gait adjustments in older adults: activity and efficacy influences

Psychology and Aging1998, Vbl. 13, Nn. 3, 375-386

Copyright 1998 by the Am :an Psychological Association, Inc.0882-7974/98/$3.00

Gait Adjustments in Older Adults: Activity and Efficacy Influences

Karl S. Rosengren and Edward McAuleyUniversity of Illinois at Urbana-Champaign

Shannon L. MihalkoPennsylvania State University

Factors that influence gait adjustments in active and sedentary older adults were examined in this

study. Fifty-five older adults (60-85 years) completed a series of physical activity and self-efficacy

measures (gait, falls) and the Berg Balance Scale (K. O. Berg, S. L. Wood-Dauphinee, J. I. Wil-

liams, &. B. Maki, 1992). Participants then completed a series of walking trials that included walking

with and without obstacles placed in their path. Sedentary older adults adopted a more cautious

walking style than active ones, exhibiting shorter step lengths and slower step velocities. Age, physical

activity level, balance, and the efficacy measures were all found to be significantly correlated with

gait speed. Hierarchical regression analyses indicated that once age, sex, and body mass index were

controlled for, gait efficacy had a significant independent effect on gait speed. These results highlight

the importance of examining multiple factors when examining the control of gait.

In a typical day, individuals make numerous adjustments in

their gait in order to avoid obstacles, maintain balance, and

navigate successfully over uneven or cluttered surfaces. With

increased age, individuals have greater difficulty with negotiat-

ing these situations, placing the older adult at risk for falling.

The goal of this investigation was to examine factors that might

influence the manner in which older adults negotiate these situa-

tions. Past research has examined changes in gait associated

with both age and activity level, but little is known about how

cognitive factors, such as self-efficacy, might also influence the

gait characteristics of older adults. In this investigation we ex-

plore the relation between these variables and examine how each

might be related to how older individuals perform a relatively

common gait task, stepping over obstacles of different heights.

The gait of older adults and the manner in which they adjust

their gait to step over obstacles have been found to differ in a

number of important ways from those of younger adults. Spe-

cifically, older adults typically walk more slowly (Bendall, Bas-

sey, & Pearson, 1989; Ferrandez, Pailhous, & Durup, 1990;

Himann, Cunningham, Rechnitzer, & Patterson, 1988; Murray,

1967), most likely because of an overall decrease in step or

stride length (Elble, Thomas, Higgins, & Colliver, 1991; Ferran-

dez et al., 1990). It has been suggested by Murray, Kory, and

Clarkson (1969) that these changes may lead to improved bal-

ance by increasing time in double support (the interval of time

Karl S. Rosengren, Departments of Kinesiology and Psychology, Uni-

versity of Illinois at Urbana-Champaign; Edward McAuley, Department

of Kinesiology, University of Illinois at Urbana-Champaign; Shannon

L. Mihalko, Department of Kinesiology, Pennsylvania State University.

This research was funded in part by an Arnold O. Beckman Research

Board Grant from the University of Illinois and National Institute on

Aging Grant AG 12113. We gratefully acknowledge Kristi Berg, Sarah

Edwards, Amy O'Malley, and Jason Metcalfe for help with data collec-

tion and Sarah Mangelsdorf for reading previous drafts.

Correspondence concerning this article should be addressed to Karl S.

Rosengren, Department of Kinesiology, University of Illinois at Urbana-

Champaign, Louise Freer Hall, 906 South Goodwin Avenue, Urbana, Illi-

nois 61801. Electronic mail may be sent to [email protected].

when both feet are in contact with the ground). The majority

of this work has examined older individuals' gait as they walk

across a smooth, uncluttered surface, a situation that we rarely

confront in our daily lives. More recent work has focused on

how older adults adjust their gait to cross over obstacles (Chen,

Ashton-Miller, Alexander, & Schultz, 1991, 1994). When con-

fronted with obstacles, older adults have been found to exhibit

slower crossing speeds, as well as shorter step lengths, and

greater obstacle-to-heel strike distances compared to younger

adults (Chen et al., 1991). Chen and his colleagues suggested,

on the basis of the gait characteristics of older adults that they

observed, that older individuals may adopt a more cautious

walking style. They did not, however, investigate this issue

directly.

A number of investigators have reported that gait declines

associated with aging are smaller in those who engage in regular

physical activity or exercise. The majority of this research has

focused almost exclusively on gait speed, showing that active

older adults have greater gait speed than sedentary older individ-

uals (Bendall et al., 1989; Binder, Brown, Craft, Schechtman, &

Birge, 1994; Martin, Rothstein, & Larish, 1992; Woo, Ho, Lau,

Chan, & Yuen, 1995). This result is not universally found, how-

ever, with a number of studies reporting little or no effect of

activity or exercise on gait speed (Judge, Whipple, & Wolfson,

1994; Leiper & Craik, 1991; Topp, Mikesky, Wigglesworth,

Holt, & Edwards, 1993). These conflicting results may be due

in part to differences in the manner in which activity level or

exercise is assessed or differences in the activity level of partici-

pants across studies. In this investigation we explored how activ-

ity level might influence kinematic variables in addition to gait

speed. Specifically, we explored how activity level might influ-

ence the average step length, step velocity, and time in single

leg support as individuals cross over obstacles of increasing

height. We hypothesized that activity level might influence each

of these parameters, especially as obstacle height increased.

An additional factor that might lead older adults to adopt a

more cautious walking style is a concern about falls or a more

general lack of confidence in their ability to successfully navi-

gate cluttered and uneven surfaces. General lack of confidence

375

376 ROSENGREN, McAULEY, AND MIHALKO

in one's ability to carry out essential activities of daily living

(ADLs), such as taking a bath or shower, reaching into cabinets,

and walking around a house, without falling has been operation-

alized by Tinetti, Richman, and Powell (1990) in their Falls

Efficacy Scale (FES). Lower levels of falls efficacy have been

associated with slower gait speeds (Tinetti et al., 1990) and

generally lower levels of physical and social function (Tinetti,

Mendes de Leon, Doucette, & Baker, 1994). In this latter study

physical functioning was assessed by querying the participants

about light and heavy housework, heavy home repair, light and

heavy yard work, sports participation, distance walked per day,

flights of stairs climbed per day, and ability to perform a number

of ADLs (eating, grooming, bathing, etc.). Social functioning

was assessed by asking the participants about their social activi-

ties (visiting friends, attending events or exhibits, participating

in group activities, volunteering, etc.).

Self-efficacy expectations are concerned with beliefs in one's

capabilities to successfully carry out courses of action (Bandura,

1986, 1997). The role of these cognitions in the prediction of

a large array of behavioral responses is well documented and

includes clinical, health, physical, and cognitive function (Ban-

dura, 1997). Relative to this study, several aspects of social

cognitive theory (Bandura, 1986), and in particular self-efficacy,

are worth consideration. First, efficacy expectations are pre-

dictive of the performance of challenging tasks. Thus, in the

context of gait patterns, higher efficacy expectations should be

related to performance under circumstances in which older

adults have to negotiate realistic challenges in the environment

(e.g., stepping down from an elevated surface, stepping over

objects in one's path, or successfully traversing a crosswalk

before the streetlight changes color). A social cognitive perspec-

tive (Bandura, 1986) would suggest that under challenging cir-

cumstances individuals must possess not only the relevant skills

and capabilities to carry out the task but also the beliefs in their

capabilities to do so. Indeed, Bandura (1997) has suggested

that the extent to which one has confidence in one's capabilities

to carry out these tasks is of greater importance.

A second point of relevance is that efficacy expectations are

both determinants and consequences of behavior. That is, they

are reciprocally determining. Efficacy expectations are often the

products of a previous successful history of task performance.

Such cognitions, in turn, influence future behavior. Take, for

example, the relationship between physical efficacy and exercise

behavior. The role of physical activity in a healthy lifestyle is

indisputably well documented (Bouchard, Shephard, & Ste-

phens, 1994). Unfortunately, epidemiological evidence (Ste-

phens & Casperson, 1994) suggests less than optimal prevalence

rates in North American adults and low adherence rates

(Dishman, 1982). Self-efficacy has repeatedly been shown to

be an important predictor of physical activity patterns in older

adults (McAuley, 1992, 1993; McAuley, Courneya, Rudolph, &

Lox, 1994; McAuley, Lox, & Duncan, 1993). Additionally, acute

and long-term exposure to physical activity provides a potent

source of efficacy information for older adults (McAuley, Cour-

neya, & Lettunich, 1991; McAuley et al., 1993).

From a theoretical perspective, we would suggest that slower

gait patterns in non-disabled older adults may well be indicative

of increased caution brought about by reduced confidence in

their ability to successfully move through the environment. Con-

sequently, a primary objective of this study was to examine the

extent to which self-efficacy cognitions were related to gait

patterns in older adults negotiating increasingly challenging ob-

stacles in their path.

In this study we addressed when and how older adults adjust

their gait in order to traverse obstacles of increasing height.

Past research on obstacle crossing has primarily focused on the

crossing step and steps immediately prior to and after the cross-

ing step. We chose to focus on a larger range of behavior because

in pilot work we had observed that a number of older adults

began to make adjustments in their step length a number of steps

prior to the obstacle. In addition, we viewed obstacles placed

in the path of motion as a relatively common, everyday perturba-

tion to the gait cycle, and we were interested in examining when

and to what extent perturbations such as this disrupted the gait

cycle. By examining a larger number of steps we could examine

how early an obstacle disrupted the gait cycle and how long it

took individuals to recover their normal gait. We also examined

how activity level, balance (as measured by the Berg Balance

Scale; Berg et al., 1992), and self-efficacy related to various

gait measures (overall speed, step length, step velocity). We

predicted that relatively inactive older adults and individuals

with relatively low self-efficacy would exhibit greater perturba-

tions to their gait (as measured by changes in step length and

step velocity) than their more active and self-efficacious peers.

Method

Participants

Fifty-five community-dwelling individuals between the ages of 60 and

85 years (M — 71.1 years; 11 men, 44 women) participated in this

study. Twenty-nine of the participants (7 men and 22 women) were

recruited from a university-supervised physical activity program de-

signed for older adults. This program, which has been in existence for

over 40 years, consists primarily of walking, swimming, and strength

training activities. The remaining participants (4 men and 22 women)

were recruited by local radio and newspaper advertisements targeting

sedentary older adults. Although the goal was to obtain very sedentary

individuals, the members of this group did vary in their levels of inactiv-

ity (see Table 1). Fifty percent of the inactive group reported no regular

physical activity, and the remainder of this group reported occasional

walking or shopping as their primary forms of activity.

All participants were offered $15 for their participation in the research

protocols. Although we did not assess cognitive functioning using a

formal assessment such as the Pfeiffer (1975), all participants were able

to follow directions in order to find our laboratory, which is in a rather

obscure location of the building. In addition, none of the participants

exhibited any difficulty with understanding the questions in the question-

naire or following directions during the gait and balance assessment.

Two additional participants (one active, one inactive) were dropped

from the analyses because they both used canes during walking. No

other participants exhibited or reported (by means of a series of brief

questions in the questionnaires) any history of gait impairments or sig-

nificant musculoskeletal problems.

Measures

Physical activity background. In order to determine overall physical

activity history, participants were given a short inventory assessing their

exercise history. This inventory assessed: (a) whether the participants

considered themselves to be exercisers or not. (b) the number of days

GAIT ADJUSTMENTS 377

Table 1

Descriptive Statistics for Age, Height, Weight, Body Mass

Index, Activity Level, Falls Efficacy, Gait Efficacy, and

Balance for Low and High Active Participants

Low active High active

Variable M SD M SD

AgeHeight (cm)Weight (kg)BMI (kg/m2)Activity level

Exercise bouts/week**

Duration/session (min.)**Intensity'*History (months)**

FESGESBalance**

71.7165.679.829.2

1.619.4

1.721.1

93.166.752.2

6.27.7

12.55.0

1.723.3

1.433.912.0

13.23.6

70.5164.8

71.926.4

4.182.43.8

124.694.2

68.455.2

5.611.3

14.54.0

1.238.9

1.0106.1

9.615.91.5

Note. BMI = body mass index; FES - Falls Efficacy Scale; GES =Gait Efficacy Scale.**p < .01.

per week that they typically engaged in physical activity, (c) the average

number of minutes per session that they engaged in physical activity,

(d) the number of months that they had participated in regular physical

activity, (e) the particular exercise mode that they typically performed,

and ( f ) the average level of intensity of their physical activity or exercise.

The level of intensity was assessed using a 7-point Likert Scale (1 =

very light to 7 = very hard). A summary of these background data is

shown in Table 1.

Self-efficacy. Typically, measures such as the FES (Tinetti et al.,

1990) have been used to predict falling, social function, and ADLs in

older adults (Tinetti et al., 1994). The FES is a 10-item scale that

examines individuals' beliefs in their capabilities to carry out basic

ADLs without falling (e.g., reaching into cabinets, taking a shower,

etc.). Past research employing this scale has shown it to be a reliable

and valid instrument (Tinetti et al., 1994). Consequently, we employed

the FES as a measure of efficacy in the current sample. Internal consis-

tency for the FES was excellent (a - .95).

Social cognitive theory (Bandura, 1986), however, dictates that mea-

sures of efficacy be composed of items that are congruent with the task

at hand. As we were interested in gait performance of older adults and

such performance under the challenge of obstacles in one's path, we

suggest that gait-related efficacy may best be assessed by items that

reflect confidence in one's capabilities to walk under challenging circum-

stances. To this end we employed a revised version of the Gait Efficacy

Scale (GES) employed by McAuley, Mihalko, and Rosengren (1997).

The original scale was a 10-item measure assessing walking confidence

under such circumstances as walking up and down stairs, over obstacles

in one's path, and so forth. We elected to add 2 items related to stepping

up and down from a curb without tripping or losing one's balance and

to remove 4 items relative to escalator use. We had originally included

the items concerning escalator use in the GES because we thought that

using an escalator might be particularly difficult for older adults and

present a situation where falls might occur. In this investigation we

dropped the escalator items because they involve stepping onto and off

of a moving surface, a task that is quite different than stepping over

obstacles. The 2 curb items were added because these two actions in-

volve similar behaviors as stepping over obstacles. Although it may

seem redundant to include items for both going up and down stairs or

stepping up and down from a curb, these actions are quite different from

a biomechanical perspective. Azar and Lawton (1964) have also reported

that older women tend to step down from heights in a less muscularly

controlled manner than older men. In addition, the consequences of

falling as one descends a flight of stairs are significantly greater than

those that are likely to occur as one ascends a flight of stairs because

of the potentially greater falling distance. To the extent that individuals

are aware of these biomechanical differences, have greater difficulty

stepping down or up from a height, or have a greater fear of falling while

descending compared to ascending stairs or curbs, we felt it important to

include both types of items. Internal consistency for the 8-itera scale

was excellent (a = .93).

The individual items in both self-efficacy measures were scored on

10-point Likert scales with 10 representing complete confidence in the

ability to carry out the behavior in question and 1 representing no

confidence at all. The structure of these scales is consistent with general

recommendations for self-efficacy assessment (Bandura, 1997).

Balance. Participants' balance was assessed using the Berg Balance

Scale (Berg et al., 1992). The Berg is a 14-item scale, scored on a 0-

4 metric (scalerange - 0-56), designed to assess how well participants

can successfully carry out activities such as standing with eyes open

and closed, turning to look behind, and standing on one foot. The internal

consistency for the Berg in this study was good (a = .90). This scale

has been used successfully to predict the occurrence of falls in older

adults, and balance scores have been found to be strongly associated

with measures of functional decline in stroke victims (Bergetal., 1992).

Gait Assessment and Procedures

Upon arrival at the laboratory, participants were asked to sign a con-

sent form and to complete the physical activity and efficacy measures.

After completing these measures reflective markers were placed on

prominent joint locations for later digitization of videotaped records

using a Peak Performance Motion Analysis System (Peak Performance

Technologies, 1994). All participants were asked to wear dark clothing

to the laboratory so that the reflective markers would be more readily

visible. Although markers were placed on all of the joints of the lower

extremities, only those on right heel (central portion of the right lateral

calcaneous), right toe (base of proximal phalanx of small toe), left heel

(central medial portion of calcaneous), and left toe (base of proximal

phalanx of left large toe) were digitized in the current investigation.

After the markers were placed on the participants, they were asked

to walk along a 1 m x 10 m pathway laid out on a gymnasium floor.

Participants were presented with six different walking conditions in the

following order: walking with no obstacle in their path, and walking

over wooden obstacles 2.5 cm, 5.0 cmf 10.0 cm, 20.0 cm, and 40.0 cm

in height (all were 10.0 cm wide). Each condition was repeated twice

before proceeding to the next and the data were averaged across the two

trials. We chose this particular order, rather than a random order, because

we were concerned that some of the participants would find the higher

obstacle conditions difficult and that these conditions might place the

participants at risk for a fall. Thus, if we encountered individuals who

had difficulty at one height, we wanted to be able to end the session

without putting the participant at risk. Obviously, practice effects could

lead to increased performance over The. course of the study. We expected

that the higher obstacle conditions would be more difficult than the no-

obstacle or lower obstacle conditions. Practice effects would serve to

mitigate any effects of obstacle height. Thus, to the extent that we

find significant effects of obstacle height given this particular order of

conditions, it is likely that our results are robust, and that even larger

effects would be obtained using a random order.

The obstacles were placed across the center of the pathway and were

easily visible to the participants. The particular obstacle heights were-

chosen in order to present participants with a range of difficulty that

included obstacle heights similar to those that might be encountered in

378 ROSENGREN, McALILEY, AND MIHALKO

the course of daily activity. For example, the 2.5-cm obstacle is similar

in height to thresholds found in homes and offices, and the 20.0-cm

obstacle is similar in height to most curbs or steps. All trials were

videotaped using a tripod-mounted Panasonic (Model No. AG450

SVHS; Secaucus, NJ) video camera positioned 10 m from the center

of the pathway in a location perpendicular to the direction of motion.

Participants took approximately 10 min to complete the walking trials.

A number of different gait measures were assessed from the videotapes.

For each trial we obtained the tune it took the participant to traverse the

entire pathway. Rom this we calculated an average gait speed over the

pathway. We also assessed the step length and velocity for the three steps

prior to the obstacle (approach steps), the step crossing over the obstacle,

and the three steps immediately following the obstacle crossing (recovery

steps). A total of seven steps (three approach, one crossing, three recovery)

were analyzed. We chose to examine three steps before and after the

obstacle for two reasons. First, we found in pilot testing that a number of

individuals began to adjust their gait a few steps prior to the obstacle.

Most research on obstacle crossing has focused primarily on the crossing

behavior or the step or two directly before and after the obstacle (Chen

et al., 1991; Patla, Reitdyk, Martin, & Prentice, 1996), thus missing some

potentially interesting behaviors that occur prior to and after the obstacle.

Second, we were interested in when and how obstacles of different heights

would cause the participants to alter their gait and how long it would take

them to return to their normal gait. In order to investigate this issue, we

needed to examine a larger number of steps than other researchers have

examined. To do this with our experimental set-up, however, required us

to have our video camera placed relatively far from the pathway, which

made it difficult to collect accurate data about foot clearance heights as

the participants crossed over the obstacle.

For each of the step lengths, the assessment was made from heel strike

to heel strike. We also obtained the distance between the obstacle and

the toe of trailing leg and heel of the lead leg. This information provides

details about how the participants were altering their step length directly

prior to and after the obstacle. Placing the feet closer to the obstacle

may indicate greater caution and less confidence in one's ability to go

over the obstacle successfully. The length of time participants were in

single leg stance as they crossed over the obstacle was also assessed.

As previously described, Murray et al. (1969) have suggested that the

gait of older adults may serve to increase double support time. However,

these same modifications (i.e., walking more slowly) may lead to an

increase in single support time when older adults are confronted with

an obstacle. Thus, increased single support time may place older individ-

uals at risk for a fall.

In order to determine whether participants were primarily controlling

their step length or their step velocity as they approached the obstacles,

we also calculated their step velocity. Step velocity for the approach,

crossing, and recovery steps was obtained by dividing the distance cov-

ered in each step by the time between heel strikes.

Results

Data Analysis Overview

We performed a series of preliminary analyses to examine

potential differences on participant characteristics and to ensure

that the active and inactive groups did in fact vary in activity

level. Our primary data analyses were designed to examine (a)

the nature and timing of gait adjustments when individuals were

confronted with obstacles of increasing height and (b) how gait

was related to physical activity, self-efficacy, and balance. After

completing the preliminary analyses we examined the extenl to

which our active and sedentary older participants differed on

the various gait measures obtained. For each of these analyses

we entered the participants' body mass index (BMI) as a covari-

ate; BMI equals (weight in kg)/(height in m)2. We chose this

approach because both height and weight have been found to

influence gait speed. Specifically, taller individuals are often

found to have faster gait speeds regardless of age (Bendall et

al., 1989; Himann et al., 1988, Woo et al., 1995), most likely

because of longer step and stride lengths (Murray, Drought, &

Kory, 1964). Additionally, Bohannon, Andrews, and Thomas

(1996) have reported that even though weight was not signifi-

cantly correlated with gait speed, in regression analyses it was

found to be a significant independent predictor of gait speed

when it was included along with gender and nondominant hip

flexion strength. We chose to control for BMI rather than control

height and weight independently in order to preserve degrees

of freedom in our analyses. In addition, BMI is recommended

as a clinical measure of obesity (National Institutes of Health

[NIH], 1985), and BMIs have been found to be significantly

related to mortality (Andres, 1990). For each of these analyses,

any significant main effects or interactions were followed up

using Tukey HSD procedures (p < .05).

For the next set of analyses, we computed a new variable

representing physical activity history by converting the fre-

quency, duration, and number of months engaged in regular

physical activity to z scores and summing these values. Intensity

of activity was excluded from this computed variable because

we were unable to determine the true intensity level (relative,

for example, to age-predicted maximal heart rate). On the other

hand, the variables employed in the standardized score are typi-

cal elements of physical activity behavior. Internal consistency

for this standardized score was adequate (a = .72).

Finally, we conducted a series of hierarchical multiple regres-

sion analyses examining the individual contributions of the self-

efficacy, balance, and physical activity variables to the variation

in the gait parameters in each of the different obstacle condi-

tions. In these analyses, sex, age, and BMI were forced into the

regression equations in which falls efficacy (FES), gait efficacy

(GES), balance, and physical activity history were systemati-

cally entered last while controlling for all other possible pre-

dictors in the equation. This procedure enabled us to determine

the unique or independent contributions of the variables influ-

encing the gait parameters.

Participant Characteristics

Table 1 presents descriptive statistics for the participants by

activity level. A 2 (activity level: active, inactive) X 2 (sex:

male, female) multivariate analysis of variance (MANOVA) ex-

amining the physical activity history of the self-classified active

and inactive participants confirmed differences between these

two groups, F(1, 51) = 41.0, MSB = 2.9, p < .001. Individuals

who classified themselves as active reported exercising more

times per week, exercising for a longer duration per session,

higher levels of intensity in their activities, and exercising for a

greater number of months.

An additional series of 2 (activity level: active, inactive) X

2 (sex: male, female) MANOVAs explored potential differences

in the participant characteristics, efficacy measures, and perfor-

mance on the Berg Balance Scale. No main effects or interac-

tions were obtained for activity level for participant age, sex,

height, weight, or BMI. Active individuals, however, were found


1.25

•WnO)a

GO

1.05-

0.95

Inactive

0.0 2.5 5.0 10.0 20.0 40.0

Obstacle Height

Figure /. Mean gait speed by obstacle height for active and inactive

participants. Error bars represent standard errors of the means.

to differ significantly from inactive ones on balance perfor-

mance, F(l, 51) = 6.8, MSB = 7.5, p < .05. As can be seen

in Table 1, individuals who classified themselves as exercisers

obtained higher scores on the Berg than individuals who re-

ported that they did not exercise. Not surprisingly, given popula-

tion demographics, the men in the sample were significantly

younger, F(l, 51) = 4.8, MSE = 32.8, p < .05; taller, F(l,

51 ) = 37.8, MSE = 8.3, p < .001; and heavier than the women,

F(l, 51) = 6.8, MSE = 733.0, p < .05. Men and women

did not differ significantly with respect to BMI. No significant

differences were obtained for either of the efficacy measures.

and 10.0-cm obstacle conditions but then decreased significantly

over the last two obstacle conditions, F(5, 255) = 28.1, MSE =

0.001, p < .001. These results suggest that the participants did

not attempt to cover the pathway in the same amount of time

across conditions, but slowed down significantly for the most

challenging obstacles. A significant Activity X Sex interaction was

also obtained, F(l, 50) = 4.5, MSE = 0.16, p < .05. Post hoc

analyses revealed that although the overall gait speed of active

women (M = 1.18 m/s, SD = 0.14) did not differ significantly

from that of inactive women (M = 1.13 m/s, SD = 0.17), active

men walked significantly faster (M = 1.36 m/s, SD = 0.18) than

inactive ones (M = 1.14 m/s, SD = 0.20).

Step length. Figure 2 shows the average step length over

the walkway for each of the six obstacle conditions. Participants

took significantly shorter initial approach steps when they were

confronted with the highest obstacle compared to the remaining

conditions, f(5, 255) = 3.9, MSE = 0.001, p < .01. Although

the final two approach steps were generally shorter for the high-

est obstacle condition than for all of the other conditions, no

significant differences were obtained for these measures across

the conditions.

None of our participants made contact with the obstacle,

tripped, or fell as they crossed over the obstacles. We found a

significant main effect of obstacle height when examining the

crossing step, F(5, 255) = 8.4, MSE = 0.001, p < .001. As

can be seen in Figure 2, participants took significantly longer

steps when they were crossing over the 2.5-, 5.0-, 10.0-, and

20.0-cm obstacles compared to the 40.0-cm obstacle. Crossing

step lengths for the four smaller obstacles were also significantly

greater than the participants' normal walking step length when

no obstacle was present. The shorter crossing step for the tallest

obstacle can be accounted for by the fact that participants' initial

foot placement following the obstacle was significantly closer

to the highest obstacle than to any of the other obstacles, F(4,

Gait Measures

A series of 2 (activity level: active, inactive) X 2 (sex: male,

female) X 6 (obstacleheight: 0,2.5,5.0,10.0,20.0,40.0) mixed

MANOV\s with BMI entered as a covariate were performed to

examine the effects of activity level, sex, and obstacle height

on gait speed, step length, step velocity, and time in single leg

support. For each of these analyses, obstacle height was a re-

peated measure variable, and activity level and sex were be-

tween-subjects variables.

We also conducted an additional 2 (activity: active, inactive)

X 2 (sex: female, male) X 7 (step: 3 approach steps, crossing

step, 3 recovery steps) MANO\A separately for each obstacle

condition to examine when individuals began to alter their gait

and recover their normal gait. These analyses were conducted

separately for step length and step velocity. BMI was entered

as a covariate in each of these analyses in order to control for

participants' height and weight.

Overall gait speed. Figure 1 shows the average gait speed for

older adults as a function of activity level and obstacle height.

Older active adults were significantly faster than inactive ones,

F(l, 50) = 6.3, MSE = 0.16, p < .05. For all participants, gait

speed was found to be relatively constant for the 0-, 2.5-, 5.0-,

X

ithJ

A 2 A 3 dossing R 1 R 2 R 3

Step

Figure 2. Mean step length for the approach steps (Al, A2, A3),crossing step (Crossing), and recovery steps (Rl, R2, R3) as a functionof obstacle height.


0.9'

a.

ono>

NJ

Of.c

EH

Inactive

Active

0.0 2.5 5.0 10.0 20.0 40.0

Obstacle Height

Figure 3. Mean time in single leg support as a function of obstacle

height and activity level. Error bars represent standard errors of the

means.

208) = 3.8, MSE = 0.001, p < .01. Men made significantly

longer crossing steps than women, F( 1,50) = 4.4, MSE = 0.04,

p < .05. Women were more likely to place their heel closer to

the obstacle after crossing over it than men, F(l, 51) = 5.2,

MSE — 0.05, p < .05. No differences in foot placement immedi-

ately prior to the obstacle were obtained for obstacle condition,

activity level, or sex.

No significant main effects were obtained for the three recovery

steps. However for each of these steps there was a significant

Activity X Sex interaction, Fs(l, 51) > 4.3, MSEs = 0.05, ps

< .05. Post hoc analyses revealed that active men took significantly

longer steps following the obstacle than inactive ones. No signifi-

cant differences were obtained for the women on the basis of

activity level.

No main effect of activity was found for any of the analyses

examining step length. This result suggests that active and inactive

individuals were using similar gait strategies to control their step

length as they approached, crossed over an obstacle, and recovered

their normal gait.

The analyses examining step length across the seven steps

analyzed revealed that the participants maintained a consistent

step length across the trial when no obstacle was present, F(6,

306) = 0.6, MSE = 0.001, p = .76, ns. For each of the remaining

obstacle conditions, significant main effects of step were ob-

tained, Fs(6, 306) > 7.6,MSEs = 0.003,ps < .05, suggesting

that participants significantly altered their step length as they

walked. For all but the highest obstacle condition, the partici-

pants took slightly smaller approach steps, significantly length-

ened their stride to cross over the obstacle, and then finished

the trials with slighter longer recovery steps than the approach

ones (see Figure 2). Post hoc analyses revealed that the crossing

step was significantly different from all of the other step lengths

for the 2.5-cm, 5.0-cm., 10.0-cm, and 20.0-cm conditions. For

the highest obstacle condition, the crossing step length was sig-

nificantly longer than the approach steps but not the recovery

steps. These results suggest that the smaller obstacles (2.5-

20.0 cm) served as brief perturbations to the gait cycle with

participants mainly compensating for them by extending their

crossing step and then returning quickly to their normal step

length. In contrast, the highest obstacle led to a much greater

and earlier perturbation. For this condition, participants began

taking significantly shorter steps than normal, but then length-

ened their step to their normal length to cross over the obstacle,

and then maintained this step length in the recovery phase.

Time in single leg support. Figure 3 shows the time partici-

pants spent in single leg support as they crossed over the obsta-

cles of different heights. A significant main effect of obstacle

condition was obtained, F(5, 255) = 110.1, MSE = 0.01, p <

.001. Post hoc analyses revealed significant differences between

each of the obstacle conditions. As can be seen in the figure,

time in single leg support increased as the height of the obstacle

increased. In addition, a significant interaction between activity

level and obstacle height was obtained, F(5, 255) = 2.3, MSE

= 0.01, p < .05. Post hoc analyses revealed that active individu-

als spent significantly longer time in single leg support when no

obstacle was present than inactive individuals. However, when

crossing over the highest obstacle, active individuals spent sig-

nificantly shorter times in single leg support than inactive ones.

Step velocity. In contrast to the results for step length, sig-

nificant main effects for exercise were obtained for each of the

approach steps, the crossing step, and all three of the recovery

steps, Fs(l, 51) > 4.1, A/SFs = 0.06, ps < .05 (see Figure

4). For each of these steps, the active participants had greater

step velocity than the inactive ones. Significant main effects for

obstacle height were obtained for the velocity of the crossing

step and first step following the obstacle, Fs(5, 255) > 4.3,

MSEs = 0.03, ps < .05. In both cases post hoc comparisons

showed that the step velocity for the crossing and first recovery

steps for the 20.0- and 40.0-cm obstacle conditions were sig-

nificantly slower than those obtained for the remaining condi-

1.4

£"3

'3

aV

i.oA l A 2 A 3 Crossing R l R 2 R 3

Step

Figure 4. Mean step velocity for the approach steps (Al , A2, A3),

crossing step (Crossing), and recovery steps (Rl, R2, R3) as a function

of activity level (inactive, active).


tions (see Figure 5). A significant Sex X Obstacle Height inter-

action was also obtained for the step velocity for the first recov-

ery step, F(5, 255) = 2.4, MSB = 0.04, p < .05. Post hoc

comparisons revealed that men had a significantly greater first

recovery step velocity than women for the highest obstacle

condition.

As can be seen in Figure 5, participants maintained a rela-

tively constant step velocity over the length of the walkway for

the no-obstacle condition and for the three smallest obstacle

conditions. For the two highest obstacle conditions, participants

slowed down significantly as they crossed over the obstacle and

during their first recovery step, Fs(6, 306) > 3.9, MSEs =

0.03, ps < 001. By the second recovery step participants' step

velocity had returned to previous levels. These results suggest

that three small obstacles did not perturb the participants' gait

speed, but that the two highest obstacles caused the participants

to slow down significantly as they crossed over the obstacles.

By the second step after the obstacle, participants had returned

to their normal walking speed.

Relations Among Age, Physical Activity, Efficacy,

Balance, and Gait Measures

The correlations among age, BMI, gait (GES) and falls (FES)

efficacy, physical activity, and balance are shown in Table 2. In-

creasing age was significantly associated with lower levels of

efficacy, activity, and poorer balance. Although the two efficacy

measures were quite strongly correlated (r = .68), neither of these

measures was significantly correlated with activity history. Higher

levels of both gait and falls efficacy were significantly related to

better balance. In addition, higher levels of activity were signifi-

cantly associated with better balance performance. BMI was not

found to correlate with any of these other measures.

Table 3 shows the correlations between age, BMI, physical activ-

ity, efficacy (FES and GES), balance, and gait speed in each of

Table 2

Bivariate Relations Among Age, Body Mass Index, Falls

Efficacy, Gait Efficacy, Physical Activity, and Balance

£>•3

"a

a.••*CA

0.8-

0.6

A l A 2 A 3 Crossing R l R 2 R 3

Step

Figure 5. Mean step velocity for the approach steps (Al, A2, A3),crossing step (Crossing), and recovery steps (Rl, R2, R3) as a functionof obstacle height.

Variable

1. Age2. BMI3. FES4. GES5. Physical activity6. Balance

1

-.10-.54**-.42**-.27*-.45**

2

-.15-.12-.19-.25

3

—.68**.21.48**

4

—.16.43**

5

.54**

Note. BMI = body mass index; FES = Falls Efficacy Scale; GES =Gait Efficacy Scale.*p < .05. **p < .01.

the different obstacle conditions. Increasing age was significantly

related to slower gait speed in all of the obstacle conditions except

for the 5.0-cm obstacle condition. In contrast, higher levels of

physical activity, efficacy, and balance were all strongly associated

with faster gait speeds for all of the obstacle conditions. Although

we expected that the efficacy measures would be more highly

related to performance of the more challenging tasks, these correla-

tions were relatively consistent across tasks.

The correlations among age, BMI, physical activity, efficacy

(FES and GES), balance, and step lengths for the last approach

step, crossing step, and first recovery step for each of the differ-

ent obstacle conditions are shown in Table 4. We chose to look

at these three steps because we expected that the greatest risk

for a fall would occur within these three steps. Increasing age

was significantly related to shorter step lengths in a few of the

obstacle conditions. Higher levels of efficacy and balance were

significantly associated with longer step lengths. Generally, BMI

and physical activity level were not found to relate to length of

the last approach step, crossing step, or first recovery step.

Table 4 also includes the correlations among age, BMI, physi-

cal activity, efficacy (FES and GES), balance, and time in single

leg stance for each of the obstacle conditions. As can be seen

in the table, time in single leg support while crossing over

the 40.0-cm obstacle was positively correlated with age but

negatively associated with physical activity, FES, and GES. In

general fewer significant correlations were obtained for the other

obstacle conditions.

A similar analysis was performed to examine these relations for

step velocity rather than step length. Overall, fewer significant

relations were obtained in these analyses, especially for the ones

involving the velocity of the first recovery step. However, higher

step velocities were generally associated with higher levels of gait

and falls efficacy (most rs > .36, p < .05) and better balance

(most rs > .30, p < .05). In contrast to the results obtained for

step lengths, the velocities of the last approach step and crossing

step were significantly related to physical activity level for four of

the six obstacle conditions (rs > .27,p < .05). This result mirrors

that obtained from the MANO\A suggesting that more active indi-

viduals generally had faster step velocities for the last approach

step and the crossing step than inactive ones.

Hierarchical Regression Analyses

The next series of analyses examined the independent effects

of physical activity, gait and falls efficacy, and balance on overall


Table 3

Bivariate Relations Among Age, Body Mass Index, Physical Activity, Falls Efficacy, Gait

Efficacy, Balance, and Gait Speed for the Six Walking Conditions

Variable/condition Age BM1 Physical activity FES GES

Note. BMI = body mass index; FES = Falls Efficacy Scale; GES = Gait Efficacy Scale.*p<.05. **p<.0l.

Balance

Overall velocityNo obstacle2.5-cm obstacle5.0-cm obstacle10.0-cm obstacle20.0-cm obstacle40.0-cm obstacle

-.34**-.33**-.26-.32*-.38**-.38"

-.25-.20-.23-.19-.22'-.18

.28*

.36**

.34*

.40**

.38**

.31*

.43**

.35**

.30**

.32**

.31*

.28*

.49*

.47*

.45*

.46*

.46*

.44*

44**

.50**

.44**

.47**47**

.39**

gait speed for each of the six conditions. In each of these analyses

age, BMI, and sex were statistically controlled by forcing this set

of variables into the regression equation first. Physical activity,

gait efficacy (GES), falls efficacy (FES), and balance were then

systematically entered last into a series of regression equations in

order to determine their unique contributions to gait speed. That

is, each of these variables were compared against all others in the

model. Tables 5 and 6 show the results for the no-obstacle and

highest obstacle conditions, respectively. For the no-obstacle con-

dition, age, BMI, sex, physical activity history, balance, and effi-

cacy accounted for a substantial portion of the variance in gait

speed, R2 = .34, F(7, 53) = 3.9, p < .01. When statistically

controlled, sex, BMI, and age together accounted for significant

variation, R2 = .18, F(3, 50) = 3.7, p < .05. Of the remaining

variables only gait efficacy accounted for significant independent

variation in gait speed, R2 = .06, F(l, 46) = 4.6, p < .05.

Similar overall patterns were found for the regression analy-

ses involving the different obstacle conditions (see Table 6 for

the results for the highest obstacle condition). For each of these

analyses only gait efficacy was found to account for a significant

Table 4

Bivariate Relations of Age, Body Mass Index, Physical Activity, Falls Efficacy, Gait Efficacy,

Balance With Selected Step Lengths, and Time in Single Leg Support

for the Six Walking Conditions

Variable/condition Age BMI Physical activity FES GES

Note. BMI = body mass index; FES = Falls Efficacy Scale; GES = Gait Efficacy Scale.*p<.05. **p<.01.

Balance

Final approach step lengthNo obstacle2.5-cm obstacle5.0-cm obstacle10.0-cm obstacle20.0-cm obstacle40.0-cm obstacle

Crossing step lengthNo obstacle2.5-cm obstacle5.0-cm obstacle10.0-cm obstacle20.0-cm obstacle40.0-cm obstacle

First recovery step lengthNo obstacle2.5-cm obstacle5.0-cm obstacle10.0-cm obstacle20.0-cm obstacle40.0-cm obstacle

Time in single leg supportNo obstacle2.5-cm obstacle5.0-cm obstacle10.0-cm obstacle20.0-cm obstacle40.0-cm obstacle

-.22-.25-.15-.17-.33*-.26

-.25-.07-.14-.20-.21-.31*

-.24-.29*-.24-.25-.31*-.34*

.06

.24

.29*

.22

.37**

.47**

-.24-.36**-.09-.11-.26-.23

-.38-.04-.25-.19-.07-.10

-.16-.26-.22-.21-.23-.06

-.31*-.04

.05

.00

.03

.10

.16

.18-.02

.04

.23

.10

.15

.14

.06

.09-.01-.10

.17

.08

.22

.31*

.25

.05

.05-.32*-.16-.36**-.22-.36**

.38**

.37**

.01

.31**

.35**

.35**

.39**

.03

.28*

.32*

.33*

.33*

.43*

.57*

.43*

.42*

.34*

.26

-.10.01

-.05-.12-.09-.30*

.46**

.48**

.07

.40**

.42**

.48**

.39**

.14

.36**

.41**

.45**

.47**

.47**

.52**

.42**

.39**

.36**

.38**

-.18-.14-.07-.20-.26-.28*

.39**

.44**

.18

.30*

.36**

.36**

.27*

.28*

.21

.30*

.30*

.22

.33*

.31*

.41**

.42**

.39**

.43**

.17-.20-.07-.21-.22-.23


Table 5

Hierarchical Regression Analyses Showing Contribution of

Efficacy, Physical Activity, and Balance to Gait Speed

(No Obstacle), Controlling For Participant Age,

Body Mass Index, and Sex

Gait speed (no-obstacle condition)

Predictor SEB

Step 1AgeBody mass indexSex

Step 2Gait efficacyFalls efficacyBalancePhysical activity

.06

.05-.02

-.02-.01-.05-.04

.02

.03

.31

.01

.02

.05

.05

.36»»

.26*-.01

-.36»-.06-.12-.11

Note. Gait efficacy, falls efficacy, balance, and physical activity historywere systematically entered last while controlling for all other possiblepredictors in the equation in order to determine the independent contribu-tions of these variables. «2 = .18 for Step 1; AS2 = .20 for Step 2.*p < .05. **p < .01.

independent variation in overall gait speed (A#2 > .07, ps <

.05).

We also performed similar analyses examining the indepen-

dent effects of physical activity, gait and falls efficacy, and bal-

ance on the step lengths for the last approach step, crossing

step, and first recovery step for each of the different obstacle

conditions. In general, the results of these regression analyses

were less consistent than those obtained for overall gait speed,

especially for the no-obstacle and lower obstacle conditions.

However, for the highest obstacle condition a number of signifi-

cant effects were obtained, but these differed for the last ap-

proach step, crossing step, and first recovery step. For the last

approach step, only gait efficacy was found to account for a

significant independent variation on step length, Aft2 > .07,

F(l, 46) = 4.8, p < .05. In contrast, for the crossing step,

only physical activity accounted for a significant independent

variation on crossing step length, A^2 > .06, F(l, 46) = 4.8,

p < .05. For the first recovery step, only balance was found to

account for a significant independent variation on step length,

Afl2 > .10, F(l, 46) = 6.1, p < .05. These results suggest that

different factors may influence the control of the final approach

step, crossing step, and first recovery step.

We next examined the independent effects of physical activity,

gait and falls efficacy, and balance on the time participants spent

in single leg support for each of the different obstacle conditions.

The results were similar to those obtained for the crossing step

length. Although no consistent pattern was obtained for the no-

obstacle and lower obstacle conditions, physical activity was

found to account for a significant independent variation on single

leg support time for the highest obstacle condition, Aft2 > .09,

F(l, 46) = 5.9, p < .05.

Our final set of analyses examined the independent effects of

physical activity, gait and falls efficacy, and balance on the step

velocities for the last approach step, crossing step, and first

recovery step for each of the different obstacle conditions. Once

again the results were not as consistent as those obtained for

overall gait speed for the lower and no-obstacle conditions.

However, in contrast to the results obtained for step length and

single leg stance time, gait efficacy accounted for independent

variation on step velocity for the last approach step, A/f2 >

.08, F(7, 46) .8, p < .05, and crossing step length, AR2 > .05,

F(l, 46) = 3.9, p < .05. No significant effects were obtained

for the first recovery step.

Discussion

Our results suggest that the manner in which older adults

modify their gait when they are confronted with obstacles in

their path is influenced by both their activity level and their

confidence in their ability to perform the task. These variables

appear to be related to different aspects of gait performance.

The changes in gait that we observed as individuals were con-

fronted with obstacles of increasing height suggest that as indi-

viduals are faced with increasingly challenging tasks they adopt

a more cautious walking style. These results appear to be quite

robust given that we statistically controlled for both participant

height and weight (using BMI) in all of our analyses, factors

previously shown to contribute to gait performance. In the re-

mainder of the discussion, we first examine the role of activity

in mediating various gait changes, and then examine how self-

efficacy is related to gait performance. Finally, we discuss how

other factors such as obstacle height and balance contribute to

gait performance.

Activity and Gait Performance

Chen et al. (1991), in an obstacle-crossing situation similar

to the one we used in this investigation, reported that older

adults took a slower approach step and crossing step than

younger adults. We found a similar overall difference between

active and inactive older adults, although we examined a greater

Table 6

Hierarchical Regression Analyses Showing Contribution of

Efficacy, Physical Activity, and Balance to Gait Speed Over

the Highest Obstacle, Controlling for Participant Age,

Body Mass Index, and Sex

Predictor

Gait speed (highest obstacle)

SEB i

Step 1AgeBody mass indexSex

Step 2Gait efficacyFalls efficacyBalancePhysical activity

.09

.06-.26

-.04.03

-.07-.04

.03

.04

.47

.02

.03

.08

.08

.37**

.19-.07

-.45*.26

-.14-.07

Note. Gait efficacy, falls efficacy, balance, and physical activity historywere systematically entered last while controlling for all other possiblepredictors in the equation in order to determine the independent contribu-tions of these variables. R2 = .19 for Step 1; Afl2 = .14 for Step 2.*p<.05. **p< .01.


number of steps prior to and after the obstacle than they did.

Specifically, we found that inactive older adults generally walked

more slowly than active ones (see Figure 1) and exhibited

slower average step velocities as they approached, crossed over

and recovered from an obstacle crossing (see Figure 4). Our

results suggest that one must consider influence of activity level

as well as age when examining the gait of older adults.

Although our results might lead one to conclude that activity

level plays a greater role in determining gait parameters than

age, the results of the correlation and regression analyses sug-

gest a more complex story. Specifically, whereas both age and

activity level were significantly correlated with overall gait

speed (see Table 3), physical activity was not significantly re-

lated to step length (except for the first recovery step following

the 10.0-cm obstacle, see Table 4). Our results suggest that

activity is primarily related to overall gait speed, average step

velocity, crossing step length, and time in single leg support.

Overall, the gait characteristics of inactive older adults suggest

that they adopt a more cautious walking style than active ones.

The regression analyses suggest that activity level was related

to greater time spent in single leg support. When no obstacle

was present both inactive and active participants spent a rela-

tively short time in single leg stance (see Figure 3). However,

as obstacle height increased, both groups spent significantly

longer times in single leg stance, with inactive individuals spend-

ing significantly longer times in single leg stance than active

ones. As discussed earlier, spending more time in double leg

support is viewed as leading to improved stability in older adults

(Murray et al., 1969). When individuals spend a greater amount

of time in single leg support they are spending a longer time in

a potentially unstable posture. Indeed, past research has shown

that older adults generally have difficulty maintaining balance

in a single leg stance (Berg et al., 1992). Thus, to the extent

that individuals spend more time in single leg stance they may

face an increased risk for a fall. On the basis of our results, it

appears that this risk increases as obstacle height increases and

activity level decreases.

Social Cognitive Factors and Gait Control

As can be seen in Tables 3 and 4, both measures of efficacy

were found to be significantly correlated with all of our gait

parameters. Likewise, in the hierarchical regression, gait effi-

cacy was the best predictor of gait speed and the final approach

step, once age, BMI, and activity level were removed. Specifi-

cally, higher levels of efficacy were associated with faster gait

speeds and a longer final approach step. These results and the

fact that we obtained significant differences in gait speed be-

tween the self-classified activity groups suggest that social cog-

nitive factors may play an important role in the control of gait

in older adults. Individuals with lower gait efficacy may ap-

proach gait tasks with more caution, exhibiting slower gait

speeds and shorter approach steps. Clearly, we cannot infer a

causal relationship between efficacy and gait performance from

the results of this study. Prospective studies are called for that

are able to tease out the causal direction of this relationship.

We had expected to find our measures of efficacy to be more

highly related to performance on the most challenging task (the

40.0-cm obstacle). As is clear from Tables 3 and 4 and the

regression analyses, we did not find much support for this pre-

diction. One possible explanation for this result is that partici-

pants found all of these tasks equally challenging. This conclu-

sion is supported by the fact that although the correlations be-

tween efficacy and gait measures did not significantly increase

with increasing obstacle height, these two variables were sig-

nificantly related. In addition, because we presented the condi-

tions in order from least difficult (no obstacle) to most difficult

(40.0-cm obstacle) it is possible that successful performance

on the initial trials led to better performance on the higher

obstacles than we would have obtained if we had used a random

order. Thus, practice effects, although not ameliorating the over-

all effect of obstacle height, may have served to moderate the

relation between efficacy and performance.

Although the two efficacy measures were significantly corre-

lated with one another (r = .68, p < .01), our results suggest

that the gait efficacy measure designed for this study is better

at predicting various gait parameters in obstacle-crossing situa-

tions than the FES (Tinetti et al., 1990). This result is consistent

with the tenets of social cognitive theory (Bandura, 1997). The

FES was designed to assess confidence in performing everyday

activities without falling. Specifically, it examines behaviors

such as reaching into cabinets and taking a shower. In contrast,

our efficacy measure was designed to assess individuals' confi-

dence in performing locomotor activities such as stair climbing

and stepping over obstacles, some of which were the exact

activities employed in our study. Bandura (1986) has argued

that measures of efficacy should be composed of items that are

congruent with the task at hand.

Effects of Obstacle Height on Gait Performance

Older adults, regardless of activity level, generally ap-

proached objects with slightly smaller steps than their normal

step length, although these approach steps were only signifi-

cantly shorter than normal step length for the highest obstacle

condition. The participants then extended their step length to

cross over the obstacles and then finished each step with slightly

longer recovery steps than normal. The crossing step for going

over the 40.0-cm obstacle was significantly shorter than that

found for any of the other obstacle conditions. The shorter cross-

ing step length for this condition can be attributed to participants

placing their heel closer to the obstacle in their initial foot place-

ment after the obstacle.

All participants generally slowed down as they were con-

fronted with obstacles of increasing height, although a signifi-

cant slowing did not occur until the obstacle height reached

20.0 cm. The analyses examining step velocity suggest that most

of the decrease in step velocity occurred during the crossing

and first recovery steps (see Figure 5). These results suggest

that obstacles of less than 20.0 cm had relatively little impact

on overall gait speed but that as obstacle height increased above

this value, older adults, regardless of activity level, tended to

adopt a more cautious walking style as they crossed over an

obstacle.

Balance and Gait Performance

Although we found that performance on the Berg Balance

Scale was significantly correlated with a number of gait parame-


ters, this measure generally did not contribute significantly to

unique variation in any of the gait parameters, except for the

length of the first recovery step. This result is likely due to

the fact that our variables (age, physical activity, self-efficacy,

balance) shared substantial variation. However, in our popula-

tion of relatively healthy active and inactive older adults who

were free from disorders that might influence balance, we ob-

tained a relatively restricted range of balance scores, with overall

performance quite high. It may be that more sensitive assess-

ments of balance control, such as measures of postural sway

(Topper, Maki, & Holliday, 1993) or computerized dynamic

posturography (Nashner, 1993), would uncover a significant

unique contribution of balance to gait performance (see

McAuley et al., 1997, for a similar argument with respect to

fear of falling). However, more dynamic measures of balance

may be needed if we are to understand the role of balance in

the control of gait. In this manner, balance may need to be

viewed as a task-specific factor, in a manner similar to that of

self-efficacy. That is, measures of serf-efficacy work best not as

assessments of individuals' general level of functioning but as

measures of confidence in their ability to perform specific tasks.

Likewise, we may need to consider balance not as a uniform

ability but one that is task-specific.

Conclusion

In recent years, a number of investigations have been con-

ducted examining how both younger and older adults adjust

their gait to compensate for obstacles in the path of locomotion

(Chen et al., 1991; Patla, Prentice, Robinson, & Neufeld, 1991;

Patla & Reitdyk, 1993; Patla et al., 1996; Sparrow, Shinkfield,

Chow, & Begg, 1996). Examining gait adjustments to perturba-

tions in the terrain is important because we rarely encounter a

perfectly smooth, even, and uncluttered surface, and trips and

falls may be a consequence of failure to negotiate these situa-

tions successfully. Whereas these studies have provided im-

portant information about the kinematic and kinetic aspects of

gait control, few studies have examined the role of social-

cognitive factors in modulating gait. To our knowledge the study

presented here is one of the first to examine in detail the role

of social-cognitive factors, such as self-efficacy, in the control

of gait. As our results suggest, confidence in one's ability to

negotiate uneven terrain successfully is significantly related to

gait adjustments made by older adults in obstacle-crossing situa-

tions. These results highlight the importance of examining multi-

ple factors in the control of gait.

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Received January 28, 1997

Revision received January 21, 1998

Accepted January 21, 1998 •

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