Success rate in tracking moving target with center of gravity in left-right direction predicts six-month fall in elderly

Joosun Hong1, Jin-young Min2, Sunyoung Kim3, Miji Kim4, Byung-sung Kim1, Hyunrim Choi1, Woo-Chul Park1, Boram Kim1, *Chang Won Won1

1Department of Family Medicine, College of Medicine, Kyung Hee University, Seoul, Korea 2Institute of Health and Environment, Seoul National University, Seoul, Korea 3Department of Medicine, Graduate school, Kyung Hee University, Seoul, Korea

4College of Medicine/East-West Medical Research Institute, Kyung Hee University, Seoul, Korea

DOI: 10.24816/jcgg.2017.v8i4.02

  • Abstract
  • Full Text
  • Reference

Background/Objectives

Balance is the ability to stabilize the body and maintain static and dynamic posture control, and failure to maintain balance increases the risk of falls; therefore, evaluating balance ability has been used to predict the risk of falling. This study aimed to investigate the efficacy of tracking moving target test with center of gravity(COG) as a predictive factor of falls.

Methods

This study was conducted at an elderly welfare center in Seoul and the participants were aged 65 years or older. Success rate in tracking moving target was measured by using a newly developed Fall Risk Assessment (FRA) device (InBody Co®) and fall rates for 6 months were evaluated by telephone survey.

Results

In the univariate analysis, the proportion of subjects experiencing falls was significantly lower with increasing success rate in tracking moving target in the left-right and diagonal B directions (p=0.0017, p=0.0203, respectively). In the left-right direction, relative to quartile 1 of success rate in tracking moving target, adjusted ORs of falling for the six-months for quartiles 2, 3, and 4 decreased to 0.36, 0.28, and 0.11, respectively (p=0.015).

Conclusions

Success rate in tracking moving target in the diagonal B directions was not a significant predictor of falling after controlling the confounding variables of age, sex, current smoking status, alcohol drinking, physical activity, BMI, and histories of falling and chronic diseases, and sleeping disorders. Therefore, success rate in tracking moving target in the left-right direction well predicted a lower six-month fall rate. This demonstrates the potential efficacy of tracking moving target test in the left-right direction as a predictive factor for fall risk.

Keywords

center of gravity, balance, fall

 

Article Outline

  1. Introduction
  2. Methods
    2.1 Subjects
    2.2 Equipment and Protocol
    2.3 Statistical Analysis
  3. Results
    3.1 Comparisons of Baseline Characteristics by the Occurrence of Falls in the Previous Six Months
    3.2 Prevalence of Falls during Six-month Follow-up
  4. Discussion
  5. Conclusions
  6. References

Abstract

Background/Objectives

Balance is the ability to stabilize the body and maintain static and dynamic posture control, and failure to maintain balance increases the risk of falls; therefore, evaluating balance ability has been used to predict the risk of falling. This study aimed to investigate the efficacy of tracking moving target test with center of gravity(COG) as a predictive factor of falls.

Methods

This study was conducted at an elderly welfare center in Seoul and the participants were aged 65 years or older. Success rate in tracking moving target was measured by using a newly developed Fall Risk Assessment (FRA) device (InBody Co®) and fall rates for 6 months were evaluated by telephone survey.

Results

In the univariate analysis, the proportion of subjects experiencing falls was significantly lower with increasing success rate in tracking moving target in the left-right and diagonal B directions (p=0.0017, p=0.0203, respectively). In the left-right direction, relative to quartile 1 of success rate in tracking moving target, adjusted ORs of falling for the six-months for quartiles 2, 3, and 4 decreased to 0.36, 0.28, and 0.11, respectively (p=0.015).

Conclusions

Success rate in tracking moving target in the diagonal B directions was not a significant predictor of falling after controlling the confounding variables of age, sex, current smoking status, alcohol drinking, physical activity, BMI, and histories of falling and chronic diseases, and sleeping disorders. Therefore, success rate in tracking moving target in the left-right direction well predicted a lower six-month fall rate. This demonstrates the potential efficacy of tracking moving target test in the left-right direction as a predictive factor for fall risk.

Keywords

center of gravity, balance, fall

1. Introduction

The frequency of falls increases with age. Elderly individuals who experience falls are not only at risk for primary complications, such as fractures or contusions, but are also at risk for secondary complications, such as restrictions on outdoor activities because of fear of falling.1 Therefore, there has been growing interest in evaluating balance among elderly population to prevent falls and maintain functional independence.

Balance is the ability to stabilize the body and maintain static and dynamic posture control.2 Failure to maintain balance increases the risk of falls; therefore, evaluating balance ability has been used to predict the risk of falling.3 The center of gravity (COG) plays an important role in balance control4 and center of gravity (COG) can bemeasured by center of foot pressure (COP), which is projection of the body center of gravity on the plantar surface. COP is the outcome of the inertial forces of the body and restoring equilibrium forces of the postural control system. COP displacement can be characterized as 1- and 2-dimensional measures.5

Increased COP instability have been used as indicators of poor balance in the elderly.6,7,8 According to previous studies, indoor falls can be predicted by measuring an individual’s COP.4 Static balance is defined as adopting a still posture against gravity, where the center of gravity (COG) falls within the base of support. Dynamic balance refers to automatic postural responses to COG disturbances caused by movement and a test of tracking moving target with COP has been suggested as a dynamic balance test.9

Research on postural control in a static, upright position includes mechanical analysis of COP,10 with quantitative evaluation of postural sway predominantly focusing on the range, average speed, and area of COP movement measured by a force platform.11,12 However, for dynamic balance test, COG movement should be analyzed through different mechanisms to enable fall prediction. Kawabata et al developed a test of coordination of the whole body, in which participants pursue a randomly moving target using the center of foot pressure (COP).13 Coordinated exertion of leg strength and balance plays an important role in performance of this test. Recently, InBody Co., Ltd developed a brand-new Fall Risk Assessment (FRA) system which is to evaluate the risk of falling. FRA system has balance tests, which consist of m-CTSIB (modified clinical test of sensory interaction in balance) test, integrated balance ability test, and reaction time. Tracking moving target test with center of gravity is one of the integrated balance ability tests, and we investigated the effect of success rate in COG shift for moving target on falls six-month fall later.

 

2. Methods

2.1 Subjects

This study was conducted at an elderly welfare center in Seoul between January 18 and 29, 2016. Volunteers out of the older adults attending the welfare center participated in the study. Subjects were community-dwelling elderly aged 65 years or older. Initially, 311 subjects those who scored 24 points or higher on the Korean Mini-Mental State Examination scale, thus capable of comprehension and communication, could participate in the baseline study. Trained investigators administered a questionnaire containing variables related with the risk of falling. Specifically, these variables included medication history (sleeping pills, sedatives, anti-depressants, blood pressure medication, and prostatic hyperplasia medication), current diseases, lifestyle habits (alcohol consumption, smoking, and exercise habits, with exercise habits assessed by the K-IPAQ (Korean version of International Physical Activity Questionnaire)), previous falls, and disease history (hypertension, angina, hyperlipidemia, anemia, diabetes, and sleep disorders). Body mass index was calculated by measuring the height and weight of the subjects. After intervals of three and six months, telephone surveys were conducted to determine the subjects’ fall frequency within the last three months, with the six-month fall frequency calculated by adding the 3- and 6-month responses.

For the final analysis, a total of 243 subjects were included after excluding five subjects who did not undergo testing for tracking moving target with center of gravity, eight subjects taking dementia medication, 12 subjects using assistive devices, and 43 subjects who did not respond to the six-month follow-up telephone survey regarding fall frequency. The protocol for this study was approved by the Institutional Review Board (IRB: 2017-02-067-003) at Kyung Hee University Hospital. Prior to the study, all subjects were thoroughly informed of the study’s intentions and provided consent.

2.2 Equipment and Protocol 

Success rate in tracking moving target with center of gravity was measured according to the following protocol. First, the subject mounts the FRA and his or her COP appears as a red spot on the screen. Then, somewhere on the screen, a round target appears, moving at a speed of 1 cm/s, 2 cm/s, 3 cm/s, 4 cm/s, or 5 cm/s in the directions of left-right, back-forth, diagonal A (front-right to back-left), and diagonal B (front-left to back-right). The subject shifts his or her COG to track the target without moving his or her feet. The device measures the percentage of time that the subject maintains his or her COP within the target. Specifically, COP position is checked once every 16 ms and recorded as either “in” or “out” of the target. Success rate in tracking moving target is then calculated as (“in” count/total count)*100 for the total examination time (Figure 1).

2.3 Statistical Analysis

Based on the results of the follow-up telephone survey, subjects were categorized into “Fallers” and “Non-Fallers”, designating those who had fallen at least once in the last six months and those who had not fallen, respectively. The success rate results of tracking moving target for each direction (left-right, back forth, diagonal A, and diagonal B) were grouped into quartiles for analysis. Subjects’ characteristics (age, gender, lifestyle, BMI, and disease history) were recorded, compared between the Fallers and the Non-Fallers. The fisher exact test was used to verify the statistical significance of differences in the distribution between the two groups (Fallers vs. Non-Fallers). The p-value of the fisher exact test value was used when there were more than 20% of the cells with an expected frequency of 5 or less. The frequencies and percentages of the two groups were calculated across the quartile distributions of the success rate in tracking moving target for each direction, and homogeneity was analyzed using the fisher exact test. A logistic regression analysis was performed to investigate the association between success rate in tracking moving target and six-month fall frequency, and odds ratios (OR) and 95% confidence intervals (95% CIs) were calculated. In the regression model, fall-related risk factors were adjusted as confounding variables. All analyses were performed using SAS 9.2 software (SAS Institute, Cary, NC, USA), and the statistical significance level was set at α=0.05.

 

Table 1. Comparisons of baseline characteristics by the occurrence of falls during the six-month follow-up
Fallers Non-Fallers p-value
N (%) N (%)
Age, year (mean ± SD) 77.14 (4.84) 76.52 (4.71) 0.5131
Age, year
65-69 2 (11.76) 15 (88.24) 0.5417
70-74 5 (7.35) 63 (92.65)
75-79 14 (14.89) 80 (85.11)
≥80 7 (10.94) 57 (89.06)
Sex
Male 8 (11.76) 60 (88.24) 0.9413
Female 20 (11.43) 155 (88.57)
Current smoking
Yes 0 (0.00) 15 (100.00) 0.1490
No 28 (12.28) 200 (87.72)
Current drinking
Yes 6 (9.84) 55 (90.16) 0.6336
No 22 (12.09) 160 (87.91)
Physical activity, Mets
<600 25 (11.26) 197 (88.74) 0.7182
≥600 3 (14.29) 18 (85.71)
BMI, kg/m2
Under/Normal weight (<25.0) 16 (14.04) 98 (85.96) 0.2489
Overweight/Obesity (≥25.0) 12 (9.30) 117 (90.70)
History of falling
Yes 7 (17.07) 34 (82.93) 0.2221
No 21 (10.40) 181 (89.60)
 

Hypertension

Yes 16 (10.96) 130 (89.04) 0.7356
No 12 (12.37) 85 (87.63)
Angina
Yes 1 (11.11) 8 (88.89) 0.7223
No 27 (11.54) 207 (88.46)
Hyperlipidemia
Yes 7 (11.11) 56 (88.89) 0.9054
No 21 (11.67) 159 (88.33)
Anemia
Yes 2 (40.00) 3 (60.00) 0.1029
No 26 (10.29) 212 (89.08)
Diabetes
Yes 4 (8.70) 42 (91.30) 0.6154
No 24 (12.18) 173 (87.82)
Sleeping disorder
Yes 4 (18.18) 18 (81.82) 0.2961
No 24 (10.86) 197 (89.14)
P-value was based on the chi-square test or the fisher exact test for categorical variables and the t-test for a continuous variable.

 

3. Results

3.1 Comparisons of Baseline Characteristics by the Occurrence of Falls in the Previous Six Months

 The mean age of the 243 final subjects in this study was 76.8 years, and 175 (72%) of the subjects were female. Table 1 presents the basic characteristics of subjects in the Fallers group and the Non-Fallers group. There were no significant differences between the Fallers and the Non-Fallers in terms of age, sex, current smoking or drinking status, physical activity, BMI, fall history, hypertension, angina, hyperlipidemia, anemia, diabetes, or insomnia (Table 1).

3.2 Prevalence of Falls during Six-month Follow-up

The correlation between success rate in tracking moving target and six-month fall frequency revealed that the proportion of subjects experiencing falls became significantly lower with increasing success rate in tracking moving target (from Quartile 1 to 2, 3, then 4) in the left-right and diagonal B directions (p=0.0109 vs. p=0.0749). There was no significant difference in fall rate according to success rate in tracking moving target in the back-forth and diagonal A directions (Table 2). Figure 2 displays the results from Table 2 in graphical form, demonstrating the decrease in proportion of fallers with increasing Success rate in tracking moving target in the left-right and diagonal B directions.

The adjusted odds ratio (OR) of falling within six months was calculated for each quartile of success rate in tracking moving target in each direction. In the left-right direction, relative to success rate in tracking moving target for quartile 1, adjusted ORs for the six-month fall for quartiles 2, 3, and 4 decreased to 0.36, 0.28, and 0.11, respectively (p=0. 015) after. adjusting for confounding variables, such as age, sex, current smoking status, current alcohol drinking status, physical activity, BMI, and histories of falling and chronic diseases, including hypertension, angina, hyperlipidemia, anemia, diabetes, and sleeping disorders (Table 3).

4. Discussion

Maintaining balance begins with keeping the body’s COG vertically above the base of support. When the COG reaches the limits of balance, the body tries to preserve its balance by rapidly adjusting its movements. When this fails, an individual loses his or her balance and subsequently falls.14 Positioning the COG within the limits of balance maintenance requires fine interactions between the sensory and motor systems. Aging not only leads to muscle weakness, but also to impaired balance and control, which is closely related to decreased function of the somatosensory system, visual system, and vestibular system.15 As a result, balance assessment using COP measurements has been used as a predictive factor for risk of falling among the elderly population.

 

Table 2. Occurrence of falls during the six-month follow up by the

quartiles of success rate in tracking moving target for each direction

Fallers Non-Fallers p-value
Success rate in tracking N (%) N (%)
Left-Right (%)
Quartile 1 (≤69.9) 14 (22.95) 47 (77.05) 0.0109
Quartile 2 (69.0 – 77.0) 6 (9.52) 57 (90.48)
Quartile 3 (77.1 – 84.2) 6 (9.68) 56 (90.32)
Quartile 4 (≥84.3) 2 (3.51) 55 (96.49)
Back-Forth (%)
Quartile 1 (≤60.5) 7 (11.48) 54 (88.52) 0.2451
Quartile 2 (60.6 – 71.8) 8 (13.56) 51 (86.44)
Quartile 3 (71.9 – 81.2) 3 (5.00) 57 (95.00)
Quartile 4 (≥81.3) 10 (15.87) 53 (84.13)
Diagonal A (%)
Quartile 1 (≤31.9) 9 (14.52) 53 (85.48) 0.5682
Quartile 2 (32.0 – 43.5) 5 (8.62) 53 (91.38)
Quartile 3 (43.6 – 58.9) 9 (14.29) 54 (85.71)
Quartile 4 (≥59.0) 5 (8.33) 55 (91.67)
Diagonal B (%)
Quartile 1 (≤40.9) 10 (17.54) 47 (82.46) 0.0749
Quartile 2 (41.0 – 52.8) 8 (13.11) 53 (86.89)
Quartile 3 (52.9 – 64.6) 8 (12.31) 57 (87.69)
Quartile 4 (≥64.7) 2 (3.33) 58 (96.67)

 

 

This study evaluated balance ability among elderly subjects by measuring the success rate in tracking moving target in subjects continuously moving their COG to follow a mobile target. This study results indicated that the proportion of subjects experiencing falls within six months decreased as success rate in tracking moving target in left-right direction increased Thus, in addition to range, mean speed, area, and instability of COG movement, success rate in tracking moving target can be an important variable for assessing balance and can be used as an indicator to predict falls.

Among the four directions of left-right, back-forth, diagonal A, and diagonal B, only success rate in tracking the left-right direction showed a significant correlation with fall risk. This is consistent with previous researches, in which COG shifts in the left-right direction were used as an important index to predict falls among the elderly.16 In addition, a study examining balance ability among the elderly found COG shifts in the left-right direction to be significant because they are highly influenced by age.17,18 However, those studies are about static lateral sway. Kawabata et al introduced a test of a tracking moving target with the center of foot pressure, but they didn’t investigate its use for fall prediction. So, to authors’ knowledge, this study for the first time showed the importance of tracking moving target test in mediolateral direction for fall prediction.

When a load is suddenly applied to the body, muscles rapidly respond to provide stabilization and maintain posture and balance. To maintain balance while moving, the body often employs an ankle strategy, a hip strategy, or both strategies simultaneously. The ankle strategy refers to the body’s ability to recover initial upright balance by contracting the muscles of the ankle.19 The ankle strategy is the first postural control strategy adopted and is typically employed when the body experiences a small disturbance while occupying sturdy ground.20 The hip strategy is a bodily response depending mostly on vestibular information, and is used when the COG moves either a very large distance, rapidly, or approaches the limits of stability, when the body occupies ground that is very narrow or unconducive of employing the ankle strategy.21 The major muscles used to stabilize the COG are the ankle dorsiflexors and plantar flexors when the body is standing comfortably, the hip flexors and extensors disturbed in anteroposterior orientation when the body is walking or standing, and the hip abductors and adductors when the body assumes the left-right orientation.22 Disturbance of the COG in the back-forth direction can be controlled by the ankle strategy. Conversely, the hip strategy provides control against disturbance in the left-right direction. Further, since left-right disturbances of the COG are controlled by the hip strategy, which is highly affected by age, COG movement success rate in the left-right direction can be an important variable to predict falls in the elderly. Thus, in the present study, the reason why success rate in tracking moving target in the left-right direction highly predicted fall rate can be explained by the employment of the hip strategy.

Until recently, out of the traditional methods used to study postural control, mean velocity of COG shifts was the most reliable parameter in assessing COG movement.23 The FRA system’s tracking moving target is a more comprehensive parameter of the coordinated function of the sensory-cognitive-motor function. The present study used a newly developed FRA device to measure success rate in tracking with COG and found success rate in tracking moving target in the left-right direction is significantly correlated with six-month fall risk.

Table 3. Odds ratio (95% CI) for the occurrence of falls in six months by the quartiles of success rate in tracking moving target with center of gravity
Unadjusted model Adjusted model

                                                                                                             

Model 1 Model 2
OR (95% CI) P for trend OR (95% CI) P for trend OR (95% CI) P for trend
Left-Right (%)
Quartile 1 (≤69.9) Reference Reference Reference 0.0150
Quartile 2 (69.0 – 77.0) 0.35 (0.13-0.99) 0.0169 0.37 (0.13-1.04) 0.0166 0.36 (0.12-1.08)
Quartile 3 (77.1 – 84.2) 0.36 (0.13-1.01) 0.34 (0.12-0.97) 0.28 (0.09-0.85)
Quartile 4 (≥84.3) 0.12 (0.03-0.57) 0.12 (0.03-0.56) 0.11 (0.02-0.56)
Back-Forth (%)
Quartile 1 (≤60.5) Reference Reference Reference 0.3050
Quartile 2 (60.6 – 71.8) 1.21 (0.41-3.58) 0.3078 1.14 (0.37-3.49) 0.3075 1.05 (0.32-3.51)
Quartile 3 (71.9 – 81.2) 0.41 (0.10-1.65) 0.37 (0.08-1.62) 0.34 (0.07-1.57)
Quartile 4 (≥81.3) 1.46 (0.52-4.11) 1.35 (0.45-4.04) 1.34 (0.42-4.22)
Diagonal A (%)
Quartile 1 (≤31.9) Reference Reference Reference 0.4729
Quartile 2 (32.0 – 43.5) 0.56 (0.18-1.77) 0.5619 0.52 (0.16-1.69) 0.5979 0.51 (0.15-1.72)
Quartile 3 (43.6 – 58.9) 0.98 (0.36-2.66) 0.87 (0.31-2.44) 0.85 (0.29-2.53)
Quartile 4 (≥59.0) 0.54 (0.17-1.70) 0.53 (0.16-1.78) 0.41 (0.12-1.48)
Diagonal B (%)
Quartile 1 (≤40.9) Reference Reference Reference 0.1461
Quartile 2 (41.0 – 52.8) 0.71 (0.26-1.95) 0.157 0.76 (0.27-2.13) 0.1423 0.67 (0.22-2.02)
Quartile 3 (52.9 – 64.6) 0.66 (0.24-1.81) 0.62 (0.22-1.76) 0.60 (0.20-1.84)
Quartile 4 (≥64.7) 0.16 (0.03-0.78) 0.15 (0.03-0.74) 0.14 (0.03-0.74)
Adjusted for age, sex, current smoking status, current alcohol drinking status, physical activity, BMI, and histories of falling and chronic diseases, including hypertension, angina, hyperlipidemia, anemia, diabetes, and sleeping disorders.

Diagonal A, front-right to back-left; Diagonal B, front-left to back-right.

 

This study has some limitations. Falls were assessed by telephone surveys, and there is the possibility that some of the elderly subjects may not have accurately remembered their falls. However, recall errors were minimized by performing the telephone surveys every three-month.

 

5. Conclusions

 

A newly developed FRA system was used to measure the success rate in COG movements while elderly subjects tracked a moving target, and higher success rate in tracking moving target in the left-right direction was associated with a lower six-month fall risk. This demonstrates the potential efficacy of using success rate in tracking moving target as a predictive factor for fall risk.

 

Conflict of interest statement

 

None of the authors had any financial or personal conflict of interest.

 

Acknowledgement

 

This study was funded by InBody Co., Ltd. All authors have met authorship criteria set by the International Committee for Medical Journal Editors and have complete control over the content of the paper.

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17. Raymakers JA, Samson MM, Verhaar HJ. The assessment of body sway and the choice of the stability parameter (s). Gait Posture. 2005;21(1):48-58.

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1.Rubenstein LZ, Josephson KR, Robbins AS. Falls in the nursing home. Ann Intern Med. 1994;121(6):442-51.

2. Mickle KJ, Munro BJ, Steele JR. Gender and age affect balance performance in primary school-aged children. J Sci Med Sport. 2011;14(3):243-8.

3. Tinetti ME, Speechley M, Ginter SF. Risk factors for falls among elderly persons living in the community. N Engl J Med. 1988;319(26):1701-7.

4. Pajala S, Era P, Koskenvuo M, Kaprio J, Törmäkangas T, Rantanen T. Force Platform Balance Measures as Predictors of Indoor and Outdoor Falls in Community-Dwelling Women Aged 63-76 Years. J Gerontol A Biol Sci Med Sci. 2008;63(2):171-8.

5. Lafond D, Corriveau H, Hébert R, Prince F. Intrasession reliability of center of pressure measures of postural steadiness in healthy elderly people. Arch Phys Med Rehabil. 2004;85(6):896-901.

6. Thapa PB, Gideon P, Brockman KG, Fought RL, Ray WA. Clinical and biomechanical measures of balance as fall predictors in ambulatory nursing home residents. J Gerontol A Biol Sci Med Sci. 1996;51(5):M239-46.

7. Maki BE, Holliday PJ, Topper AK. A prospective study of postural balance and risk of falling in an ambulatory and independent elderly population. J Gerontol. 1994;49(2):M72-84.

8. Era P, Schroll M, Ytting H, Gause-Nilsson I, Heikkinen E, Steen B. Postural balance and its sensory-motor correlates in 75-year-old men and women: a cross-national comparative study. J Gerontol A Biol Sci Med Sci. 1996;51(2):M53-63.

9. Pollock AS, Durward BR, Rowe PJ, Paul JP. What is balance? Clin Rehabil. 2000;14(4):402-6.

10. Doyle RJ, Ragan BG, Rajendran K, Rosengren KS, Hsiao-Wecksler ET. Generalizability of stabilogram diffusion analysis of center of pressure measures. Gait Posture. 2008;27(2):223-30.

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