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The relationship between accelerometer-determined physical activity (PA) and body composition and bone mineral density (BMD) in postmenopausal women
Archives of Gerontology and Geriatrics 54 (2012) e315–e321
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The relationship between accelerometer-determined physical activity (PA) and body composition and bone mineral density (BMD) in postmenopausal women Alesˇ Ga´ba a,*, Ondrˇej Kapusˇ a, Jana Pelclova´ b, Jarmila Riegerova´ a a b
Department of Natural Sciences in Kinanthropology, Faculty of Physical Culture, Palacky´ University in Olomouc, Trˇ. Mı´ru 115, 771 11, Olomouc, Czech Republic Center for Kinanthropology Research, Faculty of Physical Culture, Palacky´ University in Olomouc, Trˇ. Mı´ru 115, 771 11, Olomouc, Czech Republic
A R T I C L E I N F O
A B S T R A C T
Article history: Received 21 October 2011 Received in revised form 1 February 2012 Accepted 2 February 2012 Available online 8 March 2012
Studies of the relationships between BMD, PA and body composition have shown variable results. Therefore, the aim of this cross-sectional study was to determine the relationships between accelerometer-determined PA and selected body composition parameters to total and regional BMD of the proximal femur in postmenopausal women. BMD and body composition were measured using dual energy X-ray absorptiometry in 97 women with a mean age 63.63 5.23 years. PA was monitored using an ActiGraph GT1M accelerometer. Correlation analysis did not show significant relationships between PA variables and BMD, but increases in body composition variables were associated with increases in BMD. Lean body mass was the strongest predictor of proximal femur BMD (r = 0.18–0.37), explaining 10% of the variance for total femur, and 3–14% of the variance for regional femurs. Correlations increased when the analysis was controlled for age (rp = 0.20–0.39). A significant relationship was also found between body fat mass and BMD (r = 0.16–0.30; rp = 0.25–0.37). Analysis of differences between women with normal BMD and osteopenic women showed statistically significant differences in age (p = 0.003; h2 = 0.09) and lean body mass (p = 0.048; h2 = 0.04). In conclusion, body composition is a stronger predictor of proximal femur BMD than PA variables. However, other studies are necessary to clarify the influence of long-term PA and exercise type on proximal femur BMD. ß 2012 Elsevier Ireland Ltd. All rights reserved.
Keywords: Proximal femur area ActiGraph GT1M Walking Lean body mass Body fat mass
1. Introduction A lack of regular PA and a sedentary lifestyle affect the quality of life in elderly populations. Unfortunately, current population trends show a high prevalence of sedentary lifestyles in the European Union, and the number of inactive individuals is rapidly increasing in older age groups (Varo et al., 2003). PA is recommended as a primary means of preventing chronic diseases in adults and older adults. General recommendations for PA have focused on steps counts and intensity of PA. The most commonly cited healthy step goal is 10,000 steps per day (Hatano, 1993) or detailed step-based recommendation by Tudor-Locke and Bassett (2004). It has been suggested that adults should perform at least 150 min of moderate PA, or 75 min of vigorous PA per week (U.S. Department of Health and Human Services, 2008). In addition, the American College of Sports Medicine and the American Heart Association (ACSM/AHA) recommend engaging in moderate PA for at least 30 min five times per week or vigorous PA for at least 20 min three times per week (Haskell et al., 2007). Combinations of moderate and vigorous PA can be also performed to meet this recommendation.
* Corresponding author. Tel.: +420 777 945 875. E-mail address: [email protected] (A. Ga´ba). 0167-4943/$ – see front matter ß 2012 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.archger.2012.02.001
Strong evidence has shown that regular PA significantly affects human health. Increasing the volume and intensity of PA is associated with reduced risks of breast cancer (McTiernan et al., 2003), type 2 diabetes mellitus (Rana et al., 2007), cardiovascular disease (Albright and Thompson, 2006) and obesity in postmenopausal women (Krumm et al., 2006; Kyle et al., 2004). The benefits of regular PA are also reflected in bone health because it stimulates osteoblast activity, decreases the rate of bone loss, and substantially reduces the risk of osteoporosis in old age (Warburton et al., 2006). The ACSM recommends regular weight-bearing endurance activities in combination with resistance exercise across the age spectrum to maintain or maximize of bone mass (Kohrt et al., 2004). PA that generates moderate and high intensity loading forces may also change body composition and muscle strength. Several studies have demonstrated associations of total body mass (Reid, 2002), lean body mass (Beck et al., 2011), body fat mass (Dytfeld et al., 2011), and muscle strength (Winters and Snow, 2000) with total and regional BMD. Greater body weight has a positive effect on bone tissue due to its stimulating factor for osteogenic response. Individuals with higher body weight, and a respective higher amount of lean body mass, have a lower risk for developing health disorders (Winters and Snow, 2000; Reid, 2002). Lean body mass generally leads to increased BMD through mechanical loading of the skeleton.
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On the contrary, a decrease in muscle mass, a component of lean body mass, and a related decline in muscle strength negatively affect physical function and general stability. This may lead to a significant increase of falls, which is a common cause of serious fractures among elderly adults. Women who suffer from osteoporosis have high risk of femoral fractures (neck and trochanteric regions), which considerably affects mortality in old age (Center et al., 1999). In addition, excluding the above-mentioned factors (i.e., PA, body composition and muscle strength) the risk of osteoporosis increases with excessive use of alcohol (Maurel et al., 2011), tobacco use, eating disorders with excessive protein and calcium deficiency and corticosteroid medications. Low BMD is also influenced by heredity (Bartl and Frisch, 2009). Maintaining healthy bones and avoiding osteoporotic fractures cannot be achieved by systematic monitoring of bone tissue alone, but must be evaluated in relation to objectively measured PA and body composition. Unfortunately, the results have varied between studies, and it remains unclear how these factors affect the variability of bone tissue. Therefore, the main purpose of this crosssectional study was to analyze the relationships between accelerometer-determined PA and selected body composition parameters to total and regional BMD of the proximal femur in postmenopausal women. 2. Materials and methods 2.1. Subjects and design This study was cross-sectional, descriptive, and non-randomized analysis of quantitative data. Evaluation of BMD, PA, and body composition was performed in ninety-seven apparently healthy postmenopausal women, ranging in age from 50 to 77 years, who attended education and PA programs at the University of the Third Age at Palacky´ University in Olomouc. All participants have not been menstruating for at least one year before examination. Women who were currently taking or had taken hormone replacement therapy or any prescription medication for BMD improvements in the last two years were excluded from this research. Women who had undergone major hip or knee surgery or bone densitometry in the previous twelve months or who had physical handicap that might interfere with BMD, body composition and PA measurement (e.g. motor skills disorder, amputation, and paralysis) were also excluded. The purpose and risks of the study were explained to each participant, and written informed consent was obtained. The study plan and design was approved by the Faculty of Physical Culture Ethics Committee at the Palacky´ University in Olomouc. Research was completed in April 2010 and 2011 at the bone densitometry center in Olomouc (Czech Republic). Complete participant characteristics are shown in Table 1. 2.2. BMD and body composition assessment We assessed BMD (g/cm2) of the total body and proximal femur using a dual energy X-ray absorptiometer Lunar Prodigy PrimoTM with enCORETM software version 12.20.023 (GE Healthcare, UK). Regions of interest at the proximal femur included the femoral neck, Ward’s triangle, and the greater trochanter. The DXA device was regularly calibrated before each measurement session using a Lunar phantom with a precision error of 1%. Lean body mass (kg), body fat mass (kg), and percentage of body fat were measured via a DXA device that showed high validity and reliability in evaluating body composition (Heymsfield et al., 2005). The overall procedure (whole-body and femoral scan) took approximately 30 min and was performed with the participant lying in the supine position with their arms at their sides. All metal items were removed from the participants to ensure the accuracy
Table 1 Characteristics of study participants (n = 97).
Age (yrs.) YSM (yrs.) Height (cm) Weight (kg) BMD (g/cm2) Total body Total femur Femoral neck Ward’s triangle Greater trochanter T-score Total body Total femur Femoral neck Ward’s triangle Greater trochanter PA Light PA (min/week) Moderate PA (min/week) Vigorous PA (min/week) Steps per day Physical inactivity (hours/day) Body composition Lean body mass (kg) Body fat mass (kg) Body fat mass (%) BMI (kg/m2) FFMI (kg/m2) BFMI (kg/m2)
Mean SD
Range
63.63 5.23 12.98 6.03 160.63 5.92 69.44 11.20
50.00–77.00 1.00–27.00 146.00–179.00 44.66–98.35
1.11 0.09 0.95 0.11 0.88 0.10 0.68 0.11 0.79 0.11
0.92–1.27 0.70–1.22 0.66–1.19 0.48–0.95 0.56–1.02
0.15 1.11 0.49 0.88 1.14 0.74 1.77 0.83 0.54 0.94
2.60–1.80 2.40–1.70 2.70–1.10 3.30–0.30 2.50–1.50
531 270 225 152 9 24 9766 3644 5.00 1.52
105–1414 0–621 0–195 2945–19.960 1.62–10.62
40.65 4.42 26.46 8.16 37.34 6.42 26.92 4.22 16.65 1.52 10.27 3.20
30.30–51.87 10.50–47.95 18.90–53.00 18.45–39.84 12.74–21.26 3.98–19.54
Note: YSM – years since menopause.
of the measurement. The prevalence of osteopenia and osteoporosis was evaluated according to T-score (BMD normalized by the young adult reference BMD value) for the total femur region. According to the World Health Organization (World Health Organization, 2003), a normal BMD is defined as a T-score greater than 1.0, while a T-score between 1.0 and 2.5 indicates low bone mass (osteopenia), and a T-score lower than 2.5 indicates osteoporosis. 2.3. Anthropometric indices Anthropometric data were collected by standard methods. Standing height was recorded to the nearest 0.5 cm using an anthropometer P-375 (Trystom, Czech Republic) before the DXA procedure with subjects in light clothes without shoes. Body weight was measured by the DXA device, as total body mass, to the nearest 0.1 kg. Body mass index (BMI; kg/m2) was used as an indicator of obesity. BMI is usually calculated as body weight (kg) divided by height squared (m2). However, the significance of the BMI is not clear, because body mass is composed of two distinct components (fat-free mass and body fat mass). Therefore, we also calculated the fat-free mass index (FFMI; kg/m2) and the body fat mass index (BFMI; kg/m2). FFMI and BFMI were derived as fat-free mass (kg) and body fat mass (kg), respectively, divided by height squared (m2). For women within the normal BMI (18.5–24.9 kg/m2), derived BFMI values from 3.9 to 8.2 kg/m2 and FFMI values ranging from 14.6 to 16.8 kg/m2 indicated normal body weight. The cut-off points for FFMI and BFMI corresponding to BMI values in healthy adults were established by Kyle et al. (2004). In addition, we also used the percentage of body fat as an indicator of obesity according to percentage body fat ranges reported by Gallagher et al. (2000). 2.4. PA assessment The participants’ PA level was monitored for seven consecutive days using an ActiGraph model GT1M (3.8 cm 3.7 cm 1.8 cm;
A. Ga´ba et al. / Archives of Gerontology and Geriatrics 54 (2012) e315–e321
27 g) accelerometer (ActiGraph; LLC, Pensacola, FL, USA) that registered vertical acceleration in units of counts. Each accelerometer was calibrated according to the manufacturer’s recommendations before testing. The time sampling interval was set at 15 s using the manufacturer’s software (ActiLife version 5.8) and step mode was activated. Each accelerometer was attached to an elastic belt with a small pocket, was securely positioned near the right iliac crest, and was worn by participants beginning immediately after the densitometry scan. Participants were instructed to wear the accelerometer while awake, and to remove it for water activities. Women who accumulated fewer than 10 h of valid PA recordings per day were excluded from the research. Intensity levels (METs) were analyzed according the recommendation established by Freedson et al. (1998). Light PA (<3 METs) was defined as less than 1952 counts per minute, moderate PA was defined as between 1952 and 5724 counts per minute (3–6 METs), and vigorous PA was defined as greater than 5724 counts per minute (>6 METs). Women were considered sedentary if they accumulated fewer than 150 min of moderate PA per week (U.S. Department of Health and Human Services, 2008) or 5000 steps per day (TudorLocke and Bassett, 2004). 2.5. Statistical analysis Descriptive data are presented as mean and standard deviation (M SD). Data were verified for normality of distribution (Shapiro– Wilk test). Pearson product moment correlations (r) were calculated to determine the relationship between the dependent variables (BMD of proximal femur) and the independent variables (accelerometerdetermined PA and body composition). Partial correlation coefficients (rp) were calculated between the above-mentioned dependent and independent variables while controlling for age. For additional analysis, women were divided into two groups based on the T-score of their proximal femur: normal BMD (n = 71) and osteopenic (n = 26). No women in our study sample had a Tscore lower than 2.5. A one-way analysis of variance (ANOVA) and eta-squared (h2) were used to indicate the differences between group of women with normal BMD and osteopenic women. Etasquared was calculated via ANOVA [h2 = SSfactor/(SSfactor + SSerror)]. The values of 0.01, 0.06, and 0.14 were interpreted as small, medium, and large effect size (Morse, 1999), respectively.
[(Fig._1)TD$IG]
700
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Statistical significance was set at p < 0.05. STATISTICA version 9.0 software was used to complete all analyses. 3. Results The BMD, PA, and body composition characteristics of the study participants are listed in Table 1. The mean age of the women was 63.63 5.23 years, the mean age of menopause was 50.65 3.01 years, and the mean length of time since menopause was 12.98 6.03 years. The mean total body BMD was 0.16 g/cm2 greater than BMD of proximal femur area. Ward’s triangle had the lowest BMD (0.68 0.11 g/cm2), whereas the femoral neck showed the highest BMD (0.88 0.10 g/cm2) in the femoral head area. The average T-score of the proximal femur (0.49 0.88) was lower than the T-score of the whole body (0.15 1.11). Using the T-score of proximal femur, we found that 73% of the participants had normal BMD and 27% were osteopenic. No instances of osteoporosis were found. Thirty percent of the subjects reported being current smokers, and 5% reported having smoked previously. Women preferred light PA (531 270 min/week), and vigorous PA was achieved for 9 24 min per week (Table 1). Although the women did not meet the suggested amount of vigorous PA, the mean amount of moderate PA was above the recommended threshold. Only 38% of women performed less than 150 min of moderate PA per week. The average number of steps accumulated per day was 9766 3644, with 45% of participants accumulating more than 10,000 steps per day and 10% of participants accumulating less than 5000 steps per day (sedentary). The analysis of adherence to the PA recommendations is shown in Fig. 1. Thirty-four percent of participants (21 with normal BMD, 12 with osteopenia) accumulated fewer than 10,000 steps per day and fewer than 150 min of moderate PA per week. In contrast, we registered eighteen highly active women (13 with normal BMD, 5 with osteopenia) who accumulated more than 12,500 steps per day and 300 min of moderate PA per week. The average BMI and percentage of body fat indicated the overweight (BMI = 26.92 4.22 kg/m2; %BFM = 37.34 6.42%). The prevalence of obesity related to body fat mass was 28%, and the prevalence of being overweight was 38% (Table 1). The analysis of FFMI and BFMI is shown in Fig. 2. While the number of participants with low FFMI or BFMI was low, the number of participants with high
sedentary
osteopenia normal
600
500
400
300
200
100
2000
4000
6000
8000
10,000
12,000
14,000
steps per day Fig. 1. Adherence to the PA recommendations.
16,000
18,000
20,000
[(Fig._2)TD$IG]
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normal
22
high
very high
osteopenia normal very high
21
19
high
18 17 16
normal
fat-free mass index (kg/m2)
20
15
low
14 13 12 4
6
8
10
12
14
16
18
20
body fat mass index (kg/m2) Fig. 2. Analysis of FFMI and BFMI.
correlations were used to account for the effect of age on the relationship between BMD of proximal femur regions and PA, only the relationship between steps per day and BMD of femoral neck was significant (rp = 0.22). In contrast, correlations between body composition variables and total BMD of the proximal femur indicated that increases in all body composition variables were associated with increases in proximal femur BMD. Lean body mass was the strongest predictor of BMD of proximal femur (r values ranging from 0.18 to 0.37), explaining 10% of the variance at total femur, and 3–14% of the variance at the regional femur. Correlations increased when the
or very high FFMI and BFMI was high. We found very high FFMI and BFMI in eleven women (9 with normal BMD, 2 with osteopenia). The largest group (21 with normal BMD, 7 with osteopenia) was women with normal FFMI and high BFMI. The analysis of the relationships between accelerometerdetermined PA and body composition to proximal femur BMD is presented in Tables 2 and 3. Pearson product moment correlations revealed no significant relationship between PA variables and proximal femur regions BMD (r values ranging from 0.11 to 0.09). Because changes in bone mass are related to age, we calculated partial correlation coefficients controlling for age. When partial
Table 2 Correlations relationships between the BMD of the proximal femur and PA variables.
Light PA (min/week) Moderate PA (min/week) Vigorous PA (min/week) Steps per day Physical inactivity (hours/day) * y
BMD total femur
BMD femoral neck
BMD Ward’s triangle
BMD greater trochanter
Correlation
Partial correlationy
Correlation
Partial correlationy
Correlation
Partial correlationy
Correlation
Partial correlationy
0.08 0.08 0.03 0.01 0.02
0.13 0.02 0.01 0.09 0.03
0.08 0.02 0.02 0.08 0.06
0.16 0.09 0.04 0.22* 0.08
0.03 0.00 0.01 0.07 0.04
0.09 0.09 0.06 0.19 0.03
0.11 0.12 0.09 0.00 0.01
0.14 0.09 0.07 0.04 0.01
p < 0.05. Controlling for age.
Table 3 Correlations between the BMD of the proximal femur and body composition variables. BMD total femur
Weight (kg) Lean body mass (kg) Body fat mass (kg) Body fat mass (%) BMI (kg/m2) FFMI (kg/m2) BFMI (kg/m2) * y
p < 0.05. Controlling for age.
BMD femoral neck
BMD Ward’s triangle
BMD greater trochanter
Correlation
Partial correlationy
Correlation
Partial correlationy
Correlation
Partial correlationy
Correlation
Partial correlationy
0.36* 0.32* 0.30* 0.24* 0.31* 0.28* 0.27*
0.42* 0.34* 0.37* 0.31* 0.39* 0.33* 0.35*
0.34* 0.32* 0.26* 0.20* 0.21* 0.17 0.20
0.43* 0.36* 0.36* 0.30* 0.32* 0.24* 0.31*
0.21* 0.18 0.16 0.14 0.13 0.10 0.12
0.28* 0.20* 0.25* 0.23* 0.22* 0.15 0.22*
0.36* 0.37* 0.26* 0.19 0.27* 0.29* 0.22*
0.39* 0.39* 0.31* 0.23* 0.32* 0.32* 0.27*
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Table 4 PA characteristics and body composition among groups according to total body BMD categories.
Age (yrs.) YSM (yrs.) Height (cm) Weight (kg) PA Light (min/week) Moderate (min/week) Vigorous (min/week) Steps per day Physical inactivity (hours/day) Body composition Lean body mass (kg) Body fat mass (kg) Body fat mass (%) BMI (kg/m2) FFMI (kg/m2) BFMI (kg/m2) *
p < 0.05; yh2 > 0.01,
Normal BMD (n = 71)
Osteopenia (n = 26)
Mean SD
Mean SD
62.69 4.82 12.26 5.75 161.32 6.00 70.78 9.61
66.19 5.56 14.92 6.47 158.74 5.33 65.78 14.26
F
h2
9.26* 3.79 3.73 3.91
0.09yy 0.04y 0.04y 0.04y
542 275 240 151 9 26 9925 3426 5.05 1.47
502 259 187 150 7 19 9331 4226 4.90 1.71
0.41 2.29 0.20 0.50 0.20
0.00 0.02y 0.00 0.01y 0.00
41.19 3.95 27.16 7.94 37.82 6.03 27.26 3.91 16.77 1.48 10.49 3.00
39.19 5.32 24.52 9.88 36.04 7.36 25.99 4.92 16.33 1.64 9.66 3.68
4.02* 2.01 1.46 1.73 1.64 1.26
0.04y 0.02y 0.02y 0.02y 0.02y 0.01y
yy 2
h > 0.06.
analysis was controlling for age (rp values ranging from 0.20 to 0.39). Additionally, body fat mass explained 9% of the variance at total femur, and 3–7% of the variance at regional femur. No significant relationship was found between Ward’s triangle BMD and body composition when analysis was not controlled for age. The comparisons of PA and body composition between women with normal proximal femur BMD and osteopenic women are shown in Table 4. Statistically significant differences were found in age (p = 0.003; h2 = 0.09) and lean body mass (p = 0.048; h2 = 0.04). Furthermore, the small effect size was observed for all variables except light and vigorous PA and physical inactivity. Both groups performed more than 150 min of moderate PA per week and accumulated less than 10,000 steps per day. The average BMI and percentage body fat indicated the overweight in both groups.
4. Discussion In this study, we evaluated the relationship of objectively measured PA and body composition with total and regional BMD of the proximal femur area. BMD is commonly used for diagnosing osteoporosis. Our results confirm recent findings (Wu et al., 2011) showing that Ward’s triangle has the lowest BMD and T-score in the femoral neck. In our study, the mean T-score of total and regional femur indicated healthy bones, with the exception of Ward’s triangle (T-score lower than 1.0). In addition, although the prevalence of osteoporosis related to the femoral neck in 50 to 84year-old postmenopausal women is approximately 21% (Kanis et al., 2008), we did not find any women with osteoporosis. Because low BMD contributes to the incidence of fractures in postmenopausal women, strategies that positively stimulate bone tissue may reduce mortality related to osteoporotic fractures. Although BMD can be increased through pharmacologic therapy, PA is the only intervention that increases bone mass. For example, Douchi et al. (2000) found that exercising women have higher BMD in the dominant arm and lumbar spine than sedentary women. A cross-sectional cohort study including data from 6032 postmenopausal women between the ages of 50 and 79 years (Beck et al., 2011) demonstrated that PA positively affects bone health, with bones becoming stronger and more resistant to fracture when their geometric response to load is improved. Nevertheless, we observed no significant relationship between PA variables and proximal femur regions BMD, except for the relationship between steps per day and femoral neck BMD. Furthermore, we did not find
significant differences in PA variables between women with normal BMD and osteopenic women. In this study, we measured only the intensity and volume of PA. However, Hamilton et al. (2010) have shown that bone tissue quality is primarily dependent on exercise type. To benefit bone tissue, PA should include weight-bearing exercises, special training to strengthen the muscles and exercise with high impact loading (Kohrt et al., 2004). Previous research has highlighted the positive effect of whole-body vibration training in reducing the risk for osteoporosis by increasing lumbar BMD (Stengel et al., 2011). In contrast, lower impact exercises that reduce the effect of gravity, such as swimming, do not significantly improve BMD (Bartl and Frisch, 2009). As our data showed, postmenopausal women preferred light or moderate PA. However, we did not observe a significant relationship between these PA variables and proximal femur regions BMD. Although our study did not show a significant relationship between moderate PA and bone mass, Feskanich et al. (2002) have demonstrated that moderate PA by postmenopausal women substantially lowers the risk of hip fracture. Specifically, the risk of hip fractures decreases by 6% for every increase of 3 MET-hours per week of PA, which is roughly equivalent to 1 h of walking per week. In contrast, Chubak et al. (2006) have reported a randomized controlled study showing that yearlong moderate-intensity aerobic exercise intervention does not affect total body BMD in overweight or obese postmenopausal women. Walking, an alternative of moderate PA, is the most common type of activity among elderly adults. It has been extensively promoted as a means of preventing osteoporosis. However, studies have shown variable effects of walking on bone health. Martyn-St James and Carroll (2008) have shown that although regular walking had no significant effect on spinal BMD preservation in postmenopausal women, a significant positive effect was evident at the femoral neck. Although these findings do not confirm that regular walking preserves bone mass, several studies have demonstrated the positive consequence of walking on proximal femur BMD. For example, Boyer et al. (2011) have reported that a minimum of 4892 steps (walking at 1.00 m/s) per day is required for postmenopausal women with an average body weight (65.10 kg) to maintain a proximal femur T-score of 1.0. Substantially more steps per day are required for lighter individuals (18,568 steps per day) and less steps per day are required for heavier individuals (1638 steps per day). These results correspond with our study, in which participants (weight = 69.44 kg) who accumulated 9766 steps per day had a
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mean proximal femur T-score of 0.48. As stated above, the number of steps accumulated per day was the only PA variable that significantly correlated with proximal femur regional BMD. There has been discussion regarding the influence of lean body mass and body fat mass on bone tissue. Several studies have shown that lean body mass is a better predictor of BMD than body fat mass in young (Miller et al., 2004), premenopausal (Winters and Snow, 2000) and postmenopausal women (Gjesdal et al., 2008). Postmenopausal women with higher lean body mass have significantly stronger femur geometry than their counterparts with less lean body mass (Beck et al., 2011). Winters and Snow (2000) have shown that lean body mass explained 24% of the variance for total femur BMD and 17% of the variance for femoral neck BMD in premenopausal women. Although our findings confirm these results, we found that lean body mass explained only 10% of the variance for total femur and 3–14% for the variance at regional femur. Based on additional analysis, we also observed that lean body mass was statistically higher in women with normal BMD. In contrast, Dytfeld et al. (2011) reported a stronger association between body fat mass and femoral neck BMD than between lean body mass and femoral neck BMD in postmenopausal women with osteoporosis. Our study sample included participants who had a relatively high absolute and relative amount of body fat mass. Thus, this may explain why our study sample did not find women with osteoporosis. Although the higher proportion of body fat mass is associated with increasing of total and regional BMD, adiposity is also associated with increased risk of many chronic diseases such as cardiovascular disease (Lavie et al., 2009) or type 2 diabetes mellitus (Rana et al., 2007). This study has several limitations. The small size of study sample could limit the statistical power. Study subjects were not chosen randomly and do not represent the general population of postmenopausal women, as recent research (Ga´ba et al., 2009) has shown that female students of the University of the Third Age are often active. Furthermore, the study sample did not include subjects with T-scores lower than 2.5. Therefore, follow-up research should focus on an elderly population with insufficient PA and persons with osteoporosis. The objectivity of the presented results may also have been affected by the study design. Because this study was cross-sectional and BMD is dependent on the age, it may not provide the same results as a longitudinal or semilongitudinal study. However, other researchers have observed similar results, which support our findings. Because of the intensity and volume of PA observed in this study, follow-up research should monitor the relationship between the type of PA and bone health. Our results also show that lean body mass strongly predicts total and regional proximal femur BMD. Therefore, future research could investigate the muscle strength of lower extremity via isokinetic dynamometry that depends on lean body mass. In conclusion, no significant relationship was found between accelerometer-determined PA and proximal femur BMD. Although the correlations increased when the analysis controlled for age, only the relationship between steps per day and femoral neck BMD was significant. Our results support the hypothesis that proximal femur BMD is more dependent on body composition than PA. Lean body mass was a stronger predictor of proximal femur BMD than body fat mass. Ward’s triangle was the only area of the proximal femur that showed no significant relationship with PA, even with body composition when analysis did not control for age. Analysis of differences between women with normal BMD and osteopenic women showed statistically significant differences in age and lean body mass. Conflicts of interest None.
Acknowledgments The study was supported by a research grant from the Ministry of Education, Youth and Sports of the Czech Republic (No. MSM 6198959221) ‘‘Physical activity and inactivity of the inhabitants of the Czech Republic in the context of behavioral changes’’ and a research grant from Palacky´ University in Olomouc (ID: FTK_2011_014) ‘‘Evaluation of bone tissue of the proximal femur in women with different levels of physical activity’’. References Albright, C., Thompson, D., 2006. The effectiveness of walking in preventing cardiovascular disease in women: a review of the current literature. J. Women’s Health 15, 271–280. Bartl, R., Frisch, B., 2009. Osteoporosis: Diagnosis, Prevention, Therapy, second ed. Springer, Berlin. Beck, T.J., Kohlmeier, L.A., Petit, M.A., Wu, G.L., Leboff, M.S., Cauley, J.A., Nicholas, S., Chen, Z., 2011. Confounders in the association between exercise and femur bone in postmenopausal women. Med. Sci. Sports Exerc. 43, 80–89. Boyer, K., Kiratli, B., Andriacchi, T., Beaupre, G., 2011. Maintaining femoral bone density in adults: how many steps per day are enough? Osteoporos. Int. 1–8. Center, J.R., Nguyen, T.V., Schneider, D., Sambrook, P.N., Eisman, J.A., 1999. Mortality after all major types of osteoporotic fracture in men and women: an observational study. Lancet 353, 878–882. Chubak, J., Ulrich, C.M., Tworoger, S.S., Sorensen, B., Yasui, Y., Irwin, M.L., Stanczyk, F.Z., Potter, J.D., McTiernan, A., 2006. Effect of exercise on bone mineral density and lean mass in postmenopausal women. Med. Sci. Sports Exerc. 38, 1236–1244. Douchi, T., Yamamoto, S., Oki, T., Maruta, K., Kuwahata, R., Yamasaki, H., Nagata, Y., 2000. The effects of physical exercise on body fat distribution and bone mineral density in postmenopausal women. Maturitas 35, 25–30. Dytfeld, J., Ignaszak-Szczepaniak, M., Gowin, E., Michalak, M., Horst-Sikorska, W., 2011. Influence of lean and fat mass on bone mineral density (BMD) in postmenopausal women with osteoporosis. Arch. Gerontol. Geriatr. 53, 237–242. Feskanich, D., Willett, W., Colditz, G., 2002. Walking and leisure-time activity and risk of hip fracture in postmenopausal women. J. Am. Med. Assoc. 288, 2300–2306. Freedson, P.S., Melanson, E., Sirard, J., 1998. Calibration of the Computer Science and Applications, Inc. accelerometer. Med. Sci. Sports Exerc. 30, 777–781. Ga´ba, A., Pelclova´, J., Prˇidalova´, M., Riegerova´, J., Dosta´lova´, I., Engelova´, L., 2009. The evaluation of body composition in relation to physical activity in 56–73 y. old women. A pilot study. Acta Univ. Palacki. Olomuc., Gymn. 39, 21–30. Gallagher, D., Heymsfield, S.B., Heo, M., Jebb, S.A., Murgatroyd, P.R., Sakamoto, Y., 2000. Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am. J. Clin. Nutr. 72, 694–701. Gjesdal, C.G., Halse, J.I., Eide, G.E., Brun, J.G., Tell, G.S., 2008. Impact of lean mass and fat mass on bone mineral density. The Hordaland Health Study. Maturitas 59, 191–200. Hamilton, C.J., Swan, V.J., Jamal, S.A., 2010. The effects of exercise and physical activity participation on bone mass and geometry in postmenopausal women: a systematic review of pQCT studies. Osteoporos. Int. 21, 11–23. Haskell, W.L., Lee, I.M., Pate, R.R., Powell, K.E., Blair, S.N., Franklin, B.A., Macera, C.A., Heath, G.W., Thompson, P.D., Bauman, A., 2007. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation 116, 1081–1093. Hatano, Y., 1993. Use of the pedometer for promoting daily walking exercise. Int. Coun. Health Phys. Educ. Recreat. 29, 4–8. Heymsfield, S., Lohman, T., Wang, Z., Going, S., 2005. Human Body Composition. Human Kinetics, Champaign, IL. Kanis, J.A., Burlet, N., Cooper, C., Delmas, P.D., Reginster, J.Y., Borgstrom, F., Rizzoli, R., 2008. European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Osteoporos. Int. 19, 399–428. Kohrt, W.M., Bloomfield, S.A., Little, K.D., Nelson, M.E., Yingling, V.R., 2004. American College of Sports Medicine Position Stand: physical activity and bone health. Med. Sci. Sports Exerc. 36, 1985–1996. Krumm, E.M., Dessieux, O.L., Andrews, P., Thompson, D.L., 2006. The relationship between daily steps and body composition in postmenopausal women. J. Women’s Health 15, 202–210. Kyle, U.G., Morabia, A., Schutz, Y., Pichard, C., 2004. Sedentarism affects body fat mass index and fat-free mass index in adults aged 18 to 98 years. Nutrition 20, 255–260. Lavie, C.J., Milani, R.V., Ventura, H.O., 2009. Obesity and cardiovascular disease: risk factor, paradox, and impact of weight loss. J. Am. Coll. Cardiol. 53, 1925–1932. Martyn-St James, M., Carroll, S., 2008. Meta-analysis of walking for preservation of bone mineral density in postmenopausal women. Bone 43, 521–531. Maurel, D.B., Boisseau, N., Benhamou, C.L., Jaffre, C., 2011. Alcohol and bone: review of dose effects and mechanisms. Osteoporos. Int. [ahead of print]. McTiernan, A., Kooperberg, C., White, E., Wilcox, S., Coates, R., Adams-Campbell, L.L., Woods, N., Ockene, J., 2003. Recreational physical activity and the risk of breast cancer in postmenopausal women: the Women’s Health Initiative Cohort Study. J. Am. Med. Assoc. 290, 1331–1336.
A. Ga´ba et al. / Archives of Gerontology and Geriatrics 54 (2012) e315–e321 Miller, L.E., Nickols-Richardson, S.M., Wootten, D.F., Ramp, W.K., Herbert, W.G., 2004. Relationships among bone mineral density, body composition, and isokinetic strength in young women. Calcif. Tissue Int. 74, 229–235. Morse, D.T., 1999. MINSIZE2: a computer program for determining effect size and minimum sample size for statistical significance for univariate, multivariate, and nonparametric tests. Educ. Psychol. Meas. 59, 518–531. Rana, J., Li, T., Manson, J., Hu, F., 2007. Adiposity compared with physical inactivity and risk of type 2 diabetes in women. Diabetes Care 30, 53. Reid, I.R., 2002. Relationships among body mass, its components, and bone. Bone 31, 547–555. Stengel, S.V., Kemmler, W., Bebenek, M., Engelke, K., Kalender, W.A., 2011. Effects of whole body vibration training on different devices on bone mineral density. Med. Sci. Sports Exerc. 43, 1071–1079. Tudor-Locke, C., Bassett, D., 2004. How many steps/day are enough?: Preliminary pedometer indices for public health. Sports Med. 34, 1–8.
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U.S. Department of Health and Human Services, 2008. 2008 Physical Activity Guidelines for Americans be Active, Healthy, and Happy! U.S. Dept. of Health and Human Services, Washington, D.C.. Varo, J.J., Martinez-Gonzalez, M.A., De Irala-Estevez, J., Kearney, J., Gibney, M., Martinez, J.A., 2003. Distribution and determinants of sedentary lifestyles in the European Union. Int. J. Epidemiol. 32, 138–146. Warburton, D.E., Nicol, C.W., Bredin, S.S., 2006. Health benefits of physical activity: the evidence. Can. Med. Assoc. J. 174, 801–809. Winters, K.M., Snow, C.M., 2000. Body composition predicts bone mineral density and balance in premenopausal women. J. Women’s Health Gender-Based Med. 9, 865–872. World Health Organization, 2003. Prevention and Management of Osteoporosis: Report of a WHO Scientific Group. World Health Organization, Geneva. Wu, Q., Lefante, J.J., Rice, J.C., Magnus, J.H., 2011. Age, race, weight, and gender impact normative values of bone mineral density. Gender Med. 8, 189–201.
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Archives of Gerontology and Geriatrics journal homepage: www.elsevier.com/locate/archger
The relationship between accelerometer-determined physical activity (PA) and body composition and bone mineral density (BMD) in postmenopausal women Alesˇ Ga´ba a,*, Ondrˇej Kapusˇ a, Jana Pelclova´ b, Jarmila Riegerova´ a a b
Department of Natural Sciences in Kinanthropology, Faculty of Physical Culture, Palacky´ University in Olomouc, Trˇ. Mı´ru 115, 771 11, Olomouc, Czech Republic Center for Kinanthropology Research, Faculty of Physical Culture, Palacky´ University in Olomouc, Trˇ. Mı´ru 115, 771 11, Olomouc, Czech Republic
A R T I C L E I N F O
A B S T R A C T
Article history: Received 21 October 2011 Received in revised form 1 February 2012 Accepted 2 February 2012 Available online 8 March 2012
Studies of the relationships between BMD, PA and body composition have shown variable results. Therefore, the aim of this cross-sectional study was to determine the relationships between accelerometer-determined PA and selected body composition parameters to total and regional BMD of the proximal femur in postmenopausal women. BMD and body composition were measured using dual energy X-ray absorptiometry in 97 women with a mean age 63.63 5.23 years. PA was monitored using an ActiGraph GT1M accelerometer. Correlation analysis did not show significant relationships between PA variables and BMD, but increases in body composition variables were associated with increases in BMD. Lean body mass was the strongest predictor of proximal femur BMD (r = 0.18–0.37), explaining 10% of the variance for total femur, and 3–14% of the variance for regional femurs. Correlations increased when the analysis was controlled for age (rp = 0.20–0.39). A significant relationship was also found between body fat mass and BMD (r = 0.16–0.30; rp = 0.25–0.37). Analysis of differences between women with normal BMD and osteopenic women showed statistically significant differences in age (p = 0.003; h2 = 0.09) and lean body mass (p = 0.048; h2 = 0.04). In conclusion, body composition is a stronger predictor of proximal femur BMD than PA variables. However, other studies are necessary to clarify the influence of long-term PA and exercise type on proximal femur BMD. ß 2012 Elsevier Ireland Ltd. All rights reserved.
Keywords: Proximal femur area ActiGraph GT1M Walking Lean body mass Body fat mass
1. Introduction A lack of regular PA and a sedentary lifestyle affect the quality of life in elderly populations. Unfortunately, current population trends show a high prevalence of sedentary lifestyles in the European Union, and the number of inactive individuals is rapidly increasing in older age groups (Varo et al., 2003). PA is recommended as a primary means of preventing chronic diseases in adults and older adults. General recommendations for PA have focused on steps counts and intensity of PA. The most commonly cited healthy step goal is 10,000 steps per day (Hatano, 1993) or detailed step-based recommendation by Tudor-Locke and Bassett (2004). It has been suggested that adults should perform at least 150 min of moderate PA, or 75 min of vigorous PA per week (U.S. Department of Health and Human Services, 2008). In addition, the American College of Sports Medicine and the American Heart Association (ACSM/AHA) recommend engaging in moderate PA for at least 30 min five times per week or vigorous PA for at least 20 min three times per week (Haskell et al., 2007). Combinations of moderate and vigorous PA can be also performed to meet this recommendation.
* Corresponding author. Tel.: +420 777 945 875. E-mail address: [email protected] (A. Ga´ba). 0167-4943/$ – see front matter ß 2012 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.archger.2012.02.001
Strong evidence has shown that regular PA significantly affects human health. Increasing the volume and intensity of PA is associated with reduced risks of breast cancer (McTiernan et al., 2003), type 2 diabetes mellitus (Rana et al., 2007), cardiovascular disease (Albright and Thompson, 2006) and obesity in postmenopausal women (Krumm et al., 2006; Kyle et al., 2004). The benefits of regular PA are also reflected in bone health because it stimulates osteoblast activity, decreases the rate of bone loss, and substantially reduces the risk of osteoporosis in old age (Warburton et al., 2006). The ACSM recommends regular weight-bearing endurance activities in combination with resistance exercise across the age spectrum to maintain or maximize of bone mass (Kohrt et al., 2004). PA that generates moderate and high intensity loading forces may also change body composition and muscle strength. Several studies have demonstrated associations of total body mass (Reid, 2002), lean body mass (Beck et al., 2011), body fat mass (Dytfeld et al., 2011), and muscle strength (Winters and Snow, 2000) with total and regional BMD. Greater body weight has a positive effect on bone tissue due to its stimulating factor for osteogenic response. Individuals with higher body weight, and a respective higher amount of lean body mass, have a lower risk for developing health disorders (Winters and Snow, 2000; Reid, 2002). Lean body mass generally leads to increased BMD through mechanical loading of the skeleton.
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On the contrary, a decrease in muscle mass, a component of lean body mass, and a related decline in muscle strength negatively affect physical function and general stability. This may lead to a significant increase of falls, which is a common cause of serious fractures among elderly adults. Women who suffer from osteoporosis have high risk of femoral fractures (neck and trochanteric regions), which considerably affects mortality in old age (Center et al., 1999). In addition, excluding the above-mentioned factors (i.e., PA, body composition and muscle strength) the risk of osteoporosis increases with excessive use of alcohol (Maurel et al., 2011), tobacco use, eating disorders with excessive protein and calcium deficiency and corticosteroid medications. Low BMD is also influenced by heredity (Bartl and Frisch, 2009). Maintaining healthy bones and avoiding osteoporotic fractures cannot be achieved by systematic monitoring of bone tissue alone, but must be evaluated in relation to objectively measured PA and body composition. Unfortunately, the results have varied between studies, and it remains unclear how these factors affect the variability of bone tissue. Therefore, the main purpose of this crosssectional study was to analyze the relationships between accelerometer-determined PA and selected body composition parameters to total and regional BMD of the proximal femur in postmenopausal women. 2. Materials and methods 2.1. Subjects and design This study was cross-sectional, descriptive, and non-randomized analysis of quantitative data. Evaluation of BMD, PA, and body composition was performed in ninety-seven apparently healthy postmenopausal women, ranging in age from 50 to 77 years, who attended education and PA programs at the University of the Third Age at Palacky´ University in Olomouc. All participants have not been menstruating for at least one year before examination. Women who were currently taking or had taken hormone replacement therapy or any prescription medication for BMD improvements in the last two years were excluded from this research. Women who had undergone major hip or knee surgery or bone densitometry in the previous twelve months or who had physical handicap that might interfere with BMD, body composition and PA measurement (e.g. motor skills disorder, amputation, and paralysis) were also excluded. The purpose and risks of the study were explained to each participant, and written informed consent was obtained. The study plan and design was approved by the Faculty of Physical Culture Ethics Committee at the Palacky´ University in Olomouc. Research was completed in April 2010 and 2011 at the bone densitometry center in Olomouc (Czech Republic). Complete participant characteristics are shown in Table 1. 2.2. BMD and body composition assessment We assessed BMD (g/cm2) of the total body and proximal femur using a dual energy X-ray absorptiometer Lunar Prodigy PrimoTM with enCORETM software version 12.20.023 (GE Healthcare, UK). Regions of interest at the proximal femur included the femoral neck, Ward’s triangle, and the greater trochanter. The DXA device was regularly calibrated before each measurement session using a Lunar phantom with a precision error of 1%. Lean body mass (kg), body fat mass (kg), and percentage of body fat were measured via a DXA device that showed high validity and reliability in evaluating body composition (Heymsfield et al., 2005). The overall procedure (whole-body and femoral scan) took approximately 30 min and was performed with the participant lying in the supine position with their arms at their sides. All metal items were removed from the participants to ensure the accuracy
Table 1 Characteristics of study participants (n = 97).
Age (yrs.) YSM (yrs.) Height (cm) Weight (kg) BMD (g/cm2) Total body Total femur Femoral neck Ward’s triangle Greater trochanter T-score Total body Total femur Femoral neck Ward’s triangle Greater trochanter PA Light PA (min/week) Moderate PA (min/week) Vigorous PA (min/week) Steps per day Physical inactivity (hours/day) Body composition Lean body mass (kg) Body fat mass (kg) Body fat mass (%) BMI (kg/m2) FFMI (kg/m2) BFMI (kg/m2)
Mean SD
Range
63.63 5.23 12.98 6.03 160.63 5.92 69.44 11.20
50.00–77.00 1.00–27.00 146.00–179.00 44.66–98.35
1.11 0.09 0.95 0.11 0.88 0.10 0.68 0.11 0.79 0.11
0.92–1.27 0.70–1.22 0.66–1.19 0.48–0.95 0.56–1.02
0.15 1.11 0.49 0.88 1.14 0.74 1.77 0.83 0.54 0.94
2.60–1.80 2.40–1.70 2.70–1.10 3.30–0.30 2.50–1.50
531 270 225 152 9 24 9766 3644 5.00 1.52
105–1414 0–621 0–195 2945–19.960 1.62–10.62
40.65 4.42 26.46 8.16 37.34 6.42 26.92 4.22 16.65 1.52 10.27 3.20
30.30–51.87 10.50–47.95 18.90–53.00 18.45–39.84 12.74–21.26 3.98–19.54
Note: YSM – years since menopause.
of the measurement. The prevalence of osteopenia and osteoporosis was evaluated according to T-score (BMD normalized by the young adult reference BMD value) for the total femur region. According to the World Health Organization (World Health Organization, 2003), a normal BMD is defined as a T-score greater than 1.0, while a T-score between 1.0 and 2.5 indicates low bone mass (osteopenia), and a T-score lower than 2.5 indicates osteoporosis. 2.3. Anthropometric indices Anthropometric data were collected by standard methods. Standing height was recorded to the nearest 0.5 cm using an anthropometer P-375 (Trystom, Czech Republic) before the DXA procedure with subjects in light clothes without shoes. Body weight was measured by the DXA device, as total body mass, to the nearest 0.1 kg. Body mass index (BMI; kg/m2) was used as an indicator of obesity. BMI is usually calculated as body weight (kg) divided by height squared (m2). However, the significance of the BMI is not clear, because body mass is composed of two distinct components (fat-free mass and body fat mass). Therefore, we also calculated the fat-free mass index (FFMI; kg/m2) and the body fat mass index (BFMI; kg/m2). FFMI and BFMI were derived as fat-free mass (kg) and body fat mass (kg), respectively, divided by height squared (m2). For women within the normal BMI (18.5–24.9 kg/m2), derived BFMI values from 3.9 to 8.2 kg/m2 and FFMI values ranging from 14.6 to 16.8 kg/m2 indicated normal body weight. The cut-off points for FFMI and BFMI corresponding to BMI values in healthy adults were established by Kyle et al. (2004). In addition, we also used the percentage of body fat as an indicator of obesity according to percentage body fat ranges reported by Gallagher et al. (2000). 2.4. PA assessment The participants’ PA level was monitored for seven consecutive days using an ActiGraph model GT1M (3.8 cm 3.7 cm 1.8 cm;
A. Ga´ba et al. / Archives of Gerontology and Geriatrics 54 (2012) e315–e321
27 g) accelerometer (ActiGraph; LLC, Pensacola, FL, USA) that registered vertical acceleration in units of counts. Each accelerometer was calibrated according to the manufacturer’s recommendations before testing. The time sampling interval was set at 15 s using the manufacturer’s software (ActiLife version 5.8) and step mode was activated. Each accelerometer was attached to an elastic belt with a small pocket, was securely positioned near the right iliac crest, and was worn by participants beginning immediately after the densitometry scan. Participants were instructed to wear the accelerometer while awake, and to remove it for water activities. Women who accumulated fewer than 10 h of valid PA recordings per day were excluded from the research. Intensity levels (METs) were analyzed according the recommendation established by Freedson et al. (1998). Light PA (<3 METs) was defined as less than 1952 counts per minute, moderate PA was defined as between 1952 and 5724 counts per minute (3–6 METs), and vigorous PA was defined as greater than 5724 counts per minute (>6 METs). Women were considered sedentary if they accumulated fewer than 150 min of moderate PA per week (U.S. Department of Health and Human Services, 2008) or 5000 steps per day (TudorLocke and Bassett, 2004). 2.5. Statistical analysis Descriptive data are presented as mean and standard deviation (M SD). Data were verified for normality of distribution (Shapiro– Wilk test). Pearson product moment correlations (r) were calculated to determine the relationship between the dependent variables (BMD of proximal femur) and the independent variables (accelerometerdetermined PA and body composition). Partial correlation coefficients (rp) were calculated between the above-mentioned dependent and independent variables while controlling for age. For additional analysis, women were divided into two groups based on the T-score of their proximal femur: normal BMD (n = 71) and osteopenic (n = 26). No women in our study sample had a Tscore lower than 2.5. A one-way analysis of variance (ANOVA) and eta-squared (h2) were used to indicate the differences between group of women with normal BMD and osteopenic women. Etasquared was calculated via ANOVA [h2 = SSfactor/(SSfactor + SSerror)]. The values of 0.01, 0.06, and 0.14 were interpreted as small, medium, and large effect size (Morse, 1999), respectively.
[(Fig._1)TD$IG]
700
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Statistical significance was set at p < 0.05. STATISTICA version 9.0 software was used to complete all analyses. 3. Results The BMD, PA, and body composition characteristics of the study participants are listed in Table 1. The mean age of the women was 63.63 5.23 years, the mean age of menopause was 50.65 3.01 years, and the mean length of time since menopause was 12.98 6.03 years. The mean total body BMD was 0.16 g/cm2 greater than BMD of proximal femur area. Ward’s triangle had the lowest BMD (0.68 0.11 g/cm2), whereas the femoral neck showed the highest BMD (0.88 0.10 g/cm2) in the femoral head area. The average T-score of the proximal femur (0.49 0.88) was lower than the T-score of the whole body (0.15 1.11). Using the T-score of proximal femur, we found that 73% of the participants had normal BMD and 27% were osteopenic. No instances of osteoporosis were found. Thirty percent of the subjects reported being current smokers, and 5% reported having smoked previously. Women preferred light PA (531 270 min/week), and vigorous PA was achieved for 9 24 min per week (Table 1). Although the women did not meet the suggested amount of vigorous PA, the mean amount of moderate PA was above the recommended threshold. Only 38% of women performed less than 150 min of moderate PA per week. The average number of steps accumulated per day was 9766 3644, with 45% of participants accumulating more than 10,000 steps per day and 10% of participants accumulating less than 5000 steps per day (sedentary). The analysis of adherence to the PA recommendations is shown in Fig. 1. Thirty-four percent of participants (21 with normal BMD, 12 with osteopenia) accumulated fewer than 10,000 steps per day and fewer than 150 min of moderate PA per week. In contrast, we registered eighteen highly active women (13 with normal BMD, 5 with osteopenia) who accumulated more than 12,500 steps per day and 300 min of moderate PA per week. The average BMI and percentage of body fat indicated the overweight (BMI = 26.92 4.22 kg/m2; %BFM = 37.34 6.42%). The prevalence of obesity related to body fat mass was 28%, and the prevalence of being overweight was 38% (Table 1). The analysis of FFMI and BFMI is shown in Fig. 2. While the number of participants with low FFMI or BFMI was low, the number of participants with high
sedentary
osteopenia normal
600
500
400
300
200
100
2000
4000
6000
8000
10,000
12,000
14,000
steps per day Fig. 1. Adherence to the PA recommendations.
16,000
18,000
20,000
[(Fig._2)TD$IG]
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normal
22
high
very high
osteopenia normal very high
21
19
high
18 17 16
normal
fat-free mass index (kg/m2)
20
15
low
14 13 12 4
6
8
10
12
14
16
18
20
body fat mass index (kg/m2) Fig. 2. Analysis of FFMI and BFMI.
correlations were used to account for the effect of age on the relationship between BMD of proximal femur regions and PA, only the relationship between steps per day and BMD of femoral neck was significant (rp = 0.22). In contrast, correlations between body composition variables and total BMD of the proximal femur indicated that increases in all body composition variables were associated with increases in proximal femur BMD. Lean body mass was the strongest predictor of BMD of proximal femur (r values ranging from 0.18 to 0.37), explaining 10% of the variance at total femur, and 3–14% of the variance at the regional femur. Correlations increased when the
or very high FFMI and BFMI was high. We found very high FFMI and BFMI in eleven women (9 with normal BMD, 2 with osteopenia). The largest group (21 with normal BMD, 7 with osteopenia) was women with normal FFMI and high BFMI. The analysis of the relationships between accelerometerdetermined PA and body composition to proximal femur BMD is presented in Tables 2 and 3. Pearson product moment correlations revealed no significant relationship between PA variables and proximal femur regions BMD (r values ranging from 0.11 to 0.09). Because changes in bone mass are related to age, we calculated partial correlation coefficients controlling for age. When partial
Table 2 Correlations relationships between the BMD of the proximal femur and PA variables.
Light PA (min/week) Moderate PA (min/week) Vigorous PA (min/week) Steps per day Physical inactivity (hours/day) * y
BMD total femur
BMD femoral neck
BMD Ward’s triangle
BMD greater trochanter
Correlation
Partial correlationy
Correlation
Partial correlationy
Correlation
Partial correlationy
Correlation
Partial correlationy
0.08 0.08 0.03 0.01 0.02
0.13 0.02 0.01 0.09 0.03
0.08 0.02 0.02 0.08 0.06
0.16 0.09 0.04 0.22* 0.08
0.03 0.00 0.01 0.07 0.04
0.09 0.09 0.06 0.19 0.03
0.11 0.12 0.09 0.00 0.01
0.14 0.09 0.07 0.04 0.01
p < 0.05. Controlling for age.
Table 3 Correlations between the BMD of the proximal femur and body composition variables. BMD total femur
Weight (kg) Lean body mass (kg) Body fat mass (kg) Body fat mass (%) BMI (kg/m2) FFMI (kg/m2) BFMI (kg/m2) * y
p < 0.05. Controlling for age.
BMD femoral neck
BMD Ward’s triangle
BMD greater trochanter
Correlation
Partial correlationy
Correlation
Partial correlationy
Correlation
Partial correlationy
Correlation
Partial correlationy
0.36* 0.32* 0.30* 0.24* 0.31* 0.28* 0.27*
0.42* 0.34* 0.37* 0.31* 0.39* 0.33* 0.35*
0.34* 0.32* 0.26* 0.20* 0.21* 0.17 0.20
0.43* 0.36* 0.36* 0.30* 0.32* 0.24* 0.31*
0.21* 0.18 0.16 0.14 0.13 0.10 0.12
0.28* 0.20* 0.25* 0.23* 0.22* 0.15 0.22*
0.36* 0.37* 0.26* 0.19 0.27* 0.29* 0.22*
0.39* 0.39* 0.31* 0.23* 0.32* 0.32* 0.27*
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Table 4 PA characteristics and body composition among groups according to total body BMD categories.
Age (yrs.) YSM (yrs.) Height (cm) Weight (kg) PA Light (min/week) Moderate (min/week) Vigorous (min/week) Steps per day Physical inactivity (hours/day) Body composition Lean body mass (kg) Body fat mass (kg) Body fat mass (%) BMI (kg/m2) FFMI (kg/m2) BFMI (kg/m2) *
p < 0.05; yh2 > 0.01,
Normal BMD (n = 71)
Osteopenia (n = 26)
Mean SD
Mean SD
62.69 4.82 12.26 5.75 161.32 6.00 70.78 9.61
66.19 5.56 14.92 6.47 158.74 5.33 65.78 14.26
F
h2
9.26* 3.79 3.73 3.91
0.09yy 0.04y 0.04y 0.04y
542 275 240 151 9 26 9925 3426 5.05 1.47
502 259 187 150 7 19 9331 4226 4.90 1.71
0.41 2.29 0.20 0.50 0.20
0.00 0.02y 0.00 0.01y 0.00
41.19 3.95 27.16 7.94 37.82 6.03 27.26 3.91 16.77 1.48 10.49 3.00
39.19 5.32 24.52 9.88 36.04 7.36 25.99 4.92 16.33 1.64 9.66 3.68
4.02* 2.01 1.46 1.73 1.64 1.26
0.04y 0.02y 0.02y 0.02y 0.02y 0.01y
yy 2
h > 0.06.
analysis was controlling for age (rp values ranging from 0.20 to 0.39). Additionally, body fat mass explained 9% of the variance at total femur, and 3–7% of the variance at regional femur. No significant relationship was found between Ward’s triangle BMD and body composition when analysis was not controlled for age. The comparisons of PA and body composition between women with normal proximal femur BMD and osteopenic women are shown in Table 4. Statistically significant differences were found in age (p = 0.003; h2 = 0.09) and lean body mass (p = 0.048; h2 = 0.04). Furthermore, the small effect size was observed for all variables except light and vigorous PA and physical inactivity. Both groups performed more than 150 min of moderate PA per week and accumulated less than 10,000 steps per day. The average BMI and percentage body fat indicated the overweight in both groups.
4. Discussion In this study, we evaluated the relationship of objectively measured PA and body composition with total and regional BMD of the proximal femur area. BMD is commonly used for diagnosing osteoporosis. Our results confirm recent findings (Wu et al., 2011) showing that Ward’s triangle has the lowest BMD and T-score in the femoral neck. In our study, the mean T-score of total and regional femur indicated healthy bones, with the exception of Ward’s triangle (T-score lower than 1.0). In addition, although the prevalence of osteoporosis related to the femoral neck in 50 to 84year-old postmenopausal women is approximately 21% (Kanis et al., 2008), we did not find any women with osteoporosis. Because low BMD contributes to the incidence of fractures in postmenopausal women, strategies that positively stimulate bone tissue may reduce mortality related to osteoporotic fractures. Although BMD can be increased through pharmacologic therapy, PA is the only intervention that increases bone mass. For example, Douchi et al. (2000) found that exercising women have higher BMD in the dominant arm and lumbar spine than sedentary women. A cross-sectional cohort study including data from 6032 postmenopausal women between the ages of 50 and 79 years (Beck et al., 2011) demonstrated that PA positively affects bone health, with bones becoming stronger and more resistant to fracture when their geometric response to load is improved. Nevertheless, we observed no significant relationship between PA variables and proximal femur regions BMD, except for the relationship between steps per day and femoral neck BMD. Furthermore, we did not find
significant differences in PA variables between women with normal BMD and osteopenic women. In this study, we measured only the intensity and volume of PA. However, Hamilton et al. (2010) have shown that bone tissue quality is primarily dependent on exercise type. To benefit bone tissue, PA should include weight-bearing exercises, special training to strengthen the muscles and exercise with high impact loading (Kohrt et al., 2004). Previous research has highlighted the positive effect of whole-body vibration training in reducing the risk for osteoporosis by increasing lumbar BMD (Stengel et al., 2011). In contrast, lower impact exercises that reduce the effect of gravity, such as swimming, do not significantly improve BMD (Bartl and Frisch, 2009). As our data showed, postmenopausal women preferred light or moderate PA. However, we did not observe a significant relationship between these PA variables and proximal femur regions BMD. Although our study did not show a significant relationship between moderate PA and bone mass, Feskanich et al. (2002) have demonstrated that moderate PA by postmenopausal women substantially lowers the risk of hip fracture. Specifically, the risk of hip fractures decreases by 6% for every increase of 3 MET-hours per week of PA, which is roughly equivalent to 1 h of walking per week. In contrast, Chubak et al. (2006) have reported a randomized controlled study showing that yearlong moderate-intensity aerobic exercise intervention does not affect total body BMD in overweight or obese postmenopausal women. Walking, an alternative of moderate PA, is the most common type of activity among elderly adults. It has been extensively promoted as a means of preventing osteoporosis. However, studies have shown variable effects of walking on bone health. Martyn-St James and Carroll (2008) have shown that although regular walking had no significant effect on spinal BMD preservation in postmenopausal women, a significant positive effect was evident at the femoral neck. Although these findings do not confirm that regular walking preserves bone mass, several studies have demonstrated the positive consequence of walking on proximal femur BMD. For example, Boyer et al. (2011) have reported that a minimum of 4892 steps (walking at 1.00 m/s) per day is required for postmenopausal women with an average body weight (65.10 kg) to maintain a proximal femur T-score of 1.0. Substantially more steps per day are required for lighter individuals (18,568 steps per day) and less steps per day are required for heavier individuals (1638 steps per day). These results correspond with our study, in which participants (weight = 69.44 kg) who accumulated 9766 steps per day had a
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mean proximal femur T-score of 0.48. As stated above, the number of steps accumulated per day was the only PA variable that significantly correlated with proximal femur regional BMD. There has been discussion regarding the influence of lean body mass and body fat mass on bone tissue. Several studies have shown that lean body mass is a better predictor of BMD than body fat mass in young (Miller et al., 2004), premenopausal (Winters and Snow, 2000) and postmenopausal women (Gjesdal et al., 2008). Postmenopausal women with higher lean body mass have significantly stronger femur geometry than their counterparts with less lean body mass (Beck et al., 2011). Winters and Snow (2000) have shown that lean body mass explained 24% of the variance for total femur BMD and 17% of the variance for femoral neck BMD in premenopausal women. Although our findings confirm these results, we found that lean body mass explained only 10% of the variance for total femur and 3–14% for the variance at regional femur. Based on additional analysis, we also observed that lean body mass was statistically higher in women with normal BMD. In contrast, Dytfeld et al. (2011) reported a stronger association between body fat mass and femoral neck BMD than between lean body mass and femoral neck BMD in postmenopausal women with osteoporosis. Our study sample included participants who had a relatively high absolute and relative amount of body fat mass. Thus, this may explain why our study sample did not find women with osteoporosis. Although the higher proportion of body fat mass is associated with increasing of total and regional BMD, adiposity is also associated with increased risk of many chronic diseases such as cardiovascular disease (Lavie et al., 2009) or type 2 diabetes mellitus (Rana et al., 2007). This study has several limitations. The small size of study sample could limit the statistical power. Study subjects were not chosen randomly and do not represent the general population of postmenopausal women, as recent research (Ga´ba et al., 2009) has shown that female students of the University of the Third Age are often active. Furthermore, the study sample did not include subjects with T-scores lower than 2.5. Therefore, follow-up research should focus on an elderly population with insufficient PA and persons with osteoporosis. The objectivity of the presented results may also have been affected by the study design. Because this study was cross-sectional and BMD is dependent on the age, it may not provide the same results as a longitudinal or semilongitudinal study. However, other researchers have observed similar results, which support our findings. Because of the intensity and volume of PA observed in this study, follow-up research should monitor the relationship between the type of PA and bone health. Our results also show that lean body mass strongly predicts total and regional proximal femur BMD. Therefore, future research could investigate the muscle strength of lower extremity via isokinetic dynamometry that depends on lean body mass. In conclusion, no significant relationship was found between accelerometer-determined PA and proximal femur BMD. Although the correlations increased when the analysis controlled for age, only the relationship between steps per day and femoral neck BMD was significant. Our results support the hypothesis that proximal femur BMD is more dependent on body composition than PA. Lean body mass was a stronger predictor of proximal femur BMD than body fat mass. Ward’s triangle was the only area of the proximal femur that showed no significant relationship with PA, even with body composition when analysis did not control for age. Analysis of differences between women with normal BMD and osteopenic women showed statistically significant differences in age and lean body mass. Conflicts of interest None.
Acknowledgments The study was supported by a research grant from the Ministry of Education, Youth and Sports of the Czech Republic (No. MSM 6198959221) ‘‘Physical activity and inactivity of the inhabitants of the Czech Republic in the context of behavioral changes’’ and a research grant from Palacky´ University in Olomouc (ID: FTK_2011_014) ‘‘Evaluation of bone tissue of the proximal femur in women with different levels of physical activity’’. References Albright, C., Thompson, D., 2006. The effectiveness of walking in preventing cardiovascular disease in women: a review of the current literature. J. Women’s Health 15, 271–280. Bartl, R., Frisch, B., 2009. Osteoporosis: Diagnosis, Prevention, Therapy, second ed. Springer, Berlin. Beck, T.J., Kohlmeier, L.A., Petit, M.A., Wu, G.L., Leboff, M.S., Cauley, J.A., Nicholas, S., Chen, Z., 2011. Confounders in the association between exercise and femur bone in postmenopausal women. Med. Sci. Sports Exerc. 43, 80–89. Boyer, K., Kiratli, B., Andriacchi, T., Beaupre, G., 2011. Maintaining femoral bone density in adults: how many steps per day are enough? Osteoporos. Int. 1–8. Center, J.R., Nguyen, T.V., Schneider, D., Sambrook, P.N., Eisman, J.A., 1999. Mortality after all major types of osteoporotic fracture in men and women: an observational study. Lancet 353, 878–882. Chubak, J., Ulrich, C.M., Tworoger, S.S., Sorensen, B., Yasui, Y., Irwin, M.L., Stanczyk, F.Z., Potter, J.D., McTiernan, A., 2006. Effect of exercise on bone mineral density and lean mass in postmenopausal women. Med. Sci. Sports Exerc. 38, 1236–1244. Douchi, T., Yamamoto, S., Oki, T., Maruta, K., Kuwahata, R., Yamasaki, H., Nagata, Y., 2000. The effects of physical exercise on body fat distribution and bone mineral density in postmenopausal women. Maturitas 35, 25–30. Dytfeld, J., Ignaszak-Szczepaniak, M., Gowin, E., Michalak, M., Horst-Sikorska, W., 2011. Influence of lean and fat mass on bone mineral density (BMD) in postmenopausal women with osteoporosis. Arch. Gerontol. Geriatr. 53, 237–242. Feskanich, D., Willett, W., Colditz, G., 2002. Walking and leisure-time activity and risk of hip fracture in postmenopausal women. J. Am. Med. Assoc. 288, 2300–2306. Freedson, P.S., Melanson, E., Sirard, J., 1998. Calibration of the Computer Science and Applications, Inc. accelerometer. Med. Sci. Sports Exerc. 30, 777–781. Ga´ba, A., Pelclova´, J., Prˇidalova´, M., Riegerova´, J., Dosta´lova´, I., Engelova´, L., 2009. The evaluation of body composition in relation to physical activity in 56–73 y. old women. A pilot study. Acta Univ. Palacki. Olomuc., Gymn. 39, 21–30. Gallagher, D., Heymsfield, S.B., Heo, M., Jebb, S.A., Murgatroyd, P.R., Sakamoto, Y., 2000. Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am. J. Clin. Nutr. 72, 694–701. Gjesdal, C.G., Halse, J.I., Eide, G.E., Brun, J.G., Tell, G.S., 2008. Impact of lean mass and fat mass on bone mineral density. The Hordaland Health Study. Maturitas 59, 191–200. Hamilton, C.J., Swan, V.J., Jamal, S.A., 2010. The effects of exercise and physical activity participation on bone mass and geometry in postmenopausal women: a systematic review of pQCT studies. Osteoporos. Int. 21, 11–23. Haskell, W.L., Lee, I.M., Pate, R.R., Powell, K.E., Blair, S.N., Franklin, B.A., Macera, C.A., Heath, G.W., Thompson, P.D., Bauman, A., 2007. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation 116, 1081–1093. Hatano, Y., 1993. Use of the pedometer for promoting daily walking exercise. Int. Coun. Health Phys. Educ. Recreat. 29, 4–8. Heymsfield, S., Lohman, T., Wang, Z., Going, S., 2005. Human Body Composition. Human Kinetics, Champaign, IL. Kanis, J.A., Burlet, N., Cooper, C., Delmas, P.D., Reginster, J.Y., Borgstrom, F., Rizzoli, R., 2008. European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Osteoporos. Int. 19, 399–428. Kohrt, W.M., Bloomfield, S.A., Little, K.D., Nelson, M.E., Yingling, V.R., 2004. American College of Sports Medicine Position Stand: physical activity and bone health. Med. Sci. Sports Exerc. 36, 1985–1996. Krumm, E.M., Dessieux, O.L., Andrews, P., Thompson, D.L., 2006. The relationship between daily steps and body composition in postmenopausal women. J. Women’s Health 15, 202–210. Kyle, U.G., Morabia, A., Schutz, Y., Pichard, C., 2004. Sedentarism affects body fat mass index and fat-free mass index in adults aged 18 to 98 years. Nutrition 20, 255–260. Lavie, C.J., Milani, R.V., Ventura, H.O., 2009. Obesity and cardiovascular disease: risk factor, paradox, and impact of weight loss. J. Am. Coll. Cardiol. 53, 1925–1932. Martyn-St James, M., Carroll, S., 2008. Meta-analysis of walking for preservation of bone mineral density in postmenopausal women. Bone 43, 521–531. Maurel, D.B., Boisseau, N., Benhamou, C.L., Jaffre, C., 2011. Alcohol and bone: review of dose effects and mechanisms. Osteoporos. Int. [ahead of print]. McTiernan, A., Kooperberg, C., White, E., Wilcox, S., Coates, R., Adams-Campbell, L.L., Woods, N., Ockene, J., 2003. Recreational physical activity and the risk of breast cancer in postmenopausal women: the Women’s Health Initiative Cohort Study. J. Am. Med. Assoc. 290, 1331–1336.
A. Ga´ba et al. / Archives of Gerontology and Geriatrics 54 (2012) e315–e321 Miller, L.E., Nickols-Richardson, S.M., Wootten, D.F., Ramp, W.K., Herbert, W.G., 2004. Relationships among bone mineral density, body composition, and isokinetic strength in young women. Calcif. Tissue Int. 74, 229–235. Morse, D.T., 1999. MINSIZE2: a computer program for determining effect size and minimum sample size for statistical significance for univariate, multivariate, and nonparametric tests. Educ. Psychol. Meas. 59, 518–531. Rana, J., Li, T., Manson, J., Hu, F., 2007. Adiposity compared with physical inactivity and risk of type 2 diabetes in women. Diabetes Care 30, 53. Reid, I.R., 2002. Relationships among body mass, its components, and bone. Bone 31, 547–555. Stengel, S.V., Kemmler, W., Bebenek, M., Engelke, K., Kalender, W.A., 2011. Effects of whole body vibration training on different devices on bone mineral density. Med. Sci. Sports Exerc. 43, 1071–1079. Tudor-Locke, C., Bassett, D., 2004. How many steps/day are enough?: Preliminary pedometer indices for public health. Sports Med. 34, 1–8.
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U.S. Department of Health and Human Services, 2008. 2008 Physical Activity Guidelines for Americans be Active, Healthy, and Happy! U.S. Dept. of Health and Human Services, Washington, D.C.. Varo, J.J., Martinez-Gonzalez, M.A., De Irala-Estevez, J., Kearney, J., Gibney, M., Martinez, J.A., 2003. Distribution and determinants of sedentary lifestyles in the European Union. Int. J. Epidemiol. 32, 138–146. Warburton, D.E., Nicol, C.W., Bredin, S.S., 2006. Health benefits of physical activity: the evidence. Can. Med. Assoc. J. 174, 801–809. Winters, K.M., Snow, C.M., 2000. Body composition predicts bone mineral density and balance in premenopausal women. J. Women’s Health Gender-Based Med. 9, 865–872. World Health Organization, 2003. Prevention and Management of Osteoporosis: Report of a WHO Scientific Group. World Health Organization, Geneva. Wu, Q., Lefante, J.J., Rice, J.C., Magnus, J.H., 2011. Age, race, weight, and gender impact normative values of bone mineral density. Gender Med. 8, 189–201.