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Appendix 1

________________________________________________________________________ 160 when installing both the equipment and positioning the participant up properly. Moreover, zero values in the recorded signal were deleted and replaced by the average of the values one second before and one second after the zero value.

The vibration vector sum (total value) of the weighted root-mean-square (r.m.s.) accelerations (awv) in m/s²was calculated according to formula 1:

(1)

where awx is the frequency weighted r.m.s. for the x-axis, awyis the frequency weighted r.m.s.

for the y-axis, and awz is the frequency weighted r.m.s. for the z-axis.

The awv values were calculated for the participants in the representative sample, which were measured at their worksite. Mean values for each category of vehicles were calculated. For participants that were not measured, we extrapolated the WBV magnitude from the actual field measurements, data from earlier studies and data from existing WBV databases.^28,29 This extrapolation consisted of calculating mean values for the three WBV acceleration directions and the resulting vector sum aws and awq for each category of vehicles. Together with the data from the VIBRISKS questionnaire, we calculated for each participant the A(8) according to formula 2:

(2)

where T0 is the duration of eight hours expressed in seconds, awv2 is the squared vector sum awv, and Ti is the WBV measurement time expressed in seconds.

Statistical analysis

The statistical analysis was performed with SPSS 13.0.1 for Windows. First, we analyzed the relation between BMI and the onset of LBP 7 days or LBP in the last 12 months with a Pearson correlation coefficient.

Second, to find out if BMI affects the onset of LBP in a population already exposed to WBV, a binary logistic regression was performed. BMI and WBV were included as

independent covariates and LBP as the dependent variable. BMI and WBV were included as continuous variables and LBP was included as categorical variable. In the first logistic regression model, we analyzed the relation between BMI and LBP, without the interaction

1,4awx² + 1,4awy² + awz² awv =

A(8) = T0

1 �awv² Ti

Appendix 1

The vibration vector sum (total value) of the weighted root-mean-square (r.m.s.) accelerations (awv) in m/s²was calculated according to formula 1:

(1)

where awx is the frequency weighted r.m.s. for the x-axis, awyis the frequency weighted r.m.s.

for the y-axis, and awz is the frequency weighted r.m.s. for the z-axis.

(2)

where T0 is the duration of eight hours expressed in seconds, awv2 is the squared vector sum awv, and Ti is the WBV measurement time expressed in seconds.

Statistical analysis

Second, to find out if BMI affects the onset of LBP in a population already exposed to WBV, a binary logistic regression was performed. BMI and WBV were included as

1,4awx² + 1,4awy² + awz²

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________________________________________________________________________ 161 with WBV exposure. In the second logistic regression model this relation between BMI and LBP was analyzed with the interaction of WBV exposure. In both logistic regression models we adjusted for age (continuous variable), a well known confounder in the onset of LBP.¹ All analyses were done for LBP complaints in the last 7 days and in the past 12 months. The OR was significant, as 1.0 was not included in the 95% confidence interval (CI) and (p� 0.05).

Results

Population characteristics

Two hundred twenty one participants completed and returned the questionnaire (response rate 47.4%). From this group, 7 participants had to be excluded from the study: 3 participants were no longer exposed to WBV, for 3 participants BMI could not be calculated, and for 1 participant the vehicle driven was unknown. Table 1 reports the general

characteristics of the study population. The results were therefore based on data from the questionnaires from 214 participants.

Table 1 Characteristics, BMI and LBP outcomes of the participants (n=214)

BMI and LBP outcomes

BMI and LBP outcomes are presented in table 1. Remarkable was the high number of participants (n=124) in the second (pre-obese) BMI category (25-30 kg/m²). One hundred and forty seven participants (69% of all participants) were pre-obese or obese. LBP complaints in the last 7 days were reported by seventy four participants (35%) and LBP complaints in the past 12 months were reported by one hundred and twenty nine participants (60%).

Mean (SD) or number (%)

LBP complaints last 7 days

Yes 74 (35%)

No 137 (64%)

Missing 3 ( 1%)

LBP complaints past 12 months

Yes 129 (60%)

No 79 (37%)

Missing 5 ( 3%)

*categories are based on World Health Organisation (2000)

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Appendix 1

________________________________________________________________________ 162 WBV measurements and vibration exposure

Table 2 shows the mean values of the frequency-weighted r.m.s. accelerations measured on the seat pan of the different vehicles. Noticeable was the high awvfor the shovel measurement compared to the other vehicles. The average vibration exposure for the participants expressed in A(8) was 0.51m/s², ranging from 0.12 m/s²to 1.52 m/s². Ninety five participants exceeded the A(8) action value (0,5 m/s²) and seven participants exceeded the exposure limit (1,15 m/s²).

Table 2 Means (SD) of the frequency-weighted root-mean-square (r.m.s.) acceleration magnitude aw of the x-, y- and z-axis and their matching vector sum (av) of different types of vehicles.

Association BMI and LBP, with and without interaction of WBV exposure

We did not find a significant correlation between BMI and the onset of LBP in the last 7 days (r=0.07, p=0.34) nor for LBP in past 12 months (r=-0.30, p=0.63).

No significant increased risk for LBP in the last 7 days was observed due to an increase in BMI (OR 1.02; 95% CI:0.93-1.13) (see table 3). Introducing the interaction with WBV exposure in the logistic regression model, did not result in a significant difference in the onset of LBP in the last 7-days (OR 0.97; 95% CI:0.92-1.01).

No significant increased risk for LBP in the last 12 days was observed due to an increase in BMI (OR 0.98; 95% CI:0.89-1.09) (see table 3). Introducing the interaction with WBV exposure in the logistic regression model, did not result in a significant difference in the onset of LBP in the last 12-days (OR;0.97 95% CI:0.93-1.01).

Type of vehicle No of vehicles

measured (n=30) awx (m/s²)

Appendix 1

________________________________________________________________________ 163 Table 3: Binary logistic regression models for the relation between the onset of LBP (LBP 7 days and LBP 12 months) and BMI with or without interaction of WBV exposure while adjusting for age (n=214).

Association WBV exposure and LBP

We did not find an association between exposure to WBV and LBP in the last 7 days (OR 0.33; 95% CI: 0.94-1.15) neither for LBP in the last 12 months (OR 0.46; 95% CI: 0.15-1.39). Exposure to WBV in this study, does not seems to be associated with the onset of LBP.

Discussion

The results of this study show that BMI does not influence the risk of LBP complaints in a population of occupational participants already exposed to WBV.

Association BMI and LBP

We did not find any association between BMI and LBP. This finding is in accordance with the studies from Burdorf & Sorock (1997),⁶ Dempsey et al.(1997),⁷ Kuiper et al.(2004),⁸ Power et al.(2001).⁹ However, our results are not comparable with the results of these studies because the population in our study is at risk since the participants are exposed to WBV. The prevalence of LBP in the last 12 months is about 10 to 15% higher in our population compared to the prevalence numbers in the population in the previously mentioned studies.

The study from Bovenzi et al.(2006)²² also did not find an association between BMI and LBP in the last 12 months (OR: 0.99 95% CI:0.94-1.04, data not shown). This study population is also exposed to WBV. We found 60.3% of the participants reporting LBP in the last 12 months, while Bovenzi et al.(2002)¹⁶ and Bovenzi et al.(2006)²² reported respectively 57.8%

and 60.4%. Also the values of BMI in our population (26.5 ± 2.94) are comparable with the values in the studies from Bovenzi et al.(2002)¹⁶ and Bovenzi et al.(2006),²² respectively 26.3

± 3.40 and 26.4 ± 3.76. Based on this high comparability in actual values for BMI, number of LBP in last 12-months and study design our primary finding of no relation between BMI and LBP in a population exposed to WBV seems reliable.

LBP- 7 days LBP- 12 months

Covariates

Exp (B) [95% CI] Exp (B) [95% CI]

No WBV interaction

BMI 1.02 [0.93-1.13] 0.98 [0.89-1.09]

Age 1.00 [0.97-1.03] 0.98 [0.96-1.01]

With WBV interaction

BMI*WBV 0.97 [0.92-1.01] 0.97 [0.93-1.01]

Age 1.00 [0.97-1.03] 0.98 [0.95-1.01]

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In document UvA-DARE (Digital Academic Repository) (pagina 149-160)