1 Centre for Clinical Epidemiology and Community Studies, SMBD Jewish General Hospital, and Department of Epidemiology and Biostatistics, McGill University, Montreal, Quebec, Canada.
2 Groupe de Recherche Interdisciplinaire en Santé, Université de Montréal, Montréal, Québec, Canada.
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ABSTRACT |
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adolescence; low back pain; risk factors
Abbreviations: CI, confidence interval.
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INTRODUCTION |
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As many as 30 percent of adolescents have experienced at least one episode of back pain (47
), and Canadian youths have the highest prevalence of frequent back pain in the world (8
). With respect to incidence, a Finnish study reports 17.6 percent (9
) and a British study reports 1221.5 percent, increasing with age (10
).
To date, there is a lack of understanding of the risk factors, particularly those associated with activity and with growth and development of adolescent bones and muscles. Many clinicians believe that these problems may be due to the growth spurt in adolescents coupled with an increase in physical activity such as sports or work (11, 12
), although no direct data have been published to support or refute this. While adolescent competitive athletes develop more low back pain than do nonathletes (13
, 14
), it is unclear whether the situation is similar in teens who participate in recreational, noncompetitive sport as opposed to those who are not physically active. For example, Fairbank et al. (15
) determined that students who complained of back pain were more likely to be sports avoiders than their pain-free contemporaries, while Balagué et al. (4
) reported a positive association between sports and low back pain.
Decreased muscle flexibility and trunk strength have been postulated as risk factors for low back pain (12, 13
, 16
). Poor hamstrings flexibility has been associated with low back pain in cross-sectional studies in both adolescents and adults (6
, 17
19
), although longitudinal research in a cohort of workers has not confirmed this finding (20
). Thus, it may be that poor hamstrings flexibility is a result of low back pain (possibly due to inactivity) rather than a cause. As for muscle strength, the evidence in adolescents is scant; one study cites no association with low back pain (21
), while the other reported an association between low back pain and increased trunk flexor strength (22
). One longitudinally designed study in adults did confirm a link between the lack of trunk muscle endurance and subsequent development of low back pain (23
).
The main objectives of the present study were to describe the incidence of low back pain in a cohort of adolescents and to determine risk factors associated with this problem. In particular, a high growth spurt and possible concomitant changes in flexibility were explored.
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MATERIALS AND METHODS |
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Definition of low back pain
Low back pain occurrence at a frequency of at least once a week within the past 6 months was regarded as the primary outcome, similar to the classification used by Mikkelson et al. (24) and Brattberg (25
). It was felt that this definition of low back pain (which we termed "substantial low back pain") would reflect a more serious episode, as opposed to transient, inconsequential pain. Secondary outcomes included medication use for low back pain and disability. Disability from low back pain was assessed at the final evaluation only (12 months), defined as limitation of normal activities because of low back pain.
Definition of potential risk factors and covariates
We considered the following as possible risk factors: high growth spurt, poor muscular flexibility, poor abdominal strength, and increased level of physical activity and work. Other associated factors were age, gender, mental health status, and smoking.
A high growth spurt was defined as having grown more than 5 cm in a 6-month period, in line with the findings of average peak velocity of growth of 10 cm/year (26). Flexibility was measured via four different tests. Quadriceps flexibility was evaluated in degrees as knee flexion range of movement in the prone position (quadriceps angle), using a standard goniometer. We assessed hamstrings flexibility as the goniometric measurement of the popliteal angle (the angle of knee extension with the ipsilateral hip kept in 90 degrees of flexion). Reliability is high for both of these measures (27
29
). For both quadriceps and hamstrings flexibility, a smaller angle indicated better flexibility, that is, the closer the measurement was to 0°. The ability to sit and reach (a reliable measure of toe touch flexibility) was tested with a standard sit-and-reach box and measured in centimeters (20
, 30
33
). Lumbar flexion was checked via the Schober test that measures distraction between the midpoint of both posterior superior iliac spines and a point 10 cm above between standing upright and bending over. The Schober method was selected as opposed to the modified Schober method (34
), because minimal exposure of the lower back and sacral areas occurs in the former. Since the students were measured during their physical education class, we opted for the Schober test in order to maximize participation. The same evaluator assessed each of the flexibility measures at all three times, which improves reliability (28
, 35
).
Isometric strength of the abdominals was measured in kilograms by a portable hand-held myometer (Nicholas manual muscle tester) (36). The subject was required to do a sit-up and to stop midway, holding the position. At this point, resistance was applied to the sternum, and the amount of force was recorded by the myometer at the point when the person could no longer hold the position.
Activity participation and work participation were defined as continuous variables. They were each graded as the sum of the time categories spent in different sports activities (or work activities) over the past 6 months. The categories for activities were as follows: 1, average of less than 5 hours a week spent in the activity; 2, average of 510 hours a week; and 3, average of more than 10 hours a week. For example, if a subject spent 6 hours playing basketball (score of 2), 2 hours swimming (score of 1), and 1 hour in jazz dance a week (score of 1), the cumulative activity score was 4. Alternatively, activity was graded by metabolic equivalent values. Each activity was scored according to its metabolic equivalent value, multiplied by the frequency category, and then summed to provide a summary physical activity score (37).
Work was graded in a similar fashion to activity; however, the category definitions were different. These were as follows: 1, an average of 110 hours a week spent working at that particular job; 2, an average of 1120 hours per week; and 3, an average of greater than 20 hours per week. Work was also graded dichotomously and categorized as white collar, blue collar, or child care. Mental health status was determined by the five-item Mental Health Index from the Short Form 36 (SF36) (38) and measured concurrently with the outcome. Smoking was determined at baseline; a student was deemed a smoker if he/she answered affirmatively to presently smoking cigarettes (8
).
Analysis consisted of descriptive statistics, unadjusted comparisons, logistic regression analysis in each of the two 6-month intervals, and generalized estimating equations analysis. The generalized estimating equations model accounts for intrasubject correlations among repeated measurements on the same subject and improves power compared with treating each 6-month period separately. The dichotomous, repeated-measures outcome of substantial low back pain was modeled as a function of the time-varying factors of growth spurt, flexibility, abdominal muscle strength, activity participation (sports and work) adjusted for age, gender, smoking (as determined at t1), initial height, and time-varying covariate mental health score. Flexibility and strength measurements entered into the model were taken 6 months earlier than the determination of low back pain. This was done to ensure that flexibility and strength preceded the experience of low back pain and were not influenced by the pain. Those subjects who had low back pain 6 months prior to the measured outcome (prevalent cases) were excluded but permitted to reenter for the second interval if they did not have pain over the following 6 months.
We also analyzed the data based on the secondary outcome of medication utilization for low back pain in each of the 6-month intervals, using univariate statistics. All analyses were conducted in 1996 using SAS version 6.12 software (SAS Institute, Cary, North Carolina).
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RESULTS |
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Risk factor analysis
Univariate comparisons were carried out on the two 6-month time periods separately. In this way, seasonal influences of activity and work were separated. Table 3 describes the categorical variables, while table 4 describes continuous variables.
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It is important to note that the mental health score was assessed concurrently with outcome (as it refers to how the subject felt in the past week), because we had not asked about mental health at inception. Therefore, it is impossible to know whether mental health preceded or followed pain status. To check whether previous mental health had an effect on low back pain, we repeated the analysis using the exposure data from the 6-month time period (which included the five-item Mental Health Index) and the outcome from the 12-month period. The results were the same: a poor mental health score had an odds ratio of 0.97 (95 percent CI: 0.95, 0.99).
A second generalized estimating equations analysis was completed with work classified into three categories (white collar, blue collar, child care; reference category was not working). The estimated adjusted odds ratios for these categories were 4.85 (95 percent CI: 1.66, 14.19) for white-collar work, 1.90 (95 percent CI: 0.95, 3.80) for blue-collar work, and 1.51 (95 percent CI: 0.69, 3.29) for child care work.
Medication use for low back pain was explored at each interval. Because the numbers were small for those who took medication for low back pain, only univariate statistics were computed. The only statistically significant associations were between work and medication use in the first interval and smoking and medication use in the second interval.
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DISCUSSION |
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We found an increased incidence in the first 6-month interval (fall to spring), which corresponded to most of the school year, compared with the second 6 months (spring to fall). A possible explanation is that students who work during the school year may be at higher risk for developing low back pain as opposed to those students who work over the summer but do not have to contend with school pressures as well. Furthermore, significantly more students developed low back pain in the working group in the first 6 months than in the second 6 months. Another possible reason is that students may be more active in the summer, which might serve to protect against mechanical back pain. Conversely, training for competitive sports may be more common during the winter/spring and may serve as a possible cause for back ailments due to injury. In terms of generalized sports activity, there was no significant difference with respect to development of low back pain.
We found that students who underwent a high growth spurt were more likely to develop low back pain. Although a high growth spurt has been hypothesized to be associated with development of low back pain in adolescents by decreasing flexibility (11, 12
, 39
), we have previously found that the only flexibility measure that decreases with growth is quadriceps flexibility (40
). However, it is possible that poor flexibility by itself may be related to pain or high growth by itself may be associated with musculoskeletal pain. A high growth spurt may also be associated with changes in posture, which have been related to low back pain (41
).
This study did not find an association between development of low back pain and Schober lumbar flexion or sit and reach flexibility. Odds ratios for decreased hamstrings flexibility were similar to those for decreased quadriceps flexibility. The association between tight hamstrings and low back pain has been established in cross-sectional studies (6, 17
19
), but this is the first follow-up study to confirm this relation.
Poor isometric muscle strength of the abdominals was not found to be a risk factor for development of low back pain in this study. Newcomer and Sinaki (22) reported that increased trunk flexor strength was associated with low back pain in adolescents, although Balagué et al. (21
) found no association between isokinetic trunk strength and low back pain. Cross-sectional studies have reported associations between low back pain and decreased trunk extensor strength in adults (42
, 43
). As opposed to trunk flexor strength, good endurance of isometric trunk extensors was protective against development of low back pain in adult males (23
).
Physical activity was not associated with the development of low back pain in this cohort of adolescents. However, those adolescents who engage in higher levels of physical activity may be healthier (perhaps less likely to develop low back pain) and more able to sustain these higher levels of sports.
Smokers were more likely to develop low back pain in this cohort of adolescents. We have reported this finding previously and believe it to be the first study to determine a link between smoking and the incidence of low back pain in adolescents (37). A positive association between low back pain in adolescents and smoking was reported in a cross-sectional study; however, the authors doubted the validity of their data on smoking because the reported rates of smoking were extremely low (4
). Cross-sectional studies in adults have repeatedly found evidence supporting a link between smoking and low back pain (44
48
). Evidence from animal models and biologic studies supports an association between smoking and the health of the intervertebral disc (49
, 50
) and between smoking and decreased bone mineral densities in the lumbar spine (51
, 52
).
The present study found a positive association between poor mental health and development of low back pain, which is consistent with the literature in adults (53).
Analysis of medication use (for low back pain) supports the notion that those students who smoked or worked tended to use medication more.
Limitations of the study
There were 308 students of the original 810 who were lost to follow-up. The "lost group" was on average 4.8 months older, 2.5 kg heavier in weight, and 2.6 cm taller and included more smokers and more workers. The literature supports the notion that the incidence of low back pain increases with age in adolescence (10). Therefore, if smoking and working are both risk factors for future development of low back pain, the nonparticipants (who were older) would be more likely to develop low back pain in the upcoming 6 or 12 months than those who remained in the study. According to this scenario, our study underestimates the odds ratios for smoking and working.
Some measurement error was bound to have occurred because of inter- or intrarater reliability problems. Outcomes were all based on self-reports. Because of confidentiality, no attempts at validation of low back pain reporting (with parents) were done. There was no access to medical charts or school records regarding absences. Recall may have posed a problem by having the students try to remember the presence or absence of low back pain over a 6-month period. Because exposure was measured 6 months before low back pain status, it is very doubtful that recall of outcome (low back pain) differed within the exposure categories. Moreover, the results of the present study with respect to the incidence of low back pain are comparable with those in the scientific literature.
Misclassification because of problems with recall with respect to certain exposures may have occurred. Here again, it is doubtful that these problems resulted in differential misclassification with respect to the outcome. The levels of smoking and working are comparable with those reported in the literature (8, 54
56
). The percentage that was physically active was higher in our cohort than that reported in a cohort of Ontario teens (57
). This may be due to measuring students who all participated in physical education, while those who were excused from class or did not attend may have been more inactive.
Some unmeasured variables could possibly bias the results. One variable that may be related to low back pain is socio-economic status (58). It is conceivable that socioeconomic status would be associated with work and may perhaps serve as a confounding variable. An attempt to assess the effect of socioeconomic status in our data was done by stratifying the cohort into two strata: the public school students and those that attended private school. Separate generalized estimating equations analyses were calculated with the following variables: high growth spurt, work, activity, five-item Mental Health Index score, and quadriceps flexibility. These analyses revealed no evidence of confounding by socioeconomic status (data not shown).
Conclusion
The results of this study indicate that low back pain is common in adolescents, with a cumulative annual incidence of 17 percent. Factors associated with development of low back pain in adolescents were a high growth spurt, poor quadriceps and hamstrings flexibility, working during the school year, and smoking.
Although prevention strategies have been addressed in the occupational setting (59, 60
), our results suggest that more research is needed regarding prevention before people enter the workforce. Improved knowledge and awareness of the vulnerability of the back, the need for good flexibility, and refraining from smoking are all areas that require further investigation.
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ACKNOWLEDGMENTS |
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The authors thank the following for their assistance: Dr. P. Dobkin, Dr. J. Hanley, Dr. S. Wood-Dauphinee, Dr. J. Klvanna, and A. Hammaker.
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NOTES |
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REFERENCES |
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