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Article

Increased Motor Time in the Lower Extremity Is Related to Fall History in Older Women

by
Zongtao Li
1,*,
Qin Lai
2 and
Ronald J. Benedict II
3
1
College of Physical Education, Hebei Normal University, Shijiazhuang 050024, China
2
Kinesiology, Health & Sport Studies, Wayne State University, Detroit, MI 48202, USA
3
Wellness, Human Performance, and Recreation, Union College, Barbourville, KY 40906, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(14), 6290; https://doi.org/10.3390/app14146290
Submission received: 12 June 2024 / Revised: 9 July 2024 / Accepted: 17 July 2024 / Published: 19 July 2024
(This article belongs to the Section Applied Neuroscience and Neural Engineering)
Browse Figure
Versions Notes

Abstract

Our aim was to determine whether a temporal deterioration in central or peripheral processing was related to a history of falls in older women and observe the relationship between muscular strength in the lower extremity and information processing speed. A population of 34 older women aged 65–84 years were classified into two groups, fallers (n = 15) and non-fallers (n = 19), based on their fall history and fall risk index. Surface EMG was collected from the tibialis anterior (TA), gastrocnemius (GN), and peroneus longus (PL), which were activated in response to postural perturbation. The reaction time was fractionated into the premotor time (PMT) and motor time (MT). Three fast deep squats were performed on a force platform to record peak extensor force in the lower extremity. A 2 (Groups) × 3 (Trials) ANOVA with repeated measures for the trials revealed main group effects of MT on TA [F = 4.59, p < 0.05] and GN [F = 5.01, p < 0.05], and main trial effects of the PMT on TA [F = 3.50, p < 0.05]. A strong negative relationship was found between the motor time and peak extensor force of the left leg or both legs. The peak extensor force of the left leg was a reliable predictor for the motor time. Fallers faced a higher risk of falling and produced a longer motor time in response to the balance perturbation relative to non-fallers. Weaker explosive strength in the lower extremity, especially the non-dominant leg, was highly related to slower neuromuscular recruitment.

1. Introduction

Falling is a serious threat to seniors’ safety and health. It is a leading cause of physical injury, disability, or unnatural death in this population [1]. Most fall events are initiated by unexpected postural perturbations and result directly from the failure to perform a quick and effective reaction, i.e., latency of reaction time (RT) [2]. It is also well documented that the latency of RT increases with ageing, especially in those over 70 [3] and women [4,5].
RT is defined as the length of time elapsed from the presentation of a stimulus to the initiation of a reactive movement, influenced not only by the central processing capacity, but also by peripheral effectors, such as a muscle’s architecture and properties [6,7]. Through synchronous recording of surface electromyography (sEMG) and body reactive action, RT can be fractionated into two temporal components for central and peripheral processing, called the premotor time (PMT) and motor time (MT), respectively [6]. The PMT is estimated as the timing interval from the presentation of a stimulus to the initial sEMG burst, while MT indicates the latency from the initial sEMG burst to the initiation of movement. The fractionated RT technique has been frequently employed to observe the relationship between components of RT and ageing. Previous studies demonstrated that both the PMT and MT [8], or just MT [9,10], were significantly longer in the older women than their young counterparts.
Importantly, recent studies have focused on fractionated RT in older adults with high fall risk in comparison with their counterparts without fall risk, or with low fall risk. Wang et al. fractionated RT by defining the onset time of a muscle as the first EMG burst exceeding the baseline by 3 SD for more than 25 ms in a choice step reaction test. They both found that PMT and MT latencies were longer in the fallers than the non-fallers [11]. However, using a similar step reaction task, St George et al. failed to find any difference in PMT and/or MT between the high- and low-fall-risk groups when the fractionated RT was determined by the first normalized EMG burst to be greater than baseline by 2 SD [12]. During these choice step reaction tests, the motor time was possibly confounded with the movement time due to stepping movements. In a simple RT paradigm, Laroche et al. demonstrated that the fallers produced greater composite scores for MT from four muscles in the lower extremity compared to the non-fallers, although no difference in PMT was detected [13]. On the other hand, Crozara et al. did not find any differences in PMT or MT between the fallers and non-fallers by using a simple RT test with a seated knee or ankle response, in which EMG onset was defined by the first value greater than 55.0 of peak EMG [10]. It appears that the different research protocols, i.e., sEMG onset and the type of RT, might cause inconsistent results for fractionated RT by fall conditions among older adults. In addition, these previous studies did not make efforts to test how the subject responded to a slip or a trip. First, each of those studies used a visual stimulus in the RT test, while the natural stimulus of a fall is usually unexpected body perturbation. Second, participants in the previous studies reacted to signal stimulation with a pre-planned movement, such as stepping or knee/ankle rotation in the sagittal plane, but in restoring upright stability, an automatic postural response (APR) is typically adopted to counteract a perturbation like stumbling or slipping [14]. Thus, whether the PMT or MT predisposes the reaction time to being longer and makes older adults more susceptible to fall risk is unclear, and a postural perturbation should be used as stimulus to induce fall-related recovery action with the aim of measuring fractionated RT.
Muscle weakness in the lower extremity due to ageing has been well documented as contributing to falls [9,15,16]. Explosive muscle force or power in the lower extremities is thought to be essential in postural control and fall prevention, based on its critical role in producing fast reactive movements under the condition of perturbation. Older adults, especially women, are sensitive to muscle fiber loss and degenerative neuromuscular remodeling, which greatly decreases their ability to produce explosive force [9,16,17,18]. However, the effects of decreased muscle force in the lower extremity on latencies in central information processing and/or peripheral neuromuscular activation have not been established in the existing literature. Further, participants could anticipate the coming event by speeding up central processing [6], changing activation level [19], and making peripheral adjustments to afferent perceptual signals [20] and fusimotor output [21] to reduce RT latency, but it remains unclear whether this motor anticipation induced by multiple trials affected the central component (PMT) or peripheral component (MT).
Therefore, the present study utilized a customized trapdoor platform to induce postural recovery responses from the participants. The primary aim was to determine whether fall risk affected the PMT and/or MT during postural recovery action in older women. As an additional aspect, the secondary aim was to investigate the relationship between fractionated RT and peak extensor force (PEF) on the lower extremity. We hypothesized that a greater PEF would possibly indicate a shorter latency in neuromuscular activation. An additional aim was to identify a potential motor adaptation in the central and/or peripheral components in the repeated perturbation tests.

2. Material and Methods

2.1. Participants

In total, 34 community-dwelling older women aged 65–83 voluntarily participated in this cross-sectional study. They were randomly recruited through posting public flyers in communities and sending text massages in WeChat group with all the residents. Participants filled out a questionnaire regarding their fall history, health, and fitness within the last 12 months. The criteria for inclusion were as follows: having no severe cardiovascular diseases, such as stroke or coronary heart disease; having no neuromuscular disorders like dementia or Parkinson disease; being able to perform deep squats without assistance; having taken no medication that would interfere with their postural control; living independently and having no difficulty with activities of daily life (ADLs); feeling healthy based on self-assessment of physical fitness; being right-leg-dominant. This study conformed with the Declaration of Helsinki and was approved by the ethics committee of Hebei Normal University (2023LLSC075, 11 December 2023).
A fall refers to an event in which an individual loses balance and drops down to the ground or some lower-level surface [22,23]. Participants who reported falling one or more times in the last 12 months were assigned to the group of fallers, while the others who reported no fall were assigned to the group of non-fallers. To further confirm that the group of fallers characterized fall risk, we used the Biodex Balance (Biodex Medical System Inc., Shirley, NY, USA) to evaluate the fall risk index (FRI), in which participants were asked to maintain balance while standing on an unstable platform and to try to keep an active point still in the coordinate center of a screen for 15 s, three times. In the Biodex system, Grade 7 is specific for fall risk assessment in older adults and FRI has been proven to be a reliable measure of fall risk and is related closely to fall history in older adults [24]. See Table 1.

2.2. Experimental Task and Procedure

Participants were asked to stand on a hinged drop platform with their feet shoulder-width apart and arms straight down; the platform could be pulled down by a pneumatic actuator and tilted left when an electric current switch was pressed. The trapdoor tilted laterally down at an angle of 15°, with an average angular velocity of 118 degrees/second and an acceleration of 12.1 m/s2. A safety harness was attached to the participants’ trunk to prevent fall or injury. A MegaWin EMG 6000 system (Kuopio, Finland) was integrated and temporally synced with the trapdoor system to ensure precision when fractionating the RT (see Figure 1 for the experimental setup). Surface electromyography (sEMG) was performed on the tibialis anterior (TA), lateral gastrocnemius (GN), and peroneus longus (PL) of the left lower extremity, since they were activated as counters to the ankle inversion in the perturbation. According to the recommendations for surface electromyography (sEMG) sensors and sensor placements [25], electrode location was determined by the palpation of the muscles. sEMG was sampled at 1 k Hz, notch-filtered at 60 Hz, and bandpass-filtered at 500 Hz. In total, three trapdoor trials were performed in the study, with 10 s intervals. Participants were not provided with any warning or advance information about the upcoming perturbation of the trapdoor. Two research assistants were standing beside the participant during the experiment for additional safety and fall prevention.
The peak extensor force (PEF) of the lower extremity was measured five minutes after the end of the third perturbation trial. Participants were instructed to perform three deep squats as quickly as possible on the force platform (MES-01S20, Maidakang Medical Equipment Manufacturing Co., Ltd., Beijing, China); these were measured at 1 k Hz with their arms stretched out in front of them (90° shoulder angle), each separated by a five-second interval.

2.3. Data Processing

In the present research, the initiation of a physical response to the postural perturbation could not be conveniently determined, because it happened covertly in the phase of passive body movements caused by the trapdoor drop. Previous studies indicated that the peak EMG burst was aligned with the onset of movement or force [26,27]. Thus, this study defined the RT as the time interval from the initiation of the trapdoor drop to the peak EMG burst in the muscles for each trial. Consequently, the promotor time (PMT) was obtained as the latency from stimulus onset (trapdoor drop) to the initiation of EMG burst. The motor time (MT) was calculated as the difference between the RT and PMT. When the electric switch was pressed, it sent the signal to initialize the trapdoor and MegaWin EMG 6000 system simultaneously. The raw EMG signals were rectified with a lowpass filter using the built-in software of MegaWin ME 6000 system before determining the peak and onset EMG burst. The onset of the EMG burst was defined as being 2 SDs higher than the mean baseline.
The PEF was recorded as the vertical ground reaction force from deep squats for the left leg (L-PEF) and right leg (R-PEF) and the total force for both legs (T-PEF). The mean of the peak values of the vertical ground reaction force for three trials was used for data analysis.

2.4. Statistical Analysis

A 2 (Group) × 3 (Trial) ANOVA with repeated measures in trials was used to evaluate group and trial differences in PMT and MT. Pearson’s correlation and stepwise regression were used to analyze the relationship between the PMT, MT, and PEF (L-PEF, R-PEF, and T-PEF). All data were analyzed with SAS Windows 9.2. An alpha level of 0.05 was accepted as significant.

3. Results

3.1. Premotor Time

The two-way mixed ANOVA failed to detect a main effect of groups in any of the three muscles: TA [F (1, 32) = 0.13, p > 0.05], GN [F (1, 32) = 0.02, p > 0.05], or PL [F (1, 32) = 0.09, p > 0.05]. The repeated measures in trials revealed a significant trial difference in TA [F (2, 64) = 3.50, p < 0.05], but not in GN [F (2, 64) = 2.45, p > 0.05] or PL [F (2, 64) = 2.95, p > 0.05]. Furthermore, Duncan’s multiple range test indicated a shorter premotor time in Trials 2 and 3 relative to Trial 1 for the tibialis anterior. No interaction was found between groups and trials. See Table 2.

3.2. Motor Time

The analysis revealed a main effect of groups in TA [F (1, 32) = 4.59, p < 0.05] and GN [F (1, 32) = 5.01, p < 0.05], but not in PL [F (1, 32) = 1.36, p > 0.05]. Further, Duncan’s multiple range test demonstrated that in fallers, a longer motor time occurred in the tibialis anterior and gastrocnemius than in the non-fallers. However, no trial difference was found in any of the three muscles. The interaction was not significant between groups and trials. See Table 3.

3.3. Fractionized Reaction Time and Peak Extensor Force

Pearson’s correlation found no reliable relationship between the PMT and any measure of force. However, the analysis demonstrated that the MT had a negative relationship with T-PEF and L-PEF for all three muscles. In addition, the MT had a negative relationship with R-PEF in the gastrocnemius, and this tendency was also shown in the tibialis anterior (p = 0.055) and peroneus longus (p = 0.069). See Table 4. Moreover, analysis using stepwise regression indicated that L-PEF was a key predictor of MT in the TA [F = 25.82, r2 = 0.45, p < 0.01], GA [F = 25.61, r2 = 0.45, p < 0.01], and PL [F = 26.39, r2 = 0.44, p < 0.01].

4. Discussion

4.1. Fractionated RT and Fall Risk

The results of this study agree with some previous findings in that fallers showed a significantly longer motor time than non-fallers among older adults, while exhibiting no difference in premotor time [10,13]. This indicated that impaired neuromuscular function is associated with fall history in older women; more specifically, the longer latency of muscular activation is linked closely to falls.
As is commonly observed during an unexpected loss of balance (e.g., tripping or stumbling), the human body often responds with sudden sequential counter-movements, such as quickly stepping or jerking the body upward, to regain upright equilibrium, which is known as an automatic postural response (APR) in the realm of neuroscience. This APR, governed by central nervous system (CNS) with unconscious control, is a quick neuromuscular activity in response to a sudden postural perturbation [27]. There was no difference in PMT between the groups of fallers and non-fallers in the present study, suggesting that the central nervous system’s ability in APR, including recognizing the stimulus of perturbation, integrating sensory information, and organizing reaction, was not influenced by fall history. To our knowledge, there is no previous research designed with APR conditions investigating the central process speed, i.e., the PMT, directly, although recent results from voluntary response tests demonstrated that the central processing speed would be delayed for individuals with a fall history under complex cognitive conditions [28], such as a choice reaction task [12] or a secondary task [11]. Further, this automatic postural response could also be modulated by many other factors, like habituation, anticipation, or prior experience [27]. It can be seen that the central process in APR tends to be more influenced by task proficiency and/or task constraints and varies little between individuals in the healthy older adult population, with or without fall history.
More importantly, fallers showed a longer motor time than non-fallers in the tibialis anterior and gastrocnemius. This finding is consistent with Laroche and colleagues, who reported that fallers showed a longer motor time across the lower extremity than non-fallers in a maximal isometric task [13]. Crozara et al. [10] and Grenier et al. [29] also presented that the increase in motor time was related to a higher risk of falls. The reduced amount of muscle mass, especially type II muscle fibers [14], in older women is likely responsible for the longer MT. In neuromuscular mechanism, the excitation–contraction uncoupling, external calcium-dependent contraction, and the decline in the level of hormones and trophic factors, as well as the higher circulating level of inflammatory cytokines and pentosidine of advanced glycation End-products [30], are likely responsible for the longer MT. Practically, the slowed peripheral process in neuromuscular movement may be greatly detrimental when rapid applications of force are needed when counteracting a balance loss from tumbling or slipping. Crozara et al. found that fallers produced lower peak torques in the lower extremity compared to their counterparts [10]. In addition, older adults demonstrated a longer MT than young adults, and older women exhibited a much longer MT than older men in the previous literature [8,31]. In summary, it appears that decreased force production may increase the latency in muscle activation indexed by the MT.

4.2. MT in Relation to Muscle Strength

The motor time showed a close relationship with the peak extensor force in the lower extremity, which confirmed our assumption that a longer motor time may co-vary with reduced strength. Theoretically, the MT and peak extensor force (PEF) are both measuring parameters for working muscles. The MT is the time duration of peripheral processing for motor preparation, representing the recruiting speed of muscle fibers under neural drive [32], and PEF in fast squats is a strength variable, reflecting the maximal output of contracting muscles in the lower extremities [33,34]. Mechanically, motor preparation and ensuing muscle contraction are both determined by the rate of neural firing and properties of these muscle fibers [32]. Explosive movements require more rapid rates of cross-bridge cycling, in which the number and size of type II muscle fibers are highly related to rapid force production. Older adults experience serious muscle atrophy, especially in type II fibers, as they increase in age [35,36]. And further, this undesirable muscle atrophy was confirmed as the primary factor not only in the marked increase in MT [8,10], but also in the significant decrease in force output [37]. Fortunately, both muscle strength [38,39] and motor processing speed [40,41] could be improved with active physical activity in older adults. In addition, the present study revealed that the peak extensor force in the non-dominant leg was a strong predictor for the motor time. This indicates that a lower capacity for explosive force output in the non-dominant leg may be highly related to longer latency for neuromuscular recruitment. This result is supported, since the non-dominant leg performs a more stabilizing or supporting role in human motor movements compared to the dominant leg [42]. And the extension force in the lower extremity, especially the non-dominant leg, has been closely related to dynamic balance ability and history of falls [43].
This advocates for the establishment of exercise guidelines for fall prevention in older adults, showing that explosive strength training could be used not only to increase muscle strength and power [18,44] but also to reduce MT latency. However, in some Asian countries, gentle movement exercises such as tai-chi and yoga are very popular and regarded as a one-size-fits-all exercise to maintain older adults’ health, whereas strength training exercises are typically given little-to-no attention in this cohort. Based on the findings presented in this paper, we strongly recommend that older adults, especially those with fall history, incorporate a strength training element into their current exercise regimen, to improve both their explosive muscular strength and MT latency. And, as proposed in previous studies, exercise programs that combine resistance training with balance and functional exercises are more effective in reducing falls [45,46].

4.3. Task Anticipation and Motor Adaptation

Motor adaptation often takes place over several trials and repeated measurements. In this research, the PMT showed a downward trend with repeated trials for all three muscles, and, notably, a significant decrease was observed in the muscle TA between Trial 1 and Trials 2 and 3. This adaptation seems to comply with the schema theory of motor learning, which indicates that rule or schema learning takes place in the central nervous system rather than in the periphery [6]. Mechanically, to acquire or enhance skill proficiency during repeated perturbation tests, the neural circuitry adapts to the task novelty with greater efficiency through feedback [3], which includes the optimization of sensory organization, acceleration of information coding, and adjustments to muscular cooperation. The motor program should therefore be upgraded to allow for a faster and more successful response. And, further, the switching of postural reaction strategies from Trial 1 to Trials 2 and 3 is considered to be another method of adaptation. The postural adjustments evoked by the sudden unexpected perturbation in Trial 1 was an automatic postural response commanded by the brainstem centers without consciousness [27,32], while in Trials 2 and 3, motor anticipation took place after the acquiring of task experience. Specifically, with advanced knowledge of the upcoming perturbation and task anticipation, the participants’ ankle evertors (TA, GN, and PL) were considered to contract relatively earlier during stretching. This switch of neural networks in repeated measurements could accelerate the speed of premotor processing, i.e., the PMT could be shortened. The decreasing tendency in the PMT with the trials in the present experiment could explain a possible mechanism behind the perturbation-based balance training for fall reduction described in the previous literature [47,48].
Therefore, this research provides theoretical support for the previous findings that neuromuscular training combined with unexpected postural perturbations may improve RT latency in older participants in fall prevention programs. Furthermore, we propose postural perturbation training to help reduce the PMT and explosive strength training to aid in reducing MT latency. This research will lead to method and policy recommendations to help develop more targeted interventions and adapted environments for older women in body building and fall prevention. The delayed peripheral process should receive more attention in the diagnosis of fall risk, and explosive strength training for the lower extremities is strongly recommended as one of the essential points in exercise guidelines for older women.
The experimental task of this study does mimic a real-world scenario in terms of the type of postural perturbation. However, with the present technology of fractionating RT measurements, it was difficult to create an exactly realistic situation in which a fall would occur, like stumbling, tripling, or slipping in walking, descending, ascending stairs, etc. Another limitation was that the present study used a relatively small sample size. Participants aged 80 and over, and those who were unable to perform a deep squat, were not be included for analysis in the study. Experiments designed using a more dynamic situation, and more inclusive of participants with different motor abilities, are warranted for future study.

5. Conclusions

This study indicated that older adults with a fall history face a higher risk of falling, and show a longer motor time in response to balance perturbation relative to nonfallers. Weaker explosive strength in the lower extremity, especially the non-dominant leg, was highly related to slower neuromuscular recruitment.

Author Contributions

Conceptualization, Q.L.; Methodology, R.J.B.II.; Writing—original draft, Z.L. and R.J.B.II.; Writing—review & editing, Q.L.; Project administration, Z.L.; Funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Philosophy and Social Sciences Foundation of China (grant number: 19BTY045)

Institutional Review Board Statement

This study conformed with the Declaration of Helsinki regarding experiments involving human subjects and was approved by the ethics committee at Hebei Normal University (2023LLSC075) on 11 December 2023. Informed consent was obtained from all participants.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Acknowledgments

We express our sincere thanks to all the investigators for data collection and paper revisions.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. World Health Organization. WHO Global Report on Falls Prevention in Older Age. 2023. Available online: https://www.who.int/publications-detail-redirect/9789241563536 (accessed on 24 May 2024).
  2. Madehkhaksar, F.; Klenk, J.; Sczuka, K.; Gordt, K.; Melzer, I.; Schwen, M. The effects of unexpected mechanical perturbations during treadmill walking on spatiotemporal gait parameters, and the dynamic stability measures by which to quantify postural response. PLoS ONE 2018, 13, e0195902. [Google Scholar] [CrossRef]
  3. Luchies, C.W.; Schiffman, J.; Richards, L.G.; Thompson, M.R.; Bazuin, D.; Yong, A.J.D. Effects of age, step direction, and reaction condition on the ability to step quickly. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 2002, 57, M246–M249. [Google Scholar] [CrossRef] [PubMed]
  4. Der, G.; Deary, I.J. Age and sex differences in reaction time in adulthood: Results from the United Kingdom health and lifestyle survey. Psychol. Aging 2006, 21, 62–73. [Google Scholar] [CrossRef] [PubMed]
  5. Dykiert, D.; Der, G.; Starr, J.M.; Deary, I.J. Sex differences in reaction time mean and intraindividual variability across the life span. Develoment. Psychol. 2012, 48, 1262–1276. [Google Scholar] [CrossRef] [PubMed]
  6. Schmidt, R.A.; Lee, T.D.; Winstein, C.; Wulf, G.; Zelaznik, H.N. Motor Control and Learning: A Behavioral Emphasis, 6th ed.; Human Kinetics: Champaign, IL, USA, 2018. [Google Scholar]
  7. Benedict, R.J., II. Effect of Foreperiod Regularity and Muscle Size on Fractionated Reaction Time. Ph.D. Dissertation, Wayne State University, Detroit, MI, USA, 2016. [Google Scholar]
  8. Hong, J.; Kim, J.W.; Chung, H.Y.; Kim, H.H.; Kwon, Y.; Kim, C.S.; Ho, Y.J.; Eom, G.M.; Jun, J.H.; Park, B.K. Age-gender differences in the reaction times of ankle muscles. Geriatr. Gerontol. Int. 2014, 14, 94–99. [Google Scholar] [CrossRef] [PubMed]
  9. da Silva Alves, I.P.; Bueno, G.A.S.; Elmescany, R.B.; Borges, L.A.; Pinto, D.K.; Martins, A.C.; de Menezes, R.L. Motor Reaction Time, Sarcopenia and Functional Skills in Elderly Women: A Cross-Sectional Study. J. Nutr. Health Aging 2023, 27, 878–884. [Google Scholar] [CrossRef] [PubMed]
  10. Crozara, L.F.; Morcelli, M.H.; Marques, N.R. Motor readiness and joint torque production in lower limbs of older women fallers and non-fallers. J. Electromyogr. Kinesiol. 2013, 23, 1131–1138. [Google Scholar] [CrossRef] [PubMed]
  11. Wang, D.; Zhang, J.; Sun, Y.; Zhu, W.M.; Tian, S.L.; Liu, Y. Evaluating the fall risk among elderly population by choice step reaction test. Clin. Interv. Aging 2016, 11, 1075–1082. [Google Scholar] [CrossRef] [PubMed]
  12. St George, R.J.; Fitzpatrick, R.C.; Rogers, M.W.; Lord, S.R. Choice Stepping Response and Transfer Times: Effects of Age, Fall Risk, and Secondary Tasks. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 2007, 62, 537–542. [Google Scholar] [CrossRef]
  13. LaRoche, D.P.; Cremin, K.A.; Greenleaf, B.; Croce, R.V. Rapid torque development in older female fallers and nonfallers: A comparison across lower-extremity muscles. J. Electromyogr. Kinesiol. 2010, 20, 482–488. [Google Scholar] [CrossRef]
  14. Weerdesteyn, V.; Laing, A.C.; Robinovitch, S.N. Automated postural responses are modified in a functional manner by instruction. Exp. Brain Res. 2008, 186, 571–580. [Google Scholar] [CrossRef] [PubMed]
  15. Skelton, D.A.; Kennedy, J.; Rutherford, O.M. Explosive power and asymmetry in leg muscle function in frequent fallers and non-fallers aged over 65. Age Ageing 2002, 31, 119–125. [Google Scholar] [CrossRef] [PubMed]
  16. Takada, H.; Yamashita, K.; Osawa, L.; Komiyama, Y.; Muraoka, M.; Suzuki, Y.; Sato, M.; Kobayashi, S.; Yoshida, T.; Takano, S.; et al. Assessment of lower limb muscle strength can predict fall risk in patients with chronic liver disease. Sci. Rep. 2024, 14, 64. [Google Scholar] [CrossRef] [PubMed]
  17. Skelton, D.A.; Greig, C.A.; Davies, J.M.; Young, A. Strength, power and related functional ability of healthy people aged 65–89 years. Age Ageing 1994, 23, 371–375. [Google Scholar] [CrossRef] [PubMed]
  18. Clark, D.J.; Fielding, R.A. Neuromuscular contributions to age-related weakness. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 2012, 67, 41–47. [Google Scholar] [CrossRef] [PubMed]
  19. Sangsoo Park, S.; Caldwell, G.E. Muscle synergies are modified with improved task performance in skill learning. Hum. Mov. Sci. 2022, 83, 102946. [Google Scholar] [CrossRef] [PubMed]
  20. Mattar, A.A.; Darainy, M.; Ostry, D.J. Motor learning and its sensory effects: Time course of perceptual change and its presence with gradual introduction of load. J. Neurophysiol. 2013, 109, 782–791. [Google Scholar] [CrossRef] [PubMed]
  21. Dimitriou, M. Enhanced muscle afferent signals during motor learning in humans. Curr. Biol. 2016, 26, 1062–1068. [Google Scholar] [CrossRef] [PubMed]
  22. Song, Y.; Deng, Y.; Li, J.; Hao, B.; Cai, Y.; Chen, J.; Shi, H.; Xu, W. Associations of falls and severe falls with blood pressure and frailty among Chinese community-dwelling oldest olds: The Chinese Longitudinal Health and Longevity Study. Aging 2021, 13, 16527–16540. [Google Scholar] [CrossRef]
  23. Tsai, Y.J.; Yang, P.Y.; Yang, Y.C.; Lin, M.R.; Wang, Y.W. Prevalence and risk factors of falls among community-dwelling older people: Results from three consecutive waves of the national health interview survey in Taiwan. BMC Geriatr. 2020, 20, 529. [Google Scholar] [CrossRef]
  24. Parraca, J.A.; Olivares, P.R.; Carbonell-Baeza, A.; Aparicio, V.A. Test-Retest reliability of Biodex Balance SD on physically active old people. J. Hum. Sport Exerc. 2011, 6, 444–451. [Google Scholar] [CrossRef]
  25. Hermens, H.J.; Freriks, B.; Disselhorst-Klug, C.; Rau, G. Development of recommendations for SEMG sensors and sensor placement procedures. J. Electromyogr. Kinesiol. 2000, 10, 361–374. [Google Scholar] [CrossRef] [PubMed]
  26. Miles, M.P.; Ives, J.C.; Vincent, K.R. Neuromuscular control following maximal eccentric exercise. Eur. J. Appl. Physiol. Occup. Physiol. 1997, 76, 368–374. [Google Scholar] [CrossRef] [PubMed]
  27. Binder, M.D.; Hirokawa, N.; Windhorst, U. Automatic Postural Response in Encyclopedia of Neuroscience. In Postural Control; Springer: Berlin/Heidelberg, Germany, 2009. [Google Scholar]
  28. Little, C.E.; Woollacott, M. EEG measures reveal dual task interference in postural performance in young adults. Exp. Brain Res. 2015, 233, 27–37. [Google Scholar] [CrossRef] [PubMed]
  29. Grenier, S.; Richard-Devantoy, S.; Nadeau, A.; Payette, M.C.; Benyebdri, F.; Duhaime, M.M.B.; Gunther, B.; Beauchet, O. The association between fear of falling and motor imagery abilities in older community-dwelling individuals. Maturitas 2018, 110, 18–20. [Google Scholar] [CrossRef] [PubMed]
  30. Arnold, P.; Njemini, R.; Vantieghem, S.; Gorus, E.; Pool-Goudzwaard, A.; Buyl, R.; Bautmans, I. Reaction time in healthy elderly is associated with chronic low-grade inflammation and advanced glycation end product. Exp. Gerontol. 2018, 108, 118–124. [Google Scholar] [CrossRef]
  31. Bunce, D.; MacDonald, S.W.; Hultsch, D.F. Inconsistency in serial choice decision and motor reaction times dissociate in younger and older adults. Brain Cogn. 2004, 56, 320–327. [Google Scholar] [CrossRef] [PubMed]
  32. Kandel, E.R.; Koester, J.D.; Mack, S.H.; Siegelbaum, S.A. Principles of Neural Science, 6th ed.; McGraw-Hill Professional: New York, NY, USA, 2021. [Google Scholar]
  33. Gillen, Z.M. Leg Extension Strength, Explosive Strength, Muscle Activation, and Growth as Predictors of Vertical Jump Performance in Youth Athletes. J. Sci. Sport Exerc. 2020, 2, 336–348. [Google Scholar] [CrossRef]
  34. Tsuji, T.; Tsunoda, K.; Mitsuishi, Y.; Okura, T. Ground Reaction Force in Sit-to-Stand Movement Reflects Lower Limb Muscle Strength and Power in Community-dwelling Older Adults. Int. J. Gerontol. 2015, 9, 111–118. [Google Scholar] [CrossRef]
  35. Nilwik, R.; Snijders, T.; Leenders, M.; Groen, B.B.; van Kranenburg, J.; Verdijk, L.B.; van Loon, L.J. The decline in skeletal muscle mass with aging is mainly attributed to a reduction in type II muscle fiber size. Exp. Gerontol. 2013, 48, 492–498. [Google Scholar] [CrossRef]
  36. Kramer, I.F.; Snijders, T.; Smeets, J.S.; Leenders, M.; van Kranenburg, J.; den Hoed, M.; Verdijk, L.B.; Poeze, M.; van Loon, L.J. Extensive type II muscle fiber atrophy in elderly female hip fracture patients. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 2017, 72, 1369–1375. [Google Scholar] [CrossRef] [PubMed]
  37. Grancher, U.; Gruder, M.; Gollhofer, A. Force production capacity and functional reflex activity in young and elderly men. Aging Clin. Exp. Res. 2010, 22, 374–382. [Google Scholar] [CrossRef] [PubMed]
  38. Rantanen, T.; Era, P.; Heikkinen, E. Physical activity and the changes in maximal isometric strength in men and women from the age of 75 to 80 years. J. Am. Geriatr. Soc. 1997, 45, 1439–1445. [Google Scholar] [CrossRef]
  39. Goodpaster, B.H.; Chomentowski, P.; Ward, B.K.; Rossi, A.; Glynn, N.W.; Delmonico, M.J.; Kritchevsky, S.B.; Pahor, M.; Newman, A.B. Effects of physical activity on strength and skeletal muscle fat infiltration in older adults: A randomized controlled trial. J. Appl. Physiol. 2008, 105, 1498–1503. [Google Scholar] [CrossRef]
  40. Davranche, K.; Burle, B.; Audiffren, M.; Hasbroucq, T. Information processing during physical exercise: A chronometric and electromyographic study. Exp. Brain Res. 2005, 165, 532–540. [Google Scholar] [CrossRef] [PubMed]
  41. LaRoche, D.P.; Knight, C.A.; Dickie, J.L.; Lussier, M.; Roy, S.J. Explosive Force and Fractionated Reaction Time in Elderly Low- and High-Active Women. Med. Sci. Sports Exerc. 2007, 39, 1659–1665. [Google Scholar] [CrossRef] [PubMed]
  42. Peters, M. Footedness: Asymmetries in foot preference and skill and neuropsychological assessment of foot movement. Psychol. Bull. 1998, 103, 179–192. [Google Scholar] [CrossRef] [PubMed]
  43. Li, Z.T.; Lai, Q.; Sun, J.H.; Ge, X.F. Extension Force Deficiency of the Lower Extremity in Squat Contributing to Increased Fall Risk in the Female Elderly. Chin. J. Sports Med. 2017, 36, 700–706. (In Chinese) [Google Scholar] [CrossRef]
  44. Haghighi, A.H.; Malihe Shojaee, M.; Roya Askari, R.; Abbasian, S.; Gentil, P. The effects of 12 weeks resistance training and vitamin D administration on neuromuscular joint, muscle strength and power in postmenopausal women. Physiol. Behav. 2023, 274, 114419. [Google Scholar] [CrossRef]
  45. Sherrington, C.; Fairhall, N.J.; Wallbank, G.K.; Tiedemann, A.; Michaleff, Z.A.; Howard, K.; Clemson, L.; Hopewell, S.; Lamb, S.E. Exercise for preventing falls in older people living in the community. Cochrane Database Syst. Rev. 2019, 1, CD012424. [Google Scholar] [CrossRef]
  46. Aleixo, P.; Abrantes, J. Proprioceptive and strength exercise guidelines to prevent falls in the elderly related to biomechanical movement characteristics. Healthcare 2024, 12, 186. [Google Scholar] [CrossRef] [PubMed]
  47. Bieryla, K.A.; Madigan, M.L. Proof of concept for perturbation-based balance training in older adults at a high risk for falls. Arch. Phys. Med. Rehabil. 2011, 92, 841–843. [Google Scholar] [CrossRef] [PubMed]
  48. Mansfield, A.; Wong, J.S.; Bryce, J.; Knorr, S.; Patternon, K.K. Does perturbation-based balance training prevent falls? Systematic review and meta-analysis of preliminary randomized controlled trials. Phys. Ther. 2015, 95, 700–709. [Google Scholar] [CrossRef]
Applsci 14 06290 g001
Figure 1. Experimental image of balance perturbation text.
Figure 1. Experimental image of balance perturbation text.
Applsci 14 06290 g001
Table 1. Mean ± SD for participant characteristics and fall risk index (FRI).
Table 1. Mean ± SD for participant characteristics and fall risk index (FRI).
Fallers (n = 15)Non-Fallers (n = 19)
Age (years)75.13 ± 3.5073.00 ± 4.45
Height (m)1.55 ± 0.051.54 ± 0.04
Mass (kg)63.15 ± 9.0860.08 ± 10.34
BMI (kg/m2)26.33 ± 3.2525.17 ± 4.02
FRI2.68 ± 1.21 *1.26 ± 0.58 *
* T (for FRI) = 4.17, p < 0.05. SD, standard; BMI, body mass index; FRI, fall risk index.
Table 2. Mean ± SD for PMT (ms) in TA, GN, and PL in postural control response for fallers and non-fallers.
Table 2. Mean ± SD for PMT (ms) in TA, GN, and PL in postural control response for fallers and non-fallers.
GroupTrial
FallersNon-FallersTrial 1Trial 2Trial 3
TA124.02 ± 31.57120.82 ± 37.09131.94 ± 31.10117.50 ± 39.26 *117.32 ± 32.11 *
GN129.4 ± 27.24128.44 ± 33.54135.21 ± 28.79125.47 ± 29.33121.71 ± 28.76
PL122.49 ± 29.64120.07 ± 31.95128.82 ± 28.50117.67 ± 36.15116.88 ± 28.07
SD, standard; TA, tibialis anterior; GN, gastrocnemius; PL, peroneus longus. * p < 0.05 indicates that the difference is statistically significant compared with Trial 1.
Table 3. Mean ± SD for MT (ms) on TA, GN, and PL in postural control response for fallers and non-fallers.
Table 3. Mean ± SD for MT (ms) on TA, GN, and PL in postural control response for fallers and non-fallers.
GroupTrial
FallersNon-FallersTrial 1Trial 2Trial 3
TA 1119.93 ± 27.51101.89 ± 38.59 *109.24 ± 39.03112.23 ± 37.95109.15 ± 30.30
GN126.98 ± 27.99106.60 ± 36.72 *113.35 ± 42.88113.76 ± 31.50121.09 ± 27.81
PL113.4 ± 30.26103.51 ± 42.34106.62 ± 40.73111.50 ± 40.60107.30 ± 34.50
SD, standard; TA, tibialis anterior; GN, gastrocnemius; PL, peroneus longus; * p < 0.05 indicates that the difference is statistically significant compared with fallers.
Table 4. Pearson correlation efficiency between fractionated RT with PEF of the lower extremity.
Table 4. Pearson correlation efficiency between fractionated RT with PEF of the lower extremity.
TAGNPL
PMTMTPMTMTPMTMT
L-PEF−0.04−0.44 **0.07−0.52 **0.12−0.46 **
0.8210.0090.6930.0020.4930.007
R-PEF0.07−0.330.18−0.350.16−0.32
0.6970.0550.3090.040 *0.3720.069
T-PEF0.16−0.46 **0.22−0.47 **0.23−0.45 **
0.3550.0060.2140.0050.1980.008
RT: reaction time; PMT, premotor time; MT, motor time. TA, tibialis anterior; GN, gastrocnemius; PL, peroneus longus. L-PEF, left-leg peak extensive force; R-PEF, right-leg peak extensive force; T-PEF, total peak extensive force. * p < 0.05, correlation is significant; ** p < 0.01, correlation is highly significant.
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Li, Z.; Lai, Q.; Benedict II, R.J. Increased Motor Time in the Lower Extremity Is Related to Fall History in Older Women. Appl. Sci. 2024, 14, 6290. https://doi.org/10.3390/app14146290

AMA Style

Li Z, Lai Q, Benedict II RJ. Increased Motor Time in the Lower Extremity Is Related to Fall History in Older Women. Applied Sciences. 2024; 14(14):6290. https://doi.org/10.3390/app14146290

Chicago/Turabian Style

Li, Zongtao, Qin Lai, and Ronald J. Benedict II. 2024. "Increased Motor Time in the Lower Extremity Is Related to Fall History in Older Women" Applied Sciences 14, no. 14: 6290. https://doi.org/10.3390/app14146290

APA Style

Li, Z., Lai, Q., & Benedict II, R. J. (2024). Increased Motor Time in the Lower Extremity Is Related to Fall History in Older Women. Applied Sciences, 14(14), 6290. https://doi.org/10.3390/app14146290

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