Running head: Dual Tasking With Timed “Up & Go” Test in Patients With

Parkinson Disease

Research Report

Dual Tasking With the Timed “Up & Go” Test Improves Risk-of-Fall Detection in

Patients With Parkinson Disease

Roisin C. Vance, Helen P. French, Dan G. Healy, Rose Galvin

R.C. Vance, PT, MSc, Physiotherapy Department, Beaumont Hospital, Beaumont Road,

Beaumont Dublin, Dublin 9, Leinster, Ireland, and Physiotherapy Department, Royal

College of Surgeons, 123 Street Stepthens Green Dublin 2, Dublin, Leinster, Ireland.

Address all correspondence to Ms Vance at: roisinmoloney@beaumont.ie.

H.P. French, PT, PhD, School of Physiotherapy, Royal College of Surgeons.

D.G. Healy, MD, PhD, Neurology Department, Beaumont Hospital.

R. Galvin, PT, PhD, Physiotherapy Department, Royal College of Surgeons.

[Vance RC, French HP, Healy DG, Galvin R. Dual tasking with the Timed “Up & Go”

test improves risk-of-fall detection in patients with Parkinson disease. Phys Ther.

2014;94:xxx–xxx.]

© 2014 American Physical Therapy Association

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Published Ahead of Print: xxxx

Accepted: August 11, 2014

Submitted: August 15, 2013

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ABSTRACT

Background. Falls are a common and disabling feature of Parkinson’s disease (PD).

Early identification of patients at greatest risk is a key goal of physiotherapy

assessment. The ‘Timed up and Go’ test (TUG) is a frequently used mobility

assessment tool, which demonstrates moderate sensitivity and specificity in identifying

falls risk.

Objective. To investigate whether adding a dual task (cognitive or manual) to the TUG

increases the test’s utility to identify risk of falls in persons with PD.

Design. A retrospective cohort study of persons with PD (n=36).

Methods. Participants were compared on the basis of self-reported falls exposure in the

previous 6 months (fallers vs. non-fallers). The time taken to complete the TUG, TUG-

cognitive and TUG-manual was measured in both groups. Between-group differences

were calculated using Mann-Whitney U tests. The discriminative performance of the

test was examined at different cut-off points and estimates of sensitivity and specificity

were based on receiver operator characteristic plots.

Results. Fallers took significantly longer than non-fallers (n=19) to complete the TUG

test under all three conditions (TUG p =0.005, TUG-cognitive p =0.001, TUG-manual p

=0.005). The TUG-cognitive demonstrated optimal discriminative performance

(AUCROC=0.81, 95% CI 0.64-0.92) at a cut-off of 14.7 seconds. The TUG-cognitive is

more likely to correctly classify those at low risk (LR+=2.9) (<14.7 seconds) with

higher estimates of sensitivity (0.76, 95% CI 0.52-0.90) than specificity (0.73, 95%CI

0.51-0.88) at this threshold (LR-=0.32).

Limitations. Retrospective classification of fallers and non-fallers was used.

  4  

Conclusions. Addition of a cognitive dual task to the TUG enhances identification of

falls risk for patients with PD. The TUG-cognitive test should be considered as a

component of a multifaceted falls risk assessment in PD.

  5  

INTRODUCTION

Falls among patients with Parkinson’s disease (PD) lead to progressive loss of mobility,

independence and confidence that ultimately may reduce survival1. Prevalence rates for

falls in this patient group greatly exceed those observed in age matched controls2 and

has been reported to be as high as 87% in a longitudinal study of PD3. Considering this

high prevalence and the negative impact of falls, it is critical to continue improving

methods which can be used to accurately identify those at greatest risk of falls. The

multi-factorial nature of falls in PD means that the use of individual measures of

balance or gait may be insufficient to accurately identify falls’ risk4, 5. Therefore a

combination of tests is recommended to allow comprehensive evaluation of both

clinical and physiological measures6. The most useful outcome measures to include in a

PD falls assessment remains unclear but components such as history of falling, disease

severity and mobility or balance measures have been recommended1,5,6.

It has been shown that the PD population is significantly more susceptible to gait

changes when carrying out dual tasks compared with age-matched controls7. These gait

changes include reduced gait speed, stride length and stride pattern8,9. Decreased

automaticity and impaired attentional flexibility resulting from cortical and basal

ganglia dysfunction may play a role in this gait impairment. Automaticity is a

component of skilled movement that is used to evaluate when a skill is performed with

little demand on attentional resources10. Higher activity at premotor and prefrontal

cortical areas of the brain, measured with functional MRI, has been reported in PD

patients performing dual tasks compared with healthy age-matched controls11. The

authors suggested that the patients’ limits of capacity may have been exceeded resulting

in deterioration in the automaticity of task performance. Although the association

between dual task performance during gait and falls risk is yet to be established,

  6  

previous literature suggests PD patients prioritize concurrent tasks over postural tasks

during dual tasking7. This has been shown to decrease safety and increase fall risk in

this population7.

The measurement of automaticity allows consideration of the effects of the dual task on

performance. The automaticity index, which involves expressing the dual task

performance as a percentage of the single task, allows measurement of the dual task

cost and is a method of standardising the automaticity measurement across research

studies12,13,14.

The Timed Up and Go (TUG) test is a simple, quick and widely used clinical tool for

assessment of lower limb function, mobility and fall risk15. It has been identified as a

valid and reliable measure of mobility in PD16 and is recommended by both the

American and British Geriatric Societies as a component of a multi-factorial falls risk

assessment17. Although reported to be a moderately sensitive indicator of falls in PD18,

the TUG used alone may not be sufficiently accurate to identify falls risk. The TUG was

first modified by Lundin-Olsson to determine the effect of a second task on balance and

gait in frail older adults19. A manual task of carrying a glass of water was combined

with the TUG creating a dual task (TUG-manual). Individuals with a difference of

≥4.5seconds between the single and dual task were identified as more frail and at higher

risk of fall. The diagnostic accuracy of this test was not, however, examined in the

study. The TUG was further modified in a study of elderly fallers to include a cognitive

dual task (TUG-cognitive) that involved counting backward in threes while completing

the TUG20. The study examined both types of dual task TUG and investigated whether

the TUG-manual and TUG-cognitive were more sensitive and specific predictors of

falls in an elderly community-dwelling population than the TUG alone20. The authors

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concluded that the TUG was sensitive (87%) and specific (87%) to identify fallers and

the addition of neither the manual nor cognitive dual task increased the test’s overall

ability to predict falls. As PD patients are more susceptible to gait changes under dual

task conditions than age-matched elderly controls6, it cannot be assumed that these

findings20 can be applied to a PD population. The TUG-manual and TUG-cognitive are

now widely used in clinical practice and have been included as components of larger

outcome measures such as the Modified Parkinson Assessment Scale21 and the

BESTest22. The benefits of adding an additional component to the TUG in PD falls

assessment have yet to be fully investigated.

The primary aim of this study was to investigate whether addition of a dual task to the

TUG increases the test’s ability to identify persons with PD at risk of falls. A

secondary aim was to evaluate the optimal discriminative value of the test under single

and dual task conditions. The third aim was to ascertain if the automaticity of

movement during the dual task TUG tests was greater in PD non-fallers than fallers.

Finally, factors associated with falls recorded on assessment were examined in order to

identify potential contributing factors.

In order to address these aims the following hypothesis were generated 1) The addition

of a dual task to the TUG would increase the test’s sensitivity and specificity to identify

PD patients at risk of falls. With regard to the second aim, it was hypothesised that 2)

Fallers would be significantly slower than non-fallers when completing all three types

of TUG. The third hypothesis indicated that 3a) automaticity of gait during a dual task

TUG would be significantly greater in PD non-fallers than fallers and 3b) patients have

greater difficulty automatising their gait with a cognitive rather than a manual task.

METHODS

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Individuals with a diagnosis of PD were consecutively recruited from a tertiary hospital

in Dublin, Ireland between October 2011 and January 2012. Approval for the study was

granted from the hospital’s Research Ethics Committee. All participants provided

written consent and met the following inclusion criteria: a clinical diagnosis of

idiopathic PD, a history of no falls or two or more falls in the last six months, able to

provide written consent, independently mobile for at least nine metres (30 feet) with or

without a gait aid, Hoehn and Yahr Stage 2-423, able to provide three out of five correct

answers on the attention and calculation section of the Mini Mental State Examination

(MMSE) 24 and able to carry a cup in one hand. Participants were excluded if they had

an unstable co-existing medical condition or a history of one fall in the last six months.

Patients were categorised into one of two groups according to their self-reported falls

history: 1) No history of falls 2) Two or more falls within the last six months. Falls

were defined as ‘an event that resulted in the participant unintentionally coming to the

ground or other lower level’25. The sample size calculation for this study was based on

the difference in the TUG between PD fallers and non-fallers as data on the dual task

TUG was not available26. A mean difference in TUG speed of 5.6 seconds

(SD=5.2seconds) was used to calculate sample size. It was determined that a sample of

36 was required to provide statistically significant difference in TUG when comparing

fallers and non-fallers (power of 90%, p ≤0.05).

All assessments were conducted in a physiotherapy gym by the principal investigator

and took 35 minutes to complete. To minimise the effect of motor fluctuation all

assessments took place within two hours of the patients taking anti-parkinsonian

  9  

medication. Firstly, demographic data were collected followed by the cognitive, balance

and mobility assessments. The MMSE was carried out to ensure participants had

sufficient cognitive function to complete the dual task assessments24.

The three types of TUG tests (TUG, TUG-cognitive and TUG-manual) were timed

using a stopwatch. The three tests were completed in random order and each was

repeated three times with the average of the three scores then being used for data

analysis. The original TUG test evaluated functional mobility by measuring the time

taken to stand from a chair, walk 3 metres, turn around, return to the chair and sit down.

TUG-manual entailed completing the TUG test while carrying a glass of water in one

hand19. TUG-cognitive involved completing the TUG test while counting backwards in

threes from a random start point20. The Automaticity Index12 was calculated to assess

the impact of both dual tasks on the faller and non-faller groups. The speed under the

dual task condition is expressed as a percentage of the speed under the single task

condition;

The maximal achievable score is 100%, which indicates no decrease in performance

under dual task condition. This index was developed as a method of standardizing the

assessment of dual task performance and its effect on automaticity across research

studies12.

SINGLE TASK

DUAL TASK X 100

  10  

DATA ANALYSIS:

Data were analyzed using STATA (version 12, Stata Corp, College Station, Texas).

Descriptive statistics including means and standard deviations were computed where

appropriate. Mean differences between fallers and non-fallers were tested for

significance using independent t -tests or the Mann-Whitney U test for continuous

variables with parametric or nonparametric distribution, respectively. The following

explanatory variables; age, gender, MMSE, Berg Balance Score (BBS), the three TUG

tests and Hoehn and Yahr score were analysed to identify potential contributing factors

to risk of falling using univariate logistic regression, with faller/non-faller being the

outcome of interest. The independent variables identified as significantly associated

with falls on univariate analysis (p<0.05) were entered into stepwise multiple logistic

regression analyses to determine the best explanatory independent variables.

The discriminative value of the TUG was also examined using 2x2 tables to calculate

estimates of sensitivity and specificity and associated positive (LR+) and negative

(LR+) likelihood ratios. Receiver operating characteristic (ROC) analyses with 95%

Confidence Intervals (CIs) were also performed to describe model discrimination in all

three TUG tests. The point that simultaneously maximized sensitivity and specificity

was selected as the cut-off value. Accuracy was calculated based on the proportion of

correctly classified cases using cut-off values. The c statistic, or area under the curve

ranges from 0.5 (no discrimination) to a theoretical maximum of 1. Values between 0.7

and 0.9 represent moderate accuracy and greater than 0.9 represents high accuracy27. A

c statistic of 1 represents perfect discrimination, whereby scores for all cases of

indivials with falls are higher than those for all the non-fallers with no overlap.

Statistical significance was p<0.05

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RESULTS

Thirty six people (21 male and 15 female) were recruited. Demographic details are

shown in Table 1. The average age of participants was 71.4 years, with a data range of

66-87 years (mean 71.4, SD ± 8.3years). The faller group had an average of 3.65 falls

in the previous 6 months (range 2-8 falls) had a significantly higher Hoehn and Yahr

score and were more dependent on mobility aids than those in the non-faller group

(Table 1). There were no significant between-group differences in age (p = 0.25) or the

MMSE score of the participants.

In order to address hypothesis 1) the diagnostic accuracy statistics for the three tests are

shown in Table 3. The TUG-cognitive demonstrated the highest sensitivity (76.5%) and

specificity (73.7%) to identify PD patients’ falls risk. Sensitivity for the TUG and the

TUG-manual were lower, indicating these tests alone were insufficient to identify those

patients at risk of falls (Table 3). The highest LR score (2.9, 95%CI 1.3, 6.5) was

presented for the TUG-cognitive. This indicates that a faller was 2.9 times more likely

to be categorised correctly using this test than a non-faller (Table 3). Hypothesis two

was confirmed from Mann Whitney U test comparing speed of TUG completion in

fallers and non-fallers. Results indicated fallers were significantly slower for all three

TUG conditions (Table 2). ROC analysis for the different forms of TUG (Figures 1-3)

was used to determine the cut-off values for each test that would maximise their

sensitivity and specificity. As shown in Table 3 optimal cut-off time to discriminate

fallers and non fallers was 12s, 14.7s and 13.2s for the TUG, TUG-cognitive and the

TUG-manual, respectively.

Calculation of the Automaticity Index for each of the dual task TUG tests showed that

the TUG-cognitive demonstrated significantly lower AI scores (p =0.05) for the faller

  12  

(75.9%) than the non-faller group (83.7%) thus confirming hypothesis three. This

indicated PD patients with a history of falls had the greatest difficulty automatising their

gait pattern when carrying out a cognitive dual task. There was no significant

difference (p =0.7) in automaticity between fallers (89.1%) and non-fallers with TUG-

manual (86.3%).

Finally, univariate regression analysis demonstrated that gender and Hoehn and Yahr

stage were the only baseline measures predictive of falls with the odds of fall

significantly higher in men than in women (OR = 4.2, 95%CI 1.02, 16.67). Similarly,

progression in Hoehn and Yahr stages was associated with an increased the likelihood

of falling (OR=3.6, 95%CI 1.38, 9.26). When both variables were entered into a

multiple regression model, only Hoehn and Yahr stage remained significant (OR=3.51,

95%CI 1.28, 9.58).

DISCUSSION

This study demonstrates that the addition of a cognitive-based dual task enhanced the

TUG test’s discriminative ability to correctly identify those at high and low risk of fall.

The study identified that a cut-off score of 14.7 seconds for the TUG-cognitive

optimised the discriminative performance of the test. The TUG and TUG-manual

demonstrated lower prognostic accuracy than the TUG-cognitive in identifying those at

risk of falls. Furthermore, results demonstrated that automaticity of gait declined to a

greater degree with the addition of a cognitive rather than manual task to the TUG.

The diagnostic accuracy of each test was calculated, and included sensitivity and

specificity scores. Test sensitivity is a metric of how often TUG detected fall risk for

the participants in the falls group. Specificity, on the other hand, indicates how often the

patient was not at falls risk and was categorised as a non-faller. Cut-off scores were

used to differentiate fallers and non-fallers and were calculated using receiver operator

  13  

curves for each TUG test. The original TUG test demonstrated low sensitivity scores of

41% and higher specificity (73%) at a cut point of 12 seconds. This result can be

compared to two previous studies which investigated falls risk factors in PD18, 29. Their

data showed TUG sensitivity of 63% and specificity of 65%18 and sensitivity and

specificity score of 65% and 53%29 respectively. Both studies concluded the TUG

accuracy rate was not sufficient to predict falls risk. The TUG-manual also

demonstrated poor sensitivity (29%) and moderate specificity (68%) scores. The overall

discriminative performance of the TUG-cognitive is confirmed by the ROC curve

analysis indicating about 82% overall accuracy. Thus this combination performed better

than the other two TUG tests, demonstrating the highest sensitivity (76.5%) and

specificity (73.7%) to identify PD patients’ falls risk. Although the TUG-cognitive

showed moderate accuracy it is unlikely to be sufficient as a sole test of falls risk.

Sensitivity or specificity values that qualify as ‘high’ are usually defined as scores of

95% or greater30. Instead a combination of disease specific, balance and mobility

related measures are necessary to accurately identify falls risk in PD18. The TUG-

cognitive may be more useful as a component of a multi-factorial falls assessment

including other variables such as falls history, disease severity and freezing of gait.

These factors have been identified as having strong predictive qualities for falls risk in

PD1,5,18,

The differences identified in the discriminative ability of TUG-cognitive and TUG-

manual in the current study may in part have been due to the complexity of the task

involved. The degree of difficulty of the secondary task has been reported to affect the

pattern and speed of gait in PD31. These secondary tasks were chosen as they are widely

used in clinical practice as standard additions when performing the dual task TUG. The

automaticity index indicated that TUG-manual required less attentional demand than the

  14  

TUG-cognitive in both the faller and non-faller groups. This may indicate the secondary

task was not sufficiently complex to reach the threshold attention needed to negatively

impact the basic TUG. Overall however, while differences in task complexity between

the manual and cognitive tasks may have contributed to the discriminative ability of our

tests, we feel that our interpretation of the data is valid based on the strength of the

results reported. Future research comparing different complexities of TUG-manual

secondary tasks are likely to add to the current findings. Furthermore, comparison of

automaticity between fallers and non-fallers is yet to be examined thoroughly in people

with PD, therefore further research into the use of automaticity as a fall risk measure is

required.

Results of this study identified a significant difference in test performance between

fallers and non-fallers in all three TUG tests. These findings are in keeping with

previous research in the elderly population, indicating that fallers are slower than non-

fallers in completing the TUG under single and dual task conditions20. Non-fallers

demonstrated lower Hoehn and Yahr scores than fallers, indicating a lower disease

severity. The relationship between disease severity and risk of falling was investigated

in a recent meta-analysis of PD patients1. Those findings suggested that the Hoehn and

Yahr scale was a better predictor of falls than the Unified Parkinsons Disease Rating

Scale (UPDRS) but neither was identified as a strong predictor of falls potentially due

to ‘’the U-shaped relationship of PD with falls’’. This is a phenomenon whereby the

rate of falls increases initially with disease severity, becomes stable as compensatory

strategies are engaged and eventually decreases further due to immobility 2. The

population used in this study were independently mobile with or without an aid which

may have resulted in exclusion of those in the plateau stage, or those who had

compensated causing immobility.

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Prospective recording of falls remains the reference standard method of evaluating falls

risk, therefore retrospective categorisation of the fallers and non-fallers was a

methodological limitation of the current study. While this study is a retrospective

cohort study, efforts were made to minimise recall bias by limiting the time frame for

inclusion and excluding those with a history of one fall. A history of two or more falls

in the last six months was required for inclusion in the falls category. Future prospective

cohort studies with larger sample sizes would allow for exploration of a greater number

of predictor variables and would further validate the current study findings. Additional

research should compare the impact of TUG dual tasks of varying complexity on

identifying falls risk.

CONCLUSION:

Identification of falls risk is a critical step in developing effective therapeutic

interventions to reduce falls in PD. The TUG-cognitive was shown to have better

discriminative ability than the original TUG or the TUG- manual test. The optimal cut-

off time to discriminate fallers and non fallers was identified in this study for the TUG

=12s, TUG-cognitive=14.7s and TUG-manual=13.2s. These findings support the use of

the TUG-cognitive as a simple screening test with moderate sensitivity and specificity

that comprises a component of a multi-faceted falls assessment in people with PD to

identify those at risk of fall. The low sensitivity and specificity of the TUG-manual

indicates that use in clinical practice should be limited. Finally the study demonstrated

that the automaticity of the TUG tests in fallers was significantly less than that of non-

fallers, which may be a factor that compounds their falls risk.

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Acknowledgments

Ms Vance, Dr French, and Dr Galvin provided concept/idea/research design. Ms Vance

and Dr Galvin provided writing. Ms Vance provided data collection and analysis,

project management, and facilities/equipment. Dr Healy provided participants. Dr

French and Dr Galvin provided consultation (including review of manuscript before

submission).

DOI: 10.2522/ptj.20130386

  17  

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  21  

Table 1 Baseline Characteristics: Non-Fallers and Fallers

Variables All

Participants

(n=36)

Non-Faller

(n = 19)

Fallers

(n= 17)

Mean Diff

(95% CI)

P-value

Mean (SD)

Age (y) 71 69.9

(6.56)

73.12

(9.87)

3.22

(-2.4, 8.9)

0.25

ABC-6 (%) 52 58.84%

(31.2)

44.4%

(15.5)

-14.44

(-31.5,

2.5)

0.09

Median (IQR)

BBS (0-56) 50.4 52 (6) 51 (7) 0.17

MMSE (0-30) 27.2 28 (2) 27 (4) 0.16

N (%)

Male n % 17 5 (36.8%) 12 (70.6%) 0.04*

Hoehn &

Yahr

11

10 (52.6%)

1 (5.9%)

0.009*

  22  

Stage Two

Stage Three

Stage Four

10

15

4 (21.1%)

5 (26.3%)

6 (35.3%)

10 (58.8%)

Walking Aid

None

Walking stick

Two walking

sticks

Walking

Frame

22

10

2

2

15 (78.9%)

4 (21.1%)

0

0

7 (41.2%)

6 (35.2%)

2 (11.8%)

2 (11.8%)

0.02*

* Statistically significant at p <0.05; Mean Diff= Mean Difference

SD= Standard Deviation; 95%CI= 95% Confidence Interval; IQR= Interquartile range

  23  

Table 2 Group Differences in the three Timed Up and Go tests

Non Faller

Median (IQR)

Faller

Median (IQR)

p-value

TUG 10.12 (6.6) 13.01 (10) 0.005*

TUG-cognitive 12.09 (9.8) 17.14 (17.6) 0.001*

TUG-manual 11.72 (6) 14.6 (13.3) 0.005*

* Statistically significant at p<0.05. IQR = Interquartile Range; TUG=Timed Up and

Go

  24  

Table 3 Sensitivity, Specificity & Likelihood Ratio Scores for the TUG tests.

Variable AUC

(95% CI)

Sensitivity Specificity Positive

LR

Negative

LR

(Cut Off Time)

Percentage

(95% CI)

Percentage

(95% CI)

Likelihood

(95% CI)

Likelihood

(95% CI)

TUG

(12 secs)

0.77

(0.6; 0.89)

41%

(32, 51)

73%

(64, 82)

1.57

(0.6, 4.1)

0.8

(0.5, 1.3)

TUG-cognitive

(14.7 secs)

0.82

(0.64;

0.92)

76.5%

(52, 90.4)

73.7%

(51.2, 88.2)

2.9

(1.3, 6.5)

0.32

(0.1, 0.8)

TUG-manual

(13.2 secs)

0.78

(0.61, 0.9)

29.55%

(13, 53.1)

68.4%

(46, 84.6)

0.9

(0.34, 2.5)

1.1

(0.67, 1.6)

Sensitivity and Specificity are presented as percentage.

95% Confidence interval (95%CI), AUC= Area under the curve; LR= likelihood ratio;

Cut-off Scores in seconds (sec).

  25  

Table 4 Index of automaticity

Non Fallers Fallers p value

TUG-cognitive 83.7% 75.9% 0.05*

TUG-manual 86.3% 89.1% 0.7

* Significance value p < 0.05

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0.00

0.25

0.50

0.75

1.00

Sens

itivity

0.00 0.25 0.50 0.75 1.001 - Specificity

Area under ROC curve = 0.7740

Figure 1: ROC curve for TUG original

  27  

0.00

0.25

0.50

0.75

1.00

Sens

itivi

ty

0.00 0.25 0.50 0.75 1.001 - Specificity

Area under ROC curve = 0.8142

Figure 2: ROC curve for TUG cognitive

  28  

0.00

0.25

0.50

0.75

1.00

Sens

itivity

0.00 0.25 0.50 0.75 1.001 - Specificity

Area under ROC curve = 0.7771

Figure 3: ROC curve for TUG manual