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The Rapid Office Strain Assessment (ROSA): Validity of online worker self-assessments and the relationship to worker discomfort

  • Occupational Health Clinics for Ontario Workers, Hamilton, Canada

Abstract and Figures

The purpose of this study was to determine if office workers were capable of using an online version of the Rapid Office Strain Assessment (ROSA) tool to accurately assess musculoskeletal disorder risk factors in their own offices, and see if online training can reduce worker-reported discomfort. Fifty-five participants completed a four week program where they assessed their own office simultaneously with a trained observer, and either received or did not receive feedback on their performance. Significant differences were found between worker-and observer-reported ROSA final scores, and for the mouse and keyboard section, with workers underestimating these risk factors on average, compared to the trained observer. Worker and observer assessments of the chair, monitor and telephone were not significantly different but were significantly correlated (R values of 0.60 and 0.48). There were a greater number of significant correlations between worker-reported ROSA final scores and total body discomfort (3 instances) compared to observer-reported relationships (1 instance). Feedback appeared to have a detrimental effect on worker-assessment accuracy, and the relationship between discomfort and ROSA scores. Mean discomfort decreased across the four weeks of the study (up to a 51.6% decrease), as did ROSA final scores (3.9 to 3.5). Additional work is required to improve the validity of worker-reported scores in all sections of ROSA, but self-assessments of office workstations using the current ROSA online application do show promise in terms of assisting workers to decrease risk factors related to musculoskeletal disorders, and decrease discomfort levels.
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Occupational Ergonomics 10 (2011/2012) 83–101 83
DOI 10.3233/OER-2012-0194
IOS Press
The Rapid Ofce Strain Assessment (ROSA):
Validity of online worker self-assessments
and the relationship to worker discomfort
Michael Sonnea,b,and David M. Andrewsc
aDepartment of Kinesiology, McMaster University, Hamilton, Ontario, Canada
bLeadErgonomics Consulting Services, Windsor, Ontario, Canada
cDepartment of Kinesiology, University of Windsor, Windsor, Ontario, Canada
Abstract. The purpose of this study was to determine if ofce workers were capable of using an online version of the Rapid
Ofce Strain Assessment (ROSA) tool to accurately assess musculoskeletal disorder risk factors in their own ofces, and see
if online training can reduce worker-reported discomfort. Fifty-ve participants completed a four week program where they
assessed their own ofce simultaneously with a trained observer, and either received or did not receive feedback on their
performance. Signicant differences were found between worker- and observer-reported ROSA nal scores, and for the mouse
and keyboard section, with workers underestimating these risk factors on average, compared to the trained observer. Worker and
observer assessments of the chair, monitor and telephone were not signicantly different but were signicantly correlated (R
values of 0.60 and 0.48). There were a greater number of signicant correlations between worker-reported ROSA nal scores
and total body discomfort (3 instances) compared to observer-reported relationships (1 instance). Feedback appeared to have a
detrimental effect on worker-assessment accuracy, and the relationship between discomfort and ROSA scores. Mean discomfort
decreased across the four weeks of the study (up to a 51.6% decrease), as did ROSA nal scores (3.9 to 3.5). Additional work is
required to improve the validity of worker-reported scores in all sections of ROSA, but self-assessments of ofce workstations
using the current ROSA online application do show promise in terms of assisting workers to decrease risk factors related to
musculoskeletal disorders, and decrease discomfort levels.
Keywords: Ofce, computer, checklist, worker-assessment, online training, feedback
1. Introduction
Musculoskeletal disorders (MSD) are the number one source of lost time injuries in Ontario, and
contribute to over $12 billion in indirect and direct costs to Ontario employers per year [1]. Risk factors
related to musculoskeletal disorders in ofce work include sustained non-neutral postures of the upper
limbs [2], prolonged static sitting while using the computer [3]), awkward postures of the head and
neck [4], and increased muscular activity in the upper back and shoulders [5]. These risk factors have a
large effect on the number of musculoskeletal disorders reported every year, as over 60% of Canadian
workers require the use of a computer to perform required tasks at their jobs [6].
Attempts to proactively control these risk factors in the ofce have primarily come in the form of
training and ergonomic assessments [7]. The most effective methods of ofce ergonomics training have
Address for correspondence: Michael Sonne, Department of Kinesiology, McMaster University, 1280 Main Street West,
Hamilton, Ontario, L8S 4L8, Canada. Tel.: +1 519 996 3746; E-mail:
1359-9364/11/12/$27.50 2011/2012 – IOS Press and the authors. All rights reserved
84 M. Sonne and D.M. Andrews / Validity of online worker self-assessments and the relationship to worker discomfort
involved the participant as an active member in the training, thereby allowing him/her to make their
own workstation modications [8]. Training and additional assessment recommendations in ergonomics
can be made by using initial risk factor screening tools, such as RULA [9] and REBA [10]. However,
these tools are designed to be general enough to apply to multiple tasks, and do not necessarily apply
to the specic risk factors found in an ofce environment. While these tools have been extensively
validated, not all of the validation was conducted on computer workstations, leaving questions about the
applicability of the action levels proposed in these posture assessment tools as they pertain to computer
The Rapid Ofce Strain Assessment (ROSA) [11] is a pen and paper checklist that was developed to
quickly determine if an ofce workstationrequires additional assessment or intervention. ROSA is based
on the CSA standards for Ofce Ergonomics (CSA-Z412), and highlights MSD risk factors identied
through extensive research specictoofce and computer work. The risk factors incorporated into the
tool are organized into several subsections: chair, monitor and telephone, and mouse and keyboard.
These subsections highlight the risk factors unique to each component of the ofce workstation, and
weight risk scores based on the CSA-Z412, as well as previous research (Fig. 1). The scores recorded
in each subsection are then combined to achieve a ROSA nal score, indicative of the overall risk of
musculoskeletal discomfort, as a result of the conguration of the ofce. Initial research by Sonne et
al. [11] found a signicant relationship between worker-reported discomfort and ROSA nal scores.
Further analysis revealed that ofce workstations which were assessed to have a ROSA nal score of 5
or higher were associated with increased worker-reported discomfort. Based on this work, a ROSA nal
score of 5 was proposed to be a reasonable action level to indicate that further evaluation or intervention
is needed for ofce workstations.
A limitation of ROSA is that experts are still required to complete the initial screening assessments,
which is reective of additional costs to the workplace through the hiring of ergonomic consultants.
Additionally, ROSA may act as an effective screening tool, indicating which ofce workstations are in
need of immediate changes in order to reduce worker discomfort. However, relying solely on expert
consultants to provide this information would be very time consuming and costly for many workplaces.
The effectiveness of training on risk factor reduction in ofce ergonomics has previously been consid-
ered in the literature. Bohr [8] found that a participatory approach, in which workers were instructed how
to adjust aspects of their own ofce, was the most effective in improving the workstation, compared to a
traditional lecture-based training approach. Participatory approaches allow workers to receive feedback
from instructors and have been shown to be benecial in improving training performance [12]. If the
right type of feedback is administered in the right way and at the right time, workers can then apply this
feedback to the task in question in order to improve their performance the next time the task is performed.
If workers could be trained to perform their own ROSA assessments in an online training module, then
the initial screening process would be much quicker and less expensive. Therefore, the purposes of this
study were to determine if ofce workers were capable of using an online version of the Rapid Ofce
Strain Assessment (ROSA) tool to accurately assess musculoskeletal disorder risk factors in their own
ofces, and see if online training can reduce worker-reported discomfort associated with ofce work.
The following four research questions were of interest and guided this investigation:
1. Are ROSA subsection and nal scores reported by ofce workers using the online version of the
tool comparable to those determined by a trained observer for the same workstations?
2. What is the impact of directed expert feedback and one month of weekly assessments on the
agreement between trained observer- and worker-reported ROSA scores?
M. Sonne and D.M. Andrews / Validity of online worker self-assessments and the relationship to worker discomfort 85
3. What are the relationships between worker- and trained observer-reported ROSA scores and worker-
reported discomfort scores?
4. Is an ofce ergonomic training protocol, using ROSA online, effective in reducing musculoskeletal
discomfort in ofce workers?
Tab le 1
Mean (SE), maximum and minimum anthropometric and demographic information for participants in the
feedback (FB) and no feedback (NoFB) Groups
Feedback (n=27) No Feedback (n=28)
Mean (SE) Max Min Mean (SE) Max Min
Age (years) 37.7 (2.1) 55 23 39.4 (2.1) 59 23
Males (n) 6 9
Females (n) 21 19
Height (cm) 166.0 (0.8) 187.9 157.5 167.4 (2.7) 188.0 150.0
Body mass (kg) 71.3 (8.7) 118.2 50 73.1 (4.3) 100.9 45.9
Years at company (years) 9.7 (1.8) 25 0.8 9.8 (2.5) 43 0.5
Years at job (years) 8.8 (2.1) 25 0.8 7.3 (1.6) 43 0.5
Initial whole body discomfort (/1620) * 57.9 (13.5) 270 0 44.2 (13.4) 265 0
University of Windsor (n) 11 11
Private construction company (n) 9 11
School board (n) 4 1
Not-for-prot organization (n) 3 5
*Note: the maximum score on the Cornell University Discomfort Questionnaire [14] is 1620.
2. Methods
2.1. Participants
Participants were recruited from the administrative staff at a private construction company, a school
board’s administrative ofce, a University of Windsor ofce, and the regional ofce of a national not-for-
prot organization (Table 1). To be included in this study, workers had to use a computer workstation
for at least 50% of their normal workday, use the same computer workstation during every workday, and
had not received ergonomic training recently (within 1 year). Fifty-nine ofce workers were initially
recruited for the study and consented to participate. The procedures were approved by the Research
Ethics Board at the University of Windsor. During the course of the experiment, 4 participants dropped
out due to vacations, illness or prior commitments. Thus, 55 participants completed all 4 weeks of the
study. Participants reported their height, body mass, age, time at company, time at job, and initial level
of discomfort one week prior to the start of data collection. Participants were then assigned to one of
two groups (those who would receive feedback, and those who would not) so that they would be evenly
distributed between the groups based on these variables (Table 1). Finally, participants were asked to
refrain from buying new ofce equipment or replacing any of their existing furniture during the course
of the four weeks of the study.
2.2. Procedures
Training was performed using an online version of the Rapid Ofce Strain Assessment (outlined in
Section 2.3.1). This training consisted of two primary components – an assessment module, and an
adjustment module. The goal of this training was to give participants access to resources on how they
86 M. Sonne and D.M. Andrews / Validity of online worker self-assessments and the relationship to worker discomfort
could assess and make adjustments to their existing furniture that they felt were necessary throughout
the course of the study.
As this was an initial examination into this type of online training, a training protocol of 4 weeks was
chosen to see if results warranted further study. Weekly collection was conducted in order to see the
feasibility of such a training and assessment program. Participants received an initial training session
where they were instructed on how to use the ROSA online application. Participants registered their
username and account within the ROSA application, and completed an online form on initial discomfort
levels and biographical data (age, sex, height, body mass, years at current job, and years at the current
company). The workers in each experimental group assessed their own workstation, had a trained
observer assess their workstation, and lled out a discomfort questionnaire. The Feedback (FB) Group
received feedback on their performance from the trained observer, while the No Feedback (NoFB) Group
did not.
2.3. Worker assessment
The worker assessments were conducted once per week using the online ROSA application. Times
for the weekly assessments were scheduled either through personal contact onsite or through email, so
that the trained observer and participants did their assessments at the same time.
2.3.1. ROSA online training module
The ROSA online application contains the same risk factor identication information found in the
original ROSA tool [11]. The chair, monitor and telephone, and mouse and keyboard subsections from
ROSA were duplicated in the online version so that the participants and the trained observers used
equivalent risk factor diagrams during their assessments (Fig. 1). A sample screenshot of ROSA online
is provided in Fig. 1A for the chair subsection that assesses height. A copy of the corresponding pen and
paper checklist form for the chair height subsection is included in Fig. 1B. For all workstation subsections
covered in ROSA, risk factors in the online version were presented as text, graphics and live action in
video, with an audio narrative.
Workers navigated through the online ROSA training, selecting the risk factors that most accurately
applied to their current workstation set up. Selections were made by clicking the buttons and check
boxes corresponding to the levels of the risk factors, and the ROSA scores were automatically calculated
by the program. Workers were shown their ROSA scores, as well as information related to interpreting
the score, after the completion of their assessment. Upon logging in or completing an assessment, the
workers could also view the results from their previous assessments, thereby showing them if their scores
had increased or decreased throughout the assessment process.
The online version of ROSA was written in the PHP hypertext processor language (,
integrating a MySQL database to track and display user information. The online training module can be
found at
2.4. Trained observer assessment
A trained observer performed an assessment of the ofce workstation at the same time as the worker
assessments each week. The two trained observers who performed the assessments were graduate
students in the eld of ergonomics and biomechanics, who had previously provided ergonomic training
and assessments in a consulting role to various private and public companies. Instead of using the online
version of ROSA, the trained observers completed a paper or spreadsheet-based version of ROSA (as
M. Sonne and D.M. Andrews / Validity of online worker self-assessments and the relationship to worker discomfort 87
Fig. 1. Screenshot of the ROSA online application – chair height subsection (A). Risk factors are presented as text, graphics
and live action in video with an audio narrative. The tracking menu is to the left of the risk factors, and allows the participant
to view their progress through the assessment. This can be compared to what an ergonomist would use in the pen and paper
ROSA checklist (B) [11].
in [11]). During the course of the study, a subset of workstations (n=14) were assessed simultaneously
by the two observers, and Intra-Class Correlation Coefcients (ICCs) were calculated to determine inter-
rater reliability. ICCs of 0.69 (chair), 0.91 (monitor and telephone), 0.87 (mouse and keyboard) and 0.87
(nal score) were high in magnitude [13] and were comparable to previous results using ROSA [11].
This indicated that the use of ROSA by two observers for this study was statistically appropriate.
2.5. Trained observer feedback
For participants in the Feedback Group (Table 1), verbal feedback was given to them by the trained
observer on the accuracy of their self-assessments, based on their expert evaluation. The trained observer
indicated which assessments were incorrect and how they should have been scored. This feedback
occurred after the participant completed their assessment, but before they completed their discomfort
questionnaire (Section 2.6). To ensure that feedback was given consistently, one of the trained observers
was assigned to the Feedback (FB) Group.
2.6. Discomfort questionnaire
The Cornell University Discomfort Questionnaire [14] contains self-report information on discomfort
across 18 different body parts, which is further evaluated on the frequency of discomfort, the severity of
discomfort, and the degree of work interference that the discomfort causes. An adapted version of the
discomfort questionnaire was completed online by participants after they nished their assessments each
88 M. Sonne and D.M. Andrews / Validity of online worker self-assessments and the relationship to worker discomfort
week. Localized discomfort scores were calculated by multiplying the body part’s discomfort frequency,
severity, and work interference value. The maximum discomfort score for this questionnaire is 1620 [14].
2.7. Workstation modication videos
The workstation modication videos included in ROSA online for each subsection (e.g. Fig. 1) were
lmed in generic ofces in a local company prior to data collection. Modications that could be
made without costing the company additional money to purchase new equipment were emphasized
(such as adding a rolled up towel to the back of a chair to add lumbar support). Upon completion of
the discomfort questionnaire each week, all participants in both groups had access to the workstation
modication videos and literature provided in ROSA online. Participants were asked to try and make
changes to their workstation based on the deciencies in their current setup (as indicated by conducting
their assessment) and these videos. At the end of the study, feedback was given to all participants on
how to adjust their workstations to optimally suit their work habits and body types.
2.8. Data analysis
2.8.1. Experimental groups
To ensure that the distribution of participants between groups was comparable for all anthropometric
(height and body mass) and demographic information (time at company, time at job, initial level of
discomfort), participants were purposefully assigned and a one-way ANOVA was used to assess Group
differences (alpha set at 0.05). This process occurred after the initial training session, but prior to week
1 of the data collection.
2.8.2. Research question #1 and #2
To determine if worker-assessed ROSA scores differed from those determined by a trained observer, a
2 (Assessment Type: worker and observer) x 2 (Groups: FB, NoFB) x 4 (Time: week 1, 2, 3, 4) mixed
ANOVA was performed on the dependent variables (ROSA chair, monitor and telephone, mouse and
keyboard, and nal scores). The between-subject factor was Group and the two within-subject factors
were Assessment Type and Time. Alpha was set at 0.05 for all comparisons. Pearson Product Moment
Correlations were used to determine the relationship between worker and trained observer ROSA nal
scores. An Rvalue of less than 0.1 was considered low, 0.3 to 0.5 was considered moderate, and greater
than 0.5 was taken to be indicative of a strong positive relationship between variables [15]. Signicant
main effects of Time were further analysed with a Tukey’s HSD post hoc test.
This study was exploratory in nature and sought to establish the validity of worker-reported ROSA
scores through the online ROSA assessment process. Validity of self-assessments was deemed to have
been established if mean worker- and observer-reported scores were not signicantly different from one
another, and if they were signicantly correlated. Finally, a sensitivity and specicity analysis was
conducted on the worker and observer ROSA nal scores in reference to the previously proposed cut-off
score of 5 [11]. The cut of value of 5 is used to identify workstations that are at an increased risk of
worker-reported discomfort. This was achieved by comparing discomfort and ROSA nal score values,
and identifying where signicant increases in discomfort occurred [11].
2.8.3. Research question #3
Pearson Product Moment Correlations were calculated to establish the relationships between worker-
reported discomfort and both worker-reported and trained observer ROSA scores. Correlations between
M. Sonne and D.M. Andrews / Validity of online worker self-assessments and the relationship to worker discomfort 89
whole body and localized discomfort were made with ROSA subsection and nal scores. The localized
discomfort scores related to the expected body parts that may experience discomfort or injury as a result
of ofce work (the head and neck: [16,17], upper limbs: [18], and back: [19]) were correlated with the
ROSA nal, chair, monitor and telephone, and mouse and keyboard scores. This comparison was made
within each experimental Group (FB, NoFB), during each week of the experiment.
2.8.4. Research question #4
The effects of the two different training protocols on self-reported whole body musculoskeletal dis-
comfort over the course of the 4 week experiment were assessed using a 4 (Time: weeks 1, 2, 3 and 4) x
2 (Groups: FB, NoFB) mixed ANOVA. The between-subject factor was Group, and the within-subject
factor was Time. Alpha was set to 0.05 for all comparisons. Post hoc analysis was performed using
Tukey’s HSD test.
3. Results
3.1. Distribution of experimental groups
There were no signicant differences in mean (SE) height, body mass, time at company, time at job,
or initial level of discomfort between the two experimental Groups (p0.05) (Table 1). As previously
mentioned, 4 participants withdrew from the study for various reasons, and their data were excluded
from the analyses.
3.2. Research question #1 and #2
A signicant main effect of Assessment Type was seen in ROSA nal scores (F[1,53] =6.03, p
0.05) and mouse and keyboard scores (F[1,53] =4.73, p0.05), with worker-reported scores being
signicantly lower than observer-reported scores (Fig. 2). A signicant main effect of Group was also
seen in the ROSA nal scores (F[1,53] =4.01, p0.05], as well as mouse and keyboard scores (F[1,53]
=8.50, p0.05) (Fig. 3). On average, the group that received feedback reported signicantly lower
ROSA scores than the group that did not.
Fig. 2. Signicant main effect of assessment type in the ROSA nal and mouse and keyboard subsection (-statistically
signicant at p0.05).
90 M. Sonne and D.M. Andrews / Validity of online worker self-assessments and the relationship to worker discomfort
Fig. 3. Signicant main effect of group in the ROSA nal and mouse and keyboard subsection (-statistically signicant at p
Fig. 4. Signicant main effect of time for the ROSA nal and subsection scores over weeks 1–4 (-statistically signicant at
A signicant main effect of Time was seen for all ROSA scores. ROSA nal scores (F[3,159] =6.03,
p0.05) and mouse and keyboard scores (F[3,159] =8.07, p0.05) decreased from week 1 and 4.
Chair (F[3,159] =10.18, p0.05) and monitor and telephone ROSA scores (F[3,159] =7.16, p0.05)
followed an increasing trend during the 4 week study. Post-hoc testing revealed signicant differences in
M. Sonne and D.M. Andrews / Validity of online worker self-assessments and the relationship to worker discomfort 91
Fig. 5. Correlations between worker- and observer-reported ROSA nal and subsection scores (A), in the feedback groups (B)
and over time (C).
92 M. Sonne and D.M. Andrews / Validity of online worker self-assessments and the relationship to worker discomfort
the ROSA nal score between weeks 1 (3.90(0.12)) and 4 (3.52(0.17)) (Fig. 4A). Signicant differences
were also seen between week 1 (3.05(0.11)) and week 3 (3.47(0.12)) and week 2 (2.98(.10)) and week
4 (3.26(0.09)) in the chair subsection (Fig. 4B), as well as week 1 (2.75(0.12)) and 3 (3.28(0.14)) and
weeks 2 (2.68(0.17)) and 4 (2.94(0.16)) in the monitor and telephone subsection (Fig. 4C). Finally,
signicant differences emerged between week 1 (2.94(0.13)) and week 4 (2.53(0.13)) in the mouse and
keyboard subsection (Fig. 4D).
Correlations between worker- and observer-reported scores ranged between moderate to strong (R=
0.48 and R=0.60), with the chair subsection showing the strongest relationships (Fig. 5A). When
considering Assessment Type, worker- and observer-reported score relationships were typically stronger
in the No Feedback group. All relationships were statistically signicant and ranged between moderate
to strong (Fig. 5B). The relationship between worker and observer scores increased over the span of
the study for the ROSA nal score, as well as the mouse and keyboard subsection. In the remaining
subsections, the Rvalues peaked during week 2, followed by a decrease in the following 2 weeks
(Fig. 5C).
The sensitivity and specicity analysis yielded average values of 36% and 76%, and 40% and 80%
for worker-reported and observer-reported scores in the NoFB group, respectively (Fig. 6). In the
FB group, mean sensitivity values for weeks 1–4 were 56% for worker-reported scores, and 36% for
observer-reported scores, while the corresponding mean specicity values were 50% and 55%.
Fig. 6. Sensitivity (Sens) and Specicity (Spec) analysis results for assessment type, group (Feedback (FB) and No Feedback
NoFB), and time.
3.3. Research question #3
Signicant correlation values were larger in magnitude between the worker-related scores and discom-
fort than for the observer scores in general (Figs 7A and B). The magnitude of the correlations was the
greatest for participants in the NoFB group between worker-reported ROSA nal scores and whole body
discomfort. The fewest signicant correlations were seen between monitor and telephone ROSA scores
and localized discomfort (Fig. 7). There were far fewer signicant correlations between discomfort and
ROSA scores in the FB group (1 signicant relationship) than in the NoFB group (18 signicant rela-
tionships), and the overall mean correlation magnitude was less for the workers who received feedback
compared to those who did not.
M. Sonne and D.M. Andrews / Validity of online worker self-assessments and the relationship to worker discomfort 93
Fig. 7. Correlations between ROSA scores and discomfort for worker-reported (A) and observer-reported scores (B), during
the 4 weeks of the experiment, as well as in the Feedback (FB) and No Feedback (NoFB) Groups (-statistically signicant at
3.3.1. Additional ROSA score and discomfort relationships
Additional signicant relationships between worker-reported discomfort and ROSA scores were seen
when comparing the various discomfort regions and ROSA subsection and nal scores. These signicant
relationships varied between R=0.38 (total body discomfort and mouse and keyboard score, NoFB
Group, worker-reported ROSA score, week 3) and R=0.68 (chair-related discomfort, mouse and
keyboard score, NoFB Group, observer-reported ROSA score, week 4) (Fig. 7).
3.4. Research question #4
A trend was observed for all localized and total body discomfort measures to decrease from week 1 to
week 4 (Fig. 8). A main effect of Time on reported discomfort emerged for total discomfort [F(3,159) =
5.64, p0.05], total discomfort without leg scores [F(3,159) =4.83, p0.05], mouse and keyboard-
related discomfort [F(3,159) =3.51, p0.05], and monitor and telephone-related discomfort [F(3,159)
=3.28, p0.05] (Figs 8 A, B, D, and E). Signicant decreases in discomfort occurred between week 1
(43.2 (8.6)) and week 4 (49.9 (6.8)), as well as week 1 and week 2 (22.9 (5.0)) (Fig. 7A)). The greatest
changes in mean discomfort across Groups were seen in the total body discomfort without leg scores,
with a 51.6% decrease in reported discomfort between weeks 1 and 2 (Fig. 8B).
There were no signicant main effects of Time reported for chair-related discomfort (Fig. 8C) or Group
(FB or NoFB) in any of the discomfort categories (nal or localized discomfort). There were also no
signicant interactions between Time and Group for any discomfort score.
4. Discussion
Worker-reported scores were found to be comparable to observer-reported scores for the monitor and
telephone and chair subsections. Signicant differences were found for Assessment Type (worker or
observer) and Group (FB or NoFB) for ROSA nal scores, and for the mouse and keyboard subsections.
Worker and observer scores were signicantly correlated throughout all weeks for all subsections and
nal scores. There were signicant positive relationships between discomfort and ROSA scores for the
group that did not receive feedback, but no signicant relationships were found for the group that did
receive feedback. Finally, in general, it was found that worker-reported discomfort decreased over the
course of the four week protocol.
94 M. Sonne and D.M. Andrews / Validity of online worker self-assessments and the relationship to worker discomfort
Fig. 8. Main effects of Time on mean (SE) discomfort scores: total body discomfort (A), total body without leg discomfort
(B), chair-related discomfort (C), monitor and telephone-related discomfort (D), and keyboard and mouse-related discomfort
(E) (-statistically signicant at p0.05).
4.1. Research question #1
Worker- and observer-reported scores were signicantly different from one another in the nal score
and the mouse and keyboard subsections, suggesting that these self-reported ROSA scores are not valid,
according to the denition used in this study. The results for each subsection and for the ROSA nal
scores are discussed in turn below.
M. Sonne and D.M. Andrews / Validity of online worker self-assessments and the relationship to worker discomfort 95
4.1.1. Chair
Observer- and worker-reported ROSA scores were not different for the chair subsection (comprised
of the chair height, depth, armrest and backrest subsections), and worker and observer-reported scores
were signicantly correlated. This may be explained in part by the fact that assessments of the chair and
seated posture generally required workers to evaluate their legs and trunk. Self-assessments of postures
such as these, that involve larger segments of the body, have been shown to be moderately accurate when
compared to observer assessments in previous studies [20,21].
4.1.2. Monitor and telephone
Mean worker-reported monitor and telephone scores were not signicantly different than those reported
by the observers. In the monitor and telephone subsection of ROSA, postures of the neck and head are
assessed, with one risk factor related to reaching to the phone. Previous research on head and neck
posture self-assessment has reported less than desired accuracy compared to observer assessments [3].
However, the more positive posture results associated with the online ROSA tool may have occurred due
to unique components that other self-assessment approaches don’t utilize. The setup of pictures, text
and video in ROSA is fairly novel and may have provided enough additional information to workers to
enable them to more accurately assess these body parts.
There was a tendency for ROSA scores to increase between weeks 1 and 4 for the monitor and
telephone subsection, as well as the chair subsection. Most modern ofces are comprised of furniture
that can be adjusted. The increase in these subsection scores over time may be a result of one piece
of equipment being adjusted, which had an effect on other scores for another piece of equipment. For
example, if the chair was too high, but the monitor was at an ideal height, an adjustment to the proper
height for the chair might result in the monitor now being too high. None of the scores in week 4 for any
subsections were signicantly higher than the scores in week 1, indicating that any incorrect changesthat
possibly occurred in the middle weeks of the study may have been identied by the worker, re-assessed
and re-adjusted in subsequent evaluations.
4.1.3. Mouse and keyboard
Therewasasignicant difference between observer- and worker-reported ROSA mouse and keyboard
scores, with worker-reported scores being lower in magnitude on average. However, the magnitude of
the correlations between worker and observer scores in this subsection, were relatively high (Fig. 5).
Self-assessments have been previously shown to be effective in providing an accurate evaluation of
keyboard and mouse working posture [3]. The difference between the current and past approaches in this
regard may have been a result of the ROSA tool itself. In the evaluation of shoulder position while using
themouseinROSA,thereisaxed option to select an abducted shoulder posture, as well as an additive
option to indicate any abducted shoulder postures caused by the keyboard and mouse being on different
surfaces. Both of these risk factors have a value of 2 in ROSA. If one of these factors was consistently
missed during self-assessments, this could result in the discrepancy between observer-assessment and
worker-assessment scores observed in the present study.
4.1.4. ROSA nal score
As previously mentioned, the ROSA nal score is determined from the scores achieved from the chair,
monitor and telephone, and keyboard and mouse subsections. The ROSA nal score is achieved using
scoring charts (Fig. 1), and is highly reective of the subsection wherein the highest score lies. As
there was a signicant difference between observer- and worker-assessments in the mouse and keyboard
96 M. Sonne and D.M. Andrews / Validity of online worker self-assessments and the relationship to worker discomfort
subsection, this would have had a marked inuence on any assessment in which the mouse and keyboard
score was the highest score of the three subsections.
Worker-reported ROSA nal scores were generally lower in magnitude than the observer-reported
ROSA scores (Fig. 3), which is contrary to much of the previous research regarding the self-reporting
of risk factors. Other research has reported a trend for people to over-report when identifying risk
factors related to musculoskeletal disorders [21,22]. However, these studies focused on industrial work
primarily (in manufacturing or automotive industries), and not computer work. While Heinrich et al. [3]
also indicated that there was a tendency to over-report exposure to risk factors in the ofce environment,
this pertained only to the duration of computer use and not posture.
Even if workers have a tendency to over-report risk factors, it is entirely possible that risk factors
related to ofce and computer work could be predisposed to being under-reported. One explanation for
the under-reporting of workers’ scores in the current study is related to the current economic climate
in the participant companies and the city where the study was conducted. While Windsor has one of
the highest unemployment rates in Canada at approximately 14% [23], the majority of workers who
participated in this study worked in the public service, which is regarded as one of the most secure
industries [24]. Job security is a key component in job satisfaction [25,26]. Research has indicated
that workers with higher levels of job satisfaction are less likely to report risk factors and discomfort in
the workplace [27,28]. Systematic differences in factors such as job satisfaction between workplaces
could explain the self-report results of this study compared to others, and highlight the importance of
considering psychosocial risk factors when assessing MSD risk in the workplace; something that the
current ROSA tool does not take into account.
The ROSA nal score has an important practical application when considering the implementation
of an online training protocol into a business. Like other ergonomic risk checklists, nal evaluation on
whether a job requires additional assessment or attention is based on one number that falls within specic
intervention guidelines (e.g. REBA [10] and RULA [9]. Sonne et al. [11] found that ROSA nal scores
of greater than 5 were associated with signicant increases in discomfort, and therefore recommend that
a value of 5 be used to determine when an ofce should receive a more in-depth evaluation into the risk
factors present, and ultimately to set appropriate interventions. In the current study, self-reported scores
were signicantly different than observer scores in the mouse and keyboard subsection, as well as for the
ROSA nal scores. As the nal score is used to make judgments on if a workstation requires additional
assessment, self-reported scores using the ROSA tool cannot be considered valid at this point in time.
In summary, based on the results reported here, the use of self-assessments performed by ofce
workers of their own workstation using ROSA online, appears to be a valid method of assessing risk
factors related to the chair, monitor and telephone in an ofce environment. This conclusion is supported
by non-signicant differences in worker and observer-reported scores, and signicant, relatively large
magnitude positive correlations between these scores. The ROSA scores for the mouse and keyboard
section were signicantly different between workers and observers, but they were signicantly correlated.
Therefore, the ROSA nal and mouse and keyboard worker-reported scores cannot be considered valid
measures at this time. Future work should be conducted in an attempt to increase the ease with which
risk factors in these subsections can be identied. This could be done by improving the posture diagrams
used in the tool. While careful consideration was given to the development of the online software used in
this study, this was the rst attempt to create such a training program. Research has indicated that things
such as the size of the posture categories used in the tool [29], how many posture category boundaries
there are [30], and the salience of images used on the tool interface [31] all need to be accounted for in
order to optimize viewer performance.
M. Sonne and D.M. Andrews / Validity of online worker self-assessments and the relationship to worker discomfort 97
4.2. Research question #2
There was no signicant interaction between Assessment Type (worker or observer) and Time for the
ROSA nal score, or any of the subsection scores, indicating that there was no change in the difference
between either Assessment Type throughout the course of the 4 weeks of the study. There were also
no signicant interaction effects between Assessment Type, Time or Group, indicating that feedback
had no role in increasing or decreasing the accuracy of worker-reported scores. This result is promising
for the chair and monitor and telephone subsections, as a signicant difference between worker- and
observer-reported ROSA scores was not observed at any point during the study. However, it is noteworthy
that workers did not improve in terms of being able to assess the mouse and keyboard condition over
the course of the month during which the study took place. The signicant difference of Assessment
Type could be a result of participants not taking their time and fully completing the assessment process
(i.e. watching the videos each time they went through the assessment module). Previous research has
indicated that workers tend to terminate their learning experience early when they have control over
the training, particularly when considering computer-based applications [32], and when they receive
negative feedback on their performance [33].
Correlation coefcients between worker and observer-reported ROSA scores tended to increase be-
tween weeks 1 and 4 for all scores in the No Feedback Group. However, there was a trend for Rvalues
to peak prior to the fourth week of the study for all scores in the Feedback Group (Fig. 5). It appears that
once participants had a chance to use the ROSA online application once, they became familiar enough to
perform a more accurate assessment the second time they logged in. After this point, it is possible that
workers who were receiving feedback may not have performed their assessments with the diligence that
they did in the rst two weeks, and correlation values dropped off. This may be a result of the participants
losing interest in the training, as they were completing the same assessment repeatedly. Repeated work
can lead to decreased focus and reduced performance as a result of boredom [34].
The nature of the feedback given may have played a role in the lack of improvements in the validity
of worker-reported ROSA scores. Lee and Carnahan [35] found that when providing feedback on
performance, exact performance feedback was not as effective in improving results as providing feedback
that allowed for a margin of error, both above and below the desired target (also known as bandwidth).
Essentially, allowing workers to have a window of error that was deemed to be acceptable was seen
to increase retention over a period of time as opposed to correcting every single error. Workers in the
Feedback Group were corrected on every error they made in the current study, which may have resulted
in too much information for them to successfully process, and could have reduced the participant’s
retention of information for their next assessment. In the future, the number of pieces of feedback should
be controlled during training and be provided using the principles of bandwidth knowledge of results.
Working within an error rate of 5–10% (actual performance compared to ideal performance) has shown
to increase retention in participants when compared to those who did not receive feedback, or received
exact feedback over the course of a multi-week training program [36].
4.3. Research question #3
Signicant correlations of a magnitude comparable to those found by Sonne et al. [11] were found
in this study between discomfort and ROSA scores. Whole body discomfort and ROSA nal score
correlations varied in magnitude between R=0.40 and R=0.70 [11]. One difference between the
current and previous studies was that total body discomfort scores were more highly correlated with
ROSA scores than discomfort scores that did not include leg discomfort [11]. Ofce workers tend
98 M. Sonne and D.M. Andrews / Validity of online worker self-assessments and the relationship to worker discomfort
to sit for long periods of time throughout the day, a risk factor for the development of lumbar disc
herniation [37]. A symptom of disc herniation is sciatica (pain resulting from irritation of the sciatic
nerve, which can lead to shooting pain that extends into the leg [38]). Considering that sciatica has been
reported in up to 23% of all ofce workers [39], the authors suggest that it is important to include leg
discomfort in the analysis, as it could be a result of referred pain from a lower back injury.
The differences in the relationship between discomfort and ROSA scores between the current and
previous study in the development of ROSA [11] may partly be a result of the different factors introduced
herein. Sonne et al. [11] conducted assessments in a fairly traditional manner; workers were observed and
then they completed a paper version of the discomfort questionnaire. The introduction of feedback to the
assessment could have impacted how workers reported discomfort for a variety of reasons. The majority
of the feedback that was provided during the course of this study was negative in nature. Typically,
feedback was given to inform workers when they had scored their assessment incorrectly, and that they
needed to do something differently the next time. Van Dijk and Kluger [33] concluded that, in cases of
negative feedback, trainees may lose motivation and could possibly terminate their learning experience
early. Because the trained observers’ assessments were treated as the gold standard in this study, all
differences in worker assessment scores were effectively treated as incorrect answers. Furthermore, the
quantity of feedback that was provided may have acted against workers actually learning from their
errors. Stefanidis et al. [40] found that when attempting to learn new techniques, limited feedback
accompanied by video tutorials was more effective in improving performance than intense feedback
sessions. As feedback was given in the current study for every risk factor that was not scored the same
as the trained observer, there was a potentially large amount of information given to the worker after
the assessments. The impact of feedback may have caused the training and worker assessments to be
negatively affected, thereby preventing signicant correlations between worker-reported ROSA scores
and discomfort.
Providing feedback as done here may also contribute to the appearance of a more traditional training
program. While workers were not pressured for time during the course of their assessments, they did
know they were going to receive evaluation on how they performed. This increases the structure of
the training program, and more closely represents a less effective, more tutorial-based training program
compared to completely open access approaches [41], which have been shown to improve overall training
satisfaction and efcacy.
In addition, the mere presence of an investigator during the worker self-assessments may have reduced
the level of autonomy that workers had in performing their online training. A true self-guided program
allows workers to access training materials whenever they choose, and complete tasks at their own
pace [41]. Future research should identify if there is a benet to workers when using the online training
on their own schedule. Comparing these results to those from a control group would also ensure that the
goals of online training are actually being accomplished.
4.4. Research question #4
Decreases in discomfort over the 4 week period occurred across both feedback groups, and for both
total body discomfort as well as localized discomfort related to the monitor, telephone, mouse and
keyboard. A previous study of the effectiveness of ergonomic training on the relief of discomfort showed
that different types of ergonomic interventions can lead to reduced worker-reported discomfort [42].
Bohr [8] showed that a participatory approach to ergonomics, where workers were instructed on how to
make adjustments, followed by ergonomists helping the workers to make these changes, was the most
M. Sonne and D.M. Andrews / Validity of online worker self-assessments and the relationship to worker discomfort 99
effective in reducing symptoms of musculoskeletal disorders. The video-based training incorporated
into ROSA appears to serve a similar purpose of educating workers on how to adjust ofce furniture with
comparable results to previous studies.
Discomfort and ROSA nal scores showed similar decreasing trends over the course of the study. De-
creasing ROSA scores may be reective of risk factors being reduced or eliminated from the workstation
setup. This indicates that the changes made to ofces based on the videos, literature and assessment
structure in the ROSA online application, may have been effective in reducing discomfort.
What is promising about the changes to both ofce conditions and discomfort reported here is that
no new furniture purchases were made during the course of the study. Any changes made to the ofces
were a result of adjusting existing furniture and equipment, or using existing materials to improve the
setup of the ofces. Menozzi et al. [43] found similar results in ofce ergonomics research, with all
forms of ergonomics training proving to be effective in reducing risk factors in the ofce environment.
Amick et al. [7] found that ofce ergonomic interventions were most successful when new furniture was
brought in (primarily a new chair), and then workers were trained on adjustments. While the number of
adjustments made by workers in the present study was not recorded, the primary investigator observed
nearly all workers performing adjustments to their furniture throughout the assessment process. Using
ROSA online appears to be an effective method of getting workers to adjust their furniture, which is
a less expensive method of improving the ofce than making ofce-wide furniture purchases without
assessing the need.
Discomfort data obtained via a self-report questionnaire are inherently limited. Discomfort is a
subjective measure and relies on workers’ perceptions, which can be affected by their circumstances.
Subjective measures such as this have tended to be over-reported by workers [21,22]. However, the
ease of collecting this type of data using a questionnaire makes this method an attractive alternative for
research purposes. Comparing ROSA scores to more objective outcomes such as injury or injury claim
data over extended periods of time may be a better approach for validating the risk scores estimated by
the ROSA tool.
Similar decreases were seen in risk factors (as reected in a decrease in ROSA scores) as well as
worker-reported discomfort. While there was no control group to enable the authors to conrm that
self-guided training was more effective than the training used in this study, self-reported discomfort did
decrease in a manner similar to other training-based studies [8,43]. With this in mind, the effectiveness
of the training program described here for reducing discomfort is not fully supported, but results are
promising and suggest that future work to improve the approach seems warranted.
5. Conclusions
The results from this study can be summarized as follows:
1. Workers were able to accurately assess the risk factors associated with the chair, monitor and
telephone, but not with the mouse and keyboard or the ROSA nal scores.
2. Worker- and observer-reported ROSA nal scores had a similar decreasing trend over the 4 week
training period.
3. Providing augmented feedback to the worker on their performance negativelyaffected their reported
4. Worker-reported ROSA nal scores and total body discomfort were more highly correlated than
observer-reported ROSA scores and discomfort.
100 M. Sonne and D.M. Andrews / Validity of online worker self-assessments and the relationship to worker discomfort
5. Worker-reported discomfort decreased throughout the 4 weeks of the study.
The online version of ROSA allowed for over 200 assessments to be completed in a one-month time
period. The demonstrated speed and ease with which access to online ergonomics training and assess-
ments can be made warrants further research into how to increase the accuracy of worker-assessments
using the ROSA online tool.
Thanks to Mike Angelidis, Erika Santarrosa, Alison Schinkel-Ivy, Tim Burkhart, Jennifer Lembke,
Ryan Roach, Janice Forsythe, Anthony Lucas, the University of Windsor IT Department, and Pickering
Technical Services for their help with various aspects of this study and to the Centre of Research Expertise
for the Prevention of Musculoskeletal Disorders (CRE-MSD) for funding.
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... These advantages enable its use in large organizations to screen workstations that require intervention. ROSA can also be used online to help with office ergonomics in work-from-home situations [32]. Assessments can be made by an ergonomist or by workers on their own, and video records can be assessed (for example during the recent pandemic) if ergonomists are unable to enter workspaces in person. ...
... Assessments can be made by an ergonomist or by workers on their own, and video records can be assessed (for example during the recent pandemic) if ergonomists are unable to enter workspaces in person. Previous studies have demonstrated the versatility and specificity of the ROSA tool for risk assessments of office workstations [24,32,33]. Furthermore, to add to the growing body of literature in support of the use of this tool globally, further validation of ROSA as an effective tool for detecting changes in ergonomic risk factors is needed. ...
... The relevance of this tool increased due to the COVID-19 pandemic, as many workers transitioned to home offices but still require ergonomics evaluations. The use of video cameras in this study, in addition to previous research using ROSA which relied on self-assessments [32], and photograph-based assessments [33] provide a comprehensive way of using the tool to gain insight into office ergonomics risk without an ergonomist having to make their way onsite. This reduces risk of exposure for both the person conducting the ergonomics assessment as well as receiving the ergonomics assessment. ...
Full-text available
Background Most ergonomics studies on office workstations evaluate the effects of an intervention only by subjective measures such as musculoskeletal pain and discomfort. Limited evidence has been provided regarding risk factor reduction in office environments through standardized methods assessments. The Rapid Office Strain Assessment (ROSA) tool can provide an estimation of risk factor exposure for office workers as a means by which the outcome of interventions can be quantified. Purpose The aim of the study was to evaluate if ROSA scores reflect changes in risk factors after an ergonomics intervention among office workers. Methods Office workers (n = 60) were divided into two groups. The experimental group received a workstation intervention and the control group received no intervention. Changes in ROSA scores were compared before and after the intervention in both groups. Results Statistically significant reductions in the ROSA final and section scores occurred after the intervention in the experimental group with (mean reduction of 2.9, 0.8 and 1.6 points for sections A, B and C, respectively). In contrast, no differences were detected in the control group (mean increase of 0.1 point for sections A and C and mean reduction of 0.1 point for Section B). Conclusions These findings show that ROSA scores reflect changes in risk factors after an ergonomics intervention in an office environment. Consequently, this tool can be used for identifying and controlling risk factors among computer workers, before and after interventions.
... The scores recorded in each subsection are then combined to achieve a ROSA final score, indicative of the overall risk of musculoskeletal discomfort, as a result of the configuration of the office. [16] The ROSA was designed to quickly quantify risks associated with computer work and to establish an action level for change based on reports of worker discomfort. ROSA final scores exhibited high inter-and intra-observer reliability (ICCs of 0.88 and 0.91, respectively). ...
... ROSA proved to be an effective and reliable method for identifying computer use risk factors related to discomfort. [16] Purpose and need of study: ...
... Such a habitual awareness could result in fewer transitions between sitting positions as well as a reduction in small movements, indicating a type of avoidance learning based on the pain history. [16] Vol.11; Issue: 11; November 2021 Work-related neck pain is defined as neck pain that is caused or aggravated (or both) by work or the working environment, it is the most common complaint of those who use computers extensively at their workplace. Computer use for more than 4-6 hours was the most important predictor of work related neck pain. ...
The purpose of this study was to identify the prevalence of musculoskeletal problems in bio-pharmaceutical industry workers. A cross sectional survey was conducted on 33 bio-pharmaceutical industry workers by administering the Extended Nordic Musculoskeletal Questionnaire to quantify the musculoskeletal pain and activity limitation in 9 body regions. The Rapid Office Strain Assessment was used to assess the work-related postures and ergonomics of the computer operators in this industry. A Self-Designed Questionnaire was administered to obtain data regarding the various musculoskeletal problems faced by Bio-pharmaceutical industrial workers, work-related risk factors and various postures attained throughout the day. Out of the 33 workers investigated, 21 workers (63%) of the workers experienced musculoskeletal pain. Isolated spine pain was the commonest, and was reported in 8 out of 21 individuals (38%). Spine with upper and lower limb pain was the next most common, and was reported in 5 out of 21 individuals (24%). 4 out of 21 individuals had spine and lower limb pain (19%). The Rapid Office Strain Assessment scores of all the workers was above 5 indicating “high risk” which implied that immediate ergonomic change was necessary. This study concluded that there was 63% prevalence of musculoskeletal pain. The most common site of pain were the spine, followed by pain in the spine with both upper and lower extremities. All the workers were exposed to different ergonomic risk factors. The study concluded that implementation of ergonomic interventions may minimize the risks of work related musculoskeletal pain. Key words: Work-related musculoskeletal disorders, Extended Nordic Musculoskeletal Questionnaire, Rapid Office Strain Assessment, Ergonomic hazards.
... To evaluate computer workstations and the associated risk factors for WMSDs, office specific checklists like the OSHA [27] and the Rapid Office Strain Assessment (ROSA) [6] have been developed. Numerous studies have identified that self or remote assessments of computer workstations show moderate to low validity compared to trained professional assessments [28][29][30]. However, due to COVID-19 restrictions and the need for social distancing, remote ergonomic evaluations remain a viable option to increase our understanding of at-home workstations [31,32]. ...
Background: The recent mandate for university faculty and staff to work-from-home (WFH) during the COVID-19 pandemic has forced employees to work with sub-optimal ergonomic workstations that may change their musculoskeletal discomfort and pain. As women report more work-related musculoskeletal discomfort (WMSD), this effect may be exacerbated in women. Objective: The purpose of this study was to describe university employee at-home office workstations, and explore if at-home workstation design mediates the effect of gender on musculoskeletal pain. Methods: University employees completed a survey that focused on the WFH environment, at home workstation design and musculoskeletal pain. Descriptive statistics and regression analysis were used to analyze the responses. Results: 61% of respondents reported an increase in musculoskeletal pain, with the neck, shoulders and lower back being reported most frequently. Women reported significantly greater musculoskeletal pain, but this relationship was significantly mediated by poor ergonomic design of the home workstation. Improper seat-height and monitor distance were statistically associated with total-body WMSD. Conclusions: WFH has worsened employee musculoskeletal health and the ergonomic gap between women and men in the workspace has persisted in the WFH environment, with seat height and monitor distance being identified as significant predictors of discomfort/pain.
Abstract Background and aims: Lighting directly and indirectly affects employees' mental health and their performance. Good lighting is required for good visibility of the environment and should provide a luminous environment that is human-friendly and appropriate for the visual task performed. Optimal lighting is one of the most important issues in providing the physical conditions of different places, especially the workplace. Lighting can provide comfortable working conditions, especially visual comfort. Life on Earth cannot be imagined without light. Defects in the qualitative and quantitative aspects of lighting in the workplace can cause visual discomfort and reduce the productivity and efficiency of an individual. Therefore, monitoring the intensity of brightness and color temperature of light is essential to maintain and enhance the health of employees. The quantity and quality of lighting can also affect one's mental health. For example, one of the factors associated with depression is the defect in the quantity and quality of ambient lighting. Correlated color temperatures (CCT) of light play an important role in human psychological and physiological needs. In regards of human perception, two of the most important characteristics of lights are illumination and correlated color temperature (CCT). Studies have proven that different CCT provided by different lighting are important in affecting human beings psychologically and physiologically, through their visual and non-visual processes.. According to European standard EN 12665, visual comfort is defined as a person's mental well-being in the workplace. Studies on lighting in industrial environments have been conducted more frequently and public and office environments have received less attention. CCT is found to have effects on visual and mental fatigue. The right selection of CCT in an office environment will benefit its occupants in terms of visual comfort and reduction of daytime sleepiness. Studies have shown that insufficient and uncomfortable lightning conditions in office environments increase the risk of visual and ergonomic disorders in long term. The aim of this study was to evaluate the illumination and color temperature and its relationship with visual comfort in administrative staff in Hamadan city (west of Iran). Methods: This cross-sectional study was conducted in 50 rooms and among 70 staff of Hamadan University of Medical Sciences and random sampling. In this study, the intensity of illumination at the work surface and at the height of the individual eye The intensity of the local illumination at the work surface and at the level of the individual eye level was measured at the user's point of view and the angles and distances were accurately observed and measured with the presence of the user in the presence of semiconductors or other factors. Also intensity of the
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Introduction: Musculoskeletal disorders are one of the most common causes of occupational injuries and known as the most common cause of absenteeism. The aim of this study was to investigate the prevalence of musculoskeletal disorders and ergonomic risk factors in one of the faculty of Shahid Beheshti University of medical sciences. Materials and Methods: This cross-sectional descriptive study was done between 108 administrative staff (n=57) and professors (n=51 people) in one faculty of Shahid Beheshti University of Medical Sciences in 2020. Nordic standard questionnaire, comprehensive ergonomic checklist for office work environments and rapid assessment of administrative stress (ROSA) were used. Results: The results of the study showed that the highest prevalence of pain or discomfort in a recent year was in the shoulder (50%) and leg (50%). The evaluation by ROSA method showed that 34% of the subjects were in the area of need for ergonomic intervention. Remarkebly, there was also a significant relationship between the final score of ROSA and the three main parts of the ergonomic checklist (P<0.05). Conclusion: Given the association between prevalence of musculoskeletal disorders and workplace ergonomic risk factors, control measures should be taken to improve risk factors
Study Design : Literature Review Introduction : Computer use in the workplace has increased substantially since the start of the information age in the mid-1980s through 2020. Desktops, laptops, and tablets are essential tools for communication and project management. As a result of the COVID-19 pandemic, many workers have transitioned to work from home (WFH) to sustain public health emergency guidelines, and it is anticipated that many WFH jobs will be maintained post-pandemic. The transition to WFH occurred rapidly without time to establish ideal workstations. Ergonomic assessments that were typically performed in person needed to be performed using virtual technology. Purpose The purpose of this review is 1) to describe the components of a computer workstation evaluation; 2) to offer suggestions for identifying computer workstation problems that may be contributing to the client's musculoskeletal (MSK) pain and symptoms; 3) to provide suggestions that may improve the safety and comfort at the computer workstation, and 4) to suggest a method of completing the workstation analysis virtually, without onsite in-person evaluation. Methods and Results There is a paucity of peer-reviewed literature regarding computer workstation evaluations to be performed in person, let alone using a virtual method. The components of computer workstation evaluations have been recommended by regulatory agencies that survey injuries in the workplace. Prior to 2020, these evaluations were done in person at the office workstation. Modifications in data collection were needed to transition the analysis to a reliable virtual format. The remote method described provides a consistent approach that engages the client in the process.
The trunk posture misclassification errors made by novice and experienced operators were quantified as a function of the angular distance from posture bin boundaries, similar to those used in observation-based posture assessment tools such as 3DMatch. The effect that these misclassification errors had on cumulative and peak low back loads was also determined in three simulated lifting scenarios. Ninety subjects in 3 experience groups were randomly presented with images of known trunk angle via a monitor. Subjects were instructed to make quick and accurate bin selections using standardized pictures included below the images on the monitor. Mean % bin misclassification errors were approximately 32% and 22% for the flexion/extension and lateral bend views, respectively. More bin classification errors were made the closer a viewed image was to a posture bin boundary, regardless of expertise level, and the number of errors made decreased as operator experience increased. Approximately 99% of bin selections were made either in the correct bin or in the bins immediately adjacent to the correct bin in both views. Misclassification errors made in the 3 simulated lifting scenarios induced errors in peak and cumulative loads in 66% of the cases assessed, with an average absolute difference of 13.5% across all load variables. Future work is aimed at determining the effect of training and bin size on the error misclassification rate for all body segments and views.
The incidence of musculoskeletal injuries associated with computer use is increasing. Education has been advocated as a prevention method for reducing the incidence and severity of these injuries. Although the inclusion of education in prevention programs has become a popular practice, its efficacy is poorly defined. The present study was designed to investigate the efficacy of worker education programs in preventing musculoskeletal injuries in a population of reservation center employees who spend the majority of their workdays, using the computer. Participants were randomly assigned to one of three study groups (control, traditional education, or participatory education). Data collection utilized self-report surveys and observational checklists to collect data prior to intervention and at approximately 3, 6, and 12 months post intervention. Those who received education reported less pain/discomfort and psychosocial work stress following the intervention than those who did not receive education. There was no indication that the differences in reported pain/discomfort or psychosocial work stress were related to better work area configuration or improved worker postures. Those workers in the participatory education intervention group reported a significantly better perception of their health status than those in the control group or the traditional education group. It is unclear if the method of intervention was solely responsible for the higher rating.
In an internal campaign, a large Swiss company carried out an instruction programme with employees concerning ergonomics at a VDU. Before and after this campaign, the existing conditions were recorded regarding ergonomics at the VDU workplace. At the same time, complaints, as well as individual knowledge and interest in ergonomics, were recorded. Based on the information at hand, it can be assumed that the campaign improved the existing conditions of ergonomics, as well as the self-responsibility on the part of those involved. A compilation of the work involved indicates that a training course with the objective of influencing ergonomics at the place of work was cost effective.
Motor learning is facilitated when knowledge of results (KR) is presented in accordance with a goal-centred bandwidth (i.e. when the error exceeds a tolerance limit about a movement goal). However, under different conditions of the bandwidth procedure the frequency with which KR is provided is also affected—the wider the goal-centred tolerance limits, the lower the frequency of KR. Since low-KR frequency conditions also have been shown to facilitate motor learning, it is not known whether the bandwidth KR effect is a unique phenomenon in motor learning or is simply due to differences in the frequency of KR. In the present study we partitioned the effects due to bandwidth KR from the effects due to KR frequency using a yoking procedure. Results from the acquisition performance trials indicated that bandwidth procedures exerted both error reduction and performance stabilization influences on motor behaviour that exceeded the effects of the relative frequency control procedures. Bandwidth procedures further resulted in better performance consistency during retention than the relative frequency conditions. These findings were discussed in terms of how KR about movement error and KR about the correctness of movement affect the learning of motor skill.
Designed as a resource for foreign students, this book includes instructions not only on how to use computers, but also on how to use them to complete academic work more efficiently. Part I introduces the basic operations of mainframes and microcomputers and the major areas of computing, i.e., file management, editing, communications, databases, and spreadsheets. Part II concentrates on specific academic tasks, including library research, online searching, taking notes, organizing information, and word processing. Appendix A covers the fundamentals of three commonly used text editors: WordStar, EMACS, and MacWrite. Three major operating systems--DOS, UNIX, and Apple Macintosh System/Finder--are described in Appendix B, and a list of common computer functions is provided in Appendix C. Illustrations are used throughout and a glossary of technical terms is provided. (MES)
Alternative training methods on self-efficacy and mastery of a computer software program were compared in the context of a field experiment involving 108 university managers. A behavioral modeling approach relative to a tutorial approach yielded higher self-efficacy scores and higher performance on an objective measure of computer software mastery. Participants scoring high in self-efficacy performed significantly better than participants with low computer self-efficacy scores. Participants low in self-efficacy reported greater confidence in their ability to master the software training in the modeling compared with the tutorial conditions. Participants in the modeling training reported more effective cognitive working styles, more ease with the task, more satisfaction with training, and less frustration compared with participants in tutorial training. Implications for training interventions are discussed. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Unlabelled: Observation-based posture assessment methods (e.g. RULA, 3DMatch) require classification of body postures into categories. This study investigated the effect of improving posture category salience (adding borders, shading and colour to the posture categories) on posture selection error rates and decision times of novice analysts. Ninety university students with normal or corrected normal visual acuity and who were not colourblind, were instructed to select posture categories as quickly and accurately as possible, in five salience conditions (Plain (no border, no shading, no colour); Grey Border; Red Border; Grey Shading (GS) and Red Shading (RS)) for images presented in randomised blocks (240 classifications made by each participant) on a computer interface. Participants responded quickest in the Border conditions, classifying postures about 5% faster than in the Plain condition. Coloured diagrams significantly reduced posture classification errors by approximately 1.5%. Overall, the best performance, based on both error rate and decision time combined, resulted from incorporating a Grey Border to the posture category diagrams; a simple enhancement that could be made to most current observation-based posture assessment tools. Practitioner summary: The salience of posture diagrams used in observation-based posture assessment tools was evaluated with respect to analyst error rates and decision times. The best performance resulted from incorporating a grey border to the posture diagrams; a simple enhancement that can be made to most current observation-based posture assessment tools.
L ong gone, but still remembered by many, are typewriters, typing pools, carbon copies, adding machines and physical mail boxes. The ubiquitous personal computer has changed all this and revolutionized the workplace. .urthermore, most workers today go well beyond using their computer as a mere typewriter or calculator. As intriguing as this computer-use revolution may be, embracing information and communi-cation technology (ICT) is viewed as an essential ingredient for both businesses and individuals to remain competitive in today’s knowledge-based economy. “[A]ccess to and development of information, communication and e-commerce resources are increasingly viewed as crucial for economic and social development.” (OECD, 2001). It is argued that access to and use of ICTs can increase productivity and efficiency, enhance knowledge and skill levels, and improve the quality of work life (ILO, 2000). Concerns have been raised, however, over the uneven use of ICTs—the “digital divide”— between and within countries. .or example, only 6% of the world’s population has ever logged onto the Internet, and close to 90% of them are from industrialized countries (ILO, 2000). Dig-ital divides have been documented within indus-trialized countries as well—among individuals, households, businesses and geographic regions. This paper examines the extent of computer use by Canadian workers (see Data source and defi-nitions): which workers are most likely to use a computer at their job, how often they use it, what they use it for, and how they learned their computing skills.