University of Groningen

Functioning of Young Individuals with Upper Limb Reduction DeficienciesGolea-Vasluian, Ecaterina

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Chapter 7

A linear scoring system for the Southampton Hand Assessment

Procedure

Johannes G. M. Burgerhof

Ecaterina Vasluian

Pieter U. Dijkstra

Raoul M. Bongers

Corry K. van der Sluis

Submitted

142 | Chapter 7

Abstract

The Southampton Hand Assessment Procedure (SHAP), a test used by clinicians and

researchers, offers a functional overview of hand grips (prehensile patterns) and a general

score, the index of function (IOF). However, SHAP’s scoring system is complicated and

unclear. Furthermore, studies testing the scoring system on data from experienced prosthetic

users are scarce. This study discusses the issues with SHAP’s scoring system, provides an

alternative scoring system, and, using a substantial group of experienced prosthetic users (N

= 27), compares the original with the alternative scoring system.

SHAP includes task times that are computed into functionality scores relative to normative

data. The alternative scoring system, which is straightforward and can be calculated by the

user himself/herself, transforms the task times in a linear way relative to the same SHAP

norms. The transformed task times are computed into a linear index of functionality for each

of the prehensile patterns (LIFPP) and an overall LIF score based on all tasks. A weighted

overall LIF (W-LIF) was developed based on the extent to which prehensile patterns are used

in daily life. The prehensile patterns and the overall scores calculated for IOF and LIF were

highly correlated (LIFPP versus IOFPP ranged from r = 0.880 to r = 0.988; overall W-LIF

versus IOF: r = 0.984).

The LIF scoring system is more transparent and easier to use than the IOF system. Most

importantly, the high correlation between the two scoring systems demonstrates that their

measurements are similar. Our data provide healthcare professionals with valuable

benchmarks.

Linear scoring system for the SHAP | 143

Introduction

In rehabilitation, professionals are challenged with quantifying their patients’ overall

performance or changes in functioning in a valid and reliable way. If the response variable is

unidimensional–for example, a time or a distance–this process is rather straightforward. If the

response variable is multidimensional, however–for example, if the respondent has to

perform several tasks–the process of quantifying all the information into one single score

becomes more complex.

The Southampton Hand Assessment Procedure (SHAP)1 is a standardized instrument

that assesses the functionality of normal, impaired, or prosthetic hands. The SHAP was

recommended in a report by the international Upper Limb Prosthetics Outcome Measures

group (ULPOM) as a valid, reliable way to measure the functionality of upper limb

prostheses.2 Although its reliability has been established with a healthy population, the

ULPOM group advised further investigation into the SHAP’s psychometric properties. The

current paper seeks to follow this advice by not only critically scrutinizing the computations

underlying the SHAP scores but also by adding to the existing data by presenting the SHAP

scores of a substantial group of experienced upper limb prosthetic users.

The SHAP consists of 26 tasks: 12 with abstract objects and 14 reflecting daily living

activities. The participants’ response times were recorded for each task, and a resultant score,

the index of function (IOF), was calculated to represent the participants’ overall

performance.1 Furthermore, a SHAP functionality profile can be derived, which represents a

breakdown of the SHAP IOF score into six prehensile patterns (Spherical, Tripod, Tip,

Power, Lateral, and Extension).3 Prehensile pattern is a generic term for the functionality

score for each of the six hand grips. Each task performance requires the use of one or two

prehensile patterns, and the number of tasks used to calculate a prehensile pattern ranges

from four to seven.

In our study of the learning effects of novice prosthetic users,4 we experienced some

difficulties in understanding the SHAP scoring system, in general and the IOF scoring

system, in particular. In addition, studies presenting SHAP data from experienced prosthetic

patients are scarce. Existing studies presented SHAP data for limited samples of experienced

prosthetic users (one,5 six,6 or eight7). Furthermore, there is no insightful information into the

144 | Chapter 7

SHAP scores of data from experienced prosthetic users. Such information is needed by

clinicians and researchers as reference points for their patient SHAP data.

Therefore, we will describe the SHAP scoring system as defined by the original

authors first (I), subsequently provide our thoughts on the measure (II), suggest two

alternative scoring formulas (III), and then, using data from a group of experienced prosthetic

users, compare the alternative formulas’ functionality scores with the scores determined by

the original SHAP scoring system (IV).

I. Description of the SHAP scoring system as described by the original authors

Light provided the rationale behind the SHAP scoring system in his dissertation8 and in the

paper he wrote with colleagues.1 Twenty-four healthy volunteers performed all 26 SHAP

tasks, their performance times on these tasks were registered, and the means and standard

deviations of these times were used as normative values.

To calculate the IOF, the following steps were described:

1. For each task, a time limit (a maximum performance time) was established; that is, eight

times the mean of the normative sample time for that particular task (Table 1).

2. Total scores for all prehensile patterns were calculated based on the observed times of the

individual tasks within the prehensile patterns.

3. The total score xi, needed to perform the separate tasks within a prehensile pattern was

standardized to a z-score, based on the normative data: - ̅

, where ̅ is the mean time

for pattern i in the normative sample and si is the standard deviation of the normative data

times for prehensile pattern i.

4. The z-scores of the prehensile patterns were combined to a score d, which Light et al.1

called “Euclidian squared distance,” defined as √∑ .

5. The number d was rescaled to a value between 0 and 100, which is the index of function

IOF, indicating how well the respondent mastered the tasks (the larger the value of d, the

smaller the IOF; d = 0 will lead to IOF = 100, which indicates a very good performance,

comparable to unimpaired individuals’ performance).

Linear scoring system for the SHAP | 145

6. Light et al.1 state, “In a similar way, an individual IOF may be obtained for each of the

prehensile patterns […].”

Table 1. Mean times in the normative sample, time limits (retrieved from the thesis of Light5), and median times

derived from the experienced prosthetic users included in this study (N=27)

SHAP normative dataa

Prosthetic users data

(current study)

Task contribution

to prehensile

pattern

Task Mean time

(seconds)

Time limit

(seconds)

Median time [IQR]

(seconds)

Exceeded

time limit (n)

Spherical Light sphere 1.63 13.04 4.31 [ 6.66; 6.53] 1

Tripod Light tripod 1.66 13.28 4.94 [ 3.78; 6.22] 2

Power Light power 1.77 14.16 3.75 [ 3.16; 4.97] 1

Lateral Light lateral 1.77 14.16 5.10 [ 3.59; 5.60] 1

Tip Light tip 1.59 12.72 4.63 [ 3.44; 7.31] 1

Extension Light extension 1.78 14.24 4.75 [ 3.81; 6.65] 0

Spherical Heavy sphere 1.84 14.72 4.30 [ 3.80; 8.37] 0

Tripod Heavy tripod 1.58 12.64 4.25 [ 3.56; 6.28] 1

Power Heavy power 1.76 14.08 4.12 [ 3.13; 5.28] 1

Lateral Heavy lateral 1.84 14.72 4.97 [ 3.59; 7.15] 0

Tip Heavy tip 1.61 12.88 4.58 [ 3.87; 6.06] 0

Extension Heavy extension 1.71 13.68 4.38 [ 3.19; 6.41] 2

Tip Pick up coins 4.41 35.28 32.22 [22.4; 35.28] 13

Tripod and tip Undo buttons 6.77 54.16 28.31 [22.9; 45.91] 6

Tripod and power Food cutting 3.12 24.96 21.16 [14.81; 24.96] 8

Extension Page turning 1.80 14.40 8.07 [6.00; 11.38] 3

Spherical Remove jar lid 2.26 18.08 7.56 [6.37; 9.57] 2

Lateral Pour water from jug 4.66 37.28 13.19 [10.6; 21.50] 1

Spherical Pour water from carton 5.79 46.32 15.59 [12.06; 19.94] 1

Power Move a full jar 2.10 16.80 4.47 [3.66; 6.21] 2

Power Move an empty tin 1.77 14.16 5.75 [4.97; 6.89] 2

Lateral and extension Move a tray 3.02 24.16 6.91 [5.62; 7.78] 0

Lateral and tip Rotate a key 90° 1.78 14.24 5.31 [4.15; 10.28] 5

Lateral and tip Open/close a zip 2.43 19.44 10.00 [6.87; 16.97] 5

Power Rotate a screw 90° 3.90 31.20 17.37 [12.18; 31.20] 10

Power Rotate a door handle 1.49 11.92 3.72 [2.41; 4.78] 0

Abbreviations: SHAP, Southampton Hand Assessment Procedure; IQR, interquartile range. aThe standard deviations are not available in the literature and the time limits are calculated as 8 times the mean.

II. The difficulties we encountered in understanding the SHAP scoring system

Ad 2. It is not clear from Light et al.’s1 article how they calculated the total scores of the

prehensile patterns. From Light’s dissertation (page 100)8, we understand that this is done by

simply adding the times of all tasks within a prehensile pattern. A disadvantage of this

method is that one relatively large time on a task can substantially influence the result of the

prehensile pattern’s total score. The norm total score of a prehensile pattern is presumably

defined as the sum of the normative mean task times within the pattern, but again, this is not

clear from the literature.

146 | Chapter 7

Ad 3. It is unclear if the standard deviation of the total scores of a prehensile pattern are

based on the prehensile pattern’s individual total scores or on the (weighted) sum of the

standard deviations of the individual tasks within the prehensile pattern.

Ad 4. Apart from the fact that the given formula is not the Euclidean squared distance, this

step, as defined by Light et al.,1 can lead to wrong conclusions. If a subject performs better

than the normative value on prehensile pattern l, this will lead to a zl < 0. Because of the

square in the formula, this will give a positive contribution to d and consequently a lower

IOF, which is definitely incorrect. In Metcalf’s thesis,9 she states that the software for score

calculation has been modified to enable scores larger than 100 to reflect good performance;

however, no formulas for this correction are given.

Ad 5. Similarly, it is not clear from Light et al.’s article1 that the d-values are transformed to

the 0–100 scale in a linear way, but we are assuming that this is the case. It is not difficult to

show that, in the one-dimensional case, the combination of z transformation followed by a

linear transformation to the 0–100 scale neutralizes the effect of dividing by the standard

deviation completely. However, it is hard to see what the effect of this combination of

transformations will be in the multivariate case, without knowing the exact formulas. As we

will show later, there is hardly any effect of the standard deviations on the IOF.

Ad 6. In a prehensile pattern, there are four to seven observed task times. If we interpret the

word “similar” correctly, these individual times are transformed to z-values that are used to

calculate an IOF score for each prehensile pattern (IOFPP). This raises the question of why the

task times within a prehensile pattern can be summed directly to contribute to the calculation

of the overall IOF, whereas they have to be transformed to z-scores first to calculate the

individual IOFPP scores. There seems to be some inconsistency in reasoning here.

In our opinion, the definition of the IOF is unclear and unnecessarily complex. We

asked Southampton University’s Research and Innovation Services for clarification, but they

refused to answer our questions, claiming intellectual property. This is hard to accept from a

scientific point of view. As researchers, we would like to know what transformations and

calculations have been performed on our data instead of just receiving outcomes from a

“black box.”

Linear scoring system for the SHAP | 147

III. Possible alternative for the calculation of a functionality index: the linear index

of function (LIF)

For practical use, we would prefer an index of function that is easy to use and understand and

can be verified by others. We would like to introduce an alternative for calculating

functionality scores, the linear index of function (LIF), which is based on the same norm data

used for calculating the IOF. The LIF is based on direct linear transformations of the

observed time for each task to a functionality score on a scale from 0 (poor performance) to

100 (excellent performance). As such, we can use all 26 tasks to calculate an overall SHAP

functionality score. The functionality score of a prehensile pattern (LIFPP) will simply be the

mean of the task functionality scores within that prehensile pattern.

For each task, a norm time (n) to perform this task has to be defined. This norm time

can be based on consensus, a mean or median time based on previous experience, or the mean

or median time of the sample; however, the IOF norms may also be used (Table 1). In

addition, for each task, a maximum time (m), a time limit within which the task should be

performed, should be determined. This maximum can be, for example, a multiple of the norm

time, or it may be based on consensus. To keep our new scores, calculated according to the

linear index, comparable to the IOF scores, we used the same normative means and time

limits as in Table 1.

Every subject had an observed time t, in which they needed to complete each task. If

this time t was smaller than or equal to the norm, the functionality on this task was very good.

Consequently, t would be rescaled to 100. If the observed time of a certain task was larger

than or equal to the maximum of this task (the time limit), the functionality score for this task

would be zero. For each time between the norm and the maximum, the transformed time

score Ts will be calculated as -

- , indicating a kind of “percentage of mastery.” For a

maximum time m equal to the suggested eight times the norm time n, this equation becomes:

-

-

-

.

A functionality score on prehensile patterns like “Tip” or “Power” can be determined

by taking the mean Ts of a specific subset of tasks. The formula of this functionality score

(LIFPP) for a certain subject q, with observed time tqj on task j, is:

148 | Chapter 7

Table 2. Characteristics of the participants

Characteristics

Mean [SD] or

Number (%)

Age (years) 44 [15]

Gender

Male 21 (77.8)

Female 6 (22.2)

Cause of amputation

Trauma 17 (63.0)

Congenital 9 (33.3)

Illness 1 (3.7)

Affected hand

Left 12 (44.4)

Right 15 (55.6)

Amputation level

Transhumeral 2 (7.4)

Transradial 23 (85.2)

Wrist disarticulation 2 (7.4)

Type of myoelectric hand

Conventional 25 (92.6)

Multi-articulated 2 (7.4)

Prosthetic experience (years) 19 [15]

∑

∑(

)

∑(

)

for the k tasks (indexed with j) involved.

Patient data examples

A total of 27 experienced users of myoelectric upper limb prostheses were included in this

study. Their SHAP-IOF scores had been calculated in the past (see Table 2 for individual

characteristics).

The patient data were derived from previous

research (n = 6),6 ongoing research (n = 13),

and from patient evaluations obtained during

a regular meeting in our rehabilitation center

(n = 8). Ethical approval had previously

been obtained for the patients included in the

previous and ongoing research. For research

purposes, the use of anonymized patient data

collected during the rehabilitation meetings

does not require formal approval of the

Medical Ethical Committee. All participants

signed an informed consent form, allowing

the researchers to use their measurement

outcomes for research purposes.

Example 1. One of the participants scored 3.75 seconds on “light spherical,” 2.97 seconds on

“heavy spherical,” 7.31 seconds on “remove a jar lid,” and 16.18 seconds on “pour water

from carton. That individual’s Spherical prehensile pattern LIFSpherical was:

(

)

In fact, this participant scored IOFSpherical = 77 when using the original SHAP calculation.

Linear scoring system for the SHAP | 149

Example 2. A second participant in our dataset had an IOFTripod =17, thus displaying poor

performance. This individual’s observed times for the four tasks within this prehensile pattern

were: 34.87 seconds for “light tripod” (exceeded the time limit), 9.59 seconds for “heavy

tripod,” 100 (unable to perform the task) for “undo buttons,” and 17.35 seconds for “food

cutting.” The LIF score for this prehensile pattern was:

(

) ,

which was close to the IOFTripod = 17.

IV. Comparison of LIF and IOF scores

We calculated the median time for each of the 27 participants’ SHAP tasks (Table 1). The

LIFPP scores for the six prehensile patterns were determined in order to compare them with

the IOFPP scores (mean IOF and LIF scores, Table 3). The IOF and IOFPP scores were

generated automatically after filling in the task times on the official SHAP website

(Southampton Hand Assessment Procedure Softwarea). Statistical calculations were

performed in SPSS, Version 22b.

Table 3. The SHAP scores for the prehensile patterns and the overall scores calculated with the existing SHAP

scoring system, IOF, and the alternative scoring system LIF (N=27)

SHAP IOF score LIF score

Median [IQR] (seconds) Median [IQR] (seconds)

Prehensile pattern Spherical 75.00 [60.00; 78.00] 76.44 [60.19; 78.77] Tripod 40.00 [29.00; 55.00] 56.49 [37.43; 64.18] Power 54.00 [41.00; 63.00] 67.93 [52.50; 74.08] Lateral 62.00 [42.00; 77.00] 68.41 [51.96; 79.41] Tip 42.00 [24.00; 57.00] 55.86 [37.66; 68.30] Extension 65.00 [45.00; 76.00] 70.11 [57.25; 79.28]

Overall 62.00 [45.00; 71.00] 68.35 [50.55; 74.72] W-LIF

66.50 [47.73; 73.26]

Abbreviations: SHAP, Southampton Hand Assessment Procedure; IQR, interquartile range; IOF, index of function; LIF, linear index of function; W-LIF, weighted linear index of function.

All LIFPP scores correlated significantly with the corresponding IOFPP scores. Pearson

correlation coefficients were 0.988 (Spherical), 0.880 (Tripod), 0.901 (Power), 0.966

(Lateral), 0.957 (Tip), and 0.955 (Extension), respectively. All correlation coefficients had a

P value smaller than 0.001. Figure 1 displays six scatterplots of the IOFPP scores and LIFPP

scores. Clearly, there is very strong agreement between the IOF and LIF within each

150 | Chapter 7

prehensile pattern. This shows, in fact, that there is hardly any effect of the standard

deviations in the z transformations of the IOF, since the LIF does not make use of them.

Figure 2a shows the relationship between the IOF and the LIF scores based on all 26

tasks. The LIF score is simply calculated as the mean transformed time score of the 26 tasks.

The Pearson’s correlation coefficient for these two variables was 0.977 (P < 0.001), which

shows a strong linear relationship.

In Figure 3a, the Bland-Altman plot is presented for the LIF and IOF in order to check

the agreement between the two measurement methods.10 On average, the LIF is 4.6 higher

than the IOF. The standard deviation of the individual differences is 3.8. The relative position

of a respondent in the total sample on both indexes is almost identical (Spearman’s

correlation coefficient for overall scores is 0.945, P < 0.001).

The differences between the LIF and IOF scores can be partly explained by the fact

that the IOF is based on the six prehensile pattern scores. Five tasks (undo buttons, food

cutting, move a tray, rotate a key 90o, and open/close a zipper) are all part of two prehensile

patterns, so they contribute twice to the overall IOF score. If we consider that these five tasks

reflect important features of daily living and should have more impact on the overall score

than other tasks, we can define a weighted version of the LIF. A weighted linear index of

function (W-LIF) was calculated with a weighting factor of two for the five aforementioned

tasks and a weighting factor of one for all the other tasks. The scores on this W-LIF are, on

average, even closer to the IOF scores than the LIF scores.

The Pearson’s correlation coefficient between IOF and W-LIF, r = 0.984, is even

higher than the Pearson’s correlation coefficient between IOF and LIF, r = 0.977 (Figure 2b).

On average, the W-LIF is 2.98 higher than the IOF. The standard deviation of the individual

differences is 3.1 (Figure 3b). The W-LIF score can be calculated in a simple way: sum the

transformed time scores of all 26 tasks, add the transformed time scores of the five tasks

which are part of the two prehensile patterns, and divide the sum by 31. Equivalently, the W-

LIF can be calculated directly from the LIFPP scores of the six prehensile patterns by taking a

weighted mean of these scores, using the number of tasks in each prehensile pattern as the

weighting factor:

Linear scoring system for the SHAP | 151

Figure 1. The relationship between the SHAP index of function for prehensile patterns (IOFPP scores) and the

linear index of function scores on the six prehensile patterns (LIFPP) for all 27 participants

152 | Chapter 7

Figure 2. The relationship between the index of function (IOF) score and the linear index of function (LIF) score

(a), and the relationship between the IOF score and the weighted linear index of function W-LIF (b)

Figure 3. Bland-Altman plots of the linear index of function LIF and the index of function IOF scores (a) and the

weighted linear index of function W-LIF and IOF scores (b) based on data from 27 participants

Linear scoring system for the SHAP | 153

Discussion

Some aspects of the SHAP scoring system, in particular the index of function (IOF) as

suggested by Light et al.,1 are unclear and complex, and they may impede the development of

the sound psychometric properties of this instrument. In our opinion, the presented IOF

alternatives, the linear index of function for the prehensile patterns (LIFPP), and its weighted

version (W-LIF) for the overall score, might present vast improvements on the existing

measures. The LIF and W-LIF are based on simple formulas that do not make use of the

standard deviations of the normative sample. The IOF and the IOFPP scores, however, do

make use of these standard deviations.

Since we found that the LIF and W-LIF scores are quite similar to the IOF scores, this

indicates that the effects of the IOF standard deviations have been neutralized, for the most

part, by the chain of seemingly unnecessary transformations. The use of standard deviations

from data obtained from unimpaired individuals is not applicable to calculating the

functionality scores of impaired persons. The data from unimpaired persons had a normal

distribution,1 but this might not be the case for persons with hand impairments or amputees

using prosthetic hands. We believe that by not using standard deviation measurements, the

proposed LIF is a more reliable calculation method for SHAP prehensile pattern scores and

for the overall SHAP score. As such, clinicians and researchers using the SHAP and LIF will

benefit from a better comparison of the functionality scores of individuals with different hand

conditions.

In critically assessing the SHAP and developing an alternative way to compute scores,

we also checked the results in a sample of experienced upper limb prosthetic users (N = 27).

Our analyses showed a strong relationship between IOF and LIF scores and a high level of

agreement. In presenting these analyses, we also provided an extension to the literature with a

substantial sample of SHAP scores obtained from experienced prosthetic users. The presented

data (Tables 1 and 3, Figures 1 and 2) has great implications for use within either a clinical or

research situation.

Conclusions

Using data from a substantial group of experienced prosthetic users, we tested and compared

the original SHAP scoring system to an alternative scoring system, the LIF. The linear

154 | Chapter 7

indices LIFPP and W-LIF have several advantages: their calculations are based on a simple

and clear formula that shows very high correlations and agreement with the original IOF

scores. The formulas of the overall scores and the prehensile patterns, calculated by W-LIF

and LIFPP, are consistent with each other. Researchers and clinicians will be able to calculate

the scores for each respondent themselves. Further, and most importantly, the interpretations

of the LIF and W-LIF scores are clearer than those of the calculated IOF.

Suppliers

a. Southampton Hand Assessment Procedure software (www.shap.ecs.soton.ac.uk);

SHAP (c/o Dr Cheryl Metcalf), Room 2037, Building 45,School of Health Sciences,

University of Southampton, SO17 1BJ, United Kingdom, shap@ecs.soton.ac.uk.

b. IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk,

NY: IBM Corp, ews@us.ibm.com.

Linear scoring system for the SHAP | 155

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