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Research Reports

Relationships Between Body Mass Index and Static and Dynamic Balance in Active and Inactive Older Adults

Cancela Carral, José M. PhD; Ayán, Carlos PhD; Sturzinger, Lea MS; Gonzalez, Gema

Author Information
Journal of Geriatric Physical Therapy: October/December 2019 - Volume 42 - Issue 4 - p E85-E90
doi: 10.1519/JPT.0000000000000195
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Abstract

INTRODUCTION

The aging process involves a series of changes in functional autonomy and, as a direct consequence, balance is considerably affected.1 The decline in postural control becomes important because it is one of the major factors contributing to the higher risk of falling among older adults,2 which has made the prevention of falls among older adults a significant public health issue.3 For this reason, the development of a new line of research focused on interventions designed to reduce such risks is reasonably justified. These interventions endeavor to identify which training programs are most effective at improving balance levels4 and to pinpoint which factors may accelerate balance deterioration.5 In this respect, one of the aspects that might affect balance levels is the body mass index (BMI), since it has been argued that higher BMIs demand more displacements to maintain postural balance.6

Indeed, recent investigations have confirmed the existence of a relationship between BMI and balance in older adults.7,8 However, researchers often do not account for the influence of a variable that can have an impact on the degree of association observed between BMI and balance, which is the amount of physical activity (PA) performed. Higher levels of PA are frequently indicative of better balance, but the direction of the association between PA and BMI across BMI categories is not as clear.9,10 In addition, although researchers have observed associations between higher BMI and poorer balance in older adults,11 the role of PA in this relationship is not well established. For instance, we could hypothesize that inactive individuals might have poorer balance levels, not only because their BMI is likely to be higher but also due to their less active lifestyle. In this regard, we should consider that the normal aging process implies gradually lower PA levels, which results in decreased physical fitness.12 Therefore, the amount of PA people perform might help counteract lower balance levels related to the aging process, regardless of BMI. Consequently, since the amount of PA that older adults perform may influence both balance and BMI, we should analyze the relationship between the latter variables while bearing in mind whether the sample is active or inactive. Therefore, our aims in this study were to explore whether or not the level of PA is a confounding variable that should be taken into account when analyzing the relationship between BMI and balance in older people.

METHODS

Participants

We recruited the participants who took part in this study by means of a letter of invitation sent to a care center in a city of the north of Spain. We conducted our investigation between January 2016 and July 2016. The inclusion criteria were as follows: (a) being 65 years of age or older; (b) scoring more than 24 points in the Spanish version of the Mini-Mental State Examination13; and (c) being able to walk without assistance. We excluded those participants who were taking any kind of medication that could affect their balance (ie, sedatives, anticholinergics, or antipsychotic drugs). We determined the sample size by using the results obtained after assessing the relationship between the following parameters: BMI, balance, and postural stability with the eyes open.14 We carried out the statistical power analysis with G*Power 3.1 software,15 and set the confidence interval—or the probability of type I error—at 0.10 (1 − α), precision (d) at 3%, parameter maximization was 0.5, and expected loss was 15%. The resulting sample size was 160 participants.

Before the intervention began, we requested written consents from the participants or their relatives/caregivers, and the Local Ethics Committee for Physical Activity Research (code: 04-1302-17) gave its approval. The protocol and design of this experiment were registered in the EudraCT database under code 2017-001837-24.

Assessments

The Yale Physical Activity Survey questionnaire16,17 made it possible to estimate the amount of PA participants performed. We used this parameter to divide the sample population into 2 groups: inactive or active. As long as participants practiced 150 minutes of moderate intensity PA per week, we considered them to be active.18,19

We calculated the participants' BMI as weight (kg) divided by height (m) squared (kg/m2). We measured height (cm) by means of a mobile telescopic stadiometer (AD MZ10038-ADE) with a range of 600 to 2100 mm and precision of 1 mm. We measured weight (kg) using a Tanita innerScan BC-601 (Tanita Corporation of America, Inc., Illinois, USA) with 0.1-kg precision. We then established the following BMI categories20: normal weight (≥18.50-24.99 kg/m2), overweight (25.00-29.99 kg/m2), and obese (≥30.00-34.99 kg/m2).

We measured the static balance level of the sample on a force platform (EPS+R, Let Sense Group Technology, Loran Engineering, Italy) using the position of the feet (Romberg). The participants stood barefoot with their arms at their sides and their feet hip width apart for 30 s. They performed the Romberg position with eyes open while focusing on a target placed 2 m ahead. We took center of pressure (CoP) data samples at a rate of 100 Hz for 30 s and then low pass filtered them at 10 Hz to reduce noise. To assess body sway, we subsequently calculated the most commonly used CoP parameters: sway length (mm) and sway velocity (mm/s).21

For the measurement of dynamic balance, we analyzed the participants' performance on the Timed Up and Go (TUG) test22 by means of a Wiva science sensor (Loran Engineering, Bologna, Italy), a wireless inertial sensing device set between vertebrae L4-L5. Wiva includes an accelerometer, a magnetometer, and a gyroscope, which allow practitioners to record information about the angular velocities reached during TUG test. In addition, Wiva provides data about the partial times obtained in the main phases of TUG test (sit to stand, gait to go, turning, gait return, and stand to sit), as well as the total time needed to complete the task.

Statistical Analyses

We calculated means and standard deviations for the whole group for all continuous variables. We describe discrete variables by percentage. We stratified the sample relative to the participants' level of PA (<150 min/wk and ≥150 min/wk) and then categorized it according to BMI levels (normal weight: ≥18.50-24.99 kg/m2; overweight: 25.00-29.99 kg/m2; and obesity: ≥30.00-34.99 kg/m2). We tested all continuous variables for normality with Kolmogorov-Smirnov statistics. We also used 2-sample t tests to assess the differences between dynamic and static balance parameters in relation with the level of PA within each BMI category, as identified in the sample population. We performed a correlational analysis with the aim of determining possible connections between BMI, static balance (CoP), and dynamic balance (TUG test) using Pearson coefficients (r). We classified the resulting levels of correlation as follows: very strong (r ≥ 0.80), moderately strong (r = 0.79-0.60), fair (r = 0.59-0.30), and poor (r ≤ 0.299).23 We accepted significance at P < .05. We analyzed all data using Statistical Package for the Social Sciences 20.0.

RESULTS

A total of 160 older adults (age: 71.5 [5.6] years; BMI: 29.4 [3.7] kg/m2) took part in the study and completed the evaluations. Their characteristics, considering the amount of PA performed, are shown in Table 1. The differences found between static and dynamic balance relative to the amount of PA performed for each of the 3 pre-established BMI categories are shown in Table 2. Regarding static balance, the results of the statistical analysis indicated that the higher the BMI, the greater the CoP sway. The magnitude of this sway was higher in the case of inactive participants regardless of the BMI category they had been allocated. As for dynamic balance, we found statistically significant differences between inactive and active participants for all 3 BMI categories in the following variables: sit to stand, peak angular velocity, gait go, and TUG test total time. For both inactive and active participants, we found that higher BMI categories led to lower walking speeds. The degree of correlation between BMI and balance relative to the amount of PA performed is shown in Table 3. We found statistically significant correlations (mainly fair) between static balance, dynamic balance, and BMI in inactive normal (r = 0.280; P = .035; r = 0.300; P = .031) and inactive overweight (r = 0.395; P = .025; r = 0.339; P = .023) people. We observed no significant correlation in the case of active participants. In obese people, we found significant correlations between static and dynamic balance and BMI regardless of the amount of PA performed. For the obese participants rated as inactive, the degree of correlation between balance and BMI was moderately strong, both for static (r = 0.603; P = .028) and dynamic balance (r = 0.720; P = .020). For the active obese participants, we considered these correlations to be fair (r = 0.406; P = .037; and r = 0.378; P = .037, respectively).

Table 1. - Sample Characteristics According to the Level of Physical Activity
Variable <150 min/wk (n = 81)
Normal Weight: ≥18.5-24.99 kg/m2 (n = 16) Overweight: 25.00-29.99 kg/m2 (n = 27) Obesity: ≥30.00-34.99 kg/m2 (n = 38)
Mean (SD) Mean (SD) Mean (SD)
Age, y 68.2 (7.3) 68.7 (8.6) 71.4 (7.2)
Gender, female 66.7% 75.7% 84.2%
Height, cm 159.2 (13.4) 157.5 (8.8) 153.5 (6.9)
Weight, kg 58.9 (10.7) 68.8 (8.3) 78.5 (9.9)
BMI, kg/m2 23.0 (1.3) 27.6 (1.3) 33.2 (2.8)
Educational background, %
No studies 0.0 19.4 26.3
Primary 100.0 69.4 71.1
Secondary 0.0 8.3 2.6
University 0.0 2.8 0.0
Marital status, %
Single 0.0 5.4 2.6
Married 83.3 62.2 68.4
Widower 16.7 29.7 23.7
Separated 0.0 2.7 0.0
Divorced 0.0 0.0 5.3
Variable ≥150 min/wk (n = 79)
Normal Weight: ≥18.5-24.99 kg/m2 (n = 18) Overweight: 25.00-29.99 kg/m2 (n = 31) Obesity: ≥30.00-34.99 kg/m2 (n = 30)
Mean (SD) Mean (SD) Mean (SD)
Age, y 70.2 (4.8) 70.5 (5.9) 68.52 (8.6)
Gender, female 75.0% 75.6% 66.7%
Height, cm 158.1 (8.7) 155.2 (6.0) 157.9 (9.0)
Weight, kg 58.8 (6.7) 65.8 (5.8) 81.7 (11.6)
BMI, kg/m2 23.5 (0.9) 27.3 (1.3) 32.6 (2.4)
Educational background, %
No studies 37.5 22.0 36.7
Primary 50.0 70.7 60.0
Secondary 0.0 7.3 3.3
University 12.5 0.0 0.0
Marital status, %
Single 0.0 0.0 6.7
Married 87.5 65.9 80.0
Widower 0.0 29.3 6.7
Separated 0.0 4.9 6.7
Divorced 12.5 0.0 0.0
Abbreviation: BMI, body mass index.

Table 2. - Analysis of Balance Parameters According to BMI and the Level of Physical Activity
Balance Parameters Normal Weight: ≥18.5-24.99 kg/m2 (n = 34) Overweight: 25.00-29.99 kg/m2 (n = 58) Obesity: ≥30.00-34.99 kg/m2 (n = 68)
<150 min/wk ≥150 min/wk <150 min/wk ≥150 min/wk <150 min/wk ≥150 min/wk
Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD)
Static balance
Mean sway CoP x-axis, mm 4.8 (2.7) 2.8 (0.7) 5.1 (3.1) 3.0 (1.6) 4.4 (1.8) 3.2 (1.4)
Mean sway CoP y-axis, mm −2.6 (1.5) −1.5 (0.4) −2.7 (1.6) −1.6 (0.8) −1.8 (0.9) −1.7 (0.7)
Dynamic balance
TUG test: sit to stand
Sit to stand, s 1.8 (0.3) 1.2 (0.4a) 1.7 (0.47) 1.5 (0.5a) 1.9 (0.6) 1.6 (0.5a)
Peak angular velocity, °/s 71.8 (17.5) 87.3 (56.8a) 89.9 (93.1) 130.3 (141.3a) 75.4 (44.1) 118.7 (95.7a)
AP range, m/s2 8.6 (2.0) 10.0 (1.8) 9.5 (2.1) 10.0 (3.8) 9.3 (2.3) 10.3 (2.1)
PD range, m/s2 5.2 (1.4) 6.2 (1.0) 6.0 (2.3) 6.1 (1.4) 5.6 (1.2) 6.5 (1.6)
ML range, m/s2 2.2 (0.4) 2.9 (0.9) 2.1 (0.7) 2.8 (1.9) 2.3 (0.7) 2.4 (0.6)
TUG: Go
Gait go, s 3.1 (0.8) 2.3 (0.7a) 3.1 (1.0) 2.6 (0.6a) 3.2 (0.7) 2.7 (1.0a)
TUG: Turning
Turning, s 1.0 (0.3) 0.8 (0.3a) 1.2 (0.5) 1.1 (0.5) 1.5 (0.7) 1.2 (0.5a)
TUG: Return
Gait return, s 3.1 (0.9) 2.0 (0.5a) 2.9 (0.9) 2.5 (0.8) 2.7 (0.5) 2.6 (0.7)
TUG: Stand to sit
Stand to sit, s 2.1 (1.4) 1.6 (0.9a) 2.5 (2.0) 2.0 (0.7a) 2.3 (1.2) 2.2 (1.0)
Peak angular velocity, °/s 215.7 (44.2) 257.6 (86.9) 213.6 (72.5) 218.3 (69.1) 211.1 (65.0) 227.4 (60.7)
TUG: Total
Total time, s 11.1 (2.6) 7.9 (3.1a) 11.3 (3.4) 9.8 (2.2a) 11.6 (2.5) 10.2 (2.4a)
Speed, m/s 0.80 (0.20) 1.13 (0.29a) 0.79 (0.21) 0.91 (0.28a) 0.77 (0.27) 0.88 (0.20a)
Abbreviations: AP, anteroposterior; CoP, center of pressure; ML, mediolateral; PD, vertical; TUG, Timed Up and Go test.
aP < .05.

Table 3. - Pearson Correlation (P Value) Between BMI and CoP and TUG Test Within Different BMI and Physical Activity Categories
Variable Normal Weight: ≥18.5-24.99 kg/m2 (n = 34)
<150 min/wk ≥150 min/wk
CoP TUG CoP TUG
Body mass index 0.280 (.035) 0.300 (.031) 0.243 (.145) 0.260 (.236)
Center of pressure ... 0.800 (.010) ... 0.429 (.198)
Overweight: 25.00-29.99 kg/m2 (n = 58)
<150 min/wk ≥150 min/wk
CoP TUG CoP TUG
Body mass index 0.395 (.025) 0.339 (.023) 0.393 (.057) 0.237 (.054)
Center of pressure ... 0.471 (.021) ... 0.427 (.046)
Obesity: ≥30.00-34.99 kg/m2 (n = 68)
<150 min/wk ≥150 min/wk
CoP TUG CoP TUG
Body mass index 0.603 (.028) 0.720 (.020) 0.406 (.037) 0.378 (.037)
Center of pressure ... 0.555 (.016) ... 0.384 (.043)
Abbreviations: BMI, body mass index; CoP, center of pressure; TUG, Timed Up and Go test.

DISCUSSION

We intended this cross-sectional study to provide new information on the relationship between BMI and balance in older adults considering the amount of PA performed as an influential variable to be accounted for. The results obtained contribute to enriching the scientific evidence available, since studies on this topic are still quite scarce. The findings of the present study suggest that in older adults, both static and dynamic balance become poorer as BMI increases, a fact that may also be justified on a biomechanical basis.24 For instance, in older adults an increased amount of abdominal fat is correlated with a higher BMI.25 Relating to this, studies show that in the case of obese individuals, particularly those affected by a high adipose tissue accumulation in the abdominal area, the center of mass is displaced forward with respect to the ankle joint, which results in diminished postural control.14 Similarly, it has been observed that obese older adults show a lower plantar sensitivity derived from the hyperactivation of the plantar mechanoreceptors. This lack of sensitivity, which is a consequence of the continuous pressure of supporting a large mass,26 can lead to reduced postural control due to the limited ability to use proprioceptive information. This reasoning, which is in line with previous findings,27 might help explain why we observed a linear trend between postural stability and body weight in the present research. An additional finding of this study, following the observations of other authors, reveals that higher BMIs lead to lower walking speeds.11 Thus, it has been suggested that to move the excess of body mass, people with obesity may choose a slower gait speed as a strategy to expend minimal energy.28

One of the acknowledged limitations highlighted in previous research on the relationship between BMI and balance in older adults7,28 is the lack of inclusion of the amount of PA performed as a confounding factor. The present study found that in normal weight and overweight participants, PA levels might be considered as an additional factor that would be likely to affect balance control, since the correlation between BMI and balance was statistically significant in the physically inactive stratum but not in the case of active participants. There are several possible reasons that we can suggest to clarify this result. First, research shows that in obese older adults, physical performance assessed through speed and postural control shows an inverse association with BMI.8 Therefore, improvements in physical performance derived from PA practice could mean that, for older adults with the same BMI, an inactive lifestyle would be more likely to lead to poorer balance levels. Second, also following this line of argument, we suggest that physical fitness in older adults influences the risk of falls, which is due, among other contributing factors, to the existence of lessened aerobic capacity and a decrease in lower body muscular strength, both of them deleterious to balance.29 In relation to this, we found that in older adults their relative muscular strength decreases significantly as their BMI increases.30 Thus, obese individuals possess excessive body mass with relatively weak lower body muscle strength and as a result they show greater body sway, which is an indicator of postural instability.28 Therefore, we could hypothesize that improvements in the level of fitness derived from an increase in the amount of PA performed may translate into higher levels of muscular strength and consequently better balance levels. Indeed, research finds that strength training is an effective strategy aimed at improving the ability of obese individuals to recover balance during perturbation.31 Third, research suggests that reduced postural stability could be associated to proprioception deficits in the knee joint, even in young obese people.32 Since physical exercise has been shown to improve knee join proprioception,33 we might speculate that active obese older adults could improve their balance by increasing their proprioception levels. Finally, we should note that fat mass also affects balance in obese older adults. Fat mass increases with age while muscle mass declines,11 and this has an impact on walking performance in older adults.34 In this regard, it has been recently reported that men and women 40 to 69 years of age who started doing more PA had a lower body fat percentage regardless of their BMI level.35 Thus, PA performance could improve balance by reducing the amount of fat mass in older adults. In any case, the present study observed that the statistically significant relationship between BMI and balance was maintained in obese participants regardless of their PA level. Other authors have similarly commented on the possible existence of a threshold effect between 30 and 35 kg/m2 from which physical performance is remarkably affected,11,36 which might explain why PA performance may not be enough on its own to influence balance levels.

One of the strong points of this study is its originality, since it provides information on the confounding role PA may play on the relationship between BMI and balance in older adults. In this regard, readers should note that we assessed both static and dynamic balance through objective tests. However, several methodological limitations must be acknowledged, which may affect the interpretation of the results presented here. First, the cross-sectional design of the study does not allow for the inference of cause and effect. Second, we included no anthropometric assessments related to obesity, such as waist-to-hip ratio or abdominal circumference, so the impact of fat distribution type cannot be determined. Third, the sample of volunteers did not include any underweight participants; therefore, the analysis of the relationship between BMI and balance does not include all the categories considered in this index. Finally, we did not monitor PA levels objectively, but rather obtained them from participant self-reports concerning the amount of PA they performed. These methodological weaknesses should be considered in the design of future interventions aimed at determining the connection between PA and balance in obese older adults. It would be advisable that said investigations objectively monitor the amount of PA performed and include physical performance and physical fitness assessments.

CONCLUSIONS

The results of this study suggest that the amount of PA performed could be considered as another potential contributing factor that affects the association between BMI and balance in older persons. These findings could be of importance when identifying the main factors that influence postural control among older adults with obesity.

ACKNOWLEDGMENTS

The authors thank all the test personnel at the institutions involved in the study for their work during the data collection: Faculty of Education and Sport Sciences, Sport Sciences, University of Vigo and Concello de Sanxenxo.

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Keywords:

aging; falls; health; obesity; physical activity

© 2018 Academy of Geriatric Physical Therapy, APTA.