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The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 63:165-170 (2008)
© 2008 The Gerontological Society of America

Sex Modifies the Relationship Between Age and Gait: A Population-Based Study of Older Adults

Michele L. Callisaya, Leigh Blizzard, Michael D. Schmidt, Jennifer L. McGinley and Velandai K. Srikanth

1 Menzies Research Institute, Hobart, Tasmania, Australia.
2 Department of Medicine, Monash Medical Centre, Clayton, Victoria, Australia.
3 Murdoch Children's Research Institute, Parkville, Victoria, Australia.

Address correspondence to: Michele L. Callisaya, BSc, Menzies Research Institute, Private Bag 23, Hobart, Tasmania, Australia 7000. E-mail: michele.callisaya{at}dhhs.tas.gov.au


   Abstract
Top
 Abstract
Methods
 Results
 Discussion
 References
 
Background. Adequate mobility is essential to maintain an independent and active lifestyle. The aim of this cross-sectional study is to examine the associations of age with temporal and spatial gait variables in a population-based sample of older people, and whether these associations are modified by sex.

Methods. Men and women aged 60–86 years were randomly selected from the Southern Tasmanian electoral roll (n = 223). Gait speed, step length, cadence, step width, and double-support phase were recorded with a GAITRite walkway. Regression analysis was used to model the relationship between age, sex, and gait variables.

Results. For men, after adjusting for height and weight, age was linearly associated with all gait variables (p <.05) except cadence (p =.11). For women, all variables demonstrated a curvilinear association, with age-related change in these variables commencing during the 7th decade. Significant interactions were found between age and sex for speed (p =.04), cadence (p =.01), and double-support phase (p =.03).

Conclusion. Associations were observed between age and a broad range of temporal and spatial gait variables in this study. These associations differed by sex, suggesting that the aging process may affect gait in men and women differently. These results provide a basis for further research into sex differences and mechanisms underlying gait changes with advancing age.

Key Words: Gait • Sex • Population-based study

ADEQUATE mobility is essential for older adults to maintain an independent and active lifestyle. The prevalence of abnormal gait has been reported to be as high as 35% in adults >70 years (1). Gait problems are associated with falls (2–5), which can lead to hospitalization (5), institutionalization, and increased mortality (1). A better understanding of how gait is affected by advancing age is required to assist in targeting appropriate age groups for interventions to prevent falls and loss of independence.

Previous studies describe a decrease in speed (6–17) and step length (6,8–13,15,16,18) with age, but disagree on its effect on cadence (6,9–16,18). Few studies, however, have investigated the relationship between age and other gait variables, such as step width and double-support phase (DSP) (6,8,11,19–22). It is also unclear whether the effect of increasing age on gait variables is similar in men and women. Although previous data indicate that older women walk at a slower speed, take shorter steps, and have a faster cadence than men (23), it is unknown whether these differences are consistent across the older age range.

Prior studies of aging and gait have compared only young and old adults (6,8,12,13,18,21,22) in small samples of healthy volunteers (6–22), which limit their generalizability and usefulness in understanding how gait changes across the continuum of older age. There are very few population-based studies of age and gait (2,4,23–27), with even fewer examining more than one gait variable in both men and women (23,26). Therefore, the aims of this study are (i) to describe the associations of age with a range of temporal and spatial gait variables in a randomly selected older population-based sample and (ii) to investigate whether such associations differ in men and women.


   METHODS
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 Methods
Results
 Discussion
 References
 
Study Participants
Participants aged between 60 and 86 years were randomly selected from a comprehensive list of residents, the Southern Tasmanian electoral roll, into the Tasmanian Study of Cognition and Gait (TASCOG) (n = 223). TASCOG is a population-based study of the neural correlates of gait, balance, and cognition in older people. Southern Tasmania has a total population of 239,444 people including 46,159 persons aged at least 60 years (28), predominantly Caucasians of a northern European ancestry. Participants were excluded if they lived in a nursing home or were unable to walk without a gait aid. The Southern Tasmanian Health and Medical Human Research Ethics Committee approved this study, and written consent was obtained from all participants.

Gait Analysis
Gait variables (speed, cadence, step length, step width, and DSP) were measured using the 4.2-meter GAITRite system (CIR Systems Inc., Clifton, NJ). The GAITRite is a portable carpet walkway with embedded pressure sensors that collect gait data electronically as the participant walks over the carpet. The GAITRite system has demonstrated high concurrent validity relative to a ‘gold standard’ three-dimensional motion analysis system (29) and has excellent test–retest reliability in older adults (30). Participants started and finished walking 2 meters before and after the mat to allow for acceleration and deceleration. After two practice trials, participants performed six walks at their preferred speed, and gait measures were averaged over the six walks.

Results are presented for five gait variables (speed, cadence, step length, step width, and DSP [%]), but in exploratory data analyses we also examined DSP measured in seconds. Those results are not reported because DSP (s) is highly correlated with DSP percentage.

Other Measurements
Height (cm), weight (kg), and self-reported history of lower limb arthritis, stroke, Parkinson's disease, diabetes mellitus, and falls (in the preceding 12 months) were recorded using a standardized questionnaire. Nonresponders completed a brief phone interview providing some details about their medical history (diabetes mellitus and stroke) and history of falls in the previous 12 months.

Data Analysis
Chi-square and Student t tests were used to compare gait variables between men and women (Table 1), Spearman correlation coefficients were used to measure the associations between gait variables (Table 2), analysis of variance methods were used for the analysis of age (Table 3), and linear regression was used to estimate the cross-sectional relationship between each gait variable and age (Table 4, Figure 1). To adjust for height and weight, linear terms for these covariates were added to the regression models. In the regression analysis for women, speed and step width were log transformed, and the square of age was added as an additional covariate to capture the remaining nonlinearity for each gait variable. Two younger, slow-paced women were excluded from analysis after examination for outliers because they were highly influential in producing an implausible inverted U shape in the relationship of speed with age. For gait variables that varied nonlinearly with age, methods of calculus were used to estimate the turning points. Statistical interaction between age and sex was assessed by a test of significance of a (Age x Sex) product term for men, and by a partial F test (31) for the inclusion of two product terms (Age x Sex, Age2 x Sex) in the model for women. Similar methods were used to assess modification by height and weight. To investigate whether the findings were biased because participants tended to be younger than nonparticipants, and with a lesser prevalence of self-reported falls, the regression analyses were repeated with participants weighted by the multiple wijk = Nijk/nijk where Nijk represents the number of eligible participants (participants and nonparticipants) in a particular age (i), sex (j), and falls history (k) category, and nijk represents the number of participants in that category.


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  		Table 1. Sample Characteristics (N = 223).

 

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  		Table 2. Correlations Between Gait Variables in Men and Women (N = 221).

 

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  		Table 3. Univariable Associations Between Age and Gait Measures (N = 223).

 

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  		Table 4. Cross-Sectional Effect of an Additional Year of Age on Gait Measures (N = 221).

 

Figure 01
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  		Figure 1. Scatter plots and fitted regression lines of the relationship between age and each gait measure (n = 221). • = Men; {circ} = women; solid line = line of best fit for men; dashed line = line of best fit for women. DSP = double-support phase

 

   RESULTS
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Discussion
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The sample response proportion was 53.0% (223/420). Nonresponders were older (p <.001) and reported falling less often in the previous 12 months (p <.001), but did not differ by sex (p =.84) or prevalence of stroke (p =.20) or diabetes (p =.28).

Demographic, clinical, and gait characteristics of the sample are provided in Table 1. Men had greater step length (p <.001) and step width (p <.001) than women, but had lower cadence (p <.001) and DSP (p =.02). A greater proportion of women than men reported falling in the preceding year (p =.002). Associations between gait variables are shown in Table 2. The strongest associations were between speed and cadence, step length, and DSP for women and speed and step length for men. Speed and step length were negatively associated with age category in both men and women. Cadence was negatively associated with age category only in women. DSP (in women) and step width (in men) showed positive associations with age category (Table 3).

Fitted regression curves were used to characterize the cross-sectional relationship between age and gait variables in men and women. In men, the relationships were linear, but in women the associations of age with speed (p =.002), cadence (p =.003), step length (p =.07), step width (p =.08), and DSP (p =.01) were curvilinear (Figure 1). Peak speed, cadence, and step length were recorded by women aged 65.2 (standard error [SE] 3.1), 67.5 (SE 2.7), and 61.5 (SE 6.8) years, respectively. Lowest step width and DSP were recorded by women aged 70.0 (SE 2.9) and 65.5 (SE 3.4) years, respectively.

Results of the multivariable linear regression of age with gait measures are shown in Table 4. After adjusting for height and weight, age was significantly associated with all gait variables in men except cadence. In women, the strength of the association between age and gait variables varied across the study age range, with associations being strongest among the oldest women. Significant interactions were seen between age and sex for speed (p =.04), cadence (p =.01), and DSP (p =.03). These associations and interactions remained significant after controlling for chronic disease.

Repeating analyses with participants weighted for nonresponse confirmed that the curvilinear relationships persisted, and thus were not due to participating women being younger than nonparticipating women, or fewer of them having fallen.


   DISCUSSION
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 Methods
 Results
 Discussion
References
 
The key findings in this cross-sectional, population-based study are that age was significantly associated with a wide range of gait variables, and that these associations were most pronounced for older women. Older men and women tended to walk more slowly, with smaller steps, larger step widths, and a longer DSP, than their younger counterparts. Older women also walked with a slower cadence.

Decrease in speed (2,4,23–27) and step length (23,26) and an increase in the DSP (26) with advancing age have been found in other population-based studies of older adults and in samples of healthy volunteers (12,15,17). Only in a few (7,10,24,27) previous studies have these associations been found to be more pronounced at older ages. In the only population-based study examining both men and women, these stronger associations were found in both sexes (24), whereas in this study the stronger associations were seen in older women only. The peak speed was estimated for 65-year-old women, consistent with other studies reporting decline in the 7th decade (9,10,16,24). For men, a linear association was observed between age and speed. Previous studies (12,16) have found that declines in speed for men commence before the 7th decade, prior to the age of the youngest men in our study.

Previous results have been inconclusive regarding the association between age and cadence, with the majority of studies reporting a decrease (9–11,23), and one reporting no change with increasing age (19). In this study, cadence was negatively associated with age in older women, but not in men. Step length and cadence are the determinants of speed. It is possible that, in older women, a decrease in both step length and cadence contributes to the decrease in velocity, whereas men are able to maintain cadence well into older age.

Step width and DSP, key gait variables related to balance and falls risk in older people (9,32), were positively associated with age in both men and older women in the present study. The few previous studies of older adults show conflicting findings, suggesting that step width decreases (20) or increases with age (26) and that DSP remains unchanged (19) or increases (9,26). In this study, stronger associations were seen for older women. It is possible that older women are less able to compensate for poorer balance and therefore require a greater increase in DSP and step width in an attempt to maintain stability. Further research is needed to verify the associations and sex differences between age and step width and DSP found in this study.

The results of this study may have implications for public health, particularly with respect to the stronger associations found between gait variables and age in older women. Decreased speed, cadence, and step length, and increased DSP and step width, are predictive of future falls (3,4,19,32), loss of function, hospitalization, and increased mortality (5,33,34). Therefore, the accelerated changes in gait for women commencing in their 60s may put them at greater risk. In addition, the average walking speed for women in the 80- to 86-year age group was <1 m/s, a rate that has been reported to be predictive of major health-related events (34). Previous studies have suggested that gait speed be used as a quick and easy screening tool, as it can reflect early clinical or subclinical disease in multiple systems (5,33). Screening of gait in people, particularly women, older than 65 years may therefore help to identify those persons at high risk and potentially require preventative interventions.

One question is whether gait variables are a more proximal indicator of falls risk, reflecting the insidious effects of chronic diseases such as arthritis (35), declines in muscle strength (19), and other factors that increase the risk of falling and become increasingly more pronounced for older women (17,36). If gait parameters are better predictors of risk of falling than the factors underlying them, their ease of measurement would make them worthy candidates as a falls risk screening tool.

In interpreting these findings, it is important to recognize the intercorrelation between the gait variables. We decided to present results for five of these variables for two key reasons. First, knowledge of how these variables are associated with age is important from both biomechanical and clinical perspectives. Understanding which components of gait are age-related can help in formulating treatments to maintain walking speed in older adults. For example, our results suggest that preventive programs for men should focus on maintaining step length rather than cadence to maintain a functional walking speed, whereas in women programs should focus on both step length and cadence. Further research is needed into the mechanisms underlying change in each gait variable and which variables best predict falls and adverse health events. Second, using the method of Cheverud (37), we estimated the number of independent quantities among these five variables. The result was 4.2, suggesting that the information in those five variables would not be captured by any subset of four of them.

The specific causes underlying the observed age-related decline in gait could not be examined in this study. It is possible that age-related neurological or musculoskeletal disease contribute in part to these changes in gait (38,39). Although the presence of self-reported chronic disease in this study was independently associated with some gait variables, its inclusion as a term in the final models did not remove the independent associations between age and the gait variables. The independent association between age and gait after adjustment for chronic disease suggests that factors other than chronic disease may explain the age–gait associations. Other unmeasured factors such as decline in sensorimotor systems (2), impaired joint range of movement (38), pain (17), reduced physical activity (17), and fear of falling (40) may also contribute to impaired gait with aging and require further study.

This study adds significantly to knowledge of aging and gait by providing data on a wide range of quantitative gait measures in a large sample of both men and women. It is also one of very few population-based studies in the field, making these results more generalizable than those from smaller convenience samples reported by the majority of previous studies. However, these findings are limited by their cross-sectional nature, with longitudinal follow-up needed to characterize actual change in gait with aging. In addition, testing was performed in a flat indoor environment, with further study needed to include more challenging, ‘real life’ situations such as walking over obstacles and dual tasking.

Summary
All gait variables were associated with age, except for cadence in men. Sex differences in some of these associations suggest that the aging process may affect gait in men and women differently. Future research needs to consider these important sex differences as a basis for understanding mechanisms underlying changes in gait with advancing age.


   Acknowledgments
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 Results
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This work was supported by the National Health and Medical Research Council, the Physiotherapy Research Foundation, Perpetual Trustees, the Brain Foundation, the Royal Hobart Hospital Research Foundation, the ANZ Charitable Trust, and the Masonic Centenary Medical Research Foundation.

The information in this article was presented to the Australian Society for Geriatric Medicine ASM, Christchurch, New Zealand, September 4–6, 2006.


   Footnotes
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Decision Editor: Luigi Ferrucci, MD, PhD

Received January 29, 2007

Accepted May 15, 2007


   References
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