Associations Among Walking Performance, Physical Activity, and Subclinical Cardiovascular Disease

Authors

  • Kelley K. Pettee PhD,

    1. From the Department of Exercise and Wellness, Arizona State University, Mesa, AZ;1 and the Departments of Epidemiology2 and Medicine,3 University of Pittsburgh, Pittsburgh, PA
    Search for more papers by this author
  • 1,2 Beth M. Larouere PhD,

    1. From the Department of Exercise and Wellness, Arizona State University, Mesa, AZ;1 and the Departments of Epidemiology2 and Medicine,3 University of Pittsburgh, Pittsburgh, PA
    Search for more papers by this author
  • 2 Andrea M. Kriska PhD,

    1. From the Department of Exercise and Wellness, Arizona State University, Mesa, AZ;1 and the Departments of Epidemiology2 and Medicine,3 University of Pittsburgh, Pittsburgh, PA
    Search for more papers by this author
  • 2 B. Delia Johnson PhD,

    1. From the Department of Exercise and Wellness, Arizona State University, Mesa, AZ;1 and the Departments of Epidemiology2 and Medicine,3 University of Pittsburgh, Pittsburgh, PA
    Search for more papers by this author
  • 2 Trevor J. Orchard MD,

    1. From the Department of Exercise and Wellness, Arizona State University, Mesa, AZ;1 and the Departments of Epidemiology2 and Medicine,3 University of Pittsburgh, Pittsburgh, PA
    Search for more papers by this author
  • 2 Bret H. Goodpaster PhD,

    1. From the Department of Exercise and Wellness, Arizona State University, Mesa, AZ;1 and the Departments of Epidemiology2 and Medicine,3 University of Pittsburgh, Pittsburgh, PA
    Search for more papers by this author
  • 3 Molly B. Conroy MD,

    1. From the Department of Exercise and Wellness, Arizona State University, Mesa, AZ;1 and the Departments of Epidemiology2 and Medicine,3 University of Pittsburgh, Pittsburgh, PA
    Search for more papers by this author
  • 2,3 Rachel H. Mackey PhD,

    1. From the Department of Exercise and Wellness, Arizona State University, Mesa, AZ;1 and the Departments of Epidemiology2 and Medicine,3 University of Pittsburgh, Pittsburgh, PA
    Search for more papers by this author
  • 2 Darcy A. Underwood BS,

    1. From the Department of Exercise and Wellness, Arizona State University, Mesa, AZ;1 and the Departments of Epidemiology2 and Medicine,3 University of Pittsburgh, Pittsburgh, PA
    Search for more papers by this author
  • and 2 Lewis H. Kuller MD 2

    1. From the Department of Exercise and Wellness, Arizona State University, Mesa, AZ;1 and the Departments of Epidemiology2 and Medicine,3 University of Pittsburgh, Pittsburgh, PA
    Search for more papers by this author

Kelley K. Pettee, PhD, Department of Exercise and Wellness, Arizona State University, 7350 East Unity Drive, Mesa, AZ 85212
E-mail: kelley.pettee@asu.edu

Abstract

An objective measure of walking performance could have several potential applications in clinical settings. The cross-sectional relationships among long distance corridor walk (LDCW) time, physical activity, and subclinical cardiovascular disease (CVD) measures were examined before group randomization in 492 participants (mean age, 57.0±2.9 years) from the Women On the Move Through Activity and Nutrition (WOMAN) study, a randomized clinical trial involving postmenopausal women. Longer walk times were significantly associated with higher body mass index (P<0001), average waist circumference (P<0001), and lower levels of physical activity (P<002). The proportion of detectable coronary artery calcification and median aortic pulse wave velocity levels were significantly higher among those with slower walk times (P<002 and P<.001, respectively). Findings from the current report support the utility of the LDCW to identify women at higher risk for CVD who may be candidates for further cardiovascular testing or intensive lifestyle intervention.

Walking endurance protocols, such as the 6-minute walk test (6MWT) and the long distance corridor walk (LDCW), were developed and have been primarily used in diseased or older populations as a measure of cardiovascular health and functional status. The 6MWT has primarily been used to estimate health status in populations with debilitating conditions such as chronic obstructive lung disease, heart failure, and left ventricular dysfunction,1–3 but it has also been used to estimate functional status in older adults.4 In contrast to the 6MWT, the LDCW has been utilized in well-functioning, healthy older individuals. Results from a cross-sectional investigation suggested that walking performance was influenced by both clinical and subclinical disease and was strongly related to anthropometric measures and self-reported physical activity levels.5 The usefulness of the LDCW as a general indicator of overall health in older adults was confirmed in the follow-up longitudinal investigation, which found that exclusion or inability to complete the protocol was associated with an increased risk of mortality, incident cardiovascular disease (CVD), and mobility limitation.6 Among those who completed the protocol, each additional minute of walk time was associated with a 29% higher rate of mortality, a 20% higher rate of incident CVD, and 52% higher rates of mobility limitation and disability.6 These findings suggest that the LDCW is useful to estimate risk of mortality and/or disease among older adults.

Currently, there are no standardized methods for measuring physical activity in general clinical populations at risk for CVD and other chronic conditions. Brief lifestyle-habits surveys used in some clinical settings may provide poor estimates of physical activity, and in many clinical settings, no information is gathered.7 Using an objective measure such as the LDCW in a general clinical setting could have several potential applications such as identifying patients at higher risk for CVD and other chronic conditions, selecting patients for further CVD testing or risk reduction interventions, and monitoring the progress of patients who are involved in a lifestyle intervention program.

The primary purpose of the current report was to determine the utility of the LDCW to estimate the general health status in middle-aged, postmenopausal women by evaluating concurrent relationships between walk time from the LDCW against subjective and objective physical activity assessments and measures of subclinical CVD.

METHODS

Study Population

A total of 508 postmenopausal women were recruited for the Women On the Move Through Activity and Nutrition (WOMAN) study, primarily through direct mailing from selected zip codes in Allegheny County, Pennsylvania, from April 2002 to October 2003. The WOMAN study is a 5-year primary CVD prevention randomized clinical trial, designed to test whether an aggressive nonpharmacologic lifestyle intervention will reduce measures of sub-clinical CVD. Eligibility criteria for enrollment into the study included waist circumference (WC) ≥80 cm, body mass index (BMI) 25 to 39.9 kg/m2, and a low-density lipoprotein cholesterol (LDL-C) level 100 to 160 mg/dL as well as no physical limitations that would preclude walking, no known diabetes, no diagnosed psychotic disorder or depression, and not currently taking lipid-lowering drugs. All participants provided written informed consent and all protocols were approved by the institutional review board at the University of Pittsburgh. Results were based on data collected before randomization at the baseline clinic visit (2002–2003).

Demographics and Clinical Measures

Demographic factors and clinical measures were obtained at the baseline clinic visit. Clinical measures included height, body weight, average WC, and a fasting (12-hour) blood draw. BMI was calculated from height and weight measured with a stadiometer and calibrated balance beam scale by dividing the participant's weight in kilograms by the square of her height in meters. Average WC was measured at the navel (horizontal plane at the center of the navel) using a fiberglass retractable tape measure. To obtain fasting lipid measures, 40 cc of blood were drawn from the antecubital fossa. Levels of total cholesterol, high-density lipoprotein cholesterol, triglycerides, and glucose were determined by conventional methods. LDL-C was estimated by the Friedewald equation and insulin was measured via radioimmunoassay.

Long Distance Corridor Walk

The LDCW was a 400-m course that consisted of 10 laps along a hallway (40 m per lap) with cones set 20 m apart. Participants were instructed to walk “at a pace that [they] could maintain for the full 10 laps” and standard encouragement was given at each lap. Heart rate (HR) was monitored with a Polar Pacer heart rate monitor (Model 60905; Polar USA, Woodbury, NY). Participants with elevated blood pressure (≥200/110 mm Hg) or resting HR (>110 or <40 beats per minute [bpm]) or who reported exacerbation of chest pain, shortness of breath, or cardiac event or procedure within the past 3 months were excluded for safety reasons. The LDCW was stopped if the participant's HR exceeded 135 bpm or if a participant reported chest pain or dyspnea during the test. HR was recorded in the seated position before the walk and standing at test completion and following 2 minutes of recovery. Systolic blood pressure (SBP) was measured while seated before the walk and standing at test completion. Based on these measures, cardiovascular response variables were generated. HR and SBP response were calculated by subtracting the resting values from those obtained immediately following completion. HR recovery was calculated by subtracting the HR obtained 2 minutes after LDCW completion from the value measured at completion. On average, the entire LDCW protocol takes approximately 8 to 10 minutes to administer.

Physical Activity Measures

Physical activity levels were assessed using both subjective and objective measurement tools. The Modifiable Activity Questionnaire (MAQ) is an interviewer-administered questionnaire that assesses current leisure and occupational activities over the past year.8 Physical activity levels were calculated as the product of the duration and frequency of each activity (in hours per week), weighted by an estimate of the metabolic equivalent of that activity and summed for all activities performed; data were expressed as metabolic equivalent hours per week (MET h/wk). The MAQ has been shown to be both reliable8,9 and valid.8–10 Leisure physical activity was also measured using a past-week version of the MAQ to obtain an acute estimate of physical activity levels. Study participants were asked to record leisure activities performed during the 7 days before the clinic assessment. The past-week estimate was calculated similar to that used for the past-year version.

Objective assessments of physical activity were obtained with the Accusplit Eagle AE120 pedometer (Accusplit, San Jose, CA) in a random subgroup of WOMAN study participants (n=170; 33.5%). The participants were instructed to wear the pedometer clipped to their waistbands over the dominant hip for 1 week. Participants were provided with an activity diary and were asked to record the time the pedometer was put on in the morning and at the end of the day, the time that the monitor was taken off, and the number of steps taken. At the end of the week, the participant returned the activity diary to the investigator. The 7 daily number of steps recorded in the diary from the pedometer were averaged for the week. Total walk time in women who did not have pedometer data was not statistically different from that of women with pedometer data. The pedometer has been widely demonstrated to be a reliable and valid measure of physical activity.11–17

Subclinical CVD Measures

Coronary Artery Calcification. Electron beam tomography using the GE Imatron C-150 scanner (GE Imatron, San Francisco, CA) was used to obtain 30 to 40 contiguous 3-mm-thick images of the heart, as described previously.18 Coronary artery calcification (CAC) scores were calculated according to the Agatston method.

Intima-Media Thickness. Detailed B-mode images of the right and left common carotid artery, carotid bifurcation, and the first 1.5 cm of the internal carotid artery were obtained using a Toshiba SSA-270A ultrasound scanner (Toshiba America, Inc, New York, NY) equipped with a 5-MHz linear array imaging probe. To measure the average intima-media thickness (IMT) of each segment, lines were electronically drawn along 1-cm increments of the lumen-intima interface and the media-adventitia interface of the near and far walls of the distal common carotid artery and along the far walls of the carotid bulb and internal carotid artery. The average of these was recorded for each location. The mean of all average readings across the 8 locations (4 on each side) was calculated.

Aortic Pulse Wave Velocity. Stiffness of the aorta was measured using carotid-femoral (or aortic) pulse wave velocity (aPWV), as described previously in detail.19,20 Briefly, for each participant, 20 seconds of carotid and femoral pulse waveforms were collected simultaneously with nondirectional Doppler probes and recorded to data files. Files were “scored” using software developed by the Laboratory of Cardiovascular Science, Gerontology Research Center, National Institute on Aging. aPWV was calculated by dividing the distance traveled by the time differential between the carotid and femoral waveforms and was expressed as distance/transit time (cm/s).

Statistical Methods

Univariate analyses were conducted on measured parameters from the LDCW, demographics, physical activity levels, and measures of subclinical CVD. Normally distributed variables were reported as mean ± SD, nonnormally distributed variables were reported as median with 25th and 75th percentile, and proportions were noted for categoric variables. Descriptive statistics were used to illustrate demographics, physical activity levels, and CVD risk factors across quartiles of LDCW time. Analysis of variance was used to compare normally distributed continuous data, Kruskal-Wallis test was used to evaluate nonnormal continuous data, and chi-square test of proportions was used to compare frequency data across quartiles of LDCW time in those who completed the 400-m walk. The Jonckheere-Terpstra test was used to estimate the linear trend in continuous variables and Cochran-Armitage trend test for categoric variables. Spearman rank order correlation coefficients were used to assess the bivariate associations between walk time and cardiovascular response measures from the LDCW, demographics, physical activity levels, and CVD risk factors. Partial correlations were used to further examine these relationships adjusted for age and age and BMI. Due to the high number of planned comparisons across walk time quartiles, a P value of <.002 was used to denote statistical significance using the Bonferroni correction method.

RESULTS

Of the 508 women, 3 had missing walk times, 1 had an invalid walk time, and 1 woman did not complete the 10 laps because of elevated HR. Of those 503 (99%) women who completed the entire protocol, 11 (2%) had incomplete physical activity and/or CAC data and were excluded from the present analyses (8 of the 11 were missing past-year leisure physical activity, 2 were missing past-week leisure physical activity, and 1 woman had a missing CAC score). The current report focused on the remaining 492 WOMAN study participants with complete data.

At baseline, the mean age of the participants was 57.0 years (range, 52–62.8 years), 11.8% were African American, and 59.4% (n=292) reported taking hormone therapy. Study participants had a mean BMI of 30.8 kg/m2 (range, 23.3–41.3 kg/m2) and WC of 106.0 cm (range, 80.8–138.5 cm). Only 6.1% of women reported being current smokers at baseline. With regard to the measured parameters from the LDCW, on average the WOMAN study participants completed the walk in 301.2 s (range, 211.2–454.2 s). Mean HR and SBP increased from resting by 36.8 bpm and 4.3 mm Hg, respectively (range, −15 to 78 bpm and −32 to 52 bpm, respectively). At the end of the 2-minute recovery period, HR decreased by 20.4 bpm (range, −28 to 52 bpm), which was approximately 55% of the HR response (Table I.)

Table I.  Baseline Characteristics of the WOMAN Study Cohort (n=492)
Corridor Walk Variables
Corridor walk time, s301.2 (37.5)
HR at end of LDCW, bpm107.0 (12.5)
SBP at end of LDCW, mm Hg128.5 (18.0)
2-Minute recovery HR, bpm86.6 (13.0)
SBP response, mm Hg4.3 (13.1)
HR response, bpm36.8 (12.0)
2-Minute HR recovery, bpm20.4 (10.2)
Demographics
Age, y57.0 (2.9)
BMI, kg/m230.8 (3.8)
Average WC, cm106.0 (11.3)
African American, %11.8
High school education, %98.6
Hormone therapy use, %59.4
Current smokers, %6.1
Physical Activity Measures
Past-year leisure activity, MET h/wk11.5 (5.2, 20.5)
Past-week leisure activity, MET h/wk11.2 (5.3, 19.3)
Pedometer, steps/d (n=170)6446.9 (4801.4, 8721.6)
CVD Risk Factors
Resting HR, bpm70.2 (9.2)
SBP, mm Hg124.2 (16.3)
DBP, mm Hg76.8 (8.3)
Total cholesterol, mg/dL216.4 (28.1)
LDL-C, mg/dL128.0 (25.1)
HDL-C, mg/dL60.0 (14.2)
Triglycerides142.1 (74.3)
Insulin, mg/dL (n=432)13.6 (6.7)
Glucose, mg/dL (n=486)95.4 (9.3)
Coronary Artery Calcification
0, %49
0–10, %32.1
11–100, %13.6
≥101, %5.3
IMT (n=485)0.71 (0.65, 0.76)
Pulse wave velocity, cm/s (n=467)846.2 (737.6, 988.8)
Normally distributed variables presented as mean (SD); non-normally distributed variables presented as median (25th, 75th percentile). Abbreviations: BMI, body mass index; CVD, cardiovascular disease; DBP, diastolic blood pressure; HDL-C, high-density lipoprotein cholesterol; HR, heart rate; IMT, intima-media thickness; LDCW, long distance corridor walk; LDL-C, low-density lipoprotein cholesterol; MET h/wk, metabolic equivalent hours per week; SBP, systolic blood pressure; WC, waist circumference; WOMAN, Women On the Move Through Activity and Nutrition.

Table II describes the characteristics of the study population across quartiles of LDCW time. With regard to demographic factors, mean BMI and WC significantly increased as walk time increased. Also, the proportion of African American women was significantly higher in the slower walk time categories. With regard to the subjective physical activity measures, median physical activity levels significantly decreased across increasing walk time quartiles. The inverse relationship was validated with pedometer step count data (P<.003). The SBP and HR response to the LDCW and HR recovery decreased as mean walk time increased (SBP response, P<.004). Resting HR and insulin and glucose levels increased as time to complete the 400-m walk increased. With regard to subclinical CVD measures, the proportion of detectable CAC (ie, CAC score >0) was significantly higher among those with slower walk times. Median aPWV levels also significantly increased across increasing walk time quartiles. The association between walk time and IMT was not statistically significant.

Table II.  Characteristics of Study Participants by Quartiles of Walk Time (n=492)
 Quartile 1 <267.6 s (n=120)Quartile 2 ≥267.6–<305.4 s (n=123)Quartile 3 ≥305.4–<319.2 s (n=124)Quartile 4 ≥319.2 s (n=125)P ValueP Value for Linear Trend
Demographics
Age, y56.6 (2.8)56.6 (3.0)57.3 (2.9)57.3 (2.8).04a.01e
African American, %7.54.115.320.0003b.0002c
Hormone therapy users, %43.331.742.744.8.14b.42c
BMI, kg/m229.3 (3.3)30.1 (3.2)30.7 (3.6)32.9 (4.1)<.0001a<.0001e
Average WC, cm102.4 (10.5)104.1 (10.0)106.7 (10.8)110.5 (12.2)<.0001a<.0001e
Physical Activity (PA)
Past-year leisure PA, MET h/wk15.7 (7.5, 27.0)11.6 (5.5, 23.2)10.6 (4.2, 17.5)8.7 (4.0, 16.4)<.0001d<.0001e
Past-week leisure PA, MET h/wk13.3 (7.7, 22.2)11.7 (6.4, 22.6)9.8 (5.0, 16.7)9.9 (3.0, 15.5).01d.0007e
Pedometer, steps/d7285.2 (5670.7, 10427.6), n=366523.1 (5112.1, 8686.7), n=497132.5 (4864.6, 8426.4), n=425482.3 (4204.3, 6581.4), n=43.02d.003e
Cardiovascular Response
SBP response, mm Hg6.4 (13.6)4.6 (11.7)4.0 (12.7)2.5 (14.0).14a.0041e
HR response, bpm42.6 (12.0)37.3 (10.5)34.6 (10.9)32.9 (12.4)<.0001a<.0001e
HR recovery, bpm27.1 (9.6)21.0 (9.8)18.2 (8.8)15.6 (9.0)<.0001a<.0001e
Cardiovascular Disease Risk Factors
HR, bpm68.1 (9.3)69.8 (8.3)71.3 (9.5)71.6 (9.3).01a.0005e
SBP, mm Hg122.7 (13.2)123.8 (13.8)123.7 (13.5)126.6 (13.8).14a.05e
Diastolic BP, mm Hg77.1 (8.0)76.1 (8.7)76.7 (7.7)77.2 (8.8).69a.76e
Total cholesterol, mg/dL213.1 (24.2)215.5 (26.7)218.5 (29.4)218.5 (31.6).36a.08e
LDL-C, mg/dL124.3 (21.7)127.7 (24.3)129.1 (26.2)130.9 (27.8).22a.0510e
HDL-C, mg/dL60.9 (13.8)60.1 (13.7)61.4 (14.5)57.6 (14.7).15a.06e
Triglycerides137.2 (75.5)140.6 (79.2)139.9 (72.1)150.3 (70.3).53a.07e
Insulin, mg/dL12.0 (5.6), n=10711.7 (4.4), n=10714.4 (7.2), n=12316.1 (8.0), n=108<.0001a<.0001e
Glucose, mg/dL93.8 (7.6), n=11994.4 (8.3), n=12195.7 (9.5), n=11097.7 (11.0), n=123.006a.002e
Detectable CAC, %40.851.249.262.4.0008b.002c
IMT0.70 (0.65, 0.77) n=1150.70 (0.65, 0.77), n=1230.70 (0.65, 0.75), n=1230.72 (0.65, 0.78), n=124.62d.48e
Pulse wave velocity (cm/s)834.0 (726.7, 958.4), n=112843.3 (721.7, 945.0), n=119824.4 (731.6, 981.3), n=115916.3 (791.0, 1087.1), n=121.002d.0009e
Normally distributed variables presented as mean (SD); nonnormally distributed variables presented as median (25th, 75th percentile). aAnalysis of variance. bChi-square test of proportions. cChi-square test for trend. dKruskal-Wallis test. eJonkheere-Terpstra test for trend. Abbreviations: BMI, body mass index; CAC, coronary artery calcification; HDL-C, high-density lipoprotein cholesterol; HR, heart rate; IMT, intima-media thickness; LDL-C, low-density lipoprotein cholesterol; MET h/wk, metabolic equivalent hours per week; SBP, systolic blood pressure (BP); WC, waist circumference.

Correlations among walk time and demographics, physical activity levels, CV response measures from the LDCW, and CVD risk factors are presented in Table III. Longer walk times were significantly associated with higher BMI and average WC levels (P<.002). Walk time was inversely related to subjective and objective assessments of physical activity levels as well as HR response and recovery to the 400-m walk. Longer walk times were related to higher resting HR CAC score (continuous), and insulin and aPWV levels. The results were similar when the associations were adjusted for age with one exception; the correlation between walk time and SBP response became statistically significant. When the results were further adjusted for BMI, results were again similar; however, longer walk times were no longer significantly correlated with CAC score or aPWV levels.

Table III.  Spearman Rank Order Correlations Between Walk Time from the LDCW and Related Factors
Walk Time, s UnadjustedAdjusted for AgeAdjusted for Age and BMI
DemographicsAge, y0.13--
 BMI, kg/m20.36a--
 Average WC0.29a--
PA levelsPast-year leisure activity−0.23a−0.22a-
 Past-week leisure activity−0.18a−0.19a-
 Pedometer steps (n=170)−0.25b−0.24b-
CV response to LDCWSBP response, mm Hg−0.13−0.14b−0.22a
 HR response, bpm−0.31a−0.30a−0.38a
 HR recovery, bpm−0.44a−0.43a−0.48a
CVD risk factorsResting HR, bpm0.17b0.16b0.15b
 SBP, mm Hg0.100.090.01
 DBP, mm Hg0.010.01−0.04
 Total cholesterol, mg/dL0.080.080.06
 LDL-C, mg/dL0.080.080.06
 HDL-C, mg/dL−0.11−0.10−0.06
 Triglycerides0.090.090.04
 Insulin, mg/dL (n=432)0.26a0.26a0.15b
 Glucose, mg/dL (n=486)0.130.120.05
 CAC0.16b0.14b0.01
 IMT (n=485)0.040.02−0.04
 Pulse wave velocity (n=467)0.16b0.15b0.12
aP<0001. bP<002. Abbreviations: BMI, body mass index; CAC, coronary artery calcification; CV, cardiovascular; CVD, cardiovascular disease; DBP, diastolic blood pressure; HDL-C, high-density lipoprotein cholesterol; HR, heart rate; IMT, intima-media thickness; LDCW, long distance corridor walk; LDL-C, low-density lipoprotein cholesterol; PA, physical activity; SBP, systolic blood pressure; WC, waist circumference.

DISCUSSION

In this large cohort of healthy, postmenopausal women, 99% of study participants successfully completed the LDCW testing protocol. The high completion rate that was achieved confirms previous work suggesting that the LDCW protocol is practical to administer in larger research studies and general clinical settings. Furthermore, the significant trends that were observed between total walk time from the LDCW and physical activity levels and measures of subclinical CVD indicate that the LDCW has important psychometric properties among postmenopausal women.

In the current report, total walk time from the LDCW was inversely associated with physical activity levels as determined objectively by the pedometer as well as subjectively using the past-year and past-week versions of the MAQ.8 Unlike past studies that estimated physical activity levels using subjective questionnaires, the present investigation also included an objective physical activity measure, the pedometer. Although subjective measures have been shown to accurately assess higher intensity, structured leisure physical activities, these tools typically do a poor job when measuring lower intensity, unstructured lifestyle physical activities, such as household chores, child care, and housework.8,21 In contrast, objective measures have the ability to capture both structured and unstructured activities, thus providing a complete picture of an individual's total physical activity levels.22 The decrease in physical activity levels that was observed across increasing walk times was similar regardless of the method used to collect activity data. In the current report, the relationship of walk time was stronger with the past-year measure, as it may reflect more typical activity when compared with the past-week estimate, which may be subject to acute changes in health status, time commitments, and seasonal variation.

Nonpharmacologic interventions are receiving more attention in clinical settings because of the growing obesity epidemic and compelling evidence from successful lifestyle interventions such as the Diabetes Prevention Program.23 There is also enthusiasm for promoting physical activity and lifestyle change in clinical settings. As a result, practical tools are needed to equip health care providers with an objective assessment of physical activity levels. The LDCW may be appealing in physicians' offices or clinical settings because of its time and cost-effectiveness; ease of administration; patient familiarity of the activity mode; and lack of required medical supervision, space, and/or equipment. The use of the objective data from the LDCW protocol in clinical practice could aid in the identification of individuals at higher risk for CVD and could potentially help triage patients at highest risk to more intensive interventions and monitor progress in such programs. Furthermore, the walk time component of the LDCW could be used as a motivational tool to increase physical activity among participants in healthy-lifestyle programs. In older adults, as noted, walk time was correlated with measures of subclinical atherosclerosis and was demonstrated to be a strong predictor of cardiovascular events and death independent of most major risk factors.6 We are currently determining the effects of physical activity and weight loss on walk time in the longitudinal component of the WOMAN study.

When interpreting the findings of the present investigation, a number of limitations need to be considered. Participants in the WOMAN study were healthy, postmenopausal women who were free of diabetes, CVD, and significant mental health disease at baseline; therefore, the results may not be generalizable to other populations. Data from only one point in time, the baseline visit, were used in the analyses. Although informative, this cross-sectional investigation is limited in that it does not provide information pertaining to the direction of association or allow us to determine how the relationships among walking performance, physical activity, and CVD risk factors may change over time. Finally, the ability to complete the LDCW and timed performance were found to be important prognostic factors for total mortality, CVD, and functional ability in a previous study of older adults.6 Future work is needed to determine the predictive value of the LDCW for future morbidity and mortality in middle-aged individuals.

CONCLUSIONS

Given the growing interest in promoting physical activity for primary CVD prevention, quick and inexpensive tools are needed to ensure safe participation and/or evaluate progress in healthy lifestyle-based programs. The evidence from the current report supports the utility of the LDCW to estimate the health and functional status of middle-aged, postmenopausal women.

Acknowledgments and disclosure: The authors would like to acknowledge the contributions of the staff as well as the 508 dedicated participants of the WOMAN study. This research was funded by National Heart, Lung, and Blood Institute contract R01-HL-66468. As the principal investigator on the WOMAN study, Dr Lewis H. Kuller had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors have no financial arrangements related to the content of the paper or the research.

Ancillary