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date: 17 August 2018

Cognition and Mobility With Aging

Summary and Keywords

Research on the interplay of cognition and mobility in old age is inherently multidisciplinary, informed by findings from life span developmental psychology, kinesiology, cognitive neuroscience, and rehabilitation sciences. Early observational work revealed strong connections between sensory and sensorimotor performance with measures of intellectual functioning. Subsequent work has revealed more specific links between measures of cognitive control and gait quality. Convergent evidence for the interdependence of cognition and mobility is seen in patient studies, wherein cognitive impairment is associated with increased frequency and risk of falling. Even in cross-sectional studies involving healthy young and older adults, the effects of aging on postural control and gait are commonly exacerbated when participants perform a motor task with a concurrent cognitive load. This motor-cognitive dual-task method assumes that cognitive and motor domains compete for common capacity, and that older adults recruit more cognitive capacity than young adults to support gait and posture.

Neuroimaging techniques such as magnetic resonance imaging (MRI) have revealed associations between measures of mobility (e.g., gait velocity and postural control) and measures of brain health (e.g., gray matter volumes, cortical thickness, white matter integrity, and functional connectivity). The brain regions most often associated with aging and mobility also appear to subserve high-level cognitive functions such as executive control, attention, and working memory (e.g., dorsolateral prefrontal cortex, anterior cingulate). Portable functional neuroimaging has allowed for the examination of neural functioning during real-time walking, often in conjunction with detailed spatiotemporal measures of gait. A more recent strategy that addresses the interdependence of cognitive and motor processes in old age is cognitive remediation. Cognitive training has yielded promising improvements in balance, walking, and overall mobility status in healthy older adults, and those with age-related neurodegenerative conditions such as Parkinson’s Disease.

Keywords: aging, balance, gait, executive function, cognitive training, neural recruitment, motor control

Research studies on cognition and mobility with aging have been informed by two broad streams of work, namely studies of cognitive aging, and studies of mobility and aging. A variety of research approaches, arising from one discipline and carrying over to the other, includes behavioral cognitive methods and neuropsychological assessment, biomechanics, neuroscience, and rehabilitation. The foundational work and cross-disciplinary influences that have informed the research on cognitive-motor interactions in aging are highlighted, and the major findings in gait and balance research are reviewed separately, with common themes and future directions discussed.

Conceptual and Methodological Influences

Using a life span developmental psychology perspective, seminal work from the Berlin Aging Study revealed a link between sensory and sensorimotor aging with intellectual functioning (Baltes & Lindenberger, 1997; Lindenberger & Baltes, 1994). In this large multidisciplinary project, older adults aged 70 years and above were assessed on a variety of dimensions including standard measures of intellectual functioning, sensory functioning (auditory and visual acuity), and motor functioning (balance and gait). A strong connection was identified linking sensory and intellectual functioning, which proved to be greater in the older adult sample compared to a young and middle-aged control group. Similar patterns were found when relating basic measures of motor and intellectual functioning. Although sensory-cognitive relationships might be partially attributable to compromised sensory inputs, the relations with mobility suggest a more central common cause such as neurotransmitter depletion (Bäckman, Nyberg, Lindenberger, Li, & Farde, 2006). Others have suggested that compensatory allocation of attentional or cognitive resources might play a role in counteracting more peripheral age-related declines in vision, hearing, and motor performance (e.g., Li & Lindenberger, 2002; Schneider & Pichora-Fuller, 2000).

In parallel to developments in the cognitive aging field, an influential paper from the rehabilitation field reported that geriatric patients who stopped walking while talking were at greater risk of falling than those who continued to walk and talk at the same time (Lundin-Olsson, Nyberg, & Gustafson, 1997). This prospective study prompted a reconsideration of earlier models of the spinal control of locomotion (Dietz, 2003) and led the way to a vigorous period of research using cognitive-motor dual-task designs. Borrowing from cognitive psychological methods (e.g., Pashler, 1994; Posner & Snyder, 1975), the dual-task design typically entails a minimum comparison of two conditions: single-task and dual-task performance. The observation of age-related increases in dual-task costs in the domain of cognitive functioning are commonly interpreted as the result of competition between tasks for common resources, as advanced by Kahneman (1973). In the context of cognitive-motor dual-task designs, similar interpretations have been made.

Importantly for the dual-task motor context, the tasks may be imbalanced in terms of ecological validity, with walking or balancing holding greater survival value and hence greater priority as compared to the concurrent cognitive task demands, particularly for older adults or those with mobility limitations. For this reason, a recommended practice is to compare the magnitude of dual-task costs in both task domains (i.e., cognitive versus motor) to detect the adoption of a postural prioritization, or “posture-first,” strategy (Li, Krampe, & Bondar, 2005; Shumway-Cook, Baldwin, Polissar, & Gruber, 1997). Notably, research in training attentional allocation suggests that older adults can accurately vary dual-task priorities (e.g., perform Task A at 75%; Task B at 25%) with practice (Kramer, Larish, & Strayer, 1995), suggesting that older adults exhibit postural prioritization not because of an inability to control their attentional allocation, but because of the adaptive value of prioritization.

Based upon these early observations of dual-task motor performance, a substantial body of work has emerged from the rehabilitation and biomechanics fields, covering a range of motor behaviors such as balancing (static/dynamic balance, postural recovery following a perturbation) and gait (treadmill and over ground walking), comparing these behaviors under full attention or with a concurrent cognitive load. Major aims in this area of research include specifying which aspects or stages of motor functioning (e.g., early or late stage postural recovery; swing or stance phase of the gait cycle) are more vulnerable to cognitive interference.

With the advent of neuroimaging techniques, another major aim is to identify the neural substrates that support motor performance, and how these might change or be compensated for with aging. Offline observational studies (correlating brain volumes, cortical thickness, white matter integrity, and resting state connectivity, with balance or walking measured separately) reveal associations between brain aging and mobility (e.g., Rosano, Aizenstein, Studenski, & Newman, 2007). Online functional measures of actual walking using portable imaging techniques (e.g., Holtzer, Epstein, Mahoney, Izzetoglu, & Blumen, 2014) provide even more evidence for the utilization of prefrontal brain regions during motor performance in aging. The brain-mobility findings parallel neural recruitment models such as the compensation-related utilization of neural circuits hypothesis (CRUNCH; Reuter-Lorenz & Cappell, 2008) or hemispheric asymmetry reduction in older age model (HAROLD; Cabeza, 2002), as posited in the cognitive neuroscience of aging literature. In general, these models posit that the increased brain activity observed in older adults serves a compensatory purpose and is associated with better cognitive performance. Ultimately if cognitive capacity and, more specifically, executive functioning, plays a role in older adults’ motor performance, cognitive remediation to increase this capacity and trigger neuroplasticity in relevant brain regions may offer a promising avenue of rehabilitation or prevention.

Gait and Cognition in Aging

Definitions and Measurement

Gait can be defined as a bipedal, forward movement of the body that offsets one’s center of pressure. An extensive number of properties can be used to describe gait, including gait speed (time to walk a certain distance), cadence (steps taken within a minute), step length (average distance between each successive footfall), and gait variability (standard deviation or coefficient of variation of step time or length). One common and practical method used to measure gait speed is by stopwatch to calculate the time it takes to complete a certain distance. Instrumented walkways with embedded pressure sensors are also commonly used in research to reliably measure more complex spatial and temporal gait parameters (e.g., GAITRite, CIR Systems, Havertown, PA; Zenometrics, LLC, Peekskill, NY). Self-selected gait speed is the most frequently reported outcome measure.

Using these approaches, a number of gait parameters change normally with age, such as slower walking speed, shorter step length, and increased time in double-support, with two feet contacting the ground (Elble, Thomas, Higgins, & Colliver, 1991; Winter, Patla, Frank, & Walt, 1990). These changes in gait with age are determined in part by a person’s physical capacity, such as reduced muscle mass and maximal oxygen consumption (Schrack, Simonsick, & Ferrucci, 2011). However, it is well-established that cognition also plays a central role in the walking abilities of older adults (Demnitz et al., 2016).

Correlational Studies

Researchers have proposed that walking is less of an automated motor task in old age, and requires higher level cognitive resources (Hausdorff, Yogev, Springer, Simon, & Giladi, 2005). Evidence for cognitive involvement in gait can be found in correlational work, which demonstrates strong associations between parameters of walking (e.g., stride time variability, gait speed, and gait variability) and measures of executive function, such as category fluency (Beauchet et al., 2012; Demnitz et al., 2016; Martin et al., 2013). However, not all cognitive processes are equally associated with gait. Smaller correlations have been noted between gait and both processing speed and global cognition, and associations with measures of memory are mixed (Demnitz et al., 2017; Martin et al., 2013).

Longitudinal studies have proved useful for elucidating the temporal relationship between gait and cognitive decline in aging. For example, slow gait has been shown to predict non-Alzheimer’s dementia (Verghese et al., 2002) and mild cognitive impairment (Buracchio, Dodge, Howieson, Wasserman, & Kaye, 2010). There is also accumulating evidence in healthy older adults suggesting that slower gait speed predicts future cognitive decline (Best et al., 2016; Mielke et al., 2013; Tian, An, Resnick, & Studenski, 2017).

Convergent associational evidence is provided with structural neuroimaging, which has linked offline assessments of gait with individual variability in brain structure. The most common method used to correlate underlying neural mechanisms with gait and cognition is magnetic resonance imaging (MRI). This technique acquires high-resolution anatomic images, which allows for the measurement of region-specific grey matter volume either via semi-automated methods that incorporate a priori information about the size, shape, or location of the region or through a voxel-based method that examines whether grey matter volume varies according to a variable of interest (e.g., fitness) (Wenger et al., 2016). White matter intensities can also be measured with MRI and severity quantified by radiologists. The moderate correlations observed between behavioral measures of cognition and gait are consistent with the neurological changes associated with aging, particularly in the frontal lobes and hippocampus. Specifically, reduced gait speed is associated with smaller overall cortical gray matter volume and decreased gray matter volume in the hippocampus (Ezzati, Katz, Lipton, Lipton, & Verghese, 2015). Higher gait variability and slower gait speed have also been found to correlate with greater white matter hyper-intensities (Rosano, Aizenstein, Studenski, & Newman, 2007; Rosano, Brach, Studenski, Longstreth, & Newman, 2007), as well as lower grey matter integrity in the hippocampus and anterior cingulate gyrus (Rosso et al., 2014). Taken together, correlational studies have advanced our understanding of the relationship between cognition and gait in aging, which complements other experimental approaches.

Experimental Studies: Dual-Tasking

The dual-task paradigm, as mentioned, has proved to be a popular experimental approach for examining the relationship between cognition and gait in aging. Strong evidence has accumulated to suggest that older adults walk more slowly and have greater step variability when simultaneously completing a secondary cognitive task (Decker et al., 2016; Lundin-Olsson, Nyberg, & Gustafson, 1997; for review see Smith, Cusack, & Blake, 2016; Yogev-Seligmann, Hausdorff, & Giladi, 2008). Older adults also exhibit greater dual-task costs in gait performance compared to younger adults (Holtzer et al., 2011; Li, Abbud, Fraser, & DeMont, 2012). A U-shaped function in gait dual-task costs has been observed with increasing cognitive task difficulty (Decker et al., 2016; Lövden, Schaefer, Pohlmeyer, & Lindenberger, 2008). The left-hand side of the U is represented by single-task walking, followed by dual-task walking with increasing task difficulty. Compared to single-task walking, both older and younger adults demonstrate increased gait performance when the added cognitive load is minimal. (Li et al., 2012; Lövden et al., 2008). As cognitive load increases during dual-task walking, gait velocity decreases and stride length and stride time increase (LaRoche, Greenleaf, Croce, & McGaughy, 2014). However, in older adults, the turning point toward impaired gait performance appears to occur at a lower cognitive load, suggesting that walking requires more cognitive resources (Lövden et al., 2008). Additionally, in accordance with the posture-first hypothesis, when given instructions to equally prioritize both the motor and cognitive task, older adults prioritize walking, showing greater dual-task costs in the cognitive domain, while younger adults show more even emphasis across tasks (Li et al., 2012; Verghese et al., 2007).

These dual-task costs are even more pronounced in patient populations with poorer cognitive functioning. Under dual-task conditions, gait velocity and stride time variability are significantly impaired in older adults with mild cognitive impairment and Alzheimer’s disease as compared to age-matched controls (Muir, Gopaul, & Montero-Odasso, 2012). A greater dual-task cost in gait velocity while completing a secondary cognitive task has also been associated with an increased risk of developing dementia (Montero-Odasso, Verghese, Beauchet, & Hausdorff, 2012). Dual-task gait assessment is therefore clinically relevant in detecting the progression of cognitive decline and may help elucidate the neural mechanisms underlying cognitive and motor dysfunction with aging.

Researchers employ various techniques to infer the underlying neural mechanisms of dual-task walking. Such techniques have included measuring neural activity from imagined walking via functional magnetic resonance imaging (fMRI), correlating measures of electroencephalography (EEG) with mobility outcomes, and measuring the uptake of glucose from walking using positron emission topography (PET) (Hamacher, Herold, Wiegel, Hamacher, & Schega, 2015). Recent advances in portable neuroimaging have allowed for the measurement of brain activity during real-time locomotion using functional near-infrared spectroscopy (fNIRs). The fNIRs is a non-invasive neuroimaging technique that uses light properties within the near infrared range to gain an index about changes in cortical brain oxygenation levels (Boas, Dale, & Franceschini, 2004). Current evidence suggests that with increasing dual-task attentional demands, there is an associated increase in cerebral oxygenation in the prefrontal cortex (PFC) with decreased gait velocity (Holtzer et al., 2011; Holtzer et al., 2015; Mirelman et al., 2014). This supplementary PFC activation during dual-tasking in older adults may imply that the brain is working less efficiently or compensating for cognitive limitations (i.e., an inability to allocate attention between gait and cognitive task).

Intervention Studies

Knowledge of this interaction between cognition and gait has led researchers to examine the impact of cognitive interventions on gait in late adulthood. Use of commercially available cognitive training programs designed to improve executive function, attention, and memory has been shown to increase gait speed during normal paced walking and dual-task walking within 8–10 weeks (Smith-Ray et al., 2013; Verghese, Mahoney, Ambrose, Wang, & Holtzer, 2010). It is also well-established that exercise, such as resistance training, improves preferred walking speed in healthy older adults (see Van Abbema et al., 2015 for review). More recently, researchers have begun to examine the effect of combined physical and cognitive training on gait. Although sequentially combined cognitive and aerobic training is beneficial to single- and dual-task walking speed, recent work suggests that it is no more beneficial than single-modality cognitive or aerobic training protocols (Fraser et al., 2017).

Another multimodal approach to training involves virtual reality walking, which adds a multi-sensory component by which to improve gait in older adults. In a study comparing the added effect of virtual reality to treadmill training, it was revealed that gait velocity significantly increased following training in older adults with a history of falls, Parkinson’s disease, and mild cognitive impairment (Mirelman et al., 2016). Notably, this improvement was significantly greater following training that included virtual reality, compared to treadmill walking alone. It is anticipated that future intervention studies will continue to be grounded in basic research to better elucidate the underlying mechanisms of gait and cognition in order to optimize training gains. One current limitation in the field is the overabundance of reports on gait speed, with little attention to other gait parameters, which have been shown to predict frailty or falls (e.g., stride time variability).

Balance and Cognition in Aging

Definitions and Measurement

Balance is typically assessed using standing balance, which is also referred to as static balance (e.g., double support standing) and dynamic balance, wherein individuals respond to an environmental event such as a platform perturbation (Paillard & Noe, 2015). Balance is typically quantified in terms of changes in posture variables over time, such as center of mass (COM) and center of foot pressure (CoP) distance, area or range of excursion, or variability of movement. In parallel, one might measure muscle activations (electromyography) during a postural challenge or other dynamic balance task. Additionally, brain activation during real or imagined balance tasks can be assessed using functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and electroencephalography (EEG) (Paillard & Noe, 2015). Lastly, balance can be described by the type of postural strategy used in response to challenging situations (Paillard & Noe, 2015). With aging, upright postural sway increases (Bergamin et al., 2014) and is linked to subsequent falls (Maki, Holliday, & Fernie, 1990). In response to dynamic balance tasks, older adults generate a larger COM area and are more likely to initiate a stepping strategy at lower levels of challenge (Brown, Shumway-Cook, & Woollacott, 1999; Jensen, Brown, & Woollacott, 2011; Tsai, Hsieh, & Yang, 2014).

Correlational Studies

Evidence for the association between cognition and balance can be found in correlational studies, for example, with processing speed (Rosano et al., 2005). Changes in cortical structures (e.g., brain atrophy, cortical thinning) are negatively associated with performance on postural tasks (for review see Papegaaij, Taube, Baudry, Otten, & Hortobagyi, 2014). Gray matter volumes in specific regions of interest such as the basal ganglia, superior posterior parietal cortex, and cerebellum are correlated with balance difficulty in older adults (Rosano Aizenstein, Studenski, & Newman, 2007). Using EEG, researchers have demonstrated that cortical responses and postural reactions show adaptation in response to cues, suggesting that cognitive capacity is implicated in planning postural responses (Papegaaij et al., 2014). Moreover, with aging, postural responses to unpredictable perturbations appear to be slowed or weakened, especially in later, more controlled phases of the postural response (Maki & McIlroy, 2007). Brain activity is also modulated by task difficulty with increased activation noted in frontal and central regions during challenging dual-task conditions, including unpredictable events or conditions with low sensory input (for review see Wittenberg, Thompson, Nam, & Franz, 2017). Others have used positron emission tomography (PET) and fMRI to assess brain activity during imagined balance tasks, with increased activation in the premotor cortex, prefrontal cortex, basal ganglia, cerebellum, and brainstem, when subjects imagined themselves standing while lying in the scanner (Papegaaij, 2014). More recently, fNIRS recordings reveal that older adults recruit cerebral networks involving temporal, prefrontal, and subcortical regions to perform balance tasks (Wittenberg et al., 2017), which is exacerbated by neurodegenerative conditions such as Parkinson’s Disease (Mahoney et al., 2016).

Experimental Studies: Dual-Tasking

Complementing the correlational work, dual-task studies have demonstrated significant attentional demands associated with balance among older adults (for review see Woollacott & Shumway-Cook, 2002). In static balance conditions, older adults exhibit greater dual-task costs than young adults on either the balance task (e.g., increased CoP area), the concurrent cognitive task, or both (for review see Boisgontier et al., 2013). With increasingly challenging balance conditions, these attentional costs become more pronounced, and in line with postural prioritization, dual-task costs are often more pronounced in the cognitive domain for older adults compared to younger adults (Brown et al., 1999; Li, Krampe, & Bondar, 2005; Little & Woollacott, 2014; Redfern, Muller, Jennings, & Furman, 2002). These costs are further exacerbated in populations with cognitive (e.g., Alzheimer’s Disease) or motor (e.g., Parkinson’s Disease) impairments (for review see Fritz, Cheek, & Nichols-Larsen, 2015). Interestingly, not all secondary tasks are detrimental to balance. Similar to gait, a U-shaped function has been observed and while balancing paired with a simple task may improve motor performance, these dual-task benefits turn into costs as cognitive complexity increases (Huxhold, Li, Schmiedek, & Lindenberger, 2006). One explanation is that simple cognitive loads are facilitative because devoting full attention to a highly automated task such as balancing is unnatural and detracts from motor coordination; however, at higher levels of cognitive interference, resource competition is detrimental to motor performance (for review see Fraizer & Mitra, 2008; Huxhold et al., 2006). A final experimental approach involves manipulating sensory parameters such as altering proprioceptive information using a compliant foam surface or standing with eyes closed (Teasdale, Bard, LaRue, & Fleury, 1993). By introducing conflicting sensory information (Redfern, Jennings, Martin, & Furman, 2001), it has been shown that maintaining posture requires more attentional demands and specifically, that age-related declines in inhibition might account for age-related difficulties in sensory integration (combining visual, vestibular, and proprioceptive inputs) while balancing (Redfern et al., 2001).

As mentioned, another method of quantifying posture is using electromyography (EMG) to assess muscle activity either during single-task balancing or with a concurrent cognitive task. Reduced amplitude of muscle activation has been noted in challenging dual-task conditions, particularly for older adults, further supporting the idea that fewer attentional resources are available for balance control with age (Rankin, Woollacott, Shumway-Cook, & Brown, 2000). Qualitative changes with age have also been noted in the balance strategies exhibited in response to perturbations, wherein older adults are more likely than younger adults to initiate a stepping strategy at lower levels of postural threat (Brown et al., 1999; Little & Woollacott, 2014). Moreover, not all balance strategies are equivalent in their cognitive demands: there is evidence of an attentional continuum of balance strategies, such that ankle flexion is more commonly exhibited during low demand situations, whereas hip or stepping strategies are exhibited as cognitive load increases (Brown et al., 1999).

Intervention Studies

Given the increasing involvement of cognitive resources in balance for older adults, researchers have begun to use cognitive training as a means of improving balance. To date, computerized cognitive dual-task training has been shown to improve balance by reducing postural sway in single- and dual-task conditions (Fraser et al., 2017; Li et al., 2010). More recently, researchers have employed combined approaches incorporating both physical (e.g., aerobic) and cognitive training to improve dual-task balance, which is associated with a decreased falls risk. Broadly, dual-task training is beneficial to dual-task postural control with performance gains noted on the balance task, cognitive task, or both tasks (Agmon, Belza, Nguyen, Logsdon, & Kelly, 2014). Such studies point to the importance of incorporating cognitive training with clinical populations, such as those with motor impairment or cognitive decline.

Summary and Future Directions

To summarize, the extant literature on aging, cognition, and mobility shows a uniform picture of increasing covariation and interdependence between cognitive and motor functioning with age. The acknowledgement and subsequent investigation of cognitive-motor interactions is a relatively recent phenomenon but has accelerated along with developments in the cognitive neuroscience of aging and the research on cognitive plasticity through training interventions. Although substantial progress has been made in identifying the neural circuits and cognitive processes that contribute to motor performance in old age, more work is needed to elucidate whether cognitive involvement and prefrontal hyper-activation are always adaptive or perhaps signal inefficiency in some cases. A future research approach might consider offline neuroanatomical measures of brain aging (e.g., white matter integrity) together with functional neuroimaging and behavioral data to better characterize how brain aging and neural recruitment interact to produce motor performance.

A variety of measures typically used to quantify motor control during balance and walking have been discussed; however, motor functions used in everyday life, such as reaching, lifting, or transferring from seated to standing positions, have been less thoroughly investigated with respect to cognitive aging and deserve further study. Similarly, more studies are needed that employ naturalistic cognitive tasks such as conversing, wayfinding, and visual scene analysis. Moreover, few experimental studies of cognitive-motor dual-task performance have parametrically varied cognitive demands or individually adjusted the complexity of the cognitive task to equate single-task performance before assessing dual-task performance (cf. Doumas, Smolders, & Krampe, 2008). Doing so would clarify whether observed age differences in dual-task performance are driven by age differences in single-task baseline performance (Somberg & Salthouse, 1982). The more clinically oriented research on older adults with cognitive impairments has been covered only briefly, but more longitudinal studies that characterize the time course of motoric decline and impairment relative to the onset of cognitive impairment (e.g., motoric cognitive risk syndrome as proposed by Verghese, Wang, Lipton, & Holtzer, 2013) are expected.

Further Reading

Alexander, N. B., & Hausdorff, J. M. (2008). Linking thinking, walking, and falling. Journal of Gerontology Series A: Biomedical Sciences and Medical Sciences, 63A, 1325–1328.Find this resource:

Fraser, S. A., Li, K. Z. H., DeMont, R. G., & Penhune, V. B. (2007). The effects of balance status and age on muscle activation during walking under divided attention. Journal of Gerontology: Psychological Sciences, 62B, P171–P178.Find this resource:

Montero-Odasso, M., Verghese, J., Beauchet, O., & Hausdorff, J. M. (2012). Gait and cognition: A complementary approach to understanding brain function and the risk of falling. Journal of the American Geriatrics Society, 60, 2127–2136.Find this resource:

Rosso A. L., Cenciarini, M., Sparto, P. J., Loughlin, P. J., Furman, J. M., & Huppert, T. J. (2017). Neuroimaging of an attention demanding dual-task during dynamic postural control. Gait & Posture, 57, 193–198.Find this resource:

Rosso, A. L., Studenski, S. A., Chen, W. G., Aizenstein, H. J., Alexander, N. B., Bennett, D. A., . . . Rosano, C. (2013). Aging, the central nervous system, and mobility. Journals of Gerontology Series A: Biological Science, Medical Science, 68, 1379–1386.Find this resource:

Seidler, R. D., Bernard, J. A., Burutolu, T. B., Fling, B. W., Gordon, M. T., Gwin, J. T., . . . Lipps, D. B. (2010). Motor control and aging: Links to age-related brain structural, functional, and biochemical effects. Neuroscience and Biobehavioral Reviews, 34, 721–733.Find this resource:

Springer, S., Giladi, N., Peretz, C., Yogev, G., Simon, E. S., & Hausdorff, J. M. (2006). Dual-tasking effects on gait variability: The role of aging, falls, and executive function. Movement Disorders, 21, 950–957.Find this resource:

Stelmach, G. E., & Homberg, V. (1993). Sensorimotor impairment in the elderly. Dordrecht, The Netherlands: Kluwer Academic.Find this resource:

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