Relación entre la capacidad aeróbica, el riesgo de lesiones y tenencia para los conductores de...
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Relationship between aerobic capacity, injury risk and
tenure for new-hire delivery driversCharles K. Anderson
a
aAdvanced Ergonomics, Inc., 7460 Warren Parkway #265 Frisco, Texas, 75034-4279, USA
Version of record first published: 21 Oct 2010
To cite this article: Charles K. Anderson (2010): Relationship between aerobic capacity, injury risk and tenure for new-hire
delivery drivers, Ergonomics, 53:11, 1395-1401
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Relationship between aerobic capacity, injury risk and tenure for new-hire delivery drivers
Charles K. Anderson*Advanced Ergonomics, Inc., 7460 Warren Parkway #265 Frisco, Texas 75034-4279, USA
(Received 16 November 2009; final version received 8 September 2010)
Over a 2-year study period, aerobic capacity was measured at time of hire for 1419 delivery drivers. Injury experienceand tenure were then monitored for these new-hires during that same period. Number of strain injuries, time tofirst strain and time to termination were regressed on aerobic capacity adjusting for tenure. Statistically significant,monotonically changing relationships were found for all three outcome variables. A unit increase in aerobiccapacity was predicted to result in a 3.7% decrease in injury rate and a 1.1% decrease in risk of termination. Whenage was included in the model for time to termination, aerobic capacity was no longer a significant predictor.The findings regarding injury experience and aerobic capacity support National Institute for Occupational Safetyand Health recommendations that individuals should work at no more than 2130% of their aerobic capacity.
Statement of Relevance: Knowledge of the nature of the relationship between aerobic capacity, injury experience
and retention allows the ergonomist to determine whether there is a point of diminishing returns in interventioneffectiveness for higher levels of aerobic capacity.
Keywords: injury risk; musculoskeletal disorders; NIOSH lifting equation; personnel selection; physical workcapacity
Introduction
Physical work evaluation guidelines such as the revised
lifting equation published by the National Institute for
Occupational Safety and Health (NIOSH) identify
energy expenditure as one of the factors of concern
(National Institute for Occupational Safety and Health
1981, Waters et al. 1993). The guidelines provided inthe support material for the revised lifting equation
translate to a recommendation that the energy
expenditure for an 8-h shift should be no more than
approximately 21% of aerobic capacity, as measured
on a treadmill, when the work is primarily performed
with the arms and 30% otherwise (Waters et al. 1993).
More recently, Wu and Wang (2002) suggested energy
expenditure limits of 34% of aerobic capacity for 8 h
of work time and 31% for 10 h, based on subjects
tolerance of cycling on an ergometer at various
workloads.
The energy expenditure for a given task is just one
of several factors reflected in the calculation of the
Lifting Index in the revised NIOSH lifting equation
(Waters et al. 1993). The Lifting Index itself has been
validated in a number of ways (Hidalgo et al. 1995,
Wang et al. 1998, Marras et al. 1999, Waters et al.
1999, Lavender et al. 2009), but there have been no
studies that have provided a detailed examination of
the shape of the relationship between the percentage of
aerobic capacity utilised on the job and occupational
field measures of worker/job mismatch. Such
indications of worker/job mismatch could include
increased injury rate, increased turnover and lower
productivity. Knowledge of the shape of these
relationships could allow the ergonomist to determine
whether there is a point of diminishing returns in
intervention effectiveness for higher levels of aerobiccapacity.
Statistically significant relationships have been
found between performance on test batteries, including
a measure of aerobic capacity and subsequent injury
rates for employees in warehouse jobs (Anderson
and Catterall 1987, Craig et al. 1998, Anderson and
Briggs 2008), firefighting (Cady et al. 1979) and basic
combat training (Knapik et al. 2001, 2006). These
studies indicated that individuals with low fitness
levels had injury rates ranging from 1.4 to 9.3 times as
high as individuals with higher fitness levels. A
number of these studies did not provide the energy
expenditure for the job and all of them considered only
two, or at most three, ranges of fitness. Hence, from
these studies it is difficult to ascertain the sensitivity of
injury rate to the percentage of aerobic capacity being
used.
Two studies have reported on the relationship
between aerobic capacity and job tenure (Knapik et al.
2006, Anderson and Briggs 2008). They found that
*Email: [email protected]
Ergonomics
Vol. 53, No. 11, November 2010, 13951401
ISSN 0014-0139 print/ISSN 1366-5847 online
2010 Taylor & Francis
DOI: 10.1080/00140139.2010.524252http://www.informaworld.com
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individuals with lower fitness were 3438% less likely
to work more than 89 weeks than more-fit indivi-
duals. As with the studies of aerobic capacity and
injury rate, the results presented made it difficult to
evaluate the sensitivity of tenure to percentage of
aerobic capacity being used, particularly for time
periods other than 89 weeks. Barrick and Zimmerman(2009) noted that relationships with predictors of
retention other than personality weakened over time
up to 2 years after hire.
Data from a large sample of delivery drivers
working at a very similar average energy expenditure
provided an opportunity to more finely explore the
relationship between aerobic capacity and injury rate
and tenure. Other potential indicators of worker/job
mismatch were not archived by the company, so this
study concentrated on these two outcome measures.
Methods
Subjects
The subjects in this study were an ethnically diverse
group of 1419 full-time delivery drivers who had
been hired in 2007 or 2008 at locations of the parent
company across the United States. Demographics are
shown in Table 1.
Job analysis
These delivery drivers manually unloaded hundreds of
units of product from a delivery truck to a two-wheel
dolly or cart in the course of a series of deliveries over a
given work shift. The delivery driver then transportedthe units for a particular delivery to a clients storage
location and potentially manually handled the pro-
ducts again to place them in their final position.
The strength and endurance demands of this
delivery driver job were analysed most recently in
2006 by collecting data regarding the weights of the
product delivered, the frequency of handling various
products, the handling heights and the average energy
expenditure over the shift.
Handling heights were obtained by measuring the
heights at which products were stored on the delivery
trucks and in representative client facilities. The
majority of the manual unloading from the delivery
truck occurred above waist level, which is the region in
which NIOSH recommended that the threshold be
21% of aerobic capacity. The manual handlingassociated with rearranging product in the clients
storage locations was roughly evenly mixed between
above and below waist level.
Energy expenditures were estimated by monitoring
the heart rates of 181 experienced delivery drivers and
then adjusting the heart rate responses for their
individual fitness levels. Each driver was monitored for
an entire shift, which was typically about 10 h.
Fitness level was assessed with a multi-stage
sub-maximal step test protocol designed by Siconolfi
et al. (1985). The protocol is described in more detail in
the sub-section regarding predictor measures. The
mean overall energy expenditure for incumbentswhose heart rate was monitored was 9.86 (SD 0.45)
ml/kg per min.
Predictor measures
The 1419 delivery driver new-hires had participated in
a physical ability testing battery as part of their
screening process for employment. The battery
consisted of two strength tests and the multi-stage
sub-maximal step test. Test administrators at clinics
close to each location were trained in the test protocol
by the authors staff and the test results were
reviewed to assure compliance with the specifiedprotocol.
The strength tests consisted of lifting a box into
which the applicant added as much weight as she/he
felt she/he could safely lift and then demonstrating that
lift. The amount of weight available to place in the
box was limited to slightly more than the maximum
weight that would be routinely lifted on the job at that
location, which was where the cut-off was set. The
weight made available was limited to reduce the risk
of injury during the test. One implication of this
strength testing protocol was that individuals strength
test scores were limited to the maximum weight
available for the lift. This also meant that all
individuals who passed the test had virtually the same
amount of weight lifted for the strength tests. Hence,
strength was not used as a predictor measure in this
study since there was virtually no variance in the
measured value for these new-hires.
Aerobic capacity was assessed with the multi-stage
sub-maximal stepping protocol described by Siconolfi
et al. (1985). The protocol involved stepping up and
down on a 25.4 cm bench for 3 min, starting at a pace
Table 1. Subject demographics.
Males Females Overall
Sample size 1406 13 1419Age (years) 31.0 (6.5) 29.8 (5.8) 30.9 (6.5)Height (m) 1.78 (0.13) 1.70 (0.10) 1.78 (0.13)Weight (kg) 93.1 (18.7) 81.7 (17.4) 93.0 (18.7)Aerobic capacity
(ml/kg per min)38.6 (6.7) 35.7 (6.6) 38.5 (6.7)
Note: Values for age, height, weight and aerobic capacity are shownas mean (SD).
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of 17 steps per min. If the heart rate at the end of the
3 min was below 65% of estimated maximum heart
rate (220 minus age), the participant rested for 1 min
and then stepped another 3 min at 26 steps per min. If
the heart rate was still below 65% of the estimated
maximum at the end of that stage, the participant
rested for 1 min and then stepped another 3 min at 34steps per min. The aerobic capacities for males were
calculated using the equations provided in Siconolfi
et al. (1985). The equations for females provided by
these authors seemed to under-predict for the popula-
tion of female applicants for manual materials hand-
ling jobs, so data from Harkrider (2005) were used to
calculate equation coefficients that represented a
population of females more similar to the industrial
applicant pool. Siconolfi et al . (1985) reported a
correlation coefficient of 0.79 between predicted and
measured aerobic capacity for males. The equations
using the data from Harkrider (2005) had a correlation
coefficient of 0.89 for females. A testretest reliabilityof 0.83 was reported by Gall and Parkhouse (2004) for
the step test protocol.
Outcome measures
Hire dates, termination dates, reasons for termination
and data from first reports of injuries for the period of
2007 and 2008 were provided by the employer for the
1419 delivery drivers included in the study. Injury data
included date of occurrence, part of body involved,
type of injury (sprain, strain, contusion, etc.) and event
associated with the injury (lifting, pushing, pulling,
motor vehicle accident, etc.). The study focused onmusculoskeletal injuries that were not vehicle-related
because of their prevalence and anticipated relation-
ship to employee physical fitness level.
The outcome measures were the number of strain
injuries during the study period, time to first strain and
the number of days worked. The number of days to
first strain was calculated by determining the number
of days between the hire date and first strain (if any).
Likewise, the number of days worked for each driver
was calculated by determining the number of days
between the hire date and the earlier of the termination
date or end of the study period (31 December 2008).
The following three categories of work status were
defined for the purposes of the analysis of tenure:
. Still working: Individuals who were hired and
still working at the end of the study period.
. Terminated potentially physical ability related:
Individuals who had terminated within the study
period for reasons that may have been related
to physical ability to perform the job. The most
common examples of such reasons included
voluntary resignation, voluntary resignation with
no rehire and job abandonment.
. Terminated other: Individuals who had
terminated within the study period for reasons
unrelated to physical ability to perform the job.
Examples would be workforce reductions, return
to school and end of temporary or seasonalemployment.
The tenure analyses were restricted to those
terminations that were potentially physical
ability-related because it would not be expected that
physical ability would have a bearing on terminations
associated with return to school, workforce reduction
or end of seasonal employment.
Data analysis
Analyses of the relationships between aerobic capacity,
injury experience and tenure were performed with avariety of methods. Poisson regression adjusting for
tenure was used to develop a prediction of number of
injuries from an individuals aerobic capacity. The
assumption of equidispersion of the injury data was
evaluated by testing the over-dispersion parameter of a
negative binomial regression model against zero
(Cameron and Trivedi 1998). Cox proportional
hazards regression (Cox 1972) was used to study the
relation between time to first injury and aerobic
capacity as well as demographic variables. The same
technique was used to study the relationship with time
to termination. The Cox regression approach was
also used to assess whether there was a time-dependenteffect of aerobic capacity on time to first injury or
time to termination. The Kaplan-Meier method
(Klein and Moeschberger 1997) was used to estimate
time-to-event curves for the tenure and time to first
injury outcomes. All analyses were conducted
using Stata (version 10.1; StataCorp, College Station,
TX, USA).
Poisson regression was used for analysis of the
injury data because it allowed for consideration of all
of the injuries that occurred while adjusting for tenure.
If the alternative method of logistic regression were
used, individuals would be categorised as either having
had an injury or not, thereby disregarding injuries
beyond the first one to a given new-hire and, more
importantly, disregarding the length of time that the
individual was employed. The disadvantage of this
technique is the assumption that multiple injuries on
the same individual are independent.
The Cox proportional hazard regression approach
evaluates the time to first injury, ignoring multiple
injuries on the same individual and days worked after
the first injury. However, the assumption of the
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independence of injuries is avoided. It also enables
the consideration of a time-dependent effect on the
relationship between aerobic capacity and time to first
injury or time to termination.
Results
Injury experience
There was a total of 318 first reports of sprain-strain
injuries to 264 of the 1419 delivery drivers in the study,
within a total of 328,664 d worked. This gave an injury
rate of 35.3 injuries per 100 years worked for all
delivery drivers in the study. The mean number of
strains per driver was 0.22 with a variance of 0.26,
indicating near-equality of mean and variance
(equidispersion).
The injuries included in the study accounted for
63% of all first reports of injury for delivery drivers.
These injuries had an average of $5073 of incurred
workers compensation cost, 10.7 d lost and 13.8 d oftransitional duty.
In total, 45 of the delivery drivers had more than
one injury during the study period (3% of the whole
sample of 1419). Analysis of the 99 injuries to these 45
delivery drivers indicated that six of the injuries may
have been of the same type and to the same location of
the body as a prior injury to the same person. This was
probably an overestimate since there was not always
specific detail about the side of the body involved in
the injury and whether an injury was actually a
recurrence of a prior injury. The much more typical
pattern was for subsequent injuries to be to different
body parts and often of different types (e.g. neckstrain and cumulative trauma at the wrist). Hence,
it appeared that the assumption of independence of
injuries required for the Poisson regression was
reasonably met.
A negative binomial model was used to model the
relationship between aerobic capacity and the number
of injuries to determine if an adjustment for over-
dispersion was warranted. Using a likelihood ratio test,
the over-dispersion parameter was not found to be
significantly different from zero (p 0.19), whichcorroborated the assumption that the data were
equidispersed.
A Poisson regression performed between number
of strain injuries and aerobic capacity, adjusting for
tenure yielded a statistical significance of p 5 0.0001.
Neither ethnic group nor age was a significant
predictor in the model at a 0.05 level. The sample
consisted of only 13 females, so it was not possible to
accurately estimate the gender effect.
Figure 1 illustrates the predicted number of strains
per 100 years worked and 95% confidence bounds
based on the model, including aerobic capacity
adjusting for tenure. A downward trend in numberof injuries was found with increasing aerobic capacity.
Axis values for both aerobic capacity and
percentage of aerobic capacity at which working were
included in the figure so as to allow comparison with
the NIOSH recommendations for the threshold of
aerobic capacity at which one should work. Percentage
of aerobic capacity at which working was calculated by
dividing the average energy expenditure on the job
(9.86 ml/kg per min) by aerobic capacity. The
prediction equation was:
number of injuries per day worked
exp 0:
0376 aerobic capacity 5:
513 1
The coefficient of 0.0376 for aerobic capacity
translates into an injury rate ratio of 0.963 for a one
unit increase in aerobic capacity, which would be a
3.7% decrease. For a five-unit increase in aerobic
capacity, the injury rate would decrease by about 17%.
The predicted injury rate for the delivery driver with
the lowest measured aerobic capacity (22.75 ml/kg per
min) was close to seven times higher than the predicted
injury rate for the driver with the highest measured
aerobic capacity (73.85 ml/kg per min). As shown in
Table 2, the least-fit quartile had an actual injury
rate that was two times higher than the rate for the
most-fit quartile.
Cox proportional hazards regressions yielded the
same results as the Poisson regressions. The Wald
p-value for aerobic capacity was less than 0.001. Age
and ethnic group were not significant predictors. A
unit increase in aerobic capacity was predicted to
result in a 0.965 decrease in the hazard of injury, or
around 3.5%. There was no indication that there was a
significant time-dependent effect for aerobic capacity,
Figure 1. Predicted injury rate vs. aerobic capacity.Dotted lines indicate 95% confidence limits.
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which meant that the risk of injury associated with a
change in aerobic capacity appeared to be constant for
the time periods worked by the drivers in this study.
Tenure
A total of 112 drivers terminated during the study
period due to reductions in force, return to school or
end of seasonal employment and therefore were
removed from the tenure analysis. Of the other 1307drivers, 708 terminated during the study period (54%).
The median tenure was 260 d. A univariable Cox
proportional hazards regression indicated aerobic
capacity was significantly related to time to termina-
tion (p 0.048) for these 1307 drivers. Ethnic groupwas not related to tenure, but age was (p 0.01). Aone unit increase in aerobic capacity decreased the risk
of termination about 1.1%. A 1 year increase in age
increased the risk of termination about 1.6%. When
age was included in a multivariable model with aerobic
capacity, aerobic capacity was no longer significant
(p 0.19). A linear regression of aerobic capacity on
age showed a statistically significant inverse relation-ship (p 5 0.001), but the explained variance was less
than 8% (r2 0.077). Figure 2 illustrates the Kaplan-Meier plot of proportion employed vs. days worked for
the four age quartiles of these 1307 drivers. There was
no indication that there was a time-dependent effect for
aerobic capacity.
Discussion
Injury risk
The finding that there appeared to be a monotonically
decreasing relationship between aerobic capacity and
strain rate in this pre-screened group of delivery drivers
suggested that higher aerobic capacity had a continu-
ously increasing prophylactic effect on injury rate in
the range studied. The roughly seven-fold ratio of
predicted injury rates for the least-fit compared with
the most-fit of the new-hire delivery drivers and the
two-fold ratio in actual injury rates for the least-fit
quartile vs. the most-fit quartile of delivery drivers
were similar in magnitude to the risk ratios observed
in similar studies (Cady et al. 1979, Anderson and
Catterall 1987, Craig et al. 1998, Knapik et al. 2001,
2006, Anderson and Briggs 2008).
The increased slope of the Poisson regression
equation for lower aerobic capacities suggested thatinjury rate would be disproportionately higher for
those with aerobic capacities less than about 40 ml/kg
per min. Delivery drivers with stamina below this
level would be working at more than 25% of their
aerobic capacity, given the overall average energy
expenditure of 9.86 ml/kg per min. This supported
NIOSHs recommendation of thresholds ranging from
21% to 30% of aerobic capacity as measured on a
treadmill, depending on whether the lifting is being
performed above or below waist level, respectively
(Waters et al. 1993).
Tenure
The finding that there was a statistically significant
relationship between aerobic capacity and time to
termination was consistent with the findings of higher
termination rates for less-fit individuals reported by
Knapik et al. (2006) and Anderson and Briggs (2008).
There appeared to be a relatively small effect in this
study, however. One possible reason for this
discrepancy may have been that the delivery drivers
Table 2. Injury rates by aerobic capacity quartile.
Aerobic capacityrange (ml/kg per min) Sample size
Number ofstrain injuries Total years worked
Injury rate per 100years worked (95% CI)
22.7533.75 356 104 215.4 48.3 (39.858.5)33.7637.79 353 81 215.9 37.5 (30.246.7)37.8042.67 357 77 238.3 32.3 (25.840.4)
42.6873.85 353 56 230.2 24.3 (18.731.6)Total 1419 318 899.8 35.3 (31.739.4)
Figure 2. Proportion employed vs. days worked by agequartile.
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had been pre-screened on aerobic capacity, so the
least-fit individuals were not included in the new-hire
population that comprised the study group.
The fact that there did not appear to be a time
dependency in the relationship between aerobic capa-
city and tenure was inconsistent with the observation
of Barrick and Zimmerman (2009), who noted thatrelationships with predictors of retention other than
personality weakened over time up to 2 years after
hire. This may have been due to the fact that the
median tenure for the delivery drivers in this study was
260 d, so there may have been insufficient time for a
time dependency to have manifested.
It was interesting to find that age was a stronger
predictor of time to termination than aerobic capacity
for these delivery drivers. This suggested that there
may have been age-related psychosocial issues that
eclipsed physical ability in their impact on the
decision to terminate. It did not appear that age was
acting as a surrogate indicator of aerobic capacitysince the correlation between the two was relatively
moderate. The level of correlation was not surprising
since the group was pre-screened on aerobic capacity
(i.e. there was significant range restriction) and there
was a fairly narrow range of ages represented in the
group of delivery drivers. As both Lavender and
Marras (1994) and van Iddekinge and Ployhart (2008)
noted, withdrawal behaviour tends to be complex,
which makes measures related to it challenging to
validate. For instance, the real reasons for terminating
may be significantly different or more complicated
than the reasons reported. This made it difficult to
isolate terminations that were primarily due tomismatch between physical ability and job demand.
Conclusions
Statistically significant monotonic relationships were
found between aerobic capacity, injury rate, time to
first injury and time to termination for new-hire
delivery drivers screened on the basis of their physical
ability. A unit increase in aerobic capacity was
predicted to result in 3.7% decrease in injury rate
and a 1.1% decrease in the risk of termination. These
results support the strategy of matching individuals
and jobs as a method for reducing the injury rate
for delivery drivers. Age appeared to be a stronger
predictor of time to termination than aerobic capacity
for this group of delivery drivers screened on their
physical ability, which may be an indication of the
complexity of factors affecting withdrawal behaviour.
There appeared to be a disproportionately higher
number of injuries for those with aerobic capacities
below 40 ml/kg per min, which corresponded to
working at greater than about 25% of ones aerobic
capacity. This supported the NIOSH recommendation
that individuals should not work at more than 2130%
of their aerobic capacity, depending on the handling
heights. Further research is needed to determine if
new-hires who did not pass the physical ability test
battery would have had an even more
disproportionately higher injury rate than the least-fitdelivery drivers included in this study, as well as a
significantly higher termination rate.
Acknowledgements
The author would like to thank Gregory Young for hisassistance in data analysis and graphics development.
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