Predictive Model for Flexion Pattern of Chronic Low Back Pain Among Office Workers

doi:10.21203/rs.3.rs-4716984/v1

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Predictive Model for Flexion Pattern of Chronic Low Back Pain Among Office Workers

https://doi.org/10.21203/rs.3.rs-4716984/v1

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Background

The purpose of this study was to establish a prediction model for classifying the presence of flexion pattern chronic low back pain (CLBP) in office workers, including physical, individual, psychological, and occupational factors.

Methods

Ninety-five office workers (48 workers with CLBP and 47 workers without CLBP) participated in this study. Hip flexion ROM (HFR), knee extension ROM, knee extension ROM with ankle dorsiflexion, hip internal rotation ROM (HIR), hip flexor strength, pelvic posterior tilt angle (PPTA) in habitual sitting, and trunk extensor endurance were measured. Twelve variables (9 physical variables and age, sex, body mass index, Beck Depression Index, and working duration) were used to develop the prediction model for the presence of flexion pattern CLBP using logistic regression.

Results

The probability of the presence of flexion pattern CLBP significantly decreased with an odds ratio of HFR 0.884 (95% confidence interval [CI] = 0.817―0.957) and HIR 0.860 (95% CI = 0.799―0.926), but it increased with an odds ratio of PPTA in habitual sitting 1.190 (95% CI = 1.058―1.339). Our model showed acceptable accuracy of 82.1% and an area under the receiver operating characteristics curve of 0.898

Conclusions

HFR, HIR, and PPTA in habitual sitting could serve as guidelines for preventing and managing flexion pattern CLBP in office workers.

Chronic low back pain

Logistic regression

Prediction model

As a sedentary lifestyle is ubiquitous these days, with prolonged sitting periods in the workplace, it is essential to understand the work-related musculoskeletal disorders (WMSDs) among office workers comprehensively. Low back pain (LBP) is one of the most common health problems in office workers in the prolonged sitting posture, with a 1-year prevalence rate of between 34–51% [1, 2] and a 1-year incidence rate of 23% [3]. After an initial incidence of LBP, 44―78% of people suffer pain recurrence and 26―37% have relapses in work absence [4]. As a result, 6―23% of people with LBP have developed chronic low back pain (CLBP), defined as persisting for at least 3 months [4].

CLBP is a multifaceted disorder that combines individual, environmental, and psychosocial factors [5]. However, most CLBP cases (85%) have no critical pathoanatomic or radiologic abnormalities [6], thus frequently termed nonspecific CLBP. Previous researchers have reported risk factors of CLBP in office workers, including individual (age, sex, body weight, smoking, and pain intensity), job-related (job strain, working duration, sitting time at work, and uncomfortable ergonomic condition), and psychological factors (depression) [3, 7–15]. However, several systematic reviews of literature did not find a significant relationship between the presence of CLBP and sitting at work [16–19] or a sedentary lifestyle [20]. Difficulties in assessing CLBP are likely due to a "one-size-fits-all approach" due to the incapacity to distinguish the traits of each CLBP subgroup. If the heterogeneity of each CLBP subgroup is not considered, the traits of one subgroup may be diluted by those of another with opposing traits, resulting in the reduction of their potential benefits [21]. Therefore, subclassification approaches for nonspecific CLBP are necessary for appropriate clinical decision-making [22, 23].

Because the sitting postures cause flexion in the lumbar spine compared to the standing posture and the line of gravity shifts anteriorly [24], it is believed that office workers who suffer from CLBP have a flexion pattern disorder due to the prolonged sitting working posture [25]. Among the CLBP subgroups within maladaptive movement and motor control disorders, the flexion pattern is the most common clinical LBP pattern [26]. This flexion pattern is associated with repetitive and sustained end-range flexion loading in the lumbar spine that exceeds the neutral zone of the tissue tolerance [27]. However, few studies have considered CLBP subgroup in office workers [6, 28]. Furthermore, most studies derived their results from questionnaire-based data frequently limited to psychosocial or occupational factors or the presence or absence of pathological conditions [3, 7–10, 12–15, 29]. Few studies have considered examining not just psychological and occupational factors but also movement impairments in office workers with CLBP, such as decreased range of motion (ROM), muscle strength, muscle endurance, and unfavorable posture.

Jung et al. [30] reported that, among individuals with a CLBP subclassified a flexion pattern, the physical predictors for CLBP included reductions in the knee extension ROM (KER), knee extension ROM with ankle dorsiflexion (KEDR) and hip flexor strength (HFS). Furthermore, previous authors have focused on the relationship between the CLBP subclassified flexion pattern and trunk extensor endurance (TEE) [28, 31] and pelvic posterior tilt angle (PPTA) in habitual sitting [25, 32]. Additionally, it has been suggested that the restriction of the hip ROM, such as hip flexion ROM (HFR) and hip internal rotation ROM (HIR), which leads to repeated compensatory movements in the lumbopelvic region during daily activities, constitutes a potential factor for developing CLBP [33–37]. Although individual factors (such as age or sex) cannot be modified for physical therapy purposes, physical factors (such as body functions) can be addressed to enhance the effectiveness of interventions such as exercises to improve strength and flexibility and motor skill training [38]. Additionally, clinical practice guidelines presented by the American Physical Therapy Association suggest that clinicians should conduct evaluations of impairments in body functions during an examination [39].

Consequently, it is imperative to predict CLBP through a multifaceted approach that encompasses physical (HFR, KER, KEDR, HIR, HFS, PPTA in habitual sitting, and TEE), individual (age, sex, and body mass index (BMI)), psychological (depression severity), and occupational factors (working duration). Therefore, this study aimed to establish a prediction model for classifying the presence of flexion pattern CLBP in office workers, including movement impairment-related variables. We hypothesize that the prediction models, including movement impairment-related variables, would exhibit an acceptable performance in classifying the presence of flexion pattern CLBP in office workers.

2.1. Subjects

All subjects were included if they were full-time workers with at least a year of office-related work experience and spent at least 50% of their workday in their office, with 50% or more of this time devoted to computer work (Collins, and O'Sullivan 2015). Among the 136 office workers who responded to the survey, 95 subjects (48 office workers with CLBP and 47 without) were selected. Their characteristics are shown in Table 1. The inclusion criteria for the CLBP subclassified flexion pattern group were mechanical LBP persisting over 3 months; LBP in the flexion direction of movement (such as sitting, driving, and forward bending); and pain intensity of ≥ 3 on an 11―point numeric pain rating scale (NPRS) in the previous 3 months. Each subject’s LBP subgroup was classified using the MSI LBP diagnostic categories previously described by Sahrmann [22], based on the results of a clinical examination by a physical therapist with 4 years of experience treating people with musculoskeletal conditions. The flexion pattern CLBP was classified when symptoms were exacerbated with lumbar flexion movement tests, including forward bending, backward rocking in the quadruped position, and trunk flexion and knee extension in the sitting position [30]. The exclusion criteria were musculoskeletal problems in the hip and knee joints; spinal canal stenosis; spondylolisthesis; ankylosing spondylitis; disc herniation; radiating pain or paresthesia below the knee; previous back surgery; history of known spinal fractures; malignancy; rheumatoid arthritis; osteoporosis; known muscle, nerve, skin, or joint diseases; pregnancy; and other LBP pattern [40–42]. Prior to the beginning of the study, the researcher provided a detailed explanation of the process, and all participants voluntarily provided their informed consent, approved by the Institutional Review Board of XXXXXXXXXX (XXXX-XXXX-XXXX).

Table 1

Characteristics of subjects in this study (mean ± standard deviation)
Variables	Flexion pattern CLBP^a		p-value
Variables	Presence (N = 48)	Absence (N = 47)	p-value
Sex (male/female)	27/21	26/21	0.784
Age (years)	39.4 ± 9.0	39.3 ± 8.8	0.751
Height (㎝)	168.5 ± 7.6	168.5 ± 8.2	0.995
Body weight (㎏)	65.6 ± 9.9	66.3 ± 11.6	0.740
BMI^b (㎏/㎡)	23.0 ± 2.4	23.2 ± 2.9	0.698
NPRS^c (0―10)	5.0 ± 1.5	-	-
ODI^d (0―100%)	16.3 ± 6.5	-	-
LBP^e duration (months)	28.8 ± 36.0	-	-
BDI^f (0―63)	4.8 ± 3.5	4.3 ± 3.2	0.553
Work duration (months)	120.1 ± 86.9	139.3 ± 101.3	0.324
Working time (hours/day)	8.3 ± 0.5	8.2 ± 0.4	0.684
Sitting time at work (hours/day)	6.6 ± 0.9	6.7 ± 0.5	0.510
^aCLBP: chronic low back pain ^bBMI: body mass index ^cNPRS: numeric pain rating scale ^dODI: Oswestry Disability Index ^eLBP: low back pain ^fBDI: Beck Depression Inventory

2.2. Outcome measures

2.2.1. Questionnaire

The 11-point numeric pain rating scale (NPRS) was used to assess pain levels, with subjects rating their pain from 0 (no pain) to 10 (worst imaginable pain). The Oswestry Disability Index (ODI) was used to assess pain-related disability in persons with LBP. In this study, the Korean version of the ODI was used [43]. Also, the Beck Depression Inventory (BDI) was used to assess the degree of depression experienced by participants during the weeks leading up to the questionnaire's completion, with the 21-item instrument evaluating various cognitive and physical symptoms of depression [44].

2.2.2. Measurement of range of motion

To measure HFR, subjects were positioned supine on a table. The investigator stabilized the lumbar spine manually and passively flexed the hip joint until it reached end feel (Fig. 1). The axis of the goniometer was positioned on the greater trochanter of the femur; the stationary arm of the goniometer was aligned over the horizontal axis of the trunk, and the moving arm was aligned with lateral midline of the thigh [45]. Next, ROM was measured in the end range of hip flexion without the lumbar flexion or pelvic posterior tilt. To measure HIR, the subjects positioned the tested leg with the knee bent at 90°, and the non-tested leg was extended in the prone position. The investigator stabilized the pelvis and passively rotated the subject’s lower leg to the lateral side until it reached end feel (Fig. 2). The axis of the goniometer was positioned on the patella; the stationary arm was aligned with the vertical line, and the moving arm was aligned with the shaft of the tibia [46]. Next, ROM was measured in the end range of hip internal rotation without a pelvic rotation. To measure KER and KEDR, subjects were seated upright and performed active knee extension with the ankle in a relaxed plantar flexion and a dorsiflexion (Fig. 3). The axis of the goniometer was positioned on the lateral epicondyle of the femur; the stationary arm was aligned with the lateral midline of the thigh, and the moving arm was aligned with the line along the fibula to the lateral malleolus. The examiner also stabilized the pelvis to prevent any lumbar flexion and posterior pelvic tilting during knee extension [30]. Next, ROM was measured in the end range of knee extension without the lumbar flexion or pelvic posterior tilt. All ROMs were measured twice, and the mean values from both side measurements were calculated.

2.2.3. Measurement of hip flexor strength

Isometric HFS was measured by the Smart KEMA strength sensor (Smart KEMA strength sensor, KOREATECH, Inc., Seoul, Korea). A load cell with a measuring range of 0–200 kgf and an accuracy of ± 0.5 kgf was incorporated into the strength sensor. The sensor was fixed to a glass suction cup or a stable object with an orthopedic belt on one side and a body segment using the Smart KEMA strength strap on the other. The data obtained from the strength sensor were transmitted to an Android tablet PC (Galaxy Tab A6 10.1; Samsung, Inc., Seoul, Korea) via Bluetooth network and analyzed using the Smart KEMA application at a sampling frequency of 10 ㎐. To assess the HFS, subjects were asked to position themselves at the edge of a table in an upright sitting posture. The thigh strap was placed at the femur 2 ㎝above the popliteal fossa. Then, the examiner adjusted the length of the orthopedic belt to 5° of hip flexion in the sitting position (Fig. 4). The subjects flexed the hip joint as strongly as possible against the strap anchored by a glass suction cup for maximal voluntary isometric contraction twice for 5 s each time [47]. Furthermore, to limit lumbar flexion and trunk side bending during hip flexion, the subjects stabilized by holding the table with their hands without pushing the table hardly. HFS was measured twice, and the mean values from both side measurements were calculated. Also, HFS was normalized by body weight (% BW).

2.2.4. Measurement of trunk extensor endurance

For measuring TEE, subjects were requested to lie in a prone position and maintain an elevated position of their sternum while using a small cushion beneath the lower abdomen to mitigate the effects of lumbar lordosis (Fig. 5). The testing protocol was standardized as previously described by Ito et al. [48]. During the test, subjects were directed to sustain the chin-tuck posture while stabilizing their pelvic alignment through the contraction of the gluteal muscles. These alignments of the cervical spine and pelvis represented the optimal posture for reducing lumbar lordosis and activating the trunk extensors with the highest efficacy [49]. Then, subjects were asked to maintain the initial position for as long as possible, not exceeding a 5-minute time limit. The duration for which the position could be maintained was measured using a stopwatch and recorded in seconds; the test was terminated when the subject could no longer maintain the elevated position of their sternum. Due to fatigue in the trunk extensor muscles from maintaining the testing position for as long as possible, subjects were instructed to perform the TEE protocol once.

2.2.5. Measurement of pelvic posterior tilt angle in habitual sitting posture

The palpation meter (PALM) device (PALM, Performance Attainment Associates, St. Paul, MN, USA) was used to measure the PPTA in the habitual sitting posture. For measuring the PPTA in habitual sitting, subjects were instructed to assume a sitting position on a stool that could be adjusted in height, with no backrest, and with the hips and knees flexed at a 90° angle (Fig. 6). The feet were to be positioned shoulder-width apart, and the arms rested comfortably on the thighs [32]. Subjects then were instructed verbally: “sit on the stool as you would usually sit”. The measurement protocol using PALM was standardized as previously described by Azevedo et al. [50]. Then, the examiner holds the device with both hands and utilizes the caliper tips to identify the bony landmarks (anterior superior iliac spines and posterior superior iliac spines) of the pelvis through palpation. After palpating the anatomical landmarks, PPTA in habitual sitting was assessed using the PALM. Next, the inclinometer is used to measure the extent of deviation from the horizontal position. The subjects stood up and then sat down again to assess their habitual sitting posture, and the measurement process was performed twice.

2.3. Experimental procedure

This study was conducted between April 2022 and January 2023. The following variables were measured for all subjects in the given order: individual factors, ROM, strength, sitting posture, and muscle endurance. First, subjects were instructed to complete the questionnaires (sex, age, height, body weight, presence or absence of LBP, pain duration, working time, sitting time per day, NPRS, ODI, and BDI). Then, ROM (HFR, HIR, KER, KEDR) and HFS were measured twice on both sides in random order, made by www.randomization.com. Then, PPTA in habitual sitting posture was measured twice, and TEE was lastly measured to prevent muscle fatigue from affecting other variable data. The average data of the left and right sides were used for ROM and strength.

2.4. Statistical analysis

The Kolmogorov–Smirnov Z-test was used to assess the assumption of distribution normality. Independent t–test, Mann–Whitney U test, and Chi–square tests were used to confirm statistically significant differences in 12 independent variables, including individual (sex, age, BMI), psychological (BDI), occupational (working duration), and physical factors (HFR, HIR, KER, KEDR, HFS, PPTA in habitual sitting, and TEE) between the two groups. The level of significance was set at 0.05. It is common practice to remove variables that are not significantly correlated with the outcome to improve the predicting model's performance and reduce the possibility of false positive results in a binary logistic regression analysis. This process typically involves conducting a univariate analysis and removing variables without a significant p-value [51]. Thus, variables related to CLBP were selected from univariate analyses [52]. Ultimately, a forward multivariate logistic regression was used to evaluate our pre-established hypothesis concerning the flexion pattern of CLBP, utilizing a combination of variables related to physical, individual, psychological, and occupational factors. Lastly, the Hosmer–Lemeshow test was used to calculate Goodness-of-Fit. The area under the receiver operating characteristics curve (AUC), sensitivity, specificity, precision, negative predictive value, and accuracy were used to evaluate the predictive model's capacity to differentiate individuals with and without flexion pattern CLBP. The AUC, a widely used measure of the model's sensitivity and specificity for all possible thresholds, is the area under the receiver operating characteristic (ROC) curve by plotting sensitivity against 1–specificity at various thresholds. An AUC of 0.5 indicates no discrimination, while an AUC of 1.0 represents perfect discrimination [53]. SPSS version 25.0 (SPSS, Inc., Chicago, IL, USA) was used for statistical analysis.

3.1. Comparison of predicting factors between two groups

Table 1 presents individual, psychological, and occupational differences between the two groups, while Table 2 shows differences in physical factors. There were no differences in sex, age, BMI, BDI, working duration, HFS, and TEE (p > 0.05). All ROM variables (HFR, HIR, KER, and KEDR) were significantly greater in subjects without CLBP than with it (p < 0.05). Conversely, PPTA in habitual sitting was significantly greater in subjects with CLBP than without it (p < 0.05).

Table 2

Differences in physical factors between subjects with and without flexion pattern chronic low back pain
Variables	Group	Mean ± SD^j	Diff.	t	p-value
HFR^b (°)	CLBP^a	96.8 ± 7.1	8.0	5.090	< 0.001^**
HFR^b (°)	Control	104.8 ± 8.2	8.0	5.090	< 0.001^**
HIR^c (°)	CLBP^a	32.6 ± 9.0	5.9	3.815	< 0.001^**
HIR^c (°)	Control	42.8 ± 7.4	5.9	3.815	< 0.001^**
KER^d (°)	CLBP^a	50.2 ± 8.3	4.4	2.783	< 0.001^**
KER^d (°)	Control	56.2 ± 6.8	4.4	2.783	< 0.001^**
KEDR^e (°)	CLBP^a	46.0 ± 7.4	10.3	6.094	0.007^*
KEDR^e (°)	Control	50.4 ± 8.0	10.3	6.094	0.007^*
HFS^f (% BWⁱ)	CLBP^a	29.2 ± 10.4	-1.9	-0.093	0.926
HFS^f (% BWⁱ)	Control	29.0 ± 9.5	-1.9	-0.093	0.926
PPTA^g (°)	CLBP^a	15.7 ± 5.2	-4.4	-3.929	< 0.001^**
PPTA^g (°)	Control	11.3 ± 5.7	-4.4	-3.929	< 0.001^**
TEE^h (second)	CLBP^a	85.5 ± 40.1	31.3	1.696	0.093
TEE^h (second)	Control	116.9 ± 121.3	31.3	1.696	0.093
^aCLBP: chronic low back pain ^bHFR: hip flexion range of motion ^cHIR: hip internal rotation range of motion ^dKER: knee extension range of motion ^eKEDR: knee extension with ankle dorsiflexion range of motion ^fHFS: hip flexor strength ^gPPTA: pelvic posterior tilt angle in habitual sitting ^hTEE: trunk extensor endurance ⁱBW: body weight ^jSD: standard deviation ^p < 0.05, by independent t-test ^*p < 0.001, by independent t-test

3.2. Logistic regression

Among 12 independent variables, the following were selected using univariate logistic regression analyses: HFR (odds ratio [OR] = 0.867, 95% confidence interval [CI] = 0.809–0.929, p < 0.001), KER (OR = 0.900, 95% CI = 0.846–0.957, p = 0.001), KEDR (OR = 0.927, 95% CI = 0.875―0.982, p = 0.009), HIR (OR = 0.858, 95% CI = 0.803–0.916, p < 0.001), and PPTA in habitual sitting (OR = 1.163, 95% CI = 1.067–1.268, p = 0.001). The results of the univariate analyses of predictors of CLBP are shown in Table 3.

Using the selected predictors, multivariate logistic regression with a forward variable selection was used to predict the probability of the presence of flexion pattern CLBP (Table 4). The probability of the presence of flexion pattern CLBP was significantly decreased with OR of HFR 0.884 (95% CI = 0.817–0.957) and HIR 0.860 (95% CI = 0.799–0.926) and increased with OR of PPTA in habitual sitting 1.190 (95% CI = 1.058–1.339). This multivariate logistic regression model showed a good fit based on the Chi-square of Hosmer–Lemeshow goodness of fit statistics ($\:{\chi\:}^{2}$ = 13.279; p > 0.05). Logit model was found as follows:

$$\:\text{ln}\left(\frac{p}{1-p}\right)=15.562-0.123HFR-0.150HIR+0.174PPTA$$

3.3. Performance of prediction model

Figure 7 represents the ROC and AUC. The logistic model showed an acceptable accuracy of 82.1% and AUC of 0.898. Sensitivity was 80.4%, specificity was 78.7%, precision was 85.4%, and negative predictive value was 84.1%.

Table 3

Results of univariate analyses of predictors of flexion pattern chronic low back pain
Variables	B	S.E.	Wald	Exp(B)	95% CI^h	p-value
Age	-0.007	0.023	0.103	0.993	0.948 − 1.039	0.748
Sex	0.038	0.413	0.008	1.038	0.462–2.334	0.927
Body Mass Index	-0.030	0.077	0.154	0.970	0.835–1.128	0.694
Working duration	-0.002	0.002	0.988	0.998	0.993–1.002	0.320
Beck Depression Inventory	0.037	0.062	0.358	1.038	0.919–1.173	0.550
HFR^a	-0.142	0.035	16.258	0.867	0.809–0.929	< 0.001^*
HIR^b	-0.105	0.031	11.219	0.900	0.846–0.957	0.001^*
KER^c	-0.076	0.029	6.737	0.927	0.875–0.982	0.009^*
KEDR^d	-0.154	0.034	20.897	0.858	0.803–0.916	< 0.001^*
HFS^e	0.000	0.002	0.009	1.000	0.996–1.004	0.925
PPTA^f	0.151	0.044	11.837	1.163	1.067–1.68	0.001^*
TEEg	-0.007	0.004	2.627	0.993	0.984–1.002	0.105
^aHFR: hip flexion range of motion ^bHIR: hip internal rotation range of motion ^cKER: knee extension range of motion ^dKEDR: knee extension with ankle dorsiflexion range of motion ^eHFS: hip flexor strength ^fPPTA: pelvic posterior tilt angle in habitual sitting ^gTEE: trunk extensor endurance ^hCI: confidence interval ^*p < 0.05

Table 4

Multivariate logistic regression model using selected variables for predicting the presence of flexion pattern chronic low back pain
Model	Variables	B	S.E.	Wald	Exp(B)	95% CI^d	p-value
1	(intercept)	5.851	1.310	19.958	-	-	< 0.001^*
1	HIR^a	-0.154	0.034	20.897	0.858	0.803–0.916	< 0.001^*
2	(intercept)	18.101	4.134	19.173	-	-	< 0.001^*
	HIR^a	-0.142	0.036	15.755	0.868	0.809–0.931	< 0.001^*
	HFR^b	-0.126	0.038	11.153	0.882	0.819–0.949	0.001^*
3	(intercept)	15.562	4.368	12.692	-	-	< 0.001^*
	HIR^a	-0.150	0.038	15.864	0.860	0.799–0.926	< 0.001^*
	HFR^b	-0.123	0.040	9.316	0.884	0.817–0.957	0.002^*
	PPTA^c	0.174	0.060	8.340	1.190	1.058–1.339	0.004^*
^aHIR: hip internal rotation range of motion ^bHFR: hip flexion range of motion ^cPPTA: pelvic posterior tilt angle in habitual sitting ^dCI: confidence interval ^*p < 0.05

Office workers have a significant risk of developing CLBP due to prolonged sitting postures [12]; this is a complex condition encompassing various factors, including individual, environmental, and psychosocial components [5]. Thus, the present study developed a prediction model for the presence of flexion pattern CLBP in office workers using variables considered as risk factors, including physical, psychological, and individual factors and working duration. Among twelve variables (sex, age, BMI, BDI, working duration, HFR, HIR, KER, KEDR, HFS, PPTA in habitual sitting, and TEE), three (HFR, HIR, and PPTA) were selected as significant predictors of flexion pattern CLBP in office workers (Table 4). The predictive accuracy of our model was 82.1%, and AUC was 0.898, indicating good accuracy [54]. These results could help clinical assessment and intervention decision-making for flexion pattern CLBP related to prolonged sitting posture.

According to the kinesiopathologic model, excessive compensatory motions in the lumbar spine and pelvis produced by limited ROM of the hip joint can be a risk factor leading to LBP symptoms [33, 55]. In model 1, the OR of HIR was 0.858 (95% CI = 0.803–0.916) (Table 4), meaning that the more the hip internal rotation is restricted, the higher the probability of CLBP occurrence in office workers. In previous studies, lack of HIR has been associated with LBP in golfers [56], judo athletes [57], and tennis players [58]. Furthermore, the American Academy of Orthopaedic Surgeons reported the normal ROM of hip internal and external rotation were both 45°. Although the mean values of HIR of office workers with CLBP (32.6°) and those without CLBP (42.8°) in our study were less than 45°, the results are consistent with J Barbee Ellison, SJ Rose and SA Sahrmann [33], which found a HIR deficit in individuals with LBP. The primary factor of HIR deficit in the prone position may be the shortening of the hip external rotators. The piriformis muscle is not only essentially a hip external rotator but also a hip extensor and secondary hip abductor in a hip flexion position [59]. Given that both HIR and HFR were included as predictors for flexion pattern CLBP, shortening of the piriformis muscle may have caused a biomechanical disadvantage of the lumbo-pelvic-hip complex that produces a flexion moment in the lumbar spine during the prolonged sitting posture. Additionally, PM Barton [60] suggested that LBP symptoms associated with the shortening of this muscle were aggravated by the prolonged sitting posture.

In model 2, the OR of HFR was 0.884 (95% CI = 0.817–0.957), meaning that the more the hip flexion is restricted, the higher the probability of CLBP occurrence in office workers. Limitation of HFR in a sitting position plays a critical role in producing excessive lumbar flexion and pelvic posterior tilt [37]. Therefore, it is reasonable that prolonged sitting office workers who have an HFR deficit may develop mechanical complications in the posterior lumbar spine tissues, leading to the occurrence of LBP. Normal HFR in the supine position ranges between 110°–120° with knee flexion [61]. However, our study revealed that subjects with and without flexion pattern CLBP did not meet what was previously described as normal HFR—96.8° and 104.8°, respectively. In our study, the lumbar spine was fixed manually to prevent lumbar flexion and pelvic posterior tilt when measuring HFR. Considering the relationship between the relative stiffness of the hip joint and relative flexibility of the lumbar spine [22], preventing this faulty and uncontrolled lumbar flexion movement during HFR measurement is essential to detect clinical implications of potential disorders [62]. Office workers with flexion pattern CLBP would have compensated with a pelvic posterior tilt or a lumbar flexion for the difference in approximately 8° of HFR between the two groups.

Accordingly, in model 3, the OR of PPTA in habitual sitting was 1.190 (95% CI = 1.058–1.339), meaning that the more the pelvis is posteriorly tilted in habitual sitting, the higher the probability of CLBP occurrence in office workers. In managing LBP, physical therapists encounter an essential question: "What is the optimal sitting spinal posture?" [63]; they generally regard sitting postures that maintain the natural curvature of the lumbar spine as beneficial [63]. Although either the absence of the lordotic curve [64] or its excessive presence [65, 66] can worsen LBP symptoms, our findings are consistent with previous study that reported an interaction between LBP subclassified flexion pattern and increased PPTA in habitual sitting [6]. Evaluations of the pelvic position in habitual sitting could indicate maladaptive motor control impairment [6, 23, 67]. Thus, motor control intervention to reduce PPTA in habitual sitting should be considered when assessing and managing flexion pattern CLBP.

Using a computer with an inadequate workstation design and awkward static postures was identified as a predictive factor for developing WMSDs [68]. Furthermore, maintaining a prolonged sedentary posture is associated with increased tensile forces on the posterior lumbar structures and intradiscal pressure, resulting in CLBP development [24, 69–71]. More recently, considerable attention has been received for a psychological or individual approach to assessing CLBP.

Previously published guidelines for CLBP have recommended early identifying of psychosocial factors that can impede symptom recovery [72, 73], because these, particularly distress and depressive mood, are believed to play a role in the transition from (sub)acute LBP to CLBP [74]. Additionally, several investigators have reported that older age and female sex are more prevalent risk factors for LBP than younger age and male sex [75, 76]. These individual factors were identified as being associated with body size, muscular capacity, hormonal state, work-life balance, greater exposure to psychosocial loads, and longer job tenure [12]. However, alongside the aforementioned factors, psychosocial and individual factors have a controversial association with developing CLBP [9, 12]. Likewise, in our study, age, sex, BMI, BDI, and working duration were not included as significant independent variables in the regression model (p > 0.05) (Table 3). This could probably be explained by the lack of sufficient heterogeneity, with no significant difference between the means of the two groups (Table 1).

For clinical practitioners, the patient's subjective self-perceived reports (such as fear-avoidance or depressive mood) result in difficulty in interpreting intuitively due to biomedical confounding factors [77]. Furthermore, given that individual factors (age or sex) cannot be modified for CLBP treatment purposes, the physical factors that can be potentially improved and quantified in therapeutic sessions should be included as predictors for CLBP [78, 79].

In our study, KER, KEDR, HFS, and TEE were not included in the multivariate logistic model. It is commonly recognized that hamstring muscle tightness and sciatic nerve tension can reduce KER and KEDR, contributing to increased PPTA and decreased lumbar lordosis [80]. Inconsistent with our results, SH Jung, OY Kwon, CH Yi, SH Cho, HS Jeon, JH Weon and UJ Hwang [30] suggested KER, KED, and HFS as predictors of the dysfunction amount related to flexion pattern CLBP based on multiple regression analysis. Given that our study involved office workers who worked in a sedentary environment, it is likely that dynamic movements such as end-range knee extension or loaded hip flexion were not frequently required. Thus, HFR, HIR, and PPTA, which are required to achieve a static sitting posture with neutral lumbar lordosis, should be recognized as more crucial predictors for CLBP in office workers. This could be why KER, KEDR, and HFS were not included as predictors for flexion pattern CLBP.

The trunk extensor muscles are recognized as postural muscles that support an upright posture and regulate forward flexion of the lumbar spine [31]. In the context of LBP, it has been reported that the assessment of TEE has a greater validity to distinguish between individuals with and without LBP compared to muscle strength evaluation [81, 82]. Assessing TEE was standardized as previously described by T Ito, O Shirado, H Suzuki, M Takahashi, K Kaneda and TE Strax [48] because this method showed reduced lumbar lordosis, providing the advantage of use in individuals with CLBP. In male subjects with CLBP, T Ito, O Shirado, H Suzuki, M Takahashi, K Kaneda and TE Strax [48] found a performance time of 85.1 ± 44.8 s, significantly lower than the 208.2 ± 66.2 s observed in healthy male subjects. In our results, the value of TEE in subjects with flexion pattern CLBP group was 85.5 ± 40.1 s and 116.9 ± 121.3 s in those without it; however, there was no significant difference between the two groups (p = 0.093). Although our results use the mean value of males and females, the average performance time of TEE for individuals without CLBP was similar to a previous study [48]. In contrast, the average performance time for those with CLBP differed about two times (208.2 vs. 116.9). In a literature review of several TEE tests, CE Moreau, BN Green, CD Johnson and SR Moreau [83] reported that the performance time during TEE tests was significantly influenced by the motivation and effort exerted by subjects, which may limit their accuracy. This may be why the difference in mean performance time of TEE according to the presence or absence of CLBP was found; however, it was not included as a predictor for the presence of flexion pattern CLBP.

This study has several limitations. First, this study had a relatively small sample size. In order to enhance model performance and minimize the risk of false positive (overfitting) findings in binary logistic regression analysis, the rule of thumb of events per predictor variable (EPV) is typically utilized, which should be set to a minimum of 10 [84]. Although the 10 EPV was met, more data sets can improve the performance of the model. Second, job-related psychological factors, including job strain and work-related stress, were not measured in this study; work-related psychological factors may reflect more WMSDs occurrence than general depression measures (BDI). Third, subjects were not screened for exposure to rotation-related sports or activities. Although we recruited the subjects with CLBP subclassified flexion pattern in the sagittal plane, rotation-related components were essential in studies examining the relationship between the hip joint and LBP [34].

This study investigated predictors for CLBP subclassified flexion pattern, including physical factors (ROM, muscle strength, muscle endurance, and posture). The logistic model showed that HIR, HFR, and PPTA are predictors for the presence of flexion pattern CLBP in office workers. This study's results can guide the prediction, assessment, and management of the flexion pattern CLBP in office workers with prolonged sitting. Further studies should determine if symptoms of flexion pattern CLBP in office workers improve when these predictors (HIR, HFR, PPTA) are enhanced through an intervention program.

Ethics approval and consent to participate

This study was approved by the Institutional Review Board of Yonsei University Mirae campus (1041849-202203-BM-048-02). We confirm that all methods were performed following the relevant guidelines and regulations. Written informed consent was obtained from all study participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Funding

Not applicable.

Author Contribution

H.Y. and O.K. planned the study, S.C., H.J., and W.C. reviewed all methodology, C.Y. and W.C. analyzed the data and conducted the statistical calculations, H.Y. and O.K. drafted the manuscript and all authors reviewed and accepted the final manuscript.

Acknowledgement

Not applicable.

Data Availability

The dataset used during the current study are available from the corresponding author on reasonable request.

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Predictive Model for Flexion Pattern of Chronic Low Back Pain Among Office Workers

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

1. Introduction

2. MATERIALS AND METHODS

2.1. Subjects

2.2. Outcome measures

2.2.1. Questionnaire

2.2.2. Measurement of range of motion

2.2.3. Measurement of hip flexor strength

2.2.4. Measurement of trunk extensor endurance

2.2.5. Measurement of pelvic posterior tilt angle in habitual sitting posture

2.3. Experimental procedure

2.4. Statistical analysis

3. RESULTS

3.1. Comparison of predicting factors between two groups

3.2. Logistic regression

3.3. Performance of prediction model

4. DISCUSSION

5. CONCLUSIONS

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Funding

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Status:

Version 1