Journal of Gerontology: MEDICAL SCIENCES
2006, Vol. 61A, No. 11, 1186–1193
Copyright 2006 by The Gerontological Society of America
Lower Extremity Strength and Power Are Associated
With 400-Meter Walk Time in Older Adults:
The InCHIANTI Study
Anthony P. Marsh,1 Michael E. Miller,2 Aaron M. Saikin,1 W. Jack Rejeski,1 Nan Hu,2
Fulvio Lauretani,3 Stefania Bandinelli,3 Jack M. Guralnik,4 and Luigi Ferrucci5
1
Background. It has been suggested that lower extremity muscle power is more important for physical function in older
adults compared to strength, and that there is a nonlinear relationship between power or strength and physical function that
might be indicative of a threshold above which the association between muscle function and physical function is no longer
evident. This study examined the association between lower extremity strength or power with the time to complete a
400-meter walk, and attempted to identify thresholds within the relationship.
Methods. A cross-sectional analysis of a sample of 384 females and 336 males aged 65 years from the InCHIANTI
study (‘‘Invecchiare in Chianti,’’ i.e., Aging in the Chianti Area) was conducted. Measures included 400-meter walk time,
lower extremity strength and power, comorbidities, and sociodemographic variables (age, gender, height, education,
cognitive function, depression).
Results. Linear regression models showed that both lower extremity strength and power were significant predictors
of 400-meter walk time, although power explained marginally more of the variance in 400-meter walk time. Quadratic
models of lower extremity strength and power fit the data slightly better than the linear models. Regardless of gender,
comorbidities, or normalization scheme for strength and power, the curvilinear form of the relationship between strength
or power and 400-meter walk time remained the same.
Conclusions. Lower extremity muscle strength and power are both important predictors of the 400-meter walk time.
Although curvilinear relationships existed between muscle strength and power and the 400-meter walk time, the data do not
indicate a clear threshold for either strength or power above which the performance in the 400-meter walk test plateaus.
T
HE relationship between the capacity for body functions, activities, and participation, as defined in the
terminology of the International Classification of Functioning (1), is critical to our understanding of the determinants
of independence in older adults. Fried and colleagues have
suggested that mobility disability is a good marker for
monitoring the causal pathway leading to participation
restrictions in life situations (2). The reason is that mobility
disability occurs early in the disablement process and has
been found to be predictive of further physical and cognitive
restrictions related to social roles (3,4). The ability to walk
400 meters has been proposed as a way to objectively assess
mobility disability (5–7) and 400 meters is also comparable
to the reference distance (0.25 mile) found in questions
commonly used to assess mobility function by self-report.
It has been well documented that muscle strength, the
maximum force produced by a muscle, declines with age
(8–10). Limited data on muscle power, the product of muscle force developed, and the velocity of muscle shortening
show that muscle power declines with age earlier and at
a faster rate than muscle strength (11,12). Reductions in
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lower extremity muscle strength and power compromise
mobility when walking, climbing stairs, or rising from a
chair (13–17). It has been suggested that muscle power is
a better predictor of mobility performance than muscle
strength (14,18–20). Also, it has been suggested that the
relationship between strength and performance in walking
tests is curvilinear and, above a specific threshold, becomes
weaker (21–24). An unanswered question is whether this
curvilinear relationship also holds for muscle power and
mobility disability.
The primary aim of this cross-sectional study was to
examine the association of lower extremity muscle strength
and power with 400-meter walk time, while controlling for
age, sex, and other potential confounders. Additional aims
of this study were: (a) to determine if lower extremity
muscle power was a better predictor of 400-meter walk time
compared to strength; (b) to examine the nature of the
relationships between lower extremity strength or power and
400-meter walk time; (c) to determine if a threshold value of
strength or power exists; and (d) to determine if important
covariates (gender, disease) changed the curvilinear form of
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Department of Health and Exercise Science, Wake Forest University, Winston-Salem, North Carolina.
2
Section on Biostatistics, Department of Public Health Sciences, Wake Forest University School
of Medicine, Winston-Salem, North Carolina.
3
Florence Local Health Unit and the Tuscany Regional Health Agency, Florence, Italy.
4
Laboratory of Epidemiology, Demography and Biometry, National Institute on Aging, Bethesda, Maryland.
5
Longitudinal Studies Section, Clinical Research Branch, National Institute on Aging, Baltimore, Maryland.
STRENGTH, POWER, AND 400-METER WALK IN OLDER ADULTS
the relationship between strength or power and 400-meter
walk time. The threshold was operationalized as a value of
strength or power above which the relationship between
strength or power and 400-meter walk time flattens out, such
that additional increases in strength or power do not lead to
further reductions in 400-meter walk time.
METHODS
Participants
In August 1998, 1299 individuals aged 65 years and older
were randomly selected from the population registry from
the rural town of Greve in Chianti and the suburban town of
Bagno a Ripoli, located in the Chianti region of Tuscany,
Italy. Individuals were contacted by mail and given a
description of the study. Thirty-nine individuals were not
eligible for the study due to death or because they moved
away from the area. In those who were eligible, the response
rate was excellent (91.7%, 1155/1260). Of the initial 1155
participants, 435 participants were excluded from our
analyses, resulting in 720 participants with data available
for analyses. Participants were excluded for one or more of
the following reasons: they (a) were blind (n ¼ 24) or deaf
(n ¼ 28); (b) had a cognitive impairment defined as a MiniMental State Exam score lower than 21 (n ¼ 172); (c) had
Parkinson’s disease (n ¼ 15), a stroke (n ¼ 57), lower
extremity amputation (n ¼ 2), paralysis (n ¼ 1), or peripheral
neuropathy (n ¼ 2) as determined from both medical
documentation and a baseline medical exam; (d) had an
interview classified as unreliable, by proxy, or did not
provide consent to participate (n ¼ 97); and (e) were missing
data for lower extremity strength or power (n ¼ 194).
Reasons for missing strength or power data included: (a)
severe hip, knee, or back pain (n ¼ 45); (b) range of motion
limitation (n ¼ 44); (c) home visit (n ¼ 18); (d) unsafe,
dangerous (n ¼ 39); (e) high blood pressure (n ¼ 18); (f)
cognitive impairment (n ¼ 13); (g) paralyses/amputation
(n ¼ 6); (h) dyspnea (n ¼ 3); (i) angina (n ¼ 1); (j) low heart
rate (n ¼ 1); and (k) refusal (n ¼ 6).
In addition to these exclusions, some participants failed to
provide complete data for the 400-meter walk (n ¼ 65, see
criteria below). The final analytical sample included 655
participants.
MEASURES
Sociodemographic Variables
Participant demographic information included age, gender,
height, body mass, body mass index (BMI), education (years
completed), depressive symptoms (Center for Epidemiologic Studies Depression scale, CES-D) (26), and cognitive
function (Mini-Mental State Exam, MMSE) (27). Comorbid
conditions were confirmed for participants who had both past
medical records and a current medical exam suggestive of the
condition. Comorbidities assessed in this study included
cancer, respiratory disorder (diagnosed asthma, chronic bronchitis, and lung emphysema), dyspnea with light or mild
exertion, diabetes, cardiovascular disease (diagnosed congestive heart failure, myocardial infarction, and angina),
hypertension, peripheral arterial disease, and hip fracture.
400-Meter Walk
Participants were instructed to walk 20 laps of a 20-meter
course around 2 cones ‘‘at a steady and, if possible, constant
pace.’’ Standardized verbal encouragement was given on
each lap, directing participants to maintain their pace, and
indicating the number of laps remaining. Exclusion criteria
for the test were: heart rate , 40 bpm or . 135 bpm; within
the previous 3 months, anterior myocardial infarction, cardiac surgical intervention, angina, severe dyspnea or dyspnea at rest, loss of consciousness; systolic blood pressure
(BP) . 180 mmHg or diastolic BP . 100 mmHg, pathologic changes on electrocardiogram; difficulty keeping feet
together for 10 seconds; and difficulty in walking 8 meters.
Criteria for test termination were: palpitations; chest pain,
constriction, feeling of oppression; respiratory difficulty or
dyspnea; sensation of fainting, empty head, or postural
instability; pain in the lower limbs; vertigo; and muscle
fatigue. The 400-meter walk time was measured using an
optoelectronic system with two photo-cells connected to
a digital chronometer.
Lower Extremity Isometric Strength (LEIS)
Maximum voluntary isometric strength of eight lower
extremity muscle groups of the right and left lower extremity
(hip flexors, hip extensors, hip abductors, hip adductors, knee
flexors, knee extensors, plantarflexors, and dorsiflexors) was
measured in kilograms (kg) using a portable dynamometer
(Penny and Giles Instrumentation Ltd, Christchurch, Dorset,
England), following a standard protocol (28).
In our preliminary analyses, we considered several ways
to quantify lower extremity strength. First, the lower extremity isometric strength (LEIS) measurements of the eight
muscle groups were reduced to a single measure using
a principle component analysis. Separate analyses for the
right and left side showed that a single factor captured
approximately 81% of the variance for the measures on each
side. Because of similar factor loadings, we summed the
scores of the eight muscle groups on the right side and left
side to create an isometric strength sum score. Since the sum
scores of the right and left sides were highly correlated (r ¼
.97), we used the sum score of the right side as a measure of
maximal lower extremity isometric strength. The sum scores
were normalized by dividing by the participant’s body mass
and multiplying by the body mass sample mean. Second, we
examined single isometric strength measures at the ankle
(ankle plantarflexion) and knee (knee extension) since both
these muscle groups are important contributors to gait performance and can be assessed with little difficulty in a
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Design
We used baseline data from the InCHIANTI study
(‘‘Invecchiare in Chianti,’’ i.e., Aging in the Chianti Area)
(25), which were collected during three separate testing
sessions: a home-based interview, a medical exam, and
a functional performance evaluation. The data collection for
this study began in September 1998 and concluded in March
2000. The study protocol was approved by the Italian National
Institute of Research and Care on Aging ethics committee.
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MARSH ET AL.
clinical environment. Of these three measures of strength,
we elected to use the ankle plantarflexion isometric strength
as the predictor variable for lower extremity isometric
strength (LEIS), and our rationale is explained below.
Statistical Analyses
All analyses were performed using the SAS statistical
package (SAS Institute Inc., Cary, NC). Univariate descriptive statistics were calculated for the covariates, the 400meter walk time, and the predictor variables (LEIS, LEMP).
Because of a highly skewed distribution, log-transformed
values of 400-meter walk time were used in regression
analyses. However, the fit of models to untransformed data
was also investigated for an apparent threshold.
Examination of the regression analyses using the three
different measures of isometric strength showed that the
sum score resulted in the highest R2 (32.3%), followed by
ankle plantarflexion strength (28.6%), and the knee extension strength (23.9%). Regardless of the measure of isometric strength, the conclusions reached about the nature of
the relationship between strength and 400-meter walk time
remained the same. We used the ankle plantarflexion isometric strength measure because its predictive ability fell
between the other two measures and, pragmatically, measuring one muscle group is easier than measuring eight.
Linear multiple regression analyses and piecewise regression were used to characterize the relationships between
400-meter walk time and the predictor variables (LEIS,
LEMP), and to try to estimate a threshold in the relationship
between the predictors and 400-meter walk time. The knot
and confidence intervals (CIs) for the piecewise regression
models were estimated using PROC NLIN in SAS and
Gauss-Newton estimation. An alternative statistical method
used for fitting the relationships of interest was locally
weighted regression smoothers (loess technique) (31). This
technique resulted in plotted lines for the LEIS or LEMP
versus 400-meter walk time relationships that were almost
identical to those obtained using multiple linear regression
with higher order terms included in the model. We also
converted the 400-meter walk time into gait velocity in
meters per second and fit these data using the loess
technique. This analysis revealed even less evidence for
a threshold, that is, a plateau in the relationship between gait
speed and LEMP. Since the interpretation and conclusions
remained the same in all instances, the loess results are not
Variable
Value
Range
Female, % (n ¼ 384)
53.3
Age, mean 6 SD (n ¼ 720)
73.0 6 6.1
65–91
Height, cm, mean 6 SD (n ¼ 720)
159.4 6 9.3 133.0–189.0
Mass, kg, mean 6 SD (n ¼ 720)
69.9 6 12.3 41.0–120.0
Body Mass Index, kg/m2, mean 6 SD (n ¼ 720 ) 27.4 6 3.9
18.0–46.6
Education, y, mean 6 SD (n ¼ 720)
5.9 6 3.4
0–22
MMSE score, mean 6 SD (n ¼ 720)
26.1 6 2.4
21–30
CES-D score, mean 6 SD (n ¼ 720)
11.9 6 8.5
0–48
Cancer, % (n ¼ 45)
6.3
Respiratory disorder, % (n ¼ 63)
8.8
Dyspnea, % (n ¼ 240)
33.3
Diabetes, % (n ¼ 69)
9.6
Cardiovascular disease, % (n ¼ 68)
9.4
Hypertension, % (n ¼ 332)
46.1
Peripheral arterial disease, % (n ¼ 34)
4.7
Hip fracture, % (n ¼ 14)
1.9
No comorbidities, % (n ¼ 198)
27.5
One comorbidity, % (n ¼ 282)
39.2
Two or more comorbidities, % (n ¼ 240)
33.3
400-m walk time, s, mean 6 SD (n ¼ 655)
330.9 6 71.4 208.3–727.3
400-m walk not completed, % (n ¼ 39)
5.4
400-m walk not attempted, % (n ¼ 26)
3.6
LEMP, W, mean 6 SD (n ¼ 720)
110.3 6 63.3
4.3–340.5
LEIS, kg, mean 6 SD (n ¼ 720)
32.4 6 10.3 10.1–71.0
Note: SD ¼ standard deviation; MMSE ¼ Mini-Mental State Examination;
CES-D ¼ Center for Epidemiological Studies Depression scale; LEMP ¼ lower
extremity muscle power; LEIS ¼ lower extremity isometric strength.
presented but are available from the author. Body mass
index was not entered into the regression analyses as
a covariate because both strength and power analyses were
adjusted for body mass and height.
We examined interactions between LEIS and LEMP and
several covariates that we believed may influence the relationship between strength or power and 400-meter walk
time, and that also had a sufficient number of participants
with the covariate (approximately . 50). These included
gender, respiratory disorder, dyspnea, diabetes, cardiovascular disease, hypertension, and peripheral arterial disease.
Analysis of variance was used to explore for differences
in age, LEIS, and LEMP for three groups defined by
whether the participant completed, did not complete, or did
not attempt the 400-meter walk. Fisher’s protected least
significant difference procedure was used to examine for
differences between the group means (32). For all aforementioned analyses, p values less than .05 were used to determine statistical significance.
RESULTS
Sociodemographic Variables
Baseline characteristics of the study participants are
shown in Table 1. The sample consisted of 720 participants
with a mean (6 standard deviation [SD]) age of 73.0 6 6.1
years. Women represented approximately 53% of the
sample and were, on average, slightly older than the men.
Participants were slightly overweight with a mean BMI of
27.4 6 3.9 kg/m2 and, on average, had fewer than 6 years of
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Lower Extremity Muscle Power (LEMP)
Lower extremity muscle power in watts (W) for the right
and left sides was measured using the Nottingham leg
extensor power rig, according to the method described by
Bassey and Short (29). The maximal LEMP measurements of
the right and left lower extremities were highly correlated
(r ¼ .94). Therefore the LEMP of the right lower extremity
was used to represent a participant’s LEMP. We also conducted analyses using the normalization scheme of Lauretani
and colleagues (30) whereby the LEMP score was divided by
the participant’s body mass and multiplied by the body mass
sample mean. The conclusions reached were no different from
those using the nonnormalized measure of LEMP, and we
used the nonnormalized measure of LEMP in the article data.
Table 1. Baseline Characteristics of InCHIANTI Participants
Aged 65 Years and Older (N ¼ 720)
STRENGTH, POWER, AND 400-METER WALK IN OLDER ADULTS
formal education. Approximately 28% of the sample was
free from chronic medical conditions. The most common
comorbidity was hypertension, followed by dyspnea with
mild exertion, diabetes, and cardiovascular disease.
The mean 400-meter walk time was comparable to a
previous report (33), although the range we observed was
greater, probably reflecting the larger age range in our study
(65–91 years vs 70–78 years). The muscle power observed
in this sample was consistent with data in previous studies
that have used the power rig in older adults (19,29). The
plots of bivariate relationships between muscle strength and
power with log-transformed 400-meter walk time showed
evidence of a curvilinear relationship (Figure 1).
added. The magnitude of the difference in explained
variability (6%, 56.9 vs 50.9%) after inclusion of linear
and quadratic power versus strength terms (adjusting for
covariates other than gender and height) is larger than
observed for the adjusted analyses that included gender and
height (4.3%, 57.7 vs 53.4%), indicating more overlap in the
400-meter walk variability explained by these two covariates and power compared to strength. An alternative perspective is that the contribution of gender and height in
explaining the variability of 400-meter walk time is less
after entering power (0.8%, 57.7 vs 56.9%) in the model
compared to entering strength (2.5%, 53.4 vs 50.9%).
In Figure 1, the relationship between 400-meter walk time
and power and strength is displayed relative to the observed
data. On each figure, we identify a dashed line that
represents the fitted model from a piecewise regression that
estimated a threshold at which the quadratic equation
becomes a flat line. For LEIS, introduction of the cubic term
did not add to the explanatory ability of the quadratic term,
with the quadratic term increasing the explained variability
of the unadjusted analyses by approximately 4%. For the
piecewise regression models contained in Figure 1, the
threshold value was estimated to be 215.4 W (95% CI
178.1–242.7) for LEMP and 49.6 kg (95% CI 43.1–56.3)
for LEIS.
In analyses where we examined interaction terms between
strength or power and covariates, we found that the
curvilinear form of the relationship between strength or
power and 400-meter walk time did not change. While those
participants with dyspnea, diabetes, hypertension, and peripheral arterial disease completed the 400-meter walk in
a significantly slower time, the curvilinear relationship and
lack of strong evidence of a threshold value remained consistent for all of these analyses.
Means of strength and power measurements were
calculated for participants that completed the 400-meter
walk and those that either did not complete the walk or did
not attempt the walk. Participants who completed the
task had significantly greater strength and power than
the other two groups (Power means [W]: completed 400meter walk ¼ 115.1; did not complete 400-meter walk ¼
58.3; did not attempt 400-meter walk ¼ 67.5; Strength
means [kg]: completed 400-meter walk ¼ 33.1; did not
complete 400-meter walk ¼ 25.9; did not attempt 400-meter
walk ¼ 24.3).
DISCUSSION
This study examined whether lower extremity muscle
strength and power were associated with the time to walk
400 meters at a steady pace in adults aged 65 years and
older. Our data provide strong evidence that strength and
power are significant predictors of the time to walk 400
meters at a steady pace, independent of confounders. Further, the curvilinear form of the relationship between
strength or power and 400-meter walk time remained
unaffected by gender or several comorbidities that might
reasonably be expected to influence the 400-meter walk
time. Previous studies reported significant associations
between lower extremity strength and power with mobility
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Regression Analyses
The summaries from the unadjusted and adjusted regression analyses are shown in Table 2. In the unadjusted
models for LEIS, the addition of the quadratic term resulted
in an increase of 3.9% in total explained variability; whereas
the cubic term increased the R2 by less than 0.1%. For
unadjusted power analyses, the significant quadratic term
explained an additional 3.3% of variability, and the cubic
term an additional 0.6%. However, overall, the model containing linear, quadratic, and cubic power terms explained
35.0% of the variability in 400-meter walk time, in contrast
to 28.7% for strength. When all linear, quadratic, and cubic
terms for strength and power were entered into the model,
the total explained variability was 38.3%, indicating that the
addition of the linear and quadratic strength terms only
increased the explained variability beyond power alone by
3.3%. Inspection of Figure 1 shows that the major difference
between the quadratic and cubic models is how the models
fit those individuals with the greatest power or strength: the
cubic model for LEMP permitting a reduction in the
predicted walk time for these individuals. However, these
cubic terms explain very little variability in models containing power or strength.
In the adjusted regression analyses, a model containing
the covariates (age, gender, body mass, height, years of
education, MMSE score, CES-D score) and comorbid
conditions accounted for 48.6% of the total variability in
log-transformed 400-meter walk time. The addition of the
linear strength term resulted in an R2 equal to 52.3%, with
addition of the quadratic term increasing R2 by 1.1%, to
53.4%. The addition of the cubic strength term explained
,0.1% of the total variability. The addition of the linear
power term to the covariates resulted in an R2 equal to
55.6%, with the quadratic term increasing the R2 by an
additional 1.1% and the cubic term increasing the R2 by an
additional 0.4%. The fact that power and strength terms are
close in total explanatory power after controlling for all
covariates, even though power is the stronger predictor
in the uncontrolled analyses, indicates that some of the
covariates are more highly correlated with power than
strength. In particular, gender and height are more strongly
associated with power than strength. If these covariates are
not included, the total explained variability of covariates
alone is 42.7%, increasing by approximately 8% (to 50.9%)
if linear and quadratic strength terms are added, and by
approximately 14% (to 56.9%) if similar terms for power are
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MARSH ET AL.
1190
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STRENGTH, POWER, AND 400-METER WALK IN OLDER ADULTS
Table 2. Regression Model Results for Lower Extremity Isometric
Strength (LEIS) and Lower Extremity Muscle Power (LEMP)
Predicting Log-Transformed 400-Meter Walk Time*
Unadjustedy
Predictor
Adjustedyz
R2
Notes
24.7%
28.6% (þ3.9%)
28.7% (þ0.1%)
[1]
[1]
[2]
52.3%
53.4% (þ1.1%)
53.4% (þ0.0%)
[1]
[1]
[2]
31.1%
34.4% (þ3.3%)
35.0% (þ0.6%)
[1]
[1]
[3]
55.6%
57.7% (þ1.1%)
58.1% (þ0.4%)
[1]
[1]
[3]
33.6%
37.6% (þ1.0%)
38.3% (þ0.7%)
[1]
[1]
[4]
56.4%
58.7% (þ1.3%)
59.1% (þ0.4%)
[1]
[5]
[4]
LEIS
Linear model
Quadratic model
Cubic model
LEMP
Linear model
Quadratic model
Cubic model
LEIS and LEMP
Linear model
Quadratic model
Cubic model
Notes: *N ¼ 655 observations are used in these models.
y
Number in parentheses represents change in R2 resulting from adding the
additional polynomial term to the model specified in the previous row.
z
All models were adjusted for age, sex, height, body mass, years of formal
education, Mini-Mental State Examination score, Center for Epidemiological
Studies Depression scale score, comorbid conditions (R2 ¼ 0.486 for these
predictors).
[1] Highest order polynomial term significant at p , .001.
[2] p ¼ .34 for cubic term in unadjusted model and p ¼ .39 for cubic term
in adjusted model.
[3] p , .01 for cubic term in unadjusted and adjusted models.
[4] p ¼ .09 for both cubic terms in unadjusted model; p ¼ .04 for LEMP3;
and p ¼ .52 for cubic LEIS term.
[5] p , .01 for both quadratic terms.
performance (i.e., gait velocity, stair-climb, chair-rise) in
older adults (14,15,18,20,23,24). The percent variance in
400-meter walk time explained by strength and power is
similar to that reported by Bean and colleagues (18), who
used the 6-minute walk as the outcome of interest and
maximal strength and power at the ankle (plantarflexion),
knee (extension), and lower extremity (double-leg press) as
predictor variables. The current data are the first to show that
strength and power are important predictors of the time to
walk 400 meters, an outcome that is increasingly being used
in large longitudinal studies of older adults (7,25,34).
Overall, our regression analyses showed that lower extremity power was a slightly better predictor of mobility
performance than lower extremity strength. A cubic polynomial model including lower extremity power explained
approximately 6% more of the variance than a similar model
for strength in the unadjusted analyses predicting the time
to walk 400 meters. After the model was adjusted for
covariates, lower extremity power explained approximately
5% more of the total variance than strength in the time to
walk 400 meters. It is debatable whether these small differences in explained variance present a compelling argument that lower extremity power is a better predictor of
mobility performance over and above lower extremity
strength. The practical significance of lower extremity
power being a better predictor of mobility performance
than strength is that lower extremity power appears to be
affected more by age (11,12). Impairments in power might
be used to identify individuals at risk of mobility difficulty
and disability earlier in the disablement process than
strength. This speculation needs to be substantiated.
We also explored whether curvilinear relationships existed between lower extremity muscle strength and power
and the 400-meter walk. The quadratic terms in quadratic
models of lower extremity strength and lower extremity
power were significant predictors of the time to walk 400
meters. However, the cubic term was significant only for
power and explained approximately 0.5% of variability; the
term primarily provided additional fit to observations
obtained on those with the greatest power. For models containing power, strength, and the combination, the contribution of the higher-order polynomial terms to the linear terms
is reduced in those models that adjusted for the covariates.
Thus, some of the curvilinear relationships seen between
these predictors and 400-meter walk time can be explained
by covariates. Other investigations that inspect for these
curvilinear relationships, but do not control for covariates,
may be overstating the magnitude of the curvilinear
relationship.
It has been suggested that a quadratic, as opposed to
linear, relationship between lower extremity strength and
mobility performance may be indicative of a threshold
where additional increases in strength would not result in
improved mobility performance (22–24). Our analyses
using piecewise regression showed almost identical fits to
these data as were obtained using multiple linear regression
containing linear and quadratic terms. In addition, the confidence intervals on the threshold estimates are quite wide
and occur within a range of strength (power) where limited
data were observed, making the identification of a strict cutpoint questionable and highly subjective. Using cut-points
identified in this study would mean that the vast majority of
patients presenting in a clinic would be identified as deficient in strength or power, including those who are at the
higher end of the functional continuum. Rather, we interpret
our data to indicate that within the range of strength (power)
measured within this study, there exists a continuous relationship between strength (power) and physical function.
The notion of a threshold is very appealing from a clinical
perspective. Identifying a threshold where performance
abruptly declines might be used as a diagnostic screening
tool to target individuals at risk for mobility disability (23).
Related to this, several studies have attempted to identify
minimum lower extremity strength or power thresholds
necessary for mobility performance (35–38). In our study,
we have defined the threshold as an upper limit of strength
or power with analytic criteria. The lower limit, the minimum amount of strength or power needed to do a task, is
more difficult to characterize since there may be a wide
array of factors that prevent an individual from accomplish-
‹
Figure 1. Bivariate relationship between the predictor variables and the log-transformed 400-meter walk time (s). LEMP, lower extremity muscle power (W); LEIS,
lower extremity isometric strength (kg).
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Notes
R
2
1191
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MARSH ET AL.
Conclusion
Lower extremity muscle strength and power are important
predictors of the time to walk 400 meters at a steady pace. It
remains questionable due to the small changes in explained
variance as to whether LEMP is a better predictor of the
time to walk 400 meters than lower extremity strength.
Finally, curvilinear relationships exist between strength and
power with the time to walk 400 meters, and the curvilinear
form of the relationship is very robust to gender and
comorbidities. However, the data do not appear to be suggestive of a threshold value for strength or power.
ACKNOWLEDGMENTS
The work of A. P. Marsh and M. E. Miller was supported by the Wake
Forest University Claude D. Pepper Older Americans Independence Center
(NIA grant P30-AG-021332-01). The InCHIANTI study was supported as
a ‘‘targeted project’’ (ICS 110.1\RS97.71) by the Italian Ministry of Health,
and, in part, by the U.S. National Institute on Aging (Contracts N01-AG916413, N01-AG-821336, 263 MD 9164 13, and 263 MD 821336). None
of the sponsoring institutions interfered with the collection, analysis,
presentation, and interpretation of the data reported in this article.
Address correspondence to Anthony P. Marsh, Department of Health and
Exercise Science, Wake Forest University, Winston-Salem, NC 271097868. E-mail: [email protected] or Michael E. Miller, Department of
Biostatistical Sciences, Division of Public Health Sciences, Wake Forest
University School of Medicine, Winston-Salem, NC 27157-1063. E-mail:
[email protected]
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Received July 8, 2005
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Decision Editor: Darryl Wieland, PhD, MPH
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