PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Phys Act Health. Author manuscript; available in PMC 2010 May 10.
Published in final edited form as:
J Phys Act Health. 2007 October; 4(4): 397–410.
PMCID: PMC2866375
NIHMSID: NIHMS194365

Walking and Metabolic Syndrome in Older Adults

Abstract

Background

Little data exists describing the impact that walking has on metabolic syndrome (MetS) in a multicultural sample of older adults.

Methods

Walking was measured via pedometer in 150 older adults from 4 different ethnic categories. Steps per day were classified as low (<3100 steps/d) or high (≥3100 steps/d) for statistical analyses.

Results

Occurrence of MetS was lower in the white (33%) versus non-white population (50%). Low steps/d were related to an increase in MetS for both white (OR = 96.8, 95% CI 12.3–764.6) and non-white individuals (OR = 4.5, 95% CI 1.8–11.3). Low steps/d also increased the odds for selected components of MetS in both the white and non-white groups.

Conclusion

Low levels of walking increase the likelihood of having MetS in both white and non-white older adults. Efforts to increase walking in older adults may decrease the likelihood of developing this clustering of disease risk factors.

Keywords: pedometer, behavior, elderly, health, ethnicity

Within the last 20 to 30 years, metabolic diseases have emerged among the most prevalent causes of death in industrialized nations.1 Although individual metabolic phenotypes are important to consider, the clustering of such may be of increased importance because of its association with an increased risk for stroke,2 cardiovascular disease,3 and premature mortality.4 Metabolic syndrome (MetS) represents a clustering of metabolic phenotypes and is characterized as the presence of 3 or more of the following metabolic conditions: (1) hypertension, (2) glucose intolerance, (3) low high-density lipoprotein cholesterol (HDL-C), (4) high triglycerides, and (5) abdominal obesity.5

Older adults represent a subsection of the population that are disproportionately affected by metabolic disorders, and therefore have a higher prevalence rate of having MetS.6 Furthermore, it has been shown that certain ethnic minority populations are affected by MetS at a greater rate than their white counterparts,6 thus putting them at greater risk for early mortality.

The emergence of metabolic diseases has paralleled the increase in physically inactive behaviors. Older adults are among the most inactive segment of the population, with 51% of 65- to 74-year-olds and 65% of those >75 years old not engaging in any leisure-time physical activity (PA).7 Inactivity has also been shown to be more prevalent in minority populations.8 PA behavior, therefore, may be related to the occurrence of MetS in an older adult population, and the differences in PA levels across ethnic groups may help explain why ethnic minorities are affected by MetS at a greater rate than a white population.

PA is, however, a difficult behavior to precisely measure due to its multifaceted nature. In determining the independent effect of PA on MetS, the majority of studies have employed questionnaire-based assessments of PA,916 some producing conflicting results pertaining to the importance of PA in relation to MetS.15, 16 Questionnaire-based assessments of PA have been shown to be imprecise, especially for activities more moderate in nature, such as ubiquitous walking activities.17, 18 A limited number of studies have employed objective measures of PA, such as heart rate monitoring19, 20 and have shown a strong and consistent relation between PA and risk for development of MetS. To date, however, limited evidence exists on the relation between objectively determined PA and the occurrence of MetS in a culturally diverse older adult population.

Regardless of ethnic makeup or age, the most commonly reported PA by the general adult population is walking.21 Walking behavior can be precisely measured by the use of pedometers.22 Therefore, the purpose of this study was to explore the independent associations between PA, assessed via pedometry, and the presence of MetS in an ethnically diverse community-dwelling older adult sample.

Research Design and Methods

Participants

One-hundred fifty individuals ages 55 to 87 years took part in this study. Individuals were recruited through local community senior centers via seminars and mailings. Exclusionary criteria included any physical/orthopedic condition that prevented walking or a normal walking gait. The total sample consisted of 4 different ethnic categories, American Indian (n = 17), Asian/Hmong (n = 37), Hispanic (n = 45), and white (n = 51). Study procedures were approved by the University Institutional Review Board.

Study Design

Testing took place in the fall of 2005. All participants completed a general health history and demographic questionnaire and underwent anthropometric and metabolic assessments discussed in detail below. Individuals were shown how and asked to wear a sealed (blinded) pedometer for a consecutive 7-day period. Following the 7-day PA monitoring period, individuals returned the blinded pedometer to an investigator, and total steps taken during the 7-day period were recorded and averaged.

Health History and Demographic Questionnaire

Participants self-reported race or ethnicity as either white, black, Hispanic, Asian, American Indian, Native Hawaiian/Pacific Islander, or other. Only data on white, American Indian, Asian, and Hispanic were obtained. Individuals reported whether they currently smoke or smoked in the past and answered questions pertaining to present and past health history.

Anthropometric, Cardiovascular, and Metabolic Measures

Body mass and height were measured with minimal clothing and no shoes. Body mass was measured using a physician’s balance beam scale (Continental Scale Corporation, Bridgeview, Ill) and height was measured using a stadiometer (Continental Scale Corporation, Bridgeview, Ill). Body mass index (BMI) was calculated according to the formula, body mass (kg) divided by height squared (m2). Girth measurements were taken at the waist using a plastic tape fitted with a tension handle. All waist circumference measurements were taken in duplicate at the end of exhalation, with the average measurement recorded for analysis.

Individuals underwent measures of resting systolic and diastolic blood pressure using the auscultatory technique and a mercury sphygmomanometer following standard procedures.23

Participants were asked to fast and to refrain from smoking for 12 h prior to blood sampling. Blood samples were collected from an antecubital vein using standard venipuncture procedures. Triglyceride concentrations were determined using a three-coupled enzymatic step reagent in conjunction with a SYNCHRON LX® System (Beckman Coulter, Inc.) and a timed-endpoint method. A change in absorbance is directly proportional to the concentration of triglyceride. The coefficient of variation for this method of triglyceride determination is 3% and 3.9% at a low (81 mg/dL) and high level (272 mg/dL), respectively. HDL-C concentrations were analyzed with the same technology utilizing the HDLD reagent. The coefficient of variation for this method of HDL-C determination is 4.3% and 3.9% at a low (33 mg/dL) and high level (78 mg/dL), respectively. Glucose concentrations were analyzed with the same methodological procedures as described above utilizing the GLU reagent. The coefficient of variation for this method of glucose determination is 2.4% and 1.9% at a low (87 mg/dL) and high level (280 mg/dL), respectively.

Estimations of Physical Activity

Individuals wore a sealed electronic pedometer (SW-200, Yamax Corp., Tokyo, Japan) for a consecutive 7-day monitoring period. Previous research has shown the Yamax pedometer to be a valid and reliable assessment tool for measuring steps, distance walked, and walking behavior.24 Participants were instructed to place the pedometer on the right side of the body attached to either a belt or waistband, on the anterior midline of the thigh. Each participant completed an individualized 20-step pedometer calibration to assess the functional status of each pedometer. Individuals were given a picture showing correct placement and written instructions to increase the likelihood of proper positioning throughout the week-long period. Pedometers were worn during all waking hours, except when bathing or swimming, and removed prior to sleep at night.

Metabolic Syndrome

The Adult Treatment Panel III (ATP-III) report was used to characterize the presence of MetS.5 MetS is defined as having 3 or more of the following conditions: (1) hypertension (systolic blood pressure ≥130 mmHg and/or a diastolic blood pressure ≥85 mmHg), (2) glucose intolerance (fasting plasma glucose ≥110 mg/dL), (3) low HDL-C (<50 mg/dL in women and <40 mg/dL in men), (4) elevated triglycerides (≥150 mg/dL), and (5) central abdominal obesity (a waist circumference >88 cm in women and >102 cm in men). Those individuals taking antihypertensive medications and hyperglycemic medications were classified as having hypertension and glucose intolerance, respectively.

Data Analysis

Individuals were dichotomized into a low walking (<3100 steps/d) or high walking (≥3100 steps/d) group based on scores below and above whole group median values. Descriptive statistics were performed to examine prevalence of MetS and its components across walking categories stratified by ethnicity. Due to a limited male sample within each ethnic category, both genders were combined for ethnic analysis purposes. Where appropriate, individual race or ethnicity data were categorized as white or non-white (non-white representing American Indian, Asian, and Hispanic). Independent t tests and z tests were performed to compare gender and ethnic differences for both descriptive variables and proportions of metabolic risk factors, respectively. One-way analysis of variance was run to examine PA levels across ethnic categories. Logistic regression analysis was conducted to estimate the odds ratio (OR) and 95% confidence intervals (CI) of MetS as a function of high and low steps/d, adjusting for gender and smoking behavior. Statistical significance was set at alpha = .05. All analyses were conducted with SPSS for Windows Version 12.0 (SPSS Inc., Chicago, Ill).

Results

Demographic and metabolic characteristics of the study sample are shown in Table 1. The American Indian group had a significantly higher waist circumference than other ethnic categories (P = .02). Within the non-white category, men accumulated significantly more steps/d than women, 4698 ± 3042 versus 3178 ± 1972 steps/d (P = .01), respectively, data not shown. No significant difference was noted in accumulated steps/d between men and women in the white category, data not shown. Genders combined, the average walking within each ethnic group from the lowest to the highest was as follows: American Indian, Asian/Hmong, Hispanic, and white. The white group walked significantly more than the non-white group (P = .04).

Table 1
Demographic Characteristics of Study Participants

Table 2 shows average PA levels within each ethnic category stratified by either the high or low walking classification. The mean walking volume of the entire low-activity group was significantly lower (P = .002) than the mean walking volume of the entire high-activity group, 1960 ± 78 steps/d versus 5864 ± 315 steps/d, respectively.

Table 2
Average Steps per Day for Each Ethnic Group Stratified by Activity Level

Table 3 shows the prevalence of cumulative metabolic risk factors for MetS by ethnicity. MetS was evident in 44% of the entire older adult sample. The prevalence of MetS was the highest in the American Indian (76.5%) compared with the Asian/Hmong (45.9%), Hispanic (42.2%), and white (33.3%) groups.

Table 3
Prevalence of Metabolic Risk Factors by Ethnicity

Figure 1 shows the occurrence of MetS and its metabolic characteristics related to PA level by ethnicity. Overall, those who accumulated <3100 steps/d had a higher occurrence of MetS and its components compared to those who accumulated ≥3100 steps/d (Figure 1F). This relationship held true for the white group and all individual ethnic groups (Figure 1E, 1B, and 1C) and ethnic groups combined (Figure 1D), baring those within the American Indian category (Figure 1A). Within the American Indian category, those individuals accumulating ≥3100 steps/d had a higher occurrence of MetS, a higher triglyceride component occurrence, and also a higher central adiposity component. These results may be evident due to that fact that all individuals within the category (100%) were classified as having central adiposity.

Figure 1Figure 1Figure 1
A–F — Prevalence estimates of the metabolic syndrome and related components across low- (<3100 steps/d) and high- (≥3100 steps/d) walking groups.

Table 4 shows the relationship between MetS, its metabolic subphenotypes, and accumulated steps/d across white and non-white participants. Compared to those individuals in the high steps/d category, the odds for having high triglycerides (white: OR = 14.9, 95% CI = 1.4–160.3; non-white: OR = 2.0, 95% CI = 1.0–4.8) and developing central adiposity (white: OR = 20.5, 95% CI = 3.6–118.0; non-white: OR = 2.6, 95% CI = 1.1–6.5) was increased in the low steps/d group, after adjustment for gender and smoking. Accumulated steps/d were not predictive of developing high blood pressure, impaired fasting glucose levels, or having low HDL-C levels in either the white or non-white groups. In contrast, compared to those in the high steps/d category, those white and non-white individuals accumulating <3100 steps/d were approximately 97 times and 5 times more likely (white: OR = 96.8, 95% CI = 12.3–764.6; non-white: OR = 4.5, 95% CI = 1.8–11.3) to have MetS after adjustment for gender and smoking, respectively.

Table 4
Relationship Between Accumulated Steps per Day, the Metabolic Syndrome, and Components of the Metabolic Syndrome

Discussion

The present study shows the importance of walking behavior on the prevalence of MetS and its related subphenotypes in an older adult population. After adjusting for the effects of gender and smoking, white and non-white individuals who walked less were approximately 97 and 5 times more likely to have MetS compared to those who walked an average of 2.4 and 1.7 more miles per day, respectively, assuming an approximation of 2000 steps per mile.22 Specifically, the occurrence of MetS was higher among those who walked less compared to those who walked more for white (84% versus 8%, respectively) and for non-white participants (58% versus 32%, respectively).

Although previous studies have shown that MetS was inversely associated with participation in PA in the general population,916 some reports utilizing PA surveys have produced conflicting results. Laaksonen and colleagues15 reported an inverse association between leisure-time PA and both the prevalence and development of MetS in middle-aged men. Whereas, Palaniappan et al.16 in a community-based sample found no such relationships between leisure-time PA and its predictability of MetS.

Our data, utilizing objective PA monitoring, supports the role of PA, via accumulated walking, and the relationship with MetS in an older adult sample across white and non-white individuals. Our results demonstrate a very strong association between low levels of accumulated walking and the odds of having MetS, irrespective of gender or smoking behavior. It could be that the previously reported associations between leisure-time PA and MetS are underestimated because of the imprecision of PA recall surveys. Therefore, the strong association reported in this study could stem from the use of objective, and more accurate monitoring of PA levels, or that PA plays a more prominent role in the relationship with MetS in an older adult compared with the general population.

It has been reported that MetS and individual components of MetS affect non-white individuals to a greater extent than white individuals.6 Our results, pertaining to an older adult sample, also confirm these findings. Within our sample of older adults, 49.5% of those within the non-white group had the presence of MetS, whereas only 33.3% of those within the white group had the presence of MetS. Furthermore, PA levels have been consistently reported as being lower within ethnic minority populations compared with a white population,8 and our results of walking behavior specific to older adults support these findings. On average the white group accumulated 4556 ± 477 steps/d versus the non-white group who accumulated 3589 ± 249 steps/d.

PA levels have been shown to affect all 5 subphenotypes of MetS. PA level has been shown to be inversely associated with obesity, hypertension, triglyceride and fasting glucose levels, and positively associated with HDL-C levels, and these results are irrespective of individual ethnic background.25 Within our sample, we chose to examine the relationship between PA and the individual components of MetS by dichotomizing individuals by the median walking level for the entire group into a low- (<3100 steps/d) and high-walking group (≥3100 steps/d). Mean walking volume was 1959 ± 78 and 5864 ± 315 steps/d for the low- and high-walking groups, respectively.

Our results show that irrespective of ethnicity, those accumulating more steps/d had a lower occurrence of each individual component of MetS, resulting in a lower occurrence of MetS. In comparing the effects of PA (low and high) on individual metabolic phenotypes and the occurrence of MetS in the white population (Figure 1E) versus the non-white population (Figure 1D), it appears as if the effects of activity had a more beneficial effect on health parameters in the white population. These results can be partially explained by the fact that overall the white population had a more favorable health profile to begin with and were, on average, more physically active than the non-white group.

Similarly, the logistic regression results (reported in Table 4) revealed a stronger association with the likelihood of having MetS based upon walking less on average per day for the white versus non-white participants (OR = 96.8, 95% CI = 12.3–764.6 versus OR = 4.5, 95% CI = 1.8–11.3, respectively). The high OR reported for the white group is likely due to the small number of cases within cells having MetS in the low-walking group. Furthermore, the mean walking volume differences between the low and high groups were 4838 steps/d for the white group and 3381 steps/d for the non-white group. This difference of 1457 steps/d may further partially explain the stronger effects for PA reported for the white versus non-white individuals. Other influences that could impact ethnic differences in components of MetS and the overall role that PA may play, such as dietary composition, cannot be ignored but were not the focus of the present investigation.

Limited observational data exists examining individual components of MetS and walking volume per se. Inferring from data that those individuals with MetS have cardiovascular disease and are at higher risk for early mortality,4 studies specifically examining walking behavior in the general population also support our findings. Manson and colleagues26 found within the Women’s Health Initiative Observational Study that increasing quintiles of walking volume were associated with a reduction in relative risk for cardiovascular disease. Furthermore, Hakim et al.27 found that older men who walked more than 2 miles per day had half the mortality rate of those walking less than 1 mile per day over a 12-year follow-up period. Walking intervention studies have also documented that increases in walking behavior are associated with reductions in blood pressure28 and improvements in glucose dynamics.29

The present study findings are in agreement with other results and add further data pertinent to objectively measured walking and older adults from different ethnic backgrounds. Of further interest, the average difference in daily walking volume per day for the entire sample between our low- and high-walking groups was approximately 4000 steps/d, which equates to approximately 2 miles per day (using an approximation of 2000 steps equals 1 mile). This further extends the findings of Hakim et al.27 but also adds further evidence to the importance of United States national PA recommendations and that this recommendation can be met by briskly walking 2 miles per day.25

This current study is subject to several limitations that warrant comment. Overall sample size of the present study is limited to 150 individuals, divided into 4 different ethnic categories. The current study design is cross-sectional so causality cannot be inferred. Furthermore, the overall population was relatively healthy for their age, so results may be affected by a survival bias. In addition, within the current study, no data were available on hormone replacement therapy, a potential factor affecting blood lipid values.

Nevertheless, it seems plausible based on other research that inadequate PA levels may contribute to the development of MetS. The use of pedometers to assess walking behavior is a strength to the current study. However, although pedometers represent an accurate objective measure of walking, they are not able to distinguish the intensity or duration of the activity engaged in.

In summary, low levels of walking are a major modifiable risk factor for MetS in an older adult sample. This study shows that despite ethnic background, accumulating low volumes of walking activity throughout the day can result in a poorer cardiovascular and metabolic health profile as well as substantially increased risk for having MetS compared with those with higher volumes of daily walking. Studies with larger sample sizes examining the effects of increasing walking activity and the effects of intensity on the development of MetS among ethnically diverse men and women are warranted to confirm and extend the results of this study.

Acknowledgments

This work was partially supported by funding received from the University of Wisconsin-Milwaukee Graduate School Research Award, the Center for Age and Community Fellowship Award, the Wisconsin Department of Health and Family Services, as well as a Career Development Award from the National Institute on Aging (K01AG025962). The authors would like to thank Covenant Health Care for donations of time and resources; Mark A. Parmenter, Janet E. Laatsch, Andre Harwell, Nicholas Davis, and David Malek for data collection assistance; and Susan Cashin for statistical assistance.

References

1. Centers for Disease Control and Prevention. Surgeon General’s report on physical activity and health. JAMA. 1996;272:522.
2. Kurl S, Laukkanen JA, Niskanen L, et al. Metabolic syndrome and the risk of stroke in middle-aged men. Stroke. 2006;37:806–811. [PubMed]
3. Hassinen M, Komulainen P, Lakka TA, et al. Metabolic syndrome and the progression of carotid intima-media thickness in elderly women. Arch Intern Med. 2006;166:444–449. [PubMed]
4. Lakka HM, Laaksonen DE, Lakka TA, et al. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA. 2002;288:2709–2716. [PubMed]
5. National Institutes of Health. Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Bethesda, MD: 2001. NIH-01-3670.
6. Ford ES, Giles WH, Dietz WH. Prevalence of the metabolic syndrome among US adults: findings from the Third National Health and Nutrition Examination Survey. JAMA. 2002;287:356–359. [PubMed]
7. Centers for Disease Control and Prevention. Prevalence of no leisure-time physical activity—35 states and the District of Columbia, 1988–2002. MMWR. 2004;53:82–85. [PubMed]
8. Centers for Disease Control and Prevention. Prevalence of physical activity, including lifestyle activities among adults-United States, 2000–2001. MMWR. 2003;52:764–769. [PubMed]
9. Dubose D, Addy CL, Ainsworth BE, Hand GA, Durstine JL. The relationship between leisure-time physical activity and the metabolic syndrome: an examination of NHANES III, 1988–1994. J Phys Act Health. 2005;2:470–487.
10. Rennie KL, McCarthy N, Yazdgerdi S, Marmot M, Brunner E. Association of the metabolic syndrome with both vigorous and moderate physical activity. Int J Epidemiol. 2003;32:600–606. [PubMed]
11. Carroll S, Cooke CB, Butterly RJ. Metabolic clustering, physical activity and fitness in nonsmoking, middle-aged men. Med Sci Sports Exerc. 2000;32:2079–2086. [PubMed]
12. Irwin ML, Ainsworth BE, Mayer-Davis EJ, Addy CL, Pate RR, Durstine JL. Physical activity and the metabolic syndrome in a tri-ethnic sample of women. Obes Res. 2002;10:1030–1037. [PubMed]
13. Bertrais S, Beyeme-Ondoua JP, Czernichow S, Galan P, Hercberg S, Oppert JM. Sedentary behaviors, physical activity, and metabolic syndrome in middle-aged French subjects. Obes Res. 2005;13:936–944. [PubMed]
14. Lakka TA, Laaksonen DE, Lakka HM, et al. Sedentary lifestyle, poor cardiorespiratory fitness, and the metabolic syndrome. Med Sci Sports Exerc. 2003;35:1279–1286. [PubMed]
15. Laaksonen DE, Lakka HM, Salonen JT, Niskanen LK, Rauramaa R, Lakka TA. Low levels of leisure-time physical activity and cardiorespiratory fitness predict development of the metabolic syndrome. Diabetes Care. 2002;25:1612–1618. [PubMed]
16. Palaniappan L, Carnethon MR, Wang Y, et al. Predictors of the incident metabolic syndrome in adults: the Insulin Resistance Atherosclerosis Study. Diabetes Care. 2004;27:788–793. [PubMed]
17. Ainsworth BE, Richardson MT, Jacobs DR, Leon AS, Sternfeld B. Accuracy of recall of occupational physical activity by questionnaire. J Clin Epidemiol. 1999;52:219–227. [PubMed]
18. Strath SJ, Bassett DR, Jr, Swartz AM. Comparison of the College Alumnus Questionnaire Physical Activity Index with objective monitoring. Ann Epidemiol. 2004;14:409–415. [PubMed]
19. Ekelund U, Brage S, Franks PW, Hennings S, Emms S, Wareham NJ. Physical activity energy expenditure predicts progression toward the metabolic syndrome independently of aerobic fitness in middle-aged healthy Caucasians: The Medical Research Council Ely Study. Diabetes Care. 2005;28:1195–1200. [PubMed]
20. Wareham NJ, Hennings SJ, Byrne CD, Hales CN, Prentice AM, Day NE. A quantitative analysis of the relationship between habitual energy expenditure, fitness and the metabolic cardiovascular syndrome. Br J Nutr. 1998;80:235–241. [PubMed]
21. Crespo CJ, Keteyian ST, Heath GW, Sempos CT. Leisure-time physical activity among US adults. Arch Intern Med. 1996;156:93–98. [PubMed]
22. Bassett DR, Jr, Strath SJ. Use of pedometers to assess physical activity. In: Welk GJ, editor. Physical Activity Assessments for Health-Related Research. Champaign, IL: Human Kinetics; 2002. pp. 163–177.
23. Pickering TG, Hall JE, Appel LJ, et al. Recommendations for blood pressure measurement in humans: an AHA scientific statement from the Council on High Blood Pressure Research Professional and Public Education Subcommittee. J Clin Hyperten. 2005;7:102–109. [PubMed]
24. Schneider PL, Crouter SE, Bassett DR., Jr Pedometer measures of free-living physical activity: comparison of 13 models. Med Sci Sports Exerc. 2004;36:331–335. [PubMed]
25. US Department of Health and Human Services. Physical Activity and Health: A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion; 1996.
26. Manson JE, Greenland P, LaCroix AZ, et al. Walking compared with vigorous exercise for the prevention of cardiovascular events in women. N Eng J Med. 2002;347:716–725. [PubMed]
27. Hakim AA, Petrovitch H, Burchfiel CM, et al. Effects of walking on mortality among nonsmoking retired men. N Eng J Med. 1998;388:94–99. [PubMed]
28. Moreau KL, DeGarmo R, Langley J, et al. Increasing daily walking lowers blood pressure in postmenopausal women. Med Sci Sports Exer. 2001;33:1825–1831. [PubMed]
29. Swartz AM, Strath SJ, Bassett DR, Jr, et al. Increasing daily walking improves glucose tolerance in overweight women. Prev Med. 2003;37:356–362. [PubMed]