Janwantanakul et al. BMC Musculoskeletal Disorders 2011, 12:23
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RESEARCH ARTICLE

Open Access

Development of a risk score for low back pain
in office workers-a cross-sectional study
Prawit Janwantanakul1*, Praneet Pensri1, Patriya Moolkay2, Wiroj Jiamjarasrangsi2

Abstract
Background: Low back pain (LBP) is common among office workers and is the most common cause of workrelated disability in people under 45 years of age. The aetiology of LBP is widely accepted to be multi-factorial.
Prognostic research into office workers at risk of developing LBP has received limited attention. The aims of thisstudy were to develop a risk score to identify office workers likely to have LBP and to evaluate its predictive power.
Methods: 397 office workers filled out a self-administered questionnaire and underwent physical examination. The
questionnaire gathered data on individual, work-related physical and psychosocial data as well as the presence oflow back pain in the previous 4 weeks. The physical examination included measurement of body weight, height,
waist circumference, hamstrings length, spinal scoliosis, spinal curve, Backache Index and lumbar stability. Logistic
regression was used to select significant factors associated with LBP to build a risk score. The coefficients from thelogistic regression model were transformed into the components of a risk score.
Results: The model included six items: previous history of working as an office worker, years of work experience,
continuous standing for >2 hrs/d, frequency of forward bending during work day, chair having lumbar supportand Backache Index outcome. The risk score for LBP in office workers (The Back pain Risk score for Office Workers:
The BROW) was built with a risk score ranging from 0 to 9. A cut-off score of ≥4 had a sensitivity of 80% and a
specificity of 58%. The positive predictive value and negative predictive values were 70% each.
Conclusions: The BROW is easy and quick to administer. It appears to have reasonable sensitivity, specificity,
positive predictive value and negative predictive values for the cut-off point of ≥4. The BROW is a promising toolfor use to identify office workers in need of early interventions. Further prospective study is needed to validate thepredictive performance of the BROW.

BackgroundLow back pain (LBP) is a major health problem with
two thirds of adults suffering from LBP at some time intheir lives [1] and approximately 12% to 44% have LBP
at any given time [2]. LBP is common among office
workers with the one-year prevalence ranging from 23%
to 38% [3-5]. LBP is the most common cause of workrelated disability in people under 45 years of age andthe most expensive cause of work-related disability, interms of workers’ compensation and medical expenses[6]. In the USA, the total yearly cost of LBP exceeds 100
billion US dollars [7].

* Correspondence: prawit.j@chula.ac.th
1
Department of Physical Therapy, Faculty of Allied Health Sciences,
Chulalongkorn University, Bangkok, 10330, Thailand
Full list of author information is available at the end of the article

The etiology of LBP is not fully understood but isassumed to be of multi-factorial origin, indicating that
individual, physical and psychosocial factors can contribute to their development and persistence [8]. Previous
studies have identified several individual factors associated with LBP including female [4], level of education[9], smoking [10], sleep deprivation [11], prolonged driving [12]. Regarding work-related risk factors, accumulated computer usage has been linked to increased riskof LBP [13]. Sitting for more than half a work day in
combination with awkward postures or frequently working in a forward bent position has been found toincrease the likelihood of having LBP [14,15]. Poor
workstation ergonomics has been shown to significantly
contribute to the development of LBP [15]. Various psychosocial problems, such as high stress [16], low job
satisfaction [15], low social support [17] and effort-© 2011 Janwantanakul et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.

Janwantanakul et al. BMC Musculoskeletal Disorders 2011, 12:23
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reward imbalance [18] also contributed to an increased
occurrence of LBP. Clinical factors, such as scoliosis[19], low back muscle endurance [20], poor lumbar
stability [21] and abnormal trunk mobility [22,23], hasbeen linked to increased risk of LBP.
Having a screening tool for LBP is necessary forseveral reasons. First, a screening tool provides information about individuals’ risk of developing LBP, which
will guide health professionals and individuals in joint
decisions on further intervention. Identification of persons at risk would also mean the enhancement of
resource allocation to those most in need and most
likely to benefit from it. Without a screening tool, alarge number of people would receive intervention,
which is likely to compromise its effectiveness [24,25].
Second, a screening tool allows an examination to beheld in primary health care and workplace settings
where full clinical examinations are impractical due tolimited personnel and time [26]. Lastly, a screening tool
is beneficial for selecting relevant individuals for therapeutic research [25].
In a primary care setting, Hill et al [27] developed a brief
screening tool for identifying subgroups of patients toguide the provision of early secondary prevention in primary care. The screening tool included 9 items: referredleg pain, co-morbid pain, disability (2 items), bothersomeness, catastrophizing, fear, anxiety and depression. Von
Korff and Miglioretti [28] developed a risk score to identify
those at risk of chronic LBP. The items in the risk score
included pain severity variables (average, worst and current pain intensity), interference with usual activities,
work/housework activities and family/social activities,
number of other pain and number of days with back painin the prior six months. To our knowledge, no screening
tool to identify office workers at risk for developing LBP
has been established. Therefore, the specific purpose of
the present study was to develop a risk score to assist
health care providers in identifying office workers who
were at risk of developing LBP. The aim was achieved by
identifying important biopsychosocial predictors, assigning
relative weights to each predictor and then estimating themodel’s predictive performance.

Methods
A cross-sectional study was conducted in Chulalongkorn
University. An invitation letter and information about
the study were sent to office workers in 21 departments
within the university. Office workers were included if
aged between 18-60 years and their job involved office
work with at least one year of experience in the current
position. They were excluded if they reported pregnancy
or spinal, intra-abdominal or femoral surgery in the past
year, a history of trauma or accidents at the low backregion or had been diagnosed with rheumatoid arthritis,

Page 2 of 8ankylosing spondylitis, systemic lupus erythymatosus or
osteoporosis. Those who had any contraindications forphysical tests, such as cardiovascular diseases or severe
pulmonary diseases, were also excluded. A written
informed consent was obtained from all participants.
The study was approved by the University Human
Ethics Committee.
Subjects filled out a self-administered questionnaire
and underwent physical examination, which tookapproximately 45 minutes to complete. The questionnaire gathered data on individual, work-related physicaland psychosocial data as well as the presence of low
back pain.
Individual factors included age, gender, educational
level, marital status, leisure activities, frequency ofweekly exercise sessions, quality of sleep, average number of sleeping hours a night, smoking habits and average number of driving hours a day.
Respondents were also asked to complete the physical component of the Thai abbreviated version ofWorld Health Organization Quality of Life (WHOQOL-BREF-Thai), which consists of seven items assessing self-perception of physical health status. Items arescored on a 5-point Likert scale with responses ranging
from 1 (not at all) to 5 (an extreme value/very
satisfied). The total score ranges from 7 to 35 and are
categorized into poor (score of 7-16), fair (17-26) andgood quality of life (27-35) [29].
Work-related physical factors included the average
number of working hours a day as well as working day
a week, years of working experience and previous history of working as office workers. Respondents wereasked about whether they continuously sat or stood for>2 hours a day and the frequency of using a computer,
performing various activities during work (i.e. reaching,
forward bending, twisting, lifting moderate to heavy
objects and walking) and rest breaks. The questionnairealso asked respondents to self-rate the ergonomics oftheir workstations (i.e. height of desk and chair, thepresence of lumbar support as well as the position ofcomputer screen, keyboard and mouse) and work environment conditions (i.e. temperature, light intensity,
noise level, air circulation).
Psychosocial factors included the Suanprung stress test
(SPST-20) [30,31] and Thai version of Effort-Reward
Imbalance questionnaire (Thai ERIQ) [32].
1. The Suanprung stress test consists of 20 items
assessing stress level. Items are scored on a 5-point
Likert scale with item responses ranging from 0 (nostress) to 5 (extremely high stress). The total score of
the test ranges from 0 to 100. Total scores are categorized into 4 levels: low stress (0-24), medium stress(25-42), high stress (43-62) and extremely high stress(63-100) [30,31]

Janwantanakul et al. BMC Musculoskeletal Disorders 2011, 12:23
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2. The Thai ERIQ consists of 23 items containing
3 subscales: 6 items on effort (the demands and obligations put on the working person), 11 items on reward(offered or promised as part of social exchange in termsof money, esteem and job security/career opportunity)and 6 items on over-commitment (personal coping withdemands and reward expectancies). Responses to theitems of effort and reward are scored on a 5-point
Likert scale (disagree/agree and 4 levels of being distressed) and items of over-commitment are scored on a
4-point Likert scale (strongly disagree, disagree, agree,
and strongly agree). Total scores range from 6 to 30 for
effort, 11 to 55 for reward and 4 to 14 for over-commitment. A ratio between two scales of effort and reward(weighted by the item number) is calculated to assessthe degree of imbalance between high effort expended
and low reward received. Effort-reward ratio >1
indicates high level of stressful experience at work. A
high score of over-commitment indicates high level ofover-commitment [32].
Low back pain A picture of the body from the standardized Nordic questionnaire [33] and the question“Have you had low back pain in the previous fourweeks?” were included in the questionnaire.
Each participant underwent a physical examination
conducted by trained physical therapists according tostandardized protocol. The physical examination
included the following:
1. Body weight and height were measured by electronic digital scale and a wall-mounted standiometer,
respectively.
2. Waist circumference was measured midway
between the lower rib margin and the superior borderof the iliac crest using a tape measure [34,35].
3. Hamstring length assessment used a passive
straight-leg-raising test. With the subject in the supine
position, an examiner passively flexed a subject’s hipwith the knee extended until a subject felt strong
stretching in the posterior aspect of the leg. The examiner then recorded the hip angle [36]. A hip angle of ≥80
degree indicates normal hamstring length [37].
4. Spinal scoliosis. The subject was asked to flex forward and the examiner observed the spine from the“skyline” view. The examiner looked for a hump on one
side and a hollow on the other, indicating spinal scoliosis [38].
5. Spinal curve measurement using a flexicurve. While
the subject stood relaxed, a flexicurve was pressedagainst the subject’s back so that the upper end of theflexicurve was set at the C7 spinous process and thelower end placed at the lumbosacral joint level. The
spinal curve from the flexicurve was then traced on apaper and the indexes of thoracic and lumbar curvature
were calculated according to Milne and Lauder [39].

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6. Backache Index (BAI). The test consists of fiveactive motions of trunk in a standing position. The
examiner made her assessment by means of a scoring
system that includes pain factors obtained by asking the
subject and combined with the stiffness estimation at
the end of different lumbar motions. Each movement
was scored on the 4-point scale. The sum of the fiveoutcomes yields a BAI with a maximum of 15 points.
The higher the score indicates more restrictive spinal
movement [40].
7. Lumbar stability test. The test is used to assess theisometric contraction of abdominal and back muscles,
which provide lumbar stability. A pressure sensor (Chattanooga, USA.) was placed between L1 and S2 with thesubject in the supine position to detect motion. A seriesof 7 exercises, which required increasing levels of muscular control of the lumbar spine for stability, was performed by each subject. The subject received a pass or
fail for each exercise level based on the pressure gauge
readings and the absence of movement compensations.
The examiner recorded the highest exercise level thatthe subject attained [41].
Before data collection, the repeatability of data fromthe questionnaire and physical examination outcomes
was assessed on 30 office workers. Each subject wastested twice on two separate days with a week lapse
between the measurements.
Statistical analyses

For the reliability study, the intraclass correlation
coefficient (ICC [1,1]), Kappa coefficient and Kappa
coefficient with linear weighting were calculated forcontinuous, nominal and ordinal data, respectively.
Characteristics of subjects participating in the main
study were described using means or proportions. To
develop a risk score, a series of statistical analyses wereconducted. The associations between each factor and
LBP in the previous four weeks were evaluated using
the bivariate logistic regression analyses. Any factors
with a p-value ≤ 0.1 were eligible for addition into multivariate analysis. To retain the statistical power of thedatabase, missing data were handled by using the ‘hotdeck imputation’ procedure. A respondent was selected
at random from the total sample of the study and thevalue for that person was assigned to a case in which
this information was missing. This procedure was conducted repeatedly for each missing value until the dataset was complete [42]. Multiple logistic regression
analysis with backward stepwise selection was then performed to determine the optimal combination of biopsychosocial factors needed to predict LBP. Statisticalsignificance was set at the 5% level.
A simplified scoring system was devised on the basis ofmultiple logistic regression analytical results. Score wasJanwantanakul et al. BMC Musculoskeletal Disorders 2011, 12:23
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assigned to each variable based on the magnitude of the bcoefficient. A total score for the risk of developing LBP
was calculated as the sum of each variable. A receiveroperating characteristic curve (ROC) and the area under
the ROC (AUC) were produced to evaluate the discriminatory ability of the risk score. Sensitivity, specificity,
positive predictive value (PPV) and negative predictive
value (NPV) for several cut-off scores were calculated.
The cut-off score that gave the maximum sum of sensitivity and specificity was taken as an optimum. All statistical analyses were performed using SPSS statisticalsoftware, version 17.0 (SPSS Inc, Chicago, IL, USA).

Results
The results of reliability study showed moderate (0.49)
to very good (1.00) repeatability of the outcomes [43]. A
total of 454 office workers participated in the study. Of
these, 51 were excluded because they did not meet theinclusion criteria and 6 were excluded because they did
not provide answers about their LBP in the previous
four weeks. Thus, the final analysis was based on thedata collected from 397 office workers.
Most respondents were females with Bachelor’s
degree. Their working time resembled typical office
workers (i.e. 8 hours per day and 5 days per week). The
average body mass index of this sample was slightly
above normal range for Asians [44]. Nearly all office
workers reported fair to good quality of life but about
half of them experienced high to extremely high stress(Table 1).
Two hundred and nineteen participants (55%)
reported LBP during the previous four weeks. A varying
number of office workers reported frequent computeruse (92%), sitting for >2 hours a day (88%), forwardbending (58%), lifting moderate to heavy objects (52%),
body twisting (46%), reaching (38%) and standing for >2
hours a day (22%) during the work day. A majority ofparticipants reported satisfaction with the positions of
the computer screen (68%) and keyboard/mouse (55%).
Forty-five per cent of participants used a chair withlumbar support.
When performing bivariate logistic regression analyses,
factors showing p-value ≤ 0.1 were body weight, waist
circumference, sleep quality, smoking habits, previous
history of working as an office worker, years of work
experience, continuous standing for >2 hrs a day, frequency of computer use, reaching, forward bending,body twisting, lifting moderate to heavy objects and rest
breaks, self-perception of desk height, chair height andkeyboard/mouse positions, chair having lumbar support,
the WHOQOL-BREF-Thai score, the Suanprung stresstest score, lumbar stability test outcome, BackacheIndex outcome and Effort-Reward ratio. There was asignificant correlation between body weight and waist

Page 4 of 8

Table 1 Characteristics of study population
Characteristics

N (%)

Mean ± SD

Gender (n = 397)Male

65 (16.4)

Female332 (83.6)

Age(years) (n = 396)

41.5 ± 9.7

Body mass index (Kg/m2)(n = 395)

23.9 ± 4.6

Level of education (n = 386)
Primary school

11 (2.8)

Secondary school

16 (4.1)

College41 (10.6)

Bachelor’s degree
Higher than Bachelor’s degree

209 (54.1)
109 (28.2)

Years of work experience (years)(n = 362)

14.9 ± 9.0

Weekly working days (days perweek) (n = 363)

5.1 ± 0.6

Daily working hours (hours perday) (n = 372)

8.0 ± 1.3

Quality of life measured by the
WHOQOL-BREF-Thai (n = 391)
Poor

25.7 ± 3.5
4 (1.0)Fair

225 (57.5)

Good

162 (41.4)

Stress level measured by the
Suanprung stress test (n = 391)

42.8 ± 17.3

Low

62 (15.9)

Medium131 (33.5)

High

149 (38.1)

Extremely high49 (12.5)

circumference. Thus, body weight was selected forfurther analysis.
When performing multivariable logistic regression analyses, the results revealed that previous history of working as office workers, years of work experience,
continuous standing for >2 hrs a day, frequency of forward bending, chair having lumbar support, BackacheIndex outcome and Effort-Reward ratio strongly correlated with complaints of LBP in the preceding four weeks(Table 2). To develop a risk score for low back pain in
office workers, scores were then assigned to each variable, which resulted in a range from 0 to 14 (Table 3).
The optimal cut-off score was ≥5 (sensitivity = 74%; specificity = 63%) (Table 4). The PPV and NPV for the cutoff
score of ≥5 were 72% and 66%. The area under curve(AUC) was 0.73 (95%CI 0.68-0.78).
Because the Thai ERIQ consists of 23 questions, themodel of the risk score with the exclusion of the Thai
ERIQ was examined for its predictive power. The score
ranged from 0 to 9 (Table 5). The optimal cut-off scorewas ≥4 for a sensitivity of 80% and a specificity of 58%(Table 6). The PPV and NPV for the cut-off score of ≥4

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Table 2 Prevalence and adjusted odds ratio (ORadj) with 95% confidence interval (95%CI) of low back pain withrespect to factors in the final modelling (n = 397)
FactorsN

Prevalence n (%)

ORadj

95% CIP

173

109 (63.0)

1.72

1.08-2.74

0.023

224

110 (49.1)

1.00

< 10

122

54 (44.3)

1.00

10-19

146

96 (65.8)

3.48

1.95-6.20< 0.001≥20

129

69 (53.5)

2.03

1.14-3.62

0.017

1.03-3.33

0.039

1.12-2.83

0.014

1.04-2.63

0.035

2.52-6.42< 0.001

1.25-32.87

0.026

Previous history of working as an office worker
Yes
No
Years of work experience (years)

Continuous standing >2 hrs/dYes

86

55 (64.0)

1.86

No

311

164 (52.7)

1.00

228

137 (60.1)

1.78

169

82 (48.5)

1.00

Frequently bending forward during the work day
Yes
No
Chair having lumbar support
Yes

177

88 (49.7)

1.00

No

220

131 (59.5)

1.65

197

79 (40.1)

1.00

200

140 (70.0)

4.02

≤1

382

206 (53.9)

1.00

>1

15

13 (86.7)

6.41

Backache Index outcome
0≥1
Effort-Reward ratio

were 70% each. The predictive ability was unchanged ascompared with the model that included the Thai ERIQ
(AUC 0.73, 95%CI 0.68-0.78).

Discussion
The current study aimed to build a screening tool tojustify office workers at risk of developing LBP. Various
individual, work-related physical and psychosocial factors as well as outcomes from physical examination conducted by trained physical therapists were included in
the analysis. Data were collected from office workers inworkplace settings. The results showed that a risk scorefor LBP in office workers or “The Back pain Risk score
for Office Workers (The BROW)” comprised the following six items to calculate the total score: previous history of working as an office worker, years of work
experience, continuous standing for >2 hrs a day, frequency of forward bending during the work day, chair
having lumbar support, BAI outcome. Each item isunequal in weight. The score ranged from 0 to 9 andthe higher the score indicates higher risk of LBP.
The Thai ERIQ was excluded from the final version of
the BROW because of three reasons. First, the inclusionof the Thai ERIQ would make the BROW cumbersome
and time consuming. Second, the exclusion of the Thai
ERIQ from the BROW did not alter the predictive ability, as measured by the AUC, of the risk score and the

AUC is still at an acceptable level. This is because inthis study the number of office workers, who had effortreward imbalance, was small (14/370). Third, the exclusion of the Thai ERIQ would make the BROW accessible for those for whom the Thai ERIQ is unavailable.
The strongest predictor in the BROW was the BAI
outcome (OR adj = 4.02). The BAI is a tool aimed atassessing overall restricted spinal movement in cases of
LBP [40]. Since computer use and document workrequire a sitting posture, the strong association between
the BAI outcome and the risk of LBP may relate to theprolonged sitting posture causing lumbar stiffness. Evidence suggests that prolonged sitting may lead to lumber stiffness, which may predispose the lumbar spine to
injury during forceful loading [45].
The BROW is relatively easy to administer and can be
carried out within a short space of time because it
mainly relies on subjective information from an individual. However, the disadvantage of the BROW is therequirement to perform the BAI test with a health professional. Although the BAI test is quite easy to perform,
health professionals with less experience in doing thetest may misclassify the outcome, which potentiallycompromises the risk score’s predictive performance.
Within this limitation, the BROW is a promising tool
for the early identification of office workers at risk ofdeveloping LBP, who will receive the greatest benefit

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Table 3 Risk scores for low back pain with the inclusionof the Thai ERIQ

Table 5 Risk scores for low back pain with the exclusionof the Thai ERIQ

Factors

Factors

Beta
Coefficient

Risk
score*

Previous history of working as an office
workerYes

Beta
Coefficient

Risk
score*

0.51

1Previous history of working as an office
worker
0.54

No

1

Yes

0

No

Years of work experience (years)

0Years of work experience (years)

< 10
10-19

1.25

0
3

≥20

0.71

1

Continuous standing >2 hrs/d

< 10
10-19

1.17

0
2

≥20

0.62

1

0.59

1

Continuous standing >2 hrs/d

Yes

0.62

No

1

Yes

0

No

Frequently bending forward during thework dayYes

0Frequently bending forward during thework day
0.58

No

1

Yes

0

No

Chair having lumbar support

0.55

1
0

Chair having lumbar support

Yes
No

0
1

0.50

Yes
No

Backache Index

0.48

0
1

1.41

3

Backache Index

0
≥1

1.39

0

0

3

≥1

Effort-Reward ratio≤1* Reference groups were assigned a score of 0. b Coefficient of chair without
lumbar support was assigned a score of 1 and then the other b Coefficient
was multiplied by 2 (1/0.50) and rounded off to the nearest integer.

0

>1

1.86

4

* Reference groups were assigned a score of 0. b Coefficient of chair without
lumbar support was assigned a score of 1 and then the other b Coefficient
was multiplied by 2 (1/0.50) and rounded off to the nearest integer.

from preventive intervention. The BROW is suitable for
utilising in primary health care and workplace settings
where a full clinical examination is impractical due tolimited personnel and time.
Selection of an optimal cut-off point largely depends
on the purpose of utilizing the risk score and requires
Table 4 Sensitivity and specificity of each cut-off value
for the risk score for low back pain with the inclusionof the Thai ERIQ
Cut-off value

Sensitivity

≥1
≥2

0

knowledge of the sensitivity and specificity of a risk
score. With a cut-off score of ≥4, the sensitivity, which
represents the ability of the risk score to recognize highrisk office workers when present, is 80% and the specificity, which indicates the ability of the risk score torecognize low-risk office workers when present, is 58%.
Subsequently, the false positive rate was 42%, meaningthat 42% of low-risk office workers will be identified as
positive. Because these low-risk workers are unlikely tobenefit from a preventive intervention, a high false positive rate would increase intervention cost and time loss.
Table 6 Sensitivity and specificity of each cut-off value
for the risk score for low back pain with the exclusion of
the Thai ERIQ

Specificity

PPV

NPV

100.0

2.1

56.2

100.0

98.4

11.7

58.3

85.0

Cut-off value

Sensitivity

Specificity

PPV

NPV

≥3

90.7

28.3

61.3

70.7

≥1

100.0

1.9

56.0

100.0

≥4
≥5

83.5
74.2

48.3
63.4

60.0
71.8

70.0
66.2

≥2
≥3

98.4
88.5

12.3
33.1

58.3
62.3

86.4
69.9

≥6

51.6

78.6

75.2

56.4

≥4

79.7

57.8

70.2

69.5

≥7

30.2

91.0

80.9

51.0

≥5

63.0

69.5

72.0

60.1

≥8

22.5

95.2

85.4

49.5

≥6

44.3

83.8

77.3

54.7

≥9

8.2

97.9

83.3

46.0

≥7

23.4

95.5

86.5

50.0

≥ 10

3.3

100.0

100.0

45.2

≥8

6.8

98.1

81.2

45.8

≥ 11

2.2

100.0

100.0

44.9

≥9

0.5

100.0

100.0

44.6

Janwantanakul et al. BMC Musculoskeletal Disorders 2011, 12:23
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On the other hand, the false negative rate for the cut-off
point of ≥4 was 20%, indicating that 20% of high-risk
workers will be missed. Because these high-risk workers
may not have received deserved preventive intervention,a high false negative rate would cause greater medical
expenses and disability from a disease later on. Although
the fundamental purpose of having a screening tool is to
reduce the number of office workers with LBP, one
needs to consider the expected consequences of missinga person at risk as opposed to including a person in anintervention even though they are not at risk. Since LBP
is a non-fatal disease and taking into account the circumstances of limited resources one may want toincrease the likelihood of including those who are truly
at risk of developing LBP. Thus, a screening tool withhigh specificity would be preferable to high sensitivity.
With increasing the cut-off point to the score of ≥6, thespecificity increases to 84%, while the sensitivity drops
to 44%. The false positive rate decreased to 16%, meaning that less low-risk workers will be identified as positive. Consequently, money and time could be saved.
In practice, predictive values may be more useful for
applying the risk score in clinical decision making thansensitivity and specificity rates because predictive values
indicate the probability that the result is correct [46].
The results show that the predictive value of the cut-off
point of ≥4 was reasonably high. The PPV was 70%,
indicating that 70% of subjects with a score of ≥4 areactually at risk of developing LBP. The NPV was 70%,
meaning that 70% of subjects with a score of < 4 are
not at risk of developing LBP. Although the PPV and
NPV provide useful information for interpreting theresults of the risk score, they are highly dependent on
the prevalence of the condition of interest in the sample[46]. The lower the prevalence of LBP in a population,
the more likely a positive test will be a false positive[47]. In this study, the prevalence of 4-week LBP is considered to be high compared to that reported in previous epidemiological studies [48]. The use of a small
convenience sample of office workers may lead to selection bias and, consequently, restricts the external validityof this study. Thus, caution must be exercised whenusing the BROW in other populations.
There are two methodological limitations that are
noteworthy. First, the cross-sectional nature of the study
only allows the association between exposure and outcome to be examined. It is not possible to establish thecausal relation between exposure and outcome. For
example, it is unclear if restricted spinal movement, asassessed by the BAI, causes LBP or LBP leads torestricted spinal movement. Therefore, the findings ofthe present study should be taken as a preliminary
result. A prospective study is needed to validate thefinding of this study. Second, predictive performance ofPage 7 of 8

the BROW was tested on the same population in which
the risk score was developed. The model is likely to perform better in the development sample than in an independent sample. In other words, the predictive power islikely to be inflated [24,25]. In addition, the risk score
may be very specific to the population studies. Thus,
extrapolation of these results to other populations
should be made with caution. Further validation andimpact studies of the BROW in a new population of
office workers are suggested.

Conclusions
The risk score for LBP in office workers (The BROW)
was developed and it contained 6 items with scores ranging from 0 to 9. Using a cut-off score of ≥4, the sensitivity was found to be 80% and the specificity 58%. The
positive predictive value and negative predictive value
were both 70%. The BROW is easy and quick to complete by health care providers. The BROW is a potentially useful tool in helping clinicians make decisions
about office workers’ risk of developing LBP. However,further research is required to validate the BROW.
Acknowledgements
This work was funded by the Thai Health Foundation and Chulalongkorn
University Centenary Academic Development Project (#12). The authors
thank Dr. Surintorn Klampakorn for her advice on the Thai version of EffortReward Imbalance Questionnaire.
Author details
1
Department of Physical Therapy, Faculty of Allied Health Sciences,
Chulalongkorn University, Bangkok, 10330, Thailand. 2Department ofPreventive and Social Medicine, Faculty of Medicine, Chulalongkorn
University, Bangkok, 10330, Thailand.
Authors’ contributions
The authors have contributed in the following ways: PJ and PP provided
concept/research design, data analysis and manuscript writing. PM provided
data collection, data analysis and manuscript writing. WJ provided concept/
research design and manuscript writing. All authors read and approved thefinal manuscript.
Competing interests
The authors declare that they have no competing interests.
Received: 30 July 2010 Accepted: 25 January 2011
Published: 25 January 2011
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Pre-publication history
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doi:10.1186/1471-2474-12-23
Cite this article as: Janwantanakul et al.: Development of a risk score forlow back pain in office workers-a cross-sectional study. BMC
Musculoskeletal Disorders 2011 12:23.

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