Research article
Cardiometabolic Risk in Younger and Older Adults Across an Index of Ambulatory Activity

https://doi.org/10.1016/j.amepre.2009.05.020Get rights and content

Background

Pedometers are increasingly being used to assess population levels of physical activity and as motivational tools for individuals to increase their physical activity. To maximize their utility, a framework for classifying pedometer-determined activity into meaningful health-related categories is needed.

Purpose

This study investigated whether a pedometer step index proposed by Tudor-Locke and Bassett can effectively group younger and older adults according to cardiometabolic health status.

Methods

Analyses (conducted in 2008) used cross-sectional data from the Childhood Determinants of Adult Health study (1793 adults aged 26–36 years; collected 2004–2006) and from the Tasmanian Older Adult Cohort study (1014 adults aged 50–80 years; collected 2002–2006). Participants wore a pedometer for 7 days and the prevalence of cardiometabolic health indicators, including the metabolic syndrome, elevated Pathobiological Determinants of Atherosclerosis in Youth risk scores, and elevated Framingham risk scores, was examined across the following step categories: sedentary (<5000); low-active (5000–7499); somewhat active (7500–9999); active (10,000–12,499); and high-active (≥12,500).

Results

With the exception of younger men, individuals achieving ≥5000 steps had a substantially lower prevalence of adverse cardiometabolic health indicators than those obtaining fewer steps. Differences in the prevalence of adverse indicators were generally modest across higher steps-per-day categories. However, younger men and women in the high-active category had a substantially lower prevalence of some adverse health indicators.

Conclusions

In general, the proposed index for classifying pedometer activity effectively distinguishes cardiometabolic health risk. Pedometers may be a useful tool for objectively identifying inactive individuals at greatest risk for poor cardiometabolic health.

Introduction

Physical inactivity is an important modifiable risk factor for a range of chronic cardiometabolic health conditions such as coronary heart disease, stroke, and type 2 diabetes.1 Because of the high prevalence of both physical inactivity2 and these health conditions,3 physical inactivity is often targeted for public health surveillance and screening of at-risk individuals. Traditionally, population levels of physical activity have been estimated using various self-report survey tools, which are subject to unreliable recall and potential bias.4, 5, 6 Survey estimates have also been shown to vary widely depending on the instrument used7 and the range of activity domains assessed.8, 9

To address the above limitations, pedometers are increasingly being used to assess population levels of physical activity10, 11 and as effective motivational tools for individuals to increase their physical activity.12 To maximize the utility of pedometers for these purposes, it is necessary to have a framework for classifying pedometer steps into meaningful categories of activity analogous to those used in traditional physical activity surveys (e.g., active, insufficiently active, inactive). Current physical activity recommendations, however, do not include steps advice,13 and only a few studies, with conflicting results, have examined the validity of the widely touted target of 10,000 steps per day as a surrogate threshold for achieving recommended levels of activity.14, 15, 16, 17

To address these gaps, Tudor-Locke and Bassett18 conducted an extensive review of the literature and proposed the following five-level index for classifying pedometer data: sedentary (<5000 steps/day); low-active (5000–7499 steps/day); somewhat active (7500–9999 steps/day); active (10,000–12,499 steps/day); and high-active (≥12,500 steps/day). This index has already been used to characterize physical activity levels in a representative sample of adult residents in Colorado.11 However, the usefulness of the Tudor-Locke and Bassett step index has not been formally evaluated. For example, it is unclear whether the step index is associated with cardiometabolic risk factors commonly associated with physical (in)activity.

This study examined the prevalence of several summary measures of cardiometabolic risk across the Tudor-Locke and Bassett step index among younger and older adults. The utility of the index by age and gender was also explored.

Section snippets

Methods

This study used data from two Australian population-based cohorts: the Childhood Determinants of Adult Health (CDAH) study and the Tasmanian Older Adults Cohort (TasOAC) study, described below. The Southern Tasmanian Health and Medical Research Ethics Committee approved both studies, and written informed consent was obtained from participants.

Results

Median and 25th and 75th percentile values for age, cardiometabolic risk factors, and steps are presented by gender and age group in Table 1. As expected, body composition and blood pressure measures were higher for men than women and among older, compared with younger, participants. The prevalence of obesity (BMI ≥30 kg/m2) in the two samples was 15.4% and 14.0% in younger men and women and 24.5% and 28.4% in older men and women, respectively, which was comparable to estimates from a recent

Discussion

This study evaluated the effectiveness of the step index proposed by Tudor-Locke and Bassett in distinguishing cardiometabolic risk among younger and older adults. A consistent decrease in the prevalence of cardiometabolic risk indicators was observed across most categories of increasing ambulatory activity, suggesting that this index is effectively able to distinguish cardiometabolic risk in these two adult populations.

With the exception of younger men, participants in the sedentary category

Acknowledgments

We gratefully acknowledge the sponsors of the CDAH study (Sanitarium, ASICS, and Target) and the contributions of the CDAH study project manager, Ms. Marita Dalton; the TasOAC study project manager, Ms. Catrina Boon; and the director, Dr. Graeme Jones. We also thank Ms. Jenny Cochrane and Ms. Stella Foley for their assistance in compiling the TasOAC study data; Dr. Russell Thomson for valuable statistical advice; and all study staff, volunteers, and participants for their contributions.

The CDAH

References (40)

  • J.M. Jakicic et al.

    Accuracy of self-reported exercise and the relationship with weight loss in overweight women

    Med Sci Sports Exerc

    (1998)
  • J.F. Sallis et al.

    Assessment of physical activity by self-report: status, limitations, and future directions

    Res Q Exerc Sport

    (2000)
  • R.C. Brownson et al.

    Patterns and correlates of physical activity among U.S. women 40 years and older

    Am J Public Health

    (2000)
  • Prevalence of physical activity, including lifestyle activities among adults—United States, 2000–2001

    MMWR Morb Mortal Wkly Rep

    (2003)
  • G. McCormack et al.

    Physical activity levels of Western Australian adults 2002: results from the adult physical activity survey and pedometer study

    (2003)
  • H.R. Wyatt et al.

    A Colorado statewide survey of walking and its relation to excessive weight

    Med Sci Sports Exerc

    (2005)
  • D.M. Bravata et al.

    Using pedometers to increase physical activity and improve health: a systematic review

    JAMA

    (2007)
  • W.L. Haskell et al.

    Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association

    Circulation

    (2007)
  • A.N. Jordan et al.

    Pedometer indices for weekly physical activity recommendations in postmenopausal women

    Med Sci Sports Exerc

    (2005)
  • G.C. Le Masurier et al.

    Accumulating 10,000 steps: does this meet current physical activity guidelines?

    Res Q Exerc Sport

    (2003)
  • Cited by (76)

    • Longitudinal associations between dietary inflammatory index and musculoskeletal health in community-dwelling older adults

      2020, Clinical Nutrition
      Citation Excerpt :

      Omron and Yamax estimates have a strong linear correlation (correlation coefficient, r = 0.88), though Omron recorded higher mean steps [27]. A correction factor of 0.91 was multiplied to all Omron estimates to provide comparability between brands [31]. Smoking status was self-reported by participants using a structured questionnaire (current/past/never smoker).

    View all citing articles on Scopus
    View full text