Ciccarelli et al. - 2011 - Diversity of tasks and information technologies used by office workers at...

8/10/2019 Ciccarelli et al. - 2011 - Diversity of tasks and information technologies used by office workers at and away from

1/13

This article was downloaded by: [Instituto De Ciencias Matematicas]On: 01 November 2011, At: 09:41Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House37-41 Mortimer Street, London W1T 3JH, UK

ErgonomicsPublication details, including instructions for authors and subscription information:

http://www.tandfonline.com/loi/terg20

Diversity of tasks and information technologies used b

office workers at and away from workMarina Ciccarelli

a, Leon Straker

b, Svend Erik Mathiassen

c& Clare Pollock

d

aSchool of Occupational Therapy & Social Work, Curtin Health Innovation Research

Institute, Curtin University, Perth, AustraliabSchool of Physiotherapy, Curtin Health Innovation Research Institute, Curtin University,

Perth, AustraliacDepartment of Occupational and Public Health Sciences, Centre for Musculoskeletal

Research, University of Gvle, SwedendFaculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin Universit

Perth, Australia

Available online: 25 Oct 2011

To cite this article:Marina Ciccarelli, Leon Straker, Svend Erik Mathiassen & Clare Pollock (2011): Diversity of tasks and

information technologies used by office workers at and away from work, Ergonomics, 54:11, 1017-1028

To link to this article: http://dx.doi.org/10.1080/00140139.2011.609913

PLEASE SCROLL DOWN FOR ARTICLE

Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form toanyone is expressly forbidden.

The publisher does not give any warranty express or implied or make any representation that the contentswill be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses shouldbe independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims,proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly inconnection with or arising out of the use of this material.
http://www.tandfonline.com/page/terms-and-conditionshttp://dx.doi.org/10.1080/00140139.2011.609913http://www.tandfonline.com/loi/terg20


2/13

Diversity of tasks and information technologies used by office workers at and away from work

Marina Ciccarellia

*, Leon Strakerb

, Svend Erik Mathiassenc

and Clare Pollockd

aSchool of Occupational Therapy & Social Work, Curtin Health Innovation Research Institute, Curtin University, Perth, Australia;b

School of Physiotherapy, Curtin Health Innovation Research Institute, Curtin University, Perth, Australia; c

Department ofOccupational and Public Health Sciences, Centre for Musculoskeletal Research, University of Gavle, Sweden;

dFaculty of

Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, Australia

(Received 29 August 2010; final version received 28 July 2011)

Background. Computer use is associated with musculoskeletal complaints among office workers. Insufficientexposure diversity between tasks is a proposed etiological factor, but little information exists on diversity of tasksand information and communication technologies (ICT) among office workers. Method. Direct observation andself-report data were collected on tasks performed and ICT used among 24 office workers, over 12 h in work andnon-work environments. Self-reports were repeated on four additional days. Results. Observations were for a mean[SD] 642[40] min. Productive tasks comprised 63% of observations, instrumental 17%, self-care 12% and leisure8%. Non-ICT tasks comprised 44% of observations; New electronic-based ICT36%; Oldpaper-based ICT15%,

andCombined ICTtasks 4%. Proportions of tasks and ICT use differed between environments and days. Conclusion.Information about diversity in tasks and ICT provides the basis for future investigations into exposure variationin ICT-intensive environments and possible musculoskeletal health risks.

Statement of relevance: Information and communication technologies (ICT) provide office workers access toperform work-related tasks after work hours and in away-from-work locations. Musculoskeletal disorder riskassessment for office workers should account for actual tasks performed over a work day, including away from workexposures. This study provides rich, detailed data on occurrence of tasks performed and ICT used by office workersthroughout the day.

Keywords: office workers; ICT; tasks; direct observation

1. Introduction

Many 21st century workers use computers and other

forms of new information and communication tech-nologies (ICT) at the workplace. Rapid developments

in ICT provide office workers with more powerful and

faster work tools than their predecessors of less than

20 years ago. Ninety-four percent of all Australian

businesses access the Internet (Australian Bureau of

Statistics 2009a). Use of ICT away-from-work is also

increasingly commonplace. Recently, 78% of all

Australian homes reported having at least one

computer and 72% had Internet access (Australian

Bureau of Statistics 2009b). Expectations of where,

when and how workers are performing their jobs are

changing due to the spatial and temporal mobility that

is afforded by the use of electronic ICT.

Musculoskeletal disorders (MSDs) related to com-

puter use at work are of international concern (Gerr

et al. 2004). While surveys have reported disorder

prevalence rates among computer users (Gerr et al.

2002), actual lost-time injury data is scarce. In

Australia, 90% of lost-time injuries in clerical jobs

were reportedly due to body stressing of the

musculoskeletal system, although the proportion

directly attributable to computer use was not reported(National Occupational Health and Safety

Commission 2006).

Most clerical tasks and communications can be

performed electronically while seated at a workstation.

This contributes to growing concerns about the impact

of inadequate diversity in the task patterns and a

resulting lack of overall variation in working postures

on the health and well-being of office workers (Straker

and Mathiassen 2009).

1.1. Variation and diversity of exposures

Excessive loading of muscles and joints was widely

thought to be a major cause of computer-related

MSDs with early research and intervention focused on

reducing amplitude exposure levels (Grandjean 1969,

Aara s 1987, Jonsson 1988). However, even very low

levels of muscle loading during sedentary work

have been associated with MSDs (Westgaard and

*Corresponding author. Email: [email protected]

Ergonomics

Vol. 54, No. 11, November 2011, 10171028

ISSN 0014-0139 print/ISSN 1366-5847 online

2011 Taylor & Francis

http://dx.doi.org/10.1080/00140139.2011.609913http://www.tandfonline.com


3/13

Winkel 1996), and currently attention is being given to

the influence and effects of exposure variation and

diversity rather than to the exposure level per se.

The term variation in this context describes a

change in exposure with respect to time (Mathiassen

2006). Exposure refers to external exposures (what is

done) such as the task performed or type of ICT used,as well as internal exposures (how it is done)

including posture and muscle activity. Within a given

time period, tasks performed may involve different

actions, postures and muscle activity to different

extents, and exposure during that period may thus be

more or less variable. In jobs presenting the worker

with prolonged periods of repetitive actions or

constrained postures, more variation is generally

believed to be a necessary remedy against MSDs

(Mathiassen 2006). This conviction is based on both

epidemiologic evidence and on physiologic hypotheses

proposing that continuous activation of specific muscle

fibres is a causal mechanism for the development ofmyalgia (Ha gg 1991). Some studies do, indeed, support

that short interruptions in shoulder muscle activity

(gaps) are beneficial to musculoskeletal health

(Veiersted et al. 1993, Ha gg and A stro m 1997). These

interruptions, as well as redistributions of muscle

activity within the muscle may be triggered by

variations in load.

Diversity describes the difference in exposure

between different tasks or time periods. Combining

diverse tasks, i.e. tasks which have different actions,

postures and muscle activities, would result in greater

overall variation (Mathiassen 2006). For example,

there may be little variation in arm postures whileperforming task A during a given time period.

However, subsequent tasks B and C may involve

different arm postures and thus provide varying

postural patterns overall. Diversity may be measured

between short epochs (i.e. consecutive work cycles

within a day) or over longer periods such as across

days. Lack of diversity and variation in what workers

do and how they perform their work tasks has been

suggested as an underlying risk factor for MSDs

(Henning et al. 1997, Mclean et al. 2001, Balci and

Aghazadeh 2003).

While (lack of) diversity is recognised as an

important risk factor, surprisingly little research has

been devoted to understanding the occurrence of

diversity and variation in occupational settings, and

the effects of introducing more diverse tasks and more

variation into jobs. Workrest schedules (Henning

et al. 1997, Mclean et al. 2001), workstation exercise

(Fenety and Walker 2002) and job rotation/job

enlargement (Fernstro m and A borg 1999, Mathiassen

et al. 2003, Mo ller et al. 2004, Schneider et al. 2005,

Mathiassen 2006) have been trialled to increase

variation and thus reduce or prevent discomfort

associated with sustained postural and muscle loads,

even among people working with computers, but the

evidence has been inconclusive (Mathiassen 2006). One

reason is that the effects of these initiatives targeting

exposure variation have not been assessed in

quantitative terms. This in turn illustrates a generalneed for metrics that quantify diversity and variation,

and for the application of such metrics in jobs.

Workers exposures can be measured via

questionnaires, observations and direct measurements

(Winkel and Mathiassen 1994). To assess external

exposure, information on the tasks performed is

required. It has been suggested that worker self-reports

of time spent in different work tasks, and in computer

keyboard and mouse usage in particular, may be

substantially different from that which is identified

using observations or activity monitoring (Homan and

Armstrong 2003, Heinrich et al. 2004, Unge et al.

2005). Therefore, independent observation may bepreferable for assessing task occurrences. However,

this method is resource intensive and self-report could

be valuable in many studies if its concordance with

independent observation was known.

While work sampling (sporadic observation and

identification of tasks) within a day may be effective in

determining the structure of very regular jobs,

continuous all day observations are necessary to

accurately document the time-line of tasks in

non-cyclic or spontaneous jobs such as office work.

However, task patterns may fluctuate from day to day

and thus a better understanding of differences in tasks

between work days would also be useful. Thedifference between days is a measure of variation in

itself.

1.2. Exposure variation associated with different ICT

The type of ICT used may influence variation and

diversity in exposures. Job enlargement and job

rotation interventions among office workers are based

on the premise that interchanging tasks that are

mentally and physically diverse will reduce overall risk

levels. The variation in joint and muscle loading

within, and diversity between, computer-based and

non-computer-based tasks has been investigated by

others (Fernstro m and A borg 1999, Arvidsson et al.

2006, Richter et al. 2009); however, more information

is needed about exposure variation within and diversity

between other ICT types.

1.3. Work vs. non-work exposures

Prior research on computer-related MSDs has focused

on exposures during work tasks at the workplace.

1018 M. Ciccarelliet al.


4/13

However, non-work activities such as self-care, leisure

andinstrumentaltasks (e.g. domestic chores) may also

influence the risk of MSDs (Van Den Heuvel et al.

2005). However, it is not known whether internal

exposures during these other tasks are different to

exposures during productive tasks, and thus enhance

overall diversity, or whether internal exposures duringthese other tasks are similar and thus provide no

increase in diversity but rather increase the risk of

MSDs.

Similarly, what individuals do away-from-work

may also impact on their MSD risk. Activities in

away-from-work locations may increase diversity and

thus promote recovery from the physical and/or

mental stressors of the workplace; or may compound

the effects of awkward, constrained or prolonged

postures and sustained muscle loading because of

inadequate workstation design and/or non-work-

related psychological stressors.

It is therefore important to know how tasks aredistributed at work and away-from-work locations,

and how different ICT types are used in these tasks and

at these locations. Therefore, this study aimed to

quantify the occurrence ofproductive, self-care, leisure

and instrumental tasks and the different types of ICT

used in work and away-from-work locations among

office workers. It also aimed to compare self-report

and independent observer methods for monitoring

tasks, and whether one day of sampling will be

representative for the four following days. Internal

exposures were assessed simultaneously using direct

monitoring of participants postures and upper body

muscle activity and will be reported separately.

2. Method

2.1. Sample

A convenience sample of 24 right-handed adults

(12 female) with a mean [SD] age 38.5 [8.4] years;

height 169.0 [8.6] cm and weight 70.3 [14.1] kg was

recruited.

Participants performed office-based work at an

Australian public university, and included 14 admin-

istrative staff, 3 academic staff and 7 doctoral students.

Participants were eligible for inclusion if they reported

performing electronics-based tasks (i.e. computer,

television, telephone) at work and/or away-from-

work for at least 30 min per day, and were willing to

be observed during one entire work day. Participants

who reported having a congenital or acquired MSD

that impacted on functional performance and required

on-going medical care, and those who wore bi-focal

lenses were excluded.

This study was approved by the Human

Research Ethics Committee at Curtin University of

Technology, and participants provided written

informed consent.

2.2. Data collection

Participants were observed in real time during one

work day, over 12 h duration (9 am9 pm) toinclude work and after-work tasks, and within

participants natural environments. Direct

observations of tasks were recorded in an electronic

task log using time-stamped software

(PocketCreationsTM, OT International, Perth,

Australia), with a minute-to-minute resolution. Tasks

of less than 1 min duration were excluded.

Observations were performed by one of two observers,

trained during pilot studies to improve inter-rater

consistency of observations. Direct measurements of

posture and muscle activity were taken simultaneous

to observations; however, these results are not

presented in this article. The monitoring equipmentwas composed of inclinometers located on the head,

upper back and right upper arm, with

electromyography electrodes over the right upper

trapezius, deltoid and forearm extensor muscles along

with associated leads and data storage device worn

around the waist.

2.3. Classifying tasks

The various tasks that people typically engage in

were listed in a task observation template in the task

log. Tasks were grouped into categories including

productive, self-care, leisure, andinstrumentalactivitiesof daily living, as defined by the American

Occupational Therapy Association (Youngstrom et al.

2002). Productive tasks included work activities in

either paid or voluntary employment, or educational

activities.Self careincluded tasks related to taking care

of ones own body (e.g. toileting, bathing, dressing,

eating, sleep and sexual activity). Leisure included

non-obligatory, intrinsically-motivated tasks people do

for recreation or pleasure, such as playing a sport or

reading for pleasure. Instrumentalactivities of daily

living included complex daily tasks that individuals

complete to sustain and manage their living in the

community, and examples included management of

a household (chores), travel in the community, and

shopping.

2.4. Classifying categories of information and

communication technology (ICT)

The different types of ICT that people use, and the

input interface were included as a category of the

observation template in the task log. The following

Ergonomics 1019


5/13

definitions were developed to discriminate between

different ICT. New ICT included electronic interfaces

including desktop and laptop computers, hand-held

computers or video games, television, telephones,

calculators, photocopiers and faxes. Old ICTincluded

paper-based methods for completion of tasks such as

reading a book, and writing or drawing with a pen orpencil. Combined ICT described tasks involving

simultaneous use of New and Old ICT, for example,

composing a written document using a computer while

reading from a book or handwritten notes. Non-ICT

described tasks involving neither Old nor New ICT,

such as sports, board games, eating a meal or self-care

tasks.

2.5. Documenting observations

Observed tasks and the ICT used were logged into a

time-stamped data file by one of two trained observers,

at a 1-min resolution. For each observed taskperformed, detailed information was entered recorded

into the electronic activity log, including: (i) task

category (productive, self-care, leisure, instrumental)

and the type of task; (ii) type of ICT being used (Old,

New, Non-ICT), and the device (laptop, desktop, hand-

held computer) and control (keyboard, mouse, touch-

pad, buttons); (iii) geographical location in which the

person was functioning (work or away-from-work);

(iv) gross posture (sitting, standing, walking); and (v)

use of upper extremity support from the external

environment or the persons own body.

Reports of any discomfort experienced were also

collected during natural and scheduled breaks in tasksover the recording period. A front and rear aspect

body map with a scale of 010 (with 0 no discomfort

and 10worst possible discomfort) was used to assist

participants identify location and intensity (Straker

1999).

2.6. Task diary

At the end of the observation, participants completed

a hand-written diary of the tasks they performed that

day. The task diary (in 30 min periods) included the

main tasks performed and the ICT type and input

device used. Participants were asked if the observed

tasks were typical for that work day. Participants

also completed a hand-written task diary over

the next four consecutive work days (reported as

days 25).

2.7. Data analysis

The task log for each participant, containing time-

stamped codes for location (at work, away-from-work),

task category (productive, self-care, leisure,

instrumental) and type of ICT used (Old, New,

Combined, Non-ICT), was analysed using a

custom-designed program in LabVIEWTM (National

Instruments, Austin, Texas). Output included

descriptive statistics about the category of interest

(i.e. geographical location, task category or type ofICT used), and for each category the accumulated time

was calculated in absolute terms (minutes) and as a

proportion of the total observation period. Data from

participants who did not perform particular tasks

using the different types of ICT in a particular location

were registered as a value of 0 min to calculate group

means.

Data from the self-reported task diaries were

used to give information on the amount of time spent

in the four task categories (productive, self-care,

leisure and instrumental activities of daily living) and

using the different types of ICT (Old, New, Combined

and Non-ICT). Data from task observations on theday of recording were compared to self-reported

tasks in the diary completed by participants at the

end of the recording period, using Wilcoxon

signed-rank tests (in SPSS,v.17.0). To assess how

representative the day of recording was, Friedman

analysis of variance (in SPSS, v.17.0) was performed

on data from the task diaries, comparing time

spent performing different tasks and using different

ICT on the day of recording (day 1) and each of the

next four individual working days; as well as

comparing time spent during day 1 to the average

of days 25. A critical alpha probability level of 0.05

was used.A variance component analysis (in Excel) was

performed on the self-report diary data from the

non-observed days 25, in order to quantify variability

between days within participants as well as variability

between participants. Variability between days was

also analysed in terms of the dispersion (SD) of

grouped averages of time spent performing tasks and

using ICT across the four days.

3. Results

3.1. Tasks performed

Observation data was obtained for a mean [SD] total

of 642[40] min per participant. This is less than the

intended duration of 720 min per participant,

reflecting the time required to complete procedural

tasks related to the study that were not part of the

participants daily routine, including the end of day

diary and questionnaire. Approximately, two-thirds of

the observation time was spent at the workplace

(432[48] min) and one-third (210[28] min) away from

the workplace.



6/13

3.1.1. Total time spent in different task categories

The group mean [SD] of the participants total time

spent in productive tasks (405[122] min) accounted for

63% of the observation period1, compared to 17% for

instrumental (106[57] min), 12% for self-care (75[46]

min) and 8% for leisure (54[39] min).

3.1.2. Proportion of time spent in different task

categories at work and away-from-work

When at work, participants performedproductivetasks

for 83% of the time (356[141] min) with little leisure

time (9[16] min). However, 23% of the time away-

from-work was also spent performing productive tasks

(49[81] min). This represented more than the propor-

tion of time spent in self-care and leisure tasks when

away-from-work. Instrumental tasks comprised 39%

(79[50] min) of the time away-from-work.

3.2. ICT used

3.2.1. Total time spent using different ICT

New ICT accounted for 36% (234[118] min) of the

observation period; Old ICT accounted for 15%

(98[73] min), Combined ICT tasks 4% (24[30] min),

andNon-ICTtasks accounted for about 44% (285[89]

min).

3.2.2. Common tasks using different ICT

A summary of the tasks performed using New, Old,

Combinedand Non-ICTduring the observation periodis presented in Table 1.

3.2.3. Proportion of time using various types of ICT

at work and away-from-work

New ICTwas used 44% of the time at work

(191[126] min), compared to only 20% of the time

when away-from-work. This included using

non-computer-basedNew ICT such as photocopiers,

fax machines, telephones and television. Thecomputers used at work were predominantly desktop

computers. Laptop computers were used by four

participants; and hand-held computers by only two.

Forty percent of the time at work was spent using

computers, while only 14% of the time away-from-

work was spent using computers. Regardless of the

location, when computers were used, it was usually

without any other ICT.

Old ICTwas used 19% of the time at work

(83[63] min), and 8% of the time away-from-work

(16[39] min). Combined ICT comprised 11% (24[30]

min) of the time at work; and a negligible period

away-from-work (1[3] min). Combined ICTtasks mostoften involved the simultaneous use of the telephone

and writing information on paper. Minimal time was

spent using computer-based CombinedICT (e.g.

computer and hard copy text) at the workplace (4%)

and not at all away-from-work. Non-ICTcomprised

70% of the time away-from-work (148[103] min).

3.2.4. Time spent using different ICT when

participating in different task categories

The mean total time spent using different ICT when

performing different categories of tasks was

determined (Table 2). The most time was spent inproductivetasks using New ICT. In contrast, New ICT

Table 1. Tasks performed using different ICT during the observation period.

ICT typePerformed at

work onlyPerformed

away-from-work onlyPerformed at work and

away-from-work

Old Meeting Play with childrenTeaching

New Teaching Read emailPhotocopying Compose email

Compose documentEdit documentSearch Internet

Talk on the phoneWatch video/DVDFilm with camera

Combined Composedocument

Edit documentTalk on the phone

Non Drive car MeetingTalk to family/friends Eat meal/snackSing/act Drink coffeePlay musical instrument ToiletingTidy house Collect printingChildcare and play Prepare meal/snackLaundry Wash dishes

Ergonomics 1021


7/13

was rarely used for leisure. Old and Combined ICT

were used mostly for productive tasks. Non-ICT was

used to perform all four task categories.

3.2.5. Time spent using different ICT in different task

categories at work and away-from-work

The mean total time spent in different task categories

using different ICT at work is presented in Figure 1;

and away-from-work locations in Figure 2. The large

standard deviations indicate participants differed in the

time spent performing tasks using these ICT, and/or

that exposure differed considerably between days

within individuals. New ICT was most used when

performing productive tasks at work. Non-ICT was

used across all task categories in both work and away-

from-work locations.

3.3. Comparison of self-reported and observeddurations of tasks and ICT used

The amount of time (median[SD] hours) spent in each

task category as recorded from the task observations

compared to the self-reported end of day diary on day

1 is shown in Table 3. There were differences in the

time participants reported performing productive and

leisuretasks, and using New and Combined ICT, when

compared to the observed data.

3.4. Comparison of 5-day diary data

Twenty-three of the 24 participants completed the end

of day questionnaire. Seventeen participants reported

that the tasks performed and their durations on the

day of observation were representative of their typical

activities on that day of the week. Four participants

reported they would have usually participated in

vigorous physical exercise but did not because they

Table 2. Group mean [SD between subjects] of individualstotal time (minutes) by task category and ICT type.

Task

ICT type Productive Self-care Leisure Instrumental

Old 90 [74] 3 [10] 0 [0] 6 [20]New 211 [105] 4 [17] 16 [26] 3 [14]Combined 27 [34] 0 [0] 2 [6] 0 [0]Non 82 [55] 68 [38] 36 [40] 11 [48] Table 3. Comparison of time (median; range among

subjects) (hours) engaged in tasks on day 1 betweenobserved and self-reported diary data.

Median( range) (h) Wilcoxonsigned-Rank

testTask Observed Diary

Productive 6.6 (3.812.3) 7.5 (6.010.0) Z72.10;p .036

Self-care 1.2 (6.010.0) 1.2 (0.52.5) Z70.99;p .324

Leisure 0.8 (0.02.1) 1.2 (0.04.0) Z 72.38;

p .017Instrumental 1.5 (0.35.3) 1.0 (0.04.0) Z71.83;

p .068ICT type

Old 1.8 (0.23.5) 1.0 (0.04.5) Z70.763;p .445

New 3.9 (1.07.2) 5.5 (0.58.0) Z73.224;p .001

Combined 0.3 (0.02.1) 0.0 (0.04.5) Z72.207;p .027

Non 4.4 (1.67.0) 4.5 (0.09.5) Z70.122;p .903

Figure 2. Group mean [SD between subjects] ofindividual total time spent using different ICT duringdifferent categories of tasks away-from-work.

Figure 1. Group mean [SD between subjects] of individualtotal time spent using different ICT during differentcategories of tasks at work.



8/13

thought wearing the direct monitoring equipment

would restrict their participation. The remaining two

participants reported not going to the grocery store,

and one participant did not attend a religious function

as planned, because they felt embarrassed by wearing

the visible direct monitoring equipment in public.

3.4.1. Self-reported time spent in different task

categories across different days

Table 4 shows the mean [SD between subjects] time

participants reported performing the different task

categories during each of five work days. Friedman

analysis of variance identified a systematic difference in

productivetasks between day 1 (the day of observation;

7.9 h) and day 2 (p 0.033), day 4 (p 0.033) and day

5 (p 0.039). Time spent in productive tasks on day 1

were also different to the mean of days 25 (6.9 h;

p 0.003). For leisure tasks, time spent on day 1 was

different to the time spent on day 4 (p 0.022); andalso to the mean of days 25 (p 0.022). The increase

in leisure time on non-observed days is reflected in an

increase in total reported time on days 25; however

the data in Table 4 also suggests that there may be a

trend for less time on productivetasks on non-observed

days. There were no significant differences in the time

spent performing self-care and instrumental tasks on

the day of observation compared to the subsequent

four working days.

At both a group level and individual level, diversity

across the non-observation non-direct monitoring

days (i.e. days 25) was least for self-care tasks, while

productivetasks had the greatest variability (Table 5).

3.4.2. Self-reported ICT use across different days

Table 4 also shows the mean [SD] time spent using

different ICT on the day of observation compared to

the subsequent four working days as reported in the

task diary. Friedman analysis of variance identified

no significant differences in exposure to the different

ICT across days.

Table 5 shows that the variability (measured as SD)

between participants in time reportedly spent using

different ICT was least for Combined ICTand most for

New ICT. At a group level, variability between dayswas greatest for Non-ICTandNew ICT. At an

individual level, variability for Combined ICTwas

only about one quarter that of other ICT types.

4. Discussion

4.1. Task and ICT exposures beyond the workplace

Population studies indicate that adults do use

computers both at work and away from the workplace

duringproductive, leisure and instrumentaltasks

(Australian Bureau of Statistics 2009a, 2009b);

however, there is limited attention given to exposures

during non-productive tasks and in locations awayfrom the workplace. The participants in the current

study performed productive tasks away-from-work,

including preparation of teaching materials and

research papers. It is not uncommon for many

academics and researchers to take work home as

needed, and the university in this study offers and

promotes a formal home-based work agreement for

employees whose job duties are compatible with

working from home (Curtin University 2011). Many

office workers are using ICT to telework in away-from-

work locations including the family home (Haddon

and Silverstone 1992, Hardill and Green 2003).

Although there is debate as to what amount and typeof ICT use defines telework (Sullivan 2003), most

studies focus onNew ICT. However, the current study

showed that different ICTs includingNew, Oldand

Combined ICTare used to perform productivetasks in

away-from-work locations. Workers perform

productive work tasks away-from-work for many

Table 4. Mean[SD between subjects] hours engaged in different tasks and using different ICT as self-reported in the taskdiary over 5 days.

Days recorded in task diary

Task 1a

2 3 4 5 Mean of days 25

Productive 7.7 [1.4]* 7.1 [1.8] 7.2 [1.9] 6.3 [1.9] 7.3 [1.5] 6.9 [1.1]Self-care 1.4 [0.6] 1.6 [0.6] 1.4 [0.6] 1.5 [0.7] 1.4 [0.5] 1.5 [0.5]Leisure 1.4 [1.0]* 1.9 [1.5] 2.2 [1.4] 2.6 [1.9] 2.0 [1.3] 2.1 [1.1]Instrumental 1.3 [1.0] 1.2 [0.8] 0.9 [0.9] 1.4 [1.5] 1.0 [1.1] 1.3 [0.8]

ICT typeOld 1.7 [2.2] 1.8 [2.3] 1.8 [2.1] 1.4 [1.8] 1.5 [1.6] 1.6[ 1.1]New 4.8 [2.6] 4.8 [2.8] 4.5 [2.7] 4.4 [2.4] 5.2 [2.5] 4.7 [2.0]Combined 0.4 [1.1] 0.5 [0.9] 0.8 [1.5] 0.6 [1.6] 0.4 [0.7] 0.6 [0.9]Non 5.0 [1.9] 4.8 [2.1] 4.7 [2.4] 5.3 [2.4] 4.2 [2.2] 4.8 [1.7]

Note: aDay of observation; *Differences in time spent (p 0.05) compared to mean of days 25.

Ergonomics 1023


9/13

reasons including to work unpaid overtime to complete

the demands of the job.No other studies, to our knowledge, have described

the tasks that office workers typically participate in

beyond work-related tasks at the workplace. This

study has described the diversity of external exposure

by detailing the participation in different categories of

tasks and use of different ICT in work and away-from-

work locations. Due to the occurrence of ICT work

even at away-from-work locations, it may be insuffi-

cient to only assess an office workers pattern of daily

tasks and exposure to different ICT types only within

the workplace and during traditional 95 work hours.

Thus, our study suggests that understanding an

individuals exposure to different daily tasks, includingproductivetasks, and ICT use requires observation and

measurement in both at work and away-from-work

locations and over extended work hours.

4.2. Diversity of tasks and ICT used

The participants performed a wide range of tasks for

different proportions of time within the 12-h observa-

tion period. Not all participants used computer-based

Combined ICT; however, all other ICT types were used

by all participants for different proportions of time

during the observation. The diversity of task categories

performed and ICT used suggests a potential for

variation in overall postures and muscle activity at

work and away-from-work, provided that these tasks

and ICT types entail sufficiently different exposures.

The distribution of used ICT types depended on the

task category. New and Combined ICT were mainly

used in productive tasks. Computers were used for

email communications, composing and editing docu-

ments and Internet searches, which are similar to the

computer tasks reported in other studies of office

workers at work (Marcuset al.2002, Szetoet al.2005).

Prior studies of office workers have identified computer

use as a risk factor for MSDs (Jensen et al. 2002,

Village 2005, Wahlstro m 2005, Griffiths et al. 2007);

and specifically hours or intensity of keyboard use

(Katz et al. 2000) and mouse use (Blatter and

Bongers 2002, Ijmker et al. 2007). The office workersin the current study had mean daily exposures to

computer-basedNew ICT(2.85 h/day) that were less

than the daily exposures associated with the

development of musculoskeletal complaints reported

in prior studies. However, prior studies have relied on

self-reported estimates of total daily or weekly

computer use (Unge et al. 2005), which are suspected

to result in larger numbers than direct observations as

used in this study. We only documented the periods in

which the participants were actually using the

computer, rather than just sitting at a computer

workstation as using New ICT, and this may explain

why the exposures for computer tasks werecomparatively lower in the current study.

Furthermore, this study shows that when office

workers are not using computers they are using other

forms of ICT to perform a range of different tasks that

may impact on overall risk. Prior studies of office

workers have given little or no attention to the

potentially risk-moderating effects of performing

different tasks and using different ICT types than

those associated with core office work, and while we

suggest that the diversity represented by such activities

would be beneficial, this hypothesis needs more

research.

4.3. Impact of location on diversity and variation

On a typical work day, it is expected that productive

tasks will account for much of an office workers time

at the workplace, and the time spent in non-productive

tasks (self-care, leisure and instrumentaltasks) will be

greater in away-from-work locations.

However, as this study showed, over and above

the time at the workplace, an additional 23% of the

observation time in away-from-work locations was

also spent performing productive tasks. This time is

taken fromleisure, self-careand instrumentaltasks and

thus represents a reduction in the contribution these

tasks might have to exposure variation. For example,

late night long-distance telephone conferences with

international partners may displace leisure or

instrumentaltasks that would normally be performed

in these after work hours.

When at work, participants were observed to

self-select the tasks performed and the order in which

they were done, suggesting a degree of control over

their job tasks. High job control has been associated

Table 5. Variability (SD, hours) between and withinparticipants, of hours reported on days 25 performingdifferent tasks and using different ICT.

Variability

Betweenparticipants

Groupedbetween days

Individualbetween days

TaskProductive 1.14 0.56 1.59Self-care 0.41 0.08 0.49Leisure 0.94 0.43 1.23Instrumental 0.73 0.16 0.88

ICT typeOld 1.17 0.17 1.85New 2.04 0.32 1.74Combined 0.84 0.14 0.39Non 1.65 0.39 1.66



10/13

with worker health (Arvidsson et al. 2008, Tornqvist

et al. 2009). However, control to schedule the order

and duration of work tasks may permit unhealthy

work behaviours. For example, since participants

scheduled most tasks and breaks at their discretion,

there was the possibility that some individuals

remained on certain tasks, due to pending deadlinesor interest in the task, resulting in less diversity of tasks

across the day. Similar findings of the impact of

deadlines and worker autonomy on task variability has

been reported by others investigating task exposures

among office workers (Van Eerdet al. 2009).

Although computer-based ICT were used during

productivetasks at work, more than half of the time at

work was spent performing non-computer based tasks.

Mean total durations of computer exposure were

different between work (192 min) and away-from-

work locations (29 min). Away-from-work locations

provided less exposure toNew ICTbut more exposure

to Non-ICT tasks, probably enhancing variation.ICT exposure was influenced by natural interrup-

tions to tasks in all locations; however, location may

influence the likelihood of natural interruptions. For

example, one participant, at home alone, performed a

productive task using a computer without interruption

for in excess of 2 h.

4.4. The next step in understanding diversity and

variation

Determining the tasks performed and their occurrence

across time is only one factor in determining exposure

and thus the risk of MSDs. A change to another taskmay not create sufficient variation in biomechanical

exposures if it has similar postural and muscular

demands as the task just performed, i.e. if the tasks are

not sufficiently diverse (Richter et al. 2009). Objective

measurement of posture and muscle activity should be

matched to tasks and ICT use, at work and away-

from-work. Knowledge about tasks performed and

ICT used among office workers, combined with

knowledge of the patterns of postures and muscle

loading during these tasks, can assist in better under-

standing the relationship between diversity of external

and internal exposures, the potential for creating

exposure variation by combining the tasks, and

possibly the associated risk or no-risk of MSDs.

4.5. Independent observation versus self-report of tasks

and ICT use

The total time observed in different categories of

tasks and ICT was similar to that reported by the

participants in the task diaries, suggesting that the self-

reported time spent performing different tasks and

using ICT over the working week may be a useful

representation. However, there were some differences

suggesting caution. Time spent performing productive

andleisuretasks, andNew ICTwere over-estimated by

self-report compared to observations and Combined

ICTtime was under-estimated. The 30-min scale in

the task diary compared to the minute-to-minutesensitivity of the observations may have contributed to

these differences. Self-report times using smaller time

period increments (e.g. 15 min) may improve the

accuracy of time estimates, but may also require the

task diary to be completed more frequently throughout

the day to limit recall error. However, this would

introduce unnatural interruptions to typical task

patterns and durations, and thus change task

exposures.

Direct observation provided accurate detailed

information of participants daily task patterns.

However, within groups of office workers, e.g.

secretaries, administrative assistants, there can be awide inter-individual variation in tasks performed, and

between groups there may be even greater variation

with some office workers such as call centre operators

that are exposed to the same computer-based short

cycle tasks repeatedly, while other groups, like the

researchers in the present study, may have more

diversified tasks. Therefore, the task and ICT

exposures reported in this study are not representative

of all workers who use computers. Direct observations

or video recordings can improve precision of task

identification and duration, but these exposure

assessment methods are time-consuming and expensive

(Van Eerdet al. 2009). Self-report measures are lessburdensome to researchers, but obtaining precise data

requires greater commitment by workers to document

all tasks regularly throughout the day, and thus task

diaries are not favoured by workers (Van Eerd et al.

2009). However, self-report measures may be the

only available method practicable for large samples,

unless exposure information can be obtained from

work ICT systems, such as registrations of customer

contacts at call centres, or from registration software

downloaded to the computers of the participants

(Blangstedet al. 2004, Richter et al. 2008, Chang

et al. 2010).

4.7. Sampling tasks and ICT use on 1 day versus

5 days

At a group level, the time spent using different ICT on

the day of observation was similar to that during days

25, suggesting that the pattern of ICT use did not

depend to any notable extent on the day of the week.

Self-careand instrumentaltasks were also

performed for similar amounts across the five work

Ergonomics 1025


11/13

days; however, productive tasks on the day of

observation were performed for significantly longer

than on days 25; while time in leisure tasks during

the observation was less than on days 25. This may

have been due either to systematic effects of having

an observer present or the inability to participate in

certain leisure tasks because of the direct monitoringequipment worn by participants. When participants

were asked about the day of observation, those who

reported differences stated the monitoring equipment

was the reason.

While the observation day did, in some respects,

seem to differ systematically in exposure from non-

observation days, we found a generally large varia-

bility between days in the proportions of task

categories and ICT use of a specific individual. This

suggests that exposure variation for the individual is

increased by doing different tasks on different days, as

compared to the variation obtained during one specific

day.

5. Limitations

The labour intensive nature of the direct observation

limited observations of each participant to one

work day in the current study. More days of

observation may better determine the typical activity

patterns across the week, if indeed such patterns exist

(Wahlstro m et al. 2010). Further studies comparing

tasks and ICT use by office workers on workdays

versus non-workdays are also recommended, to

determine if non-work days enhance exposure diversity

and thus may reduce the risk of MSDs.The study sample was purposively selected from

staff and graduate students at a University. This

combined with the small sample size limits the

generalisability of the study results beyond the study

sample and populations in similar settings.

6. Conclusion

This study has provided the first rich description of the

occurrence of productive, self-care, leisure and instru-

mentaltasks and ICT use of office workers at work and

away-from-work. When performing productive, self-

care, leisureand instrumentaltasks, the 24 participants

used different ICT. Computer-based New ICT and

Combined ICT tasks were alternated with tasks

involving Old and Non-ICT, thereby contributing to

the participants overall daily diversity of tasks, and

possibly even increased exposure variation.

The location of office workers influenced the tasks

performed and ICT used and thus diversity. The

proportion of time spent engaged in the different tasks

changed between work and away-from-work locations.

New ICTwas used more at the workplace during

productive tasks, whilst more Non-ICTwas used

when performing instrumentaland self-care tasks in

away-from-work locations.

There were differences in self-reported and

independent observations of time spent performing

tasks and using ICT. Participant self-reportsover-estimated the time spent using New ICTand

performingproductive and leisure tasks, compared to

observation data. Differences in the time-resolution of

the two measurement methods may have contributed

to over- or under-estimation in the self-report data.

This information about diversity in tasks and ICT,

as well as their diversity between days, provides an

elementary understanding of external exposure to risks

associated with office work, especially computer-based

tasks. This work provides a basis for matching tasks

and the ICT used with associated postures and muscle

loads, which will assist in assessing diversity of internal

exposures at work and away-from-work, anddetermining relationships between exposure variation

and risks of developing MSDs among office workers.

Acknowledgements

The authors wish to thank Mr Paul Davey for writing theLabVIEWTM software programme used for the dataprocessing and Mr James Lyra for assistance with taskobservations. A National Health and Medical ResearchCouncil of Australia Public Health Scholarship and a researchgrant from the Occupational Therapists Registration Boardof Western Australia supported this study.

Note

1. The sum of time spent in different task categories wasless than the total observation period due to roundingerror.

References

Aara s, A., 1987. Postural load and the development ofmusculo-skeletal illness. Scandinavian Journal ofRehabilitation Medicine, 18, 535.

Arvidsson, I.,et al., 2006. Changes in physical workload withimplementation of mouse-based information technologyin air traffic control. International Journal of IndustrialErgonomics, 36 (7), 613622.

Arvidsson, I., et al., 2008. Neck postures in air trafficcontrollers with and without neck/shoulder disorders.Applied Ergonomics, 39 (2), 255260.

Australian Bureau of Statistics, 2009a. 8129.0 Business use ofinformation technology, 200708[online]. Available from:http://www.abs.gov.au/AUSSTATS/[email protected]/Lookup/8129.0MainFeatures1200708?OpenDocument[Accessed 8 August 2011].

Australian Bureau of Statistics, 2009b. 8146.0 Householduse of information technology, Australia, 200809.Canberra: Australian Bureau of Statistics.

Balci, R. and Aghazadeh, F., 2003. The effect of work-restschedules and type of task on the discomfort andperformance of VDT users. Ergonomics, 46 (5), 455465.

http://www.abs.gov.au/AUSSTATS/[email protected]/Lookup/8129.0Main+Features12007-08?OpenDocumenthttp://www.abs.gov.au/AUSSTATS/[email protected]/Lookup/8129.0Main+Features12007-08?OpenDocumenthttp://www.abs.gov.au/AUSSTATS/[email protected]/Lookup/8129.0Main+Features12007-08?OpenDocumenthttp://www.abs.gov.au/AUSSTATS/[email protected]/Lookup/8129.0Main+Features12007-08?OpenDocumenthttp://www.abs.gov.au/AUSSTATS/[email protected]/Lookup/8129.0Main+Features12007-08?OpenDocumenthttp://www.abs.gov.au/AUSSTATS/[email protected]/Lookup/8129.0Main+Features12007-08?OpenDocument


12/13

Blangsted, A., Hansen, K., and Jensen, C., 2004. Validationof a commercial software package for quantificationof computer use. International Journal of IndustrialErgonomics, 34, 237241.

Blatter, B. and Bongers, P., 2002. Duration of computer useand mouse use in relation to musculoskeletal disordersof neck or upper limb. International Journal of IndustrialErgonomics, 30 (45), 295306.

Chang, C., et al ., 2010. Daily self-reports resulted ininformation bias when assessing exposure duration tocomputer use. American Journal of Industrial Medicine,53, 11421149.

Curtin University, 2011.Ethics, equity and social justice: workand family [online]. Available from: http://eesj.curtin.edu.au/employment/workandfamily.html [Accessed 8September 2011].

Fenety, A. and Walker, J., 2002. Short-term effects ofworkstation exercises on musculoskeletal discomfort andpostural changes in seated video display unit workers.Physical Therapy, 82 (6), 578589.

Fernstro m, E. and A borg, C., 1999. Alterations in shouldermuscle activity due to changes in data entry organisation.International Journal of Industrial Ergonomics, 23 (3),

231240.Gerr, F., et al., 2002. A prospective study of computer

users:1. Study design and incidence of musculoskeletaldisorders. American Journal of Industrial Medicine, 41(4), 221235.

Gerr, F., Marcus, M., and Monteilh, C., 2004. Epidemiologyof musculoskeletal disorders among computer users:lesson learned from the role of posture and keyboarduse. Journal of Electromyography & Kinesiology, 14 (1),2531.

Grandjean, E., 1969. Fitting the task to the man. London:Taylor & Francis.

Griffiths, K., Mackey, M., and Adamson, B., 2007. Theimpact of a computerized work environment on profes-sional occupational groups and behavioural and physio-

logical risk factors for musculoskeletal symptoms: aliterature review. Journal of Occupational Rehabilitation,17 (4), 743765.

Haddon, L. and Silverstone, R., 1992. Information andcommunication technologies in the home: the case forteleworking [online]. Available from: http://www.mot.chalmers.se/dept/tso/haddon/cict.pdf [Accessed 5 May2006].

Ha gg, G., 1991. Static work loads and occupational myalgia a new explanation model. In: P. Anderson, D. Hobart,and J. Danoff, eds. Electromyographical kinesiology.Amsterdam, Elsevier Science, 141144.

Ha gg, G. and A stro m, A., 1997. Load pattern and pressurepain threshold in the upper trapezius and psychosocialfactors in medical secretaries with and without shoulder/neck disorders. International Archives of Occupationaland Environmental Health, 69 (6), 423432.

Hardill, I. and Green, A., 2003. Remote working alteringthe spatial contours of work and home in the neweconomy. New Technology, Work and Employment, 18(3), 212222.

Heinrich, J., Blatter, B., and Bongers, P., 2004. A compar-ison of methods for the assessment of postural load andduration of computer use. Occupational and Environ-mental Medicine, 61 (12), 10271031.

Henning, R., et al., 1997. Frequent short rest breaks fromcomputer work: effects on productivity and well-being attwo field sites. Ergonomics, 40 (1), 7891.

Homan, M. and Armstrong, T., 2003. Evaluation of threemethodologies for assessing work activity duringcomputer use. American Industrial Hygiene AssociationJournal, 64 (1), 4855.

Ijmker, S., et al., 2007. Should office workers spend fewerhours at their computer? A systematic review of theliterature. Occupational and Environmental Medicine, 64(4), 211222.

Jensen, C., et al., 2002. Musculoskeletal symptoms andduration of computer and mouse use. InternationalJournal of Industrial Ergonomics, 30 (45), 265275.

Jonsson, B., 1988. The static load component in musclework. European Journal of Applied Physiology, 57 (3),305310.

Katz, J., et al., 2000. Prevalence of upper extremitymusculoskeletal disorders in college students. AmericanJournal of Medicine, 109 (7), 586588.

Marcus, M., et al., 2002. A prospective study of computerusers: II. Postural risk factors for musculoskeletalsymptoms and disorders. American Journal of IndustrialMedicine, 41 (4), 236249.

Mathiassen, S., 2006. Diversity and variation inbiomechanical exposure: what is it, and why would we

like to know? Applied Ergonomics, 37 (4), 419427.Mathiassen, S., Mo ller, T., and Forsman, M., 2003.

Variability in mechanical exposure within and betweenindividuals performing a highly constrained industrialwork task. Ergonomics, 46 (8), 800824.

Mclean, L., et al., 2001. Computer terminal work and thebenefit of microbreaks. Applied Ergonomics, 32 (3),225237.

Mo ller, T., et al., 2004. Job enlargement and mechanicalexposure variability in cyclic assembly work. Ergonomics,47 (1), 1940.

National Occupational Health and Safety Commission, 2006.The national workers compensation statistics database[online]. Commonwealth of Australia. Availablefrom:http://nosi2.nohsc.gov.au/site.taf?goassisted&_

UserReference

1F4B68F43BE6F41F44826574[Accessed 18 May 2006].Richter, J., et al., 2009. Differences in muscle load between

computer and non-computer work among office workers.Ergonomics, 52, 15401555.

Richter, J., et al., 2008. Computer work duration and itsdependence on the used pause definition. AppliedErgonomics, 39, 772778.

Schneider, S., Davis, K., and Jorgensen, M., 2005. Prosand cons of job rotation as a means of reducing injurycosts. Journal of Occupational and EnvironmentalHygiene, 2 (1), D1D3.

Straker, L., 1999. Body discomfort assessment tools. In: W.Karwowski and W. Marras, eds. The occupationalergonomics handbook. 1st ed. Boca Raton, USA: CRCPress, 12391252.

Straker, L. and Mathiassen, S.E., 2009. Increasedphysical work loads in modern work a necessity forbetter health and performance? Ergonomics, 52 (10),12151225.

Sullivan, C., 2003. Whats in a name? Definitions andconceptualisations of teleworking and homeworking.New Technology, Work and Employment, 18 (3), 158165.

Szeto, G., Straker, L., and Osullivan, P., 2005. Acomparison of symptomatic and asymptomatic officeworkers performing monotonous keyboard work-2:neck and shoulder kinematics. Manual Therapy, 10 (4),281291.

Ergonomics 1027
http://www.mot.chalmers.se/dept/tso/haddon/cict.pdfhttp://www.mot.chalmers.se/dept/tso/haddon/cict.pdfhttp://nosi2.nohsc.gov.au/site.taf?go=assisted_UserReference=1F4B68F43BE6F41F44826574http://nosi2.nohsc.gov.au/site.taf?go=assisted_UserReference=1F4B68F43BE6F41F44826574http://nosi2.nohsc.gov.au/site.taf?go=assisted_UserReference=1F4B68F43BE6F41F44826574http://nosi2.nohsc.gov.au/site.taf?go=assisted_UserReference=1F4B68F43BE6F41F44826574http://nosi2.nohsc.gov.au/site.taf?go=assisted_UserReference=1F4B68F43BE6F41F44826574http://nosi2.nohsc.gov.au/site.taf?go=assisted_UserReference=1F4B68F43BE6F41F44826574http://nosi2.nohsc.gov.au/site.taf?go=assisted_UserReference=1F4B68F43BE6F41F44826574http://nosi2.nohsc.gov.au/site.taf?go=assisted_UserReference=1F4B68F43BE6F41F44826574http://www.mot.chalmers.se/dept/tso/haddon/cict.pdfhttp://www.mot.chalmers.se/dept/tso/haddon/cict.pdf


13/13

Tornqvist, E., et al ., 2009. The influence of workingconditions and individual factors on the incidence ofneck and upper limb symptoms among professionalcomputer users. International Archives of Occupationaland Environmental Health, 82, 689702.

Unge, J., et al., 2005. Validity of self-assessed reports ofoccurrence and duration of occupational tasks. Ergo-nomics, 48 (1), 1224.

Van Den Heuvel, S., et al., 2005. The effect of physicalactivity in leisure time on neck and upper limbsymptoms.Preventive Medicine, 41 (1), 260267.

Van Eerd, D., et al., 2009. Task exposures in an officeenvironment: a comparison of methods. Ergonomics, 52(10), 12481258.

Veiersted, K., Westgaard, R., and Andersen, P., 1993.Electromyographic evaluation of muscular work patternas a predictor of trapezius myalgia. Scandinavian Journalof Work Environment and Health, 19 (4), 284290.

Village, J., 2005. Musculoskeletal disorders of the upperextremity associated with computer work: a systematicreview. Occupational Ergonomics, 5 (4), 205.

Wahlstro m, J., 2005. Ergonomics, musculoskeletal disordersand computer work. Occupational Medicine, 55 (3),168176.

Wahlstro m, J., et al., 2010. Upper arm postures andmovements in female hairdressers across four fullworking days. Annals of Occupational Hygiene, 54 (5),584594. doi:10.1093/annhyg/meq028.

Westgaard, R. and Winkel, J., 1996. Guidelines for

occupational musculoskeletal load as a basis forintervention: a critical review.Applied Ergonomics, 27 (2),7988.

Winkel, J. and Mathiassen, S., 1994. Assessment of physicalwork load in epidemiologic studies: concepts, issuesand operational considerations. Ergonomics, 37 (6),979988.

Youngstrom, M.,et al., 2002. Occupational therapy practiceframework: domain and process. American Journal ofOccupational Therapy, 56 (6), 609639.


Ciccarelli et al. - 2011 - Diversity of tasks and information technologies used by office workers at...

Documents

Transcript of Ciccarelli et al. - 2011 - Diversity of tasks and information technologies used by office workers at...