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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).

1396 C.K. Anderson


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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

Ergonomics 1397


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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.

1398 C.K. Anderson


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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.

Ergonomics 1399


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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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