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Archives of Gerontology and Geriatrics
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Comparison of a combination of upper extremity performance measures andusual gait speed alone for discriminating upper extremity functional limitationand disability in older women
Satoshi Seino a,b,*, Noriko Yabushita a, Mi-ji Kim c, Miyuki Nemoto a, Songee Jung a, Yosuke Osuka a,
Yoshiro Okubo a, Tomoaki Matsuo a, Kiyoji Tanaka aaGraduate School of Comprehensive Human Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, JapanThe Japan Society for the Promotion of Science, 8 Ichiban, Chiyoda, Tokyo 102-8472, Japanc
The Research Team for Promotion Independence of the Elderly, Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology, 35-2 Sakae, Itabashi, Tokyo 173-0015, Japanb

A R T I C L E I N F O

A B S T R A C T

Article history:
Received 28 June 2011
Received in revised form 21 October 2011

Although usual gait speed (UGS) is considered an indicator of overall well-being, it is unclear whether
upper extremity performance (UEP) measures provide a similar, additive contribution to functional
status. We aimed to identify whether combining UEP measures can more accurately discriminate upper
extremity functional limitation (UE limitation) and disability compared to UGS. We conducted a crosssectional analysis on data from 322 community-dwelling older women, aged 65–96 years. Trained
testers assessed UGS, and hand-grip strength (GRIP), functional reach (FR), back scratch, manipulatingpegs (PEG), and moving beans with chopsticks as UEP measures. We assessed three functional statuses:
UE limitation, activities of daily living (ADLs) and instrumental ADLs (IADLs) disabilities using selfreported questionnaires. Areas under the receiver operating characteristic curves (AUCs) were used tocompare the discriminating power of UGS, with the individual and combined UEP measures for eachstatus. Among UEP measures, only GRIP (AUC = 0.68 for UE limitation, 0.81 for IADLs disability, and 0.84
for ADLs disability) could accurately discriminate each status as well as UGS (AUC = 0.65, 0.83, and 0.91,
respectively). Furthermore, UGS alone could discriminate UE limitation almost as well as the
combination of GRIP, PEG, and FR (AUC = 0.70). Combining other UEP measures did not help discriminate
further. There were few advantages to combining UEP measures, and UGS or GRIP alone may sufﬁce forassessing UE limitation and disability. However, the UGS should be the test of ﬁrst choice, certainly morethan GRIP, in routine assessment of functional limitation and disability, including UE limitation.
ß 2011 Elsevier Ireland Ltd. All rights reserved.

Accepted 21 October 2011
Available online xxx
Keywords:
Upper extremity performance
Usual gait speed
Performance measures
Functional limitation
Disability
Older adults

1. Introduction
Numerous studies have demonstrated that poor lower extremity performance (LEP) not only correlates cross-sectionally withfunctional status (e.g., functional limitation and disability), but also
predicts them prospectively (Guralnik et al., 1994, 1995, 2000;
Shinkai et al., 2000; Cesari et al., 2005; Kim et al., 2010). In
particular, gait speed, chair rising, and balance tests, which areincluded in the short physical performance battery (SPPB)(Guralnik et al., 1994), have been studied to determine the added
value of each additional LEP measure in predicting adverse healthrelated outcomes. Furthermore, UGS has been considered to be the* Corresponding author at: Graduate School of Comprehensive Human Sciences,
University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan.
Tel.: +81 29 853 5600x8365; fax: +81 29 853 2986.
E-mail address: se-no@stat.taiiku.tsukuba.ac.jp (S. Seino).

most important predictor of adverse health-related outcomesamong LEP measures (Cesari et al., 2009).
Thus, LEP measures, particularly UGS, are largely representativeof a person’s general health condition, but UEP is also likely to betightly associated with a person’s functional status because of thefollowing: (1) physical functioning can be divided into threecomponents, upper extremity, basic lower extremity, and advanced lower extremity functions (Haley et al., 2002); and (2)several common ADLs, such as dressing, eating, and personal
hygiene are mostly upper extremity-related tasks. Notably, thevast majority of women also engage in upper extremity-related
IADLs tasks (e.g., cooking, housekeeping, and doing the laundry).
Indeed, Hazuda et al. (2005) have shown that their UEP battery of
testing makes an independent contribution beyond the SPPB in
explaining disability and dependence.
Although several UEP measures are widely used in older adults,
it is unclear whether any or all of them provide a similar, additive
contribution to our determination of functional status. Compared
to one measure alone, combining several UEP measures may

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doi:10.1016/j.archger.2011.10.011

Please cite this article in press as: Seino, S., et al., Comparison of a combination of upper extremity performance measures and usual gait
speed alone for discriminating upper extremity functional limitation and disability in older women. Arch. Gerontol. Geriatr. (2011),
doi:10.1016/j.archger.2011.10.011

G ModelAGG-2630; No. of Pages 62

S. Seino et al. / Archives of Gerontology and Geriatrics xxx (2011) xxx–xxx

capture more manifestations of disability, however, it has yet to be
determined which, if any, combination of UEP measures is most
efﬁcient at detecting functional limitation and disability.
The purpose of this study was to identify whether acombination of UEP measures is better at detecting UE limitationand disability compared to one measure alone, and if so, which
combination of UEP measures is most accurate. To verify the valueof UEP measures, we compared the discriminating power of each
UEP measure alone and in different combinations with thecommonly used UGS test, which is well established as a measureof general health (Cesari et al., 2005).
2. Methods
2.1. Participants
A total of 343 community-dwelling older Japanese womenparticipated in this study in 2010. The participants were recruitedfrom the towns of Ibaraki, Chiba, and Fukushima, Japan, as part of anursing care prevention program or day-care service. Almost all ofparticipants were recruited through local advertisements andﬂyers. The eligibility criteria were as follows: (1) they must be
community dwellers aged 65 years or older; and (2) participants
must be able to understand the instructions of performance tests
and questionnaires. Participants who required assistance or were
too functionally limited to perform the tests safely and participants
with missing data of UEP measures were excluded. The remaining
322 participants included in this study ranged in age from 65 to 96
years. All participants provided written informed consent. This
study was conducted in accordance with the guidelines proposedin the Declaration of Helsinki, and the study protocol was approvedby the Ethics Committee of the University of Tsukuba, Japan.
2.2. Measurements
2.2.1. UGSParticipants were instructed to stand with their feet behind andjust touching a starting line marked with tape at 0 m and, on
receiving the tester’s command, to start walking at their normal
pace along a 7 m course. The actual walking speed was measured
over 5 m starting with the ﬁrst footfall past the 1 m mark andending with the ﬁrst footfall after the 6 m mark. Participants
performed two trials with results averaged to the nearest 0.01 m/s(Shinkai et al., 2000). The reliability of UGS was excellent, with an
intraclass correlation coefﬁcient (ICC) of 0.97.
2.2.2. UEP measures
UEP components for performing ADLs included upper bodystrength, ﬂexibility, and dexterity. We selected the following
performance tests as indicators for these components: hand-grip
strength (GRIP), functional reach (FR), back scratch (BS), manipulating pegs in a pegboard (PEG), and moving beans with chopsticks(BEAN). In selecting these items for assessing UEP, we referred to test
selection criteria (Rikli and Jones, 1999) and studies by Hazuda et al.
(2005), Tanaka et al. (1995), Shigematsu and Tanaka (2000), Syddall
et al. (2003), Rikli and Jones (1999), and others. Participants could
complete the 5 tests within 20 min and were not fatigued.
GRIP. We measured GRIP using a hand-held dynamometer(GRIP-D, T.K.K 5401; Takei Scientiﬁc Instruments, Tokyo, Japan).
Participants were in a standing position with their arms hanging
naturally at their sides. They were instructed and verballyencouraged to squeeze the hand-grip as hard as they could. Grip
size was adjusted to a comfortable level for the participant.
Participants performed two trials with each hand alternately, andthe results were average to the nearest 0.1 kg. The reliability of the
GRIP was excellent, with an ICC of 0.95.

FR. According to the measuring method devised by Duncan et al.
(1992), participants stood with their feet together, their bodies
perpendicular to and with one shoulder adjacent to, but not touching,a wall which had a measuring yardstick afﬁxed to it horizontally. They
raised their arms in front of them to a horizontal position with theirtips of the middle ﬁngers positioned at the zero end of the measuring
yardstick. They reached forward as far as possible, bending asnecessary but keeping their arms straight and horizontal and theirfeet in the starting position. The distance from beginning position to
ending position as measured at the tips of the middle ﬁngers was theFR value. We measured FR two times and recorded the average to thenearest 1 cm. Although the FR test was originally developed as ameasure of dynamic balance, it involves movement of the upper
extremities and is required for many upper body tasks (Hazuda et al.,2005). The reliability of FR was excellent, with an ICC of 0.95.
BS. Participants were asked to place the preferred hand behindthe same-side shoulder, palm toward back and ﬁngers extended,reaching down the middle of the back as far as possible (elbow
pointed up) in a standing position. They placed the other handbehind the back, palm out, reaching up as far as possible in an
attempt to touch or overlap the extended middle ﬁngers of both
hands. We measured the distance between (or the overlap of) the
middle ﬁngers behind the back two times and recorded the averageto the nearest 1 cm. If their middle ﬁngers could not touch, werecorded the value as minus. If their middle ﬁngers could overlap,
we recorded the value as plus. The participants were not allowed to
grab ﬁngers together and pull (Rikli and Jones, 1999). The
reliability of the BS was considered acceptable, with an ICC of 0.88.
PEG. For this test, we used a pegboard (hand working test
instrument, T.K.K 1306; Takei Scientiﬁc Instruments, Tokyo, Japan)
consisting of 48 pegs arranged in a six by eight matrix on the side of
the board distal to where the participants stood. With the board
situated close to and at the midline of the body, participants were
instructed to manipulate the pegs as fast as possible, one by one,using both hands, from the far side of the board to the near side. We
recorded the number of pegs relocated within 30 s during 1 trial(Shigematsu and Tanaka, 2000). We evaluated this test’s reliability
using 22 of the participants; we considered it acceptable, with an
ICC of 0.88. Shigematsu and Tanaka (2000) demonstrated an ICC
with the PEG test of 0.82.
BEAN. The participants used chopsticks to transfer as many beans
as possible (approximately 0.8 cm in diameter) from one dish(2.0 cm in depth, 20.0 cm in diameter) to another (3.5 cm in depth,
6.0 cm in diameter) within 30 s. The dishes were 20 cm apart. We
recorded the number of beans correctly transferred during one 30 strial (Shigematsu and Tanaka, 2000). This evaluation was modiﬁed
from a previous study by Kim and Tanaka (1995) in which pincers
rather than chopsticks were used. Shigematsu and Tanaka (2000)
arrived at an ICC of BEAN of 0.84, which was considered acceptable.
2.2.3. UE limitation and disability status
From a self-reported questionnaire, we determined a participant’s UE limitation using a severity of UE limitation scale(Simonsick et al., 2001), which assesses the degree of difﬁculty inperforming three primarily upper extremity actions (i.e., using
ﬁngers to grasp or handle something, lifting and carrying 10 lbs,
and raising arms up over the head). Response categories were no
difﬁculty, a little difﬁculty, some difﬁculty, a lot of difﬁculty, andunable to do. Participants who reported any difﬁculty with thesethree activities were rated as having UE limitation.
Disability status was assessed using IADLs (Lawton and Brody,1969) and ADLs (Mahoney and Barthel, 1965) scales. The IADLs
include the ability to use the telephone, shop, prepare food,
perform housekeeping chores, do laundry, use a mode oftransportation, maintain responsibility for own medications, andhandle ﬁnances. The ADLs include aspects of eating, moving from

Please cite this article in press as: Seino, S., et al., Comparison of a combination of upper extremity performance measures and usual gait
speed alone for discriminating upper extremity functional limitation and disability in older women. Arch. Gerontol. Geriatr. (2011),
doi:10.1016/j.archger.2011.10.011

G ModelAGG-2630; No. of Pages 6
S. Seino et al. / Archives of Gerontology and Geriatrics xxx (2011) xxx–xxx

bed to chair, grooming, toilet use, bathing, ambulation, negotiating
stairs, dressing, emptying bowels and bladder. IADLs and ADLs
disabilities were deﬁned as a participant being unable to performor needing human help with one or more IADL or ADL tasks,
respectively (Lawton and Brody, 1969).
2.2.4. Potential confounders
Several potential confounders were included in our analyses:
age; body mass index (BMI), deﬁned as body weight divided byheight squared (kg/m2); frequency of weekly outings; clinicalconditions (history of stroke, hypertension, diabetes mellitus,
heart disease, respiratory disease, and dyslipidemia); and joint
pain (presence of shoulder pain, low-back pain, or knee pain). All ofthese were computed on the basis of self-report questions.
2.3. Statistical analyses
We used descriptive statistics to characterize the study
participants and performed multiple logistic regression analyses
to evaluate whether UGS and each UEP measure alone weresigniﬁcantly associated with UE limitation, IADLs disability, or ADLs
disability. Cesari et al. (2005) demonstrated that the prognostic
value of UGS for identifying people at high risk of health-related
outcomes was 1.0 m/s. In our analyses, we used the 1.0 m/s cut-off
value to dichotomize UGS into high- and low-performance groups.
GRIP, FR, BS, PEG, and BEAN were dichotomized using the same
percentile (21.7%) as the chosen UGS cut-off value. By choosing thissame threshold to identify individuals at a low-performance level,
we determined equal distributions of the performance measures ofinterest, consequently allowing fair comparisons (Cesari et al.,2009). We calculated the odds ratio (OR) and 95% conﬁdence interval(95% CI) for each functional status (i.e., UE limitation, IADLs
disability, and ADLs disability) according to our two categories
after adjusting for potential confounders: the high-performance
category, which we considered a reference group; and the lowperformance category. We also performed these analyses considering continuous variables for each UEP measure. The continuous
variables of performance measures were rescaled to standardized
score (i.e., average per standard deviation).
To compare the discriminating power of an individual UEP
measure and their combination for each status, we conducted
receiver operating characteristic (ROC) analyses. We compared theareas under the ROC curves (AUCs) using the DeLong method (DeLong
et al., 1988) implemented in the statistical software Analyse-It for
Microsoft Excel. An AUC between 0.7 and 0.8 is considered acceptablediscrimination, between 0.8 and 0.9 is considered excellent
discrimination, and greater than 0.9 is considered outstanding
discrimination (Hosmer and Lemeshow, 2000).
We used an alpha level of 0.05 to determine statisticalsigniﬁcance, and all statistical analyses were performed using
SPSS statistics Version 18.0 (SPSS Inc., Chicago, IL, USA).
3. Results
Table 1 summarizes descriptive details of the study participants. Mean age  standard deviation of the study participants was
75.6  6.7 (range 65–96). The numbers of participants reporting UE
limitation, IADLs disability, and ADLs disability were 117 (37.6%), 68(22.4%), and 40 (12.5%), respectively.
Table 2 presents ORs and 95% CIs for UE limitation, IADLs
disability, and ADLs disability according to performance measuresresults with adjustments for potential confounders. In the sample
participants, the 1.0 m/s cut-off value for UGS corresponded to the
21.7th percentile. We used the same percentile to identify the cut-off
values for GRIP (low-performance group < 16.8 kg), FR (low-performance group < 20.1 cm), BS (low-performance group < 15.0 cm),

3

Table 1
Characteristics of the study participants (n = 322).
Characteristics

Mean  SD or n (%)

Age (years) (n = 322)
Height (cm) (n = 322)
Weight (kg) (n = 322)
BMI (kg/m2) (n = 322)
Frequency of weekly outings (days/wk) (n = 314)
Conditions, n (%)
Stroke (n = 316)
Hypertension (n = 317)
Diabetes mellitus (n = 317)
Heart disease (n = 317)
Respiratory disease (n = 317)
Dyslipidemia (n = 317)
Shoulder pain (n = 318)
Low back pain (n = 318)
Knee pain (n = 318)
UGS (m/s) (n = 317)
UEP measures (n = 322)
GRIP (kg)FR (cm)BS (cm)
PEG, number of pegs
BEAN, number of beans
Self-reported functional status, n (%)
UE limitation (n = 311)
IADLs disability (n = 304)
ADLs disability (n = 319)

75.6  6.7
146.9  6.4
50.6  7.8
23.5  3.4
6.1  1.810(3.5)148(46.7)17(5.4)34(11.0)13(4.1)58(18.3)32(10.1)
90(28.3)
100(31.4)
1.23  0.32
20.1  4.6
24.8  7.1
6.7  11.0
38.4  7.0
9.2  3.6
117(37.6)68(22.4)40(12.5)

SD, standard deviation; BMI, body mass index; UGS, usual gait speed; GRIP, hand-grip
strength; FR, functional reach; BS, back scratch; PEG manipulating pegs; BESN,
moving beans with chopsticks; UE limitation, upper extremity functional limitation;
IADLs, instrumental activities of daily living; ADLs, activities of daily living.

PEG (low-performance group < 34 pegs), and BEAN (low-performance group < 7 beans). The UGS was consistently associated witheach functional status as both categorical and continuous variables.
When looking at individual UEP measures, only GRIP and PEG tests
were consistently associated with each status as both categorical andcontinuous variables. Although BS was signiﬁcantly associated with
IADLs and ADLs disabilities as both categorical and continuous
variables, it was not associated with UE limitation. As a categoricalvariable, FR was signiﬁcantly associated with each status, whereas, asa continuous variable, it was not associated with IADLs disability.
BEAN was not signiﬁcantly associated with any status as either acategorical or a continuous variable.
Through multiple logistic regression analyses, we set up thefollowing 4 combination patterns of UEP measures: (1) GRIP + PEG,
which was consistently associated with each status as bothcategorical and continuous variables, (2) GRIP + PEG + FR, (3)GRIP + PEG + BS and (4) GRIP + PEG + FR + BS. Each combination
was represented by simple addition of the standardized score ofindividual measures. Since BEAN was not associated with any
status, we did not include it in the combination patterns.
Table 3 shows the AUC and 95% CI for UGS, each individual UEP
measure, and combinations of UEP measures for each status. All
ROC curves were signiﬁcantly different from a diagonal line
(AUC = 0.5) that indicates zero discriminating ability of the tests.
Among the individual measures, GRIP had the greatest AUC for UE
limitation (0.68), and UGS had the greatest AUCs for IADLs and
ADLs disabilities (0.83 and 0.91, respectively). Only GRIP’s AUCs
were not signiﬁcantly different from the AUCs of UGS for any
status. The AUC for PEG alone was nearly equal to that for GRIP
alone. The AUCs of FR and BS were consistently lower than any
other measures alone or in combination.
For the UE limitation, we did not detect any signiﬁcantdifferences between the AUCs of UGS (0.65) and the individual
UEP measures. The AUCs of GRIP + PEG (0.69) and GRIP + PEG + FR
(0.70) for UE limitation were signiﬁcantly greater than UGS alone,
but the 4–5% difference between AUCs was not substantial.

Please cite this article in press as: Seino, S., et al., Comparison of a combination of upper extremity performance measures and usual gait
speed alone for discriminating upper extremity functional limitation and disability in older women. Arch. Gerontol. Geriatr. (2011),
doi:10.1016/j.archger.2011.10.011

G ModelAGG-2630; No. of Pages 6
S. Seino et al. / Archives of Gerontology and Geriatrics xxx (2011) xxx–xxx

4

Table 2Adjusted odds ratio for each status according to performance measures results (n = 322).
Performance measures

UGS>=1 m/s (high-performance)<1 m/s (low-performance)Continuous
GRIP>=16.8 kg (high-performance)<16.8 kg (low-performance)ContinuousFR>=20.1 cm (high-performance)< 20.1 cm (low-performance)ContinuousBS>= 15.0 cm (high-performance)< 15.0 cm (low-performance)Continuous
PEG>=34 pegs (high-performance)<34 pegs (low-performance)Continuous
BEAN>=7 beans (high-performance)<7 beans (low-performance)Continuous

Cases/participants (%)

Adjusted odds ratio (95% conﬁdence interval)

UE limitation
117/311 (37.6)

IADLs disability
68/304 (22.4)

ADLs disability
40/319 (12.5%)

UE limitation

IADLs disability

ADLs disability

77/242 (31.8)
38/64 (59.4)

28/235 (11.9)
38/64 (59.4)

6/245 (2.4)
32/69 (46.4)

1 (reference)
2.20 (1.10–4.40)*
0.65 (0.46–0.91)*

1 (reference)
3.00 (1.30–6.89)*
0.36 (0.21–0.60)***1 (reference)
21.60 (6.70–69.65)***0.09 (0.04–0.21)***

74/246 (30.1)
43/65 (66.2)

32/242 (13.2)
36/62 (58.1)

13/251 (5.2)
27/68 (39.7)

1 (reference)
2.94 (1.45–5.96)**0.54 (0.39–0.75)***1 (reference)
3.20 (1.39–7.36)**
0.40 (0.25–0.65)***1 (reference)
6.18 (2.38–16.04)***0.28 (0.16–0.49)***78/244 (32.0)
39/67 (58.2)

32/238 (13.4)
36/66 (54.5)

17/249 (6.8)
23/70 (32.9)

1 (reference)
1.99 (1.03–3.84)*
0.64 (0.45–0.87)**

1 (reference)
2.93 (1.35–6.34)**
0.69 (0.46–1.03)

1 (reference)
2.92 (1.20–7.09)**
0.63 (0.40–0.97)*

83/247 (33.6)
34/64 (53.1)

41/244 (16.8)
27/60 (45.0)

19/253 (7.5)
21/66 (31.8)

1 (reference)
1.55 (0.81–2.97)
0.79 (0.59–1.05)

1 (reference)
2.66 (1.11–6.35)*
0.54 (0.35–0.83)**

1 (reference)
4.40 (1.73–11.24)**
0.38 (0.24–0.61)***80/251 (31.9)
37/60 (61.7)

36/247 (14.6)
32/57 (56.1)

15/256 (5.9)
25/63 (39.7)

1 (reference)
2.78 (1.39–5.58)**
0.35 (0.21–0.58)***1 (reference)
2.61 (1.12–6.06)*
0.40 (0.24–0.65)***1 (reference)
4.14 (1.63–10.55)**
0.35 (0.21–0.58)***77/235 (32.8)
40/76 (52.6)

41/231 (17.7)
27/73 (37.0)

22/243 (9.1)
18/76 (23.7)

1 (reference)
1.88 (0.95–3.34)
0.80 (0.61–1.04)

1 (reference)
1.84 (0.80–4.23)
0.68 (0.44–1.04)

1 (reference)
2.37 (0.99–5.63)
0.65 (0.40–1.06)

UE limitation, upper extremity functional limitation; IADLs, instrumental activities of daily living; ADLs, activities of daily living; UGS, usual gait speed; GRIP, hand-grip
strength; FR, functional reach; BS, back scratch, PEG manipulating pegs; BESN, moving beans with chopsticks. Odds ratio: adjusted for age, body mass index, frequency ofweekly outings, stroke, hypertension, diabetes mellitus, heart disease, respiratory disease, dyslipidemia, shoulder pain, low back pain, knee pain. The continuous variables of
performance measures were rescaled to standardized score (i.e., average per standard deviation).
*p < 0.05.
**
p < 0.01.
***
p < 0.001.

For the IADLs disability, we did not detect a difference between
the AUCs of UGS (0.83), GRIP (0.81) and PEG (0.83), however, allthree were signiﬁcantly higher than the AUCs of FR and BS (0.73
and 0.68, respectively). Although the AUC of GRIP + PEG (0.86) was
signiﬁcantly greater than the AUC of GRIP alone, the difference wasonly 5%.
For the ADLs disability, the AUCs of the individual UEP
measures, with the exception of GRIP, were signiﬁcantly lower
than the AUC of UGS (0.91). When we increased the number of
combined UEP measures, the differences were not signiﬁcantbetween the AUC for any combination compared to the AUCs of
UGS, GRIP (0.84), or PEG (0.81).

4. Discussion
Among UEP measures, only GRIP could accurately discriminate
each status as well as UGS. Interestingly, despite UGS being a test oflower extremity function, this single test could discriminate UE
limitation as well as the GRIP test. Moreover, even when we added
PEG and then FR in combination with GRIP, there was only a 4–5%
difference in their AUCs for UE limitation compared with UGS
alone. Adding any other UEP measure to the GRIP + PEG combination did not increase the discriminating power for each status.
Therefore, our study suggests that combining UEP measures has
few advantages, and the implementation of UGS should be

Table 3
AUCs for each status according to each performance measure alone and combinations of UEP (n = 322).
Performance measures

UGS
UEP measures
GRIP
PEG
FR
BS
Combinations of UEP measures
GRIP + PEG
GRIP + PEG + FRGRIP + PEG + BS
GRIP + PEG + FR + BS

AUC* (95% conﬁdence interval)
UE limitation

IADLs disability

ADLs disability

0.65 (0.58–0.71)

0.83 (0.78–0.89)

0.91 (0.85–0.97)

0.68 (0.62–0.74)
0.66 (0.60–0.73)
0.63 (0.57–0.70)
0.59 (0.53–0.66)b

0.81 (0.75–0.88)
0.83 (0.77–0.89)
0.73 (0.66–0.81)a,b,c
0.68 (0.60–0.75)a,b,c

0.84 (0.77–0.92)
0.81 (0.73–0.90)a
0.78 (0.70–0.87)a
0.77 (0.70–0.85)a

0.69 (0.63–0.75)a,c,d,e
0.70 (0.64–0.76)a,c,d,e
0.65 (0.59–0.72)e
0.67 (0.61–0.73)e

0.86 (0.80–0.91)b,d,e
0.85 (0.80–0.91)d,e
0.81 (0.75–0.87)d,e
0.83 (0.77–0.88)d,e

0.86 (0.78–0.93)d0.87 (0.81–0.94)d0.87 (0.82–0.93)d,e
0.88 (0.83–0.94)d,e

AUC, area under the receiver operating characteristic curve; UGS, usual gait speed; UEP, upper extremity performance; GRIP, hand-grip strength; PEG manipulating pegs; FR,
functional reach; BS, back scratch; UE limitation, upper extremity functional limitation; IADLs, instrumental activities of daily living; ADLs, activities of daily living.
ap < 0.05 vs UGS.
b
p < 0.05 vs GRIP.
cp < 0.05 vs PEG.
dp < 0.05 vs FR.
e
p < 0.05 vs BS.
*
Range 0.5–1.0. Degree of discrimination: 0.7–0.8 acceptable, 0.8–0.9 excellent, 0.9–1.0 outstanding.

Please cite this article in press as: Seino, S., et al., Comparison of a combination of upper extremity performance measures and usual gait
speed alone for discriminating upper extremity functional limitation and disability in older women. Arch. Gerontol. Geriatr. (2011),
doi:10.1016/j.archger.2011.10.011

G ModelAGG-2630; No. of Pages 6
S. Seino et al. / Archives of Gerontology and Geriatrics xxx (2011) xxx–xxx

encouraged in routine assessment of functional limitation anddisability, including assessment of UE limitation.
Although we should consider a statistically signiﬁcant difference as meaningful when comparing AUCs, Guralnik et al. (2000)
have concluded that a 3–5% difference between AUCs is negligible.
Furthermore, since the 95% conﬁdence intervals largely overlappedin our study, we consider the 4–5% difference in the AUCs between
UGS alone and GRIP + PEG or GRIP + PEG + FR to be insubstantial.
Onder et al. (2005) demonstrated that LEP measures, particularly
UGS, showed a greater predictive ability than UEP measures forincident disability outcomes including upper extremity disability.
Therefore, the UGS is considered a general measure of health andphysical performance, and not just a speciﬁc indicator of localized
poor function.
This is consistent with our ﬁndings, and we also showed thatwhen combining UEP measures, the discriminating power for UE
limitation did not increase meaningfully compared to UGS alone.
These results would discourage the use of PEG, FR, BS, and BEAN inclinical practice and reinforce using UGS as the preferredperformance measure, even more than a combination of UEP
measure for assessing physical function.
Guralnik et al. (2000) showed that UGS alone, which is part of
the SPPB, performed almost as well as the full SPPB in predicting
incident disability. Onder et al. (2005) also demonstrated that UGS
is nearly as good as their lower-extremity summary performance
score in predicting incident disability. Moreover, Viccaro et al.
(2011) recently evaluated the predictive ability of UGS and timed
up-and-go for adverse-health outcomes. The UGS and timed upand-go predicted outcomes equally well, but combining the two
measures did not add to the predictive ability. These results also
showed that, even when combining LEP measures, predictive
ability does not substantially increase over UGS alone. The
performance measure (e.g., GRIP and UGS), which has the strongest
impact on discerning disablement, may include the vast majority
of information generally obtained through the other performancemeasures.
In the present study, only GRIP could discriminate eachfunctional status almost as well as UGS among individual UEP
measures. There are several explanations for this. Numerous
studies have consistently demonstrated that GRIP is an independent predictor of frailty (Sayer et al., 2006), disability (Rantanen etal., 1999), and cause-speciﬁc and total mortality (Rantanen et al.,2003). Thus, GRIP is also considered representative of a person’s
general health condition. On the other hand, Onder et al. (2005)
showed that GRIP is a very speciﬁc predictor of future incident
progressive upper extremity disability. Rantanen et al. (1999) also
found that the GRIP test performed in middle age predicts
functional limitation in terms of UE tasks 25 years later in oldage. As shown by these studies, GRIP may be clinically meaningful
as an indicator for both disablement and primary upper extremity
function (using ﬁngers to grasp or manipulate).
Although the importance of both UGS and GRIP as screening
measures is conﬁrmed, UGS may have greater value than GRIP. As
shown in the present study, UGS can discriminate UE limitation
almost as well as combined UEP measures. On the other hand, ourprevious study results (Seino et al., 2011) demonstrated that thediscriminating power of GRIP for mobility limitation was 15%
lower than the discriminating power of combined LEP measures inolder women, indicating that the difference in discriminating
power between the two methods was substantial. Moreover, asystematic review and meta-analysis (Cooper et al., 2011) showedthat associations between physical performance measures and allcause mortality in community-dwelling older adults. The summary hazard ratios for mortality, when comparing the best 25% withthe worst 25% of performance measures, were 2.87 for UGS (ﬁvestudies, 14,692 participants), and 1.67 for GRIP (14 studies, 53,476

5

participants). These studies indicate that the UGS is certainly more
important than GRIP in the routine assessment of older adults.
There were several limitations in this study. First, population
studies of older adults may sometimes be affected by a selection bias,
because relatively healthier people tend to participate. Second, thisstudy was a cross-sectional study, which does not allow evaluationof the predictive ability of the UEP measures and the combinations
we studied. Moreover, we could not obtain more direct and clinicallymeaningful results (e.g., hazard ratio) in terms of the strength of thedifferent combinations of UEP measures because our study design
and the small sample size of participants did not permit this. Further
research is needed to conﬁrm the predictive validity of our ﬁndings
in longitudinal studies. Third, although we were able to adjust ouranalyses for health information with this study, there could beunmeasured confounders for which we could not adjust. Finally,
although we reinforced the importance of UGS and GRIP as
performance measures, exercise interventions may have a differential impact on different regions and should be monitored withappropriate regional measures.
In conclusion, the results of the present study showed that, in our
attempt to reﬁne our ability to discern UE limitation and disability,
the advantages of combining UEP measures were less than we
expected. Our results suggest that UGS alone or GRIP alone have
similar utility, and they may be adequate for assessing UE limitationand disability. In particular, UGS should be considered the ﬁrst
choice of performance measures, certainly more than GRIP, inroutine assessment of functional limitation and disability amongolder women because the UGS test can discriminate UE limitation
almost as well as a combination of different UEP measures.
Conﬂict of interest statement
None.
Acknowledgments
We are grateful to the participants and our staff members. This
study was supported by the Japan Society for the Promotion of
Science (JSPS), Grant-in-Aid for Scientiﬁc Research (A) # 19200047
and Grant-in Aid for JSPS Fellows # 231009.
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Please cite this article in press as: Seino, S., et al., Comparison of a combination of upper extremity performance measures and usual gait
speed alone for discriminating upper extremity functional limitation and disability in older women. Arch. Gerontol. Geriatr. (2011),
doi:10.1016/j.archger.2011.10.011