the Fugl-Meyer Assessment, producing correlations of r=0.96 and r=0.88 (Malouin et al 1994, Poole and Whitney 1988). One limitation is that the MAS does not discriminate between early stages of motor recovery (Hill et al 1997), as there are no criteria for flaccidity and the movement synergies typical in early stroke onset. Several studies have also confirmed a limitation in the dimensionality and scalability of the measure as it can be seen that top and/or bottom levels are overrepresented and other items show clustered middle levels (Aamodt et al 2006, Brock et al 2002, Dean and Mackey 1992, English et al 2006).

The ability to accurately predict discharge destination and functional outcomes following rehabilitation could result in improved planning, more efficient service utilisation and reduction in overall inpatient rehabilitation costs. In one recently published report, discharge destination was predicted with 87% accuracy, based on pre-stroke residential status, age and MAS scores for walking (item 5) and supine to side lying (item 1) (Brauer et al 2008). An earlier study suggested that recovery of motor function at discharge (total MAS discharge score) could be predicted based on age, balanced sitting (item 3) and combined arm scores (items 6-8) on admission (Loewen and Anderson 1990).

The present study aimed to examine, within an Australian aged care context, the extent to which MAS admission scores could be used to predict mobility outcomes at discharge, as well as LOS and discharge destination after stroke. It was hoped that this information would assist the therapist with goal setting and overall management of clinical loads, as well as determination of treatment priorities. In addition to informing therapeutic planning, this insight into an individual's potential for recovery could assist the patient, their family and the healthcare service from both financial and social perspectives.



This was a retrospective audit of patients admitted to the Stroke Rehabilitation Unit (SRU) at Osborne Park Hospital, Perth, Western Australia during the period June 2001 and January 2007. The SRU provides comprehensive, multidisciplinary rehabilitation after stroke for adults aged 65 years or older. Individuals entered the SRU by referral from a range of acute medical and neurosurgical services. Stroke survivors were not considered for admission if they were not deemed to have rehabilitation potential, for instance, if they had a shortened life expectancy due to co-morbidity (e.g., cancer), significant co-morbidity likely to impair ability to participate in rehabilitation (e.g., severe cardiac disease), poor functional level prior to admission or a cognitive impairment severe enough to impair rehabilitation potential.

Data acquisition

We interrogated the SRU database to extract the required data on all patients admitted during the study period of June 2001 and January 2007. Patients were identified only by Unit Medical Record Number (UMRN) and variables recorded included: gender, age, date of birth, affected side, stroke classification (see Table 1) and stroke type (infarct or haemorrhage) (Bamford et al 1991), length of inpatient stay, admission and discharge MAS scores (total and individual component scores) and discharge destination.

Initial assessment

Motor ability was assessed using the MAS within the first 1-2 days of admission to the SRU by one of four senior physiotherapists. To investigate the confounding influence of multiple assessors, evaluation of the inter-rater reliability was undertaken utilising a video of five stroke patients, with varying degrees of disability, performing the tasks within each subset of the MAS. Each physiotherapist scored the patients' performance using the MAS and scores were compared for percentage exact agreement.

Data analysis

Descriptive analyses were undertaken to describe the sample participants and inspect the total and individual subsections of the MAS on admission and discharge, as well as discharge destination and LOS. The total MAS scores on admission were recoded into four categories (<20, 20-29, 30-39, 40+). The term 'mobility' included the first 5 items on the MAS; while the ability to 'walk' at discharge was based on only item 5 (walking), which was dichotomised further into "walking with assistance" (scoring 1-3) or "walking independently" (scoring 4-6). The database recorded discharge destination as either being independent at home, home with carer or other support or discharge to an institution. For the prediction of discharge destination in this study, the variables were dichotomised to being discharged home (with or without support) or discharge to an institution.

The predictive value of initial MAS scores to determine LOS was investigated using a multiple linear regression procedure, while prediction of discharge destination and mobility on discharge utilised multiple logistic regression. The variables entered into each regression equation were age, gender, side, type and classification of stroke and MAS score on admission. Length of stay had a skewed distribution so these data were logarithm transformed to create a normal distribution. All analyses were performed using SPSS v 15.0. A probability of p<0.05 was considered to represent meaningful differences for all statistical tests.


Baseline Descriptive Data

Data were collected on a total of 253 people admitted to the SRU between June 2001 and January 2007. Data from fourteen individuals were excluded from analysis: seven died during their hospital admission period, two were transferred to another hospital and discharge clinical outcomes were not measured, two had other diagnoses, two had a second admission due to a stroke during the time period of this study, and in one case clinical data was incomplete (see figure 1). Consequently data from 239 individuals were included in the analysis. Demographic data for the study sample are presented in Table 2.

Interrater Reliability

Percentage exact agreement between raters' scores of the eight MAS items for the five patient videos ranged from 55% for the balanced sitting (item 3) to 100% for the task of supine to sitting over side of the bed (item 2). The mean percentage agreement for the four raters over the five patients was 71%.

Relationships between MAS admission scores, patient demographics, length of stay, mobility on discharge and discharge destination.

1. Length of stay--There were moderate correlations between LOS and total MAS admission scores (r=-0.706) and admission mobility scores (items 1-5) (r=-0.716); with lower scores on admission indicating a greater likelihood for a longer LOS. Age, gender, type and side of stroke showed no association with LOS.

2. Mobility on discharge--Total MAS scores on admission were strongly associated with walking function (item 5) on discharge (#2= 119.4, 45DF, p=0.001). The lower the total MAS admission score, the less likely the patient was to be 'walking independently' (scoring between 4-6 for this item) on discharge.

3. Discharge destination--Correlations are summarised in Table 3. A significant association was observed between discharge destination and total MAS admission scores and mobility on admission (MAS items 1-5). Of the 169 patients discharged home, 68.3% had a total admission MAS score >30, whereas 85.7% of the 70 patients discharged to an institution had an MAS score <30. When the mobility component scores are considered, the score for supine to sitting over the side of bed (item 2) on admission showed greatest association with eventual discharge destination, followed by balanced sitting (item 3).


Predicting Discharge Destination

The MAS admission scores as well as age were included in the final model ([R.sup.2] = 0.249, P<0.001), (Table 4). The regression analysis illustrated that the lower the total MAS admission score, the more likely an individual was to be discharged to an institution (p<0.001). Similar trends were observed for age, with older individuals more likely to be discharged to an institution (p<0.001). This model correctly predicted a greater percentage of those discharged home, (with or without support) compared to discharge to an institution (Table 5).

Predicting Walking Function at Discharge

Of the variables examined (age, gender, side, type and classification of stroke and MAS admission scores) only age and total MAS admission scores were significantly related to the likelihood of walking independently on discharge (Table 6). The likelihood of walking also was shown to decrease with age (see table 6).


This study aimed to explore the utility of admission MAS scores to predict LOS, mobility at discharge and discharge destination, as well as to explore associations between other variables and these outcomes. Of the patient demographics explored for possible relationships with LOS, discharge destination and walking function, affected side and type of stroke, as well as gender showed no association with any of the discharge outcomes. Age was only significant in relation to predicting discharge destination and walking function on discharge.

Moderate associations were identified between eventual LOS admission and total MAS as well as MAS mobility items (r=-0.706 and r=0.716 respectively). However accurate prediction of LOS was limited by the retrospective nature of the study. While Tooth et al (2005) found the variables predictive of a longer LOS were poor admission FIM scores, living alone, longer acute LOS, co-morbidity and stroke classification, our model revealed total MAS scores on admission to be the only significant variable predicting LOS. Both models accounted for around half of the variance associated with LOS. This underlines the fact that there are many potential confounding factors that need to be taken into consideration when predicting LOS for an individual undergoing rehabilitation post stroke, some of which reflect social and economic perspectives. Overall LOS is affected by time spent in the acute setting as well as time spent waiting for placement. For instance, Wee et al (2003) found that one factor responsible for increased LOS in the stroke unit was the lack of bed availability in the intended discharge destination.

Another component of this study was to investigate the potential of admission MAS scores to predict discharge destination. Like many previous investigations (Bohannon et al 2002, Brauer et al 2008, Loewen and Anderson 1990, McKenna et al 2002, Tooth et al 2005, Wee et al 2003), our data show a strong association between lower MAS admission scores and the likelihood of being discharged to an institution; a relationship which is not unexpected. Using multiple regressions to predict discharge destination, Brauer et al (2008) found that previous living situation and age, as well as MAS scores for walking (item 5) and supine to side lying (item 1) were able to predict discharge destination with 86% accuracy ([r.sup.2] = 0.373, p < 0.001). Our logistic regression analyses correctly predicted a higher percentage of those who were discharged home, with or without support (86.4%) compared with only 51.4% of those discharged to an institution. Our data differed from Brauer et al's (2008) in the variables entered into the final regression equation. These researchers reported that pre-stroke residence was the most significant as this greatly influences patient's subsequent discharge destination; however this variable was not available in our database. Many factors such as cognition, age and social support determine requirements for high levels of care following a stroke, which partly explains the lower prediction accuracy for institutionalised care compared to predicting discharge home. The strongly predictive nature of our model (86.4% correct) indicates that age and total MAS admission scores can be considered to be key considerations when determining those who are likely to be discharged home following stroke.

Different correlations between MAS sub-sections and discharge destination have been reported previously. In Brauer's study (2008) the greatest association was with the MAS item 5 (walking) followed by MAS item 1: supine to side lying and then MAS item 2: supine to sitting over side of bed. In our study, MAS item 2 was most highly correlated with discharge destination, followed by item 4: sit to stand and item 3: balanced sitting. Although different sub-sections of the MAS have been reported to predict discharge destination in different studies, it is clear that it is the mobility items (MAS items 1-5), rather than arm and hand function, that appear to be the most influential. It has been hypothesised that the complexity of these tasks, which integrate motor control, perceptual ability, upper limb strength and trunk control, is the reason for their high predictive value of eventual discharge outcome. This premise is supported by previous findings that the presence of perceptual problems (Kalra et al 1997) and poor trunk control (Massucci et al 2006) are outcomes highly predictive of discharge to an institution.

One aspect of this investigation was to explore the value of the MAS to predict walking ability on discharge, an outcome of considerable importance to patients and their families post-stroke. The multiple logistic regression model indicated a limited ability to predict discharge walking function based solely on MAS total admission scores. The variables entered in the multiple logistic regression were age and MAS admission scores, although these were associated with significant variance. A major factor limiting the ability to predict gait function on discharge from admission scores was that most individuals scored 0 on the MAS item 5 (walking) on admission. This has been previously described as a downfall of the MAS due to the floor effect of this walking measure (Aamodt et al 2006, Brock et al 2002, English et al 2006). Loewen and Anderson (1990) have reported that balanced sitting score on admission (item 3), and MAS admission walking score (item 5) and bowel control scores (from the Barthel Index) were significant predictors of discharge walking function. These authors also found that integration of a second outcome measure, the Barthel Index, accounted for a greater proportion of the variability as it included impairments that are not measured by the MAS. It is apparent that prediction of walking function is difficult using a scale that measures only motor recovery, as the majority of patients score 0-1 on admission.

One limitation on the present study was that the MAS data utilised were collected by four different therapists over the time period under evaluation. The circumstances under which the data were collected in this investigation represent real life situations where frequently patients are assessed and treated by more than one therapist during their inpatient stay. An inter-rater reliability study was undertaken to investigate the impact that this may have had on the data utilised. The variations in scoring the five cases by four senior physiotherapists, while surprising, in part reflects the use of videoed cases for this procedure. In contrast to the study performed by Carr & Shepherd (1985), therapists did not receive any specific training and did not review the guidelines for scoring the MAS prior to the evaluation of video cases. Percentage agreements are considered to be a 'stringent measure of consistency' (Carr et al 1985). However it would have been unlikely that raters would have 100% percent exact agreements on scores made from video recordings of patients performing the movement tasks, compared to actual assessments. In a previous exploration of inter-rater reliability, Carr and Shepherd (1985) showed that balanced sitting (item 3) had the highest degree of consistency between raters; however the opposite was true for our data. Raters in the present investigation indicated that this was due to the difficulty of assessing equal weight distribution in sitting, based on the video camera angle. It was apparent from our data that some variation exists in the manner that the test is routinely administered, even by experienced staff. From these findings it might be recommended that formal training be undertaken by new staff using this scale and regular review of scoring consistency be undertaken if the MAS is to be considered to be a reliable tool for patient assessment across multiple therapists or rehabilitation settings. Wherever possible, investigations of this nature should use the same therapist to record all evaluations for a single patient or establish clearly inter-rater reliability between different evaluators prior to commencement of the data collection.

Attention to inter-rater reliability issues in clinical as well as in research settings is critical to obtain data that is useful for outcome prediction.

Another limitation of the present study was the use of retrospective data. It is recommended that further investigation of the value of specific stroke outcome measures for discharge prediction should use a prospective study design. Further research is needed into the effect that stroke complications and other comorbidities have on LOS and discharge destination, as these factors have been shown to be significant confounders in some previous investigations.


This study has provided further support of the utility of the Motor Assessment Scale to measure and predict outcomes after stroke using data from a large, representative cohort of patients in an Australian aged care stroke rehabilitation setting. Total MAS admission score, combined with age, could predict discharge destination with an overall accuracy of 76.2%. Total MAS and MAS mobility scores (items 1-5) on admission were only modestly correlated with eventual LOS, however total MAS scores and age significantly predicted the ability to walk independently on discharge. These data have potential to assist the rehabilitation physiotherapist with treatment planning as well as to inform patients and carers regarding likely outcomes post rehabilitation.


Thank you to Karen Joesbury for assistance with data analysis and to Leanne Cormack, Tracy Beckwith, and Carol Unkovich for their involvement in the set up of the database and data collection,


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Claire Tucak BSc (Physio) (Hons),

Senior Physiotherapist, Osborne Park Hospital, Perth, Australia

Jennifer Scott, B Physio (Hons),

Physiotherapy, School of Health Sciences, University of Notre Dame Australia

Alison Kirkman, BSc (Physio),

Lecturer, Physiotherapy, School of Health Sciences, University of Notre Dame Australia

Barbara Singer, Dip PT, MSc, PhD

Associate Professor, Physiotherapy, School of Health Sciences, University of Notre Dame Australia


Alison Kirkman, Division of Physiotherapy, School of Health Sciences, The University of Notre Dame, Fremantle, WA 6959, Australia, Email:; Phone: +61 8 94330902; Fax: +61 8 94330210
Table 1: Classification of Stroke in the study *

Stroke Classification       Features

Total anterior cerebral     Deficits of higher cerebral function,
infarct (TACI),             homonymous hemianopia and
                            ipsilateral hemiparesis with or
                            without sensory loss.

Partial anterior cerebral   Only two features of TACI present
infarct (PACI),

Lacunar anterior cerebral   Pure or combined sensorimotor
infarct (LACI)              deficit, ataxic hemiparesis or
                            acute movement disorder without
                            disturbances of higher cerebral
                            function or consciousness.

Posterior circulation       Presence of signs of brainstem,
infarct (POCI),             cerebellum, vertebrobasilar
                            or occipital lobe dysfunction,
                            including ipsilateral cranial
                            n erve palsy, bilateral motor
                            and/or sensory deficit, disorder
                            of conjugate eye movement,
                            cerebellar dysfunction without
                            ipsilateral long tract deficit or
                            isolated hemianopia or cortical

* after Bamford et al

Table 2: Demographics of study sample n= 239

                        Characteristic     Number   Mean+SD

Age                                                 78.1 [+ or -] 7.1
                        Males              125      76.2 [+ or -] 6.9
                        Females            114      80.1 [+ or -] 6.8

Side of stroke
                        Left               115
                        Right              118
                        Bilateral          6

Type of stroke

Ischaemic                                  193
                        TACI               25
                        PACI               105
                        POCI               33
                        LACI               30

Haemorrhagic                               46

Length of Stay (LOS)
                        LOS Total                   50.4 days
                                                      [+ or -] 26.8
                        LOS Acute                   17.9 days
                                                      [+ or -] 12.4
                        LOS SRU                     32.6 days
                                                      [+ or -] 20.74

MAS admission score
                        <20                79
                        20-29              48
                        30-39              75
                        40+                37

Discharge Destination

Home                    Independent        104
                        Home with carer    65
Institution                                70
                        Other hospital     13
                        Slowstream rehab   22
                        Hostel             14
                        Nursing home       21

                        Characteristic     Percentage

                        Males              52.3%
                        Females            47.7%

Side of stroke
                        Left               48.1%
                        Right              49.6%
                        Bilateral          2.5%

Type of stroke

                        TACI               13.0%
                        PACI               54.4%
                        POCI               17.1%
                        LACI               15.5%


Length of Stay (LOS)
                        LOS Total

                        LOS Acute

                        LOS SRU

MAS admission score
                        <20                33.1%
                        20-29              20.1%
                        30-39              31.1%
                        40+                15.5%

Discharge Destination

Home                    Independent        43.5%
                        Home with carer    27.2%
Institution                                29.3%
                        Other hospital     5.4%
                        Slowstream rehab   9.2%
                        Hostel             5.9%
                        Nursing home       8.8%

Table 3: Associations between total MAS admission scores
and discharge destination, and individual MAS items and
discharge destination

MAS Item on Admission     square]   df      P

1--Supine to side lying    35.43    2     0.001
2--Supine to sitting
   over side of bed        64.04    2     0.001
3--Balanced Sitting        50.22    2     0.001
4--Sit to Stand            33.48    2     0.001
5--Walking                 18.02    2     0.001
Mobility--Items 1-5        56.09    4     0.001
Total MAS scores           62.22    6     0.001

Table 4: Prediction of discharge destination using
total MAS admission scores (a,b,c)

Variable              B             Standard   P
                      Coefficient   Error

MAS admission score   -0,109        0,016      0,001
Age                    0,064                   0,001

(a) Discharge destination dichotomised as either home
(with or without support) or discharge to an institution

(b) adjusted [R.sup.2]= 0,249

(c) non-significant variables: gender, type,
side and stroke classification,

Table 5: Actual versus predicted discharge destinations

                 Home with/
                 without      PREDICTED
ACTUAL           care         Institution   % Correct

Home with/
  without care   146          23            86,4
Institution      34           36            51,4
Overall %                                   76,2

Table 6: Prediction of the likelihood
of walking on discharge (a) using total
MAS admission scores (a,b,c)

Variable              B        Standard     P
                 Coefficient    Error

MAS admission
  score             0,146       0,026     0,001
Age                -0,094       0,026     0,001

(a) Walking defined as MAS item 5 score with assistance,
criterions 1-3 or without assistance, criterions 4-6

(b), adjusted R2= 0,3851

(c), non-significant variables: gender, type, side
and stroke classification,
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Title Annotation:Research Report
Author:Tucak, Claire; Scott, Jennifer; Kirkman, Alison; Singer, Barbara
Publication:New Zealand Journal of Physiotherapy
Article Type:Report
Geographic Code:8AUST
Date:Mar 1, 2010
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