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Gait Variability Detects Women in Early Postmenopause With Low Bone Mineral Density

Kerstin M. Palombaro, Laurita M. Hack, Kathleen Kline Mangione, Ann E. Barr, Roberta A. Newton, Francesca Magri, Theresa Speziale
DOI: 10.2522/ptj.20080401 Published 1 December 2009
Kerstin M. Palombaro
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Laurita M. Hack
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Kathleen Kline Mangione
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Ann E. Barr
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Roberta A. Newton
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Francesca Magri
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Theresa Speziale
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Abstract

Background Women in early postmenopause and with low bone mineral density (BMD) may exhibit early markers for physical frailty as a result of sarcopenia and osteopenia.

Objective The purpose of this study was to determine whether women in early postmenopause and with low BMD exhibit decreased physical performance and differences in gait variability and fall and fracture rates.

Design This study was an observational cohort design with participants assigned to groups on the basis of BMD status.

Methods Fifty-four women, 31 with low BMD and 23 with normal BMD, participated. This study was conducted in a university research facility. Physical performance was measured by assessment of dynamic balance (timed backward tandem walk test), strength (handheld dynamometry of isometric quadriceps muscle force production), and free gait speed. Gait variability was assessed on the basis of the coefficient of variation for temporal-spatial gait characteristics. Falls and fractures were assessed for the year after initial testing.

Results Significant between-group differences were found for step time and stance time variability.

Limitations The limitations of this study included group assignment on the basis of the results of the most recent bone density scan within the preceding 2 years.

Conclusions Women in early postmenopause and with low BMD exhibited increased gait variability in step time and stance time but did not exhibit differences in balance, strength, or gait speed. Gait variability may be more sensitive for detecting differences in women in early postmenopause and with or without low BMD than more typical measures of physical performance.

Physical frailty describes a process in which multiple organ systems deteriorate. This deterioration both results from and causes physical inactivity.1 Markers for frailty include osteopenia, which is the loss of bone tissue, and sarcopenia, which is the loss of muscle tissue.2 As women age, their risk for frailty increases.3 Physical frailty contributes to dependency in daily tasks,4 longer periods of disability after illness,4 and increased mortality.5,6

More than 44 million Americans 50 years of age or older have osteoporosis or osteopenia, with women representing 30 million of those cases.7 People are diagnosed with osteopenia when their bone mineral density (BMD), determined by dual x-ray absorptiometry (DXA) scanning, falls between 1.1 and 2.5 standard deviations below the mean for adults who are healthy; people are diagnosed with osteoporosis when their BMD falls more than 2.5 standard deviations below the mean for adults who are healthy.7 Osteoporosis is a systemic skeletal disease that is marked by decreased bone mineral mass and compromises in bone architecture.8 Women with primary osteoporosis, that is, bone demineralization attributable to aging and menopause, are at increased risk for frailty9 because they experience greater loss of muscle and bone tissue than their peers who are healthy.10

One definition of frailty is a syndrome in which 3 or more of the following criteria are present: unintentional weight loss of greater than 4.5 kg (10 lb) in a year and self-reported exhaustion, weakness, decreased physical activity, and slow walking speed.5 Free gait speed is predicted by knee extensor muscle strength (force-generating capacity)11 and is correlated with BMD at various skeletal sites.12 Variability in temporal-spatial gait characteristics, such as step width and step length, has been investigated in other studies in relation to both falls13–15 and the transition to frailty.16 Because older women with osteoporosis tend to be more frail than their peers who are healthy,17 women in early postmenopause and with low BMD may exhibit early markers for physical frailty, including changes in balance and gait.

Sarcopenia associated with aging and frailty is related to poorer physical performance,18–23 which in turn could result in falls.3 Approximately half of women who are postmenopausal will sustain an osteoporosis-related fracture in their lifetime, and 15% will sustain a hip fracture. If women in early postmenopause are at risk for frailty, their risk of fracture from an injurious fall may be elevated because of low BMD8 as well as weakness and impaired balance.24 To date, no studies have examined these variables in women in early postmenopause and with low BMD. If deficits in balance, strength, and gait were identified in this population, then physical performance measures could provide an earlier indication of risk for frailty.

The primary aim of this study was to determine whether women in early postmenopause and with low BMD exhibit poorer performance in dynamic balance, quadriceps femoris muscle strength, and free gait speed than women without low BMD. The secondary aims were to determine whether women in early postmenopause and with or without low BMD exhibit differences in variability in temporal-spatial gait characteristics and to determine whether there are differences in fall and fracture rates in the year after initial testing.

Method

Study Design

This study was an observational cohort study. Participants were assigned to groups on the basis of BMD status. Participants were assigned to the low BMD group if their BMD was ≥2.0 standard deviations below the mean for adults who are healthy at one or more skeletal sites in accordance with the DXA scan results.8 The results of each DXA scan were confirmed by the radiologist's written DXA scan report. Participants were assigned to the normal BMD group if their BMD was within 1.0 standard deviation of the mean for adults who are healthy. Participants were excluded from the latter group when any skeletal site had a bone density of 1.1 to 1.9 standard deviations below the mean for adults who are healthy. This exclusion criterion recognizes that although severe osteopenia is defined as 2.0 standard deviations below the mean for adults who are healthy,25 the range of 1.1 to 1.9 is still classified as osteopenia.

All testing occurred at Arcadia University. Pilot testing was conducted to estimate sample size. Eight women, 4 with low BMD and 4 with normal BMD, completed the pilot testing. These women had a mean (SD) age of 59.1 (3.1) years; had been postmenopausal for, on average, 6.3 (SD=2.2) years; had a mean (SD) body mass index (BMI) of 24.3 (1.5) kg/m2; and had a mean (SD) physical activity level of 2,214.1 (189.8) kcal/d, as determined with the Stanford 7-Day Physical Activity Recall Questionnaire. Results from the pilot study indicated that women in early postmenopause and with low BMD exhibited differences in backward tandem walk time and quadriceps femoris muscle strength compared with their peers who were healthy. Effect sizes were large (r=−.75) for backward tandem walk time and medium for the strength of both quadriceps femoris muscles (r=.43). The study was powered with a medium effect size because it represented the lowest effect size for the specific aims. Given a power of at least 0.80, a medium effect size of r=.43, and an alpha level of .05, a target sample of 28 women per group, for a total of 56 participants, was assumed.26 Data collection for the main study took place from August 2006 to April 2007. Performance data were collected in one session, which lasted approximately 1 hour. Participants were contacted by telephone 1 year after performance testing.

Participants

Participants were recruited through informational flyers placed in Arcadia University–area community buildings for people aged 55 years and older, churches, gymnasiums, synagogues, physical therapy clinics, and libraries and advertisements placed in church bulletins and local newspapers. A total of 82 women responded to recruitment flyers and advertisements.

After potential participants made contact with the principal investigator (K.M.P.), they were telephoned and screened with inclusion and exclusion criteria. To be considered for inclusion in the study, participants had to be community-dwelling women who were 50 to 65 years of age and 3 to 10 years after menopause, who had DXA test results from within the preceding 2 years, and who were able to ambulate independently. Years after menopause was assessed by reading the definition of menopause, which is “cessation of the menstrual period for a span of time of at least 12 months,”27 and then asking, “At what age did your period cease?”

Participants were excluded if they were organ transplant recipients, had active, severe liver or kidney disease, were undergoing chemotherapy or renal dialysis, had chemical menopause or menopause because of oophorectomy, had chronic, severe cardiac or pulmonary disease, had a history of bone cancer or bone metastases, used corticosteroids for a long period of time, or had medical issues that affected the lower extremities and that, in turn, could affect ambulation. Examples of such medical issues were joint replacement; joint inflammation causing swelling, tenderness, or both; lower-extremity surgery within the preceding 6 months; or neurological diseases that could affect ambulation, such as Parkinson disease, multiple sclerosis, or residual weakness from a cerebrovascular accident. Twenty-four women did not meet all of the inclusion criteria (age, n=1; years after menopause, n=12; DXA scan test date, n=2; chronic lung disease or use of asthma inhalers, n=2; menopause because of oophorectomy, n=4; lower-extremity joint replacement, n=2; and neurological disease affecting the lower extremities, n=1), and 1 woman declined to participate without financial compensation.

Potential participants who met the inclusion criteria were scheduled for assessment. Participants were informed of the study objectives and provided written informed consent before data collection. Fifty-seven women, 33 with low BMD and 24 with normal BMD, participated in the testing for this study. Two participants were excluded from the low BMD group because their physical activity levels were greater than 2 standard deviations above the participant mean (4,130.23 and 4,166.20 kcal/d, respectively), and 1 participant was excluded from the normal BMD group because both her physical activity level and BMI were greater than 2 standard deviations above the group mean (3,953.73 kcal/d and 42.2, respectively) (Fig. 1). Four participants (3 in the normal BMD group and 1 in the low BMD group) were lost to follow-up for the 1-year evaluation of falls and fractures despite numerous attempts to contact them.

Figure 1.
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Figure 1.

Flow diagram of study recruitment. BMD=bone mineral density, BMI=body mass index.

Demographic Variables

The participants completed a demographic and health status questionnaire that included questions about age, number of years after menopause, date and results of the last DXA scan, medical history, current medications, and use of calcium and vitamin D supplements. With the exception of the DXA scan results, these variables are associated with BMD,28–31 physical performance,32–34 or both. Participants’ height (in meters) and weight (in kilograms) were measured on a calibrated physician scale,* and BMI (kg/m2) was calculated from these values. The BMI was calculated because it is associated with sarcopenia35 and BMD.28

Next, the participants were administered the Stanford 7-Day Physical Activity Recall Questionnaire.36 Physical activity level was measured as a potential covariate because activity level could affect both physical performance37–39 and BMD.40 The Stanford 7-Day Physical Activity Recall Questionnaire was selected because it has been used with both older women41 and women who are postmenopausal and have low BMD.42–45 This questionnaire asks people to recall the number of hours slept each night as well as the number of hours spent in moderate, hard, and very hard activities during the preceding 7 days. Examples of moderate, hard, and very hard physical, social, and occupational activities are provided to each individual. A description of exertion levels also is provided. Energy expenditure is calculated from the numbers of hours spent in sleep and in activity and is reported as kilocalories per day. The reliability36 and validity46 of Stanford 7-Day Physical Activity Recall Questionnaire scores have been reported.

Of the 57 women who participated in the testing for this study, 55 were white and 2 were African American. On average, the women were 58.8 (SD=3.3) years of age and had been postmenopausal for 6.1 (SD=2.5) years. Analysis of the results indicated significant between-group differences for BMI and level of physical activity. Participants with low BMD had a mean BMI that fell within the healthy category, and those with normal BMD had a mean BMI that fell in the overweight category. Participants with low BMD had a mean (SD) physical activity level of 2,290.61 (376.32) kcal/d, or 13 h/wk, and those with normal BMD had a mean (SD) physical activity level of 2,683.79 (573.05) kcal/d, or 15 h/wk. All participants walked at speeds that exceeded reported thresholds for successful community ambulation, such as crossing the street within the timing of a traffic light (1.0–1.2 m/s).47

The most common types of medications included over-the-counter supplements, daily aspirin, statins, and osteoporosis medications for participants in the low BMD group. The most common comorbidities were high cholesterol levels and hypothyroidism (Tab. 1).

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Table 1.

Demographic Characteristics of Participants

Physical Performance Variables

Physical performance measures included assessment of dynamic balance, quadriceps femoris muscle strength, and gait. The order of testing was the same for all participants. Dynamic balance was assessed with a timed, backward tandem walk test.48,49 Participants were instructed to walk backward as quickly as possible in a heel-to-toe fashion on a 2.44-m line. This test is timed with a stopwatch.37 Participants were allowed 1 practice trial to become accustomed to the testing procedure. They then performed 2 trials of the test. The time for the 2 trials was recorded in seconds, and the mean time for the 2 trials was calculated. This test was selected to minimize the ceiling effect that can occur when subjects under age 65 are tested; other balance measures that use ordinal scales, such as the Berg Balance Scale, have a documented ceiling effect for such subjects.50 The backward tandem walk test has been used for older adults49 and people with osteoporosis.48 The validity of the backward tandem walk test for isokinetic knee extension strength has been reported.49

Isometric quadriceps femoris muscle force production for each leg was measured with a MicroFET 2 handheld dynamometer.† Participants sat upright on a plinth of sufficient height to prevent the feet from touching the floor. The upper thighs were strapped to the plinth to prevent compensatory movements, and the knees were flexed to 90 degrees.51 The dynamometer was placed on the anterior tibia just proximal to the malleolus.51 Participants were asked to push as hard as possible for 5 seconds while the investigator matched the resistance (make test). The left lower extremity was tested first. Participants performed 2 practice trials and then a minimum of 2 maximal-effort trials with a 1-minute rest period between the trials. The peak force for each trial was recorded, and the mean for the 2 trials was calculated.52 In the event that the 2 trials were not within 10% of each other (∼50% of the participants), additional trials were performed. The quadriceps femoris muscle complex was chosen because quadriceps femoris muscle strength has been found to predict gait speed11 and is associated with static and dynamic balance in women with osteoporosis.42 Handheld dynamometry has been reported to have reliability42 and validity.53 Handheld dynamometry52 and similar testing procedures42–44 have been used to assess lower-extremity strength in women with osteoporosis.

Free gait speed data were collected with the GaitMat II‡ to determine temporal and spatial gait characteristics. The GaitMat II is a 3.87-m walkway containing pressure-sensitive switches54 (Fig. 2). Free gait speed was chosen as a variable because walking speed is one of the criteria included in the definition of frailty.5 The participants completed 2 trials of free speed with the instruction to “walk across the mat at your normal speed.” Individually determined rest periods were given between the trials. Gait variability was calculated from the walking trials as the coefficient of variation of free speed step length, stride length, step width, step time, and stance time as described by Brach et al13 (Fig. 1). The coefficient of variation, calculated as (standard deviation/mean) × 100,13 represents the variability within a distribution of numbers. The reliability and validity of data obtained with the GaitMat II have been reported.54

Figure 2.
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Figure 2.

(A) GaitMAT II System. (B) Step length, defined as the distance between contacts of the contralateral extremities. Step length is defined with respect to the advancing limb.77 (C) Step width, defined as the lateral distance between contralateral footfalls. Thus, right step width is defined with respect to the left trailing limb and left step width is defined with respect to the right trailing limb.77 (D) Stride length, defined as the distance from the contact of one foot to the subsequent contact of the same foot. Initial contact typically is used as the measurement reference. One stride represents the completion of one cycle of gait.77 (E) Step time, defined as the time necessary to complete a right or left step length.77 (F) Stance time, defined as the time a limb is in contact with the ground.77 Photographs used with permission of E.Q. Inc.

Data on Falls and Fractures

Data on falls and fractures were collected for the year after initial testing. Participants were contacted 1 year from their initial test date and were provided with the definition of a fall: “A fall is defined as unintentionally coming to rest on the ground, floor, or other lower level.”55(p1619)

Participants then were asked whether they had fallen in the preceding year and, if so, how many falls they had sustained. Participants also were asked whether they had sustained any fractures or broken bones in the preceding year and, if so, which bones they had broken. They were not questioned about the circumstances of the fall or whether a hospitalization resulted from the fall.

Data Analysis

All data were analyzed with SPSS software, version 14.0.§ Descriptive statistics were determined for all demographic and health-related variables. Data were excluded from the analyses when a participant's physical activity level or BMI exceeded 2 standard deviations of the group mean to prevent outliers from skewing the results. Independent sample t tests were performed to assess between-group differences for all demographic variables and to assess potential between-group differences between the pilot sample and the full-study sample for both the low BMD group and the normal BMD group.

Analyses of variance were performed to test differences in physical performance variables (primary aim) and differences in gait variability (secondary aim). Chi-square analyses were performed to test differences in rates of falls and fractures (secondary aim). We treated falls and fractures as dichotomous variables instead of using the numbers of falls and fractures as metrics. Statistical significance was set at P<.05.

Role of the Funding Source

This study was funded, in part, by grants from the Pennsylvania Physical Therapy Association and the Section on Women's Health of the American Physical Therapy Association. The funding received for this study allowed for office supply costs, such as postage and purchases of paper and statistical software; photocopying costs; advertising costs for recruitment purposes; and partial salary support for the principal investigator (K.M.P.). The principal investigator was required to submit a poster presentation of preliminary results to the Pennsylvania Physical Therapy Association and one article related to her dissertation to the Journal of Women's Health Physical Therapy as part of the funding stipulations. This study was conducted in partial fulfillment of Dr Palombaro's doctoral degree.

Results

There were no significant between-group differences in backward tandem walk time, free gait speed, or isometric quadriceps femoris muscle force. However, women with low BMD had poorer balance (slower times on the backward tandem walk test), slower walking speeds, and less strength (isometric quadriceps femoris muscle force) than women with normal BMD (Tab. 2).

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Table 2.

Results of Data Analysis of Primary Variables

Significant between-group differences were found for step time and stance time variability. Additionally, a tendency toward increased variability in step width was found for the low BMD group (Tab. 3). Nine women with low BMD (30%) and 5 women with normal BMD (25%) sustained at least 1 fall in the year after physical performance testing. Two women with low BMD sustained a total of 3 fractures. These differences were not statistically significant.

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Table 3.

Results of Analysis of Variance (ANOVA) for Gait Variabilitya

Discussion

The primary aim of the present study was to address potential physical performance differences in women in early postmenopause and with or without low BMD. Although there was a tendency for women in early postmenopause and with low BMD to demonstrate poorer performance in the physical performance measures than their peers with normal BMD, no statistically significant differences were found. The tendency toward poorer performance did not translate to clinically meaningful between-group differences for balance (1.28 seconds) and strength (4.58–10.63 N).

Although other studies12,42,44 have demonstrated that women in late postmenopause and with low BMD exhibit significantly poorer physical performance in measures of balance, quadriceps femoris muscle strength, and gait speed, the differences in samples as well as the types of tools used to measure physical performance may partially explain the conflicting results. In the present study, on average, women were 59 years of age and had been postmenopausal for 6 years, had a mean BMI of 25 kg/m2, and had a mean physical activity level of 2,457.65 kcal/d (14 hours of activity per week). In earlier research, subjects had an age range of 68.3 to 69.4 years, a BMI range of 24.7 to 25.9 kg/m2, and total physical activity times ranging from 6 to 21 h/wk.12,42,44 Two studies included women who had been postmenopausal for at least 5 years,42,44 and 1 study did not have minimum or maximum cutoffs for years after menopause.12 Thus, the subjects in earlier research were, on average, older, more sedentary, and likely to be in late postmenopause because of age and menopause inclusion criteria. The between-group differences in these studies12,42,44 may have been larger because of the effects of aging and physical activity on bone density and physical performance. Finally, the tests and measures used in the present study to assess physical performance and activity levels may not be sensitive enough to detect changes at the level of functional limitations in women in early postmenopause.

Earlier research focusing on women in late postmenopause revealed a relationship between physical performance and BMD.12,42,44 Our results for dynamic balance and isometric force output testing contrasted with those in the studies of Carter et al,42 and Liu-Ambrose et al,44 who found associations of quadriceps femoris muscle strength and dynamic balance (as measured with the Figure-of-8 Test) with BMD in older women (mean age of >65 years) who were postmenopausal. Our free gait speed results also differed from the findings of Lindsey et al,12 who reported an association between free gait speed and BMD.

A secondary aim of the present study was to explore more-sensitive measures of gait variability to determine whether subtle changes were occurring in gait at the level of impairment. Our study revealed statistically significant differences in step time and stance time; women with low BMD exhibited increased variability in both of these variables. Women with low BMD also demonstrated a tendency toward increased step width variability. As in our study, Hausdorff et al15 found increased stance time variability in older adults who had fallen. Maki14 found increased step time variability and a tendency toward step width variability in older adults who had fallen. Brach et al13 found an association between increased step width variability and falls in older adults walking at normal speeds. Kressig et al16 found that stride length variability increased in older adults who were transitioning to frailty, a finding that supports the hypothesis that spatial gait characteristics may change before changes in strength and balance are detectable. Because step width variability may be a more-sensitive measure for differentiating people who have fallen from those who have not fallen, step time and stance time may be more-sensitive measures for detecting progression to frailty in women in early postmenopause.

In earlier research on gait variability,13–16 older adults with mean ages ranging from 79 to 82 years were examined. The participants in our sample, with a mean age of 59 years, represent an earlier point on the age continuum of gait variability. However, the percentage differences in variability between the group with low BMD and the group with normal BMD in the variables of step time (9% versus 7%) and step width (17% versus 12%) mirror the findings of Brach et al.13 Our findings may indicate that women with low BMD are beginning to experience some balance instability. This notion is supported by our finding of a tendency toward increased time on the backward tandem walk test in women with low BMD.

Another aim of the present study was to determine whether there were differences in fall and fracture rates between women who were in early postmenopause and had low BMD and women who were in early postmenopause and did not have low BMD. Although no significant between-group differences were found, 9 women with low BMD (30%) fell, and 2 women with low BMD sustained a total of 3 fractures. The percentage of falls in our sample of women with osteoporosis mirrors the reported rate of falls of 32%,56 whereas the percentage of fractures in women in the low-BMD group who fell (10%) exceeds the reported rate of fractures of 2.8% in the age group of 50 to 64 years.57

The detection of differences in gait variability and at the level of functional limitations in balance, strength, and gait speed was explored as a possible method for detecting and thus preventing58 early frailty. Poorer performance on tasks related to functional limitations may be subtle in a younger cohort of women. Fried et al59 defined preclinical disability as an increase in the time needed to complete a task, modification of a task, or a decrease in the frequency of task performance. In community-dwelling people, compensatory strategies may mask early changes that indicate the potential to progress to frailty.60 Compensatory strategies may have accounted for the tendency toward poorer performance in all physical performance tasks and the significant differences in gait variability observed in the present study.

Physical activity level and resultant BMI may have been important mediators in the present study. Participants in the low-BMD group had a significantly lower BMI and lower activity levels than those in the group with normal BMD. Changes in total daily physical activity are associated with 3-year changes in mobility performance, as measured by deficits in the time needed to rise from a chair and timed walking.61 Physical activity and mobility limitations were found to be mediated by knee extension strength in a sample of 3,075 adults who were 70 to 79 years of age.62 Increasing physical activity levels from the onset of middle age results in a slower progression of functional limitations and prevention of disability.61,63,64 Although women with low BMD in the present study exhibited significantly lower physical activity levels than women with normal BMD, there were no observable deficits in balance, strength, and gait speed. Addressing physical activity in women in early postmenopause and with low BMD (similar to the women in our sample) might serve as a preventative intervention to limit progression to frailty.

Clinical Relevance

Early detection of functional limitations and disability is vital to preventing frailty.58 A recently published consensus report recommended focusing on domains such as mobility, muscle strength, balance, endurance, fatigue, and physical activity in older adults who are likely at risk for frailty because these areas are strong, independent predictors of frailty.65 That report also emphasized that interventions to prevent frailty should target people most likely to benefit. Those who would likely benefit the most might be similar to the women in the present study who, although not exhibiting changes in physical performance measures, were beginning to demonstrate changes in the more-sensitive measure of gait variability.

As part of their role in health and wellness promotion, clinicians should ask women who are in early postmenopause (women in their mid-40s to mid-50s) and are being seen by physical therapists for examination or screening whether they have had a DXA scan and what the DXA scan results were. Although most clinicians do not have access to instrumented gait analysis systems, they nevertheless should be aware that women in early postmenopause and with low BMD might have subtle changes in gait variability that are not detected by more-typical measures of physical performance, such as balance, strength, and gait speed. Knowledge that potential subclinical changes could be occurring would allow physical therapists to provide their patients in early menopause with education about the benefits of regular physical activity that includes weight-bearing and resistance exercises66 to preserve physical function3,18,21,63 and to reduce the risk of injurious falls.37,43 Clinicians could use the results of a physical activity questionnaire as a starting point for patient education because patient education programs about self-management of osteoporosis have been reported to be successful.67 Physical activity could be assessed with standardized tools, such as the one used in the present study (Stanford 7-Day Physical Activity Recall Questionnaire), or tools that might be less time-consuming, such as the Rapid Assessment of Physical Activity68 and the International Physical Activity Questionnaire.69 These tools might be more useful in a clinical setting. Such programs would address prevention of the transition to frailty in women in late postmenopause and with low BMD while simultaneously addressing factors that lead to injurious falls in adults.

A dose-response effect of exercise on fracture risk has been reported in the literature, with higher activity levels being related to lower fracture risk.70,71 This finding has particular bearing in younger people with low BMD, who may be at risk for distal radius fractures. Distal radius fractures often are the initial symptom of low BMD.72 People who are 50 years of age and sustain a distal radius fracture have a 17% lifetime risk of hip fracture; the risk in the general population is 11%.73 These findings support physical therapist interventions that target frailty-related musculoskeletal changes with the aim of reducing injurious falls in women with low BMD.

Additionally, there is a lack of published literature about physical performance variables in women in early postmenopause. Although no between-group differences in measures of balance, strength, and gait speed were found in the present study, the physical performance data from this study can provide clinicians with initial values with which to compare measures from their own patients in early postmenopause.

Limitations

There were several limitations of the present study. The pilot study results indicated significant between-group differences in balance and strength. The study was powered with a medium effect size. Power was reanalyzed at the completion of data collection. Effect size was small for all variables and indicated that 175 women would have to be recruited to detect between-group differences. Data analysis showed that the pilot sample was different from the full-study sample for all variables for the low-BMD group and for mean quadriceps femoris muscle force output for the group with normal BMD. These differences might have resulted in underpowering of the present study. Replicating the study with a larger sample might demonstrate between-group differences in measures of physical performance. Additionally, a larger sample might have introduced greater variability in demographic variables, such as the number of years after menopause or age, which are mediators of bone density. The lack of variability in these variables inhibited the consideration of their impact on BMD and physical performance.

Another limitation was that leg length was not measured; therefore, step length was not adjusted for leg length. No significant between-group differences were found for height, but without adjustment for leg length. It is unknown whether the between-group differences for gait speed were larger or smaller than those in the unadjusted data.

A final limitation was the reliance on the results of the most recent DXA scan to categorize BMD status. Current guidelines for DXA scanning suggest testing every 2 years for people with osteopenia or osteoporosis and every 5 years for people with normal BMD.74–76 In an attempt to be more stringent, we required all participants to have had a DXA scan within the preceding 2 years. It would have been most accurate to test participants’ BMD just before group assignment because some women with normal BMD, as determined from their most recent DXA scan, might have been categorized in the low-BMD group or excluded when their BMD fell into the range of 1.1 to 1.9 standard deviations below the mean. However, the physical performance of the 2 groups was similar, so this limitation in methods might not have affected the final results of the present study.

Conclusion

The results of the present study demonstrated a tendency toward poorer physical performance and a tendency toward increased rates of falls and fractures in women in early postmenopause and with low BMD but no significant between-group differences. Significantly increased step time and stance time variability and a tendency toward increased step width variability were found. Gait variability might be more sensitive than more typical measures of physical performance for detecting differences in women in early postmenopause and with or without low BMD. The subtle changes in gait variability observed in the present study indicated a need for preventive physical therapist interventions to maintain and improve current physical functioning in women in early postmenopause and with low BMD, thus addressing the potential for the transition to frailty in this population. Research examining physical performance in a wider age range of women in postmenopause and with or without low BMD might help to identify the point at which the progression to frailty occurs and would provide a more complete description of the transition to frailty in women in early postmenopause and with low BMD.

Footnotes

  • Dr Palombaro, Dr Hack, Dr Mangione, Dr Barr, and Dr Newton provided concept/idea/research design and writing. Dr Palomaro provided data collection, project management, fund procurement, and participants. Dr Palombaro, Dr Hack, Dr Magri, and Dr Speziale provided data analysis. Dr Hack provided facilities/equipment. Dr Hack and Dr Newton provided institutional liaisons. Dr Hack, Dr Mangione, Dr Barr, and Dr Newton provided consultation (including review of manuscript before submission).

  • This study was conducted in partial fulfillment of Dr Palombaro's doctoral degree.

  • The institutional review boards for the protection of human subjects of Arcadia University, Widener University, and Temple University approved the examination procedures before data collection.

  • This study was funded, in part, by grants from the Pennsylvania Physical Therapy Association and the Section on Women's Health of the American Physical Therapy Association.

  • A poster containing the physical performance data was presented at the Pennsylvania Physical Therapy Association Annual Conference; October 26–28, 2007; Pittsburgh, Pennsylvania.

  • ↵* Health O Meter Inc, 11800 S Austin Ave, #B, Alsip, IL 60803.

  • ↵† Hogan Health Industries, 8020 South 1300 West, West Jordan, UT 84084.

  • ↵‡ E.Q. Inc, PO Box 16, Chalfont, PA 18914-0016.

  • ↵§ SPSS Inc, 233 S Wacker Dr, Chicago, IL 60606.

  • Received December 16, 2008.
  • Accepted August 3, 2009.
  • © 2009 American Physical Therapy Association

References

  1. 1.↵
    1. Bortz WM
    . A conceptual framework of frailty: a review. J Gerontol A Biol Sci Med Sci. 2002;57:283–288.
    OpenUrlCrossRef
  2. 2.↵
    1. Vanitallie TB
    . Frailty in the elderly: contributions of sarcopenia and visceral protein depletion. Metabolism. 2003;52:22–26.
    OpenUrlCrossRefPubMedWeb of Science
  3. 3.↵
    1. Muhlberg W,
    2. Sieber C
    . Sarcopenia and frailty in geriatric patients: implications for training and prevention. Z Gerontol Geriatr. 2004;37:2–8.
    OpenUrlCrossRefPubMed
  4. 4.↵
    1. Hardy SE,
    2. Dubin JA,
    3. Holford TR,
    4. Gill TM
    . Transitions between states of disability and independence among older persons. Am J Epidemiol. 2005;161:575–584.
    OpenUrlAbstract/FREE Full Text
  5. 5.↵
    1. Fried LP,
    2. Tangen CM,
    3. Walston J,
    4. et al
    . Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56:146–156.
    OpenUrlCrossRef
  6. 6.↵
    1. Gill TM,
    2. Gahbauer EA,
    3. Allore HG,
    4. Han L
    . Transitions between frailty states among community-living older persons. Arch Intern Med. 2006;166:418–423.
    OpenUrlCrossRefPubMedWeb of Science
  7. 7.↵
    1. National Osteoporosis Foundation
    . America's bone health: the state of osteoporosis and low bone mass in our nation (2005). Available at: http://www.nof.org/advocacy/prevalence/. Accessed December 7, 2008.
  8. 8.↵
    1. World Health Organization
    . Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: report of a WHO Study Group. World Health Organ Tech Rep Ser. 1994;843:1–129.
    OpenUrlPubMed
  9. 9.↵
    1. Kotz K,
    2. Deleger S,
    3. Cohen R,
    4. et al
    . Osteoporosis and health-related quality-of-life outcomes in the Alameda County study population. Prev Chronic Dis. 2004;1:1–9.
    OpenUrl
  10. 10.↵
    1. Rutherford OM,
    2. Jones DA
    . The relationship of muscle and bone loss and activity levels with age in women. Age Ageing. 1992;21:286–293.
    OpenUrlAbstract/FREE Full Text
  11. 11.↵
    1. Rantanen T,
    2. Guralnik JM,
    3. Ferrucci L,
    4. et al
    . Coimpairments as predictors of severe walking disability in older women. J Am Geriatr Soc. 2001;49:21–27.
    OpenUrlCrossRefPubMedWeb of Science
  12. 12.↵
    1. Lindsey C,
    2. Brownbill RA,
    3. Bohannon RA,
    4. Ilich JZ
    . Association of physical performance measures with bone mineral density in postmenopausal women. Arch Phys Med Rehabil. 2005;86:1102–1107.
    OpenUrlCrossRefPubMedWeb of Science
  13. 13.↵
    1. Brach JS,
    2. Berlin JE,
    3. VanSwearingen JM,
    4. et al
    . Too much or too little step width variability is associated with a fall history in older persons who walk at or near normal gait speed. J Neuroeng Rehabil. 2005;2:21.
    OpenUrlCrossRefPubMed
  14. 14.↵
    1. Maki BE
    . Gait changes in older adults: predictors of falls or indicators of fear. J Am Geriatr Soc. 1997;45:313–320.
    OpenUrlPubMedWeb of Science
  15. 15.↵
    1. Hausdorff JM,
    2. Rios DA,
    3. Edelberg HK
    . Gait variability and fall risk in community-living older adults: a 1-year prospective study. Arch Phys Med Rehabil. 2001;82:1050–1056.
    OpenUrlCrossRefPubMedWeb of Science
  16. 16.↵
    1. Kressig RW,
    2. Gregor RJ,
    3. Oliver A,
    4. et al
    . Temporal and spatial features of gait in older adults transitioning to frailty. Gait Posture. 2004;20:30–35.
    OpenUrlCrossRefPubMedWeb of Science
  17. 17.↵
    1. Lane JM,
    2. Russell L,
    3. Khan SN
    . Osteoporosis. Clin Orthop Relat Res. 2000:139–150.
  18. 18.↵
    1. Janssen I,
    2. Heymsfield SB,
    3. Ross R
    . Low relative skeletal muscle mass (sarcopenia) in older persons is associated with functional impairment and physical disability. J Am Geriatr Soc. 2002;50:889–896.
    OpenUrlCrossRefPubMedWeb of Science
  19. 19.↵
    1. Riggs BL,
    2. Melton LJ III.
    . The prevention and treatment of osteoporosis. N Engl J Med. 1992;327:620–627.
    OpenUrlCrossRefPubMedWeb of Science
  20. 20.↵
    1. Judge JO,
    2. Underwood M,
    3. Gennosa T
    . Exercise to improve gait velocity in older persons. Arch Phys Med Rehabil. 1993;74:400–406.
    OpenUrlPubMedWeb of Science
  21. 21.↵
    1. Rantanen T,
    2. Guralnik JM,
    3. Sakari-Rantala R,
    4. et al
    . Disability, physical activity, and muscle strength in older women: the Women's Health and Aging Study. Arch Phys Med Rehabil. 1999;80:130–135.
    OpenUrlCrossRefPubMedWeb of Science
  22. 22.↵
    1. Vetta F,
    2. Ronzoni S,
    3. Taglieri G,
    4. Bollea MR
    . The impact of malnutrition on the quality of life in the elderly. Clin Nutr. 1999;18:259–267.
    OpenUrlCrossRefPubMedWeb of Science
  23. 23.↵
    1. Dutta C
    . Significance of sarcopenia in the elderly. J Nutr. 1997;127:992S–993S.
    OpenUrlWeb of Science
  24. 24.↵
    1. Stone KL,
    2. Seeley DG,
    3. Lui LY,
    4. et al
    . BMD at multiple sites and risk of fracture of multiple types: long-term results from the study of osteoporotic fractures. J Bone Miner Res. 2003;18:1947–1954.
    OpenUrlCrossRefPubMedWeb of Science
  25. 25.↵
    1. Siris ES,
    2. Chen YT,
    3. Abbott TA,
    4. et al
    . Bone mineral density thresholds for pharmacological intervention to prevent fractures. Arch Intern Med. 2004;164:1108–1112.
    OpenUrlCrossRefPubMedWeb of Science
  26. 6.↵
    1. Cohen J
    . Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale, NJ: Erlbaum Associates ; 1988.
  27. 27.↵
    1. Greendale GA,
    2. Lee NP,
    3. Arriola ER
    . The menopause. Lancet. 1999;353:571–580.
    OpenUrlCrossRefPubMedWeb of Science
  28. 28.↵
    1. Johnell O
    . Advances in osteoporosis: better identification of risk factors can reduce morbidity and mortality. J Intern Med. 1996;239:299–304.
    OpenUrlCrossRefPubMedWeb of Science
  29. 29.↵
    1. Heaney RP
    . Bone mass, nutrition, and other lifestyle factors. Am J Med. 1993;95:29S–33S.
    OpenUrlCrossRefPubMed
  30. 30.↵
    1. Dhesi JK,
    2. Moniz C,
    3. Close JC,
    4. et al
    . A rationale for vitamin D prescribing in a falls clinic population. Age Ageing. 2002;31:267–271.
    OpenUrlAbstract/FREE Full Text
  31. 31.↵
    1. Bischoff-Ferrari HA,
    2. Borchers M,
    3. Gudat F,
    4. et al
    . Vitamin D receptor expression in human muscle tissue decreases with age. J Bone Miner Res. 2004;19:265–269.
    OpenUrlCrossRefPubMedWeb of Science
  32. 32.↵
    1. Montero-Odasso M,
    2. Duque G
    . Vitamin D in the aging musculoskeletal system: an authentic strength preserving hormone. Mol Aspects Med. 2005;26:203–219.
    OpenUrlCrossRefPubMed
  33. 33.↵
    1. Chapuy MC,
    2. Arlot ME,
    3. Duboeuf F,
    4. et al
    . Vitamin D3 and calcium to prevent hip fractures in the elderly women. N Engl J Med. 1992;327:1637–1642.
    OpenUrlCrossRefPubMedWeb of Science
  34. 34.↵
    1. Dawson-Hughes B,
    2. Harris SS,
    3. Krall EA,
    4. Dallal GE
    . Effect of calcium and vitamin D supplementation on bone density in men and women 65 years of age or older. N Engl J Med. 1997;337:670–676.
    OpenUrlCrossRefPubMedWeb of Science
  35. 35.↵
    1. Aloia JF,
    2. McGowan DM,
    3. Vaswani AN,
    4. et al
    . Relationship of menopause to skeletal and muscle mass. Am J Clin Nutr. 1991;53:1378–1383.
    OpenUrlAbstract/FREE Full Text
  36. 36.↵
    1. Sallis JF,
    2. Haskell WL,
    3. Wood PD,
    4. et al
    . Physical activity assessment methodology in the Five-City Project. Am J Epidemiol. 1985;121:91–106.
    OpenUrlAbstract/FREE Full Text
  37. 37.↵
    1. Nelson ME,
    2. Fiatarone MA,
    3. Morganti CM,
    4. et al
    . Effects of high-intensity strength training on multiple risk factors for osteoporotic fractures: a randomized controlled trial. JAMA. 1994;272:1909–1914.
    OpenUrlCrossRefPubMedWeb of Science
  38. 38.↵
    1. Kaneko M,
    2. Morimoto Y,
    3. Kimura M,
    4. et al
    . A kinematic analysis of walking and physical fitness testing in elderly women. Can J Sport Sci. 1991;16:223–228.
    OpenUrlPubMedWeb of Science
  39. 39.↵
    1. Malmberg JJ,
    2. Miilunpalo SI,
    3. Vuori IM,
    4. et al
    . A health-related fitness and functional performance test battery for middle-aged and older adults: feasibility and health-related content validity. Arch Phys Med Rehabil. 2002;83:666–677.
    OpenUrlCrossRefPubMedWeb of Science
  40. 40.↵
    1. Kemmler W,
    2. Weineck J,
    3. Kalender WA,
    4. Engelke K
    . The effect of habitual physical activity, non-athletic exercise, muscle strength, and VO2max on bone mineral density is rather low in early postmenopausal osteopenic women. J Musculoskelet Neuronal Interact. 2004;4:325–334.
    OpenUrlPubMed
  41. 41.↵
    1. Nichols JF,
    2. Omizo DK,
    3. Peterson KK,
    4. Nelson KP
    . Efficacy of heavy-resistance training for active women over sixty: muscular strength, body composition, and program adherence. J Am Geriatr Soc. 1993;41:205–210.
    OpenUrlPubMedWeb of Science
  42. 42.↵
    1. Carter ND,
    2. Khan KM,
    3. Mallinson A,
    4. et al
    . Knee extension strength is a significant determinant of static and dynamic balance as well as quality of life in older community-dwelling women with osteoporosis. Gerontology. 2002;48:360–368.
    OpenUrlCrossRefPubMedWeb of Science
  43. 43.↵
    1. Carter ND,
    2. Khan KM,
    3. McKay HA,
    4. et al
    . Community-based exercise program reduces risk factors for falls in 65- to 75-year-old women with osteoporosis: randomized controlled trial. CMAJ. 2002;167:997–1004.
    OpenUrlAbstract/FREE Full Text
  44. 44.↵
    1. Liu-Ambrose T,
    2. Eng JJ,
    3. Khan KM,
    4. et al
    . Older women with osteoporosis have increased postural sway and weaker quadriceps strength than counterparts with normal bone mass: overlooked determinants of fracture risk? J Gerontol A Biol Sci Med Sci. 2003;58:862–866.
    OpenUrlWeb of Science
  45. 45.↵
    1. Liu-Ambrose T,
    2. Eng JJ,
    3. Khan KM,
    4. et al
    . The influence of back pain on balance and functional mobility in 65- to 75-year-old women with osteoporosis. Osteoporos Int. 2002;13:868–873.
    OpenUrlCrossRefPubMedWeb of Science
  46. 46.↵
    1. Richardson MT,
    2. Ainsworth BE,
    3. Jacobs DR,
    4. Leon AS
    . Validation of the Stanford 7-day recall to assess habitual physical activity. Ann Epidemiol. 2001;11:145–153.
    OpenUrlCrossRefPubMedWeb of Science
  47. 47.↵
    1. Robinett CS,
    2. Vondran MA
    . Functional ambulation velocity and distance requirements in rural and urban communities: a clinical report. Phys Ther. 1988;68:1371–1373.
    OpenUrlAbstract/FREE Full Text
  48. 48.↵
    1. Arnold CM,
    2. Busch AJ,
    3. Schachter CL,
    4. et al
    . The relationship of intrinsic fall risk factors to a recent history of falling in older women with osteoporosis. J Orthop Sports Phys Ther. 2005;35:452–460.
    OpenUrlPubMedWeb of Science
  49. 49.↵
    1. Topp R,
    2. Mikesky A,
    3. Wigglesworth J,
    4. et al
    . The effect of a 12-week dynamic resistance strength training program on gait velocity and balance of older adults. Gerontologist. 1993;33:501–506.
    OpenUrlAbstract/FREE Full Text
  50. 50.↵
    1. Steffen TM,
    2. Hacker TA,
    3. Mollinger L
    . Age- and gender-related test performance in community-dwelling elderly people: Six-Minute Walk Test, Berg Balance Scale, Timed “Up & Go” Test, and gait speeds. Phys Ther. 2002;82:128–137.
    OpenUrlAbstract/FREE Full Text
  51. 51.↵
    1. Bohannon RW
    . Reference values for extremity muscle strength obtained by hand-held dynamometry from adults aged 20 to 79 years. Arch Phys Med Rehabil. 1997;78:26–32.
    OpenUrlCrossRefPubMedWeb of Science
  52. 52.↵
    1. Mangione KK,
    2. Palombaro KM
    . Exercise prescription for a patient 3 months after hip fracture. Phys Ther. 2005;85:676–687.
    OpenUrlAbstract/FREE Full Text
  53. 53.↵
    1. Wadsworth CT,
    2. Krishnan R,
    3. Sear M,
    4. et al
    . Intrarater reliability of manual muscle testing and hand-held dynametric muscle testing. Phys Ther. 1987;67:1342–1347.
    OpenUrlAbstract/FREE Full Text
  54. 54.↵
    1. Barker S,
    2. Craik R,
    3. Freedman W,
    4. et al
    . Accuracy, reliability, and validity of a spatiotemporal gait analysis system. Med Eng Phys. 2006;28:460–467.
    OpenUrlCrossRefPubMedWeb of Science
  55. 55.↵
    1. Lamb SE,
    2. Jorstad-Stein EC,
    3. Hauer K,
    4. et al
    . Development of a common outcome data set for fall injury prevention trials: the Prevention of Falls Network Europe consensus. J Am Geriatr Soc. 2005;53:1618–1622.
    OpenUrlCrossRefPubMedWeb of Science
  56. 56.↵
    1. Tinetti ME,
    2. Speechley M,
    3. Ginter SF
    . Risk factors for falls among elderly persons living in the community. N Engl J Med. 1988;319:1701–1707.
    OpenUrlCrossRefPubMedWeb of Science
  57. 57.↵
    1. Cranney A,
    2. Jamal SA,
    3. Tsang JF,
    4. et al
    . Low bone mineral density and fracture burden in postmenopausal women. CMAJ. 2007;177:575–580.
    OpenUrlAbstract/FREE Full Text
  58. 58.↵
    1. Wolinsky FD,
    2. Miller DK,
    3. Andresen EM,
    4. et al
    . Further evidence for the importance of subclinical functional limitation and subclinical disability assessment in gerontology and geriatrics. J Gerontol B Psychol Sci Soc Sci. 2005;60:S146–S151.
    OpenUrlAbstract/FREE Full Text
  59. 59.↵
    1. Fried LP,
    2. Bandeen-Roche K,
    3. Chaves PH,
    4. Johnson BA
    . Preclinical mobility disability predicts incident mobility disability in older women. J Gerontol A Biol Sci Med Sci. 2000;55:43–52.
    OpenUrl
  60. 60.↵
    1. Carriere I,
    2. Colvez A,
    3. Favier F,
    4. et al
    . Hierarchical components of physical frailty predicted incidence of dependency in a cohort of elderly women. J Clin Epidemiol. 2005;58:1180–1187.
    OpenUrlCrossRefPubMedWeb of Science
  61. 61.↵
    1. Visser M,
    2. Pluijm SM,
    3. Stel VS,
    4. et al
    . Physical activity as a determinant of change in mobility performance: the Longitudinal Aging Study Amsterdam. J Am Geriatr Soc. 2002;50:1774–1781.
    OpenUrlCrossRefPubMedWeb of Science
  62. 62.↵
    1. Visser M,
    2. Kritchevsky SB,
    3. Goodpaster BH,
    4. et al
    . Leg muscle mass and composition in relation to lower extremity performance in men and women aged 70 to 79: the Health, Aging and Body Composition Study. J Am Geriatr Soc. 2002;50:897–904.
    OpenUrlCrossRefPubMedWeb of Science
  63. 63.↵
    1. Binder EF,
    2. Schechtman KB,
    3. Ehsani AA,
    4. et al
    . Effects of exercise training on frailty in community-dwelling older adults: results of a randomized, controlled trial. J Am Geriatr Soc. 2002;50:1921–1928.
    OpenUrlCrossRefPubMedWeb of Science
  64. 64.↵
    1. Frontera WR,
    2. Hughes VA,
    3. Lutz KJ,
    4. Evans WJ
    . A cross-sectional study of muscle strength and mass in 45- to 78-yr-old men and women. J Appl Physiol. 1991;71:644–650.
    OpenUrlAbstract/FREE Full Text
  65. 65.↵
    1. Ferrucci L,
    2. Guralnik JM,
    3. Studenski S,
    4. et al
    . Designing randomized, controlled trials aimed at preventing or delaying functional decline and disability in frail, older persons: a consensus report. J Am Geriatr Soc. 2004;52:625–634.
    OpenUrlCrossRefPubMedWeb of Science
  66. 66.↵
    1. Kemmler W,
    2. Engelke K,
    3. Lauber D,
    4. et al
    . Exercise effects on fitness and bone mineral density in early postmenopausal women: 1-year EFOPS results. Med Sci Sports Exerc. 2002;34:2115–2123.
    OpenUrlPubMedWeb of Science
  67. 67.↵
    1. Kanat AA,
    2. Yurtkuran M
    . Efficacy of a self-management program for osteoporotic subjects. Am J Phys Med Rehabil. 2007;86:633–640.
    OpenUrlCrossRefPubMedWeb of Science
  68. 68.↵
    1. Topolski TD,
    2. LoGerfo J,
    3. Patrick DL,
    4. et al
    . The Rapid Assessment of Physical Activity (RAPA) among older adults. Prev Chronic Dis. 2006;3:A118.
    OpenUrlPubMed
  69. 69.↵
    1. Craig CL,
    2. Marshall AL,
    3. Sjöström M,
    4. et al
    . International Physical Activity Questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35:1381–1395.
    OpenUrlCrossRefPubMedWeb of Science
  70. 70.↵
    1. Gregg EW,
    2. Cauley JA,
    3. Seeley DG,
    4. et al
    . Physical activity and osteoporotic fracture risk in older women. Ann Intern Med. 1998;129:81–88.
    OpenUrlCrossRefPubMedWeb of Science
  71. 71.↵
    1. Paganini-Hill A,
    2. Chao A,
    3. Ross RK,
    4. Henderson BE
    . Exercise and other factors in the prevention of hip fracture: the Leisure World study. Epidemiology. 1991;2:16–25.
    OpenUrlPubMed
  72. 72.↵
    1. Hegeman JH,
    2. Oskam J,
    3. van der Palen J,
    4. et al
    . The distal radial fracture in elderly women and the bone mineral density of the lumbar spine and hip. J Hand Surg Br. 2004;29:473–476.
    OpenUrlAbstract/FREE Full Text
  73. 73.↵
    1. Lauritzen JB,
    2. Schwarz P,
    3. McNair P,
    4. et al
    . Radial and humeral fractures as predictors of subsequent hip, radial or humeral fractures in women, and their seasonal variation. Osteoporos Int. 1993;3:133–137.
    OpenUrlCrossRefPubMedWeb of Science
  74. 74.↵
    1. Heinemann DF
    . Osteoporosis: an overview of the National Osteoporosis Foundation clinical practice guide. Geriatrics. 2000;55:31–36.
    OpenUrl
  75. 75.↵
    1. Nelson HD,
    2. Helfand M,
    3. Woolf SH,
    4. Allan JD
    . Screening for postmenopausal osteoporosis: a review of the evidence for the U.S. Preventive Services Task Force. Ann Intern Med. 2002;137:529–541.
    OpenUrlPubMedWeb of Science
  76. 76.↵
    1. Brown JP,
    2. Josse RG,
    3. et al
    . 2002 clinical practice guidelines for the diagnosis and management of osteoporosis in Canada. CMAJ. 2002;167(10 suppl):S1–S34.
    OpenUrlPubMed
  77. 77.↵
    1. Craik RL,
    2. Oatis CA
    1. Craik RL,
    2. Dutterer L
    . Spatial and temporal characteristics of foot fall patterns. In: Craik RL, Oatis CA , eds. Gait Analysis: Theory and Application. St Louis, MO: Mosby ;1995:143–158.
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Vol 96 Issue 12 Table of Contents
Physical Therapy: 96 (12)

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  • myMoves Program: Feasibility and Acceptability Study of a Remotely Delivered Self-Management Program for Increasing Physical Activity Among Adults With Acquired Brain Injury Living in the Community
  • Application of Intervention Mapping to the Development of a Complex Physical Therapist Intervention
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Gait Variability Detects Women in Early Postmenopause With Low Bone Mineral Density
Kerstin M. Palombaro, Laurita M. Hack, Kathleen Kline Mangione, Ann E. Barr, Roberta A. Newton, Francesca Magri, Theresa Speziale
Physical Therapy Dec 2009, 89 (12) 1315-1326; DOI: 10.2522/ptj.20080401

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Gait Variability Detects Women in Early Postmenopause With Low Bone Mineral Density
Kerstin M. Palombaro, Laurita M. Hack, Kathleen Kline Mangione, Ann E. Barr, Roberta A. Newton, Francesca Magri, Theresa Speziale
Physical Therapy Dec 2009, 89 (12) 1315-1326; DOI: 10.2522/ptj.20080401
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