University of Iowa
Iowa Research Online
Theses and Dissertations
Summer 2016
Age differences in kinesthetic and static-position
sense of the upper limb in unconstrained 3-D tasks
Christopher Ross Coffman
University of Iowa
Copyright 2016 Christopher Ross Coffman
This thesis is available at Iowa Research Online: http://ir.uiowa.edu/etd/2059
Recommended Citation
Coffman, Christopher Ross. "Age differences in kinesthetic and static-position sense of the upper limb in unconstrained 3-D tasks." MS
(Master of Science) thesis, University of Iowa, 2016.
http://ir.uiowa.edu/etd/2059.
Follow this and additional works at: http://ir.uiowa.edu/etd
Part of the Exercise Physiology Commons
AGE DIFFERENCES IN KINESTHETIC AND STATIC-POSITION SENSE OF THE
UPPER LIMB IN UNCONSTRAINED 3-D TASKS
by
Christopher Ross Coffman
A thesis submitted in partial fulfillment
of the requirements for the Master of Science
degree in Health and Human Physiology in the
Graduate College of
The University of Iowa
August 2016
Thesis Supervisor:
Professor Warren G. Darling
Copyright by
CHRISTOPHER ROSS COFFMAN
2016
All Rights Reserved
Graduate College
The University of Iowa
Iowa City, Iowa
CERTIFICATE OF APPROVAL
____________________________
MASTER'S THESIS
_________________
This is to certify that the Master's thesis of
Christopher Ross Coffman
has been approved by the Examining Committee for
the thesis requirement for the Master of Science degree
in Health and Human Physiology at the August 2016 graduation.
Thesis Committee:
____________________________________________
Warren G. Darling, Thesis Supervisor
____________________________________________
Kelly J. Cole
____________________________________________
Stacey L. DeJong
To my parents, Rodney and Audrey Coffman
ii
ACKNOWLEDGEMENTS
I would like to acknowledge those who have supported and assisted in my
education in completing this document. I could not have completed writing this thesis
without the strong support provided by family and friends. First and foremost, I could not
have completed this experiment without the guidance of my mentor, Warren Darling. I
also drew from the experience of my two lab mates and friends, Sara Hussain and Steph
Hynes. I also appreciated the emotional support provided by my good friends and family,
Tony Sapochetti, Sam Wallace, and Mary Coffman.
iii
ABSTRACT
We compared sense of movement and position in unconstrained 3-dimensional
arm movement tasks in younger and older adults to investigate whether older adults have
diminished kinesthetic sense. Active and passive kinesthesia were compared in a novel
dynamic-position sense task and also in a static-position sense task. Older (65-85 years)
and younger (18-22) adults performed tasks in which they moved the right arm to touch
the right index tip to the moving and stationary left index (target) fingertip in different
conditions.
In the dynamic task the participant or experimenter moved the left upper limb
and, after a variable delay, the subject moved the right arm to attempt to touch the right
index-tip to the moving target index-tip. Participants performed the dynamic task with
vision actively moving both limbs (VDA), without vision while actively moving both
limbs (NVDA), and without vision with the experimenter moving the target limb
(NVDP). In the static task the participant (NVSA) or experimenter (NVSP) moved the
target limb to a position and held it stationary while the participant moved the right arm
to attempt to touch the right index tip to the target fingertip.
Both younger and older adults performed the dynamic task remarkably accurately
with errors averaging less than 1.6 cm across the 3 conditions. Mean 3-dimensional
distance errors averaged slightly (0.19 cm) larger in older adults in the dynamic task
(F1,25=5.88, p=0.02). Variable distance errors did not differ between age groups in the
dynamic task (F1,25=0.90, p=0.35). Small errors were observed in all conditions. NVDP
had the largest mean distance errors (1.81 cm) of moving conditions, followed by NVDA
(1.65 cm), and VDA had the smallest errors (1.27 cm) (F2,50=49.55, pcorr<.001, all post
iv
hoc tests less than p<0.05). There was no evidence of errors depending on target index-tip
peak speed or location. Interestingly, distance errors in the static tasks averaged 3.0 cm
and were clearly larger than in the dynamic tasks (F1,25=57.78, p<0.001). Within the two
static conditions, average errors were 0.5 cm larger in the NVSP condition than in the
NVSA condition (F1,25=7.56, p=0.01). Average distance errors trended to being larger in
older adults in static conditions (F1,25=3.53, p=0.07). Variable distance errors were
similar for the two age groups in the static conditions (F1,25=.25, p=0.35), averaging 1.77
cm in NVSP and 1.38 cm in NVSA (F1,25=.7.98, p<0.01).
These results suggest that regardless of age, availability of visual information,
active/passive target limb movement, or reaching to static versus moving targets that
adults are generally quite accurate at localizing fingertip position. The finding that
accuracy in the static and dynamic tasks when vision was not allowed was only slightly
better when the subjects actively moved the target arm (i.e., NVDA, NVSA) than when
the target arm was moved by the experimenter (NVDP, NVSP) indicates that internal
models may contribute only very slightly to proprioceptive localization of the upper limb.
However, it is clear that kinesthetic sensory information from the periphery is sufficient
to allow the central nervous system to accurately calculate position of the endpoint of the
limb (tip of the index) while unconstrained in 3-dimensional space.
v
PUBLIC ABSTRACT
Proprioception is the sensation of the position of one’s limbs provided by sensory
receptors in muscles, skin and ligaments, allowing people to touch their nose, clap their
hands, and type even with eyes closed. We were interested in how the ability to locate
position of the limbs might change with aging. We therefore took a group of healthy
community dwelling older adults (65-83 years), and a group of younger adults (18-22
years), and asked them each to perform tasks in which they moved one arm to touch its
index to the other index under dynamic (moving target) and static (stationary target)
conditions.
Older adults were slightly less accurate than younger adults at locating the tip of
the index finger within a large space under both dynamic and static conditions while
blindfolded. Both younger and older adults were slightly better at locating the index-tips
when moving than when stationary. Errors were only slightly larger when the
experimenter, instead of the subject, moved the target limb, suggesting that the brain is
capable of locating the limbs based purely on non-visual sensory information without
using information from the movement plan to predict where the limb will be located.
Thus, healthy aging has little influence on awareness of limb position.
vi
TABLE OF CONTENTS
LIST OF TABLES ........................................................................................................... viii
LIST OF FIGURES ........................................................................................................... ix
INTRODUCTION .............................................................................................................. 1
METHODS ......................................................................................................................... 9
Participants ...................................................................................................................... 9
Experimental Setup ....................................................................................................... 10
Instructions .................................................................................................................... 11
Task Practice ................................................................................................................. 13
Data Collection and Analysis ........................................................................................ 16
RESULTS ......................................................................................................................... 23
Dynamic Task ............................................................................................................... 23
Static Task ..................................................................................................................... 26
Combined Task Analysis .............................................................................................. 28
DISCUSSION ................................................................................................................... 45
Overall Performance ..................................................................................................... 45
Dynamic Task Performance .......................................................................................... 46
Target Speed and Distance Errors ................................................................................. 47
Movement Strategies and Characteristics during Task Performance............................ 47
Static Task Performance................................................................................................ 49
Directional Errors and Internal Models ......................................................................... 51
Performance across Dynamic and Static Conditions .................................................... 53
Limitations and Future Directions................................................................................. 54
Clinical Applications ..................................................................................................... 55
APPENDIX – ADDITIONAL STATISTICAL ANALYSES .......................................... 57
REFERENCES ................................................................................................................. 65
vii
LIST OF TABLES
Table 1: Trials eliminated from each condition from 3 SD elimination. .......................... 19
Table A.1: Additional ANOVA statistics for directional error magnitude in dynamic
conditions. ............................................................................................................. 57
Table A.2: Condition by direction interaction of the magnitude of directional distance
errors ..................................................................................................................... 58
Table A.3: Additional ANOVA statistics for directional variable errors in dynamic
conditions .............................................................................................................. 59
Table A.4: Condition by direction interaction of the directional variable errors.............. 59
Table A.5: Additional ANOVA statistics for directional error magnitude ....................... 60
Table A.6: Age x direction interaction of directional errors magnitude in static
conditions .............................................................................................................. 60
Table A.7: Direction x condition interaction for directional errors magnitude in static
conditions .............................................................................................................. 61
Table A.8: Additional ANOVA statistics for directional variable errors in dynamic
conditions. ............................................................................................................. 61
Table A.9: Condition by direction interaction for directional variable errors in dynamic
conditions .............................................................................................................. 62
Table A.10: Workspace size ANOVA statistics ............................................................... 63
Table A.11: Age by condition interaction post-hoc testing for workspace ANOVA ....... 64
viii
LIST OF FIGURES
Figure 1: Top, front, and side views of table .................................................................... 10
Figure 2: Moving target angles of kinesthetic (moving) task ........................................... 13
Figure 3: Static target positions ........................................................................................ 15
Figure 4: Adjusted movements ......................................................................................... 17
Figure 5: Examples velocity traces of trials which were eliminated ................................ 18
Figure 6: Accuracy in the dynamic task ........................................................................... 29
Figure 7: Three-dimensional position and tangential index-tip speed versus time for
representative younger (left side) and older (right side) subjects ......................... 30
Figure 8: Commonly observed differences in performance by older adults ..................... 30
Figure 9: Horizontal plane position traces of H60 degree, V0 degree movement in an
older and a younger subject .................................................................................. 31
Figure 10: Mean distance error for the dynamic conditions ............................................. 31
Figure 11: Mean variable errors in dynamic conditions. .................................................. 32
Figure 12: The average number of trials in each group when categorized by the time
between voluntary arm stop and target arm stop per subject ................................ 32
Figure 13: Mean distance and variable errors within each group in trials categorized
by time between voluntary arm stop and target arm stop.. ................................... 33
Figure 14: Mean distance and variable errors in older and younger adults by condition
in trials in which the time between the voluntary arm stop and target arm stop
was between -200 ms and 200 ms......................................................................... 33
Figure 15: Magnitude of directional errors in dynamic conditions. ................................. 34
Figure 16: Variable directional errors in dynamic conditions .......................................... 34
Figure 17: Examples of scatterplots of distance error versus peak velocity of target
index tip motion of selected subjects/conditions. . .............................................. 35
Figure 18: Selected prediction plots from error and target location in moving
conditions .............................................................................................................. 36
Figure 19: Position plots of an older and a younger subject in static conditions .............. 37
ix
Figure 20: Typical velocity and position traces of an older and younger adult within
the static conditions............................................................................................... 38
Figure 21: Distance error in static condition..................................................................... 39
Figure 22: Variable error in static conditions ................................................................... 39
Figure 23: Selected prediction plots from static target position regressions predicting
distance error.. ....................................................................................................... 40
Figure 24: Workspace size correlations with distance and variable errors in both
moving and static conditions. ............................................................................... 41
Figure 25: Magnitude of directional errors in static conditions. ....................................... 41
Figure 26: Variable directional errors in static conditions................................................ 42
Figure 27: Distance errors among moving and static conditions. ..................................... 42
Figure 28: Variable errors among moving and static conditions. ..................................... 43
Figure 29: Directional distance errors in all conditions. ................................................... 43
Figure 30: Workspace sizes in older and younger adults in all conditions ....................... 44
x
INTRODUCTION
Proprioception is the awareness of the location of body parts without vision and can be
broken down into multiple categories. Vestibular sense is the awareness of angular and linear
acceleration and tilt, of the head. Position sense is the sensation of position of the limbs, torso,
and head relative to each other and/or gravity. Kinesthetic sense is the sensation of movement
of the limbs, head, or torso. Position sense and kinesthesia are thought to arise from sensory
input from muscle spindles, joint mechanoreceptors, and stretch receptors in the skin
surrounding joints. Muscle spindles are currently thought to be the primary sensory inputs
providing for position and movement sense, based on research showing that muscle tendon
vibration creates illusions of movement errors in perception of limb position (Goodwin et al
1972).
It has been reported that proprioceptive acuity is better when the hand/arm is moved
by oneself (active movement) versus by an outside entity (passive movements) (Paillard and
Brouchon 1968, Adamovich et al 1998, Gritsenko et al 2007, Monaco et al 2010, Fuentes and
Bastian 2009). The difference in accuracy could be accounted for based on the sensitivity of
receptors activated during active versus passive movement. During the active movements there
is beta-gamma activation of muscle spindle fibers in active muscles that produces greater
muscle spindle sensitivity (Paillard and Brouchon 1968). It has also been previously shown that
efference copies of motor commands conveyed by collaterals of corticospinal tract axons to
other brain areas can contribute to position sense (Gandevia et al 2006) and could be used by
the brain to predict the location of the limbs in space when the limb is actively moved. In
contrast, under passive conditions of imposed motion, the lack of motor commands to move the
limb would be expected to decrease accuracy of limb position sense (Wolpert et al 1995,
Wolpert and Kawato 1998). Finally, greater slackness of a muscle in passive than in active
1
conditions may reduce accuracy of proprioceptive localization because spindles would fire at a
slower rate than in active muscle (Proske et al 2014).
Internal models are a concept that involves the nervous system predicting sensory
outcomes of motor commands, or necessary motor commands to achieve a sensory outcome
based on desired goals of movement. Internal models originate from control theory, and have
been invoked to explain the coordination of fast movements, because it is thought that motor
feedback loops are too slow to obtain accurate movements through feedback control. Forward
models make use of efference copies of motor commands, and predict the sensory outcomes of
the motor commands based on the mechanical properties of the limb and external
environment. The difference between the predicted and the actual sensory outcomes can be
used to adjust the system to improve future movements. Inverse models make use of current
sensory information, as well as the goal for movement, and then predict the necessary motor
commands necessary for the desired sensory outcome (as reviewed by Kawato 1999). The best
evidence for the use/existence of internal models in motor control originates from research
showing that grip force for an object held between the fingertips is adjusted before a person
makes arm movements. This suggests that the brain predicts the necessary grip force changes
ahead of movement (Flanagan and Wing 1997).
There have been some contrasting experimental results. Capaday et al (2013) failed to
find a statistically significant difference between active and passive proprioception with static
targets in an unconstrained 3-d position matching task. All of the previous studies of active
versus passive proprioception had used single joint angle matching (e.g. Gritsenko et al 2007) or
constrained limb movement with experimental apparatuses, such as the 1-D sliders used by
Paillard and Brouchon (1968). From their experiment, they concluded that under unconstrained
conditions only sensory information from proprioceptive localization of the upper limb was
2
necessary for accurately specifying location of the limb endpoint. Use of an internal model, as
postulated previously to be necessary for accurate position sense because of noise in
proprioceptive sensory signals, was therefore deemed unnecessary for an accurate sense of
limb position. One other study, also reported similar accuracy under active and passive
movements (Jones et al. 2010).
There is some evidence of reduced proprioceptive acuity in older adults, referred to as
presbypropria (Boisgontier et al 2012). Joint angle matching research as early as 1984 reported
larger errors when matching angles at the knees in older adults (Skinner et al 1984). They took a
large sample across a wide age range (20-82 years) and, using simple linear regression,
determined that each year of aging past 20 years resulted in an average loss of 0.059 degrees of
proprioceptive acuity. In contrast, Stelmach and Sirica (1986) found only small differences (0-3
cm between age groups). Distance errors were only larger in medium and long movements
within older adults (70-80 years old) and younger adults (20-25 years old) using a onedimensional upper limb position matching task involving linear sliders. They also noted that
errors were greatest for those in which the target had moved the farthest, and older adults
were slightly more accurate (by about 0.7 cm) when they pre-selected matching targets than
when the experimenter selected the targets, similar to active and passive conditions in other
experiments. Younger subjects performed about the same under both experimenter- and
subject-selected targets.
A variety of research has been conducted on joints of the upper and lower limb
regarding potential changes in joint position sense in older adults. Adamo et al (2007) found
average errors ranging from 1-10 degrees in an elbow matching task, and statistically smaller
errors by younger adults (5 degrees less than older). In a follow-up study comparing young and
older adults who participated in upper limb activities, and older adults who did not, older adults
3
were 4 degrees less accurate than their younger counter parts, and acuity was about 2 degrees
better in older adults who participated in upper limb physical activity compared to those who
did not (Adamo et a. 2009). Similar age-related differences (2-4 degrees) were observed by
Goble et al (2012), but with much larger errors (10 degrees) in older adults performing a
cognitive task (the “Stroop” test) while performing an angle matching task, suggesting that
conscious proprioception requires more attentional resources in older adults. Kalisch et al
(2012) found older adults made a greater number of errors (averaged 1.97 more errors than
young adults, who averaged 1.2) when comparing the size of larger and smaller equally
weighted Styrofoam balls while blind folded. They argued that being able to compare ball sizes
without vision required proprioceptive input from joints in the fingers and hand. Schapp et al
(2015) also found larger errors in older adults using a 3-D, unconstrained limb position matching
task; however, the workspace used within the experiment was limited, and the task involved
proprioceptive memory. Subjects were asked to reach to a point in space, then remember the
location for 3 seconds, and then reach to that location again or mirror the final limb
configuration with the other arm.
Other studies have suggested that proprioception is minimally impacted, if at all, with
aging. In 1990, Lovelace and Aikens performed a two-dimensional matching task in which
participants made pen-marks to indicate the position of an unseen finger on the other side of
the board. They also asked subjects to touch facial features with their fingertips without vision.
Older (55-85 years old) and younger adults (18-24 years old) performed similarly in these tasks.
They concluded that proprioceptive decrements only occur late in life and hypothesized that
such decrements may be more related to pathological aging than healthy aging. Similarly,
Boisgontier and Nougier (2013) found older adults were about as accurate as younger adults at
an ankle matching task, but were less accurate (by 8.4o) when asked to complete the matching
4
as quickly as possible. This suggests that proprioception is less accurate for high velocity
movements, but it is unclear whether the error originates from errors in proprioception, or
errors in motor commands. Herter et al (2014) studied a position matching task constrained to a
horizontal plane. Participant’s arms were passively moved by a robot to a location and then
participants were asked to mirror the orientation and the position of the arm with the other
arm. They found older adults were less slightly less accurate than younger adults at matching at
the shoulder, elbow, and hand positions. Interestingly, the variable error did not change in older
adults for shoulder joint angles.
Studies reporting larger proprioception errors in older adults have some similar
characteristics compared to those reporting similar errors in proprioceptive tasks. Movements
were usually restricted in some way, shape, or form (e.g. 1 dimensional sliders or isolated joint
angle matching) or required memory of targets in experiments finding larger errors in older
adults. Even in these studies the difference in magnitude of errors between younger and older
adults were relatively small, calling into to question the clinical significance of such findings.
The, mostly small, deficits in proprioceptive acuity with aging may be attributed to a
variety of factors. It is well known that there is a loss of neurons at all levels of the nervous
system in older adults (Callaghan et al 2014, Gong et al 2014, and Agosta et al 2007). This could
lead to a loss of resources necessary to transmit and process proprioceptive input. It has also
been observed that there is a loss of larger neurons leading to skeletal muscle in a lower limb
nerve (Swallow 1966), which most likely includes the large Ia afferents that innervate muscle
spindles). Furthermore, there is evidence of intrafusal muscle fibers lacking afferent innervation
in aged rats (Desaki and Nishida 2010). In rodents, it has also been observed that there is a loss
of intrafusal muscle fibers, as well as increased spindle capsular thickness (Swash and Fox,
1972). This probably leads to lower sensitivity to stretch in spindles (Kim et al 2007), which are
5
thought to be the major sensory input underlying proprioception and kinesthesia. Compounding
these effects is a loss of cutaneous mechanoreceptors in older adults (Bolton et al 1966, also
reviewed in depth by Shaffer and Harrison 2007), and a loss of joint mechanoreceptors (Aydog
et al 2006, Morisawa 1998) that could result in additional sensory information loss for
perception of joint angles and limb orientation.
It is possible older adults compensate for muscle spindle structural changes with age by
changing movement strategies during tasks testing accuracy of proprioception. A study using an
ankle joint angle matching task suggested that older adults co-contract muscles more than
younger adults while performing such tasks. This may increase gain of muscle spindle activity to
partially compensate for age-related changes to spindles and their innervation. However,
differences between old and young adults were small in proprioceptive accuracy (Madhavan
and Shields, 2005). Older adults move slower during the honing in phase of reaching to a target
(Ketcham and Stelmach 2004). Perhaps this slower movement also applies during proprioceptive
tasks, as larger errors have been reported when older adults are required to complete an angle
matching task as fast as possible (Boisgontier and Nougier 2013).
Very little work has been done investigating kinesthetic (or moving) position sense, and
such research is virtually absent in older adults. At present, research involving kinesthetic sense
is limited to movement threshold detection at isolated joints and speed matching tasks. Of note,
Kokmen et al (1978) observed no difference in the movement detection threshold in younger
and older adults. Angular velocity matching tasks have been performed in younger adults (Goble
and Brown 2009, Goble and Brown 2010) but not in older adults.
The present study expands on the existing literature by measuring static and dynamic
(kinesthesia) proprioceptive acuity under completely physically unconstrained conditions in
6
three dimensions. The present study is unconstrained in terms of physical movement.
Participants were not restricted to moving their limbs in one or two directions, nor were any
joints restrained. The workspace, however, was somewhat limited in that the chosen movement
directions and target locations in both tasks were pre-selected.
Previous studies have used matching tasks at individual joints, typically in a constrained
unidimensional device, or in two dimensions such that either the limb, the target locations, or
both are constrained. The work described herein is fully physically unconstrained, and involves
sensing of the 3-dimensional location of the limb endpoint, the tip of the index finger, which is
important in many basic daily tasks involving coordination of the two hands. Right-handed
participants were tested for self-determined and passively imposed left index-tip target
locations and directions of movement. The reaching movements of the right arm to attempt to
touch the right index-tip to the moving or stationary left index-tip mimic the movements of daily
life such as passing a small object from one hand to another or in buttoning a shirt. To our
knowledge, this is also the first study to address kinesthetic-position sense or localization of a
limb in motion, in older adults, as well as to compare dynamic-position sense with staticposition sense.
Given the previous work in the field, we hypothesized that older adults would have
greater errors when locating the target arm in all conditions, including with vision, due to likely
structural differences in the CNS, muscle spindles and other sensors contributing to awareness
of upper limb position and motion (i.e., cutaneous receptors, joint receptors). We also predicted
errors would be greater when targets were selected or moved by the experimenter, due to the
lack of an efference copy of motor commands during passive movement and less gamma and
beta activation to increase spindle sensitivity. We hypothesized that static targets would have
greater errors than moving targets because greater information about limb position is available
7
to the central nervous system during movement. This greater information may be available
because 1a afferents from muscle spindles are more sensitive to velocity changes than to
position changes, and also because sensory adaptation of cutaneous and joint
mechanoreceptors could occur with static targets, leaving less information available to the CNS
for limb localization. That is, during movement, the CNS would be receiving more input from
fast-adapting mechanoreceptors about target position than during static conditions.
8
METHODS
Participants
13 older community dwelling adults (5 males, 8 females, aged 65-83 years of age, 73 ±
5.1 (SD) years) and 14 younger adults (5 males, 9 females, aged 18-22 years of age, 20 ± 1.4 (SD)
years) participated in this study. Participants self-identified as right handed, good health, no
cardiac pacemaker, no history of neurological disease, and no arthritis of either upper limb. All
participants gave written informed consent, and the experiment was approved by the University
of Iowa institutional review board.
Tasks
We compared the performance of older and younger adults in a dynamic (moving
target) task, as well as a static (non-moving target) task. Participants left index tip served as a
target and the right arm served as a voluntary reaching arm used to attempt to touch the target
(left index tip) with the right index tip. The dynamic task was performed by both young and
older adult groups with full vision moving the target arm actively (VDA) and without vision (NV)
to test whether vision was necessary for accurate performance. Without vision, participants
were able to actively move their target arm (NVDA) during some trials and, in other trials, the
experimenter passively moved the target arm (NVDP). During the static task, participants either
self-selected a left index tip position with their target arm (NVSA), or the experimenter moved
the arm to a static target position (NVSP), and then participants located the stationary target left
index tip by actively moving the voluntary arm to touch the right index tip to the target.
9
Experimental Setup
Figure 1: Top, front, and side views of table, consisting of start button for left hand, starting
sticker for right hand, and table at 0, 30, and 60 degrees in the horizontal plane.
Participants were seated in a stationary chair in front of a table such that the xiphoid
process of their sternum was one-half the length of the arm (distance from the coracoid process
of the scapula to the tip of the index finger) from the table (Fig. 1). Participants were centered
between a button placed on the table that served as a start position for the left index tip, and a
sticker that served as a tactile cue for the start position for the right index tip. The sticker and
the button were placed 51 cm apart, and 2 cm from the edge of the table. Colored tape was
placed on the table along horizontal plane directions of 0 (i.e., straight rightward motion toward
the right index), 30, and 60 degrees from the left start button to help familiarize subjects with
the directions of left hand motion during the task (Figure 1). A six degree of freedom motion
sensor (Ascension Technologies TrakSTAR, Burlington, VT, USA) was attached to the fingernail of
each index finger with double sided tape and secured with surgical tape. The cable of each
motion sensor was taped to the hand dorsum such that the cable did not interfere with natural
10
wrist and index movements. During all tasks, participants used the left index tip as a target and
voluntarily moved the right arm to touch the right index tip to the left index tip. It should be
noted that the two motion sensors used cannot occupy the same space, therefore the distance
(error) between them cannot be zero. To document positioning of the sensors on subjects
finger, and to get an estimate of the minimum possible distance between the sensors, we took
the minimum distance error from each subject within the task involving vision.
Instructions
Participants were instructed to move the right arm to attempt to touch the right index
tip to the left index tip while it was moving under 3 experimental conditions: (1) VisionDynamic-Active (VDA) – full vision was allowed, both arms were moved voluntarily by the
subject, (2) No Vision–Dynamic-Active (NVDA) – subject was blindfolded, both arms were moved
voluntarily by the subject and (3) No Vision-Dynamic-Passive (NVDP) – subject was blindfolded,
the target (left) arm was moved by the experimenter and the subject voluntarily moved the
right arm. During passive conditions (NVDP, NVSP), the experimenter supported the elbow with
one hand, and the wrist with the other hand while moving the arm. If resistance to the
movement was felt by the experimenter, the trial was stopped and repeated. Note that in the
NVDP condition subjects were not told the direction of the imposed movements. Thus, they had
to predict the location of the target index tip based on kinesthetically sensed motion of the
target arm. We also assessed ability of blindfolded subjects to move the right index tip to touch
the statically positioned left index tip under two conditions: (1) Active (NVSA) – left index tip
positioned by the subject and (2) Passive (NVSP)– left index tip positioned and left arm
supported at the elbow and wrist by the experimenter.
11
Before starting, participants were permitted to practice the task with vision until they
felt comfortable with the instructions, and then motion sensors were attached and we started
recording. The VDA condition was always performed first, and then the active and passive
moving conditions without vision (NVDA, NVDP) were performed in a random order. The static
task was always performed after the moving condition, and the active and static conditions
were tested in a randomized order. In conditions where the experimenter moved the
participants arm, the experimenter attempted to match the participant’s movement speed.
To start each trial, the left index finger was placed on the starting button, and the right
index finger was placed on the starting sticker. Other digits were lightly curled under each palm.
Participants were verbally given a vertical and horizontal angle to move their left finger during
VDA and NVDA conditions. Participants were then given a “go” command which signaled they
were to release the button and move their left hand in the given direction. After the start button
was released by left hand movement, it emitted a beep delayed randomly by 50-300 ms which
served as a signal to begin right (voluntary) arm movement. Participants then moved the arm to
touch the right index tip to their already moving left index tip. Participants were instructed to
use a smooth motion, and not to attempt to correct after either making contact with the left
hand or if they missed the left hand. 26 total trials were performed for each condition involving
13 discrete movements repeated twice for each combination of horizontal and vertical
directions (horizontal angles 0, 30, 60 degrees; vertical angles 0, 30, 60, 90 degrees) (Fig. 2). If
participants visibly began to move their left and right arms simultaneously or moved the target
12
hand in the wrong movement direction, the trial was repeated. A trial was also repeated if the
cables interfered with hand movement in any way.
Figure 2: Moving target angles of kinesthetic (moving) task. A is a top view showing the 0o, 30o
and 60o directions of motion in the horizontal plane. B is a front view showing 0o, 30 o, 60 o and
90 o directions in the vertical plane. C is a picture of a subject positioned in front of the table and
also shows the location of the transmitter for the Trakstar system.
Task Practice
To explain the dynamic task to participants, we first informed them that the overall goal
of the task was for them to touch their right index fingertip as close to the left fingertip as
possible. After this, we told them that left and right hand needed to start after the left hand
movement at different times. The target finger would start on the button and would be moved
when the experimenter instructed “Go”. After the left hand left the button, the subject would
hear a beep, which was the signal for them to start moving their right hand to touch the left
index tip. Subjects were also told to move their right hand in a single smooth movement and to
not correct their right hand movements or, when blindfolded, to grope for the target left index
13
tip. We started practicing with only the horizontal movement directions. Some participant
needed reminder about interpreting geometric angles, but all were able to perform the task
properly. After mastering moving in horizontal directions, we added the vertical directions, and
had them practice until they were more comfortable with performing the task with vision. After
they clearly understood the delay between the right and left arm movement starts, and could
interpret the verbal directional instructors, we affixed the motion sensors to their fingertips and
began recording the VDA condition trials followed by the NVDA and NVDP conditions in random
order.
During the static proprioception task, participants were verbally instructed to move
their left arm to 1 of 12 target positions. After holding the target position for about 1 second,
participants were given a “go” command that signaled they were to move their right index
finger to the touch the stationary index finger of the left arm in a single smooth movement.
Participants were told not to attempt to correct errors, and only the first movement toward the
target of each trial was analyzed. Target positions were defined by an imaginary grid
immediately above the table. There were 12 target positions on the grid (4 horizontal levels:
outer left, inner left, inner right, outer right horizontal positions - Three vertical levels at each
horizontal level: just above the table, sternum, and shoulder level vertical positions)(Fig. 3). One
trial was performed for each target position.
14
Figure 3: Static target positions. The top panel shows a front view of the table and approximate
target positions (green stars), and the bottom panel shows a side view with the subject in
position and approximate height of the targets (red stars). The targets were arranged on a grid
in the Y-Z plane positioned over the table.
The static task was performed under two blindfolded conditions. During the active
condition, subjects moved the left arm to the instructed target location by themselves, and
during the passive condition, the experimenter moved the participant’s left arm to the target
location and supported the elbow of the target arm for the duration of each trial. These two
conditions were done in random order.
15
Data Collection and Analysis
Data were collected at 240 Hz using a trakSTAR electromagnetic tracking system
(Ascension Technology Corporation, Shelburne VT) with a custom MATLAB program. Data were
output to a text file and analyzed using DataPac 2k2 (Run Technologies, Mission Viejo CA) to
identify movement onset and termination and associated kinematic data of interest which were
then output for further analysis in Microsoft Excel. A low pass butterworth filter at 15 Hz rolloff
frequency with zero phase lag was applied to the kinematic data. Statistical tests were
performed with Microsoft Excel, SAS 9.3, and Statistica 5.1.
In the dynamic task left and right index tip tangential speed (S) at time t were computed
using the central difference method over 5 samples using the formula 𝑆𝑡 =
√(𝑥𝑡+8.14 𝑚𝑠 −𝑥𝑡−8.14 𝑚𝑠 )2 +(𝑦𝑡+8.14 𝑚𝑠 −𝑦𝑡−8.14 𝑚𝑠 )2 +(𝑧𝑡+8.14 𝑚𝑠 −𝑧𝑡−8.14 𝑚𝑠 )2
.
𝑡
(t is the time in 4.17ms
intervals, and x,y,z refers to the index tips’ positions at the time point referenced in the
subscript. Mean distance and directional (X-forward/backward, Y-right/left and Z-up/down)
errors and variable errors within each task (VDA, NVDA, NDVP) were measured at the end of
both the target and voluntary hand movements. The start and end of movements were selected
using a “filter” set to identify movements with a threshold speed of 2.3 cm/s to identify the start
(when index tip speed exceeded threshold) and end (when speed fell below threshold) of all arm
movements between 250 ms to 3000 ms in duration. Movements not corresponding to
movements to touch the index tips were then discarded (e.g. returning hands to start positions,
participant fidgeting, and removing blindfold at end of a trial condition). The 250 – 3000 ms
duration filter was occasionally adjusted to accept longer duration movements for subjects with
particularly slow movement speeds by some of the older adults. The start and end of
movements were adjusted visually when the filter selection did not appear to represent the true
16
ending of a movement, for example, if the movement was so slow such that the 2.3 cm/s
threshold did not represent the true end of a limb’s movement) (e.g., Fig. 4). The peak
tangential index tip speed of each movement was recorded, and the straight line distance and
direction (within horizontal and vertical planes) of each target index tip movement from start to
end were calculated.
Figure 4: Adjusted movement endpoints. The right panel shows example of a trial that required
filter modification; The duration filter needed to be adjusted because of the long movement
duration (exceeding 3000 ms). The right panel shows a trial in which the subject made multiple
movements with each hand. These would have been taken as separate movements had the
movement start and end times not been adjusted visually.
During analysis trials were discarded if there were more than two movements by the
right arm evident in its velocity trace (e.g., Fig. 4, right side) as this suggested the participant was
groping for the target rather than simply proceeding directly towards the predicted target index
tip location, which was of primary interest (Fig. 5). A total of 3 or 0.11% of trials were eliminated
across all trials and subjects in this manner. If both hands moved at completely separate times,
that is, the left hand nearly finished moving before the right hand started, the trial was
eliminated; two such trials (0.07% of all trials) were eliminated for this reason. One trial (.04% of
all trials) was eliminated because the movement tracing was very erratic, making it impossible to
clearly identify movement onset and end. Additionally, trials in the static condition were
eliminated if it was visually evident that the target arm was not held moderately still, indicating
17
that the static task was performed incorrectly (Figure 5). Two trials from one subject were
eliminated because of this (0.6% of trials by all subjects in the static active condition; 2 of 12 or
17% of the static active condition by a single young subject).
Figure 5: Examples velocity traces of trials which were eliminated. The top left panel shows a
trial in which the target arm had nearly stopped before the volutary arm started moving. The top
right panel shows a trial in which the voluntary arm made more than two movements before
stopping. The bottom panel shows a static trial in which the participant moved both the
voluntary and target arm; the target would not have been static in this trial.
Distance errors were computed on each trial using 𝐷𝑒𝑟𝑟 =
√(𝑥𝑟 − 𝑥𝑙 )2 + (𝑦𝑟 − 𝑦𝑙 )2 + (𝑧𝑟 − 𝑧𝑙 )2 , using the positions of the target (xl, yl, zl) and voluntary
index-tips (xr, yr, zr) at the end of the voluntary index-tip’s movement. Mean distance errors
were computed for each subject within each condition. Variable distance errors were computed
as the standard deviation of the individual trial distance errors of each subject within each
condition.
18
After elimination of trials described above, all trials with a distance error of more than 3
standard deviations from the mean distance errors of each condition for individual subjects
were flagged and individually inspected. A total of 39 of 135 subjects/conditions were flagged
(29% of subject/conditions). Trials with voluntary arm distance errors greater than 3 standard
deviations from the mean within a condition for a subject were eliminated, unless there were
multiple trials with similar errors, or if the variable error was very small (i.e. around 0.5 cm or
less). We removed these trials because we did not feel they properly reflected an individual
subject’s performance at the task. A total of 20 trials from the flagged subjects/conditions (less
than 0.7% of all trials) were eliminated across all subjects in this manner (Table 1).
Trials eliminated from each condition from 3 SD elimination
VDA
NVDA
NVDP
NVSA
Older
3 (0.89%)
2 (0.60%)
3 (0.89%)
0 (0%)
Younger
1 (0.27%)
3 (0.83%)
4 (1.10%)
0 (0%)
Table 1: Trials eliminated from each condition from 3 SD elimination.
NVSP
4 (2.54%)
0 (0%)
Mean distance errors were compared among the moving conditions using 2x3 (group young/old x condition – VDA, NVDA, NDVP) repeated measures analysis of variance (rmANOVA)
and in the static conditions were compared using 2x2 (group – young/old x condition – NVSA,
NVSP) ANOVA. Variable distance errors were compared among conditions in the moving and
static tasks in a similar manner. Directional errors were compared using 2x3x3 (group –
young/old x condition – VDA, NVDA, NVDP x error direction – X, Y, Z) ANOVA. Directional
constant errors were compared using 2x2x3 (group – young/old x condition – NVSA, NVSP x
error direction – X, Y, Z) ANOVA. Variable directional errors were compared similarly. HuynhFeldt adjustments were applied to significant repeated measures factors with 3 or more levels
with probability values reported as pcorr. Significant main effects and interactions were further
investigated using Tukey’s post-hoc tests. Directional errors were also tested for bias using a
one-sample t-test comparing the mean directional error to a hypothesized mean of 0 in each
19
direction and condition in older and younger adults separately. Because there were 5 conditions
and 3 directions within each condition. We use a Bonferroni adjusted alpha of 0.003333 (i.e.,
0.05/15) to establish statistical significance.
In the moving target conditions we also examined differences in peak target index tip
speed and speed variability (S.D.) using 2x3 (group x condition) rmANOVA. Near significant main
effects and interactions were further investigated using Tukey’s post-hoc tests. We were
interested in comparing target speeds between active and passive conditions to ensure that the
experimenter was moving the participant’s arm at speeds similar to those the participant used
in the VDA and NVDA condition.
Potential effects of target location and target arm speed on distance errors in individual
trials were examined to test whether errors depended on target location and, in the dynamic
tasks, on speed of target index tip movement. To test whether distance errors differed in a
predictable manner on target location and motion direction, multiple linear regression analyses
were performed on each participant’s individual results by condition with distance errors for
individual trials as the dependent variable and target finger tip’s vertical angle of movement,
horizontal angle of movement, and linear distance traveled by target arm as independent
variables (predictors). To test for potential effects of speed of target index tip movement,
voluntary arm distance errors were correlated to peak target index tip speed on individual trials
within each condition of the dynamic task using Pearson’s R. For peak speed correlations and
location regressions we adjusted the alpha value using the Bonferroni method to 0.017 (i.e.,
0.05/3) to correct for the multiple significance tests on each of 3 conditions within subjects.
We also tested whether distance errors on individual trials were associated with target
location in a predictable manner within static conditions. Multiple regression analyses were
20
performed within each participant’s individual results by condition but target x, y, and z values
were used as independent variables (predictors), and distance error was used as the dependent
variable. Because two regressions were ran within each subject, we adjusted alpha to 0.025 (i.e.,
0.05/2) for these multiple regressions.
We also tested whether the range of the target locations, that is, the workspace size,
was correlated with distance errors. We approximated the workspace size of each condition in
each subject using the x, y, and z ranges as primary axes on an ellipsoid. Workspace volume was
4
computed using the formula for volume of an ellipsoid, 𝑉 = 3 𝜋𝑎𝑏𝑐, where a, b, and c are the
axis radii set to one-half the length of each of the x, y, and z ranges. We then correlated the
workspace sizes (independent variable) with voluntary arm distance errors and distance variable
errors (dependent variables) in each condition using Pearson’s R. Because 3 correlations were
performed within each subject, we adjusted the alpha value to 0.017 to correct for multiple
correlations.
We noted that some participants moved their trunk in coordination with their arms, and
would bend the trunk forward, especially during movements of 60 degrees direction in the
horizontal plane (i.e., H60V0, H60V30, H60V60). We were interested in whether or not moving
at the trunk would impact the distance errors. We compared the mean distance error of those
trials at 60 degrees horizontally (H60V0, H60V30, H60V60) with those at 0 degrees horizontally
(H0V0,H0V30, H0V60) using a paired t-test.
The voluntary arm and target arm movements did not end synchronously in the
dynamic task. We therefore assessed whether stopping right and left arm movements at
different time intervals relative to each other affected errors. Time between endpoints of the
two arm was calculated as voluntary arm endpoint time minus target arm endpoint time. We
21
sorted each subject’s errors into five time interval categories as follows: 400 ms and earlier, 400 to -200 ms, -200ms to +200ms, +200 to +400 ms, +400 and greater ms). After sorting, we
averaged errors across each time group for each subject, and then aggregated all errors by task
condition. We compared mean distance errors and variable distance errors on a reduced data
set (right and left arm movements ending within 200 ms of each other) using 2x3 (age –
young/old x condition – VDA, NVDA, NVDP) repeated measures ANOVAs for comparison to the
ANOVA results using all errors.
To compare errors between dynamic and static tasks, we used 2 (groups) x 2 (conditions
– dynamic/static) x 2 (tasks – active/passive) repeated measures ANOVA. Mean distance errors
and variable distance errors were dependent variables for these ANOVAs. We also compared
voluntary arm movement durations between dynamic and static conditions using a similar 2 x 2
x 2 repeated measures ANOVA to assess whether voluntary arm movements were of similar
duration in the two conditions.
22
RESULTS
Dynamic Task
Both younger and older subjects were remarkably accurate in the moving target index
tip task. Average distance errors in each moving condition were small (1 – 3 cm) and not much
larger than the minimum distance error when vision was allowed (mean ± S.D. = 0.79 cm ±
0.21cm). The errors for individual targets in individual subjects were small and very similar
whether vision was allowed or not and whether the subject moved the target arm voluntarily or
the experimenter moved the target arm (Fig. 6).
Some aspects of task performance differed between younger and older subjects. Older
participants tended to move at more variable speeds, often slower than younger participants,
and therefore had longer movement durations during both dynamic and static tasks. Older
adults also usually had multiple index tangential velocity peaks during the reaching movement
(Fig. 7) whereas younger subjects usually had a single index tangential velocity peak in both
arms. In addition, older adults also generally had more difficulty starting the voluntary arm
movement after target arm movement began, and therefore we more frequently repeated
“practice” trials until the older adults were able to start the voluntary arm movements after the
audible beep indicating target arm movement onset. Even after accomplishing temporal
separation of left and right arm movement onsets, older adults more frequently displayed
changes in the motion of the target arm when they initiated movement with the voluntary arm
in the dynamic NVDA task. They would either slow movement of the target arm during the
initiation of the target arm, and/or they would initiate movement with both arms and then
attempt to stop the voluntary arm until cued to start by the tone (Fig. 8). When initiating the
target hand movement both older and younger subjects apparently predicted the moving
23
target’s location trajectory fairly accurately as the movements of the voluntary index tip were
almost straight towards the location where the target hand eventually stopped moving (Fig. 9).
Interestingly younger and older subjects both chose similar peak target hand speeds
(F1,25=0.19, p=0.66). The main effect of condition trended on significance (F2,50=3.15, p=0.051),
however, post-hoc testing failed to reveal significant differences; NVDP compared with VDA was
the closest test to reaching significance (p>0.05). The interaction of age and condition neared
significance (F2,50=2.95, p=0.06), though only one post-hoc test neared significance (Young NVDP
compared with young VDA, p>0.05). This suggests that the experimenter succeeded at choosing
target speeds similar to those chosen by the participants.
Distance errors averaged 0.19 cm greater in older adults than younger adults across the
3 dynamic conditions (Fig. 10, F1,25=5.88, p=0.02). As expected, the vision (VDA) condition
averaged the lowest errors (1.27 cm) , followed by NVDA (1.65 cm) and NVDP (1.81 cm). (Fig. 10,
F2,50=49.55, pcorr<.001, p<0.05 for all post hoc tests). The average distance errors were similar
for younger and older subjects in each condition (group x condition interaction: F2,50=0.57,
p=0.57). Results were similar for variable distance errors as these also did not differ between
age groups (F1,25=0.90, p=0.35), but differed among the 3 conditions, being greatest in NVDP
(0.72 cm), followed by NVDA (0.58 cm), and VDA (0.33 cm) (Fig. 11, F2,50=28.67, pcorr<.001,
p<0.05 for all post hoc tests). There were no interactions between age group and condition
within either distance or variable errors in dynamic conditions (p>0.47 for all interaction
effects).
We noted that some participants in both age groups moved at the trunk, especially
when moving in directions further away from their person (e.g. H60). This was not observed in
all participants and was never observed for movements in the H0 direction. Trunk movement
24
did not appear to affect accuracy of perception of target index motion as there was no
difference in mean distance errors for movements in the H60 directions (H60V0, H60V30,
H60V60) compared with those in the H0 directions (H0V0,H0V30, H0V60) across all subjects
(t25=0.41, p=0.68).
After segregating trials in categories based on the time between the ends of target and
voluntary movements, it was clear that in most trials the two arms stopped moving within 200
ms of each other (i.e., -200 to 200 millisecond category in Fig. 12). Larger errors typically
occurred when the voluntary arm stopped moving before the target arm because the distance
error was measured at voluntary arm stop. Distance errors were expected to be larger
depending on how early the voluntary arm stopped motion relative to the target arm. However,
the errors were only slightly larger when the voluntary arm stopped before the target arm (Fig.
13). If the full dataset of the experiment is reduced to include only trials in the ±200 second
range of voluntary arm stop relative to target arm stop, all comparisons of mean distance and
variable errors between groups and across conditions maintain the same statistical significance
as reported for the entire dataset, with one exception: NVDA versus NVDP variable error posthoc test fails to reach significance but is a strong trend for differences (p=.051) (Fig. 14)
Within dynamic conditions, the directional errors were small in magnitude and did not
differ between age groups (Fig. 15, F1,25=1.09, p=0.30) or between conditions (F2,50=2.00,
p=0.14). However, the magnitudes of directional errors were larger on the left/right axis (ydirection) by 0.2 cm than the forward/backward axis (x-direction) (F2,50=8.77, pcorr<0.001, posthoc p>0.001). Interestingly, distance errors within the left/right axis were largest in the vision
condition, and errors within the superior/inferior direction were largest in the passive condition
(F4,100=15.02, p<0.001, all post-hoc tests p<0.05). Complete statistics for the condition by
direction ANOVA can be found in Appendix A.
25
Variable directional errors were also small (Fig. 16) and averaged only slightly (0.08 cm)
larger in older adults than younger adults (Fig. 16, F1,25=4.34, p=0.047). Variable errors were
largest in NVDP (1.01 cm), followed by NVDA (0.91 cm), and, as expected, smallest in VDA (0.56
cm) (F2,50=78.19, pcorr<0.001, all post hoc tests p<0.05). Variable errors were also 0.11 cm larger
in the forward/backward axis than in the left/right axis (F2,50=7.41, pcorr<0.01, post-hoc p=0.02).
The directional variable errors within the vision condition were smaller than all other conditions
with the exception of the NVDP condition anteroposterior errors (F4,100=4.92, p<0.01, p<0.05 for
all relevant post-hoc tests). Complete statistics for the condition by direction ANOVA are in
appendix A.
Distance errors on individual trials within the dynamic task were not well correlated
with peak target index-tip speed. Most correlations (75 of 81) were non-significant (e.g., Fig.
17), with an average Pearson r across all conditions of 0.05. Distance errors were not correlated
with final target index-tip position in the moving conditions (average R2=0.20 for all multiple
regressions) (Fig. 18). Of the 81 (27 subjects x 3 conditions) multiple regressions performed, only
7 regressions were statistically significant (3 VDA, 2 NVDA, and 2 NVDP). Among the 7
significant regressions, the R2 value averaged 0.49.
Static Task
Overall, participants were also fairly accurate at performing the static task (e.g., Fig. 19).
Older and younger subjects typically made a single smooth movement to the target, and the
older adults moved the voluntary index-tip slower on average than younger adults (Fig. 20).
Both age groups occasionally failed to make contact with the target index tip, or otherwise erred
by 5 or 6 cm from the target fingertip. Average distance errors were only 0.5 cm larger in the
NVSP condition than in the NVSA condition (Fig. 21, F1,25=7.56, p=0.01) and trended to being
26
larger in older adults (F1,25=3.53, p=0.07). There was no group by condition interaction
(F1,25=0.10, p=0.75). Variable errors were similar in the two age groups (Fig. 22, F1,25=.25, p=0.35)
but were larger in in NVSP (1.77 cm) compared with NVSA (1.38 cm) (Fig. 22, F1,25=.7.98,
p<0.01).There was no group by condition interaction (F1,25=0.92, p=0.34).
Distance errors showed no dependence on target location within the static conditions
(average R2=0.38 for all multiple regressions) (Fig. 23). Only 3 of the 54 (27 subjects x 2
conditions) multiple regressions performed were significant (p < 0.05). Average and variable
distance errors were not correlated with workspace size in the static conditions. (Fig. 24).
The magnitudes of directional errors in static conditions were similar in the two age
groups (F1,25=0.76, p=0.39) but were larger in the passive (NVSP) condition (0.95 cm) than in the
active (NVSA) condition (0.71 cm) when averaged across all directions (Fig. 25, F1,25=7.06,
p=0.01). The magnitude of directional errors were larger (by 0.52 cm) on the superior/inferior
(z) axis than the right/left (y) axis (F2,50=7.65, pcorr<0.01, post-hoc p=0.01). Anteroposterior (x)
errors were larger in older adults (1.17cm) than younger adults (0.57 cm) (F2,50=4.13, pcor=0.03,
post hoc p=.04). NVSA errors were significantly smaller than NVSP errors on the anteroposterior
axis (F2,50=3.32, p=0.04, post-hoc p=0.01). Other age x direction interactions and condition x
direction post-hoc tests are in appendix A. Direction variable errors in static conditions were
0.36 cm larger in older adults (Fig. 26, F1, 25=4.62, p=0.04), and were also larger in the NVSP than
in the NVSA condition by 0.25 cm (Fig. 26), F1, 25=5.64, p=0.03). Variable directional errors were
larger on the superior/inferior axis (z axis) than on other axes (Fig. 26, F2,50=14.86, pcorr<0.001,
p<0.001 for relevant post-hoc tests).
27
Combined Task Analysis
Although overall errors were small, the mean distance and variable errors in the
dynamic tasks without vision were substantially smaller than those in the static task. Mean
distance errors without vision averaged 1.23 cm smaller in the dynamic than in the static tasks
(Fig. 27) (F1,25=57.78, p<0.001). Averaged across static and dynamic tasks, mean distance errors
were slightly smaller (by 0.34 cm) in active conditions than passive conditions (F1,25=13.22,
p=0.001). Variable errors within dynamic conditions were about half the size of those in static
conditions (0.93 cm smaller) (Fig. 28, F1,25=38.56, p<0.001). Averaged across static and dynamic
tasks, variable errors were 0.26 cm smaller in active conditions compared to passive conditions
(F1,25=13.83, p=0.001).
Older subjects moved the voluntary arm 23% slower than young adults in static and
dynamic conditions without vision (F1,25=5.52, p=0.03). The voluntary arm movement durations
did not differ between static and moving conditions (F1,25=0.18, p=0.68). However, the voluntary
arm movement durations averaged 94 ms longer in active conditions (F1,25=6.38, p=0.02).
Movement durations in NVSP and NVDP averaged 147 ms shorter duration than in NVDA
(F1,25=6.13, p=0.02, p<0.05 for post-hoc tests).
The voluntary arm index-tip tended to be placed slightly anterior, right, and above the
target arm index-tip at the ends of movements in the moving conditions (Fig. 29), whereas in
static conditions the voluntary arm index-tip tended to be behind and below the target arm
index-tip (Fig. 29). A few statistically significant, yet small directional biases were observed.
Within the vision condition, in both older and younger adults, a slight rightward (y) bias (0.69
cm) was observed (older: t11=10.43, p<0.003, younger: t12=9.07, p<0.003). Older adults exhibited
no other biases. Younger adults also exhibited a slight upward (z) bias (0.50 cm) in NVDP
28
(t12=5.508, p<0.003), a slight backward bias (0.69 cm) in NVSP (t12=3.73, p=0.003), and larger
upward bias (1.26 cm) in NVSP (t12=3.77, p=0.003).
Although there were no main effects of age (Fig. 30, F1,25=1.007, p=0.32), the older
subjects had smaller workspaces than the younger subjects in the NVDA condition (group x age
interaction: F4,100=4.88, pcorr<0.01, post-hoc p<0.01). Full statistics for workspace age x condition
interactions are in appendix A.
Figure 6: Accuracy in the dynamic task. Voluntary index tip position versus target index tip
position of an older and a younger subject in the dynamic tasks. Each plotted point represents
data from a single trial in a single condition. The plotted line is the line of identity. Deviations
from the line of identity represent errors in a given direction. Note that axes are not scaled the
same for different directions.
29
Figure 7: Three-dimensional position and tangential index-tip speed versus time for
representative younger (left side) and older (right side) subjects. The top row shows x, y, and z
positions of the target and voluntary index-tips and the bottom row shows the tangential speeds
of the target and voluntary index-tips throughout the movements.
Figure 8: Commonly observed differences in performance by older adults that were not typically
seen in younger adults. The left panel shows an example of an older adult slowing target arm
movement at voluntary arm movement onset. The right panel shows an older adult starting
both arms simultaneously instead of delaying voluntary arm start as instructed.
30
Figure 9: Horizontal plane position traces of H60 degree, V0 degree movement in an older and a
younger subject. Note that the voluntary index tip moves relatively straight towards the position
reached eventually by the target index tip.
Figure 10: Mean distance error for the dynamic conditions. Each bar represents the mean
distance error for 15 younger adults (black bars) and 13 older adults (gray bars). Error bars are 1
S.E.M. * significant main effect of age p<0.05, # significant main effect of condition p<0.05.
31
Figure 11: Mean variable errors in dynamic conditions. Each bar represents the mean
distance error for 15 younger adults (black bars) and 13 older adults gray bars).
Error bars are 1 S.E.M. # significant main effect of condition p<0.05.
Figure 12: The average number of trials in each group when categorized by the time between
voluntary arm stop and target arm stop per subject. From left to right, under each set of bars for
a condition, the time categories are: 400ms and longer early voluntary arm stop, 200 to 400 ms
early voluntary arm stop, -200 to 200 ms voluntary arm stop relative to target arm stop, 200 to
400 ms voluntary arm stop after target arm stop, and greater than 400ms voluntary arm stop
after target arm stop. Error bars represent ±1 SD from mean.
32
Figure 13: Mean distance and variable errors within each group in trials categorized by time
between voluntary arm stop and target arm stop. From left to right, under each condition,
400ms and larger early voluntary arm stop, 200 to 400 ms early voluntary arm stop, -200 to 200
ms voluntary arm stop after target arm stop, 200 to 400 ms voluntary arm stop after target arm
stop, and greater than 400ms voluntary arm stop after target arm stop. Error bars represent 1
S.E.M.
Figure 14: Mean distance and variable errors in older and younger adults by condition in trials in
which the time between the voluntary arm stop and target arm stop was between -200 ms and
200 ms each bar represents the mean of 15 younger subjects or 13 older subjects. Error bars are
1 S.E.M. # significant main effect of condition p<0.05. & NVDA and NVDP significantly different
from VDA p<0.05, but NVDA and NVDP are not different from each other.
33
Figure 15: Magnitude of directional errors in dynamic conditions. Each bar represents
the mean error for 15 younger subjects or 13 older subjects. Error bars are 1 S.E.M. $
significant effect of direction p<0.05. $ Significant main effect of direction p<0.05. @
Condition x direction significant interaction p<0.05.
Figure 16: Variable directional errors in dynamic conditions. Each bar represents the mean of
variable distance errors for 15 younger subjets or 13 older subjects. Error bars are 1 S.E.M. *
Significant main effect of age p<0.05. # Significant main effect of condition p<0.05. $ Significant
main effect of direction p<0.05.
34
Figure 17: Examples of scatterplots of distance error versus peak velocity of target index tip
motion of selected subjects/conditions. Each graph is from a different subject. From left to right
the top row panels were from VDA, NVDA, and NVDA. The bottom row of panels were from
NVDP, NVDP, NVDA. Each plotted point is data from a single trial within one condition.
35
Figure 18: Selected prediction plots from error and target location in moving conditions. All
panels are from different subjects. Each point represents the errors from a single trial within one
condition. From left to right, the top row panels are from NVDP, VDA, and NVDA. The bottom
row panels are from VDA, VDA, and VDA.
36
Figure 19: Scatterplots of target index tip position versus voluntary index tip position of
representative older and younger subjects in the static conditions. Deviations from the line of
identity represent errors in a given direction. Note that axis unit divisions are not the same for
different directions.
37
Figure 20: Typical velocity and position traces of the target and voluntary index tips an older and
younger adult within the static conditions. Note that the speed (Y) axes are not all on the same
scale.
38
Figure 21: Distance error in static conditions. Error bars represent 1 S.E.M. # significant main
effect of condition p<0.05.
Figure 22: Variable error in static conditions. Error bars represent 1 S.E.M. # significant main
effect of condition p<0.05.
39
Figure 23: Selected scatterplots of distance errors versus predicted distance errors from target
location in the static conditions. All panels are from different conditions by different subjects.
From left to right, top to bottom, panels are from NVSA, NVSP, NVSP, NVSP, NVSA, NVSP.
40
Figure 24: Scatterplots of mean and variable distance errors versus workspace size (volume) in
dynamic and static tasks. Each plotted point is the mean or variable distance error plotted
against workspace volume for a single subject under one experimental condition.
Figure 25: Magnitude of directional errors in static conditions. Each bar represents the mean of
15 younger subjects or 13 older subjects. Error bars represent 1 S.E.M. # significant main effect
of condition p<0.05. $ Significant main effect of direction p<0.05. @ Condition x direction
significant interaction p<0.05. % Condition x age significant interaction p<0.05.
41
Figure 26: Variable directional errors in static conditions. Error bars represent 1 S.E.M. *
Significant main effect of age p<0.05. # Significant main effect of condition p<0.05. $ Significant
main effect of direction p<0.05.
Figure 27: Mean distance errors in moving and static conditions. Error bars represent 1 S.E.M. !
Significant main effect of dynamic/static condition p<0.05. $ Significant main effect of
active/passive condition p<0.05.
42
Figure 28: Mean variable errors in dynamic and static conditions. Each bar is the mean of 15
younger subjects or 13 older subjects. Error bars represent 1 S.E.M. ! Significant main effect of
dynamic/static condition p<0.05. $ Significant main effect of active/passive condition p<0.05.
Figure 29: Mean directional distance errors in all conditions. Error bars represent 1 S.E.M.
* Significant bias for given direction/condition/age-group p<0.0033.
43
Figure 30: Mean workspace sizes (volumes) of older and younger adults in all conditions. Error
bars represent 1 S.E.M. * Significant age x condition interaction p<0.05.
44
DISCUSSION
Overall Performance
All subjects performed very accurately in dynamic and static conditions. Mean distance
errors by group in experimental conditions without vision ranged from 1.55cm-3.8cm, which is
quite small considering that the minimum distance error in each subject in the vision condition
averaged 0.79 cm ± 0.21cm (SD), which the sensors represent as the closest possible positioning
of the index tips. The mean variable errors by groups and conditions without vision was also
small, ranging from 0.52 cm to 1.98 cm. These results suggest that regardless of age, availability
of visual information, active/passive movement, or reaching to static/moving targets that
younger and older adults are generally quite accurate at localizing the ends of their upper limbs
(tips of the index fingers). Moreover, the finding that mean errors in passive conditions
averaged less than 1 cm larger than in the active conditions without vision shows a very
minimal, if any, contribution from internal models to kinesthetic/proprioceptive localization of
the upper limb finger tips under unconstrained conditions.
It should be noted that there is no known “fingertip position receptor” located in the
fingertip. Yet this study, as well as Capaday’s (2013) study, suggests that people are very
proficient at locating their fingertips in 3D space based on proprioceptive information. Fingertip
position must be computed from the proprioceptors signaling joint angles proximal to the
fingertip, which includes joints of the index finger, wrist, elbow, and shoulder. Given the
previously reported angular errors at each of these isolated joints (e.g. Gritsenko et al 2007,
Fuentes and Bastian 2009, Adamo et al 2007) and the lengths of the arm and forearm, we
should expect to see much larger errors at the fingertips, but clearly this is not the case. If a
participant with a 73 cm length arm erred by only 5 degrees at the shoulder, at the fingertip we
45
would expect to see a 6.4 cm error. This better performance on the higher order task of locating
the endpoint of the arm suggests that the errors in previous tasks are more likely due to the
cognitive and foreign nature of joint matching/memory paradigms, rather than limitations on
proprioceptive capabilities. People do not usually need to match elbow angles, except perhaps
when lifting a load with both arms, and even then, balance and weight distribution is the
primary task, not joint angle matching. A similar conclusion about greater limb endpoint
accuracy than at isolated joints was also reached by Fuentes and Bastian (2009); however, the
task used to assess proprioceptive acuity in that work involved use of vision. Participants visually
estimated their unseen fingertip location and elbow angles in different conditions; therefore,
one could argue that the task used was not truly proprioceptive. It actually involved
transformation of proprioceptive inputs into a visual representation. Our study rules out the
possibility that vision was influencing endpoint location accuracy and precision as subjects were
blindfolded.
Dynamic Task Performance
The small difference in distance errors between older and younger adults in dynamic
tasks, along with the lack of a difference between the variable errors of both age groups
suggests that kinesthesia is well maintained into late adulthood. The small observed differences
are unlikely to affect the performance of many bimanual motor tasks, except for those perhaps
requiring the finest precision, such as buttoning an 8mm diameter shirt button without vision.
Notably, the worst performing younger subject (18 years) only slightly outperformed the oldest
(83 years) subject by 0.55 cm on mean distance error with nearly identical variable errors (0.08
cm different) in the NVDA and NVDP conditions.
46
It is doubtful that an internal model using efference copies of motor commands in the
active conditions contributes to prediction of target arm motion as distance errors were only
slightly larger (by 0.17 cm) in the NVDP condition than in the NVDA condition. Participants
apparently accurately predicted the outcome of the movement imposed on the target limb
based on non-visual sensory inputs and then moved the right fingertip in a relatively straight
path to meet the left index tip at its endpoint (Fig. 9).
Target Speed and Distance Errors
The low average correlation between peak target index tip speed and distance error
(R=0.05) suggests that variations in speed of the moving target do not affect the accuracy of
localizing the index-tips, at least across the speeds selected in this experiment. That is, people
are similarly accurate at localizing the endpoints of their upper limbs regardless of the speed the
target arm is moving under normal conditions. Within the workspace used in this experiment,
the regressions using target location as predictors for distance error accounted for little of the
variance in distance errors, suggesting that proprioceptive localization is fairly similar across
locations within a large workspace. Workspace size for the static and dynamic tasks was about
the same for younger and older adults, and did not correlate well with mean or variable distance
errors in either static or moving tasks. This suggests that the CNS is capable of sensing moving
and static limb end positions well over the entire workspace.
Movement Strategies and Characteristics during Task Performance
The greater difficulty of older adults in delaying the start of the voluntary arm
movement may reflect age-related differences in interhemispheric inhibition. The corpus
callosum, the main bundle of connecting axons between the hemispheres of the brain
responsible for interhemispheric communication, thins with aging (Sullivan et al 2002). A
47
growing body of evidence suggests that the amount of interhemispheric inhibition, which is
conveyed largely through the corpus callosum, is reduced in older adults (reviewed by Fling et al
2011). Furthermore, fMRI studies such as Naccarato et al (2006) indicated that older adults
exhibit less lateralized processing than younger adults during a simple thumb to fingers
opposition task. Previous research has also shown that older adults have more difficulty
suppressing tension production of a muscle in one limb when asked to contract the homologous
muscle in the opposite limb, which was true for both isometric and aninsometric contractions of
the FDI (Shinohara et al 2003). This could explain the greater difficulty older adults had
suppressing the onset of voluntary arm movement until after the target arm movement began
despite the delayed auditory starting cue for the voluntary arm movement. Alternatively,
perhaps the dynamic task may be better thought of as a dual-task paradigm rather than just a
bimanual movement. Moving the target limb and moving the voluntary limb independently
could be considered as two separate tasks. It is well documented that older adults struggle more
with performing dual-tasks than younger adults (see Verhaeghen et al 2003 for a meta-analysis
of such studies). Despite the issues with older adults delaying the voluntary arm movement and
the research showing mirror activation of muscles (e.g. Shinohara et al 2003), older adults were
still able to control limbs independently very well, as evidenced by the small errors (1.74 cm
distance, 0.64 cm variable) in NVDA condition.
Many subjects also employed trunk movement during performance of the dynamic task
because of its unconstrained nature. Although we did not measure trunk movement, such
movement clearly did not increase errors by any substantial amount. Subjects typically
employed relatively large trunk movements in trials with a horizontal angle of 60 degrees, and
would even lean over the table slightly during such movements. Even though the previously
described regression analyses showed no specific relationships between target location and
48
distance error, we specifically tested for a difference between H60 trials (when participants
were most likely to employ trunk movement), and H0 trials (in which participants were least
likely to employ trunk movement) across all older and younger subjects. We found no difference
between errors of H60 and H0 movements. This accords with previous research showing arm
movements and trunk movements are highly coordinated such that trajectories of the hand can
be nearly identical for arm only movements and combined trunk/arm movements to the same
target (e.g., Kaminski et al 1995, or Pigeon et al 2000).
Older and younger adults used similar speeds of target arm movement, but older adults
were slower at moving the voluntary arm to touch the index tip to the target arm’s index tip.
Neither the peak target speeds chosen by subjects in active conditions, nor their variability,
differed by age. There was only a small decrease in workspace size found in the NVDA condition
along with a slowing of voluntary arm movement by an average of 23% (246 ms longer duration)
in older adults. The longer durations might reflect a compensatory strategy to increase the
amount of time to process input from peripheral receptors to predict location of the target in
the dynamic task. If so, it is a relatively small compensation. It should also be noted that it is
well documented that older adults move slower during targeted reaching and fine motor
movements than younger adults (Smith et al 1999). Thus, it is also possible that the longer
movement duration does not reflect a compensatory strategy in this task but instead represents
slower comfortable speed movements of older adults.
Static Task Performance
It would appear that static position sense is also well maintained into older adulthood.
There was some indication of larger mean distance errors by older adults in the static tasks (p =
0.07). However, older adults’ mean distance errors averaged only 0.61 cm higher than in
49
younger adults. Our oldest subject, at age 83, outperformed the worst performing young subject
on mean distance error within static tasks by 2.47 cm, and by 2.89 cm in mean variable error.
This suggests, similar to previous conclusions by Lovelace and Aikens (1990), that position sense
is well maintained into late adulthood, and this can be extended to both static and dynamic
tasks.
We observed a small increase in mean distance error (0.51 cm) and variable error (0.39
cm) in the passive static condition compared with the active static condition. This finding
appears to contradict the findings of Capaday et al (2013), in which a very similar static positionsense task was performed. This is probably attributable to the increased statistical power as the
previous study had 11 subjects, whereas the present study involved 27 total subjects (14 young,
13 older). This suggests that the additional information from the motor outflow, increased
gravity from holding the limb, and increased alpha-gamma coactivation slightly improved
localization of the limb endpoints. The error differences we observed were smaller than those
observed by Adamovich et al (1998), perhaps because memory of fingertip position was
required in that study, but not in the present study. Irrespective of statistical significance it
seems clear that accuracy of limb end-point localization without vision is very good whether the
limb is actively displaced by the subject or passively displaced by the experimenter as the errors
observed in both conditions by both age groups were small.
Distance errors on individual trials in each subject showed no evidence of dependence
on target location. Despite the moderately strong average coefficient of determination (0.38),
most of the multiple regressions predicting distance errors from target locations were nonsignificant (51 of 54 regressions across all subjects and conditions). This suggests little
dependence of errors on target location within the large workspace participants used during the
static condition.
50
Directional Errors and Internal Models
Directional mean distance and variable errors were small in all directions under dynamic
and static conditions, and exhibited no strong biases in any direction (under 1 cm on average),
similar to results of the static task in Capaday et al (2013). However, there was some evidence
of biases as some directional constant errors statistically differed from 0 (p < 0.05). Such biases
were not observed by Capaday et al (2013), but this is not likely to reflect contradictory results
because of the higher statistical power from the larger number of subjects in the present work.
Also, the two observed biases were relatively small (max of 1.26 cm in up/down (z) direction).
These findings extend the results of Capaday et al. (2013) by showing that large directional
biases do not exist under either dynamic or static conditions. In the VDA condition, the slightly
larger rightward bias (0.69 cm) is likely explained by the use of the right arm as the voluntary
reaching arm. When reaching from the right side towards the left side, the point where the
fingertips will touch is shortest on the right side of the left index tip. The interaction found
between condition and direction within the directional constant errors under dynamic
conditions is somewhat interesting because this small rightward bias was reduced without
vision. The slight loss in spatial acuity without vision (i.e., larger mean distance errors in NV
conditions) could lead to participants touching the top/bottom of the left index fingertip more
often than on the side, and/or slightly changing the orientation of the finger. Assuming they
were targeting the right side of the left fingertip with vision, this would reduce the slight
rightward bias, while overall slightly increasing the absolute distance error due to larger but less
biased vertical errors.
The lack of strong biases in this experiment do not support the notion that internal
models are necessary for limb localization. The lack of any clear overshoot biases in the both of
the active conditions without vision contradicts Wolpert et al’s (1995) suggestion that overshoot
51
biases under such conditions are due to use of internal models for predicting limb location.
Others (e.g., Gritsenko et al 2007, Fuentes and Bastian 2009) also failed to observe such biases
and reported overshoot errors in both active and passive conditions. Similarly, Monaco et al
(2010) also noted a small (0.5 cm) decrease in variable errors, but not in constant errors, under
active conditions (compared to passive conditions). They suggested that efference copies of
motor commands might increase the precision of localizing position of an actively moved body
part because it could add information about individual joint angles for use by the nervous
system. The lack of biases observed in the present and previous experiments does not
completely eliminate the potential for the involvement of forward internal models for localizing
the limbs in active conditions. Instead, the slightly smaller errors in active than in passive
conditions suggest that an internal model may be used for slight improvement of
kinesthetic/proprioceptive localization of the limbs. However, one could argue that enhanced
spindle sensitivity and reduced slack of muscles, rather than use of efference copy/internal
model predictions, underlie the slightly smaller errors in active conditions.
Furthermore, the utility and existence of internal models making predictions from
efference copies is brought into question from grip studies such as Monzée et al (2003). Strong
initial support for internal models came from the observations of Flanagan and Wing (1997) that
grip force adjustments by the digits were adjusted with a small safety margin before moving a
limb. Because these adjustments were initiated prior to movement onset, or a change in
movement direction, it was thought that an internal model based on efference copy of motor
commands was used to predict the small increases in grip forces needed to counteract inertial
forces arising during movement. Monzée et al anesthetized the cutaneous receptors on the
digits of the limb after practicing this task to allow development of an internal model. If a
forward internal model was mostly responsible for adjusting grip force ahead of predicted
52
movements, it would be expected that the anesthesia of the cutaneous receptors would not
largely change the small safety margin. Monzée et al saw that after the anesthesia, grip forces
applied to the object greatly increased. Efference copies of motor commands clearly were not
sufficient for predicting the necessary grip force to make adjustments while moving. Large
safety margins were applied presumably to prevent dropping the object. If a forward model is
used, it must rely on existing sensory input to assist in making predictions of the necessary grip
force.
The utility of efference copies to estimate limb endpoint position from motor
commands to contribute to position sense is questionable. Severe interventions were required
to invoke any illusion of movement in an anesthetized/paralyzed arm. Participants needed to
exert very large efforts (i.e., 20-50% of maximum voluntary effort) to induce relatively small
illusions of movement (20-40 degrees) of an unloaded hand at the wrist (Gandevia et al. 2006).
This “sense of movement” they inferred from the large perceived effort is unlikely to be of much
benefit under normal physiological conditions.
Performance across Dynamic and Static Conditions
The greater mean distance errors (by 1.23 cm) and variable error (by 0.93 cm) in the
static conditions (NVSA, NVSP) compared to the dynamic conditions (NVDA, NVDP) suggests that
localization of a moving limb is better than that of a statically positioned limb. This could be due
to greater activity of Ia afferents from muscle during motion, which could increase the amount
of available information to the CNS to make predictions regarding limb location. This better
accuracy while moving is probably assistive when performing bimanual tasks such as typing on a
keyboard, buttoning a shirt, or playing a musical instrument without vision.
53
Limitations and Future Directions
This study involved locating only one self-target (tip of the index), and we concluded
that individuals can accurately locate the endpoints of the upper limbs. The question still
remains whether this would apply to other spatial locations on the body. For instance, while a
person may be very aware of their fingertip location given its importance for many activities of
daily living, are they very aware of the location of their elbow, knee, or toe? Could potential
deficits in spatial acuity for these joints explain bumped elbows and stubbed toes? This could be
tested by repeating this task, but rather than having the voluntary fingertip locate a target
fingertip; the voluntary fingertip could be used to locate an elbow target (e.g., olecranon
process or medial condyle) or a toe-tip target. Lovelace and Aiken’s (1990) study would suggest
that localization of static locations would still be excellent (erred on average by 0.71cm for both
older and younger adults) for many facial features they studied a task in which subjects were
asked to touch features such as the nose and ear lobes while wearing a blindfold.
We did not collect EMG data from any of the participant’s target arm muscles in active
or passive conditions. Therefore, we cannot conclude that subjects were truly relaxed or that
there was no alpha-gamma coactivation during passive conditions. However, if the
experimenter felt resistance to movement from the participant, then trials were repeated. This
would minimize this potential confounding factor. If there was some resistance to movement,
that would not change the passive component of the movement because an outside force is still
driving the movement.
Another potential limitation of the present study is that we did not at any point swap
roles of the left and right finger tips. The left always served as the target, and the right always
served as the voluntary arm. Some research suggests that there is no difference in
54
proprioceptive acuity of the dominant and non-dominant arms (Lephart et al 1994, Jerosch and
Prymka 1996), though others have argued a proprioceptive advantage for the non-dominant
arm (reviewed by Goble and Brown, 2008). However, it could also be argued that a left/right
arm advantage would not give benefit to either limb in our task because participants had to
know where both fingertips were located to touch the fingertips together.
Clinical Applications
Various disorders can affect proprioception. Peripheral neuropathy, such as that caused
by diabetes mellitus, has been shown to negatively impact proprioception and postural stability
(e.g., van Deursen and Simoneau 1999). Damage to tracts in the spinal cord which carry
proprioceptive information can also cause proprioceptive deficits (Maynard et al 1997).
However, some issues have been raised with current methods of assessing proprioception in
clinical settings, concerning which tests are the best and most valid for measuring
proprioception (Han et al 2016). Currently, proprioception is most frequently measured using
joint matching paradigms, threshold to detection of passive movement, or by asking participants
to sense which direction a joint is moving. These methods do not measure proprioception in a
way that it is used in daily life. For rehabilitation, additional methods of measurement may
provide better measures of functional proprioception.
Perhaps joint matching paradigms are not the best method for measuring
proprioceptive function. Further development should be made on tests that assess the ability to
touch other body parts in the absence of vision. Moving the hand/digits to various body
locations without vision simplifies the task to something done every day (people regularly
manipulate their hair/ears/or scratch various locations on the body without vision) whereas
matching of joint angles is almost never done except in a laboratory. Our method of testing in
55
this experiment could be used in the clinic more easily than goniometers to measure joint angles
and movement threshold tests. A clinician can easily see whether or not a patient could touch
various locations on the body with precision and accuracy without vision and performance could
be scored based on whether the patient accurately touched a location (e.g., ear lobe), made a
small error (touched another part of the ear) or large error (missed the ear completely). The
motor component of moving the arm could also be removed from the test for cases where
damage to sensory and motor systems needs to be evaluated separately as a clinician could
passively move a limb and ask the patient around which body part is their limb located in the
absence of vision. A kinesthetic component could be added to this task by asking a participant to
touch a passively moved limb, or to identify which part of the body a passively moved limb is
being moved toward/near.
56
APPENDIX – ADDITIONAL STATISTICAL ANALYSES
Main Effects and Interactions: Directional Error Magnitude in Dynamic Conditions
Df
MS
df
MS
Effect
Effect Error
Error
F
p-level
pcorr
Age
1
0.24
25.00
0.22
1.10
0.30
Condition
2
0.12
50.00
0.06
2.00
0.15
Direction
2
0.81
50.00
0.09
8.77
0.00
0.00
Age x
2
0.13
50.00
0.06
2.20
0.12
Condition
Age x
2
0.01
50.00
0.09
0.10
0.91
Direction
Condition x
4
0.82
100.00 0.05
15.02
0.00
Direction
Condition x
Direction x
4
0.05
100.00 0.05
0.98
0.42
Age
Table A.1: Additional ANOVA statistics for directional error magnitude in dynamic conditions.
Pcorr is the Huynh-Feldt adjusted p-value.
57
Directional Error Magnitude: Condition by Direction Interaction
Distance Error
(cm)
VDA x X
VDA x Y
VDA x Z
0.28
0.69
0.24
VDA x X
0.00
NVDA x X
NVDA x Y
0.25
0.42
NVDA x Z
0.34
NVDP x X
0.32
NVDP x Y
0.33
NVDP x Z
0.56
1.00
1.00
0.27
0.98
0.99
0.99
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.54
1.00
0.09
0.81
0.90
0.86
0.00
0.15
0.91
0.96
0.94
0.00
0.91
0.82
0.86
0.41
1.00
1.00
0.01
1.00
0.01
VDA x Y
0.00
VDA x Z
1.00
0.00
NVDA x X
1.00
0.00
1.00
NVDA x Y
0.27
0.00
0.09
0.15
NVDA x Z
0.98
0.00
0.81
0.91
0.91
NVDP x X
0.99
0.00
0.90
0.96
0.82
1.00
NVDP x Y
0.99
0.00
0.86
0.94
0.86
1.00
1.00
NVDP x Z
0.00
0.54
0.00
0.00
0.41
0.01
0.01
0.01
0.01
Table A.2: Condition by direction interaction of the magnitude of directional distance errors. Average error (cm) for each condition is displayed in
the first row, and all other values are p-values from Tukey post-hoc tests.
58
Main Effects and Interactions: Directional Variable Errors Dynamic Conditions
df
MS
df
MS
Effect
Effect
Error
Error
F
p-level
pcorr
Age
1
0.47
25.00 0.11
4.34
0.05
<.001
Condition
2
4.49
50.00 0.06
78.19
0.00
Direction
2
0.24
50.00 0.03
7.41
0.00
0.00
Age x Condition 2
0.06
50.00 0.06
1.06
0.35
Age x Direction
2
0.02
50.00 0.03
0.59
0.56
Condition x
100.0
4
0.11
0.02
4.92
0.00
Direction
0
Condition x
100.0
4
0.03
0.02
1.45
0.22
Direction x Age
0
Table A.3: Additional ANOVA statistics for directional variable errors in dynamic conditions. Pcorr
Is the Huynh-Feldt adjusted p-value.
Directional Variable Error: Condition by Direction Interaction
Variable
Error
(cm)
VDA x X
VDA x
X
VDA x
Y
VDA x
Z
NVDA
xX
NVDA
xY
NVDA
xZ
NVDP
xX
NVDP
xY
NVDP
xZ
0.65
0.46
0.59
0.86
0.91
0.98
1.00
0.97
1.09
0.65
0.46
0.59
0.86
0.91
0.98
1.00
0.97
1.09
0.00
0.89
0.00
0.00
0.00
0.00
0.00
0.00
0.04
0.00
0.00
0.00
0.00
0.00
0.00
VDA x Y
VDA x Z
0.00
NVDA x
0.89
0.04
0.00
0.00
0.00
0.00
0.00
0.00
X
NVDA x
0.00
0.00
0.00
0.96
0.08
0.02
0.17
0.00
Y
NVDA x
0.00
0.00
0.00
0.96
0.65
0.31
0.85
0.00
Z
NVDP x
0.00
0.00
0.00
0.08
0.65
1.00
1.00
0.15
X
NVDP x
0.00
0.00
0.00
0.02
0.31
1.00
0.99
0.42
Y
NVDP x
0.00
0.00
0.00
0.17
0.85
1.00
0.99
0.07
Z
Table A.4: Condition by direction interaction of the directional variable errors. Average error (cm)
for each condition is displayed in the variable error row, and all other values a p-values from
Tukey post-hoc testing.
59
Main Effects and Interactions: Directional Error Magnitude in Static Conditions
df
MS
df
MS
Effect
Effect
Error
Error
F
p-level
p-corr
Age
1
0.96
25.00
1.26
0.76
0.39
Condition
1
2.46
25.00
0.35
7.06
0.01
Direction
2
3.63
50.00
0.47
7.65
0.00
0.00
Age x Condition 1
0.03
25.00
0.35
0.08
0.78
Age x Direction 2
1.96
50.00
0.47
4.13
0.02
0.03
Condition x
2
0.89
50.00
0.27
3.33
0.04
Direction
Condition x Age
2
0.52
50.00
0.27
1.93
0.16
x Direction
Table A.5: Additional ANOVA statistics for directional error magnitude. Pcorr Is the Huynh-Feldt
adjusted p-value.
old x old y
Directional Error
Magnitude (cm)
1.17
0.55
Age x Direction Interaction
old z
young x
1.02
0.58
young y
young z
0.57
1.13
old x
0.03
0.97
0.04
0.03
1.00
old y
0.03
0.16
1.00
1.00
0.04
old z
0.97 0.16
0.21
0.19
0.99
young x
0.04 1.00
0.21
1.00
0.04
young y
0.03 1.00
0.19
1.00
0.04
young z
1.00 0.04
0.99
0.04
0.04
Table A.6: Age x direction interaction of directional errors magnitude in static conditions. The
directional errors magnitude (cm) is in the row labeled directional error magnitude. The p-values
for Tukey post-hoc testing is displayed for each age x direction.
60
Direction x Condition
Directional Error
Magnitude (cm)
NVSA x
NVSA y
0.63
0.57
0.93
1.11
0.55
1.21
1.00
0.27
0.01
0.99
0.00
0.12
0.00
1.00
0.00
0.79
0.08
0.36
0.00
0.98
NVSA x
NVSA Z
NVSP x
NVSA y
1.00
NVSA z
0.27
0.12
NVSP x
0.01
0.00
0.79
NVSP y
0.99
1.00
0.08
0.00
NVSP z
0.00
0.00
0.36
0.98
NVSP y
NVSP z
0.00
0.00
Table A.7: Direction x condition interaction for directional errors magnitude in static conditions.
Average directional distance error (cm) for each condition/direction is displayed in the first row,
and all other values are p-values from Tukey post-hoc tests.
Main Effects and Interactions: Directional Variable Errors
Df
MS
df
MS
Effect
Effect
Error
Error
F
Age
1
0.46838
25
0.107881
4.34162
0.04757
Condition
2
4.49233
50
0.057455
78.18861
4.05E-16
Direction
2
0.236598
50
0.031929
7.410213
0.001519
p-level
Age x
2
0.060839
50
0.057455
1.058891
0.354488
Condition
Age x
2
0.018838
50
0.031929
0.590008
0.558136
Direction
Condition
4
0.105057
100
0.021332
4.924812
0.00115
x Direction
Condition
4
0.030862
100
0.021332
1.446756
x Direction
0.224231
x Age
Table A.8: Additional ANOVA statistics for directional variable errors in dynamic conditions.
61
Condition x Direction Interaction, Directional Variable Errors in Dynamic Conditions
Variable Error
(cm)
VDA, x
VA, x
VDA, y
VDA, z
NVDA, x
NVDA, y
NVDA, z
NVDP, x
NVDA, y
NVDA, z
0.65
0.46
0.59
0.86
0.91
0.98
1.00
0.97
1.09
0.00
0.89
0.00
0.00
0.00
0.00
0.00
0.00
0.04
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.96
0.08
0.02
0.17
0.00
0.65
0.31
0.85
0.00
1.00
1.00
0.15
0.99
0.42
VDA, y
0.00
VDA, z
0.89
0.04
NVDA, x
0.00
0.00
0.00
NVDA, y
0.00
0.00
0.00
0.96
NVDA, z
0.00
0.00
0.00
0.08
0.65
NVDP, x
0.00
0.00
0.00
0.02
0.31
1.00
NVDP, y
0.00
0.00
0.00
0.17
0.85
1.00
0.99
NVDP, z
0.00
0.00
0.00
0.00
0.00
0.15
0.42
0.07
0.07
Table A.9: Condition by direction interaction for directional variable errors in dynamic conditions. Average variable error (cm) for each condition
and direction is displayed in the first row, and all other values are p-values from Tukey post-hoc tests.
62
Additional workspace size ANOVA statistics
Main Effects and Interactions: Workspace Size
df
MS
df
MS
Effect
Effect
Error
Error
F
p-level
pcorr
Age
1
2.6E+10 25
2.58E+10 1.00
0.325
Condition
4
2.07E+11 100
8.64E+09 23.94
0.000 <.001
Age x Condition 4
4.23E+10 100
8.64E+09 4.89
0.001
0.008
Table A.10: Workspace size ANOVA statistics. Pcorr is the Huynh-Feld corrected p-value. The main
condition effect is not important because the differences were between active and passive
conditions. The experimenter determined the workspace size by the imposed movements in
passive conditions.
63
Mean
(cm3)
Old
Old
Old
Old
Old
Vision
Old
Active
197445.5
230142.3
Age by Condition post-hoc (Tukey unequal HSD)
Old
Old
Old
Young
Young
Passive
Static
Static
Vision
Active
Active
Passive
271038.3 178014.7 93019.34 340327.1 300860.4
Young
Passive
274856.6
Young
Static
Active
124336.3
Young
Static
Passive
68130.88
Vision
0.996
0.589
1.000
0.130
0.006
0.139
0.516
0.598
0.020
Active
0.996
0.981
0.915
0.010
0.088
0.643
0.966
0.119
0.001
Passive
0.589
0.981
0.255
0.000
0.669
0.998
1.000
0.004
0.000
Static
Active
1.000
0.915
0.255
0.379
0.001
0.034
0.207
0.900
0.090
Old
Static
Passive
0.130
0.010
0.000
0.379
0.000
0.000
0.000
0.997
1.000
Young Vision
0.006
0.088
0.669
0.001
0.000
0.981
0.693
0.000
0.000
Young Active
0.139
0.643
0.998
0.034
0.000
0.981
0.999
0.000
0.000
Young Passive
0.516
0.966
1.000
0.207
0.000
0.693
0.999
0.002
0.000
Young Static
Active
0.598
0.119
0.004
0.900
0.997
0.000
0.000
0.002
0.845
Young Static
Passive
0.020
0.001
0.000
0.090
1.000
0.000
0.000
0.000
0.845
Table A.11: Age by condition interaction post-hoc testing for workspace ANOVA. Average error (cm3) for each condition/age group is displayed in
the first row, and all other values are p-values from Tukey post-hoc tests.
64
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