Poor physical function is common among older adults who are obese (37). Obesity is also associated with an increased risk of all-cause and cardiovascular disease mortality, but these associations are largely explained by poor cardiorespiratory fitness (15). Regular aerobic activity is recommended for older adults due to its recognized benefits on healthy weight maintenance and cardiorespiratory fitness (19).
Walking, a form of aerobic activity, has been shown to be the most popular form of physical activity among older Australian adults, with participation rates of at least 45% for those aged over 45 yr (11). Pedometers record vertical accelerations of the hips during ambulatory activity (AA) and provide a convenient monitoring tool for measuring amounts of walking (36). They have been shown to be useful for assessing physical activity levels in epidemiological studies of free-living populations (27), and although they do not provide information on intensity or duration of physical activity, they have good accuracy in counting steps (2).
Several studies have examined associations between pedometer-determined AA and body composition in adults. Inverse associations have been demonstrated between pedometer-determined AA and body composition measures, including waist circumference, hip circumference, waist-hip ratio, body mass index (BMI), and body fat percentage in middle-aged subjects (7,32,33). Although a previous study found that postmenopausal women who averaged more steps per day also generally demonstrated lower BMI, waist-hip ratios, and body fat percentages (16), the associations between pedometer-determined AA and body composition in older adults remain unclear.
Sarcopenia, the age-related loss of skeletal muscle mass, makes a significant contribution to the decreases in muscle strength that usually accompany aging (13). Resistance-training programs are known to be successful at increasing strength in older adults and are recommended as a means of offsetting age-related strength declines (19). Moreover, it has been observed that older adults who participate in moderate-intensity training programs, generally designed to improve only cardiovascular fitness, demonstrate greater strength than their less active counterparts (5,31). This raises the possibility that even participation in low-intensity exercise, such as walking, may be sufficient to ameliorate the age-related decline in strength and muscle mass.
Poor leg strength has been associated with risk of falls (10), poor mobility (17), disability (23), and mortality (21), highlighting the importance of strength maintenance in ageing populations. To the best of our knowledge, only one study has examined the relationship between pedometer-determined AA and leg strength in community-dwelling older adults. It was found that the recorded amount of walking was significantly positively correlated with muscle strength of the triceps surae in men but not in women (4). However, this previous study used an older-style mechanical pedometer, and there is some evidence to suggest that electronic pedometers demonstrate greater accuracy in measuring absolute step scores than their mechanical counterparts (3). The associations between AA assessed by electronic pedometer and measures of leg strength require further clarification.
To effectively prescribe pedometers as a means of encouraging and assessing AA directed at improving body composition and strength of older adults, it is necessary first to understand the associations between pedometer-determined AA and these health benefits. Thus, the purpose of the present study was to examine cross-sectional associations between habitual walking activity, assessed by electronic pedometer, and body fat measures in community-dwelling older adults. We further attempted to examine associations between pedometer-determined AA and leg muscle parameters, including leg lean mass, leg strength, and leg muscle quality.
MATERIALS AND METHODS
This study was conducted as part of the Tasmanian Older Adult Cohort Study (TASOAC), an ongoing, prospective, population-based study primarily aimed at identifying factors associated with the development and progression of osteoarthritis and osteoporosis in community-dwelling 50- to 79-yr-olds. The study was approved by the Southern Tasmanian Health and Medical Human Research Ethics Committee, and written informed consent was obtained from all participants.
The cohort consisted of 1100 males and females aged between 50 and 79 yr, selected from the roll of electors in southern Tasmania (population = 229,000) using stratified simple random sampling without replacement (response rate = 57.2%). As TASOAC was designed to examine community-dwelling older adults, institutionalized older adults were excluded from participating in the study. Participants were also excluded due to contraindication for magnetic resonance imaging (MRI), as MRI tests were required to examine osteoarthritis progression. Enrolled participants attended a clinic at the Menzies Research Institute between March 2002 and September 2004.
Height was measured to the nearest 0.1 cm (with shoes, socks, and headwear removed) using a Leicester stadiometer (Invicta, Leicester, UK). Weight was measured to the nearest 0.1 kg (with shoes, socks, and bulky clothing removed) using electronic scales (Heine, Dover, NH) that were calibrated using a known weight at the beginning of each clinic. Body mass index (BMI; kg·m−2) was calculated from these measurements.
Each participant underwent a whole body scan by dual-energy x-ray absorptiometry (DEXA) using a Hologic Delphi densitometer (Hologic, Waltham, MA), from which soft tissue composition was determined. The analysis provided mass in grams of total body fat and trunk fat as well as lean mass of the left and right legs. A variable of leg lean mass was calculated as the sum of lean mass in the left and right legs. Participants were excluded from the DEXA scans if their weight exceeded 130 kg because their bodies were too wide for the scan field (n = 3). They were also excluded if they were unable to remain supine for the duration of the scanning procedure (n = 2) or if they had an artificial limb (n = 2).
Leg strength and muscle quality.
Leg strength was measured to the nearest 1.0 kg in both legs simultaneously, using a dynamometer (TTM Muscular Meter, Tokyo, Japan). Participants stood on the back of the dynamometer platform, with back straight against a wall and knees flexed to an angle of 115°. A bar connected by a chain to the dynamometer was held on the front of the thighs. Using only their legs and keeping the back and the neck straight, the participant was then instructed to lift the bar upward with maximum force. The correct technique was demonstrated by the examiner before testing. This test examines isometric strength, predominantly of the quadriceps and hip extensors.
Two trials were recorded, with the mean score taken as the criterion value for leg strength. Reproducibility of this test showed a high correlation of 0.96 between trials 1 and 2. Some participants were excluded from performing the leg strength test as a result of reporting pain or recent surgery (n = 42). Further participants were excluded from the second trial after experiencing pain in the first trial (n = 14).
Specific force or muscle quality (strength per unit of muscle mass) has been shown to decrease with age and as a result has gained popularity in studies of aging muscle (20). We calculated leg muscle quality as the magnitude of leg strength from the leg strength test divided by the combined lean mass of the legs from the DEXA scans using the following formula:
Leg muscle quality (kg·kg−1) = leg strength (kg) / [left leg lean mass (kg) + right leg lean mass (kg)].
AA was measured over seven consecutive days after the clinic in which participants were familiarized with use of the pedometer (Omron HJ-003 & HJ-102; Omron Healthcare, Kyoto, Japan). Participants were shown how to attach the pedometers to their waistband or belt and were instructed to wear the pedometer above their dominant leg. Pedometers were calibrated at the clinic with the participant present, using a 100-pace walking test.
Participants were provided with a pedometer diary and were instructed to record their steps daily for seven consecutive days from the following day. The start and the finish times of pedometer use were recorded on each day, and participants also reported the duration and the type of physical activity for any periods in which they did not wear the pedometer. Finally, participants were requested to report circumstances they believed may have affected the pedometer reading on any day.
Some participants refused to take a pedometer for the assessment of AA (n = 13), and other participants returned the pedometer and the pedometer diary unused (n = 25). Participants were excluded if it was determined that any unusually high pedometer readings were caused artificially (e.g., report of work with heavy machinery in the pedometer diary; n = 1). Furthermore, participants were only included in data analyses if they wore the pedometer on at least 5 d and for at least 8 h on each of these days, excluding reported duration of pedometer removal. Further participants (n = 15) were excluded from the study because they did not meet these criteria.
Examination of pedometer data revealed a significant seasonal influence on AA, with participants who wore the pedometer in summer months completing more steps per day than those who wore the pedometer in cooler months. Seasonal variations in pedometer recordings were removed by fitting a sinusoidal model to the data. Residuals were calculated to find the component of daily steps not explained by monthly predictions from the sinusoidal model. The overall mean steps per day was then added to these residuals to restore the scale of the measurements.
Simple Pearson correlations were used as an initial means of examining associations between AA, fat, and muscle parameters. Stata's (StataCorp, Texas, College Station) fracpoly function was used to identify any nonlinear pattern in the relationships between AA and total body fat, AA and leg lean mass, and AA and muscle strength to determine whether a continuous effect or a threshold effect fitted the data better.
Individuals were categorized into five groups according to their average daily AA to examine AA-related differences in anthropometric and leg strength measures. These groups were classified as sedentary (<5000 steps per day), low active (5000-7499 steps per day), somewhat active (7500-9999 steps per day), active (10,000-12,499 steps per day), and highly active (≥12,500 steps per day). The groups were chosen based on previous pedometer indices designed to classify physical activity levels in adults (34). ANOVA tests were then used to compare means of continuous data variables between these groups, whereas a chi-square test was used to compare gender proportions. In the absence of any evidence of threshold relationships between AA and fat and muscle parameters, a test of linear trend was applied across these groups.
Because age is a potential confounder of these associations, multivariable regression analyses were also performed to identify associations between several study outcome factors and seasonally adjusted AA (steps per day × 10−3), independent of age. These outcome factors included total body fat, trunk fat, leg lean mass, leg strength, and leg muscle quality. We further controlled for the amount of time participants reported removing the pedometer and the type of activity completed during this period.
A P value of less than 0.05 (two-tailed) or a 95% confidence interval (CI) not including the null point was considered statistically significant. All statistical tests were performed using Intercooled Stata 9.2 for Windows (StataCorp).
Of the 1100 participants recruited for the TASOAC study, 982 (89.3%) provided complete DEXA, leg strength, and pedometer data and were therefore included in the data analyses. The average age of participants in the group (51% female) was 62 ± 7 yr. No significant differences in age or BMI were observed between this group and those who were excluded.
Seasonally adjusted average daily AA was 9622 ± 4018 steps per day. There were no significant differences in AA between men and women (9821 ± 4205 vs 9432 ± 3798 steps per day; P = 0.139). A total of 182 (18.5%) participants reported removing the pedometer on at least one occasion (mean = 1.09 times, range = 1-7) during the 7-d recording period. The average duration of pedometer removal for these participants was 0.44 ± 0.48 h·d−1, and a variety of activities were reported during these times. There were no significant differences in average AA, fat, or muscle parameters observed after grouping those who reported removing the pedometer and comparing to those who did not. Furthermore, examining activities reported in the pedometer diary while the pedometer was removed, we found that those participants who performed swimming (n = 54), cycling (n = 7), or resistance training (n = 11) demonstrated no significant differences in AA, fat, or muscle parameters to the rest of the cohort.
Table 1 presents participant characteristics by AA categories of sedentary (3372 ± 1041 mean steps per day), low active (6372 ± 759 mean steps per day), somewhat active (8769 ± 771 mean steps per day), active (11,060 ± 754 mean steps per day), and highly active (15,469 ± 2667 mean steps per day). ANOVA tests for linear trends demonstrated that more active participants were younger, had lower body weight, BMI, total body fat, and trunk fat mass. They also had higher leg strength and muscle quality, although no trend was observed for leg lean mass.
Pearson correlations examined initial associations between AA and the study outcome factors. Figure 1 demonstrates the modest but significant negative correlation between seasonally adjusted AA and total body fat (r = −0.22, P < 0.001). Conversely, a significant positive correlation was observed between AA and leg muscle quality (r = 0.17, P < 0.001), as demonstrated in Figure 2. The relatively mild skew of data observed in Figures 1 and 2 did not artificially inflate the reported correlations because correlations calculated from the ranks (Spearman correlations) provided similar results (r = −0.23, P < 0.001 and r = 0.18, P < 0.001, respectively). A similar correlation to that observed between AA and total body fat was observed between AA and trunk fat mass (r = −0.23, P < 0.001). There was no significant association between AA and leg lean mass (r = 0.04, P = 0.140); however, a significant positive correlation was observed between AA and leg strength (r = 0.16, P < 0.001). These associations were found to be continuous, and the data did not support the concept of a threshold model between AA and body fat, AA and leg strength, or AA and leg lean mass.
Table 2 presents nonstandardized coefficients from multivariable regressions of outcome variables and seasonally adjusted average AA, stratified by gender. These regressions were adjusted for age as outlined in the methods; however, controlling for the amount of time the pedometer was removed, and the type of activity reported did not significantly change the reported associations. As a result, these variables were not included in the final model. The results demonstrated that total body fat was significantly negatively associated with AA after adjustment for age in both men (partial R2 = 0.03) and women (partial R2 = 0.09). Similarly, trunk fat mass was significantly associated with AA after adjustment for age in men (partial R2 = 0.03) and women (partial R2 = 0.09).
The associations of AA with the muscle parameters of leg lean mass and leg muscle quality were nonsignificant in men after adjustment for age. The association between AA and leg strength was also nonsignificant, although the results indicate that this association approaches significance at a level of P = 0.056. In women, AA demonstrated significant positive associations with leg strength (partial R2 = 0.01) and muscle quality (partial R2 = 0.02); however, a significant negative association between AA and leg lean mass (partial R2 = 0.02) was observed after adjustment for age.
The primary findings of this investigation were the significant negative associations of both total body and trunk fat mass with pedometer-determined AA in men and women. Positive associations were also observed for both leg strength and muscle quality with AA in women only after adjustment for age.
The observed negative association between pedometer-determined AA and total body fat mass supports previous studies which have shown that amount of walking assessed by electronic pedometer is inversely associated with body fat in middle-aged men and women (32,33). Along with reductions in physical activity leading to a decrease in overall energy expenditure, aging is associated with decreases in fat oxidation during exercise (28). A 16-wk endurance-training program has previously resulted in significantly improved fat oxidation in a study of older subjects (29). The association between pedometer-determined AA and body fat observed in the present study may suggest that aerobic exercise in the form of walking plays a role in reducing body fat in older adults by increasing energy expenditure, improving fat oxidation, or a combination of both.
The importance of maintaining a lower body fat mass is particularly apparent for elderly populations, given research which has demonstrated that higher levels of body fat are associated with lower physical functioning and greater disability (18,30,38). It also possible that muscle weakness associated with frailty leads to decreased physical activity levels and subsequent increases in body fat, demonstrating a cyclic relationship between frailty and obesity (24). Villareal et al. (37) found that 96% of elderly subjects classified as obese demonstrated mild to moderate physical frailty, indicating that interventions involving weight loss therapy may be effective in preventing disability in older adults as well as disorders associated with obesity.
These obesity-related disorders include the components of the metabolic syndrome, which is a primary risk factor for cardiovascular disease and type II diabetes and is defined as dyslipidemia, insulin resistance, hypertension, and visceral obesity (1). Trunk fat mass, an indicator of visceral obesity, was found to be negatively associated with AA in both men and women in the present study. The metabolic syndrome has been found in older adults with low relative body fat who demonstrate high levels of visceral obesity (14). This raises the possibility that increases in pedometer-determined AA resulting in decreased trunk fat mass may be preventative of the metabolic syndrome in older adults, even without reductions in total body fat.
We are aware of only one previous study that has examined pedometer-determined AA and leg strength in older men and women. This study found that the recorded amount of walking, assessed by a mechanical pedometer, was significantly positively correlated with triceps surae strength in men over the age of 65 yr (4). Although this previous study found that walking speed was significantly associated with strength after controlling for age, no associations were reported between amount of walking and strength independent of age. The present study is the first to demonstrate that the amount of AA, assessed by electronic pedometer, is significantly positively associated with both leg strength and muscle quality in women independently of age. It is also worth noting that the association of pedometer-determined AA with leg strength was approaching significance in men (P = 0.056).
Although significant, the associations observed in women for AA with leg strength and muscle quality were modest (partial R2 = 0.01 and 0.02, respectively), suggesting that AA is not a major determinant of leg strength in either sex. The reason for this is uncertain but it may be that walking only provides sufficient loading to improve strength and muscle quality in those with muscles of smaller size, explaining why significant associations were present in women but not in men. Male leg strength may also be more greatly influenced by other determinants, such as sex hormones.
The fact that AA was significantly inversely associated with leg lean mass in women would suggest that any possible benefits it might provide to muscle strength in older adults may be mediated through pathways other than increased muscle mass, such as cellular or neurological factors. This negative association between AA and leg lean mass in women was unexpected, although it may possibly be attributed to the strong influence that weight loss can have on lean tissue in older adults (12). A study examined community-dwelling older men and women and found that over 4 yr, a loss of around 5% of initial lean mass occurred in those that lost body weight (22). It may be that weight loss due to increased AA leads to a net loss of muscle mass in women; however, we acknowledge that this may have been a chance finding and that longitudinal studies are required to clarify this association.
Although the present study did not demonstrate strong associations between AA and muscle parameters in older adults, the associations between AA and fat measures possibly warrant further consideration in the context of age-related muscle decline. It has been proposed that the muscle loss that occurs with sarcopenia contributes to the fat gains that occur with aging, and these fat gains lead to further muscle loss in a process termed "sarcopenic obesity" (24). Physical activity, an important stimulus for muscle synthesis, declines with age, and this can cause a positive energy balance leading to fat gain (25). Furthermore, alterations in the metabolic function of muscle can cause insulin resistance resulting in the metabolic syndrome (39), and obesity can increase circulating levels of inflammatory cytokines, which may have a deleterious effect on muscle (26). Given this possible synergistic relationship between fat and lean tissue, the finding that AA is negatively associated with fat may hold significance for older adults. Specifically, habitual walking may indirectly provide a beneficial effect to muscle mass by reducing the amount of fat gain that occurs with age.
The associations between pedometer-determined AA and health indices may in the future further encourage their use as an assessment tool for interventions aimed at increasing physical activity in older adults. Interventions using pedometers have previously been successful in increasing average AA of adult workers (6,8) and older adults with type II diabetes (35). If longitudinal studies confirm the associations between AA and body composition identified in this study, pedometers may be an effective instrument for promoting healthy weight maintenance in older adults.
This study has many potential limitations. The cross-sectional design prevents any comments on causality. It is possible that participants with lower fat and greater muscle masses are simply capable of doing more walking rather than higher levels of AA providing a benefit in body composition and leg strength. Longitudinal studies will be required to resolve this issue.
There is also possibility that older adults with greater leg strength, muscle mass, or muscle quality were participating in some form of strength training before the study because this information was not collected. No differences were observed in leg strength, lean mass, or muscle quality between participants who reported removing the pedometer to perform resistance training and the rest of the group. It is possible, however, that a larger number of participants were involved in resistance training and that this was not accounted for because they chose not to remove the pedometer while completing this activity. Furthermore, it should be pointed out that although leg muscle quality is commonly examined in aging studies, there is currently no "gold standard" of measurement and no evidence of the validity or reliability of the DEXA method used in this study. Future studies that use other methods to quantify muscle mass, such as MRI, computed tomography, ultrasound, or anthropometry, may observe different associations between pedometer-determined AA and leg muscle quality in older adults.
It is unknown whether the use of pedometers as an assessment tool influenced participant activity levels in any way because no information was collected on previous physical activity levels of participants. As stated earlier, longitudinal studies are required to clarify the associations observed in this study and may determine the possible effects of changes in physical activity over time on fat and muscle parameters. Another major limitation of pedometers is that they cannot be used to quantify nonambulatory physical activities such as cycling and swimming. However, the results obtained from this study would suggest that those who reported removing the pedometer for swimming or cycling demonstrated similar levels of pedometer-determined AA, body fat, and muscle strength to those who did not.
We acknowledge that the conclusions drawn from this study may apply only to community-dwelling older adults and may not be relevant to those who are institutionalized. Indeed, it has been shown that pedometers are not a reliable instrument for measuring AA in nursing home residents despite demonstrating good accuracy in healthy community-dwelling populations (9), among whom walking is known to be the most popular form of leisure time physical activity (11). The observed associations between AA and fat and muscle parameters may also apply only to adults aged between 50 and 79 yr and may be different for adults over the age of 80. It is further possible that associations between AA and fat parameters differ for individuals who weigh over 130 kg. Participants who exceeded this weight could not be analyzed in this study because they were unable to undergo a DEXA scan, although only three participants were excluded for this reason. Finally, older adults with contraindication for MRI, who were excluded due to the requirements of the larger TASOAC study, may demonstrate different associations between pedometer-determined AA and fat and muscle parameters.
In conclusion, this study demonstrated a negative association between pedometer-determined AA and body fat measures in older men and women. A significant positive association between AA and leg strength and leg muscle quality was observed in women only. These findings open the possibility that participation in regular walking activity assessed and encouraged by the use of pedometers may be preventative of fat gains in both sexes as well as age-associated muscle strength losses in women, potentially reducing the prevalence of obesity-related disorders and disability in older adult populations.
The authors thank the TASOAC staff and volunteers, particularly the study coordinator Catrina Boon. The authors are also extremely grateful to the TASOAC subjects, whose participation made this study possible.
This study was supported by the National Health and Medical Research Council of Australia, the Arthritis Foundation of Australia, the Tasmanian Community Fund, and the University of Tasmania Institutional Research Grants Scheme.
The conclusions drawn from the results of the present study represent the opinions of the authors and do not constitute endorsement by American College of Sports Medicine.
1. Alberti KGMM, Zimmet P, Shaw J. The metabolic syndrome-a new worldwide definition. Lancet
2. Bassett DR. Validity and reliability issues in objective monitoring of physical activity. Res Q Exerc Sport
3. Bassett DR, Ainsworth BE, Leggett SR, et al. Accuracy of five electronic pedometers for measuring distance walked. Med Sci Sports Exerc
4. Bassey EJ, Bendall MJ, Pearson M. Muscle strength in the triceps surae and objectively measured customary walking activity in men and women over 65 years of age. Clin Sci
5. Brown M, Holloszy JO. Effects of walking, jogging and cycling on strength, flexibility, speed and balance in 60- to 72-year olds. Aging (Milano)
6. Chan CB, Ryan DAJ, Tudor-Locke C. Health benefits of a pedometer-based physical activity intervention in sedentary workers. Prev Med
7. Chan CB, Spangler E, Valcour J, Tudor-Locke C. Cross-sectional relationship of pedometer-determined ambulatory activity to indicators of health. Obes Res
8. Croteau KA. A preliminary study on the impact of a pedometer-based intervention on daily steps. Am J Health Promot
9. Cyarto EV, Myers AM, Tudor-Locke CE. Pedometer accuracy in nursing home and community-dwelling older adults. Med Sci Sports Exerc
10. de Rekeneire N, Visser M, Peila R, et al. Is a fall just a fall: correlates of falling in healthy older persons. The health, aging and body composition study. J Am Geriatr Soc
11. Exercise, Recreation and Sport Survey 2006
. Australian Sports Commission; 2007. 118 p.
12. Forbes GB. Longitudinal changes in adult fat-free mass: influence of body weight. Am J Clin Nutr
13. Frontera WR, Hughes VA, Fielding RA, Fiatarone MA, Evans WJ, Roubenoff R. Aging of skeletal muscle: a 12-yr longitudinal study. J Appl Physiol
14. Goodpaster BH, Krishnaswami S, Harris TB, et al. Obesity, regional body fat distribution, and the metabolic syndrome in older men and women. Arch Intern Med
15. Katzmarzyk PT, Church TS, Janssen I, Ross R, Blair SN. Metabolic syndrome, obesity, and mortality: impact of cardiorespiratory fitness. Diabetes Care
16. Krumm EM, Dessieux OL, Andrews P, Thompson DL. The relationship between daily steps and body composition in postmenopausal women. J Womens Health
17. Lauretani F, Russo CR, Bandinelli S, et al. Age-associated changes in skeletal muscles and their effect on mobility: an operational diagnosis. J Appl Physiol
18. Lebrun CEI, van der Schouw YT, de Jong FH, Grobbee DE, Lamberts SW. Fat mass rather than muscle strength is the major determinant of physical function and disability in postmenopausal women younger than 75 years of age. Menopause
19. Mazzeo RS, Cavanagh P, Evans WJ, et al. American College of Sports Medicine Position Stand. Exercise and physical activity for older adults. Med Sci Sports Exerc
20. Metter EJ, Lynch N, Conwit R, Lindle R, Tobin J, Hurley B. Muscle quality and age: cross-sectional and longitudinal comparisons. J Gerontol A Biol Sci Med Sci
21. Newman AB, Kupelian V, Visser M, et al. Strength, but not muscle mass, is associated with mortality in the health, aging and body composition study cohort. J Gerontol A Biol Sci Med Sci
22. Newman AB, Lee JS, Visser M, et al. Weight change and the conservation of lean mass in old age: the Health, Aging and Body Composition Study. Am J Clin Nutr
23. Rantanen T, Avlund K, Suominen H, Schroll M, Frändin K, Pertti E. Muscle strength as a predictor of onset of ADL dependence in people aged 75 years. Aging Clin Exp Res
. 2002;14(3 suppl):10-5.
24. Roubenoff R. Sarcopenic obesity: does muscle loss cause fat gain? Lessons from rheumatoid arthritis and osteoarthritis. Ann N Y Acad Sci
25. Roubenoff R. Sarcopenic obesity: the confluence of two epidemics. Obes Res
26. Schrager M, Metter EJ, Simonsick EM, et al. Sarcopenic obesity and inflammation in the InCHIANTI study. J Appl Physiol
27. Sequeira MM, Rickenbach M, Wietlisbach V, Tullen B, Schutz Y. Physical activity assessment using a pedometer and its comparison with a questionnaire in a large population survey. Am J Epidemiol
28. Sial S, Coggan AR, Carroll R, Goodwin J, Klein S. Fat and carbohydrate metabolism during exercise in elderly and young subjects. Am J Physiol Endocrinol Metab
29. Sial S, Coggan AR, Hickner RC, Klein S. Training-induced alterations in fat and carbohydrate metabolism during exercise in elderly subjects. Am J Physiol Endocrinol Metab
30. Sternfeld B, Ngo L, Satariano WA, Tager IB. Associations of body composition with physical performance and self-reported functional limitation in elderly men and women. Am J Epidemiol
31. Tarpenning KM, Hamilton-Wessler M, Wiswell RA, Hawkins SA. Endurance training delays age of decline in leg strength and muscle morphology. Med Sci Sports Exerc
32. Thompson DL, Rakow J, Perdue SM. Relationship between accumulated walking and body composition in middle-aged women. Med Sci Sports Exerc
33. Tudor-Locke CE, Ainsworth BE, Whitt MC, Thompson RW, Addy CL, Jones DA. The relationship between pedometer-determined ambulatory activity and body composition variables. Int J Obes
34. Tudor-Locke CE, Bassett DR. How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med
35. Tudor-Locke CE, Bell RC, Myers AM, et al. Controlled outcome evaluation of the First Step Program: a daily physical activity intervention for individuals with type II diabetes. Int J Obes Relat Metab Disord
36. Tudor-Locke CE, Williams JE, Reis JP, Pluto D. Utility of pedometers for assessing physical activity. Sports Med
37. Villareal DT, Banks M, Siener C, Sinacore DR, Klein S. Physical frailty and body composition in obese elderly men and women. Obes Res
38. Visser M, Harris TB, Langlois J, et al. Body fat and skeletal muscle mass in relation to physical disability in very old men and women of the Framingham Heart Study. J Gerontol A Biol Sci Med Sci
39. Wolfe RR. The underappreciated role of muscle in health and disease. Am J Clin Nutr