Exploring Time-Dependent Symptom Outcomes

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Human Factors and Ergonomics in Manufacturing, Vol. 00 (0) 113 (2009)C 2009 Wiley Periodicals, Inc.

Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/hfm.20151

Exploring Time-Dependent Symptom Outcomesin Office Staff

Xiaoshu LuFinnish Institute of Occupational Health, Topeliuksenkatu 41 a A,FIN-00250 Helsinki, Finland

Risto Toivonen

Human Factors at Work, Ergonomics and Usability, Finnish Institute ofOccupational Health, Topeliuksenkatu 41 a A, FIN-00250 Helsinki, Finland

Esa-Pekka TakalaFinnish Institute of Occupational Health, Topeliuksenkatu 41 a A,FIN-00250 Helsinki, Finland

ABSTRACT

This article illustrates the application of a new mathematical model developed for the study oftime-dependent health outcomes for office staff during computer work. The model describes the time-dependent associations of computer usage with outcomes expressed as discomfort in multiple bodyregions. The association is explicitly presented with a functional relationship that is parameterized

by body regions. The validation of the model demonstrated accuracy in reproducing the observedquantities for the study population. Therefore, we used this model to assess the impact of computer-related work exposure on discomfort in different body regions among office staff to better understandthe behavior of musculoskeletal and other symptoms. The exposures and outcomes were recordedparallel in time as usage of keyboard and mouse and with diaries of discomfort. The body regionsof neck/shoulders, eyes, head, shoulder joint/upper arm, and upper back were identified to have thehighest discomfort levels and rates for the development of discomfort parallel with exposures. Mostof our findings are consistent with the literature. The developed mathematical methodology maybe used to understand how the human body reacts to computer work to further prevent potentialmusculoskeletal and other disorders. C 2009 Wiley Periodicals, Inc.

1. INTRODUCTION

Musculoskeletal disorders (MSDs) are common among office workers and have been asso-

ciated with prolonged computer use and repetitive typing or use of a mouse (Gerr, Marcus,

Ensor, et al., 2002; Juul-Kristensen & Jensen, 2005; Punnett & Bergqvist, 1997a; Rempel,

Krause, Goldberg, et al., 2006). Additional health problems such as eye strain, eye irritation,

headache, and fatigue have been reported. The origin of the last mentioned problems is not

clear, and environmental factors like sick building syndrome or electromagnetic fields

may be of importance (Clements-Croome, 2004; Sandstrom, 2006).

Correspondence to: Xiaoshu Lu, Finnish Institute of Occupational Health, Topeliuksenkatu 41 a A,

FIN-00250 Helsinki, Finland. E-mail: [email protected]

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2 LU, TOIVONEN, AND TAKALA

There is also a lack of studies on the dynamic process of musculoskeletal and human sen-

sory outcomes and discomfort associated with computer-related workload as an exposure.

The hampering factors exist in both measurement techniques and analysis methodologies.

From a measurement point of view, the use of only questionnaires may not be accurate

enough or may overestimate the magnitude of the problem. However, measuring the ex-

posures by observation or direct measures is expensive and therefore not feasible in largeepidemiological studies (Winkel & Mathiassen, 1994). In this study, a combination of direct

measure and survey techniques was adopted for data collection.

Methodologically, the data are often nonlinear, requiring special analysis. The commonly

used statistical analysis models (analysis of variance [ANOVA], e.g.) are not well-suited for

nonlinear data (Kristensen & Hansen, 2004). The application of linear models as primary

tools may not be able to capture the complexity presented in the data on a dynamic process.

The interpretability of the model may be low. In mathematical modeling, one of the main

objectives is to select a suitable model for analysis purposes. When coping with large

size data with nonlinear features, as the ones resulting from many subjects in a poorly

understood process, we do not know in advance the most suitable model. Therefore, thefirst aim should be to discover dominant patterns so that the possible nonlinear models

can be easily identified. As the pain and discomfort in the musculoskeletal system may

be the first indications of MSDs (National Research Council, 2001), the study of temporal

relationship or exposure history can bring us further toward effective interventions to prevent

computer-related health problems and disorders.

To fill the gap in the literature, the Singular Value Decomposition (SVD)-based mod-

eling method was developed to establish temporal relationships between computer-related

workload and musculoskeletal and other discomfort among the study population. The col-

lected data served as a basis for the model. The modeling method consists of generating a

sample matrix from the observation data, applying SVD to capture the dominant patterns,

regressing toward the dynamic dominant patterns, and finally applying standard statisticalsoftware to estimate the standard errors of the model coefficients. Using the collected data,

a dose-response function was obtained to indicate the nonlinear relationship, yielding a

model that is highly efficient while applying various analyses. The validation of the model

gave a good accuracy, demonstrating that the model could reproduce the observed quan-

tities and help to gain a deeper understanding on the discomfort felt in different parts of

the body.

2. DATA AND MATHEMATICAL MODEL

2.1. Data

The subjects (n= 103) were office staff in Finland. They did office work with a video display

unit for at least four hours a day, and reported a moderate amount of musculoskeletal and

other symptoms. Information on computer work and health outcomes was gathered by

continuous measurements and self-administered questionnaire-diaries. The computer work

was assessed by counting keyboard and mouse clicks with special software (Work-Pace TM,

Niche Software Limited, Christchurch, New Zealand). Data recorded with the accuracy of

10 milliseconds were summed up for a daily base. The questionnaire-diaries consisted of

items presenting localization of musculoskeletal and sensory outcomes for 15 body parts.

Each item was scored 1 to 5 according to the discomfort severity such as 5 = feel good

and 1 = feel very uncomfortable. The subjects were requested to fill in the diaries three

Human Factors and Ergonomics in Manufacturing DOI: 10.1002/hfm


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EXPLORING TIME-DEPENDENT SYMPTOM OUTCOMES IN OFFICE STAFF 3

times a day. A detailed description of the study population was given by Ketola, Toivonen,

Hakkanen, Luukkonen, Takala, and Viikari-Juntura (2002).

One major problem with the collected data was that a large portion of missing data

existed because of some technical and human factors, which is common in human study

settings. Another problem associated with the data was that the measurement periods varied

among the subjects and were too short. Because of these shortcomings, the study includeda total of 69 subjects, and we decided to average the data to use a weekly model. Another

important reason behind the selection of the weekly model was that we believe that long-

term variations in musculoskeletal and sensory outcomes can be superimposed to changes

in weekly patterns.

2.2. Mathematical Model

Based on the data, a mathematical model was developed that describes the temporal asso-

ciations between the computer-related workload and discomfort in multiple body regions,

and the associations were formulated in an explicit form (Lu & Takala, 2007). The stages of

the proposed methodology, including basic concepts of SVD, are briefly given as follows:

Generating a sample matrix from the original time series data set; Applying SVD to the matrix to capture the dominant temporal patterns; Regressing toward the dominant temporal patterns; Summarizing the model equations; Applying standard statistical software to estimate the standard errors of the model

parameters.

2.2.1. Generation of Sample Matrix. Consider a sample mn matrix at generated

from data as

at =

a1(t1) a1(t2) . . . a1(tn1) a1(tn)

a2(t1) a2(t2) . . . a2(tn1) a2(tn)

. . . . . . . . . . . . . . .

am1(t1) am1(t2) . . . am1(tn1) am1(tn)

am(t1) am(t2) . . . am(tn1) am(tn)

, (1)

where aj (ti ), j = 1 . . . m & i = 1 . . . n presents a regional musculoskeletal or sensory

discomfort rating for sample j at time ti . The matrix may also present a single samples

measures with periodic patterns depending on the data structure and study purpose.

2.2.2. Application of SVD. Applying SVD (Golub & Van Loan, 1996) to Equation 1gives

at = UDVTt =

ni=1

ui di vTit, (2)

where columns ui and vi ofUand V are called the left and right singular vectors, respectively.

The diagonal elements di ofD, sorted in descending order with upper left value the largest,

are called the singular values. The singular values are the square roots of the eigenvalues of

the matrix ataTt or aTt at whereas the singular vectors are the correspondent eigenvectors.



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One key in applying SVD is that the truncated matrixr

i=1 ui di vTi is the closest rank-r

matrix to at (Golub & Van Loan 1996). Note that r is always smaller than n. This property is

extremely useful when r is much smaller than n. In practical applications, di , n i r + 1,

may not be zero because of the presence of noise such as individual disturbances in the

measurement data, but they are very close to zero. Then by dropping the last n r sin-

gular values, a good approximation of at is obtained with an r-dimensional matrix. The

corresponding singular values can be used as a measure of relative significance of the ap-

proximation for explaining at. Very often, a good matrix approximation can be obtained

with only a small fraction of the singular values.

Applying Equations 1 and 2 to our measurement data and checking the rank of at, we

found that r = 1 and the approximation u1d1vT1t can explain 90% of the variation ofat. So

we get the following approximation:

at =

a1(t1) a1(t2) . . . a1(tn1) a1(tn)

a2(t1) a2(t2) . . . a2(tn1) a2(tn)

. . . . . . . . . . . . . . .am1(t1) am1(t2) . . . am1(tn1) am1(tn)

am(t1) am(t2) . . . am(tn1) am(tn)

u1d1vT1t = uv

Tt , (3)

where u = u1d1 = (u1 u2 . . . u(m1) um)T and vt = d1v1t = (v(t1), v(t2) . . . v(tn1),

v(tn))T.

It is easy to see that a time-dependent model problem at is simplified through Equation 3.

The time series outcomes for all subjects are expressed in terms of the time series vt with

linear combination coefficients of the elements ofu which describe the differences among

all subjects. Therefore, vt presents the captured dominant time pattern and u the individual

sample differences. The next step deals with the regression ofv

t and at.

2.2.3. Regression of the Dominant Dynamic Patterns. To test whether the captureddominant time pattern vt is linear, nonlinear, or just random noise, we plotted vt and made

a visual inspection in data variability and goodness of fit through regression analysis. We

found that vt obeys certain nonlinear properties. A dose-response relationship was identified

and proved to be significant. It is worth mentioning that this method, by incorporating visual

inspection of the plotted curve and nonlinear regression analysis, can reduce the potential

errors introduced, especially by large unseen data.

The regression function a(t) ofat can then be obtained based on the regression function

v(t) ofvt through Equation 3 as

a(t) = u v(t). (4)

To make Equation 4 more clear, remember the following point: a(t) denotes the regional

musculoskeletal or sensory discomfort in continuous time, v(t) the dominant dynamic

patterns, and u the correspondent linear coefficient vector that presents kind of individual

differences of the outcomes in response to the dominant pattern. Averaging u over the

studied time period results in a population model where individual differences are neglected

as

a(t) = uv(t). (5)



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Monday

0

1

2

3

4

5

6

1 11 21 31 41 51 61

Subjects, indicated by numbers

Discomfortrating

Figure 1 Comparison of the approximated and the observed musculoskeletal discomfort ratings(body region neck) (5 = feel good, 1 = feel very uncomfortable). Vertical lines represent thedifference between the model and observations.

We call Equation 4 the stochastic model. Figure 1 is an example that displays the compar-

ison result of musculoskeletal discomfort rating for the body region neck. Vertical linesrepresent the difference between the observed and the model-approximated values for all

the study subjects. Figure 1 shows that the model approximation is accurate.

In this study we are less focused on the stochastic model and more interested in the

population model as we aim to study how broad office staff reacted and how computer-

related workload played a role generally. Therefore, for the rest of this article, model or

model equation refers to the population model, Equation 5, only.

2.2.4. Summary of the Model Equations. The model characteristics can be summa-rized in the following:

Computer-related workload: The computer-related workload varied linearly with time.

This simple linear dependence suggests that it is adequate to study time-dependent

health outcomes. Two benefits can be obtained from such study: a general dynamic

variation of the epidemiological behaviors can be analyzed; the analyzed result can

be easily applied to the workload-induced epidemiological behaviors because the

computer-related workload depends linearly on the time variable. Musculoskeletal and other outcomes: The developed explicit model equations can be

expressed with the following general functional form from Equation 5 as

a(t) =

1 +

2 1

1+ 10t3

, (6)



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0

1

2

3

4

5

6

DiscomfortRatingof'Eyes

'

observed

predicted95% confidence interval

Simulation period Forecast

period

Figure 2 Simulation and forecast of musculoskeletal discomfort ratings (5= feel good, 1= feelvery uncomfortable) in body region eyes for an individual subject. Based on the measured ratingsduring the first period (Simulation period) a forecast was fitted (stars) and compared to observations(circles). The horizontal dotted lines show the 95% confidence limits for the predicted period.

where a(t) presents the time-dependent musculoskeletal or sensory discomfort rating

ranging from 1 to 5, and 1, 2, and 3 are body regiondependent parameters.

Equation 6 is parameterized by body region parameters.

Model validation: The validation is performed by direct comparison of observationsand its forecast as illustrated in Figure 2. The accuracy is good given the fact that the

results are not adjusted for unknown confounding factors involved in the study.

2.2.5. Application of Standard Statistical Software. Next we have to give theparameter estimates and their standard errors for the equation (Equation 6). In some extreme

cases, evaluation of the standard error of the estimates can be difficult because of the

complicated design of the analyzed data, for example, data that are collected repeatedly

over time on the subjects. Such data often have greater complexity than cross-sectional

data because the correlations of the measurements within each subject must be considered

in the analysis. In this study, discomfort data were collected from each subject each daythroughout the week. It is very likely that discomfort outputs were correlated by day of

the week. Failing to account for the correlation among the time points can have serious

consequences. The error variance of the fitting estimates can be underevaluated, and the

confidence intervals can be substantially narrower than they should be (Diggle, Heagerty,

Liang, & Zeger, 2002).

Statistical software package SAS (PROC NLMIXED) was used to evaluate the standard

errors of the estimates. The evaluation addresses the sequential correlation issue directly

by modeling the covariance structure. Table 1 gives these parameter values (Lu & Takala,

2008). As a single dose-response model could not be fitted to all the curves, the data for the

outcome mood were modeled separately with an extra exponential function represented

through the parameters 4 and 5. These parameters were not statistically discernible at the



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TABLE 1. Fitted Parameters a1,a2,a3,a4, and a5 in Model Equation 6 for Dose-ResponseRelationship

Body Regions a1 (SE) a2(SE) a3(SE) a4(SE) a5(SE)

Head 3.87

(0.08) 4.00

(0.08) 2.63

(0.38) Eyes 3.72(0.09) 3.89(0.10) 2.29(0.49)

Neck 3.58(0.10) 3.74(0.10) 2.19(0.38)

Shoulder 3.64(0.10) 3.77(0.10) 2.25(0.39)

Shoulder joint/Upper arm 3.75(0.10) 3.87(0.10) 2.63(0.48)

Forearm 4.02(0.10) 4.07(0.10) 3.15(1.33)

Wrist 4.02(0.10) 4.09(0.10) 3.73(0.65)

Fingers 3.99(0.11) 4.07(0.11) 3.83(0.41)

Upper back 3.75(0.11) 3.87(0.11) 2.78(0.38)

Low back 3.84(0.11) 3.89(0.11) 3.43(1.06)

Hips 4.29(0.09) 4.30(0.09) 3.51(1.24)

Thighs 4.30(0.09) 4.32(0.10) 3.44(1.37)

Knees/Shin 4.21(0.11) 4.26(0.11) 3.72(0.63)

Feet 4.18(0.10) 4.23(0.10) 4.14(0.93)

Mood 4.04(0.79) 3.57(0.49) 2.88(0.39) 0.41 (0.77) 0.41 (0.61)

Note. SE= standard error.p < 0.001, p < 0.05.

5% probability level; therefore, they were eliminated from the model. Figure 3 displays the

model a(t) for 15 body parts ranging from 1 to 5. The x-axis represents time in days or

scaled computer-related workload because of their linear relationship.

3. RESULTS

3.1. Ranking and Classifying the Discomfort Severities

First we performed two WallerDuncan k-ratio t tests for a(t) (Equation 6 and Figure 3)

to investigate detailed contrast of musculoskeletal and sensory outcomes in different body

regions. Tables 2 and 3 show the results.

The results show that the severity levels of musculoskeletal and sensory discomfort can

be grouped into the following categories roughly from severe to moderate as Level 1 (neck

and shoulder); Level 2 (eyes, shoulder joint/upper arm, and upper back); Level 3 (low

back and head); Level 4 (fingers, forearm, and wrist); Level 5 (feet, knees/shin, hips, andthighs); and Level 6 (mood). It is worth mentioning that this testing is for multiple means

comparison, which probably has classification results that are too rough for nonlinear data.

In the following text, we give a much more detailed classification scheme for the severity

levels of musculoskeletal and sensory discomfort.

Regarding weekly severity changes over time, Table 4 demonstrates some of the elemen-

tary evaluations. It can be seen that the changes of musculoskeletal and sensory discomfort

ratings of mood, eyes, neck, head, shoulder, shoulder joint/upper arm, and upper back are

maximal and those of fingers, wrist, low back, forearm, knees/shin, feet, thighs, and hips

are minimal.

The discomfort ratings of hips and thighs were nearly constant over the work week,

meaning that there was practically no association between the computer-related workload



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3.4

3.6

3.8

4

4.2

4.4

0 1 2 3 4 5 6

Time (day)/Computer-Related Workload

SymptomO

utcomes

neck

shoulder

eyes

upper back

shoulder joint/upper arm

thighs

hips

knees/shin

feet

wrist

forearm

fingers

head

low back

Figure 3 Estimates of discomfort ratings (5= feel good, 1 = feel very uncomfortable) over awork week (1 = Monday, 5 = Friday).

and fatigue symptoms in these body regions. A weak association exists between computer-

related workload and fatigue symptoms in fingers, wrist, low back, forearm, knees/shin, and

feet.

3.2. Dose-Response Curve Fitting and Analysis

Recall that the dose-response relationship between the time and discomfort ratings has been

described in Equation 6 with three parameters (a1, a2, and a3) dependent on body region as

listed in Table 1. The model parameters have biological meanings, supposing accumulated

fatigue caused by the exposures leading to discomfort. We provide results only for the

following body regions that showed the maximal change of discomfort during the weekly

work hours: eyes, neck, head, shoulder, shoulder joint/upper arm, and upper back.

The parameters a1, a2, and a3 represent the baseline response, the maximum response,and the time that provokes a response halfway between the baseline and the maximum a3,

respectively. From Table 1 we can see that a3, describing the halfway result between the

outcomes from Monday (t= 1) to Friday (t= 5), presents the minimum value (2.19 days)

for the neck. This means that the neck got tired much more quickly than the other body

regions. The halfway results of the studied body regions in increasing order are: neck (2.19

days), shoulder (2.25 days), eyes (2.29 days), head (2.63 days), shoulder joint/upper arm

(2.63 days), and upper back (2.78 days). The order is consistent with that presented in the

previous section (Tables 2 and 3). Note that all of these body regions have the halfway

outcomes on the days before Wednesday (values are less than three or t = 3). However,

such a halfway outcome for feet appears at day 4.14, which means that no discomfort or a

little discomfort was developed in feet among the study subjects (see Table 1).



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TABLE 2. Results of WallerDuncan k-Ratio t Test forDiscomfort Rating Model

Waller Grouping Mean Body Region

A 4.58 Mood

B 4.31 Thighs

B 4.30 Hips

B 4.24 Knees/Shin

B 4.22 Feet

C 4.07 Wrist

C 4.05 Forearm

C

C 4.04 Fingers

C

D C 3.93 Head

D

D E 3.87 Low back

D E

D E F 3.80 Upper back

D E F

D E F 3.80 Shoulder joint/Upper arm

E F

E F 3.78 Eyes

F

GF 3.67 Shoulders

GG 3.63 Neck

Note. Means of ratings with the same letter are not significantly differentat p < 0.05. Severity levels are ranked in alphabetical order.

With respect to the changes of discomfort ratings during the work week presented as

a2 a1, the decreases in units of discomfort are in descending order: eyes (0.17), neck

(0.16), head (0.13), shoulder (0.13), shoulder joint/upper arm (0.12), and upper back (0.12).

The discomfort rating for mood exhibited somewhat different features. An upside downU-shaped association between the computer-related workload or the time duration and

discomfort rating is demonstrated. Starting from the lighter discomfort outcome of mood

on Monday (characterized by a low discomfort rating), the severity increases until Thursday,

when the discomfort rating reaches the maximum, and then the severity decreases on Friday

marked by the highest comfort rating.

4. DISCUSSION

We present the epidemiology and biological plausibility of the obtained analysis results in

this section.



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TABLE 3. Results of WallerDuncan k-Ratio t Test forMusculoskeletal and Sensory Discomfort Rating Model

Waller Grouping Mean Body Region

A 4.07 WristA 4.05 Forearm

A 4.04 Fingers

B 3.93 Head

B 3.87 Low back

C 3.80 Upper back

C 3.80 Shoulder joint/Upper arm

C 3.78 Eyes

D 3.67 Shoulders

D 3.63 Neck

Note. Means of ratings with the same letter are not significantlydifferent at p < 0.05. Severity levels are ranked in alphabeticalorder.

4.1. Findings

The analyses show first that the largest change of discomfort level over a weeks time was

found in eyes, followed by neck, head, shoulders, shoulder joint/upper arm, and upper back.

The weekly discomfort level of hips and thighs kept nearly constant, meaning that computer-

related workload or time duration seemed to be unrelated to discomfort in these body parts.Second, the fastest rate of change was discovered in neck, shoulders, eyes, head, shoulder

joint/upper arm, and upper back (in descending order). The rate varied nonlinearly over the

TABLE 4. Change of Discomfort in Different Body Regions During a Week

Body Regions Weekly Change Weekly Change/Initial Discomfort Rating

Head 0.126 0.032

Eyes 0.161 0.042

Neck 0.150 0.040

Shoulder 0.123 0.033Shoulder joint/Upper arm 0.117 0.030

Forearm 0.049 0.012

Wrist 0.066 0.016

Fingers 0.075 0.018

Upper back 0.117 0.030

Low back 0.049 0.012

Hips 0.010 0.002

Thighs 0.019 0.004

Knees/Shin 0.047 0.011

Feet 0.044 0.010

Mood 0.948 0.019



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week. The rate of change was faster in the neck at the beginning of the week and gradually

slowed down over the week. A similar pattern was seen in the shoulders, upper arms, eyes,

and head. Nevertheless, we identified that the major discomfort problems for office staff

with computer-related workload existed in eyes, neck, shoulder, head, shoulder joint/upper

arm, and upper back.

Surveys of computer users show that eye and vision problems are the most frequentlyreported health-related problems, generally occurring in 70% to 75% of computer users

(Collins, Brown, Bowman, & Caird, 1991; Dain, McCarthy, & Chan-Ling, 1988; Smith,

Cohen, & Stammerjohn, 1981). A survey by the American Optometric Association con-

cluded that eyestrain, blurred vision, and headache are the top three vision complaints

associated with video display unit work (Dainoff, Happ, & Crane, 1981; Sheedy, 1992).

Moreover, the relationship between computer use and MSDs of the neck and upper extrem-

ity has been well documented (Fahrbach & Chapman, 1990; Punnett & Bergqvist, 1997b).

Therefore, our findings are consistent with those reported in the literature.

The relationship between the computer-related workload or time duration and the muscu-

loskeletal outcomes has not been consistent in the literature. A report by Swanson, Galinsky,and Cole (1997) explored the effects of a number of different keyboard designs and found

that reported levels of discomfort and fatigue were low for all keyboard conditions. Increased

activity in the deltoid and trapezius muscles has been found when the use of a mouse has

been compared with that of the keyboard, explaining the development of discomfort in the

neck and shoulders. For the hand/wrist disorders, a combination of ergonomic risk factors

(mechanical pressure on the soft tissue of the forearm and wrists) and work organizational

factors (hours of keying a day) have been identified as risk factors (Shuval & Donchin,

2005). A possible explanation for the weak relationship between computer-related workload/

time duration and the musculoskeletal outcomes on the body regions of the forearm, fingers,

and wrist in our study includes the lack of highly repetitive typing work. Skilled typists

can easily exceed 500 keystrokes per minute, for a rate of 30,000 keystrokes per hour ofcontinuous typing (Nelson, Treaster, & Marras, 2000). In this study, the average typing

rate was less than 900 keystrokes per day. Hence, both the frequency and the time duration

of the typing work were too low to demonstrate a strong causal relationship between the

computer-related workload/time duration and discomfort in forearm, fingers, and wrist.

The analysis demonstrated development of discomfort toward the end of the week in

several parts of the body. Although it had the biggest change in discomfort over time, the

outcome of mood exhibited a somewhat different feature. Unlike other body regions, the

discomfort rating of mood decreased until the threshold day (Thursday), and then increases.

Individual differences in mood were large compared with body regions (Lu & Takala, 2008).

Psychological factors play a major role in moods outcome pattern.Our findings also suggest that the weekend break significantly reduced the discomfort

problems in the eyes and head. Even though the discomfort changes over time were biggest

in these body regions, the general discomfort severity levels were not the highest ones. An

effective workrest schedule can be an economical way to reduce musculoskeletal and other

problems of computer users.

4.2. Limitations

This study has some limits, which may limit the interpretation. First, the size of the study

population was not large. Missing data existed in the data sets especially for the survey data

of the musculoskeletal and other outcomes. Moreover, collection of time series of these data



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was short, and the sample sizes varied very much in the data sets. The longest duration was

three weeks. The collected time duration varied, and it was impossible to select a common

time duration for all the subjects. Therefore, our study was limited to a weekly model only.

However, in practice, people recover during the weekends, so the musculoskeletal and other

discomfort often demonstrate periodic features across work weeks. Hence, a weekly model

should be appropriate for this study.Second, assessment of the computer-related workload on the basis of cumulative duration

of keystrokes and mouse clicks was somewhat crude. This studys population consists of,

for example, secretaries, technicians, architects, and engineers whose work is composed of

multiple tasks, each of them with its own specific exposure profile. The associations with

work-related exposures occurring as use of keyboard or mouse in combination with other

tasks should be considered in future studies. Our exposure measurement did not include

environmental factors that can affect discomfort on eyes and head (e.g., lighting, ambient

temperature, humidity).

Third, self-reported response for musculoskeletal outcomes was adopted. Self-reports

might be prone to error; however, the occurrence of mistakes in objectively measuredMSDs is so low in this kind of work that the subjective measures remain the best option.

Finally, the dose-response curve was relatively flat, which means that discomfort had

a weak response on the computer-related workload in this study. Because of the above-

described limits as well as the light repetitive computer work, it is not surprising that such a

dose-response curve was obtained. At this point, it is not disputable as to the dose-response

relationship among intensity of computer-related workload and fatigue changes in different

body regions as the workload might be too small to generate a detectable fatigue response.

Longer time series data are needed to examine the relationship.

5. CONCLUSIONS

The new mathematical model handled the time-dependent relationships of the data on the

use of computers and subjective symptoms of office workers well. It is obvious that analysis

of cross-sectional data, which is the most common technique in musculoskeletal studies,

cannot provide such broad findings, especially related to dynamic changes. Most of our

findings are consistent with those in the literature, which demonstrates that the developed

mathematical methodology is a flexible and broadly applicable one, and may be used by

a variety of epidemiological researchers. Moreover, the identified findings can lead us to

characterize the evolution of musculoskeletal and other symptoms in relation to computer-

related work, thus helping to prevent these disorders.

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of Optometry and Physiological Optics, 65, 162167.Dainoff, M. J., Happ, A., & Crane, P. (1981). Visual fatigue and occupational stress in VDT operations.

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