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Sensori-motor function, gait patterns and falls in community-dwelling women.

Key words: Gait, Ageing, Women, Sensori-motor function, Physiology.


A number of cross-sectional studies have now shown that clinical measures of gait and mobility as well as specific gait parameters, such as velocity and stride length, show age-related changes [1-5]. The decrements found in these studies vary to a considerable degree, which is due in the main to differing subject selection criteria as to whether older persons with chronic conditions and impairments are included in the study samples. For example, it has been suggested that gait changes should not be regarded as normal concomitants of ageing but result from chronic conditions and physiological impairments of postural control [6-8].

Significant age-related declines in all the major sensory and motor systems that are considered to be important for balance and mobility have been reported [9-12]. However, only a few studies have examined relationships between reduced functioning in these systems and gait patterns in older people. Significant associations between walking speed and quadriceps strength have been reported in healthy older women [13] and nursing-home residents [14]. Similarly, Bassey et al. [15] have found that older community-dwelling men and women with reduced ankle dorsiflexor strength have slow self-selected walking speeds. One study has also reported a significant association between increased postural sway and reduced walking speed [16].

The above studies have limited their investigations to a single 'postural control system' in their attempts to elucidate mechanisms or mediating factors for age-related changes in gait. Thus, little is known of the relative contributions of the many sensori-motor factors to stable gait, and to what extent the demonstrated age-related declines in each of these systems play in the decline in mobility that occurs with age. In one study which has addressed this issue, Duncan et al. [8] examined the effect of physiological impairments of balance on functional mobility in 39 older men classified as functionally high, intermediate or low. While impairments in components of postural control were rarely different between the three groups, the total number of impaired components was significantly different. They concluded that the decline in physical function that occurs with age may be better explained by the accumulation of deficits across multiple domains than by any specific impairments.

A second important research question is the role impaired gait plays in predisposing older people to fall. A number of investigators have found that older persons who perform poorly in clinical tests of gait and mobility are at increased risk of falling [17]. However, only preliminary work has been undertaken as to whether specific gait parameters are significant predictors of falls [16, 18, 19].

In this paper, we have assessed concurrently the major sensory and motor factors involved in postural control [20] and collected sophisticated measures of gait in a large community population of women to examine (i) whether sensori-motor and balance measures can explain age-associated changes in walking speed and related parameters and (ii) whether these gait parameters differ in elderly fallers and non-fallers.


Subjects: One hundred and eighty-three women comprised the study sample. The 96 subjects aged 65 years and over (mean age 72.8 years, SD 6.2) were drawn from the 414 women who took part in the Randwick Falls and Fractures Study [9]. These women, who were living in private households, were recruited from randomly selected census districts in the Randwick local government area in Sydney, Australia. Consecutive subjects recruited in the later phase of the study (1990-91) who were able to walk unaided on the walkway underwent the assessment of gait. All women in the target age group living within these districts (who were identified using extracted information from the electoral roll) were invited to take part in the study. The only exclusion criteria were not living at the dwelling at the time of the study or having no or very little English. Participation was voluntary and informed consent was sought at the commencement of the study. A full description of the sample characteristics and recruitment procedures for the Randwick Falls and Fractures study has been reported elsewhere [9]. The only exclusion criteria for this phase of the study were an inability to walk along the walkway without an aid or a stride length too small to avoid the placement of two consecutive steps on the force plate, which invalidated the stance duration measure.

In addition, 87 women, aged 22-64 years comprised a younger sample. These women were generally a convenience sample. The younger sample was selected so as to include approximately 20 subjects per decade. In all, the sample comprised 21 women aged 20-29 years, 20 aged 30-39 years, 19 aged 40-49 years, 18 aged 50-59 years, nine aged 60-64 years, 37 aged 65-69 years, 22 aged 70-74 years, 26 aged 75 to 79 years and 11 aged 80 years and over.

The assessment of gait: The gait assessment was carried out in a Biomechanics Laboratory at the University of New South Wales, Australia. The apparatus consisted of a heavy, rigid wooden decked walkway (11.2 m in length) containing a KISTLER 92/81B 11 load platform (KISTLER Instruments AG, Winterthur, Switzerland) at its centre. This platform, which measured ground reaction forces of a single foot strike, was mounted level with the walkway on a concrete base. The signals from the load platform were passed through KISTLER charge amplifiers to the data collection computer.

The subject's heel strikes were detected by two sensitive accelerometers attached to the walkway. The timing of the electronically processed signal from the accelerometers was automatically recorded by the data collection computer which permitted step, stride duration and cadence to be measured. This method has been shown to be more accurate than heel switches and affords no encumbrance to the subject [21]. Walking speed was measured using two proximity sensors at a set distance apart with the load table centred in between. Time to traverse this distance was recorded automatically by the data collection computer.

In each walk, the foot which struck the load platform (right or left) was recorded so as to calculate the left-to-right and right-to-left step durations. From the cadence value, stride duration was calculated. Stance duration was determined from the vertical ground reaction force signal, which when subtracted from stride duration, gave swing duration.

All subjects walked barefoot, so as to control for the effects of different shoes. In addition, subjects wore a standard set of test clothing which consisted of a pair of close-fitting stretch bicycle shorts and a sleeveless shirt. The trials were undertaken at a self-selected comfortable walking speed, and data collection commenced only after subjects were accustomed to walking in the laboratory environment. Data were collected for up to 20 walks--ten when the left foot hit the force platform and ten when the right foot hit the force platform.

The gait facility also permits sagittal plane motion to be recorded using an NAC HSV400 camera with retro-reflective markers on major body landmarks. These data, with the synchronized ground reaction forces, permit both a kinematic and joint kinetic analysis of gait; however such analyses will not be presented in this paper.

Sensori-motor function assessments: The test battery included 11 tests of individual sensory and motor systems and five 'composite' tests of reaction time and stability. The sensory and motor tests included three visual tests: high and low contrast visual acuity and contrast sensitivity; three tests of sensation in the leg--touch thresholds at the ankle, vibration sense at the knee and a test of proprioception; three tests of vestibular function--the Vertical X Writing Test, the Vestibular Stepping Test and a test of vestibular optical stability; and quadriceps and ankle dorsiflexion strength. The composite tests included tests of reaction time and body sway on firm and compliant (foam rubber) surfaces. The tests were administered in the following order: sway (standing), strength (sitting), stepping test (walking), contrast sensitivity, reaction time, Vertical X Writing Test, proprioception, touch, vibration sense (sitting), vision on treadmill (walking), vision while sitting (sitting). Thus, the sequence was designed to separate the more 'arduous' tests. Testing was usually completed within one hour and no participants tired during the test period.

Visual acuity was measured using a dual contrast visual acuity chart [22]. This chart consisted of a high-contrast visual acuity letter chart (similar to a Snellen scale) and a low-contrast (10%) letter chart (where contrast = the difference between the maximum and minimum luminances divided by their sum). Acuity was measured binocularly for both high- and low-contrast scales with subjects wearing their best correction at a test distance of 4 m. Visual acuity was measured in terms of logarithm of the minimum angle resolvable (MAR) in minutes of arc. Low-contrast acuity was then compared with high-contrast acuity. This difference was also measured in terms of the logarithm of the minimum angle resolvable.

Contrast sensitivity was assessed using the Melbourne Edge Test--a non-grating test specifically designed for screening purposes [22].

Touch thresholds at the lateral malleolus were measured with a Semmes-Weinstein Pressure Aesthesiometer [23]. Vibration sense was measured using an electronic device which drove a 200 Hz vibration of varying intensity. The vibration was applied to the tibial tuberosity of the knee and was measured in microns of motion perpendicular to the body surface. The tibial tuberosity was chosen as the test site to minimize variations due to differing thickness of subcutaneous tissue, as this site is a prominent bony landmark, and to ensure a continuous measure as it has been found that a percentage of older subjects are unable to perceive any vibration more distally (i.e. at the ankles) [24].

Proprioception was tested using apparatus based on a design by De Domenico and McCloskey [25]. With their legs outstretched, seated subjects (with eyes closed) attempted to place the big toe of each foot at the same position but opposite side of a vertical perspex sheet (60 cm x 60 cm x 1 cm) simultaneously. Errors in matching the two toes were measured by reading from a protractor inscribed on the sheet. Subjects had two practice trials, then five experimental trials. The subject's score was the mean error in matching the two toes measured in degrees.

Vestibular sense was assessed using three tests: the Vertical X Writing Test, the Vestibular Stepping Test and a test of vestibulo-ocular stability. The Vertical X Writing Test measured subjects' ability to write columns of 'X' characters for up to 20 cm down a vertically mounted piece of paper [27, 28]. For this test, subjects sat at a desk with a blank piece of paper mounted vertically in front of them. The arms and body were kept free of contact with the desk or paper and only the pencil tip was allowed to touch the paper. The subjects performed the task with eyes open once, and then five times with the eyes closed. Any vertical deviation was determined by drawing a line from the centre of the top X character to the centre of the bottom X character and measuring the angle between this line and the vertical plane. The average angle of deviation from the vertical for the five trials undertaken with the eyes closed was used as the test measure. Stoll has found that performance in this test discriminated between healthy persons and those with vestibular lesions, with the vestibularly impaired subjects showing marked deviations from the vertical [28]. He suggested that this simple test can be used for objective identification of vestibulo-spinal deviation.

The Vestibular Stepping Test measured subjects ability to remain stationary and oriented in the one plane whilst 'walking on the spot' with the eyes closed for a period of I min [26] whilst the Vestibular-Optical Stability Test measured any difference between visual acuity at rest and while walking on a treadmill, recorded in logarithms of visual angle.

Quadriceps strength was measured in the sitting position. A strap (which was connected to a spring gauge) was placed around the subject's dominant (stronger) leg. The angles of the hip and knee were 90[degrees], the strap was placed 10cm above the ankle, and the angle of pull was perpendicular to the lower limb segment. Ankle dorsiflexion strength was measured by having the subject place the foot of the dominant (stronger) leg on a foot-rest. A strap (which protruded through the footrest) was placed over the top of the foot just proximal to the commencement of the little toe. The strap was connected to the base of the foot-rest so that when the subject (seated on the chair) attempted to raise the front of the foot (whilst keeping the heel placed on the foot-rest) a spring gauge was extended giving a measure of maximal ankle dorsiflexion strength. In both strength tests, subjects had three experimental trials and the greatest extension of the spring gauge was recorded.

Reaction time was assessed with a simple reaction-time task, using a light as the stimulus and depression of a switch (by the hand) as the response. Reaction time was measured in milliseconds (ms).

Sway was measured using a swaymeter that measured displacements of the body at the level of the waist. The device consisted of a rod attached to the subject at waist level by a firm belt. The rod was 40 cm in length and extended behind the subject. A sheet of graph paper (with a millimetre square grid) was fastened to the top of an adjustable height table which was positioned behind the subject. The height of the table was adjusted so that the rod was in a horizontal plane and the tip of a pen (mounted vertically at the end of the rod) could record the movements of the subject on the graph paper. Testing was performed on a firm surface (a linoleum covered floor) and on a piece of foam rubber (70 cm by 62 cm by 15 cm thick) with the subject standing in the centre. The same test was repeated on both surfaces with the subject's eyes closed. The foam rubber was used to reduce proprioceptive input from the ankles and cutaneous inputs from the soles of the feet so that subjects would be required to rely on visual and vestibular cues to maintain a steady stance. Four testing conditions were employed: condition A -- firm surface, eyes open; condition B -- firm surface, eyes closed; condition C -- compliant surface, eyes open; and condition D -- compliant surface, eyes closed. The number of square millimetre squares traversed by the pen in the 30-s periods was recorded for the four test conditions. This measure closely approximated the total length of the sway path. Subjects who could not perform the sway tests on the foam because of poor balance were given scores equal to three standard deviations above the mean score for these measures.

Full descriptions of the apparatus and procedures, along with test-retest reliability scores for the test measures have been reported elsewhere [9, 20].

Falls: A fall was defined as an event which resulted in a person coming to rest unintentionally on the ground or other lower level, not as the result of a major intrinsic event or an overwhelming hazard [30]. Questionnaires were mailed to residents every 2 months (with a reply-paid envelope). The questionnaire contained questions seeking details on the number of falls in the past 2 months, the location, the cause and any injuries suffered. If subjects failed to return their questionnaire, the relevant information was obtained by telephone interview.

Statistical analysis: The sensori-motor and gait parameters were coded as continuous variables. For variables with right skewed distributions: vibration sense, proprioception, vestibular function (as measured by all three tests), quadriceps strength, reaction time and sway, logs of the variables were analysed.

The gait parameters were significantly intercorrelated, with a very strong association evident between velocity and stride length and very strong inverse association evident between cadence and stance duration (Table I). A composite gait measure was, therefore, derived from the first principal component of the factor analysis of the five individual gait measures: velocity, cadence, stride length, stance duration and stance percentage--this factor was the only factor extracted with an eigenvalue greater than 1, accounting for 77.5% of the variance in the gait parameters.
Table I. Correlations among the gait measures
                  Cadence   Stride length  Stance duration  Stance %

Velocity          0.73(**)   0.91(**)        - 0.80(**)   - 0.79(**)
Cadence            --        0.39(**)        - 0.97(**)   - 0.60(**)
Stride length      --         --             - 0.51(**)   - 0.71(**)
Stance duration    --         --              --          - 0.76(**)

(**) p < 0.001.

Pearson correlation and hierarchical multiple regression analyses were used to assess the associations between the sensori-motor variables and the individual gait parameters and the composite gait measure. In the regression analyses, where the individual gait parameters were the dependent variables, stepwise procedures were used initially to identify the set of sensori-motor variables that significantly and independently explained part of the variance in each gait parameter. Only one measure from each of the visual, strength and sway variable sets were included in the models, as these measures were strongly inter-correlated. Age was then forced into the regression equations using forward selection, to assess whether this variable could explain any more of the variance in the gait parameters. In the regression analysis where the composite gait measure was the dependent variable, sensori-motor measures identified as significant and independent predictors for one or more of the individual gait parameters were included as a block at the first step, then age was included at step two.

Beta weights for each independent variable included in the regression equations and the multiple r2 values at each step are presented. Beta weights (the coefficients of the independent variables included in the regression equation) are expressed in a standardized (z score) form. As the units of each measure have been standardized, the beta weights give an indication of the relative importance of each variable in explaining the variance in the dependent variable (although they do not in an absolute sense reflect the importance of the various independent variables).

Finally, differences in the means of the individual gait parameters and the composite gait measure between the multiple faller and non-multiple faller groups were assessed using analysis of covariance, controlling for age. This criterion was used as it has been suggested that multiple falling may indicate physiological impairment or the presence of chronic conditions whereas single falls are less predictable and are more likely to result from external factors [30, 31]. The data were analysed using the SPSS computer package [32].


Associations with age: Correlations between the gait parameters and age and mean scores for the gait parameters for 10-year age groups up to 59 years and 5-year age groups for those aged 60 years and over (with a final age group of 80 plus years) are shown in Table II. All of the gait measures were significantly associated with age.


Sensori-motor correlates of gait: Table III shows the associations expressed as Pearson correlation coefficients, between the individual sensori-motor system measures and the five gait parameters (Table III). All four visual measures, touch, vibration sense, performance in the Vestibular X Writing Test, quadriceps and ankle dorsiflexion strength and reaction time were significantly associated with all five gait parameters. All sway measures were associated with percentage of the stride in the stance phase, and the sway on foam measures was also associated with velocity and stride length. The only sensori-motor measures not associated with any of the gait parameters were proprioception in the lower limbs, and performances in the vestibular stepping and vestibular ocular stability tests.


Table IV shows the sensory and motor system variables that were included in the multiple regression equations for the five individual gait parameters and the composite gait measure derived from the factor analysis. The stepwise regression procedures identified subsets of the sensori-motor measures that accounted for significant amounts of the variance in each individual gait parameter, with multiple Rs ranging from 0.32 for cadence to 0.69 for stride length. Quadriceps strength was included as a predictor variable for every gait parameter. When age was subsequently included in the regression models, r2s were significantly increased for velocity (1.8%), stride length (4.0%) and stance percentage (3.6%) but not for cadence (0.1%) and stance duration (0.5%).
Table IV. Heirarchical multiple regression analaysis showing
standardized beta weights and [r.sup.2]
after entry of each successive
block of variables into the equation

Dependent variables      Predictor variables    Beta     [r.sup.2]

Velocity                 LC visual acuity       -0.161   0.422(**)
                         Vestibular function    -0.157
                         Quadriceps strength     0.368
                         Reaction time          -0.141
                         Age                    -0.274   0.440(**)
Cadence                  Touch                  -0.173   0.104(**)
                         Quadriceps strength     0.218
                         Age                    -0.037   0.105
Stride length            LC visual acuity       -0.175   0.479(**)
                         Vibration sense        -0.163
                         Vestibular function    -0.153
                         Quadriceps strength     0.353
                         Age                    -0.449   0.519(**)
Stance duration          Quadriceps strength    -0.300   0.156(**)
                         Reaction time           0.175
                         Age                     0.106   0.161
Stance percentage        LC visual acuity        0.198   0.294(**)
                         Quadriceps strength    -0.377
                         Sway (EO) floor         0.152
                         Age                     0.301   0.330(**)
Composite gait measure   Contrast sensitivity    0.153   0.374(**)
                         Touch                  -0.097
                         Vestibular function    -0.096
                         Quadriceps strength     0.289
                         Reaction time          -0.095
                         Vibration sense        -0.062
                         Sway (EO) floor        -0.093
                         Age                    -0.194   0.382

Asterisks indicate differences in [r.sup.2] change when blocks of variables
are entered into the regression equations: (**) p < 0.01. For the individual
gait measures, only those physiological variables which were identified as
significant and independent contributors to [r.sup.2] (at p < 0.05) in a
stepwise procedure were included in the initial block. For the composite gait
measure, physiological measures identified as significant contibutors to r2
for any individual gait measure were included in the initial block.

Overall, seven sensori-motor measures were identified as significant predictors for one or more of the gait parameters: low contrast visual acuity, touch, vibration sense, vestibular X test writing performance, quadriceps strength, reaction time and sway. These variables had a multiple R with the composite gait measure of 0.61. The subsequent inclusion of age into the model did not significantly add to the variance explained in the composite gait measure (0.8%).

Falls: Of the 96 subjects aged 65 years and over, 67 (69.8%) had no falls in the follow-up year, 18 (18.8%) had one fall and 11 (11.5%) had two or more falls. Table V shows the mean scores plus standard deviations for the gait parameters for the non-fallers, once only fallers and multiple fallers. Women who fell on two or more occasions in the follow-up year had reduced and more variable cadence and increased stance duration (as a crude measure or as a percentage of stride), than those who did not fall or fell on one occasion only. Multiple fallers also performed worse on the composite gait measure compared with non-multiple fallers (F = 5.51, df = 1, 93, p < 0.05).
Table V. Non-faller--once only faller--multiple faller comparisons

                   Non-fallers   Once only fallers  Multiple fallers
                    Mean (SD)     Mean (SD)          Mean (SD)
Velocity           1.07 (0.19)   1.09 (0.14)        0.99 (0.17)
Cadence            114.9 (10.0)  113.7 (8.8)        108.1 (9.6)(*)
Cadence SD         1.76 (0.49)   1.94 (0.67)        2.23 (0.79)(*)
Stride length      1.12 (0.13)   1.15 (0.12)        1.10 (0.16)
Stance duration    676 (70)      678 (56)           734 (91)(*)
Stance percentage  64.2 (1.5)    63.9 (1.2)         65.5 (2.3)(**)

Significant difference between multiple and non-multiple fallers controlling
for age: (*) p < 0.05; (**) p < 0.01.


The controversy among researchers concerning the extent to which gait patterns change with age is due to fundamental differences in the definition of the term 'normal ageing'. At one extreme, normal older persons can be defined as only those free from all pathology, whilst at the other, all older people, with no exclusion criteria whatsoever and hence representative of general communities, can be considered normal. Clearly, both perspectives on selection criteria are valid, but lead to differing results, depending on whether pathological conditions are considered as a normal concomitant of the ageing process. Our approach to this problem has been based on the assessment of functional performance, rather than the identification of diseases or disorders, and has placed major emphasis on the quantitative measurement of visual processes, vestibular function, peripheral sensation, muscle strength, reaction time and body sway--factors outlined in our conceptual model [5] as the major body systems that contribute to balance control. Within this conceptual framework, we suggest that any debilitating medical condition (whether diagnosed or not) would be manifest by reduced functioning in one or more sensorl-motor system.

Within our study population, age was significantly associated with slower walking speed, reduced stride length and cadence and increased stance duration as well as reduced functioning in most of the visual, vestibular and sensori-motor systems assessed. The strength of these associations is probably underestimated to some extent, however, as the women with the most impaired gait, who were most often in the oldest age groups, could not undertake the gait assessment.

In all, seven sensori-motor measures were identified as significant predictors for one or more of the gait parameters: low-contrast visual acuity, touch, vibration sense, vestibular X test writing performance, quadriceps strength, reaction time and sway. The finding that reduced strength and increased sway are associated with slow walking speed is in accord with most previous research [13-16]. Of all the sensori-motor predictor variables, quadriceps strength was found to be the most important. It was included in all the regression models and in each case it had the strongest beta weight. The findings that other sensori-motor factors such as vision, vestibular function, lower-limb sensation and reaction time are also associated with gait support the claim by Duncan et al. [8] that the age-related decline in mobility is related to impairments in multiple domains.

The hierarchical regression analysis revealed that for certain gait parameters there was a residual effect of age which suggests that there are variables unaccounted for in the present study. It is possible that joint instability and/or pain, and specific health problems affecting the neurological and musculoskeletal systems not accounted for in our conceptual model could be included among these variables. None the less the derived models account for large amounts of the variance in all of the gait parameters and provide a valuable means of assessing the importance of various sensori-motor factors in predicting functional gait.

In clinical studies of falls risk factors in older people, gait measures such as decreased arm swing, increased trunk sway, slow walking speed, unequal stepping and broad based gait have been found to be significantly more common in fallers compared with non-fallers [17, 29, 30]. However, in the studies that have assessed specific gait parameters, some disparate findings have emerged. In a convenience sample of communitydwelling older people, Imms and Edholm [16] found that fallers had reduced velocity and shorter stride lengths. Similar results have also been reported in nursing-home residents [18]. In contrast, Gehlsen and Whaley [19] found no differences between fallers and non-fallers in an array of temporal gait parameters, although this may be partially explained by the artificiality of the assessment which required the subjects to walk on a treadmill moving at two preset velocities--4 km/in and 6 km/h. Finally, Guimaraes and Isaacs [33] found that among older hospitalized persons, fallers had reduced cadence with considerable variability in step length.

We found that multiple fallers had reduced and more variable cadence and greater stance durations than nonmultiple fallers. The present study has advantages over all previous studies that have examined relationships between gait parameters and falls in that it was prospective in design, studied a large sample, and used sophisticated apparatus for measuring gait. It was, therefore, not subject to limitations of inadequate power, the uncertainty as to whether the fall may have caused the gait abnormality or difficulties with artificial gait assessments. However, as indicated above, one of the limitations of the study was that those with the poorest balance and mobility could not undertake the gait testfrequently because their stride length was too short to obtain a valid assessment. In consequence, the analysis was restricted to those with better mobility and lower falls risk as evidenced by the fact that only 11.5% of the sample who undertook the gait test suffered multiple falls in the follow-up year compared with 20.8% of the Randwick Falls and Fractures study as a whole [34]. It is extremely likely that if these women had been included in the study, reduced stride length would also have emerged as a risk factor for falling.

In conclusion, the concurrent assessment of a range of sensori-motor systems involved in postural control along with gait measures assessed with sophisticated equipment has elucidated some mechanisms for the observed age-related changes in gait and why certain older people fall.


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Authors' addresses

S. R. Lord Prince of Wales Medical Research Institute High Street, Randwick, N. S. W. 2031 Australia

D. G. Lloyd, Sek Keung Li Department of Safety Science, University of New South Wales, Australia

Received in revised form 12 January 1996
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Author:Lord, Stephen R.; Lloyd, David G.; Li, Sek Keung
Publication:Age and Ageing
Date:Jul 1, 1996
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