Skip to content

Advertisement

Open Access

Development of a new instrument for evaluating leg motions using acceleration sensors (II)

  • Takaki Itoh1Email author,
  • Yukie Kumagai2,
  • Ikuharu Morioka3,
  • Shinji Mae4,
  • Toshihiro Naka5,
  • Hiroyasu Uenishi6 and
  • Toshiko Matsuoka6
Environmental Health and Preventive Medicine201117:242

https://doi.org/10.1007/s12199-011-0242-x

Received: 17 January 2011

Accepted: 13 September 2011

Published: 22 October 2011

Abstract

Objective

The purpose of this study was to investigate the characteristics of the average acceleration of elderly people during walking.

Method

The subject cohort comprised nine men and 21 women aged ≥63 years. Subjects walked a 10-m straight course (walk test) which required stepping over six obstacles (hurdle walk test). The average acceleration was calculated from the accelerograms. Functional reach test scores and the Tokyo Metropolitan Institute of Gerontology (TMIG) index of competence, fall risk assessment, fall experience within the last year, and carelessness were used as standard indices to estimate the dynamic postural movement and fall risk.

Results

The average acceleration during the walk test was not significantly correlated with the standard indices. The average accelerations at the lumbar and knee positions clustered with fall experience and carelessness, while those at the ankle and toe positions clustered with the hurdle walk test, TMIG index of competence, and fall risk assessment. Between the high- and low-risk groups classified by the conventional indices, there was a significant difference in the average acceleration at some measurement positions. The receiver operating characteristic analysis showed the possibility to discriminate the high-risk group according to the standard indices with average acceleration.

Conclusions

The average acceleration during walking may be a composite index that encompasses standard indices and discriminate the high-risk group. As such, it may be a useful tool to estimate the dynamic postural movement and fall risk at all measurement positions.

Keywords

Leg motionThree-dimensional acceleration sensorsAccelerogramWalk testFall risk

Introduction

An instrument for evaluating leg motions using three-dimensional (3D) acceleration sensors was reported in an earlier publication by our group [1, 2]. This new instrument allowed us to measure leg motions during physical fitness activities without subjecting the subjects to any physical burden. Based on the results of our analysis, we then suggested that acceleration during physical fitness activities may reflect the muscle power of the legs and movement coordination and that it may be used as a new index for evaluating leg motions during dynamic postural movement, such as physical fitness activities.

Since falls often occur while walking, it is necessary to measure the momentary postural movement during walking with accuracy [35]. The key feature of the new instrument is its ability to measure leg motions at four points simultaneously. As a result, this instrument may enable movement coordination of dynamic postural movement to be measured during walking. However, information on the average acceleration during walking is currently lacking.

The aim of this study was to establish the validity of average acceleration as one of the indices of the dynamic postural movement. We investigated the characteristics of accelerations during the walk test and hurdle walk test and attempted to clarify the relationship of average accelerations to the indices used for evaluating the dynamic postural movement and fall risk.

Materials and methods

Subjects

The study cohort comprised 30 participants (9 men, 21 women) aged ≥63 years. Of the nine men, two were aged 65–74 years and seven were 75–84 years old; of the 21 women, one was aged 63–64 years, ten were 65–74 years old, eight were 75–84 years old, and one was aged ≥85 years. None of the participants had leg diseases or difficulty in performing the usual activities of daily living (ADL).

Public health nurses in a health center explained the outline of this study to the subjects beforehand. The researcher in charge explained the aims and methods of the study, the protection of individual information, and the possibility of removing oneself from the study with an oral explanation and by providing documents on the chosen day. Those who agreed to participate were asked to provide written informed consent. This study was approved by the Ethical Committee of Wakayama Medical University.

Methods

A representation of the measuring system is shown in Fig. 1. The measuring system comprises acceleration sensors (MA3-10Ac; MicroStone, Japan), a data logger, a data reader, and a personal computer. Four 3D acceleration sensors were placed on the body surface at the lumbar, knee, ankle, and toe positions. The three axes of the acceleration sensor are back–forward (B–F), right–left (R–L), and up–down (U–D) with the subject in a standing position. These directions, however, change depending on the position of the body during movements because the measuring directions of the sensors are originally fixed at mutually vertical positions. The data logger can accumulate 12 output signals from four sensors for about 20 min with a 10-ms time interval. The accumulated data in the EEPROM (electrically erasable and programmable read-only memory) in the data logger is transferred through a data reader to a personal computer by RS232C. The area of acceleration is integrated using the trapezoidal rule and then the integrated value is divided by measured time to obtain the average acceleration. A detailed explanation of the measuring system has been reported previously [1].
Fig. 1

Representation of the measuring system for leg motions. EEPROM Electrically erasable and programmable read-only memory, B–F back–front, R–L right–left, U–D up–down

This average acceleration does not directly show muscle power of the legs or the movement coordination because the accelerogram only shows instantaneous leg motions.

Subjects walked the 10-m straight course (walk test) which included stepping over six obstacles (W10 × L50 × H20 cm3) separated by 1.5 m (hurdle walk test). A schematic diagram of the walk test and hurdle walk test is shown in Fig. 2. The subjects were given no instructions on walking speed or style.
Fig. 2

Schematic diagram of the 10-m straight course (walk test) and hurdle walk test

As standard indices, we used the results of the functional reach test (FRT) [6, 7], hurdle walk test [8, 9], the Tokyo Metropolitan Institute of Gerontology (TMIG) index of competence [10], fall risk assessment [11], fall experience within 1 year (fall experience), and the answer to the question: “Are you careless?” (carelessness).

The result of the FRT was the maximum distance that an individual could reach forward while standing and maintaining a fixed base support [6, 7], and that of the hurdle walk test was the time the individual took to complete the hurdle walk test [8, 9].

The indices, TMIG index of competence [10], fall risk assessment [1113], fall experience, carelessness were assessed in the questionnaire.

Statistical analyses

Although there are significant differences in the average acceleration between sexes, we evaluated data from the combined group since our aim was to establish the validity of average accelerations for indices of the dynamic postural movement and fall risk.

Student’s t test was used to evaluate the differences in the means between the walk test and hurdle walk test, and between high- and low-risk groups. The Spearman rank correlation was calculated to assess the correlation among average acceleration, FRT, hurdle walk test, TMIG index of competence, and fall risk assessment. In all analyses, the level of statistical significance was set at 0.05. The cluster analysis was used to identify the dissimilarity with items examined in this study. Receiver-operating characteristic (ROC) analysis was used to estimate the discrimination of an individual in the high-risk group based on the standard indices with the average acceleration. We calculated the area under the curve (AUC) as the index. Since the AUC is a portion of the area of the unit square, its value varies between 0 and 1.0.

Statistical analysis was carried out using R 2.10.0 for Windows® Project for Statistical Computing; R Foundation for Statistical Computing, Vienna, Austria.

Results

The measuring time used by the new measuring system was about 5 min. No one complained about the measuring activities.

We calculated the mean and standard deviation (SD) of the average acceleration at all sensor positions during the walk test and hurdle walk test. The results are shown in Fig. 3. There were no significant differences in the means between the walk test and hurdle walk test. We then used the average acceleration value during the walk test for further analysis.
Fig. 3

Means and standard deviation of average accelerations at all sensor positions during the walk test and hurdle walk test

The Spearman rank correlation coefficient between average acceleration, FRT, hurdle walk test, TMIG index of competence, and fall risk assessment, respectively, is shown in Table 1. A significant correlation was obtained between FRT and the hurdle walk test, and between the TMIG index of competence and fall risk assessment. Average acceleration was not significantly correlated to the standard indices.
Table 1

Correlation coefficient between average acceleration, FRT, hurdle walk test, TMIG index of competence, and fall risk assessment, respectively

Position

Axes/direction of the acceleration sensor

FRT

Hurdle walk test

TMIG index of competence

Fall risk assessment

Lumbar

B–F axis

0.216

−0.134

0.037

0.180

R–L axis

0.202

−0.069

−0.144

0.141

U–D axis

0.204

−0.083

−0.074

0.123

Knee

B–F axis

0.194

−0.191

−0.240

−0.193

R–L axis

0.299

−0.273

−0.095

0.293

U–D axis

0.322

−0.355

−0.293

0.051

Ankle

B–F axis

0.183

−0.252

0.021

0.034

R–L axis

0.126

−0.313

0.123

0.228

U–D axis

0.177

−0.068

0.007

−0.068

Toe

B–F axis

−0.120

−0.014

−0.127

−0.098

R–L axis

−0.120

−0.014

−0.127

−0.098

U–D axis

0.119

−0.050

−0.270

−0.216

FRT

 

−0.450*

−0.195

−0.143

Hurdle walk test

  

0.102

0.000

TMIG index of competence

   

0.549**

* P < 0.05, ** P < 0.01

FRT Functional reach test, TMIG Tokyo Metropolitan Institute of Gerontology, B–F back–front, R–L right–left, U–D up–down

The results of the cluster analysis are shown in Fig. 4. Three groups appeared in the cluster analysis: (1) the average acceleration at the lumbar position on the B–F and U–D axes and at the knee position on all axes clustered with fall experience and carelessness; (2) the average acceleration at the lumbar position on the R–L axis and at the ankle and toe positions on all axes clustered with the hurdle walk test, TMIG index of competence, and fall risk assessment; (3) FRT appeared in a separate cluster from other conventional indices.
Fig. 4

Dendrogram of average acceleration at all points during walk, FRT, hurdle walk test, TMIG index of competence, fall risk assessment, fall experience, and carelessness

Subjects were divided into two groups based on the results of the different tests. For the FRT, the high-risk group comprised individuals whose results were <20 cm (n = 6), and the low-risk group comprised those individuals whose results were ≥20 cm (n = 24). For the hurdle walk test, the high-risk group were those individuals who took longer than 10 s (n = 14), while individuals in the low-risk group took ≤10 s (n = 16). By the TMIG index of competence, the high-risk group comprised individuals with ≤10 points (n = 6), and the low-risk group of individuals with ≥11 points (n = 24). By fall risk assessment, individuals in the high-risk group had ≤10 points (n = 6) and those in the low-risk group had ≥11 points (n = 24). By fall experience, the high-risk group (yes) comprised seven persons and the low-risk group (no) 23 persons. By carelessness, the high-risk group (yes) consisted of eight persons, and the low-risk group (no) of 22 persons.

We calculated the means and SD of average acceleration at all sensor positions during the walk test by high- and low-risk groups. The results are shown in Table 2. In the case of the high-risk group according to the FRT, the average acceleration at the lumbar position on the R–L axis was significantly lower than that in the low-risk group. In the case of the risk classification of individuals according to the hurdle walk test, the average acceleration at the ankle position on the B–F axis was lower in the high-risk group than in the low-risk group. In case of classification by fall experience, the average acceleration at the lumbar position on the R–L axis and U–D axis was lower in the high-group than in the low-risk group. In case of classification by carelessness, the average acceleration at the lumbar position on R–L axis was lower in the high-risk group than in the low-risk group.
Table 2

Means and standard deviations of average acceleration at all points during walk by high and low risk groups classified by the standard indices

Standard indices

Location

B–F axis

R–L axis

U–D axis

High risk

Low risk

High risk

Low risk

High risk

Low risk

FRT

Number of subjects: high risk group 6, low risk group 24

Lumbar

1.8 ± 0.4

2.1 ± 2.1

3.9 ± 0.5*

5.1 ± 2.0

1.8 ± 0.9

2.1 ± 0.6

Knee

1.5 ± 0.7

2.0 ± 1.6

1.1 ± 0.2

1.8 ± 2.2

1.8 ± 0.5

2.3 ± 1.6

Ankle

7.6 ± 2.4

8.4 ± 3.3

3.9 ± 1.5

3.3 ± 1.0

5.1 ± 1.7

5.3 ± 1.6

Toe

8.5 ± 1.8

7.9 ± 1.8

6.8 ± 1.6

7.9 ± 1.8

4.9 ± 1.7

5.3 ± 1.9

Hurdle walk test

Number of subjects: high-risk group 14, low-risk group 16

Lumbar

1.7 ± 0.4

2.5 ± 2.7

4.6 ± 1.6

5.2 ± 2.2

2.1 ± 0.8

1.9 ± 0.5

Knee

1.6 ± 0.5

2.3 ± 2.0

1.2 ± 0.4

2.1 ± 2.8

1.8 ± 0.4

2.6 ± 2.1

Ankle

7.1 ± 2.5*

9.5 ± 3.4

3.2 ± 1.1

3.8 ± 1.0

5.2 ± 1.9

5.3 ± 1.3

Toe

8.0 ± 1.7

8.1 ± 1.9

6.5 ± 1.6

7.6 ± 1.8

5.0 ± 1.6

5.5 ± 2.1

TMIG index of

competence

Number of subjects: high-risk group 6, low-risk group 24

Lumbar

1.5 ± 0.5

2.2 ± 2.1

4.7 ± 1.7

5.0 ± 1.9

2.0 ± 0.8

2.0 ± 0.7

Knee

1.6 ± 0.5

2.0 ± 1.6

1.1 ± 0.3

1.8 ± 2.2

1.8 ± 0.4

2.3 ± 1.6

Ankle

8.8 ± 3.6

8.1 ± 3.1

3.5 ± 1.5

3.4 ± 1.0

4.8 ± 2.3

5.4 ± 1.5

Toe

8.4 ± 1.7

8.0 ± 1.8

6.3 ± 0.9

7.2 ± 1.9

5.6 ± 1.5

5.2 ± 1.9

Fall risk assessment

Number of subjects: high-risk group 6, low risk-group 24

Lumbar

1.5 ± 0.4

2.2 ± 2.1

4.5 ± 1.8

5.0 ± 1.9

2.0 ± 0.6

2.0 ± 0.7

Knee

1.8 ± 0.5

1.9 ± 1.6

1.2 ± 0.3

1.8 ± 2.2

1.9 ± 0.5

2.2 ± 1.6

Ankle

7.8 ± 3.6

8.4 ± 3.1

3.2 ± 1.6

3.5 ± 1.0

6.0 ± 2.5

5.1 ± 1.3

Toe

8.7 ± 2.4

7.9 ± 1.6

6.9 ± 1.5

7.1 ± 1.8

6.0 ± 2.1

5.1 ± 1.7

Fall experience

Number of subjects: high-risk group 7, low-risk group 23

Lumbar

1.8 ± 0.4

2.2 ± 2.2

3.7 ± 0.4**

5.3 ± 2.0

1.5 ± 0.1**

2.2 ± 0.7

Knee

2.1 ± 0.8

1.8 ± 1.6

1.0 ± 0.2

1.8 ± 2.2

1.9 ± 0.4

2.3 ± 1.7

Ankle

8.5 ± 2.7

8.2 ± 3.3

3.2 ± 0.8

3.5 ± 1.2

5.9 ± 2.4

5.0 ± 1.3

Toe

9.7 ± 0.7

7.5 ± 1.6

6.8 ± 0.5

7.1 ± 2.0

5.8 ± 1.3

5.1 ± 2.0

Carelessness

Number of subjects: high-risk group 8, low-risk group 22

Lumbar

1.7 ± 0.3

2.2 ± 2.2

3.9 ± 0.5**

5.2 ± 2.1

1.9 ± 0.6

2.0 ± 0.7

Knee

1.4 ± 0.3

2.1 ± 1.6

1.2 ± 0.3

1.8 ± 2.3

1.7 ± 0.4

2.3 ± 1.4

Ankle

8.7 ± 3.6

8.1 ± 3.0

3.7 ± 1.7

3.4 ± 0.9

5.0 ± 1.1

5.3 ± 1.8

Toe

7.1 ± 1.1

8.3 ± 1.9

7.8 ± 1.9

6.8 ± 1.6

4.8 ± 1.8

5.4 ± 1.8

* P < 0.05, ** P < 0.01 (high-risk group vs. low-risk group)

We calculated the AUC for ROC analysis to estimate the discrimination of the average acceleration to high- and low-risk groups classified by the conventional indices. These results are shown in Table 3. The AUC was high (>0.650): (1) at the lumbar position on the R–L axis and U–D axis and at the knee position on the B–F axis and R–L axis when the groups were classified by FRT; (2) at the ankle position on the R–L axis when the groups were classified by the hurdle walk test; (3) at the lumbar position on the B–F axis when the groups were classified by the fall risk assessment; (4) at the lumbar position on the R–L axis and U–D axis and at the knee position on the R–L axis when the groups were classified by fall experience; (5) at the knee position on the U–D axis when the groups were classified by carelessness.
Table 3

The area under the receiver operator characteristic curve to discriminate the high- and low-risk groups classified by the standard indices

Standard indices

Location

B–F axis

R–L axis

U–D axis

FRT

Lumbar

0.514

0.653

0.701

Knee

0.660

0.722

0.625

Ankle

0.500

0.382

0.486

Toe

0.306

0.521

0.507

Hurdle walk test

Lumbar

0.549

0.487

0.460

Knee

0.558

0.554

0.647

Ankle

0.625

0.705

0.563

Toe

0.415

0.616

0.473

TMIG index of competence

Lumbar

0.625

0.458

0.542

Knee

0.500

0.604

0.556

Ankle

0.375

0.479

0.646

Toe

0.361

0.611

0.347

Fall risk assessment

Lumbar

0.653

0.563

0.500

Knee

0.313

0.576

0.417

Ankle

0.528

0.632

0.389

Toe

0.403

0.486

0.292

Fall experience

Lumbar

0.497

0.801

0.795

Knee

0.242

0.770

0.485

Ankle

0.385

0.522

0.441

Toe

0.745

0.460

0.335

Carelessness

Lumbar

0.534

0.619

0.648

Knee

0.500

0.591

0.682

Ankle

0.415

0.511

0.483

Toe

0.585

0.296

0.568

Discussion

In this study, we used a new instrument to measure average acceleration during the walk test and hurdle walk test—without receiving any complaints, even in the aged group. We investigated the relationship of acceleration during the walk test to the conventional indices of dynamic postural movement and fall risk.

The average acceleration during the walk test was not significantly correlated, according to the Spearman rank coefficient, to FRT, the hurdle walk test, TMIG index of competence, and fall risk assessment. This lack of correlation indicates that average acceleration at a position on one axis has no specific characteristics to correlate with these four indices.

The values for average acceleration were divided into two clusters. Those at the lumbar position and knee position clustered with fall experience and carelessness, while those at the ankle position and toe position clustered with the hurdle walk test, TMIG index of competence, and fall risk assessment.

Fall experience easily leads to muscle weakness and a reduced range of joint motion due to the anxiety associated with falling [14]. Carelessness relates to a low self-efficacy to present falls, such as anxiety disorder [15, 16]. Consequently, this cluster, which includes fall experience and carelessness, reflects the functionality of, for example, muscle power and range of motion. The average acceleration at the lumbar and knee positions thus indicates walking ability.

The hurdle walk test is affected by balance ability [17]. The TMIG index of competence and fall risk assessment are related to the maintenance of physical fitness [18]. This cluster, which includes the hurdle test, TMIG index of competence and fall risk assessment, thus reflects balance ability and ADL. The average acceleration at the ankle and toe positions reflects the coordination needed to maintain the ability of dynamic postural balance and ADL.

FRT is the index related to dynamic and static postural balance ability [19, 20]. The center of foot pressure through the muscles and hip joints for attitude dynamics and control in the aged group was different from that in the young group. As a result, FRT in the aged seems to include the risk factor for falls, with the exception of ability of dynamic postural balance [21]. In our study, the average acceleration showed high dissimilarity to FRT.

The average acceleration includes conventional indices to estimate the dynamic postural movement and fall risk, except for FRT. Thus, the average acceleration at all positions on all axes may be a composite index that packages the conventional indices. We found that the average acceleration shown by the high- and low-risk groups was significantly different when classified by the conventional indices. The average acceleration in the high-risk group at the lumbar position was significantly lower than that in the low-risk group when classified by fall experience and carelessness. These results are associated with a decline in walking ability since muscle weakness leads to significant postural instability as well as inactivity in daily life [22]. The average acceleration at the lumbar position in the aged group was higher than that in the young group due to trunk instability in the former [23]. However, as a result of a further decline in walking ability in the high-risk group, the average acceleration might become lower in the low-risk group.

The aged walk with a flat-footed action because of muscle weakness of the lower leg and show poor coordination during walking [24]. The average acceleration at the toe position was therefore higher in the aged group than in the young group [23]. However, as a result of further reduced coordination due to advanced muscle weakness, the average acceleration in the high-risk group was lower than in the low-risk group.

The average acceleration in the high-risk group at the lumbar position during the walk test was significantly lower than that in the low-risk group when classified by FRT. Although FRT is a valid index to estimate the dynamic postural movement in a young group [25, 26], FRT in the aged is assumed to be different from that in the young group. The elderly use more hip movements to maintain the body equilibrium [27, 28], while the young use ankle movements. Because of this strategy [23], the average acceleration at the lumbar position in the aged group was higher than that in the young group. However, in the high-risk group, the instant movement of the hip joint is not able to be used for body equilibrium because of advanced muscle weakness and, consequently, the average acceleration at the lumbar point was lower in the high-risk group than in the low-risk group.

The results of the ROC analysis show that average acceleration at the ankle position may screen the high-risk group classified by the hurdle walk test, that average acceleration at the lumbar and knee positions may screen group by fall experience and carelessness, and that average acceleration at the lumbar and knee positions may screen groups by FRT. The hurdle walk test had a low dissimilarity to the average accelerations at the ankle and toe positions. The average acceleration at the ankle position was able to discriminate the high-risk group classified by the hurdle walk test. Fall experience and carelessness had a low dissimilarity to the average accelerations at the lumbar and knee positions. The average accelerations at the lumbar and knee positions were able to discriminate the high-risk groups classified by fall experience and carelessness. In the case of FRT, the average acceleration at the lumbar position was significantly lower in the high-risk group than in the low-risk group. The average acceleration at the lumbar position was able to discriminate the high-risk group classified by FRT. However, the reason why the average acceleration at the knee position was able to discriminate the high-risk group classified by FRT is unknown.

We calculated the root mean square of three axes to estimate the total effects and found that the results agreed with those using 12 measurement points, with the exception of the AUC for the ROC analysis. The AUC values at the lumber position to discriminate to the high-risk group classified by fall risk assessment and at the knee position to discriminate to the high-risk group classified by fall experience using root mean square were lower than those using the 12 measurement points; for these measures calculation of the root mean square had no total effect. We therefore used the data of average acceleration at the 12 measurement points.

The measuring system introduced here is hassle free with respect to subject involvement and is a convenient tool by which researchers can measure average acceleration, even among the elderly. This system may enable the dynamic postural movement and fall risk to be measured by assessing the overall rating of average acceleration at the lumbar, knee, ankle, and toe positions and to discriminate the risk group. Consequently, the average acceleration may be a useful measure to estimate the dynamic postural movement and fall risk by means of any measurement position simultaneously in one measurement.

There were no significant differences in male–female distribution between the high- and low-risk groups, with the exception of the hurdle walk test, and no significant differences in mean age, except for FRT. However, there was an insufficient number of subjects to estimate the influence of differences in male–female distribution and mean age between the high- and low-risk groups. We therefore used the combined group of men and women in this study. Further study is necessary to clarify the influences of sex distribution and age variation.

Functional asymmetry in both lower extremities causes a fall, and this should be taken into consideration to prevent falls. Future assessment of the differences in acceleration between the left and right lower limbs is required.

In conclusion, we measured that the average acceleration, based on accelerograms, of 30 subjects aged ≥63 years during the walk test. The Spearman rank correlation was not significant in terms of the conventional indices for evaluating the dynamic postural movement and fall risk, which indicates that the average acceleration at one axis at a position has no specific characteristics to correlate with the standard. Average acceleration was divided into two clusters: accelarations at the lumbar and knee positions clustered together with fall experience and carelessness; accelerations at the ankle and toe positions clustered with the hurdle walk test, TMIG index of competence, and fall risk assessment. Average acceleration may be a composite index that encompasses the standard indices. There was a significant difference in the average acceleration at some measurement positions between high- and low-risk groups classified by conventional indices. The average acceleration has the possibility to discriminate the high-risk group classified by the standard indices. As such, it may be a useful tool to estimate the dynamic postural movement and fall risk by means of all measurement positions.

Declarations

Acknowledgments

The authors are grateful to Ms. Emiko Horiuchi, Ms. Yuki Sakamoto, Ms. Mari Shimohigashi, Ms. Kimiko Yuba, and all of the elderly people in Kimino Town and Katsuragi Town, Wakayama Prefecture (Japan) who participated in our study, for their support in the conduct of this study. This study was supported by the Medicine Promotion Association in Wakayama Prefecture.

Authors’ Affiliations

(1)
Department of Electronics Industry, Industrial Technology Center of Wakayama Prefecture, Wakayama City, Japan
(2)
Wakayama Nursing Institute, Kinokawa City, Japan
(3)
School of Health and Nursing Science, Wakayama Medical University, Wakayama City, Japan
(4)
Nature Core Science Inc, Kainan City, Japan
(5)
Faculty of Education, Wakayama University, Wakayama City, Japan
(6)
Wakayama Medical University, Wakayama City, Japan

References

  1. Itoh T, Kumagai Y, Morioka I, Mae S, Naka T, Uenishi H, et al. Development of new instrument for evaluating leg motions using acceleration sensors. Environ Health Prev Med. 2007;3:111–8.View ArticleGoogle Scholar
  2. Itoh T, Kumagai Y, Mae S, Naka T, Uenishi H, Morioka I. Estimation of leg motions with acceleration sensor (I). J Educ Health Sci. 2005;51:56–7.Google Scholar
  3. Rubenstein ZL, Robbins AS, Schulman BL, Rosado J, Osterweil D, Josephson KR. Falls and instability in the elderly. J Am Geriatr Soc. 1988;36:266–78.PubMedGoogle Scholar
  4. Rubsenstein ZL, Josephson RK. The epidemiology of falls and syncope. Clin Geriatr Med. 2002;18:141–58.View ArticleGoogle Scholar
  5. Stalenhoef PA, Diederiks JP, Knottnerus JA, Kester AD, Crebolder HF. A risk model for prediction of recurrent falls in community-dwelling elderly: a prospective cohort study. J Clin Epdermiol. 2002;55:1088–94.View ArticleGoogle Scholar
  6. Duncan PW, Studenski S, Chandler J, Prescott B. Functional reach: predictive validity in a sample of elderly male veterans. J Gerontol. 1992;47:M93–8.PubMedGoogle Scholar
  7. Duncan PW, Weiner DK, Chandler J, Studenski S. Functional reach: a new clinical measurement of balance. J Gerotol. 1990;45:M192–7.Google Scholar
  8. Nishijima N, Takahashi S, Ohishi T, Nakano T, Suzuki K, Yamada H, et al. The sensitivity of the Japan fitness test in elderly people to assess the effects of aging. Int J Sport Health Sci. 2006;4:583–90.View ArticleGoogle Scholar
  9. Nishijima N, Takahashi S, Ohishi T, Nakano T, Suzuki K, Yamada H, et al. A comparison of estimation models of physical fitness age for elderly people using the Japan fitness test. Int J Sport Health Sci. 2006;4:591–605.View ArticleGoogle Scholar
  10. Koyano W, Shibata H, Hakazato K, Haga H, Suyama Y. Measurement of competence: reliability and validity of the TMIG index of competence. Arch Gerontol Geritr. 1991;13:103–16.View ArticleGoogle Scholar
  11. Suzuki T. Development and its use of a falling risk assessment chart for the elderly population. In: Health Assessment Committee. Health Assessment Manual (article in Japanese with English abstract). Kosei Kagaku Kenkyusho; 2001. p. 142–52.Google Scholar
  12. Yokoya T, Demura S, Sato S. Fall risk characteristics of the elderly in a exercise class. J Physiol Anthropol. 2008;27:25–32.PubMedView ArticleGoogle Scholar
  13. Yokoya T, Demura S, Sato S. Relationships between physical activity, ADL capability and fall risk in community-dwelling Japanese elderly population. Environ Health Prev Med. 2007;12:25–32.PubMedView ArticleGoogle Scholar
  14. King MB. In: Hazzard WR et al., editors. Principle of geriatric medicine and gerontology. 5th ed. NY: McGraw-Hill; 2003. p. 1517–29.Google Scholar
  15. Vellas BJ, Wayne SJ, Romero LJ, Baumgartner RN, Garry PJ. Fear of falling and restriction of mobility in elderly fallers. Age Ageing. 1997;26:189–93.PubMedView ArticleGoogle Scholar
  16. Binda SM, Culham EG, Brouwer B. Balance, muscle strength, and fear of falling in older adults. Exp Aging Res. 2003;29:205–19.PubMedView ArticleGoogle Scholar
  17. Nishigaki T, Koshimizu E, Aoki A, Toji H, Suei K, Iwasaki H. The relationship between physical fitness and activities of daily living in older adults. Hum Perform Meas. 2005;2:1–9.Google Scholar
  18. Yokoya T, Demura S, Sato S. Relationships between physical activity, ADL capability and fall risk in community-dwelling Japanese elderly population. Environ Health Prev Med. 2007;12:25–32.PubMedView ArticleGoogle Scholar
  19. Shimada H, Uchiyama Y, Kakurai S. Specific effects of balance and gain exercises on physical function among the frail elderly. Clin Rehabil. 2003;17:472–9.View ArticleGoogle Scholar
  20. Seidler RD, Martin PE. The effects of short term balance training on the postural control of older adults. Gait Posture. 1997;6:224–36.View ArticleGoogle Scholar
  21. Wernick-Robinson M, Krebs DE, Giorgetti MM. Functional reach: does it really measure dynamic balance? Arch Phys Med Rahabil. 1999;80:262–9.View ArticleGoogle Scholar
  22. Andersson E, Oddsson L, Grundström H, Thorstensson A. The role of the psoas and iliacus muscles for stability and movement of the lumbar spine, pelvis and hip. Scand J Med Sci Sports. 1995;5:10–16.PubMedView ArticleGoogle Scholar
  23. Itoh T, Morioka I, Naka T. Estimation of leg motions with acceleration sensor (IV). J Educ Health Sci. 2007;53:46–7.Google Scholar
  24. Takahasi K, Takahashi HE, Nakadaira H, Yamamoto M. Different changes of quantity due to aging in the psoas major and quadriceps femoris muscles in women. J Musculoskelet Neuronal Interact. 2006;6:201–5.Google Scholar
  25. Cavanaugh JT, Shinberg M, Ray L, Shipp KM, Kuchibhatla M, Schenkman M. Kinematic characterization of standing reach: comparison of younger vs. older subjects. Clin Biomech (Bristol, Avon). 1999;14:271–9.View ArticleGoogle Scholar
  26. Jonsson E, Henriksson M, Hirschfeld H. Does the functional reach test reflect stability limits in elderly people? J Rehabil Med. 2003;35:26–30.PubMedView ArticleGoogle Scholar
  27. Horak FB. Postural orientation and equilibrium: What do we need to know about neural control of balance to prevent falls? Age Ageing. 2006;35-S2:ii7–ii11.View ArticleGoogle Scholar
  28. Okada S, Hirakawa K, Takada Y, Kinoshita H. Age-related differences in postural control in humans in response to a sudden deceleration generated by postural disturbance. Eur J Appl Physiol. 2001;85:10–18.PubMedView ArticleGoogle Scholar

Copyright

© The Japanese Society for Hygiene 2011

Advertisement