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

* These authors contributed equally
Medicine

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Summary

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.

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Wei, W., Zhào, H., Liu, Y., Huang, Y. Traditional Trail Making Test Modified into Brand-new Assessment Tools: Digital and Walking Trail Making Test. J. Vis. Exp. (153), e60456, doi:10.3791/60456 (2019).

Abstract

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.

Introduction

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’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 (1–25) on a test paper in ascending order (1->2->3…), whereas the latter requires the test-taker to set numbers and letters (1->A->2->B…) 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−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—TMT completion time is calculated in seconds16; (2) error analysis—different types of TMT errors are classified and quantified14; (3) intermanual differences—different abilities of completing TMT between the dominant hand and the nondominant hand are compared17; and (4) derived Trail Making Test indices—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’s Disease, Alzheimer’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.

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Protocol

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

  1. 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).
  2. 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).

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.

  1. dTMT-A procedure
    1. 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.
    2. Ask the participants to sit in front of a desk, and adjust the computer position, background light, and the electronic pen.
    3. 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.
    4. Show the instructions of dTMT-A as follows: Please draw a line as rapidly as possible joining consecutive numbers (i.e., 1->2->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.
    5. 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.
    6. 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.
    7. 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.
  2. dTMT-B procedure
    1. Repeat step 2.1.
    2. 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->graphic 1->2->graphic 1graphic 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.
    3. 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.

3. Direct Data Collection and Definitions in dTMT

  1. Determine the total time to completion: the time taken (ms) to draw a line connecting all circles in the correct order.
  2. Determine the number of errors: the number of times a line is drawn to a circle in the incorrect order.
  3. Determine the time to completion for each step: the time taken in milliseconds to draw each step.
  4. Determine the time inside each circle: the time spent in milliseconds to draw inside circles.
  5. Determine the inside circle percentage (%): time inside each circle divided by total time to completion.
  6. Determine the time inside each tolerance circle: the time spent in milliseconds to draw inside tolerance circles.
  7. Determine the inside circle tolerance percentage (%): time inside each tolerance circle divided by total time to completion
  8. 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.
  9. Determine the optimal pathway of each step: the nearest line in millimeters of each step.
  10. Determine the actual pathway of each step: the actual line in millimeters of each step.
  11. Determine the pathway deviation of each step: the actual line in millimeters minus the nearest line in millimeters of each step.
  12. Determine the variability of pathway deviation: Coefficient of the variation of the pathway deviation of each step.
  13. 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.

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.

  1. WTMT-A procedure
    1. 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.
    2. Connect the Intelligent Device for Energy Expenditure and Activity (IDEEA) to the PC and enter the subject's anthropometric data.
    3. 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.
    4. After the device is equipped, ask participants to walk up and down a walkway without any targets at a comfortable walking speed to warmup.
    5. 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->2->3…15) in the coins randomly distributed on the floor.
    6. Encourage the participants to walk fluently, but as accurately as possible; However, no priority is given. Perform WTMT-A only once.
    7. 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.
  2. WTMT-B procedure
    1. Repeat the steps as in Section 4.1.1.
    2. 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->graphic 1->2->graphic 1graphic 1>8) in the coins randomly distributed on the floor. Make sure all the Chinese characters are recognized by the participants.
    3. Perform WTMT-B only once.
    4. 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.

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.

  1. Determine the steps (n): the number of steps completed during level walking, including the right and left limbs.
  2. Determine the swing duration (%): the phase percentage starting from toe-off until initial ground or stair contact for any given foot.
  3. Determine the stance duration (%): phase percentage between the heel strike of one foot and the heel strike of the contra-lateral foot.
  4. Determine the speed (m/s): the average velocity over two consecutive strides.
  5. 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.
  6. 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).
  7. 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.

6. Data Collection and Statistics

  1. 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 < 0.05 was considered to indicate a statistically significant difference.

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Representative Results

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 > 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 (P= 0.017, P= 0.048), a larger variability of pathway deviation (P= 0.000, P= 0.000), and a lower velocity of drawing of each step (P= 0.001, P= 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 (P= 0.000, P= 0.002), step length (P= 0.004, P= 0.016), stride length (P= 0.005, P= 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 (P= 0.001, P= 0.000) and more steps to complete the test (P= 0.000, P= 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, P= 0 .002), stride length (0.91 m ± 0.04 vs. 0.96 m ± 0.03, P= 0.012), and Gait variability of step length (0.112 ± 0.0030 vs. 0.120 ± 0.0034, Pc = 0.000).

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). Please click here to view a larger version of this figure.

Figure 2
Figure 2: IDEEA. Device for WTMT-A and WTMT-B. Please click here to view a larger version of this figure.

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. Please click here to view a larger version of this figure.

Figure 4
Figure 4: IDEEA accelerometers and the location. The figure showed how to wear the IDEEA accelerometers correctly. Please click here to view a larger version of this figure.

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. Please click here to view a larger version of this figure.

Healthy Elderly Elderly with MCI Elderly with PD P Value
N = 7 N = 7 N = 7
Age 67.14 ± 4.22 65.14 ± 3.39 66.29 ± 3.90 0.63
Gender(M:F) 4:03 5:02 4:03 0.589
Dominant hand(R%) 100 100 100
Education (yrs) 10.00 ± 1.91 11.43 ± 2.51 10.14 ± 1.36 0.353
MMSE 29.00 ± 1.15 27.86 ± 1.35 28.43 ± 1.27 0.263
CDR 0.14 ± 0.24 0.5 ± 0.00 0.29 ± 0.39 0.066
GDS 2.28 ± 0.49 2.71 ± 0.76 2.29 ± 0.75 0.487
TUG (S) 10.07 ± 1.51 11.02 ± 0.60 11.72 ± 1.24 0.052

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

Healthy Elderly Elderly with MCI Elderly with PD P Value
N = 7 N = 7 N = 7
Total time to Completion 18.15 ± 5.12 19.67 ± 7.12 19.85 ± 3.89 0.821
Number of Errors 0.14 ± 0.38 0.29 ± 0.49 0.29 ± 0.49 0.796
Total time inside each circle 6.94 ± 1.99 6.91 ± 3.31 7.81 ± 2.46 0.773
Inside circle percentage 39.13 ± 7.70 35.42 ± 10.25 40.02 ± 11.63 0.665
Total time inside each tolerance circle 1.57 ± 0.80 2.09 ± 0.88 1.85 ± 0.49 0.442
Inside tolerance circle percentage 8.74 ± 3.02 10.80 ± 3.07 9.61 ± 3.55 0.498
Total Line cancelling times 0.14 ± 0.38 0.29 ± 0.49 0.14 ± 0.38 0.764
Total pathway deviation of each step 38.41 ± 2.52 39.30 ± 3.07 42.99 ± 3.99b, c 0.039
Variability of pathway deviation 1.72 ± 0.24 2.36 ± 0.55a 3.66 ± 0.46b, c 0
Velocity of drawing of each step 21.38 ± 2.59 19.00 ± 2.40 15.70 ± 2.55b, c 0.002

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 < 0.05 Elderly with MCI relative to Healthy Elderly; b = P < 0.05 Elderly with PD relative to Healthy Elderly; c = P < 0.05 Elderly with PD relative to Elderly with MCI.

Healthy Elderly Elderly with MCI Elderly with PD P Value
N = 7 N = 7 N = 7
Total time to Completion 32.07 ± 10.93 67.56 ± 9.87a 89.95 ± 12.12b,c 0
Number of Errors 0.14 ± 0.38 2.86 ± 1.07a 1.29 ± 0.49b,c 0
Total time inside each circle 6.03 ± 1.72 27.83 ± 5.05a 7.81 ± 2.46b,c 0
Inside circle percentage(%) 19.16 ± 3.86 41.47 ± 6.76a 22.46 ± 3.35c 0
Total time inside each tolerance circle 3.51 ± 0.91 9.73 ± 1.46a 3.93 ± 2.21c 0
Inside tolerance circle percentage(%) 11.26 ± 2.20 14.47 ± 1.62a 4.57 ± 2.86b,c 0
Total Line cancelling times 0.29 ± 0.38 0.86 ± 1.07 0.43 ± 0.53 0.35
Total pathway deviation of each step 86.02 ± 7.36 95.36 ± 6.76a 95.56 ± 8.78b 0.051
Variability of pathway deviation 2.158 ± 0.173 2.024 ± 0125 2.659 ± 0.332b,c 0
Velocity of drawing of each step 16.85 ± 1.79 8.41 ± 1.09a 4.91 ± 0.91b, c 0

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 < 0.05 Elderly with MCI relative to Healthy Elderly; b = P < 0.05 Elderly with PD relative to Healthy Elderly; c = P < 0.05 Elderly with PD relative to Elderly with MCI.

Healthy Elderly Elderly with MCI Elderly with PD P Value
N = 7 N = 7 N = 7
Total time to Completion 68.43 ± 4.86 76.57 ± 7.66 98.29 ± 9.36b,c 0
Number of Errors 0.29 ± 0.49 0.29 ± 0.49 0.57 ± 0.53 0.487
Steps (n) 80.86 ± 2.34 81.29 ± 3.30 81.71 ± 3.90 0.886
Swing duration (%) 36.86 ± 1.32 35.03 ± 0.84a 35.48 ± 1.25b 0.022
Step duration (%) 63.00 ± 1.35 64.97 ± 0.84 a 64.52 ± 1.25b 0.014
Speed (m/s) 1.01 ± 0.10 0.82 ± 0.57a 0.68 ± 0.04b,c 0
Step length (m) 0.51 ± 0.02 0.50 ± 0.01 0.49 ± 0.02b,c 0.01
Stride length (m) 1.02 ± 0.04 1.00 ± 0.02 0.96 ± 0.04b,c 0.011
Gait variability of step length 0.111 ± 0.0011 0.112 ± 0.0011 0.113 ± 0.0014 0.156

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 < 0.05 Elderly with MCI relative to Healthy Elderly; b = P < 0.05 Elderly with PD relative to Healthy Elderly; c = P < 0.05 Elderly with PD relative to Elderly with MCI.

Healthy Elderly Elderly with MCI Elderly with PD P Value
N = 7 N = 7 N = 7
Total time to Completion 78.57 ± 4.86 92.29 ± 7.72a 109.00 ± 5.66b,c 0
Number of Errors 0.57 ± 0.79 1.14 ± 1.07 0.86 ± 0.69 0.479
Steps (n) 89.71 ± 2.63 96.71 ± 2.29a 100.57 ± 3.74b,c 0
Swing duration (%) 37.20 ± 1.21 36.56 ± 1.23 36.47 ± 1.15 0.476
Step duration (%) 62.80 ± 1.21 63.44 ± 1.23 63.53 ± 1.15 0.476
Speed (m/s) 0.98 ± 0.06 0.83 ± 0.08a 0.73 ± 0.03b,c 0
Step length (m) 0.51 ± 0.02 0.49 ± 0.02 0.45 ± 0.02b,c 0
Stride length (m) 1.01 ± 0.04 0.96 ± 0.03a 0.91 ± 0.04b,c 0
Gait variability of step length 0.114 ± 0.0033 0.120 ± 0.0034a 0.112 ± 0.0030c 0.001

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 < 0.05 Elderly with MCI relative to Healthy Elderly; b = P < 0.05 Elderly with PD relative to Healthy Elderly; c = P < 0.05 Elderly with PD relative to Elderly with MCI.

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Discussion

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.

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Disclosures

The authors have nothing to disclose.

Acknowledgments

The authors thank Xiaode Chen for digital technology support.

Materials

Name Company Catalog Number Comments
Minisun LLC Intelligent Device for Energy Expenditure and Activity (IDEEA)
Surface Pro 2 Microsoft computer

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References

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