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Aging, Visual Intermittency, and Variability in Isometric Force Output

¹ Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign.
² Department of Kinesiology, The Pennsylvania State University, University Park.

Address correspondence to Jacob J. Sosnoff, Department of Kinesiology and Community Health, 207 Freer Hall (MC 052), University of Illinois at Urbana-Champaign, 906 S. Goodwin Avenue, Urbana, IL 61822. E-mail: jsosnoff{at}uiuc.edu

	Abstract

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We tested the hypothesis that increases in minimal visual motor processing (VMP) time in older adults contribute to age-related increases in force variability. We manipulated the intermittency rate of visual information feedback over a 100-fold range as young (20–29 years old) and old (60–79 years old) participants produced isometric force output to a visually presented target. The force output of the old adults was more variable and more structured, and the old adults had an increase in minimal VMP time compared with the young adults. However, there was no significant relation between VMP time and force variability. We propose that the age-related changes in variability are a reflection of information-processing capacity limitations and not a decrement in minimal VMP time.

Recently, the impact of the aging process on the consistency of cognitive performance has been a topic of discussion within the gerontology community (Hultsch & MacDonald, 2004; Nesselroade & Salthouse, 2005). However, motor variability and aging has been investigated for the past quarter century (see Spirduso, 1995 for review).

Traditionally, age differences in performance have focused on the mean behavioral output and the variance around it. However, with the introduction of nonlinear dynamics and chaos theory into behavioral science, researchers realized that distributional measures of performance are unable to adequately quantify time-sequential properties of performance (Newell & Slifkin, 1998; Ward, 2002). It is important that the time-sequential properties of behavioral output are indicative of the organization of motor variability, with less adaptability being associated with increased time-dependent properties (i.e., structure) of the within-subject variability (Lipsitz, 2002; Lipsitz & Goldberger, 1992; Vaillancourt & Newell, 2002).

In constant force tasks involving the digits of the hand, researchers have consistently reported that older adults are more variable and have increased time-dependent properties in contrast to young adults, especially at low force levels (Galganski, Fuglevand, & Enoka, 1993; Vaillancourt & Newell, 2003). The long-standing hypothesis that has implicitly or explicitly been involved in discussions of age-related variability is the notion that there is enhanced noise in the neural control system with advancing age (Kail, 1997; Welford, 1965, 1984).

There is growing evidence, however, that deficits in visual motor integration in elderly persons could partially be responsible for age-related differences in the control of isometric force production. For instance, when visual feedback of a constant isometric task is taken away, the magnitude and structure of force variability increase in healthy young adults (Slifkin, Vaillancourt, & Newell, 2000). This change in the amount and time-dependent properties of force variability with the removal of vision mirrors age-related decrements in force production (Vaillancourt & Newell, 2003). Specifically, it has been proposed that age differences in force control are related to increases in minimal visual motor processing (VMP) time (Vaillancourt, Slifkin, & Newell, 2001), the amount of time needed to perceive and subsequently correct for an error in motor output (Carlton, 1992; Keele & Posner, 1968; Vince, 1948; Zelaznik, Hawkins, & Kisselburgh, 1983).

Sosnoff and Newell (2005a) have shown that the influence of vision at the higher frequencies (4–12 Hz) of the force spectrum are most apparent at lower force levels, a finding that fits well with the notion that age-related increases in variability are magnified at lower force levels (Galganski et al., 1993). These observations, when considered in conjunction with the structural (Weale, 1992) and functional (Spear, 1993) age-related changes in the visual system, lead to the suggestion that VMP deficits that are due to aging are a contributing mechanism to age-related decrements in the magnitude and structure of force variability. However, other researchers have observed no influence of visual feedback in force control (Christou & Carlton, 2001; Taylor, Christou, & Enoka, 2003), or even a negative influence in specific experimental protocols (Christou, Jakobi, Critchlow, Fleshner, & Enoka, 2004).

Additional evidence for this association between VMP deficits and increased variability is that older adults have increased activity in the lower frequency bandwidth in their force signal (Vaillancourt & Newell, 2003); this is the region associated with sensory motor processing (Miall, Weir & Stein, 1993). However, these investigations lacked a fine-grain analysis of sensory motor processing, leaving it unclear what specific sensory motor processing deficits with age contribute to force variability. In the experiment reported here, we directly examine the hypothesis that older adults have greater force variability and increased structure as a result of their increased minimal VMP time. This proposition is based on the concept that individuals act as simple feedback control systems and that an increase in processing time results in larger deviations from the target (Craik, 1947a, 1947b).

One approach to the assessment of minimal VMP time is to manipulate the intermittency of the presentation of visual information in the maintenance of a steady-state position or force level (Slifkin et al., 2000). In this task the stability properties of the action are high and the change in the force output is minimal, both features that reduce the minimal VMP time (Carlton, 1992). In our experiment we tested the hypothesis that age differences in minimal VMP time contribute to age-related increases in the magnitude and structure of force variability (Vaillancourt et al., 2001). In order to test this hypothesis, we had young (20–29 years old) and old (60–79 years old) adults produce isometric force by means of index finger abduction while they received visual feedback of their force trajectory at varying intermittency rates (i.e., temporal duration between visual presentation of the force output time series on a computer monitor). We will obtain support for the postulation that the age-related increase in VMP time contributes to deficits in force control if (a) age differences in force control are only observed when visual feedback is presented at high intermittency rates and (b) there is a significant relation between minimal VMP time, task performance, and the structure of force variability.

	METHODS

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Participants
We tested a total of 48 participants (24 men and 24 women) in three different age groups. The groups were the 20-year-old group (n = 15; 20–29 years old, M = 24.9 years, SD = 3.8 years; 9 men and 6 women), the 60-year-old group (n = 15; 60–69 years old, M = 65.7 years, SD = 3.1 years; 8 men and 7 women), and the 70-year-old group (n = 18; 70–79 years old, M = 75.2 years, SD = 4.2 years; 7 men and 11 women). To be included in the study, participants met the following selection criteria: (a) lived independently; (b) were free of any neuromuscular disorders, severe arthritis, cognitive impairments, or diabetes; (c) had normal or corrected to normal vision; (d) were able to ambulate without the use of an assistive device; (e) were right-hand dominant; and (f) reported no falls in the past year. The subjects signed informed consent forms that had been approved by the University Institutional Review Board.

Apparatus
Isometric force recording
Each participant was seated in a chair facing a 17-in. (43-cm) video monitor (CTX International, California), with his or her dominant hand placed in a prone position in an apparatus designed to isolate abduction force of the index finger. Orthogonal to the index finger and 36 cm from the subject's midline was an Eltran EL-500 load cell (diameter 1.27 cm). The load cell measured compressive force produced by the index finger. The lateral side of the distal portion of the index finger was in constant contact with the load cell producing compressive force.

We amplified voltage changes from the load cell by using a Coulbourn (V72-25) resistive bridge strain amplifier with an excitation voltage of 10 V and an amplifier gain of 100. We sampled the analog force signal at 140 Hz with a 16-bit analog-to-digital converter. The smallest increment of change in force that we could detect was.0016 N. Prior to being saving it on the hard drive of the computer, we passed the force signal through an eighth-pole Butterworth low-pass filter (858L8B-1, Frequency Devices, Inc.) with a cutoff frequency of 25.6 Hz.

At each sampling interval the force produced was presented on the monitor. The monitor had a viewing area of 1,200 horizontal pixels and 1,000 vertical pixels. The monitor was approximately 50 cm from the volunteer's eyes and 100 cm from the ground. We set the display to control gain at 50 pixels/N.

Procedures
Estimation of maximal voluntary contraction
We determined the participant's maximal voluntary contraction (MVC) strength at the start of the experimental session. The experimenter instructed the participant to produce the maximal amount of isometric force possible by abducting her or his index finger. The participant produced maximal force while pressing against the load cell. The force applied to the load cell was displayed on the monitor to provide visual feedback. Three 6-s maximal contractions were recorded with 30 s of rest between each contraction. We determined the participant's MVC to be the highest force produced over the three trials.

Experimental design and instructions
Each participant adjusted his or her force output to match a red target line displayed on the monitor and viewed online feedback of his or her performance in the form of a series of yellow dots that corresponded to the force trajectory that moved left to right across the screen with time. The target line corresponded in independent conditions to 5% and 25% of the participant's MVC. We experimentally manipulated the rate of visual feedback presentation (i.e., the time between successive presentations of visual feedback) of the participant's force trajectory. We presented eight distinct frequencies of intermittent visual feedback (0.21, 0.42, 0.83, 1.67, 3.33, 6.67, 10, and 20 Hz) at each force target (Slifkin et al., 2000). For example, for the 20-Hz condition, we presented visual feedback corresponding to the force output every 50 ms; for the 0.21-Hz condition, we presented visual information every 4,800 ms. We also performed a no-vision condition, in which there was a presentation of visual feedback at 10 Hz for the initial 4 s of the 25-s trial so that participants could achieve the force target and then try to sustain the prescribed force level without visual feedback. We proposed that if age differences in the control of force output are only due to age differences in minimal VMP time, then there would be minimal age differences in force variability in the no-vision condition.

Each testing session consisted of two blocks of 27 trials (9 intermittency conditions x 3 trials). We randomly presented the eight visual conditions within a block of trials at each force level. We performed the no-vision condition at the end of each block of trials to ensure that participants were familiar with the force requirement. Half of the participants performed the lower force target block first, followed by the higher force target block. We performed three 25-s trials at each unique force-intermittency condition. In order to minimize their fatigue, we gave participants a 30-s rest between trials and a 2-min rest between blocks.

We instructed participants to minimize the deviations between the yellow force trajectory and the target line throughout all trials. We presented each participant a feedback score at the end of the trial to encourage performance. The score was the root mean square error, and we calculated it with this equation: [(s – f_i )²/n – 1]^1/2, where s is the value of the target, f_i is the ith force sample, and n is the number of data samples.

Data Analysis
We removed the initial 4 s and final 1 s of force data from each trial prior to analysis to avoid the initial force stabilization or premature cessation of force production. We performed all data processing with software written in Matlab Version 7 (The Mathworks).

Task Performance
In order to access task performance as a function of age, we calculated the force level and visual feedback the within trial mean and coefficient of variation (SD/M) of the force data.

Structure of Force Output
We examined the influence of intermittent visual feedback, force level, and age on the structure of force output in the time domain. We assessed the time domain structure of force output with approximate entropy (ApEn; Pincus, 1991). ApEn yields a single value that quantifies the regularity (structure) of a time series. A very regular (i.e., structured) signal such as an ideal sine wave would have an ApEn value approaching 0, whereas a random time series (white noise) would have a value close to 2. Increases in ApEn have been interpreted as a decrease in the signal's time-sequential structure (Pincus). The appendix in Slifkin and Newell (1999) provides a brief explanation of how ApEn is calculated.

Visual Minimal Processing Speed
Central to this investigation is the question of whether age-related differences in minimal VMP time relate to differences in force control. In order to calculate the minimal VMP time, we used the same trend and breakpoint analysis as that of Slifkin and colleagues (2000). First, we fit either an increasing (Equation 1) or decreasing (Equation 2) hyperbolic function to the data, depending on the variable of interest:

We used these functions to describe changes in the group mean as well as the individual subject trends. We then used a quasi-Newtonian nonlinear estimation procedure to isolate the feedback frequency rate at which the variable stopped changing. This feedback frequency rate served as an estimate of the amount of time needed to perceive and correct for a deviation from the force target, which is the minimal VMP time.

Statistical Analysis
We placed each of the dependent variables discussed herein independently in a three-way (3 x 2 x 9) mixed analysis of variance with age as a between-group factor and force level and visual intermittency as within-group factors. When it was relevant, we used Tukey's Honestly Significant Difference test to determine the specific effects contributing to the general analysis of variance. We evaluated all statistics as significant when there was less than a 5% chance of making a Type I error (p <.05), and we report only significant effects. We completed all statistical analyses by using the Statistica statistical package (StatSoft Inc., OK).

	RESULTS

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Task Performance
Our analysis of the MVC revealed that the 20- and 60-year-old groups were stronger (21.69 N and 21.51 N, respectively) than the 70-year-old group (16.21 N); F(2, 45) = 4.68, p <.05. Our analysis of mean force output in the accuracy task revealed a main effect for age group [F(2, 45) = 6.2; p <. 05], force level [F(1, 45) = 670.1; p <.05], and visual intermittency [F(8, 360) = 7.0; p <.05]. We found the 20- and 60-year-old groups (3.176 N and 3.085 N, respectively) to have greater mean force output than the 70-year-old group (2.347 N). The 25% MVC level had greater mean force output than the 5% target (4.745 vs 0.994 N), confirming that participants scaled their force output to the target. Our post hoc analysis revealed that the main effect of visual intermittency resulted because the no-vision condition (2.70 N) had less mean force output than all of the other visual intermittency conditions (2.90 N).

Figure 1 depicts the two-way interaction between force level and visual intermittency [F(8, 360) = 6.75; p <.05]. Specifically, there was a increase in mean force output as a visual intermittency increased in the 25% force target (ranging from 4.68 N at 0.21 Hz to 4.85 N at 20 Hz), but a decrease in mean force output as a visual intermittency increased in the 5% level (ranging from 1.05 N at 0.21 Hz to 0.98 N at 20 Hz). This shows that the effect of visual intermittency is dependent on force level.

View larger version (14K): [in this window] [in a new window]   Figure 1. Mean force output as a function of visual intermittency and force level

View larger version (18K): [in this window] [in a new window]   Figure 2. Coefficient of variation (CV) of force output as a function of age group, force level, and visual intermittency. The filled circles depict the 20-year-old age group, the open circles represent the 60-year-old age group, and the filled triangles depict the 70-year-old group

Force Structure
We calculated the ApEn in order to examine the influence of age, force magnitude, and visual intermittency on the dynamical structure of force output. The ApEn was greater at the 5% MVC level than it was at the 25% force level (0.262 vs 0.152); F(1, 45) = 93.88, p <.05. Figure 3 shows that ApEn increased as a function of visual intermittency rate ranging from 0.116 at the 0.21-Hz condition to 0.277 at the 20-Hz condition [F(8, 360) = 72.31; p <.05] and that there was an interaction between age and visual intermittency [F(16, 360) = 1.78; p <.05]. A post hoc analysis revealed that the 70-year-old group (0.22, 0.23, and 0.25) had a lower ApEn in the higher intermittency rate conditions (20–6.67 Hz) than the 60-year-old (0.29, 0.28, and 0.27, respectively) and 20-year-old groups (0.32, 0.31, and.30, respectively). The observation that age differences in the time-dependent structure of force variability are only apparent at high intermittency rate conditions is supportive of the hypothesis that age differences in the structure of force variability are associated with decreases in minimal VMP time.

View larger version (17K): [in this window] [in a new window]   Figure 3. Approximate entropy (ApEn) as a function of visual intermittency and age group. The filled circles depict the 20-year-old age group, the open circles represent the 60-year-old age group, and the filled triangles depict the 70-year-old group

Minimal VMP time for relative task performance (CV) was 167.0 ms. A statistical analysis revealed a main effect for force [F(1, 45) = 3.91; p <.05] and a two-way interaction between age and force level [F(1, 45) = 2.63; p <.05]. The 5% force level had a shorter VMP time (163 ms) than the 25% target (207 ms). A post hoc analysis showed that the two-way interaction resulted because the 20-year-old age group had a lower VMP time (150.8 ms) than did the 60-year-old age group (167.2 ms) and the 70-year-old age group (174.0 ms), but only at the low force level. The minimal VMP time for the ApEn was 154.8 ms. We found a two-way interaction between age and force level [F(1, 45) = 3.19; p <.05]. A post hoc analysis revealed that the 20-year-old group had a lower VMP time for ApEn (150.2 ms) than did the 60- and 70-year-old groups (175.1 ms and 181.8 ms, respectively), but only at the lower force level.

Once we confirmed the age-related increase in VMP time, it was of interest to examine if this decrement was related to changes in the magnitude and structure of motor performance. Consequently, we conducted a linear regression analysis between VMP time and task performance (CV) as well as VMP time and force structure (ApEn) at each intermittency rate condition during the 5% MVC target. It is clear in Figures 4A through 4F that there is no relation between VMP time and task performance in all age groups, regardless of the visual intermittency rate. The amount of variance in task performance accounted for by VMP time ranged from 0% to 27%. None of the slopes of the linear regression functions were significantly different from 0 (p's >.05; see Table 1). We found this null effect across both force levels and all age groups. The amount of variance in the structure of force output accounted for by minimal VMP time ranged from 0% to 13%. Again, we found none of the slopes of the linear regressions to be significantly different from 0 (p's >.05; see Table 1). The lack of a significant relation between minimal VMP time and force variability and irregularity is in contrast to the notion that age differences in force control are related to increases in minimal VMP time.

View larger version (25K): [in this window] [in a new window]   Figure 4. The relationship between visual motor processing speed and task performance (CV) at the 5% maximal voluntary contraction force target. The top two figures depict the 20-year-old group, the middle two figures depict the 60-year-old age group, and the bottom two figures depict the 70-year-old age group. The left column is the relationship in the high intermittency rate conditions (20–3.33 Hz) and the right column is the relationship in the low intermittency rate conditions (1.67–0.21 Hz). Each symbol represents a single participant's CV as a function of visual motor processing speed at a specific intermittency condition

	DISCUSSION

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It is also possible that the decrease in muscular strength of the 70-year-old age group is indicative of declines in health, which were not adequately screened for. Fried and colleagues (Fried, Ferrucci, Darer, Williamson, & Anderson, 2004) maintain that a loss of muscular strength is an important risk factor for frailty and mortality. Therefore, the increase in variability in the weakest age group is supportive of the association between increased variability and mortality (Lipsitz, 2002; Rowe & Kahn, 1987).

Several experiments have found little to no age differences in force output, but the majority of the studies that have documented no effect for age have examined the force output of large muscle groups (Christou & Carlton, 2001; Schiffman, Luchies, Richards, & Zebas, 2002). This trend raises the possibility of an interaction between task and age that may be due to several factors (Enoka et al., 2003). These task differences may be a result of the inability of both young and old adults to quickly alter force output in larger effectors as a result of their greater mass. This latter proposition is in agreement with the findings of Sosnoff and Newell (2005a), who demonstrated that there is greater contribution of faster control processes in the lower levels of force output.

It has also been shown that there is no effect of visual feedback on the precision of force variability (Christou & Carlton, 2001; Taylor et al., 2003). These reports are not only in contrast to the current experiment, but also to the well-established benefit of visual feedback in motor performance (Carlton, 1992; Woodworth, 1899).

Methodological differences between the studies explain this discrepancy. Investigations that report no effect of vision often examine the variability of force output very close to the time at which visual feedback is removed. This approach holds limitations, because differences in force control with and without visual feedback materialize 1 s after visual information is removed (Vaillancourt & Russell, 2002). It is also possible that the practice of examining force variability over short time scales minimizes the influence of visual feedback (Sosnoff & Newell, 2005a).

As we expected, we found that force variability decreased as a function of increased visual intermittency rate in all age groups (Slifkin et al., 2000; Sosnoff & Newell, 2005a; Vaillancourt et al., 2001). We found age differences in the amount of variability between the 20-year-old group and 70-year-old group across all intermittency rate conditions, which is in contrast to the hypothesis that deficits in VMP contribute to decrements in isometric force control in older adults. However, in line with previous findings, we found older adults to have, on average, a 20- to 25-ms increase in VMP time (Vaillancourt et al.). The average minimal VMP time of 167 ms is congruent with previous reports (Carlton, 1992; Elliott, Helsen, & Chua, 2001). It is important that this increase in VMP time was only significant at the low force level for both task performance and force complexity. Combined with the well-documented finding that older adults are more variable at low force levels (Galganski et al., 1993), this result offers indirect evidence for the hypothesis that minimal VMP speed contributes to age differences in force control. However, the direct analysis of this relation showed that there was no significant association between VMP speed and task performance or force structure in any of the age groups. Consequently, there was minimal evidence for the hypothesis that decreases in VMP speed contribute to age-related performance decrements.

The findings also clearly show that the no-vision feedback condition is not a simple extension of low intermittency rate conditions. There appears to be a different organization of control mechanisms operating in these conditions, and a direct comparison should be made cautiously. Methodological differences between conditions may lead to the use of different control processes. In order to ensure that the participants were able to achieve the force target, we provided visual feedback at 10 Hz for the initial 4 s during the no-vision condition. However, we did not provide similar "set-up" time in the low frequency feedback conditions. Consequently, we presume that participants utilized a memory-based feedforward control strategy in the no-vision condition (Vaillancourt & Russell, 2002), but a visual-feedback-dominated control in the low intermittency rate conditions (Sosnoff & Newell, 2005a). Congruent with these differences in motor behavior is the observation that different cortical networks are active during visual and nonvisual guided force production (Vaillancourt, Thulborn, & Corcos, 2003).

The well-documented decline in memory in older adults combined with the persistence of age differences in the no-vision condition leads to the proposition that older adults have a decreased visual motor memory and that this contributes to age differences in the no-vision condition. Although intriguing, there has been minimal investigation of age differences in motor memory (cf. Smith, Walton, Loveland, Umberger, & Kryscio, 2005). Furthermore, the persistence of age differences in the no-vision condition is also congruent with the notion of decreased precision of feedforward control processes in older adults (Christou & Carlton, 2001).

In contrast to the amount of force variability findings, the examination of the structure of the force output revealed that the 70-year-old age group's force output was less complex (Vaillancourt & Newell, 2003) and that this age difference was most evident at high frequency feedback conditions. This is supportive of the hypothesis that VMP deficits contribute to age-related differences in the structure of force output. The findings offer further support to Vaillancourt and Newell's (2002) proposition that the aging process is not characterized by a loss of complexity per se (cf. Kyriazis, 2003; Lipsitz & Goldberger, 1992), but rather a loss of adaptability. This loss of adaptability can be characterized by either a decrease or increase in complexity, depending on the influence of the task on the intrinsic dynamics of the system (Vaillancourt & Newell, 2002; Vaillancourt, Sosnoff, & Newell, 2003). In addition, the observation that the rate of visual feedback presentation influenced age differences in the time-dependent properties of force output, but did not effect differences in the amount of force variability, leads to the postulation that distinct mechanisms are driving the structure and magnitude of behavioral variability.

The contradictory findings that age differences in force structure are greatest at high feedback rates but that there is no significant relation between minimal VMP speed and motor output suggest that other VMP components are responsible for differences in the structure of force control. It has been proposed that VMP is composed of multiple feedback and feedforward control processes (Paillard, 1996; Pew, 1974). Within this conceptualization, minimal VMP speed is only the fastest feedback control loop (Carlton, 1992; Keele & Posner, 1968; Vince, 1948; Zelaznik et al., 1983) and as such is not sufficient in and of itself to describe the influence of the multiple time scales of VMP on force output (Sosnoff & Newell, 2005a, 2005b). In addition, it is proposed that the increased minimal VMP time in older adults gradually increases with the aging processes. In this way, the well-documented plasticity of the sensorimotor system (Bock & Schneider, 2002) enables the older adults to adapt and utilize other, less impaired feedback and feedforward control loops.

Intermittency rate determines the amount of visual feedback information per unit time. Consequently, it is possible that the observed age-related deficits in force control are due to older adults' limited visual motor information-processing capacity (Cerella, 1990; Cerella & Hale, 1994; Walsh, 1988). It has been previously shown that incremental increases in information lead to initial improvements in performance, but once the information-processing capacity of the system is reached, any further increase in information leads to a decrease in performance (Newell & Kennedy, 1978; Rogers, 1974). In line with this notion, the 70-year-old group's force output became more variable and more structured at the 10- and 20-Hz intermittency rate conditions (see Figures 2 and 3). Finally, results from our laboratory demonstrate that older adults' decreased visual motor information-processing capacity is associated with increased force variability (Sosnoff & Newell, in press).

	Acknowledgments

 
This study is supported in part by grants from the National Institute on Aging (Grants T32-AG00048 and 1 R03 AG023259). J. Sosnoff was supported by a Kligman Research Fellowship.

	Footnotes

 
Decision Editor: Thomas M. Hess, PhD

Received for publication April 14, 2005. Accepted for publication September 26, 2005.

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