Difference in trunk stability during semicircular turns with and without a bag in elderly women

Abstract

BACKGROUND:

Direction changes while walking are more likely to cause a hip fracture than is falling while walking in a straight line. Trunk stability is an important contributor to safe and effective walking, and arm movements influence trunk movement while walking. However, the difference in the trunk stability during semicircular turns performed by elderly women with a light bag has not been examined.

OBJECTIVE:

To investigate the effects of carrying a bag on trunk stability during semicircular turns in elderly women.

METHODS:

We enrolled 15 community-dwelling elderly women capable of independent walking. Participants walked with and without a bag at a self-selected speed along a marked path, which included semicircular turns, while fitted with an accelerometer attached over the L3 spinous process.

RESULTS:

Gait velocity was faster during semicircular turning with a bag versus without a bag. The normalized medial-lateral center of mass acceleration was lower during semicircular turning with a bag versus without a bag.

CONCLUSIONS:

We suggest that a light additional arm load and increased arm swing contributes to trunk stability and efficient walking during semicircular turning by elderly women.

Keywords

Center of mass acceleration gait velocity older adults semicircular turning trunk stability

1. Introduction

Walking is an essential part of independent daily activities, and it is vital in terms of the quality of life of older adults [1]. However, a large proportion of falls in older adults occurs while walking; in particular, falls during direction changes while walking are 7.9-fold more likely to cause a hip fracture than is falling while walking in a straight line [2, 3]. When facing greater balance threats, such as turning while walking, older adults commonly adopt cautious gait patterns, characterized by slower walking velocity, shortened stride length, and reduced arm swing [4].

Trunk stability is an important contributor to safe and effective walking. The trunk plays a key role in providing a stable platform for the head and lower extremities by regulating the amplitude and structure of gait-related oscillations and by providing input to the visual and vestibular senses [5]. Additionally, the trunk is linked to the arms through the shoulder joints, and arm movements influence trunk movement while walking. For example, rhythmic arm swings are made for postural stability during walking, which increases gait velocity and promotes balanced angular momentum in the lower body [6, 7].

Many studies have demonstrated the effects of arm swinging on gait parameters in older adults and patients with conditions, such as stroke and Parkinson’s disease (PD). These studies have shown that the arm-swing effect changes gait parameters and improves trunk stability [4, 8, 9]. Additionally, a review article regarding arm swing suggested that arm swinging may have therapeutic value [10]. Recently, several studies investigated the effects of added arm weights on arm swinging and gait parameters in patients, such as those with stroke and PD. Yoon et al. [11] suggested that adding weights to the arm during walking may facilitate arm and pelvic movements, resulting in changes in gait patterns. The therapeutic use of additional arm weights could be considered for gait rehabilitation in PD to improve gait impairment. Lee et al. [7] reported that applying the optimal load to the less affected upper limb may benefit people with a hemiplegic gait and could improve the efficiency of gait performance.

Carrying a bag is common extrinsic load in daily activities that affects many of the body’s movements [12]. Bampouras and Dewhurst [13] assessed carrying no bag, one 1.5-kg bag, and one 3-kg bag. They suggested that carrying shopping bags did not negatively affect postural stability or gait variables in either group. Further, in older individuals, a decrease in sway velocity was found when holding bags during the postural stability assessment, suggesting that carrying a bag, regardless of load distribution, may have a stabilizing effect during quiet standing. However, this study examined only straight walking conditions, and static postural stability was assessed with participants standing quietly, not walking. Matsuo et al. [14] compared balance during asymmetric load-carrying as well as the effects of asymmetric loading on lower limb coordination during gait in young versus middle-aged adults. They reported that intra-limb and inter-limb coordination in the trunk, head, and upper arm did not vary under the different load conditions. The only difference observed between the groups was in contralateral shoulder abduction. However, they did not report gait parameters and used relatively heavy bags in older adults.

Thus, in this study, we investigated the differnece in trunk stability while elderly women with and without a light bag performed semicircular turns. We hypothesized that carrying a bag would increase gait velocity and decrease trunk acceleration amplitude in elderly women.

2. Methods

2.1 Subjects

In total, 15 elderly women (aged 66–86 years) living in Gyeongsangnam-do, South Korea participated. The inclusion criteria were: (1) age $>$ 65 years with the ability to walk independently (no assistive device) and no participation in regular exercise programs, such as lower extremity strengthening exercises or balance or gait training, within the last 6 months; and (2) a score $\geqslant$ 24 on the Korean version of the Mini-Mental State Exam (MMSE-K). Exclusion criteria were: (1) past or present neurological disorder; (2) major orthopedic diagnosis (bone fracture, joint fusion or replacement, or limb amputation) in the lower back, pelvis, or lower extremities; (3) significant visual, auditory, or vestibular impairment; and (4) treatment with drugs that could influence the study results (e.g., antidepressants, sedative-hypnotics, and antipsychotic medications). Ethical approval was obtained from the Inje University Ethics Committee for Human Investigations. Written informed consent was obtained from all participants.

2.2 Materials

2.2.1 Tri-axial accelerometer

Gait velocity and center-of-mass (COM) acceleration during a semicircular turning gait were measured using a tri-axial accelerometer (Fit Dot Life, Suwon, Korea). The size and weight of the accelerometer were 35 $\times$ 35 $\times$ 13 mm and 13.7 g, respectively. A sensor range of $-$ 8 G to $+$ 8 G could be selected with the acquisition software (Fitmeter Manager 2; ver. 1.2.0.14, Fit Life, Inc., Korea). Raw data were measured using $x$ , $y$ , and $z$ acceleration axes; data were transferred automatically to a computer using a USB cable. A sensor range of $-$ 2 G to $+$ 2 G was used. The accelerometer was attached over the L3 spinous process using double-sided adhesive tape [15]. Another accelerometer, with start and stop buttons operated by a hand switch, was used by the investigator. Before the test, we synchronized the two accelerometers with the acquisition software. Data were collected at a sampling rate of 32 Hz.

2.2.2 Semicircular turning (SCT) course

The SCT course consisted of walking 3 m in a straight line, a 2.355-m semicircular curve with a radius of 0.75 m and a 3-m return straight path. Colored tape (5 cm wide) was used to delineate the path [15].

2.2.3 Bag

A bag (350 $\times$ 270 $\times$ 20 mm and 200 g weight) with two straps (width: 15 mm; Fig. 1) and hand straps were selected for the participants to carry with their right hands (Fig. 2).

Figure 1.

Semicircular turning.

Figure 2.

Bag.

2.2.4 Data processing

Walking velocity was calculated by dividing the distance by the time required to complete the locomotor task [15], expressed in centimeters per second (cm/s). Gait time was calculated by reference to the accelerometer data.

$\displaystyle\text{Velocity}=\text{835.5/time (cm/s)}$

COM acceleration in the anterior-posterior (AP), medial-lateral (ML), and vertical (VT) directions was calculated using the root mean squares (RMS) of the different directions. RMS values (AP, ML, and VT) of the different directions were normalized to [(direction RMS/RMS) $\times$ 100], expressed as % RMS [15]:

$\displaystyle\text{AP}=\left({\left.{\sum\limits_{{i}=1}^{n}\sqrt{z_{i}^{2}}}% \right/\sum\limits_{{i}=1}^{n}\sqrt{x_{i}^{2}+y_{i}^{2}+z_{i}^{2}}}\right){% \times}100$ $\displaystyle\text{ML}=\left({\left.{\sum\limits_{{i}=1}^{n}\sqrt{x_{i}^{2}}}% \right/\sum\limits_{{i}=1}^{n}\sqrt{x_{i}^{2}+y_{i}^{2}+z_{i}^{2}}}\right){% \times}100$ $\displaystyle\text{VT}=\left({\left.{\sum\limits_{{i}=1}^{n}\sqrt{y_{i}^{2}}}% \right/\sum\limits_{{i}=1}^{n}\sqrt{x_{i}^{2}+y_{i}^{2}+z_{i}^{2}}}\right){% \times}100$

2.3 Procedures

Lower limb dominance was assessed prior to data collection by asking participants to kick a soccer ball [16]. All participants were right-limb dominant. All tests were explained to participants in advance, after which they were asked to walk along the colored tape. An accelerometer was attached with double-sided adhesive tape over the L3 spinous process; the participants held a bag with hand straps in the right hand and were asked to walk on the pathway (left turn) with bare feet at a self-determined speed. Participants began walking from a location 2 m before the onset of the marked path and finished 2 m beyond the end of the path to avoid any influence of acceleration and deceleration [17]. After completing two practice trials, participants completed three measured courses, resting for 1 min between trials to avoid muscle fatigue.

2.4 Statistical analysis

Data were analyzed using the SPSS software (ver. 18.0 for Windows; SPSS, Inc., Chicago, IL, USA). Differences in gait velocity and normalized COM acceleration during SCT task were analyzed using paired $t$ -tests. A value of $p<$ 0.05 was considered to indicate statistical significance. All data were analyzed to verify the assumption of normality using the Shapiro-Wilk test.

3. Results

3.1 Participant characteristics

Participant ages, heights, weights, and body mass indexes (BMI) are provided in Table 1. Their average age, height, weight, and BMI were 75.60 $\pm$ 6.86 (mean $\pm$ SD) years, 149.87 $\pm$ 3.89 cm, 52.91 $\pm$ 6.07 kg, and 23.55 $\pm$ 2.37 kg/m ${}^{2}$ , respectively.

Table 1
General characteristics of the participants ( $N=$ 15)

Variables	Mean $\pm$ SD		Range
Age (years)	75.60	$\pm$ 6.86	66	$\sim$ 86
Height (cm)	149.87	$\pm$ 3.89	143.4	$\sim$ 155.9
Weight (kg)	52.91	$\pm$ 6.07	45	$\sim$ 65
BMI (kg/m ${}^{2}$ )	23.55	$\pm$ 2.37	20.4	$\sim$ 27.7

All values are mean $\pm$ standard deviation. Abbreviation: BMI, body mass index.

Table 2

Comparison of gait velocity and normalized COM acceleration during SCT with and without a bag

Segment	Without a bag		With a bag		CI		p
Velocity (cm/sec)	60.61	$\pm$ 8.72	68.27	$\pm$ 12.42	$-$ 12.95	$\sim$ $-$ 2.37	0.008 ${}^{*}$
Anterior-posterior (% RMS)	54.52	$\pm$ 9.85	52.07	$\pm$ 6.16	$-$ 8.76	$\sim$ 3.86	0.419
Medial-lateral (% RMS)	47.99	$\pm$ 10.93	43.52	$\pm$ 6.76	$-$ 8.26	$\sim$ $-$ 0.67	0.024 ${}^{*}$
Vertical (% RMS)	47.23	$\pm$ 9.76	46.68	$\pm$ 7.42	$-$ 6.17	$\sim$ 5.06	0.836

All values are mean $\pm$ standard deviation. Abbreviation: CI, confidence interval. ${}^{*}p<$ 0.05.

3.2 Walking velocity

Walking velocity increased slightly when partici- pants walked carrying a bag (68.27 $\pm$ 12.42) versus when they walked with no bag (60.61 $\pm$ 8.72; $p=$ 0.008; Table 2).

3.3 COM acceleration

Normalized ML COM acceleration with a bag (43.52 $\pm$ 6.76) was significantly lower than that without a bag (47.99 $\pm$ 10.93; $p=$ 0.024; Table 2).

4. Discussion

We examined the difference in trunk stability with and without a bag during SCT tasks in terms of gait velocity and normalized COM acceleration in elderly women. Gait velocity and COM acceleration are clinically important measurements for evaluating trunk stability during walking in older adults, and they can predict the risk of falling [1, 18, 19]. Increases in walking speed are associated with better functioning, diminished risk, and a higher possibility of survival [1]. Typically, when encountering greater poise threats, such as turning while walking, older adults commonly adopt a cautious gait pattern, characterized by slower walking velocity, shorter stride length, and reduced arm swinging [4]. In the present study, gait velocity was significantly faster, whereas normalized ML COM acceleration was slower, with a bag versus with no bag. These results indicated that the trunk was more stable under the with-a-bag condition. That is, COM movement velocity changed more frequently with no bag than with a bag. One possible reason is that arm swing influences trunk movement and gait velocity, playing a counterbalancing role against the contra lateral leg and pelvic movement.

In this study, participants walked with a bag and this condition involved an additional arm load. Our findings are consistent with those of Lulić et al. [20], who reported that deliberate upper limb swinging changed the central dynamic moments of inertia of the whole body, thus influencing the dynamic characteristics of gait and changing gait efficiency. Additionally, Bruijn et al. [21] reported that arm movement led to control of body angular momentum. Control of lateral stability was reported to be more important during walking, and walking stability in the ML direction could better predict age-related falls [22]. Turning requires shifting the body COM toward the inside of the turn. Thus, during turning, the body has an increased risk of imbalance in the ML direction [23]. In particular, semicircular turning requires greater ankle push-off force on the outside limb to push the COM in the direction of the turn and to rotate the trunk toward the new direction, such that ankle power is increased in the outside leg. Thus, the body has an increased threat to its balance in the ML direction [24]. Our findings indicate that older adults changed ML COM velocity more frequently during walking without a bag than with one. Our results are consistent with those of Nakakubo et al. [4], who assessed trunk stability under three walking conditions: normal, no swing, and deliberate arm swinging. They reported that trunk stability in the ML direction increased when elderly individuals walked with a deliberately emphasized arm swing. Wu et al. [25] investigated the effects of natural and active arm-swinging conditions and reported that active arm swinging may facilitate improved dynamic stability of the trunk in the ML direction. Additionally, upper limb movement is necessary to produce smooth, non-jerky movement [20]. For these reasons, walking velocity increased slightly and normalized ML COM acceleration decreased when participants walked carrying a bag versus carrying no bag.

In this study, there was no significant difference in normalized AP or VT COM accelerations between tasks. AP COM acceleration is related to velocity [26]. Movement velocity in the AP direction was increased. Thus, our results indicated that, in carrying a bag, participants maintained trunk stability in the AP direction. VT COM acceleration confers shock absorption in the foot and energy efficiency [10, 27]. Thus, carrying a bag did not influence shock absorption. Lulić et al. [20] reported that deliberate upper limb swinging led to better vertical shock absorption. A possible reason for the different result is that participants in this study were elderly women, whereas Lulić et al. examined young adults. Older adults generally have a decreased ankle range of motion and power versus young adults. Additionally, participants in this study performed a semicircular turning task, whereas participants in the study conducted by Lulić et al. performed a straight-ahead task. The turning task required shifting the body COM toward the inside of the turn. Thus, ML directional movement increased over that observed in a straight-ahead task. Moreover, our data were normalized and expressed as % RMS.

Our study had several limitations. First, all participants were elderly women, so we cannot generalize the results to all older adults. Second, we did not directly measure arm-swing magnitude. Third, the SCT task mixed straight and turning tasks. We did not classify the course between straight and turning sections. Fourth, we used only one weight and size of bag. Thus, we cannot fully explain the effects of other weights and bag sizes on stability. Future studies should investigate the contribution of arm movements used for upper and lower trunk acceleration during turning.

5. Conclusions

In the present study, we compared trunk stability in SCT tasks with and without carrying a bag in terms of gait velocity and COM acceleration in elderly women. Walking velocity increased and ML COM acceleration decreased when participants walked carrying a bag versus when they walked with no bag. Thus, we suggest that a light additional arm load contributes to trunk stability during SCT task in elderly women.

Conflict of interest

The authors have no conflict of interest related to the present article.

Footnotes

Acknowledgments

This work was supported by the 2016 Inje University research grant.

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Difference in trunk stability during semicircular turns with and without a bag in elderly women

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2. Methods

2.1 Subjects

2.2 Materials

2.2.1 Tri-axial accelerometer

2.2.2 Semicircular turning (SCT) course

2.2.3 Bag

2.3 Procedures

2.4 Statistical analysis

3. Results

3.1 Participant characteristics

Table 1 General characteristics of the participants ( N = 15)

3.3 COM acceleration

4. Discussion

5. Conclusions

Conflict of interest

Footnotes

Acknowledgments

References

Table 1
General characteristics of the participants ( $N=$ 15)