Data from previously published findings of a targeted bone and muscle loading exercise program (resistance plus impact exercise) were used to determine the influence of initial values on exercise-induced changes in BMD, strength, power, and stability (23).
Data from 31 women, who had completed 12 months of resistance plus impact exercise training, were used for these analyses (Table 1). Before study entry, women were screened for the following: history of chronic disease known to affect bone metabolism or exercise capacity, smoking, breast feeding, intention to become pregnant within the next year, irregular menstrual cycles, or regular participation in high-intensity resistance training or in activities including high-impact movements (e.g., volleyball, basketball). All eligible subjects provided written informed consent approved by the Oregon State University Institutional Review Board and then underwent initial testing followed by 12 months of exercise training and follow-up testing.
The training program occurred 3× wk−1 and consisted of 100 jumps and 100 repetitions of lower-body resistance exercises per session. The training program consisted of nine sets of 10–12 jumps and nine sets of 10–12 repetitions of lower-body resistance exercises. Repetitions were performed in succession, but approximately 15–30 s of rest between jumps and 2–3 min of rest between resistance exercise sets was given. Proper form was encouraged when executing all exercises, and, thus, speed was deemphasized in favor of safe and controlled movements.
A variety of jumping routines was used to prevent monotony in training. Jumps were performed off the ground, off 12-inch wooden boxes, in the forward and side directions, and in single or double-leg stances. In general, each session consisted of equal repetitions of the following types of jumps: two-footed jumps off the ground, two-footed jumps onto and off of a 12-inch wooden box, two-footed side-to-side hops, and one-footed hops. Subjects performed the jumps on 2-inch gymnastics mats and were instructed to jump with shoes off and to land flat-footed with approximately 30° of knee flexion. Pilot data, collected in the laboratory, generated ground reaction forces of 4–5 times body weight for jumps and are categorized as high-intensity exercise. We define high-intensity activities as those that produce forces greater than four times body weight (24). Lower-body resistance exercises (squats, lunges, and calf raises) were performed immediately after the jump exercise. Squats were performed in a wide stance to 90° of knee flexion, lunges were performed in the forward, side, and backward directions to 90° of knee flexion in the lead leg, whereas calf raises were performed off the toes to slightly less than 90° of plantar flexion. In general, each session consisted of three sets of 10–12 squats, six sets of 10–12 lunges (two sets in each direction), and two sets of calf raises.
Intensity for both jump and resistance exercise was increased using weighted vests and calculated as a percentage of body weight (%BW), such that each woman had the same relative intensity. Jump and resistance intensity were increased over the first 10 months at rates of 1% BW per month and 1.25% BW per month, respectively, and remained at 10%BW for jumps and 13%BW for resistance exercises during the final 2 months. Women recorded their training on individual logs kept in the exercise room and also maintained records of physical activities performed outside of class.
Height and weight.
Measurement of height and weight was performed with subjects in regular dress clothing but without shoes. Standing height was measured to the nearest 0.5 cm, using a wall-mounted stadiometer. Body weight was measured on a digital scale to the nearest 0.1 kg.
BMD and body composition.
BMD (g·cm−2) of the total hip (Thip), greater trochanter (GT), femoral neck (FN), and lumbar spine (L2–L4; LS) was measured via DXA (Hologic QDR 1000-W, software version 4.74). Lean and fat masses were determined from whole-body scans. The same individual conducted all scans and analyses. In-house coefficients of variation (CV) on a subsample of women (N = 10) similar to our study population are <1.0% for hip and spine measures and <1.5% for body composition measures.
Maximal knee extensor and hip abductor strength (N·m) were assessed via isokinetic dynamometry (Kin-Com 500-H, Chattex, TN). All strength values were corrected for the effect of gravity on the limb in the horizontal position. This instrument has been shown to provide valid and reliable estimates of muscle strength (9), and our in-house CV is <4%. A detailed description of the testing protocol is described elsewhere (23).
Peak leg power.
Peak muscular power (W) of the legs was assessed using a modified version of the Wingate anaerobic power test on a Monark bicycle ergometer (Varberg, Sweden). The test consisted of a 3- to 5-min warm-up period of low-intensity cycling at 60–70 rpm, followed by 15 s of maximal pedaling against a resistance set at 7.5% of body weight. The highest value obtained during the 15-s trial was taken to reflect peak leg muscle power. The Wingate test has been shown to be a valid and reliable measure of muscular power in younger women (14), and our in-house CV for maximal power is <4%.
Dynamic postural stability.
The ability of subjects to balance themselves for 30 s on an unstable surface was tested using a stability platform (Biodex Medical Systems, NY). Values are expressed in terms of the Stability Index (SI), a unitless measure that represents the variance of platform displacement in degrees from level. Higher SI values indicate greater deviation from level and thus poor stability. Likewise, improvements in stability are indicated by a reduction in SI scores. A detailed description of the testing protocol is described elsewhere (23). The in-house CV for this measure is <10%.
Descriptive data are presented as mean ± standard deviation. Correlation and regression analyses were only run for those variables that significantly changed from the training program. Pearson-product moment correlation coefficients were used to determine relationships between initial value and percent change for each measure of fracture risk.
To determine the predictive power of initial values, we performed separate stepwise regression analyses for each variable including the following dependent variables: age, initial value, highest weight lifted during training, and total exercise sessions completed. The selection of regression coefficients was based on the following rationale: initial value, based on findings that other physiological systems demonstrate that exercise-induced changes depend partly on initial values (7); age, based on exercise intervention data across age groups that suggests that younger individuals may respond better to similar osteogenic exercise (3,21,23); highest weight lifted during training, as an index of intensity, was included to examine the training principle of overload and the influence of intensity on the magnitude of the exercise response; and total exercise sessions completed, as an index of exercise duration and program compliance, also included to examine the principle of overload and the influence of training duration on the magnitude of the exercise response.
The significance criterion of the critical F value for entry into the regression equation was set at P < 0.05. All analyses were performed using the SPSS statistical software program, version 11.0.
We found significant inverse correlations (r = −0.43 to −0.54) between initial values and annual percent change for trochanteric BMD, hip abductor strength, leg power, and postural stability (Fig. 1). Highest weight lifted was significantly correlated with percent improvement in stability (r = 0.4). In follow-up stepwise regression analyses, initial value consistently emerged as the best predictor of these variables as well, such that lower initial values predicted a better response to exercise than age or indices of overload (Table 2). Highest weight lifted was also in the regression model for change in leg power, such that greater weight lifted during training predicted a greater change in leg power. These regression models predicted 15–49% of the variance in percent change of the dependent variables. Neither percent change in total hip BMD nor knee extensor strength was predicted by any independent variable.
To further explore the effect of initial values on the magnitude of exercise-induced adaptations, we divided subjects into quartiles based on their initial values for each variable and compared the average magnitude of response between subjects below the 25th percentile (N = 8) and those above the 75th percentile (N = 8). In all cases, average percent change for the lowest quartile of initial values was greater than the average percent change for those in the upper quartile (greater trochanter BMD: 3.8% vs 1.7%; hip abductor strength 41.3% vs 7.8%; leg power: 43.1% vs 9.6%; and stability: 32.8% vs 10.8% for lower and upper quartiles, respectively).
This study is the first specific examination of the role of initial values in exercise-induced reductions in fracture risk factors. We found that in most cases, initial values independently predicted the response of the musculoskeletal system (hip BMD, hip strength, leg power, and stability) to resistance plus impact training. Further analysis showed that women with initial values in the lowest quartile had 2–5 times greater improvements in response to exercise than women with values in the upper quartile. These data suggest that targeted bone and muscle loading exercise will be most effective in those at greatest risk.
Despite the uniqueness of this study, it also has limitations that must be noted. Although we were fortunate to be able to analyze data from our previous intervention study to address our research aim, we did not specifically design the study to target women with a poor fracture risk profile. In the original study, our group of women had average height, weight, percent body fat, and BMD for their age, whereas normative data were unavailable to evaluate strength, power, or stability. Despite the low fracture risk of our group as a whole, we did have a fairly heterogeneous population with respect to baseline values of musculoskeletal measures, allowing us to explore the initial values question (Table 1). Another limitation was our sample size. Though most of our findings were significant, our low sample size likely contributed to the rather large SEE in the regression equations (Table 2). Most of our SEE were similar to the average percent change from the original exercise study, yet more subjects would have yielded less error and more confidence in our prediction equations. Regardless of these limitations, we believe the data yield useful information regarding the ability of targeted bone and muscle loading exercise to best affect those with poor initial musculoskeletal mass and function and suggest that future exercise interventions specifically target at-risk populations. Logically, though, it must also be noted that this particular exercise program may not be able to produce appreciable gains in women with high initial values of musculoskeletal performance measures. Exercise-induced increases in women in the highest quartile of initial values for all variables, except trochanteric BMD, were similar to control changes (23). Women with better musculoskeletal mass and function may be better served by an exercise program that targets otherwise identified areas of physiologic weakness. However, participation in this exercise program in middle age may help promote long-term exercise adherence that could potentially offset musculoskeletal declines that occur in later years and, importantly, would help to establish long-term exercise behavior (8).
Questions have been raised in the exercise literature regarding the variability in individual responses to training. Numerous studies have reported large interindividual variation in the metabolic response to exercise despite strict uniformity in training volume among study participants (4). Several factors that may determine the magnitude of exercise adaptations have been proposed including, age, sex, race, genetic predisposition, or initial values. For various fitness and metabolic variables, age does not appear to influence trainability (16,17,19,20), yet conflicting evidence exists in the literature with regard to muscle strength (13,22). One study has examined age-related changes in power in response to resistance training and found that younger and older adults increase power similarly in response to training (15). To our knowledge, no direct comparison of stability improvements from training between older and younger adults has been published. Although it is known that stability declines with age (2) and that older adults can improve balance in response to training, without direct age comparisons, it is difficult to establish the influence of age on balance responsiveness to exercise. With regard to bone, however, age appears to influence the magnitude of exercise-induced gains in skeletal mass. For example, similar impact interventions across age groups suggest an age-dependent BMD response to exercise such that children increase BMD 5–6% (10), premenopausal women 2–4% (3,23), whereas postmenopausal women maintain BMD at best (18,21). Differences in training responsiveness across ages may also be related to the relative effort exerted by varying age groups. Within our sample of premenopausal women, as expected, age did not influence any training response, though this is almost certainly due to the limited age range of our subjects.
The training principles of overload and frequency state that a training stimulus must be of sufficient magnitude and applied consistently in order to result in physiologic improvements. Our index of overload was the highest weight put in the weighted vests during training, and our index of frequency was the total number of exercise sessions completed during the 1-yr program. Only weight lifted emerged as an independent predictor of the improvement in leg power, though by examination of standardized beta coefficients, initial values remained the strongest predictor (−0.8 and 0.5 for initial values and weight lifted, respectively). It is not surprising that weight lifted did not emerge as the best predictor of the overall training response, because we attempted to train each woman at the same relative intensity by setting the resistance as a percentage of each woman’s body weight. Thus, most women were exerting the same relative effort. Similarly, total exercise sessions could not predict the training response likely because the median attendance for our group was 77%, and thus most women attended a high percentage of the proscribed exercise sessions. Our original study was not designed to address the responsiveness of the musculoskeletal system to varying intensities nor frequencies of exercise training, and future studies must be designed specifically to address these questions.
Our data support the principle of initial values when predicting those premenopausal women who are most likely to improve musculoskeletal health from participation in impact and resistance exercise. Inserting data from our study population into the derived regression equations, a woman with the lowest value for hip BMD, hip strength, leg power, and stability would be predicted to have absolute percent increases that were 3%, 37%, 37%, and 43% greater, respectively, than a woman with the highest value of each variable. Although unlikely that such equations would be useful in a practical setting, these estimates further illustrate the applicability of the principle of initial values when targeting exercise to those most likely to benefit from specific training.
Perhaps the most intriguing data from our study come from comparison of percent changes between women in the lower and upper most quartiles of initial values. It has been previously stated that the mean response to an exercise program can be misleading and obscures the fact that some individuals experience much lower gains and some individuals much higher gains than the mean (4). In our study, the improvements in BMD, strength, power, and stability for women in the lowest quartile of initial values were ∼1.5- to twofold greater than mean improvements and ∼two- to fivefold greater than changes in those women in the upper quartile of initial value. For example, at the hip, the average percent change in BMD for women in the lowest quartile of initial values was 3.8%, compared with the mean increase of 2.5% and the upper-quartile increase of 1.7%. A recent meta-analysis of exercise trials designed to improve BMD in premenopausal women estimated that exercise could result in an approximate 1.0% gain in BMD per year (25). Our data, however, suggest that the training effect might be larger if higher risk (e.g., osteopenic) populations were targeted. Though speculative, it is possible that previous exercise reports may underestimate the ability of exercise to promote bone gain in higher risk premenopausal women when reporting mean increases for women of varying skeletal status. Reexamination of data with this issue in mind is of interest and future studies in premenopausal women should target those with low initial BMD in order to confirm our observations.
Our results suggest that an exercise-training program aimed at reducing risk factors for osteoporosis and falls in premenopausal women should target those at highest risk. Women with lower initial values for strength, power, and stability benefited more from our resistance and impact-training program than those with higher values. These data encouragingly confirm that targeted muscle and bone-loading exercise is most effective in premenopausal women with poor values of musculoskeletal risk factors for fracture. When prescribing exercise programs for the individual, initial values should be considered, and specific exercise should be targeted to areas of greatest weakness and specifically to their disease and injury risk profile.
This work was funded by the National Aeronautics and Space Association Graduate Research Program and LifeFitness, Inc.
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