The authors thank Xiaode Chen for digital technology support.

The development of the dTMT and initial application was approved by The Seventh Medical Center of PLA Army General Hospital Review Board. Subjects signed approved informed consent documents prior to testing TMT.
1. General Method Development
<ol>
	<li>Use a tablet (e.g., Microsoft Surface Pro 2) with high-quality inertial sensors embedded within the device and a compatible electronic pen (Figure 1).</li>
	<li>Use the Intelligent Device for Energy Expenditure and Activity (IDEEA) monitor, composed of five sensors (each 16 x 14 x 4 mm3, 2 g), with one attached over the sternum, two attached to the front side of each thigh, and the other two attached under each foot. Connect the sternum and thigh sensors via a solid cable to a small 32-bit microprocessor (70 x 44 x 18 mm3, 59 g), and wire the foot sensors (Figure 2).</li>
</ol>
2. Design and Testing of the dTMT
NOTE: As mentioned earlier, dTMT has two parts: dTMT-A and dTMT-B. These two tests should be performed sequentially (dTMT-A proceeding dTMT-B), without being reversed.
<ol>
	<li>dTMT-A procedure
	<ol>
		<li>Carry out the dTMT-A in a quiet and comfortable environment. 
		NOTE: Participants enrolled to complete dTMT should have the educational level of more than 2 years of preliminary school; otherwise, they might have difficulty in reading and recognizing Chinese characters in dTMT-B. Meanwhile, ensure that the participants have no obvious visual and upper limb disability.</li>
		<li>Ask the participants to sit in front of a desk, and adjust the computer position, background light, and the electronic pen.</li>
		<li>Check the near visual acuity of participants to ensure that they can easily read the numbers on the screen. 
		NOTE: Some aged subjects maybe need a pair of glass in case that the circles on the screen are too small for the subjects with presbyopia.</li>
		<li>Show the instructions of dTMT-A as follows: Please draw a line as rapidly as possible joining consecutive numbers (i.e., 1-&gt;2-&gt;3…9) in the circles randomly distributed on the screen. A pre-test trial (150 s maximum) is necessary because most participants need to familiarize how to draw on the surface of a computer.</li>
		<li>Demonstrate the major differences between dTMT-A and standard TMT-A. First, if the circle is correctly lined, its color can be changed. Second, if the circle is not correctly lined, its color remains unchanged, and the subjects need to re-line it from the last circle. 
		NOTE: Connecting all the circles fluently with straight lines is encouraged.</li>
		<li>Advise the participants to avoid errors and time wastage. Encourage the participants to draw the line fluently, but as accurately as possible; however, give no priority.</li>
		<li>Ask the participants to select PartA on the screen (Figure 1 lower panel) to complete dTMT-A without interruption. All the dTMT-A data are gathered on the computer automatically. 
		NOTE: If data are collected for investigating intermanual differences, one more test needs to be carried out with the other hand. The sequence of left-/right- hand test is at random.</li>
	</ol>
	</li>
	<li>dTMT-B procedure
	<ol>
		<li>Repeat step 2.1.</li>
		<li>Show the instructions of dTMT-B as follows: Please draw a line as rapidly as possible joining the numbers and Chinese characters (i.e., 1-&gt;<img alt="Molecular structure diagram with atomic models depicting pyrolysis process for materials research." src="/files/ftp_upload/60456/img1.jpg" style="opacity: 0.9;" data-altupdatedat="1747911167000" data-alt="graphic 1">-&gt;2-&gt;<img alt="Chromatography system; chemical separation process; flow diagram; analytical method." src="/files/ftp_upload/60456/img2.jpg" style="opacity: 0.9;" data-altupdatedat="1747911167000" data-alt="graphic 1">…<img alt="Graph theory; adjacency matrix; mathematical representation; connectivity; vertex-edge relation." src="/files/ftp_upload/60456/img3.jpg" style="opacity: 0.9;" data-altupdatedat="1747911167000" data-alt="graphic 1">) alternatively in the circles randomly distributed on the screen. 
		NOTE: Make sure all the Chinese characters are recognized by subjects. A pre-test trial (150 s maximum) is also necessary because some participants need to familiarize how to draw in the numbers and Chinese characters alternatively on their own.</li>
		<li>Ask the subjects to select PartB on the screen (Figure 1 lower panel) to complete dTMT-B without interruption. All the dTMT-B data are gathered in computer automatically. 
		NOTE: If data are collected for investigating intermanual differences, one more test needs to be carried out with the other hand. The sequence of left-/right- hand test is at random.</li>
	</ol>
	</li>
</ol>
3. Direct Data Collection and Definitions in dTMT
<ol>
	<li>Determine the total time to completion: the time taken (ms) to draw a line connecting all circles in the correct order.</li>
	<li>Determine the number of errors: the number of times a line is drawn to a circle in the incorrect order.</li>
	<li>Determine the time to completion for each step: the time taken in milliseconds to draw each step.</li>
	<li>Determine the time inside each circle: the time spent in milliseconds to draw inside circles.</li>
	<li>Determine the inside circle percentage (%): time inside each circle divided by total time to completion.</li>
	<li>Determine the time inside each tolerance circle: the time spent in milliseconds to draw inside tolerance circles.</li>
	<li>Determine the inside circle tolerance percentage (%): time inside each tolerance circle divided by total time to completion</li>
	<li>Determine the line canceling times in each step: the times a line is canceled in each step. The tolerance circle has a diameter five times more than that of a real circle.</li>
	<li>Determine the optimal pathway of each step: the nearest line in millimeters of each step.</li>
	<li>Determine the actual pathway of each step: the actual line in millimeters of each step.</li>
	<li>Determine the pathway deviation of each step: the actual line in millimeters minus the nearest line in millimeters of each step.</li>
	<li>Determine the variability of pathway deviation: Coefficient of the variation of the pathway deviation of each step.</li>
	<li>Determine the velocity of drawing of each step: the actual line in millimeters of each step divided by the time to completion for each step. 
	NOTE: The average value was calculated by summing up the values collected step by step. Indirect data reflecting different points between hands or parts were derived based on the direct data.</li>
</ol>
4. Design and Testing of the WTMT
NOTE: Similar to dTMT, WTMT also has two parts: WTMT-A and WTMT-B. These two tests should be performed sequentially (WTMT-A proceding WTMT-B), without being reversed.
<ol>
	<li>WTMT-A procedure
	<ol>
		<li>Carry out WTMT-A in a quiet and comfortable environment. Ensure that there is room light. Randomly distribute coins with numbers at each of 15 positions in a 16 m2 area (4 x 4 m2). Draw a 30 cm diameter around each coin (Figure 3). 
		NOTE: The participants enrolled to complete WTMT should have the educational level of more than 2 years of preliminary school; otherwise, they might have difficulty in reading and recognizing Chinese characters in WTMT-B. Meanwhile, ensure that the participants have no obvious visual and lower limb disability.</li>
		<li>Connect the Intelligent Device for Energy Expenditure and Activity (IDEEA) to the PC and enter the subject's anthropometric data.</li>
		<li>Attach five biaxial mini-accelerometers (16 x 14 x 4 mm3, 2 g) with medical tape over the sternum, to the front side of each thigh and under each foot (Figure 4). Connect all the accelerometers through thin, flexible cables to a microprocessor/storage unit (70 x 44 x 18 mm3, 59 g) attached with a clip to the clothes. 
		NOTE: The IDEEA is a multiple accelerometer-based system comprising five biaxial accelerometers located on the upper trunk, thighs and feet. The IDEEA was initially developed to estimate energy expenditure during activities of daily living22,23, but has an additional capability to quantify many of the commonly used gait cycle parameters24.</li>
		<li>After the device is equipped, ask participants to walk up and down a walkway without any targets at a comfortable walking speed to warmup.</li>
		<li>Show the instructions of WTMT-A as follows: Please walk on numbered targets in a sequential order as rapidly as possible joining consecutive numbers (i.e., 1-&gt;2-&gt;3…15) in the coins randomly distributed on the floor.</li>
		<li>Encourage the participants to walk fluently, but as accurately as possible; However, no priority is given. Perform WTMT-A only once.</li>
		<li>Ensure the safety of the participants, because dual-task walking in a challenging environment may increase the risk of falling25. For both pre- and post- tests, a 5 s step pause is needed for IDEEA to discriminate walking from standing. 
		NOTE: Either footstep on the coin is considered as on the target. If the participants walk in the wrong order, guide them until they walk in the right order. All the WTMT-A data are gathered in the IDEEA microprocessor/storage unit automatically.</li>
	</ol>
	</li>
	<li>WTMT-B procedure
	<ol>
		<li>Repeat the steps as in Section 4.1.1.</li>
		<li>Show the instructions of WTMT-A as follows: Please walk on numbered targets in a sequential order as rapidly as possible joining consecutive numbers (i.e., 1-&gt;<img alt="Chromatography setup diagram; protein separation process; solvent flow and sample injection points." src="/files/ftp_upload/60456/img4.jpg" style="opacity: 0.9;" data-altupdatedat="1747911167000" data-alt="graphic 1">-&gt;2-&gt;<img alt='Chemical symbol equation, diagram; educational, symbol for "Er" chemical identification and learning.' src="/files/ftp_upload/60456/img5.jpg" style="opacity: 0.9;" data-altupdatedat="1747911167000" data-alt="graphic 1">…<img alt="Drying curve diagram; moisture content vs. time chart for material drying analysis." src="/files/ftp_upload/60456/img6.jpg" style="opacity: 0.9;" data-altupdatedat="1747911167000" data-alt="graphic 1">&gt;8) in the coins randomly distributed on the floor. Make sure all the Chinese characters are recognized by the participants.</li>
		<li>Perform WTMT-B only once.</li>
		<li>Ensure the safety of the participants, because dual-task walking in a challenging environment may increase the risk of fallings25. For both pre- and post- tests, a 5 s step pause is needed for IDEEA to discriminate walking from standing. 
		NOTE: Either footstep on the coin is considered as on the target. If the subjects walked in the wrong order, guide them until they walk in the right order. All the WTMT-B data are gathered in the IDEEA microprocessor/storage unit automatically.</li>
	</ol>
	</li>
</ol>
5. Direct Data Collection and Meaning Explanation in WTMT
NOTE: As shown in Figure 5, the human gait cycle has been divided into different subphases. In detail, spatial and temporal parameters are defined and calculated as follows.
<ol>
	<li>Determine the steps (n): the number of steps completed during level walking, including the right and left limbs.</li>
	<li>Determine the swing duration (%): the phase percentage starting from toe-off until initial ground or stair contact for any given foot.</li>
	<li>Determine the stance duration (%): phase percentage between the heel strike of one foot and the heel strike of the contra-lateral foot.</li>
	<li>Determine the speed (m/s): the average velocity over two consecutive strides.</li>
	<li>Determine the step length (m): the difference in length between the initial heel strike of the right or left foot and the heel strike of the contralateral foot.</li>
	<li>Determine the stride length (m): the distance between the successive points of the initial contact of the same foot, right-left-right (R-L-R) or left-right- left (L-R-L).</li>
	<li>Determine the gait variability of step length: coefficient of the variation of step length. 
	NOTE: Completion time and errors are also collected and counted by the examiner, instead of IDEEA.</li>
</ol>
6. Data Collection and Statistics
<ol>
	<li>Use one-way-ANOVA and Fisher’s LSD to compare the differences between the groups. The demographic data are listed in Table 1. dTMT-A, dTMT-B, WTMT-A, and WTMT-B data are shown in Tables 2-5 respectively. A P &lt; 0.05 was considered to indicate a statistically significant difference.</li>
</ol>

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The authors have nothing to disclose.

Traditional paper-pencil TMT has been well used worldwide for more than 50 years. However, digital TMT is advantageous. First, traditional TMT is considered as an executive function tool, while both dTMT and WTMT have aspects reflecting motor ability besides cognitive function. Considering that the cognitive-motor dual task has gained great attention in recent years26, digital technologies can provide researchers with more information on this integrated task compared with the traditional TMT27. Second, digital TMT is a sensitive tool compared with the traditional version. Digital TMT does not need additional time relative to traditional ones, which has enough compliance of subjects.
A critical step in the protocol is to perform dTMT and WTMT with no interruption, because both tests collected time variables. Subjects need to complete the tests fluently. Any delay induced by examiners, or misunderstanding, distraction, etc., should be minimized or eliminated.
There are two modifications to be mentioned. First, for dTMT, the real-time pressure of the stylus onto the screen is a sensitive variable for drawing, which has been confirmed in a digital Clock Drawing Test28. With more development, software that could detect the stylus pressure onto the screen during dTMT will give physicians more information in future. Second, for WTMT, a new device that can detect and analyze trunk sway might be helpful to find more evidence in movement disorder patients29,30, because IDEEA only provides gait data. However, as far as we know, IDEEA is the first digital accelerometry used in WTMT.
The current study introduced two types of TMTs in a digitized version. These new types of TMTs were derived, rather than being an exact copy of the traditional TMT. Robert P. Fellows found that the computerized TMT needed fewer circles compared to the traditional TMT, in case the circles were too crowded31. However, this difference cannot impede the wide use of the digital TMT in the future.
Since digital technology is becoming more and more popular in our daily life, digital devices should be used in early diagnosis of cognitive disorders and movement disorders32. dTMT and WTMT are both derived from traditional TMT but can capture more variables than the paper-based TMT. Both new modified TMTs could be used to screen patients with cognitive disorders and movement disorders. Particularly for those patients with upper limb disability, WTMT is particularly useful.
A limitation of the present study was its small sample size. Consequently, the sensitivity and specificity of digital TMT could be demonstrated. However, dTMT and WTMT could find additional information for the physicians to determine the cognitive function and motor ability of the participants. However, more studies are needed to validate the findings.

With a rapidly aging population, dementia is considered to be a major public health concern. The number of elderly patients with dementia worldwide is about 47 million according to the World Health Organization1. Executive function impairment is not only a common type of cognitive dysfunction in aged individuals, but has been reported as a predictor of progression from mild cognitive impairment (MCI) to clinical Alzheimer&#8217;s disease (AD)2,3. As the third most widely used test in neuropsychology4, the Trail Making Test (TMT) is employed as a well-accepted tool to evaluate executive functions, especially sustained attention and set-shifting5, even in elderly patients6.
The standard TMT is a paper-pencil test consisting of two parts: tMT-A and TMT-B5. The former calls for the test-taker to draw lines connecting randomly distributed numbers (1&#8211;25) on a test paper in ascending order (1-&#62;2-&#62;3&#8230;), whereas the latter requires the test-taker to set numbers and letters (1-&#62;A-&#62;2-&#62;B&#8230;) alternatively. The performance of TMT is generally scored in the time taken to complete each part correctly7. TMT has been translated into different languages. The Chinese version of TMT was developed in 20068. Since Chinese characters are quite distinct from English letters, the Chinese version of TMT was used in our procedure.
Apart from the standard version, TMT has been modified in different ways by researchers (e.g., oral TMT9, driving TMT10, walking TMT (WTMT)11) to assess specific populations or find details under different conditions, such as driving and walking. Of note, some studies conferring different numbers compared with the standard TMT are also reported to be of high validity and reliability. For example, THINC-Integrated Tool (THINC-it) developed by the McIntyre group used 9 numbers and letters for TMT-B12; WTMT reported by Schott and colleagues used 15 numbers for TMT-A13. In the same way, many evaluating systems of TMT have been built beyond the complete time scoring, which are reported to be helpful in finding more items besides executive dysfunction, or to be accessible for participants who are not suitable to complete the standard TMT. For example, some researchers investigated the errors in TMT and found that errors in TMT-B were associated with mental tracking and working memory in patients with psychiatric disorder14. Another group from Greece suggested derived TMT scores [TMT-(B&#8722;A) or TMT(B/A)] as indices to detect impairment in cognitive flexibility across the adult life span15. Generally, alternative evaluating systems of TMT can be summarized as follows: (1) completion time analysis&#8212;TMT completion time is calculated in seconds16; (2) error analysis&#8212;different types of TMT errors are classified and quantified14; (3) intermanual differences&#8212;different abilities of completing TMT between the dominant hand and the nondominant hand are compared17; and (4) derived Trail Making Test indices&#8212;different characterizations between completing TMT-A and TMT-B are analyzed15. The alternative scoring methods provide additional information. For example, the utility of TMT error analysis could reveal cognitive deficits not traditionally captured using completion time as the sole outcome variable in patients with schizophrenia and depression14. The lack of any significant intermanual difference helped to discriminate the cognitive dysfunction from the influence of the motor disorder17. Derived TMT indices could detect impairment in cognitive flexibility across the adult life span and minimize the effect of demographics and other cognitive background variables15.
With advances in modern technology, computer-based digital applications have been increasingly integrated into traditional cognitive interventions, most of which are designed as similar to the original test as possible, rather than created as new tools. Digital or computerized TMT (dTMT) has been proven to have the potential to capture additional information, with the structure of the existing test mainly unchanged in recent years18,19.
This study aimed to introduce a computer-based Chinese version of dTMT-A and dTMT-B, as well as a WTMT. Both are modified TMTs and have been confirmed to have high sensitivity and specificity to screen patients with MCI, Parkinson&#8217;s Disease, Alzheimer&#8217;s Disease, and so forth, based on the movement of upper and lower limbs20,21. Detailed scoring methods were also presented because digital technologies incorporated into dTMT and WTMT might help capture more information compared to the paper-pencil version of TMT.

<table><tbody><tr><td>Minisun</td><td>LLC</td><td></td><td>Intelligent Device for Energy Expenditure and Activity (IDEEA)</td></tr><tr><td>Surface Pro 2</td><td>Microsoft</td><td></td><td>computer</td></tr></tbody></table>

traditional trail making test modified into brand-new assessment tools: digital and walking trail making test

The Trail Making Test (TMT) is a well-accepted tool for evaluating executive function. The standard TMT was invented more than 60 years ago and has been modified into many versions. With the development of digital technologies, TMT is now modified to a digitized version. The present study demonstrated digital TMT (dTMT) performed on a computer, and Walking TMT (WTMT) on the floor. Both revealed more information compared with the traditional version of TMT.

Seven aged patients with Mild Cognitive Impairment (Elderly with MCI), seven aged subjects with Parkinson’s Disease (Elderly with PD), and seven aged healthy individuals (Healthy Elderly) were recruited, and dTMT-A, dTMT-B, WTMT-A, and WTMT-B, were performed. After the tests, data were collected and analyzed using SPSS software.
As a whole, the demographical data of participants showed that all groups were matched well in terms of age, gender, educational level, dominant hand, Clinical Dementia Rating (CDR) score, Global Deterioration Scale (GDS) score, TUG: timed Up and Go Test (TUG), and so forth (p &gt; 0.05).
As shown in Table 2, most of the data of dTMT-A between Healthy Elderly, Elderly with MCI, and Elderly with PD were similar, such as Total time to completion (18.15 ± 5.12 s vs. 19.67 ± 7.12 s vs. 19.85 ± 3.89, P = 0.812), Number of Errors (0.14 ± 0.38 vs. 0.29 ± 0.49 vs. 0.29 ± 0.49, P = 0.796), and so forth. This means all the participants had similar scores if they are assessed by traditional TMT-A. However, there existed some different variables captured by dTMT-A. As shown in Table 2, Elderly with PD exhibited a larger total pathway deviation of each step (Pb = 0.017, Pc = 0.048), a larger variability of pathway deviation (Pb = 0.000, Pc = 0.000), and a lower velocity of drawing of each step (Pb = 0.001, Pc = 0.025) compared with Elderly with MCI and Healthy Elderly, respectively.
As shown in Table 3, the differences in completing dTMT-B were reflected in more aspects relative to dTMT-A. Aged patients with MCI needed a longer time of completion (P = 0.000) and had more errors (P = 0.000), more time inside the circle (P = 0.000) or tolerance circle (P = 0.000), more pathway deviation (P = 0.035), and lower velocity in drawing (P = 0.000) compared with healthy elderly. Meanwhile, Elderly with PD needed a longer time of completion (P = 0.000), and had more errors (P = 0.000), more time inside the circle (0.000) but less time inside the tolerance circle (P = 0.000), more pathway deviation (P = 0.032), larger variability of pathway deviation (P = 0.001), and obviously lower velocity of drawing of each step (P = 0.000) compared with aged healthy individuals. All the results indicated that dTMT can detected amount of significant differences between aged healthy participants and aged patients.
As shown in Table 4, gait data in WTMT-A could detect more differences between Elderly with PD in comparison with other individuals, especially in terms of speed (Pb = 0.000, Pc = 0.002), step length (Pb = 0.004, Pc = 0.016), stride length (Pb = 0.005, Pc = 0.019), and so forth. All these data implied that WTMT-A could capture obvious differences between aged PD patients and aged healthy participants.
As shown in Table 5, gait data in WTMT-B could find more differences between groups. Aged patients with MCI and PD needed a longer time (Pa = 0.001, Pb = 0.000) and more steps to complete the test (Pa = 0.000, Pb = 0.000). Their step and stride length seemed shorter relative to aged healthy participants. In addition, aged patients with PD showed even more severe trend in comparison with MCI subjects. The marked differences are step length (0.045 m ± 0.02 vs. 0.049 m ± 0.02, Pc = 0 .002), stride length (0.91 m ± 0.04 vs. 0.96 m ± 0.03, Pc = 0.012), and Gait variability of step length (0.112 ± 0.0030 vs. 0.120 ± 0.0034, Pc = 0.000).
<img alt="Trail Making Test (TMT) interface on tablet; cognitive assessment software with keyboard setup." class="xfigimg" src="/files/ftp_upload/60456/60456fig1.jpg" data-altupdatedat="1747911167000" data-alt="Figure 1"> 
Figure 1: Computer. Computer for dTMT-A and dTMT-B (upper panel), print screen of dTMT, subjects choose Part A to start dTMT-A, or Part B to start dTMT-B (lower panel). <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/60456/60456fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt='&lt;!DOCTYPE html&gt;
&lt;html lang="en"&gt;
&lt;head&gt;
&lt;meta charset="UTF-8"&gt;
&lt;title&gt;Alt Text Example&lt;/title&gt;
&lt;/head&gt;
&lt;body&gt;
Electrophysiology measurement setup with control unit and electrodes for data analysis.
&lt;/body&gt;
&lt;/html&gt;' class="xfigimg" src="/files/ftp_upload/60456/60456fig2.jpg" data-altupdatedat="1747911167000" data-alt="Figure 2"> 
Figure 2: IDEEA. Device for WTMT-A and WTMT-B. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/60456/60456fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="WTMT-A and WTMT-B maze diagram comparison; pathfinding analysis and strategy visualization." class="xfigimg" src="/files/ftp_upload/60456/60456fig3.jpg" data-altupdatedat="1747911167000" data-alt="Figure 3"> 
Figure 3: Example of WTMT-A and WTMT-B. As shown in the figure, subjects need to begin from START and walk to the END. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/60456/60456fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Human motion sensors on body for gait analysis; wearable tech for movement study." class="xfigimg" src="/files/ftp_upload/60456/60456fig4.jpg" data-altupdatedat="1747911167000" data-alt="Figure 4"> 
Figure 4: IDEEA accelerometers and the location. The figure showed how to wear the IDEEA accelerometers correctly. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/60456/60456fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Gait cycle diagram; phases labeled with new and classic terms; stance and swing phases shown." class="xfigimg" src="/files/ftp_upload/60456/60456fig5.jpg" data-altupdatedat="1747911167000" data-alt="Figure 5"> 
Figure 5: Human gait cycle divided into different subphases. Stand phase was about 60% of gait cycle, and Swing phase was about 40% of gait cycle. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/60456/60456fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td rowspan="2"></td>
			<td>Healthy Elderly</td>
			<td>Elderly with MCI</td>
			<td>Elderly with PD</td>
			<td rowspan="2">P Value</td>
		</tr>
		<tr>
			<td>N = 7</td>
			<td>N = 7</td>
			<td>N = 7</td>
		</tr>
		<tr>
			<td>Age</td>
			<td>67.14 ± 4.22</td>
			<td>65.14 ± 3.39</td>
			<td>66.29 ± 3.90</td>
			<td>0.63</td>
		</tr>
		<tr>
			<td>Gender(M:F)</td>
			<td>4:03</td>
			<td>5:02</td>
			<td>4:03</td>
			<td>0.589</td>
		</tr>
		<tr>
			<td>Dominant hand(R%)</td>
			<td>100</td>
			<td>100</td>
			<td>100</td>
			<td></td>
		</tr>
		<tr>
			<td>Education (yrs)</td>
			<td>10.00 ± 1.91</td>
			<td>11.43 ± 2.51</td>
			<td>10.14 ± 1.36</td>
			<td>0.353</td>
		</tr>
		<tr>
			<td>MMSE</td>
			<td>29.00 ± 1.15</td>
			<td>27.86 ± 1.35</td>
			<td>28.43 ± 1.27</td>
			<td>0.263</td>
		</tr>
		<tr>
			<td>CDR</td>
			<td>0.14 ± 0.24</td>
			<td>0.5 ± 0.00</td>
			<td>0.29 ± 0.39</td>
			<td>0.066</td>
		</tr>
		<tr>
			<td>GDS</td>
			<td>2.28 ± 0.49</td>
			<td>2.71 ± 0.76</td>
			<td>2.29 ± 0.75</td>
			<td>0.487</td>
		</tr>
		<tr>
			<td>TUG (S)</td>
			<td>10.07 ± 1.51</td>
			<td>11.02 ± 0.60</td>
			<td>11.72 ± 1.24</td>
			<td>0.052</td>
		</tr>
	</tbody>
</table>
Table 1: Demographic data of participants. Mean ± SD. M:F = Male: Female; R% = Right hand percentage; yrs = years; MMSE = Mini Mental State Examination.; MCI = Mild Cognitive Impairment; PD = Parkinson’s Disease; CDR = Clinical Dementia Rating; GDS = Global Deterioration Scale; TUG = timed Up and Go Test; S = Seconds
<table border="1" fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td rowspan="2"></td>
			<td>Healthy Elderly</td>
			<td>Elderly with MCI</td>
			<td>Elderly with PD</td>
			<td rowspan="2">P Value</td>
		</tr>
		<tr>
			<td>N = 7</td>
			<td>N = 7</td>
			<td>N = 7</td>
		</tr>
		<tr>
			<td>Total time to Completion</td>
			<td>18.15 ± 5.12</td>
			<td>19.67 ± 7.12</td>
			<td>19.85 ± 3.89</td>
			<td>0.821</td>
		</tr>
		<tr>
			<td>Number of Errors</td>
			<td>0.14 ± 0.38</td>
			<td>0.29 ± 0.49</td>
			<td>0.29 ± 0.49</td>
			<td>0.796</td>
		</tr>
		<tr>
			<td>Total time inside each circle</td>
			<td>6.94 ± 1.99</td>
			<td>6.91 ± 3.31</td>
			<td>7.81 ± 2.46</td>
			<td>0.773</td>
		</tr>
		<tr>
			<td>Inside circle percentage</td>
			<td>39.13 ± 7.70</td>
			<td>35.42 ± 10.25</td>
			<td>40.02 ± 11.63</td>
			<td>0.665</td>
		</tr>
		<tr>
			<td>Total time inside each tolerance circle</td>
			<td>1.57 ± 0.80</td>
			<td>2.09 ± 0.88</td>
			<td>1.85 ± 0.49</td>
			<td>0.442</td>
		</tr>
		<tr>
			<td>Inside tolerance circle percentage</td>
			<td>8.74 ± 3.02</td>
			<td>10.80 ± 3.07</td>
			<td>9.61 ± 3.55</td>
			<td>0.498</td>
		</tr>
		<tr>
			<td>Total Line cancelling times</td>
			<td>0.14 ± 0.38</td>
			<td>0.29 ± 0.49</td>
			<td>0.14 ± 0.38</td>
			<td>0.764</td>
		</tr>
		<tr>
			<td>Total pathway deviation of each step</td>
			<td>38.41 ± 2.52</td>
			<td>39.30 ± 3.07</td>
			<td>42.99 ± 3.99b, c</td>
			<td>0.039</td>
		</tr>
		<tr>
			<td>Variability of pathway deviation</td>
			<td>1.72 ± 0.24</td>
			<td>2.36 ± 0.55a</td>
			<td>3.66 ± 0.46b, c</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Velocity of drawing of each step</td>
			<td>21.38 ± 2.59</td>
			<td>19.00 ± 2.40</td>
			<td>15.70 ± 2.55b, c</td>
			<td>0.002</td>
		</tr>
	</tbody>
</table>
Table 2: dTMT-A data of participants. Mean ± SD. MCI = Mild Cognitive Impairment; PD = Parkinson’s Disease. One-way-ANOVA and post hoc analysis with LSD. a = P &lt; 0.05 Elderly with MCI relative to Healthy Elderly; b = P &lt; 0.05 Elderly with PD relative to Healthy Elderly; c = P &lt; 0.05 Elderly with PD relative to Elderly with MCI.
<table border="1" fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td rowspan="2"></td>
			<td>Healthy Elderly</td>
			<td>Elderly with MCI</td>
			<td>Elderly with PD</td>
			<td rowspan="2">P Value</td>
		</tr>
		<tr>
			<td>N = 7</td>
			<td>N = 7</td>
			<td>N = 7</td>
		</tr>
		<tr>
			<td>Total time to Completion</td>
			<td>32.07 ± 10.93</td>
			<td>67.56 ± 9.87a</td>
			<td>89.95 ± 12.12b,c</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Number of Errors</td>
			<td>0.14 ± 0.38</td>
			<td>2.86 ± 1.07a</td>
			<td>1.29 ± 0.49b,c</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Total time inside each circle</td>
			<td>6.03 ± 1.72</td>
			<td>27.83 ± 5.05a</td>
			<td>7.81 ± 2.46b,c</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Inside circle percentage(%)</td>
			<td>19.16 ± 3.86</td>
			<td>41.47 ± 6.76a</td>
			<td>22.46 ± 3.35c</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Total time inside each tolerance circle</td>
			<td>3.51 ± 0.91</td>
			<td>9.73 ± 1.46a</td>
			<td>3.93 ± 2.21c</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Inside tolerance circle percentage(%)</td>
			<td>11.26 ± 2.20</td>
			<td>14.47 ± 1.62a</td>
			<td>4.57 ± 2.86b,c</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Total Line cancelling times</td>
			<td>0.29 ± 0.38</td>
			<td>0.86 ± 1.07</td>
			<td>0.43 ± 0.53</td>
			<td>0.35</td>
		</tr>
		<tr>
			<td>Total pathway deviation of each step</td>
			<td>86.02 ± 7.36</td>
			<td>95.36 ± 6.76a</td>
			<td>95.56 ± 8.78b</td>
			<td>0.051</td>
		</tr>
		<tr>
			<td>Variability of pathway deviation</td>
			<td>2.158 ± 0.173</td>
			<td>2.024 ± 0125</td>
			<td>2.659 ± 0.332b,c</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Velocity of drawing of each step</td>
			<td>16.85 ± 1.79</td>
			<td>8.41 ± 1.09a</td>
			<td>4.91 ± 0.91b, c</td>
			<td>0</td>
		</tr>
	</tbody>
</table>
Table 3: dTMT-B data of participants. Mean ± SD. MCI = Mild Cognitive Impairment; PD = Parkinson’s Disease. One-way-ANOVA and post hoc analysis with LSD. a = P &lt; 0.05 Elderly with MCI relative to Healthy Elderly; b = P &lt; 0.05 Elderly with PD relative to Healthy Elderly; c = P &lt; 0.05 Elderly with PD relative to Elderly with MCI.
<table border="1" fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td rowspan="2"></td>
			<td>Healthy Elderly</td>
			<td>Elderly with MCI</td>
			<td>Elderly with PD</td>
			<td rowspan="2">P Value</td>
		</tr>
		<tr>
			<td>N = 7</td>
			<td>N = 7</td>
			<td>N = 7</td>
		</tr>
		<tr>
			<td>Total time to Completion</td>
			<td>68.43 ± 4.86</td>
			<td>76.57 ± 7.66</td>
			<td>98.29 ± 9.36b,c</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Number of Errors</td>
			<td>0.29 ± 0.49</td>
			<td>0.29 ± 0.49</td>
			<td>0.57 ± 0.53</td>
			<td>0.487</td>
		</tr>
		<tr>
			<td>Steps (n)</td>
			<td>80.86 ± 2.34</td>
			<td>81.29 ± 3.30</td>
			<td>81.71 ± 3.90</td>
			<td>0.886</td>
		</tr>
		<tr>
			<td>Swing duration (%)</td>
			<td>36.86 ± 1.32</td>
			<td>35.03 ± 0.84a</td>
			<td>35.48 ± 1.25b</td>
			<td>0.022</td>
		</tr>
		<tr>
			<td>Step duration (%)</td>
			<td>63.00 ± 1.35</td>
			<td>64.97 ± 0.84 a</td>
			<td>64.52 ± 1.25b</td>
			<td>0.014</td>
		</tr>
		<tr>
			<td>Speed (m/s)</td>
			<td>1.01 ± 0.10</td>
			<td>0.82 ± 0.57a</td>
			<td>0.68 ± 0.04b,c</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Step length (m)</td>
			<td>0.51 ± 0.02</td>
			<td>0.50 ± 0.01</td>
			<td>0.49 ± 0.02b,c</td>
			<td>0.01</td>
		</tr>
		<tr>
			<td>Stride length (m)</td>
			<td>1.02 ± 0.04</td>
			<td>1.00 ± 0.02</td>
			<td>0.96 ± 0.04b,c</td>
			<td>0.011</td>
		</tr>
		<tr>
			<td>Gait variability of step length</td>
			<td>0.111 ± 0.0011</td>
			<td>0.112 ± 0.0011</td>
			<td>0.113 ± 0.0014</td>
			<td>0.156</td>
		</tr>
	</tbody>
</table>
Table 4: WTMT-A data of participants. Mean ± SD. MCI = Mild Cognitive Impairment; PD = Parkinson’s Disease. One-way-ANOVA and post hoc analysis with LSD. a = P &lt; 0.05 Elderly with MCI relative to Healthy Elderly; b = P &lt; 0.05 Elderly with PD relative to Healthy Elderly; c = P &lt; 0.05 Elderly with PD relative to Elderly with MCI.
<table border="1" fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td rowspan="2"></td>
			<td>Healthy Elderly</td>
			<td>Elderly with MCI</td>
			<td>Elderly with PD</td>
			<td rowspan="2">P Value</td>
		</tr>
		<tr>
			<td>N = 7</td>
			<td>N = 7</td>
			<td>N = 7</td>
		</tr>
		<tr>
			<td>Total time to Completion</td>
			<td>78.57 ± 4.86</td>
			<td>92.29 ± 7.72a</td>
			<td>109.00 ± 5.66b,c</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Number of Errors</td>
			<td>0.57 ± 0.79</td>
			<td>1.14 ± 1.07</td>
			<td>0.86 ± 0.69</td>
			<td>0.479</td>
		</tr>
		<tr>
			<td>Steps (n)</td>
			<td>89.71 ± 2.63</td>
			<td>96.71 ± 2.29a</td>
			<td>100.57 ± 3.74b,c</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Swing duration (%)</td>
			<td>37.20 ± 1.21</td>
			<td>36.56 ± 1.23</td>
			<td>36.47 ± 1.15</td>
			<td>0.476</td>
		</tr>
		<tr>
			<td>Step duration (%)</td>
			<td>62.80 ± 1.21</td>
			<td>63.44 ± 1.23</td>
			<td>63.53 ± 1.15</td>
			<td>0.476</td>
		</tr>
		<tr>
			<td>Speed (m/s)</td>
			<td>0.98 ± 0.06</td>
			<td>0.83 ± 0.08a</td>
			<td>0.73 ± 0.03b,c</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Step length (m)</td>
			<td>0.51 ± 0.02</td>
			<td>0.49 ± 0.02</td>
			<td>0.45 ± 0.02b,c</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Stride length (m)</td>
			<td>1.01 ± 0.04</td>
			<td>0.96 ± 0.03a</td>
			<td>0.91 ± 0.04b,c</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Gait variability of step length</td>
			<td>0.114 ± 0.0033</td>
			<td>0.120 ± 0.0034a</td>
			<td>0.112 ± 0.0030c</td>
			<td>0.001</td>
		</tr>
	</tbody>
</table>
Table 5: WTMT-B data of participants. Mean ± SD. MCI = Mild Cognitive Impairment; PD = Parkinson’s Disease. One-way-ANOVA and post hoc analysis with LSD. a = P &lt; 0.05 Elderly with MCI relative to Healthy Elderly; b = P &lt; 0.05 Elderly with PD relative to Healthy Elderly; c = P &lt; 0.05 Elderly with PD relative to Elderly with MCI.

Watch this Scientific Journal Video about Traditional Trail Making Test Modified into Brand-new Assessment Tools: Digital and Walking Trail Making Test at JoVE.com

Traditional Trail Making Test Modified into Brand-new Assessment Tools: Digital and Walking Trail Making Test

Here, we present a protocol to show how to perform two types of cognitive assessment tools derived from the paper-pencil version of the Trail Making Test.

The Trail Making Test (TMT) is a well-accepted tool for evaluating executive function. The standard TMT was invented more than 60 years ago and has been ...

traditional-trail-making-test-modified-into-brand-new-assessment

Universidad San Sebastian

Research

JoVE Journal

Medicine

11.0K Views.  The Seventh Medical Center of PLA Army General Hospital. Here, we present a protocol to show how to perform two types of cognitive assessment tools derived from the paper-pencil version of the Trail Making Test.

Video: Traditional Trail Making Test Modified into Brand-new Assessment Tools: Digital and Walking Trail Making Test

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Recent studies support the involvement of supraspinal networks in control of bipedal human walking. Part of this evidence encompasses studies, including our previous work, demonstrating that gait kinematics and limb coordination during treadmill walking can be inferred from the scalp electroencephalogram (EEG) with reasonably high decoding accuracies. These results provide impetus for development of non-invasive brain-machine-interface (BMI) systems for use in restoration and/or augmentation of gait- a primary goal of rehabilitation research. To date, studies examining EEG decoding of activity during gait have been limited to treadmill walking in a controlled environment. However, to be practically viable a BMI system must be applicable for use in everyday locomotor tasks such as over ground walking and turning. Here, we present a novel protocol for non-invasive collection of brain activity (EEG), muscle activity (electromyography (EMG)), and whole-body kinematic data (head, torso, and limb trajectories) during both treadmill and over ground walking tasks. By collecting these data in the uncontrolled environment insight can be gained regarding the feasibility of decoding unconstrained gait and surface EMG from scalp EEG.

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Development of an effective brain-machine-interface (BMI) system for restoration and rehabilitation of bipedal locomotion requires accurate decoding of user's intent. Here we present a novel experimental protocol and data collection technique for simultaneous non-invasive acquisition of neural activity, muscle activity, and whole-body kinematics during various locomotion tasks and conditions.

Recent  studies support the involvement of supraspinal networks in control of bipedal  human walking. Part of this evidence  encompasses studies, ...

Behavior

Assessing Spatial Memory Impairment in a Mouse Model of Traumatic Brain Injury Using a Radial Water Tread Maze

Despite the recent increase in use of mouse models in scientific research, researchers continue to use cognitive tasks that were originally designed and validated for rat use. The Radial Water Tread (RWT) maze test of spatial memory (designed specifically for mice and requiring no swimming) has been shown previously to successfully distinguish between controlled cortical impact-induced TBI mice and sham controls. Here, a detailed protocol for this task is presented. The RWT maze capitalizes on the natural tendency of mice to avoid open areas in favor of hugging the sides of an apparatus (thigmotaxis). The walls of the maze are lined with nine escape holes placed above the floor of the apparatus, and mice are trained to use visual cues to locate the escape hole that leads out of the maze. The maze is filled with an inch of cold water, sufficient to motivate escape but not deep enough to require that the mouse swim. The acquisition period takes only four training days, with a test of memory retention on day five and a long-term memory test on day 12. The results reported here suggest that the RWT maze is a feasible alternative to rat-validated, swimming-based cognitive tests in the assessment of spatial memory deficits in mouse models of TBI.

Here we present a protocol for a mouse-specific test of cognition that does not require swimming. This test can be used to successfully distinguish controlled cortical impact-induced traumatic brain injury mice from sham controls.

Despite the recent increase in use of mouse models in scientific research, researchers continue to use cognitive tasks that were originally designed and ...

The Ladder Rung Walking Task: A Scoring System and its Practical Application.

Progress in the development of animal models for/stroke, spinal cord injury, and other neurodegenerative disease requires tests of high sensitivity to elaborate distinct aspects of motor function and to determine even subtle loss of movement capacity. To enhance efficacy and resolution of testing, tests should permit qualitative and quantitative measures of motor function and be sensitive to changes in performance during recovery periods. The present study describes a new task to assess skilled walking in the rat to measure both forelimb and hindlimb function at the same time. Animals are required to walk along a horizontal ladder on which the spacing of the rungs is variable and is periodically changed. Changes in rung spacing prevent animals from learning the absolute and relative location of the rungs and so minimize the ability of the animals to compensate for impairments through learning. In addition, changing the spacing between the rungs allows the test to be used repeatedly in long-term studies. Methods are described for both quantitative and qualitative description of both fore- and hindlimb performance, including limb placing, stepping, co-ordination. Furthermore, use of compensatory strategies is indicated by missteps or compensatory steps in response to another limb&#x2019;s misplacement. 

The ladder rung walking task is a new test to assess skilled walking and measure both forelimb and hindlimb placing, stepping, and inter-limb co-ordination.

Progress in the development of animal models for/stroke, spinal cord injury, and other neurodegenerative disease requires tests of high sensitivity to ...

Biology

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard scores from paper and pencil symptom- measures traditionally used to track disease progression are too coarse (discrete) to capture the granularity of the clinical phenomena under consideration, in the face of tremendous symptom diversity. For this reason, sensors, wearables, and mobile devices are increasingly incorporated into PD research and routine care. These digital measures, while more precise, yield data that are less standardized and interpretable than traditional measures, and consequently, the two types of data remain largely siloed. Both of these issues present barriers to the broad clinical application of the field&#8217;s most precise assessment tools. This protocol addresses both problems. Using traditional tasks to measure cognition and motor control, we test the participant, while co-registering biophysical signals unobtrusively using wearables. We then integrate the scores from traditional paper-and-pencil methods with the digital data that we continuously register. We offer a new standardized data type and unifying statistical platform that enables the dynamic tracking of change in the person&#8217;s stochastic signatures under different conditions that probe different functional levels of neuromotor control, ranging from voluntary to autonomic. The protocol and standardized statistical framework offer dynamic digital biomarkers of physical and cognitive function in PD that correspond to validated clinical scales, while significantly improving their precision.&#160;

This protocol offers a digitization of portions of traditional clinical tasks commonly used to measure cognition and motor control in Parkinson&#8217;s disease. Clinical tasks are digitized while biophysical rhythms are co-registered from different functional levels of the nervous systems, ranging from voluntary, spontaneous, automatic to autonomic.

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard ...

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision

We describe an indoor, portable, standardized course that can be used to evaluate obstacle avoidance in persons who have ultralow vision. Six sighted controls and 36 completely blind but otherwise healthy adult male (n=29) and female (n=13) subjects (age range 19-85 years), were enrolled in one of three studies involving testing of the BrainPort sensory substitution device. Subjects were asked to navigate the course prior to, and after, BrainPort training. They completed a total of 837 course runs in two different locations. Means and standard deviations were calculated across control types, courses, lights, and visits. We used a linear mixed effects model to compare different categories in the PPWS (percent preferred walking speed) and error percent data to show that the course iterations were properly designed. The course is relatively inexpensive, simple to administer, and has been shown to be a feasible way to test mobility function. Data analysis demonstrates that for the outcome of percent error as well as for percentage preferred walking speed, that each of the three courses is different, and that within each level, each of the three iterations are equal. This allows for randomization of the courses during administration.
Abbreviations: 
preferred walking speed (PWS) 
course speed (CS) 
percentage preferred walking speed (PPWS)

We describe an indoor, portable, standardized course that can be used to evaluate obstacle avoidance in persons who have ultralow vision. The course is relatively inexpensive, simple to administer, and has been shown to be reliable and reproducible.

We describe an indoor, portable, standardized course that can be used to evaluate obstacle avoidance in persons who have ultralow vision. Six sighted ...

The Multiple Sclerosis Performance Test (MSPT): An iPad-Based Disability Assessment Tool

Precise measurement of neurological and neuropsychological impairment and disability in multiple sclerosis is challenging. We report a new test, the Multiple Sclerosis Performance Test (MSPT), which represents a new approach to quantifying MS related disability. The MSPT takes advantage of advances in computer technology, information technology, biomechanics, and clinical measurement science. The resulting MSPT represents a computer-based platform for precise, valid measurement of MS severity. Based on, but extending the Multiple Sclerosis Functional Composite (MSFC), the MSPT provides precise, quantitative data on walking speed, balance, manual dexterity, visual function, and cognitive processing speed. The MSPT was tested by 51 MS patients and 49 healthy controls (HC). MSPT scores were highly reproducible, correlated strongly with technician-administered test scores, discriminated MS from HC and severe from mild MS, and correlated with patient reported outcomes. Measures of reliability, sensitivity, and clinical meaning for MSPT scores were favorable compared with technician-based testing. The MSPT is a potentially transformative approach for collecting MS disability outcome data for patient care and research. Because the testing is computer-based, test performance can be analyzed in traditional or novel ways and data can be directly entered into research or clinical databases. The MSPT could be widely disseminated to clinicians in practice settings who are not connected to clinical trial performance sites or who are practicing in rural settings, drastically improving access to clinical trials for clinicians and patients. The MSPT could be adapted to out of clinic settings, like the patient&#8217;s home, thereby providing more meaningful real world data. The MSPT represents a new paradigm for neuroperformance testing. This method could have the same transformative effect on clinical care and research in MS as standardized computer-adapted testing has had in the education field, with clear potential to accelerate progress in clinical care and research.

The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool

Precise measurement of neurological and neuropsychological impairment and disability in multiple sclerosis is challenging. We report methodologic details on a new test, the Multiple Sclerosis Performance Test (MSPT). This new approach to the objective of quantification of MS related disability provides a computer-based platform for precise, valid measurement of MS severity.

Precise measurement of neurological and neuropsychological impairment and disability in multiple sclerosis is challenging. We report a new test, the ...

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

To cross a road successfully, individuals must coordinate their movements with moving vehicles. This paper describes use of a walking simulator in which people walk on a treadmill to intercept gaps between two moving vehicles in an immersive virtual environment. Virtual reality allows for a safe and ecologically varied investigation of gap crossing behavior. Manipulating the initial starting distance can further the understanding of a participant&#8217;s speed regulation while approaching a gap. The speed profile may be assessed for various gap crossing variables, such as initial distance, vehicle size, and gap size. Each walking simulation results in a position/time series that can inform how velocity is adjusted differently depending on the gap characteristics. This methodology can be used by researchers investigating pedestrian behavior and behavioral dynamics while employing human participants in a safe and realistic setting.

This protocol describes use of a walking simulator that serves as a safe and ecologically valid method to study pedestrian behavior in the presence of moving traffic.

To cross a road successfully, individuals must coordinate their movements with moving vehicles. This paper describes use of a walking simulator in which ...

Revised and Neuroimaging-Compatible Versions of the Dual Task Screen

Dual task paradigms simultaneously assess motor and cognitive abilities, and they can detect subtle, residual impairments in athletes with recent mild traumatic brain injury (mTBI). However, past dual task paradigms have focused solely on lower extremity skills and have relied on cumbersome, expensive laboratory equipment - thus limiting their practicality for everyday mTBI evaluation. Subsequently, we developed the Dual Task Screen (DTS), which takes &#60;10 minutes to administer and score, uses low-cost portable equipment, and includes lower extremity (LE) and upper extremity (UE) subtasks. The purpose of this manuscript was twofold. First, we describe the administration protocol for the revised DTS, which we revised to address the limitations of the original DTS. Specifically, the revisions included additions of smart devices to acquire more detailed gait data and inclusion of single cognitive conditions to test for disrupted cognitive performance under dual task conditions. Importantly, the revised DTS is a measure intended for future clinical use, and we present representative results from three male athletes to illustrate the type of clinical data that can be acquired from the measure. Importantly, we have yet to evaluate the sensitivity and specificity of the revised DTS in athletes with mTBI, which is the next research initiative. The second purpose of this manuscript is to describe a neuroimaging-compatible version of the DTS. We developed this version so we could evaluate the neural underpinnings of single and dual task performance, for a better empirical understanding of the behavioral deficits associated with mTBI. Thus, this manuscript also describes the steps we took to enable simultaneous functional near-infrared spectroscopy (fNIRS) measurement during the DTS, along with how we acquired and completed first-level processing of the fNIRS data.

We developed the original Dual Task Screen (DTS) as a portable, low-cost measure that can evaluate athletes with sports-induced mild traumatic brain injury. We revised the original DTS for future clinical use and developed a neuroimaging-compatible version of the DTS to measure neural underpinnings of single and dual task performance.

Dual task paradigms simultaneously assess motor and cognitive abilities, and they can detect subtle, residual impairments in athletes with recent mild ...

Implementing Vestibular Disorder Screening Tests

Screening People on Standing Balance with Romberg Testing and Walking Balance with Tandem Walking

The goal of this protocol is to inform readers about the exact procedures to use to perform two screening tests for vestibular disorders: tandem walking (TW) with eyes closed, also known as heel-toe walking, and the Clinical Test of Sensory Integration and Balance (CTSIB), which is also known as the modified Romberg. The study describes the steps for performing each test and each of the three CTSIB subtests so that the reader will be able to replicate the test conditions for use in the clinic, in the clinical laboratory, or in any other situation needing valid and reliable screening for balance skill which might be affected by changes in vestibular system function. The procedures detailed here can be easily administered and take less than 1 min per trial. References to published papers with normative data are provided. The representative results section includes examples of data collected with these screening tests.

Author Spotlight: Developing Low-Tech Balance Assessment Methods for Broad-Spectrum Healthcare Applications 

This article describes procedures for screening people for standing and walking balance impairments using two normed, rapid, low-tech balance tests.

The goal of this protocol is to inform readers about the exact procedures to use to perform two screening tests for vestibular disorders: tandem walking ...

Motor Dual-Tasks for Gait Analysis and Evaluation in Post-Stroke Patients

Eighteen stroke patients were recruited for this study involving the evaluation of cognition and walking ability and multitask gait analysis. Multitask gait analysis consisted of a single walking task (Task 0), a simple motor dual-task (water-holding, Task 1), and a complex motor dual-task (crossing obstacles, Task 2). The task of crossing obstacles was considered to be equivalent to the combination of a simple walking task and a complex motor task as it involved more nervous system, skeletal movement, and cognitive resources. To eliminate heterogeneity in the results of the gait analysis of the stroke patients, the dual-task gait cost values were calculated for various kinematic parameters. The major differences were observed in the proximal joint angles, especially in the angles of the trunk, pelvis, and hip joints, which were significantly larger in the dual motor tasks than in the single walking task. This research protocol aims to provide a basis for the clinical diagnosis of gait function and an in-depth study of motor control in stroke patients with motor control deficits through the analyses of dual-motor walking tasks.

This paper presents a protocol specifically for dual motor task gait analysis in stroke patients with motor control deficits.

Eighteen stroke patients were recruited for this study involving the evaluation of cognition and walking ability and multitask gait analysis. Multitask ...