1

Japanese Journal of Comprehensive Rehabilitation Science (2020)

Original Article

Feasibility of the International Classification of Functioning,
Disability and Health Rehabilitation Set for inpatient rehabilitation:
Selection and validity of a set of categories for inpatients in aconvalescent rehabilitation ward
Reisuke Funahashi, MD,1,2 Masahiko Mukaino, MD, DMSc,1 Yohei Otaka, MD, PhD,1
Yuki Senju, MD,1,3 Chikako Yoneda, MD, DMSc4 Yasunori Ozeki, MD, DMSc,5
Yasuhiro Shimizu, MD, DMSc,6 Tomoharu Koike, MD,7 Eiichi Saito, MD, DMSc1
Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Aichi, Japan
Department of Rehabilitation Medicine, Fujita Health University Nanakuri Memorial Hospital, Mie, Japan3
Department of Rehabilitation, Ise Municipal General Hospital, Mie, Japan
4
Department of Rehabilitation, Ukai Rehabilitation Hospital, Aichi, Japan
5
Department of Rehabilitation, Uno Hospital, Aichi, Japan
6
Department of Rehabilitation, Kizankai Memorial Hospital, Nagano, Japan
7
Department of Rehabilitation, Sankuro Hospital, Aichi, Japan1
2

ABSTRACT
Funahashi R, Mukaino M, Otaka Y, Senju Y, Yoneda
C, Ozeki Y, Shimizu Y, Koike T, Saito E. Feasibilityof the International Classification of Functioning,
Disability and Health Rehabilitation Set for inpatient
rehabilitation: Selection and validity of a set of
categories for inpatients in a convalescent rehabilitation
ward. Jpn J Compr Rehabil Sci 2020; 11: 1‐8.
Objective: To evaluate the feasibility of the International
Classification of Functioning, Disability and Health(ICF) Rehabilitation Set for inpatients in a convalescent(Kaifukuki) rehabilitation ward.
Methods: Overall, 295 inpatients in convalescentrehabilitation wards were rated using the ICF
Rehabilitation Set, and the proportion of missing
values was investigated. Categories with missing
values <10% were selected, and internal constructvalidity of the total score of the selected categories
was examined using Rasch analysis.
Results: Missing values were detected in 25 items, ofwhich seven had missing values of ≥10%. No ceilingor floor effects were noted. Rasch analysis of 23
categories with missing values <10% showed a good
Correspondence: Masahiko Mukaino, MD, DMSc
Department of Rehabilitation Medicine I, School of
Medicine, Fujita Health University, 1‐98 Dengakugakubo,
Kutsukake, Toyoake, Aichi 470‐1192, Japan.
E-mail: mmukaino@fujita-hu.ac.jp
Accepted: September 11, 2019
COI: The authors have no conflict of interest directly
relevant to the content of this article.
©Kaifukuki Rehabilitation Ward Association 2020
doi.org/10.11336/jjcrs.11.1

fit to the model after applying testlet solution and item
splitting, which supported the internal constructvalidity of the ICF Rehabilitation Set.
Conclusion: This identified the set of categories of the
ICF Rehabilitation Set that could be used for evaluating
rehabilitation inpatients. These categories had goodinternal construct validity based on Rasch analysis.
Key words: ICF, ICF Rehabilitation Set, Rasch analysis,
convalescent rehabilitation
Introduction
The International Classification of Functioning,
Disability and Health (ICF) was developed by theWorld Health Organization and provided acomprehensive framework for describing health-related
functioning profiles. ICF classifies functioning based ona wide range of categories, including body functions,activities and participation, and environmental factors,
which more broadly contextualize additional variables
that affect a patient’s functioning [1]. ICF has > 1,400
classification categories that describe each aspect of apatient’s functioning. Furthermore, ICF provides acoding system to describe the extent of patients’problems within each category.
However, it is difficult to apply all the categories ofthis comprehensive classification directly forevaluation. Thus, studies aimed to develop ICF Core
sets, which are a more focused collection of categories
related to specific diseases and health conditions as
determined by international clinical experts [2‐7]. The
ICF Generic Set comprising seven categories and the
Jpn J Compr Rehabil Sci Vol 11, 2020

2

Funahashi R et al.: ICF Rehabilitation Set for inpatients

ICF Rehabilitation Set comprising 30 categories wererecently introduced for evaluating functioning acrossvarious populations [8, 9]. The ICF Generic Set wasdeveloped based on a psychometric study in which
regression analyses were performed using databasedata. The ICF Rehabilitation Set is an extended versionof the ICF Generic Set and is more focused on thepatient population receiving rehabilitation [8]. The
ICF Rehabilitation Set was developed using both theregression analysis of the ICF database data and expert
reviews of the existing ICF Core Sets [9].
The ICF Rehabilitation Set covers extended areas ofhuman functioning, thereby enabling comprehensive
assessment of a patient’s functioning by assessing abroader range of patient variables compared withexisting clinical scales for evaluating functioning[10, 11]. However, there are categories, including
using transportation (ICF#: d470) and remunerative
employment (ICF#: d850) are difficult to evaluate in
hospitalized patients and thus possibly have missing
values. Excessive categories with missing data make it
difficult to describe the extent of a patients’ problem
using a single total score, which is a practical attributeof medical scales used in the clinic. Identifying
common categories with complete data (i.e., no
missing data) would be helpful in implementing ICF,
and in using the ICF for statistics of rehabilitation.
For use in real-world clinical settings, the validityof the selected categories of the ICF Rehabilitation Set
should be confirmed. In many cases, the external
criteria are used for assessing criterion-related validityof a clinical scale. The activities and participation
items of the ICF Rehabilitation Set have criterionrelated validity compared with the Functional
Independence Measure [12]. Conversely, internal
construct validity is an important aspect of validity
and is often conducted to support the validity of ascale [13, 14]. Internal construct validity refers towhether the scale has a theoretically correct structure
for evaluating the concept of interest, and Raschanalysis is frequently performed to evaluate internal
construct validity [15]. Rasch analysis is a probabilistic
approach for estimating the difficulty of items anddifferent levels of personal ability [16]. The validity of
the internal construct is verified by assessing constructirrelevant variance and construct underrepresentationof scales [15, 17]. The existence of construct irrelevant
variance indicates sub-dimensions irrelevant to the
focal construct of the scale, which is evident by thepoor item fit and existence of differential item
functioning (DIF).
Construct underrepresentation indicates theimperfectness of tests for assessing all features of aconstruct [15], thereby possibly manifesting as gaps inthe distribution of the item and person location.
To determine a commonly collectable dataset thatcan be used for comparison across different healthconditions, this study aimed to assess the feasibility of
Jpn J Compr Rehabil Sci Vol 11, 2020

the ICF Rehabilitation Set to evaluate functioning insubacute patients receiving inpatient rehabilitation.
Moreover, Rasch analysis was performed to investigate
internal construct validity of the acquired data set,
which is evident by the absence of construct irrelevant
variance or construct underrepresentation.
Methods
In this study, inpatients from convalescentrehabilitation wards were included. The convalescentrehabilitation ward, supported by the Japanese healthinsurance system, provides intensive rehabilitation
during the subacute phase (up to 180 days after onset)
of diseases and conditions such as stroke and spinal
cord injury [18].
The ICF Rehabilitation Set, developed by Prodinger
et al. [9], was used. The raters used the following
ratings based on ICF qualifiers to evaluate eachcategory of the ICF Rehabilitation Set. The qualifiers,
i.e., the rating system for ICF categories, are describedas follows: 0: no problem (0%-4%), 1: mild problem(5%-24%), 2: moderate problem (25%-49%), 3:
severe problem (50%-95%), 4: complete problem(96%-100%), 8: not specified, and 9: not applicable[1]. Based on this rating system, a physiatrist, physicaltherapist, or occupational therapist in charge of thepatient rated each category. To record the ICF ratings,
we used the Japanese version of simple, intuitive
descriptions and the rating reference guide previously
developed as a clinical tool to support the use of ICF
in clinics [19]. Because the ability of patients to walkindoors and outdoors significantly differed, only thatof patients to walk indoors was evaluated.
1. Statistical analysis
To identify categories with missing values whenevaluating inpatients, the proportion of missing values,
rating 8 or 9, in each of the 30 categories of the ICF
Rehabilitation Set was assessed. Because excessive
missing values can bias results, categories with <10%of missing data were selected for inclusion in the study
for validation [20].
After identifying the categories, the total score of
the selected categories was calculated. The ceiling andfloor effects were analyzed to measure the percentage
of subjects with the best and worst possible scoreobtained, respectively. Ceiling or floor effects wereassigned when >10% of the responses had the best or
worst possible score, respectively.
2. Rasch analysis
Rasch analysis was performed to examine theinternal construct validity of the ICF Rehabilitation
Set. The overall fit of the data into the Rasch model
was examined using χ2 statistics. A non-significant χ2(>0.05) value was considered to indicate an overall
good fit [21]. A testlet approach was employed toFunahashi R et al.: ICF Rehabilitation Set for inpatients

accommodate a high local dependency of the items,
which could cause a problem in the fit of the scale tothe model [22]. The testlets were constructed byaggregating items with high residual correlations into
super-items, wherein the same iterative process ofscale adjustment as a single-item design was applied.
Principal component analysis (PCA) of thestandardized Rasch residuals was performed to
examine the unidimensionality of the scale [23].
Unidimensionality was assessed using t-tests thatcompared pairs of ability estimates from separate
Rasch calibration of the two sets of items, either
loading positively or negatively on the first componentof PCA. To achieve strict unidimensionality, theproportion of significant t-tests had to be <5%. The
lower bound of the binomial confidence interval for
proportions <5% would then be acceptable.
The lack of DIF is an important assumption in scaleevaluation using the Rasch model [24, 25]. The
absence of DIF indicates that an individual can achieve
comparable levels of ability regardless of groupcharacteristics such as age and disease. In this study,
DIF was investigated using an analysis of variancetest for sex (male and female), age groups (<40,
40–60, 60–80, and ≥80 years), and disease groups(neurological, musculoskeletal, and others). DIF could
be resolved by splitting an item showing DIF for
certain groups into group-specific items. If DIF wasobserved in either of the group characteristics, theitem was split to assess whether this solution would
improve the item fit [26].
In this study, to examine whether there was adifference in the distribution of item difficulty andperson ability estimate, we prepared a person-item
threshold map using Rasch analysis. The upper half ofthe map was a histogram of the person ability estimate,
whereas the lower half was a histogram of the itemdifficulty. The difference in the difficulty level of thescale and the ability of the measurement object canthus be assessed [27]. The distribution of item difficulty
was revealed based on the threshold from thecategories. The ICF Rehabilitation Set is structured sothat each category is given a rating of 0–4. Therefore,
there are four thresholds for each item to divide theratings. In this study, the distribution of the difficultyof each item was represented by the distribution of the
difficulty of those thresholds.
All analyses were performed using JMP 11 software(SAS Institute Inc., Cary, NC, USA) and RUMM2030
software (RUMM Laboratory, Perth, Australia).
Results
1. Patient characteristics
Overall, 295 inpatients from six convalescentrehabilitation wards were included. The age of theparticipants ranged from 17 to 101 years (median: 74
years); 177 patients were male, and the remainder were

3

female. Results of assessing the patients’ conditions
were as follows: 91 patients had hemorrhagic stroke, 64
had ischemic stroke, 17 had subarachnoid hemorrhage,
27 had hip fracture, 23 had spinal cord injury, and 73
had other diseases such as various orthopedic andneurological diseases. The patient’s days after onset
ranged from 17 to 177 days (median: 74 days).
2. Missing values
The number of missing values and the score for eachcategory are shown in Table 1. Missing values were
found in 25 categories. The categories with >10%
missing values were sexual function (ICF#: b640),
using transportation (ICF#: d470), doing housework(ICF#: d640), assisting others (ICF#: d660), intimate
relationships (ICF#: d770), remunerative employment(ICF#: d850), and recreation and leisure (ICF#: d920).
Data of the remaining 23 categories were further
analyzed. For the 23 categories, the total score wascalculated, and their ceiling and floor effects wereassessed. We found that no cases had the maximum
total score value, whereas two cases had the minimum
total score value. No ceiling or floor effects wereobserved.
3. Results of the Rasch analysis
Rasch analysis was performed using the total scoreof 23 selected categories (Table 2). The results of theinitial Rasch analysis revealed that the data did not fit
the assumptions of the Rasch model. The residual
correlations indicated a robust local dependency
among several items. For example, correlations wereobserved between categories related to mental
functions or activities and between categories related
to physical activities. Disease- and gender-related
DIFs was observed in several categories (Table 3).
Given the strong local dependency among items, we
attempted a testlet approach by grouping items into
four groups. Body function categories were subdivided
into two groups (Categories related to mental function[b130, b134, and b152] and physical function [b280,
b455, b620, b710, and b730]), and activity andparticipation categories were subdivided into twogroups (categories related to mental activity [d230,
d240, and d710] and physical activity [d410, d415,
d420, d450, d455, d465, d510, d520, d530, d540,
d550, and d570]). Using this strategy, the fit of themodel improved, which was supported by a nonsignificant χ2 value (χ2=12.18, p=0.73). The percentage
of significant pairwise comparison of the principal
components of the Rasch residuals was 5.3%, and thelower 95% CI was 2.7%.
However, DIF related to disease between patientswith neurological diseases and those with otherdiseases was still observed. However, after item
splitting of the physical function item groups, DIF was
not observed, and the fit of the model improved (Table2; χ2=14.83, p=0.78). The findings of the initial RaschJpn J Compr Rehabil Sci Vol 11, 2020

4

Funahashi R et al.: ICF Rehabilitation Set for inpatients

Table 1. Distribution of ratings and number of missing value

b130
b134
b152
b280
b455
b620
b640
b710
b730
d230
d240
d410
d415
d420
d450
d455
d465
d470
d510
d520
d530
d540
d550
d570
d640
d660
d710
d770
d850
d920

Energy and drive functions
Sleep functions
Emotional functions
Sensation of pain
Exercise tolerance functions
Urination functions
Sexual functions
Mobility of joint functions
Muscle power functions
Carrying out daily routine
Handling stress and otherpsychological demands
Changing basic body position
Maintaining a body position
Transferring oneself
WalkingMoving around
Moving around using equipment
Using transportation
Washing oneself
Caring for body parts
Toileting
Dressing
EatingLooking after one’s health
Doing housework
Assisting others
Basic interpersonal interactions
Intimate relationships
Remunerative employment
Recreation and leisure

2: Moderate
problem
n (%)

n (%)
118 (40.0)
162 (54.9)
147 (49.8)
119 (40.3)
64 (21.7)
138 (46.8)
138 (46.8)
86 (29.2)
29 ( 9.8)
77 (26.1)
93 (31.5)

70 (23.7)
78 (26.4)
73 (24.7)
103 (34.9)
89 (30.2)
38 (12.9)   9 ( 3.1)
98 (33.2)
86 (29.2)
50 (16.9)
73 (24.7)

50 (16.9)
43 (14.6)
41 (13.9)50 (16.9)
76 (25.8)
43 (14.6)   1 ( 0.3)
81 (27.5)
109 (36.9)
58 (19.7)
53 (18.0)

34 (11.5)5 ( 1.7)
18 ( 6.1)
14 ( 4.7)
41 (13.9)
13 ( 7.8)
6 ( 2.0)22 ( 7.5)
59 (20.0)53 (18.0)
34 (11.5)

18 ( 6.1)   5 ( 1.7)
13 ( 4.4)   2 ( 0.7)
21 ( 7.1)
62 (21.0)
16 ( 5.4)   8 ( 2.7)
11 ( 3.7)
54 (18.3)
32 (10.8)   5 ( 1.7)   2 ( 0.7)   3 ( 1.0)   7 ( 2.4)   4 ( 1.4)   1 ( 0.3)
125 (42.4)   0   1 ( 0.3)   3 ( 1.0)
10 ( 3.4)

✓
✓
✓
✓
✓
✓

44 (14.9)
72 (24.4)
80 (27.1)
37 (12.5)
23 ( 7.8)
99 (33.6)   8 ( 2.7)
46 (15.6)
74 (25.1)
91 (30.8)
97 (32.9)
158 (53.6)
68 (23.1)
14 ( 4.7)
14 ( 4.7)
132 (44.7)
144 (48.8)   5 ( 1.7)
27 ( 9.1)

96 (32.5)
114 (38.6)
116 (39.3)
90 (30.5)
54 (18.3)
62 (21.0)
22 ( 7.5)
60 (20.3)
63 (21.4)
48 (16.3)
50 (16.9)
73 (24.7)
78 (26.4)
19 ( 6.4)   8 ( 2.7)
59 (20.0)32 (10.8)   4 ( 1.4)
32 (10.8)87 (29.5)
63 (21.4)
54 (18.3)
65 (22.0)55 (18.6)
39 (13.2)   9 ( 3.1)
83 (28.1)
77 (26.1)
49 (16.6)
64 (21.7)
20 ( 6.8)
51 (17.3)
11 ( 3.7)
12 ( 4.1)
52 (17.6)
24 ( 8.1)   4 ( 1.4)
37 (12.5)

36 (12.2)
19 ( 6.4)
16 ( 5.4)
34 (11.5)
18 ( 6.1)
19 ( 6.4)
9 ( 3.1)
44 (14.9)
37 (12.5)
48 (16.3)
44 (14.9)
11 ( 3.7)
28 ( 9.5)
6 ( 2.0)
6 ( 2.0)26 ( 8.9)
15 ( 5.1)
3 ( 1.0)46 (15.6)

31 (10.5)
27 ( 9.1)
28 ( 9.5)
63 (21.4)
121 (41.0)
61 (20.7)
125 (42.4)
58 (19.7)
44 (14.9)
59 (20.0)39 (13.2)
33 (11.2)
67 (22.7)
122 (41.4)
113 (38.3)
20 ( 6.8)
19 ( 6.4)
108 (36.6)
100 (33.9)   1 ( 0.3)   0   1 ( 0.3)   6 ( 2.0)
24 ( 8.1)
15 ( 5.1)
122 (41.4)   4 ( 1.4)
   0
   0
   1 ( 0.3)   0   3 ( 1.0)
123 (41.7)
142 (48.1)   6 ( 2.0)
61 (20.7)
171 (58.0)
53 (18.0)

✓
✓
✓
✓
✓
✓

analysis are shown in Table 4. Figure 1 demonstrates
the distribution of individuals (top panel) and item
thresholds (bottom panel) for the total score of the ICF
Rehabilitation Set. The detailed findings of the models
are reported in Table 3, with the item difficulty, fit
statistics, and DIF.
Discussion
In this study, 30 categories of the ICF Rehabilitation
Set data were identified. Of the 30 categories, 25 had
missing values, of which seven had >10% of their data
missing and therefore were excluded from subsequent
analysis. This study was conducted using the total
scores of the remaining 23 categories that demonstrated
no ceiling or floor effects. The Rasch analysis revealed
the good internal construct validity of the a scale,
which was evident by the total score of the 23
categories.
1. 
Feasibility of the ICF Rehabilitation Set inrehabilitation clinics
Some categories were difficult to use for inpatient
rehabilitation. In this study, among the body function
categories, the category with the highest number ofmissing values was sexual functions (ICF#: b640).
This could be because of the older age of patients who
participated in this study. Moreover, there is apossibility that the evaluators avoided inherently
Jpn J Compr Rehabil Sci Vol 11, 2020

3: Severe
problemn (%)

4: Complete
problemn (%)

Categories withless than 10% of
missing value

1: Mild
problemn (%)

0: No problem

Missing value
n (%)

✓
✓
✓
✓

✓
✓
✓
✓
✓
✓

✓

private questions regarding sexual function.
Several categories with missing values are possibly
difficult to assess while patients stay in hospitals.
Previous studies on the ICF Generic Set discussed thatthe inclusion of remunerative employment (ICF#:d850) may not be suitable for evaluating patientsduring their hospital stay or at the age of retirement[28, 29]. Consistently, we found a high frequency of
missing values in several categories related to bodyfunctions or activities that inpatients did not frequently
use or experience during their hospital stay. Because
the high rate of missing values can create a bias in thestatistical analysis [20], the functioning score used forthe analysis should only include categories with a low
amount (<10%) of missing values. Although several
categories were excluded from the data set forcomparison, our analysis still used several categories
traditionally not considered by existing clinical scales.
For example, categories such as energy and drive
functions (ICF#: b130), sleep functions (ICF#: b134),and handling stress and other psychological demands(ICF#: d240) have not been included in most existing
scales of functioning assessment used in rehabilitation
clinics [30]. The categories of the ICF Rehabilitation
Set are selected by assessing functioning profiles thatare strongly related to self-reported general health.
Inclusion of these categories in the data set may resultin a new perspective for clinical assessment of patients’
functioning, which would be beneficial from a patient-



Yes

Yes

0.98
0.91
2.70
2.28
0.34
0.32
0.00
0.00

-0.47
-0.49

0.68
0.71

-0.03
-0.04

-0.36
-0.33

12.18
14.83

16
20

0.73180.7862

0.85
0.85

0.86
0.86

15

284

5.3

2.7‐7.9Yes
16.7‐21.7
19.2
292
56
0.95
0.95
0.000092434.99
1.45
-0.25
3.38
-0.16
1.40
-0.64
0.61
0.00

ICF Rehabilitation Set
23 categories(Basic module)
4 Testlets
4 Testlets (Item split)

SD
Mean
SD

Analysis

Mean

Item

SD

Mean

SD

Mean

Item
Persons
Location

Table 2. Fit statistics of ICF Rehabilitation Set 23 categories.

Fit residual

Persons

Value

df

p

Item-trait interaction
chi square

WITH
extremes

Reliability
PSINO
extremesnSignificant
tests

Sample% PST

Unidimensionality
paired t-tests

CI % PSTLD

DIF

Funahashi R et al.: ICF Rehabilitation Set for inpatients5

centered point of view.
2. Internal construct validity of the ICF
Rehabilitation Set
Despite the large variety of categories included in
the ICF Rehabilitation Set, the fit to the Rasch model
was generally good. The existence of constructirrelevant variance indicates that there aresubdimensions irrelevant to the focal construct of thescale, which could demonstrate a poor item fit and thepresence of DIF. In this study, although the initial
Rasch analysis showed a poor fit to the Rasch model,
a testlet approach according to the chapter structure of
the ICF significantly improved the fit to the Raschmodel. Furthermore, unidimensionality of the scale
was acceptable.
We noted DIF related to the disease types after
applying the testlet approach. This could indicate thatusing this scale for comparisons across populations
having different diseases may be problematic [24].
DIF can be resolved by splitting an item showing DIF
for certain groups into group-specific items[31, 32], as
performed in this study. However, it may be necessary
to consider the stratification of data when dealing withsamples with more diverse backgrounds in the future.
In such cases, comparisons between different diseasegroups can be made by transforming scores tostandardized metrics based on person ability estimatesof the results of Rasch analysis.
The well-balanced distribution of person ability
estimate and item difficulty was shown in the personitem threshold map, negating the existence of construct
underrepresentation [15]. This could be interpreted
because the set of categories used in our study waswell targeted to patients who participated. Overall, thestudy results negate the existence of constructirrelevant variance and construct underrepresentation,
thereby supporting the good internal construct validityof the selected 23 categories from the ICF
Rehabilitation Set.
Limitations
This study included subacute patients receiving
inpatient rehabilitation at convalescent rehabilitation
wards. The feasibility and validity of the ICF
Rehabilitation Set for patients receiving rehabilitationin the acute phase of the disease should be further
investigated.
In this study, seven categories with high rates of
missing values were excluded from the total scoreanalysis. However, the categories excluded from thescore could still be considered important even if it was
not used for statistical analysis to compare patients’
functioning in general. These sparse categories, if
present in the functioning profile of the patient, may
serve as additional independent indicators to help
reveal a more complete picture of the patient’s status,
Jpn J Compr Rehabil Sci Vol 11, 2020

Jpn J Compr Rehabil Sci Vol 11, 2020

Energy and drive functions
Sleep functions
Emotional functions
Sensation of pain
Exercise tolerance functions
Urination functions
Mobility of joint functions
Muscle power functions
Carrying out daily routine
Handling stress and otherpsychological demands
Changing basic body position
Maintaining a body position
Transferring oneself
WalkingMoving around
Moving around using equipment
Washing oneself
Caring for body parts
Toileting
Dressing
EatingLooking after one’s health
Basic interpersonal interactions

0.07
0.084
0.075
0.08
0.073
0.059
0.077
0.081
0.063
0.067
0.075
0.076
0.075
0.068
0.067
0.061
0.068
0.067
0.062
0.066
0.069
0.062
0.069

-0.2460.186
0.228-0.807-1.513-0.183-0.761-0.266-0.369-0.0650.449-0.524
0.469

SE

0.465
1.17
0.795
0.932
0.086
-0.0390.576-0.156-0.4370.01

Location

Item difficulty

Df, degrees of freedom; Prob, probability; SE, standard error.

d410
d415
d420
d450
d455
d465
d510
d520
d530
d540
d550
d570
d710

b130
b134
b152
b280
b455
b620
b710
b730
d230
d240

Item

-4.176-3.893-3.74-3.155-0.357-1.17-4.841-3.395-3.988-4.815
2.928
1.284
2.93

0.184
6.492
2.024
5.414
0.702
1.21
4.119
0.823
-0.6422.27

Fitresiduals

274.16275.11
274.16269.45252.49260.97271.34275.11275.11
274.16275.11
272.28269.45270.4
273.22272.28268.51271.34275.11275.11
274.16272.28265.68Df

19.03519.01511.723
12.6128.537
5.827
25.77416.17912.84424.6995.52
13.661
11.494

1.95
78.2
8.205
100.43
1.217
9.107
29.543
4.153
12.7382.533

χ2Item fit

4
4
4
4
4
4
4
4
4
4
4
4
4

4
4
4
4
4
4
4
4
4
4

Df

0.000774
0.000782
0.019536
0.013339
0.073785
0.212417
0.000036
0.002789
0.012067
0.000059
0.237998
0.008462
0.021542

0.745008
00.084337
00.875372
0.058489
0.000006
0.385731
0.012631
0.638733

Prob

DiseaseGenderGender

DiseaseDifferential item
functioning (DIF)
sub-groups with DIF

Table 3. Findings of initial Rasch analysis based on individual categories of the ICF Rehabilitation Set (selected 23 categories).

b152d420, d520, d510

d520, d540
d510, d540

d415, d420, d450
d410, d420
d410, d415, d450, d540
d410, d420, d455
d450

b152

d530
b280

b130, d240, d710
b710

b152

Local item dependency
residual correlations >0.3

PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
MA

MF
MF
MF
PF
PF
PF
PF
PF
MA
MA

Grouping into
the testlet

6
Funahashi R et al.: ICF Rehabilitation Set for inpatients7

Funahashi R et al.: ICF Rehabilitation Set for inpatients

Table 4. Individual item fit statistics in the testlet analysis.
Item split for DIF

Item difficulty
Location

SE

Item fit
Fit residual

Df

ChiSq

Df

Prob

4 Testlets

Cognitive function
Physical function
Cognitive activities
Physical activities

0.43
0.088
-0.155-0.363

0.033
0.025
0.028
0.013

2.382
-0.307
1.526-3.717202.32200.88195.12
175.68

3.242
3.049
4.678
1.206

4
4
4
4

0.51810.5973 Disease
0.32190.8770DIF

4 Testlets(Item split)

Cognitive function
Cognitive activities
Physical activities
Physical function
Neurological disease
Orthopaedic and theother diseases

0.449-0.159-0.37
0.195-0.116

0.034
0.029
0.013
0.032
0.045

2.493
1.565
-3.424-0.692-0.157201.27194.11
174.77
131.8
68.05

1.409
5.067
1.75
3.344
3.257

4
4
4
4
4

0.84260.28050.7816
0.50200.5157

Figure 1. A person-item threshold map for the International Classification of Functioning, Disabilityand Health (ICF) Rehabilitation Set.
The bar graph shows the distribution of person ability estimates (red bars) and item thresholds (bluebars).
as demonstrated in previous studies [28, 29].
Conclusion
In this study, we assessed data on the functioning ofpatients receiving inpatient rehabilitation using the
ICF Rehabilitation Set. We identified the set of
categories with few missing values from the ICF
Rehabilitation Set. Furthermore, Rasch analysis revealed
that the calculated score from the selected set of
categories had good internal construct validity inevaluating inpatients in rehabilitation wards.
Acknowledgment
This research was supported by the Grants for
Research on Health and Welfare for the research
project titled “The establishment of a mechanism
for evaluation and information sharing using theInternational Classification of Functioning, Disabilityand Health to integrate medical and long-term careservices” (Principal Investigator: Masahiko Mukaino).


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