Abstract
Background
Low back pain (LBP) is a public health problem. General health perception is the best predictor of healthcare utilization and mortality. Identifying predictors of health perception helps understand how people with LBP live, implement the appropriate treatment, and improve the quality of care.
Objective
This study aimed to estimate the relationships between pain intensity, psychological distress, self-efficacy, functional ability, and healthcare utilization among individuals with chronic LBP over a period of six-months and to estimate the impact of these relationships on general health perception.
Methods
This is a secondary analysis of data from a longitudinal study that assessed the health outcomes of individuals with chronic LBP. Structural equation modeling (SEM), based on health frameworks, was used to estimate the predictors of health perception among people with LBP at baseline and 6-months.
Results
314 individuals with LBP were included in the analysis. The final SEM model had good fit statistics and explained 48% of health perception variance at 6-months. The model showed that health perception was significantly affected by pain intensity (β = 0.29, β = 0.21), psychological distress (β = 0.51, β = 0.44) and self-efficacy (β = 0.4, β = 0.36) cross-sectionally and overtime, respectively.
Conclusion
Different health outcomes could affect the health perception among people with low back pain. This requires holistic approaches to treatment, involving self-management and cognitive behavioral therapy, as well as improved self-efficacy to improve their health.
Introduction
Low back pain (LBP) is the most common musculoskeletal condition worldwide [1,2]; approximately 7.5% of the global population suffers from LBP [2]. LBP affects people physically, psychologically, and economically. It is also a major contributing factor to the number of years that a person lives disabled [2]. LBP may lead to problems with physical function, and psychological disorders such as anxiety and depression [3–6]. The literature shows that chronic LBP (CLBP) causes pain interference in activities of daily living and function, poor health-related quality of life (HRQL), and disability [7–11]. An estimated $6–$12 billion is spent on LBP medical care each year in Canada [12].
General health perception (GHP) is considered an important health outcome that can be affected by LBP and other health conditions [13]. It was defined by Wilson and Cleary (1995) as “a subjective overall rating of health”, and it has been shown that GHP can be affected by different health outcomes (e.g., pain, depression, and functional status) [13]. GHP is considered the best predictor of healthcare utilization and mortality [13]. Identifying predictors of GHP helps with understanding the progress of LBP, adjusting treatment to the needs of individuals with LBP, and reinforcing the requirement of multidisciplinary intervention. These may result in optimizing functional ability, GHP, and QoL [14–17]. In addition, studying the GHP indicators and addressing them may prevent the deterioration of patients’ health and enhance self-management approach [18].
The literature shows the factors that significantly enhance GHP in individuals with LBP, CLBP, or chronic pain (including CLBP) include less pain severity, fewer psychological symptoms (i.e., depression and anxiety), higher self-efficacy, and higher functional status [19–32].
However, the studies that were used to conclude the statistical correlations between the different health outcomes relied mainly on cross-sectional design and regression/bivariate statistical approaches. With such approaches, it is not possible to evaluate the interplay between the variables that influence GHP, and it is difficult to indicate the sequence of the factors that influence the disease or the progression of it [33]. Therefore, studying the predictors of GH overtime is important to accurately estimate the effects of different health and social factors on GHP. Furthermore, a more sophisticated analytical approach is needed to incorporate the relationships between predictors of GHP, which may help evaluate the combined effect of these variable on GHP.
The objective of this study was to estimate the relationships between pain intensity, psychological distress, self-efficacy, functional ability, and healthcare utilization among individuals with CLBP over a period of six-months and to estimate the impact of these relationships on GHP.
Guided by Wilson and Cleary and the International Classification of Functioning, Disability, and Health (ICF) models [13, 34, 35], a specific theoretical model was proposed in this study to specify the relationships between GHP and other health outcomes over time in individuals with CLBP.
Materials and methods
Population
The study sample included individuals with CLBP who were enrolled in a primary care interdisciplinary LBP program, were 18 years and older, and could speak English and/or French.
Study Design and Procedure
This study is a secondary analysis using data from a longitudinal study that assessed the health outcomes of individuals with CLBP at baseline and 6-months follow-ups. The original study was conducted to develop and evaluate the impact of interdisciplinary intervention and self-management on different patient outcomes and healthcare utilization over a 2-year period [36]. The program was implemented in four Health and Social Services Centres (CSSSs) in Quebec. The interdisciplinary program included interventions by physicians, nurses, physiotherapists, and psychologists.
The current study’s population was recruited from four interdisciplinary intervention centers from August 1, 2012, to February 20, 2016. The participants completed a package of measurement tools at baseline and at 3 and 6-month follow-up. The nurse case manager provided the patients with the questionnaires during their visits to the clinic. The McGill University Health Center IRB provided ethics approval for the original study (#MP-CUSM-12–220 GEN/2013–999). A written consent form was signed by participants to participate and use their data for research purposes. Participants who only completed questionnaires at both baseline and 6-month follow-up were included in this study. Therefore, the follow-up rate was 100%.
Measures & Data Collection
All the following measurement tools were completed by participants at baseline and at 6-month follow-up during the clinic visit. Table 1 presents the study’s latent variables and their indicators.
Table 1. Latent factors’ indicators and the measurement model.
| Variable | Indicators | Factor Loading |
|---|---|---|
| Latent Variable | ||
| Self-Efficacy | SE1 SE2 SE3 SE4 SE5 SE6 SE7 SE8 |
0.68 0.47 0.8 0.75 0.8 0.74 0.92 0.83 |
| Pain | BPI-Severity BPI-Interference SF-12 Bodily Pain |
0.71 0.71 0.87 |
| Psychological Distress | HADS – Depression HADS – Anxiety PHQ-9 |
0.92 0.72 0.86 |
| Functional Status | ODI SF-12 Physical Function SF-12 Role Physical SF-12 Social Function |
0.88 0.62 0.72 0.92 |
BPI: Brief Pain Inventory; HADS: Hospital Anxiety and Depression Scale; ODI: Oswestry Disability Index; PHQ: Patient Health Questionnaire; SE: Self-efficacy; SF-12: Short Form 12.
Outcome: GHP
SF-12: General Health Subscale.
The SF-12 is a self-administered short-form questionnaire with 12 items that measure eight dimensions: bodily pain, vitality, mental health, physical functioning, role-physical, role-emotional, social functioning, and general health. The general health item of the SF-12 was used in this study as the outcome. It is rated on a 5-point scale, from poor to excellent [37]. The literature supports the validity and reliability of using a single-item measure [38,39].
Predictor variables
Pain.
Pain was measured by the subscales of the BPI: pain severity and pain interference, and the bodily pain subscale of the SF-12.
Brief Pain Inventory (BPI):
Pain intensity and Pain Interference
The BPI is a self-administered tool used to assess pain severity and the impact of the pain on the main aspects of life. BPI has fifteen items and two subscales: pain severity and pain interference. Each item is rated on an eleven-point scale, from 0 to 10, where 0 indicates no problems and 10 indicates maximal problems [40]. The reliability and validity of BPI are supported by the literature [41].
SF-12: Bodily Pain Subscale.
The bodily pain subscale of the SF-12 was used in this study as one of the indicators of pain. It includes one item rated on a 5-point scale, from “not at all” to “extremely” and transformed to 0–100 [37].
Psychological Distress
Depression and anxiety were used as indicators of psychological distress.
Patient Health Questionnaire, PHQ-9.
PHQ-9 is a self-administered nine-item questionnaire used to assess depression. Each item is rated on a four-point scale, from 0 (not at all) to 3 (very often). The score range of PHQ-9 is between 0 and 27. Zero indicates minimal depression symptoms, and 27 indicates severe depression [42]. The reliability and validity of PHQ-9 are supported by the literature [42]. Cronbach’s alpha of PHQ-9 is 0.89, and the construct validity (correlation for PHQ-9 and mental health scale of SF-20) is 0.73.
Hospital Anxiety and Depression Scale (HADS)
HADS is a self-administered questionnaire used to screen for depression and anxiety in non-psychiatric clinical populations [43]. It includes fourteen items and two subscales: depression and anxiety. There are seven-item an each subscale, each item is rated on a four-point scale. The score range of each subscale is between 0 and 21 [44]. Zero indicates no case of depression or anxiety, and 21 indicates a severe case of depression or anxiety [44]. The reliability and validity of HADS are supported by the literature [45]. Cronbach’s alphas for the depression and anxiety subscales are 0.79 and 0.85, respectively. The correlations for the life satisfaction questionnaire and HADS-depression and anxiety (construct validity) are respectively -0.66 and -0.42.
Self-efficacy
Self-Efficacy Scale.
Self-efficacy can be defined as the confidence and belief of a person in their ability to avoid back pain symptoms and exacerbations to achieve optimal health. [46]. Self-efficacy scale (SES) adapted from the Kate Lorig scale was used in this study to assess self-efficacy [47]. The self-efficacy scale includes 8 items measured on a 10-point scale, where 1 indicates very uncertain and 10 indicates very certain [47]. The reliability and validity of SES are supported by the literature. The test-retest reliability of SES was 0.87, and there was a significant correlation between self-efficacy and health status with perceived ability [47].
Functional status.
Functional status was measured by the following indicators: the Oswestry Disability Index, and the physical function, role physical and social function subscales of SF-12.
Oswestry Disability Index (ODI)
The ODI is a self-administered tool developed to assess pain-related disability in individuals with LBP. It includes 10 items on the main daily activities; each item is rated on a six-point scale. The score range of the ODI is between 0 and 100. Zero indicates minimal disability, and 100 indicates “bed-bound or exaggerated symptoms” [48]. The reliability and validity of ODI are supported by the literature. Test-retest reliability of the ODI ranges from 0.81–0.99, and convergent validity (correlation for the ODI and Roland-Morris Questionnaire) is 0.82 [48].
SF-12: Physical Functioning, Role-Physical, and Social Functioning Subscales
The following SF-12 subscales were used in this study as indicators of functional status: physical functioning, role-physical and social functioning.
The physical functioning subscale includes two items rated on a 3-point scale, “Yes, limited a lot” to “No, not limited at all” [37]. The role-physical subscale includes two items rated on a 5-point scale, “All of the time” to “None of the time”. Social functioning includes one item rated on a 5-point scale, “All of the time” to “None of the time”. The scores of the physical functioning and role-physical subscales were transformed to 0–100, where 0 indicates a worse condition and 100 indicates a better condition.
Measurement tools for other explanatory variables.
Sociodemographic Variables
Sociodemographic characteristics included patient sex (men/women), age, education (university or college, secondary and primary school), and marital status (never married, married or common law, divorced or separated, and widowed). Each one of these characteristics was assessed by one question.
Healthcare Utilization
Healthcare utilization was assessed by the total number of patient’s visits to the physician, physiotherapist, nurse, and psychologist.
Statistical Analysis
All descriptive and correlation statistical analyses in this study were done using SAS 9.4 [49], and Structural Equation Modeling (SEM) with ten imputations for missing data was conducted using MPLUS 8 [50]. Pearson correlation was used to assess the correlation between predictor variables to determine multicollinearity. P-values of parameter estimates larger than 0.05 support rejecting a hypothesis. All the studied variables in this project were considered continuous, except for GHP, sex, education, and marital status.
A measurement model was specified for each latent construct (Table 1). SEM was used to assess the relationships between health outcomes and personal and environmental factors and the impact of these relationships on GHP. SEM is an alternative approach to regression used to assess the correlation among health outcomes over time. SEM overcomes the limitations of the regression approach; it enables researchers to use more than one outcome at the same time and to estimate both direct and indirect effects of predictors on an outcome. It allows the simultaneous use of a variable as an outcome and a predictor. This makes it possible to more precisely simulate the causal relationships between studied variables in order to understand the overall relationships among them. SEM comprises both path and measurement models, where the measurement models represent latent variables and provide a more accurate adjustment for a measurement error [51,52].
Five steps were conducted to apply the SEM model: 1) specification: the model should be specified based on a theory; 2) identification: the number of observations in the model should be equal to or more than the number of parameters; 3) estimation: applying various techniques of estimation (e.g., WLSMV); paths in SEM indicate the “beta coefficients”; 4) testing and diagnostics: the goodness of fit of the SEM is examined by Comparative Fit Index (CFI) and Root Mean Square Error of Approximation (RMSEA), RMSEA value ≤ 0.05 and CFI values ≥ 0.9 indicate a good fit model [53]. Lastly, 5) re-specification: according to the results of the model fit, we applied adjustments by changing direction, deleting, or adding some paths to the model [54].
A literature review was firstly conducted to develop the proposed theoretical model. The review summarized the theoretical and statistical relationships in order to strengthen and support the model hypotheses. The theoretical base refers to theories and hypotheses that explain the correlation between the studied variables, while the statistical (empirical) base refers to the significance of the statistical correlation between the studied variables. The summary of the literature supporting for these two bases is presented in S1 Table.
The SEM model is provided in Fig 1. For clarity, only structural paths for the studied variables were included in Fig 1. The proposed model includes four latent variables measured at baseline (self-efficacy, pain, psychological distress, and functional ability) and the other seven observed variables (GHP at baseline, GHP at 6-month follow-up, healthcare utilization, sex, age, education, and marital status). For all latent variables and GHP, higher scores indicate better outcomes.
Fig 1. Predictors of health perception model at 6-month - structural paths model.
It shows the model of the predictors of health perception overtime, it includes only the structural paths.
The model specified that self-efficacy was directly affected by age, sex, education, and marital status, while pain was directly affected by age, sex, education, marital status, and self-efficacy. Also, it was hypothesized that psychological stress was directly affected by age, sex, education, marital status, pain, self-efficacy, and functional status. In addition, the model specified that functional status was directly affected by age, sex, education, marital status, pain, self-efficacy, and psychological distress. Lastly, healthcare utilization at 6-month was directly affected by age, sex, education, marital status, pain, psychological symptoms, functional status, and GHP at baseline.
Furthermore, the GHP at baseline was affected directly by age, sex, education, marital status, pain, psychological symptoms, self-efficacy, and functional status at baseline. Lastly, it was hypothesized that the GHP at 6-month was affected directly by age, sex, education, marital status, pain, psychological symptoms, self-efficacy, functional status, GHP at baseline, and healthcare utilization at 6-month. The unit loading identification (ULI) constraint was used for each latent variable.
Sample Size
Kline recommends that 200 subjects are sufficient to run a structural equation model [55].
Results
The baseline characteristics of the 314 individuals with LBP are presented in Table 2. The mean age was 62.9 ± 34.57 years, and 60% were women. The participants had moderate pain intensity on the BPI scale (the mean score was 4.75 ± 1.85), mild disability on the ODI (the mean score was 33.1 ± 15.8), and on average within the normal range for depression and anxiety on HADS (6.05 ± 4 and 8.55 ± 3.76, respectively). Only 21 (6.7%) and 37 (11.8%) participants rated their health as “excellent” at baseline and 6-months, respectively.
Table 2. Participants’ characteristics at baseline.
| Variable | N | Mean (sd) | Missing Data% |
|---|---|---|---|
| Age | 314 | 62.9 (34.57) | 6% |
| BPI-Pain Severity | 314 | 4.75 (1.85) | 12% |
| BPI-Pain Interference | 314 | 4.65 (2.39) | 8% |
| ODI | 314 | 33.1 (15.8) | 0.6% |
| Self-Efficacy | 314 | 6.07 (2.05) | 15% |
| HADS – Depression | 314 | 6.05 (4) | 8% |
| HADS – Anxiety | 314 | 8.55 (3.76) | 7% |
| PHQ-9 | 314 | 7.4 (5.85) | 8% |
| SF-12 Bodily Pain | 314 | 35.2 (11.35) | 8% |
| SF-12 Physical Function | 314 | 36.65 (11.6) | 4% |
| SF-12 Role Physical | 314 | 36.25 (9.75) | 5% |
|
SF-12 General Health/ Baseline Excellent Very Good Good Fair Poor |
314 | 21 (6.7%) 77 (24.4%) 131 (41.7%) 62 (19.7%) 23 (7.5%) |
3% |
|
SF-12 General Health/ 6 months Excellent Very Good Good Fair Poor |
314 | 37 (11.8%) 94 (30%) 125 (39.7%) 43 (13.7%) 15 (4.8%) |
3% |
|
Changes in SF-12 General Health overtime No change Improvement Deterioration |
314 | 161 (51.3%) 108 (34.4%) 45 (14.3%) |
BPI: Brief Pain Inventory; HADS: Hospital Anxiety and Depression Scale; ODI: Oswestry Disability Index; PHQ: Patient Health Questionnaire; SF-12: Short Form 12.
SEM model fit statistics
To achieve an acceptable statistical fit of the model, four error correlations were set between certain indicators that were presented in Table 3. The fit statistics showed that the model was statistically fit (RMSEA = 0.05 and CFI = 0.9). The model explained 37% and 48% of the variance of GHP at baseline and 6-month, respectively.
Table 3. The correlation between variable’s errors that was included in the SEM model.
| SF12-physical function and SF12-role physical |
|---|
| SF12-social function and ODI |
| SF12-social function and pain severity of BPI |
| First and second items of self-efficacy scale |
BPI: brief pain inventory; ODI: Oswestry disability index; SF12: Short form 12.
Factors of self-efficacy
The model showed that aging was the only significant factor that affected self-efficacy (β = 0.13, p-value = 0.03), while sex, education, and marital status did not significantly affect self-efficacy.
Factors of pain
The model showed that high self-efficacy (β = 0.43, p-value = 0.000), higher educational level (β = 0.18, p-value = 0.006), and being male (β = -0.16, p-value = 0.01) significantly reduced pain intensity. However, age and marital status did not significantly affect the level of pain.
Factors of psychological distress
The results showed that psychological distress improved with lower pain levels (β = 0.58, p-value = 0.00), and high self-efficacy (β = 0.49, p-value = 0.00). In contrast, age, sex, educational level, and marital status did not significantly affect psychological distress.
Factors of functional status
The results showed that functional status was significantly affected by pain (β = 0.87, p-value = 0.00), self-efficacy (β = 0.44, p-value = 0.00), psychological distress (β = 0.19, p-value = 0.052), and being male (β = -0.12, p-value = 0.048). On the other hand, age, educational level and marital status did not significantly affect functional status.
Factors of healthcare utilization at 6-months
The model showed that low pain intensity (β = -0.19, p-value = 0.01) reduced healthcare utilization. Self-efficacy, psychological distress, functional status, health perception, age, sex, educational level, and marital status did not significantly affect healthcare utilization at 6-month.
Factors of GHP at baseline
The total effect in the SEM model showed that lower pain levels (β = 0.29, p-value = 0.00), lower psychological distress (β = 0.51, p-value = 0.00), higher self-efficacy (β = 0.4, p-value = 0.00), and higher educational level (β = 0.15, p-value = 0.04) significantly improved GHP at baseline. On the other hand, functional status, age, sex, and marital status did not significantly affect GHP at baseline. Table 4 presents the factors of GHP at baseline.
Table 4. The effects of the factors of general health perception (GHP) at baseline.
| Factor | Total Effect (β) (p-value) [95% Confidence Interval] |
Direct Effect (β) (p-value) [95% Confidence Interval] |
Indirect Effect (β) (p-value) [95% Confidence Interval] |
|---|---|---|---|
| Pain | 0.29 (0.00) [0.18-0.41] |
-0.1 (0.6) [-0.42-0.22] |
0.39 (0.019) [0.12-0.67] |
| Self-efficacy | 0.4 (0.00) [0.31-0.49] |
0.15 (0.02) [0.045-0.26] |
0.25 (0.00) [0.18-0.32] |
| Psychological distress | 0.51 (0.00) [0.38-0.65] |
0.49 (0.00) [0.36-0.62] |
0.02 (0.53) [-0.036-0.08] |
| Functional status | 0.12 (0.51) [-0.18-0.65] |
0.12 (0.51) [-0.18-0.42] |
– |
| Gender | 0.04 (0.51) [-0.06-0.15] |
0.1 (0.054) [0.013-0.19] |
-0.06 (0.13) [-0.13-0.01] |
| Age | 0.01 (0.82) [-0.09-0.11] |
-0.03 (0.54) [-0.11-0.05] |
0.04 (0.22) [-0.01-0.1] |
| Education | 0.15 (0.04) [0.03-0.27] |
-0.1 (0.1) [-0.03-0.2] |
0.07 (0.1) [0-0.13] |
| Marital status | 0.04 (0.5) [-0.06-0.14] |
0.09 (0.11) [-0.09-0.08] |
0.05 (0.24) [-0.02-0.11] |
Factors of GHP at 6-month
The total effect in the SEM model showed that some health outcomes significantly improved GHP over time: lower pain levels (β = 0.21, p-value = 0.004), lower psychological distress (β = 0.44, p-value = 0.00), higher self-efficacy (β = 0.36, p-value = 0.00), and GHP at baseline (β = 0.59, p-value = 0.00). On the other hand, functional status, age, sex, education, marital status, and healthcare utilization over the period of 6-months did not significantly affect GHP at 6-month. Table 5 presents the factors of GHP at 6-month.
Table 5. The effects of the factors of general health perception (GHP) at 6-month.
| Factor | Total Effect (β) (p-value) | Direct Effect (β) (p-value) | Indirect Effect (β) (p-value) |
|---|---|---|---|
| Pain | 0.21 (0.004) [0.09-0.33] |
-0.03 (0.85) [-0.33-0.26] |
0.24 (0.13) [-0.02-0.5] |
| Self-efficacy | 0.36 (0.00) [0.26-0.46] |
0.07 (0.28) [-0.04-0.18] |
0.29 (0.00) [0.21-0.37] |
| Psychological distress | 0.44 (0.00) [0.29-0.58] |
0.13 (0.13) [-0.01-0.27] |
0.31 (0.00) [0.21-0.41] |
| Functional status | 0.06 (0.74) [-0.26-0.38] |
-0.02 (0.91) [-0.3-0.26] |
0.08 (0.46) [-0.1-0.27] |
| GHP - Baseline | 0.59 (0.00) [0.52-0.67] |
0.6 (0.00) [0.53-0.67] |
-0.006 (0.32) [-0.015-0.005] |
| Healthcare providers visits | -0.048 (0.22) [-0.11-0.02] |
-0.05 (0.22) [-0.11-0.017] |
– |
| Gender | -0.005 (0.94) [-0.11-0.01] |
-0.02 (0.77) [-0.11-0.07] |
0.01 (0.82) [-0.07-0.09] |
| Age | -0.002 (0.98) [-0.01-0.09] |
-0.03 (0.47) [-0.09-0.035] |
0.03 (0.56) [-0.05-0.1] |
| Education | 0.07 (0.27) [-0.04-0.18] |
-0.04 (0.51) [-0.13-0.06] |
0.11 (0.04) [0.02-0.19] |
| Marital status | -0.004 (0.95) [-0.11-0.1] |
-0.03 (0.56) [-0.13-0.06] |
0.03 (0.5) [-0.04-0.1] |
Discussion
To our knowledge, this is the first study to use SEM to assess the impact of pain, self-efficacy, psychosocial distress, functional status, patient characteristics, and healthcare utilization on GHP overtime among people with LBP. Pain, psychological distress, self-efficacy, and GHP at baseline had a significant impact on GHP cross-sectionally and overtime. The results showed that people with low levels of pain had higher levels of GHP. This was supported in the literature, where increasing pain severity decreased GHP among people with LBP [20–22]. Chronic pain may lead to some consequences like physical, psychological, and economic burden [56], which may affect the health status of patients. The model in this study showed that pain affected GHP mainly through psychological distress.
This study also presented that psychological distress at baseline was significantly associated with a low level of GHP at 6-month. The literature shows that people with depression and/or anxiety are more likely to have low GHP [26,27]. Pain, depression, and anxiety may affect activities of daily living, physical health, and mental health, as well as social health [57], which may in turn decrease and deteriorate GHP.
Self-efficacy was shown in this study as a significant predictor of GHP among people with LBP. The model showed that self-efficacy improved GHP through the reduction of psychological distress. Two cross-sectional studies showed that self-efficacy was significantly correlated with GHP among individuals with chronic musculoskeletal pain including osteoarthritis and rheumatoid arthritis [31,32]. According to Social Cognitive Theory [30], self-efficacy promotes and improves GHP by allowing individuals to participate in a healthy lifestyle.
The literature supports that the association between functional status and GHP, tested using multiple regression, is significant among individuals with LBP, where increasing function increases GHP [23, 24, 29]. However, in this study, functional status did not significantly affect GHP at baseline, and 6-months. In part, this is likely related to the difference in the estimation approach used in this study (i.e., regression vs. SEM). In addition, patient characteristics (age, sex, education, and marital status) did not significantly affect GHP cross-sectionally and overtime.
In the proposed model in this study, psychological distress was modeled as a mediator between pain, and self-efficacy and GHP. Lower pain and higher self-efficacy were associated with higher levels of GHP through the reduction of psychological distress. According to Bandura’s study in 1994, self-efficacy is a key factor controlling thought processes and regulating thoughts that produce stress and depression [58]. Also, social cognitive theory and experimental studies support the fact that people who do not believe that they are able to manage threatening events may have high levels of anxiety arousal [59–61]. In addition, the concept of self-efficacy reflects the ability of individuals to avoid the exacerbation of a condition and its comorbidities; by using coping behaviors, self-efficacy may decrease the severity of depression and anxiety [46]. Pain and depression share the same neurological pathway [62]. Normally, response to painful physical stimuli is moderated in the brain by serotonin and norepinephrine. If this mechanism becomes impaired, it affects mood which explains why individuals with LBP generally experienced a state of sadness, hopelessness, or anxiety [63]. In addition, psychological models of pain propose that pain may activate negative emotions including anxiety [64].
Lastly, this study showed changes in the scores of ‘SF-12 General Health’ between the baseline and the 6-month follow-up (Table 2). These changes may reflect the effects of the multidisciplinary intervention received by the participants. This intervention was individualized and applied to all participants, which was controlled in the analysis, reflecting the real life of people with LBP.
Clinical Implications
This study identified pain intensity, psychological distress, self-efficacy, and GHP at baseline significantly associated with GHP 6 months later among individuals with LBP. Therefore, it is important to assess these health factors to define people with high risk of having low GHP. This can be done by assessing people with LBP using patient-reported outcome measures (PROMs) at the initial visit to refer them to the appropriate treatment. PROMs can be used in clinics for different purposes: to screen for problems, monitor progress over time, and facilitate patient-centred care [65–67]. Administering the PROMs before the clinical visit may reduce the time needed for it by helping clinicians focus on areas most relevant for a given patient; studies have shown that areas flagged as below normal limits are more likely to be discussed in the consultation [65].
Identifying any issues with pain intensity, psychological distress, and/or self-efficacy may contribute to optimizing functional ability, GHP, and QoL to prevent deterioration of patients’ health, enhance self-management approaches, and decrease the cost of health care to society. Also, doing so may contribute to improving healthcare services for individuals with LBP. These contributions will be derived from a better understanding of LBP progression, personalised interventions to individuals with LBP needs, and emphasizing the need for interdisciplinary intervention. The results of this study support the importance of including in LBP programs interventions that improve individuals’ confidence to manage their symptoms and their impact on function. The multidisciplinary/interdisciplinary intervention for LBP is recommended by guidelines for evidence-based primary care management, this interdisciplinary intervention includes physicians, nurses, physical therapists, occupational therapists, psychologists, chiropractors, and pharmacists [68]. The literature supports the effect of interdisciplinary and multidisciplinary treatment among people with LBP; it improves pain, depression, physical and social function, mental health, kinesiophobia, general health, and return to work [69–71]. In addition, cognitive-behavioral therapy (CBT) and self-management has been shown to be an effective treatment to improve self-efficacy among people with LBP [72,73]. The literature shows that high self-efficacy is a significant indicator of return to work among people with LBP [74,75].
Lastly, healthcare providers can use the results of this study to target interventions to people who are more likely to have poor health and focus on improving the significant predictors of GHP. Addressing issues related to pain, psychological distress, and self-efficacy may improve individuals’ GHP. Different treatment procedures can be used to address those issues, such as self-management [76,77], cognitive behavioural therapy [73], and multidisciplinary interventions.
Limitation
Ideally, all factors within the Wilson and Cleary Model would have been included in the SEM. In this study, we were limited to the variables collected within the CLBP primary project. Some predictors were not covered in the current study, like fatigue and sleep impairment. The findings of this study can be generalized only to people with LBP who receive health care from primary care centers, but not to those who receive care from specialty care. Lastly, the study did not perform sex- or gender-based analyses and age-stratified analyses since the sample sizes for each category were small. It is possible that there are variations in health outcomes and perceptions that are influenced by age-specific pathophysiological factors that this study did not capture.
Conclusions
GHP at 6-month among people with LBP was found to be associated with pain, psychological distress, self-efficacy, and GHP at baseline. Programs that measure and target these factors as part of the intervention plan can increase individuals’ GHP over time, thereby increasing the likelihood of a successful return to work and increasing social participation. A holistic approach for managing chronic pain is needed to incorporate self-management strategies that can improve self-efficacy, which in turn can help address psychological distress and assist individuals in adopting strategies to reduce the interference of pain on function.
Supporting information
It summarizes the theoretical and statistical association between the studied variables and the proposed hypotheses.
(DOCX)
It shows the effects of the factors of the outcomes included in the SEM model.
(DOCX)
(DOCX)
Acknowledgments
The authors would like to thank the research assistants who collected the data and the people who participated in the study.
Data Availability
The reason for the restriction is patient confidentiality, as enforced by the McGill University Health Centre. Only the research team and ethics boards related to the study have access to participants’ identifiable information. We are providing a non-author contact for data requests. Dr. Benjamin Smith. McGill University Health Centre benjamin.m.smith@mcgill.ca (514) 934-1934 ext. 32235.
Funding Statement
The author(s) received no specific funding for this work.
References
- 1.Hoy D, Bain C, Williams G, March L, Brooks P, Blyth F, et al. A systematic review of the global prevalence of low back pain. Arthritis Rheum. 2012;64(6):2028–37. doi: 10.1002/art.34347 [DOI] [PubMed] [Google Scholar]
- 2.Wu A, March L, Zheng X, Huang J, Wang X, Zhao J, et al. Global low back pain prevalence and years lived with disability from 1990 to 2017: estimates from the Global Burden of Disease Study 2017. Ann Transl Med. 2020;8(6):299. doi: 10.21037/atm.2020.02.175 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Wettstein M, Eich W, Bieber C, Tesarz J. Pain Intensity, Disability, and Quality of Life in Patients with Chronic Low Back Pain: Does Age Matter?. Pain Med. 2019;20(3):464–75. doi: 10.1093/pm/pny062 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Glombiewski JA, Hartwich-Tersek J, Rief W. Depression in chronic back pain patients: prediction of pain intensity and pain disability in cognitive-behavioral treatment. Psychosomatics. 2010;51(2):130–6. doi: 10.1176/appi.psy.51.2.130 [DOI] [PubMed] [Google Scholar]
- 5.Hagen EM, Svensen E, Eriksen HR, Ihlebaek CM, Ursin H. Comorbid subjective health complaints in low back pain. Spine (Phila Pa 1976). 2006;31(13):1491–5. doi: 10.1097/01.brs.0000219947.71168.08 [DOI] [PubMed] [Google Scholar]
- 6.Rudy TE, Weiner DK, Lieber SJ, Slaboda J, Boston RJ. The impact of chronic low back pain on older adults: a comparative study of patients and controls. Pain. 2007;131(3):293–301. doi: 10.1016/j.pain.2007.01.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Mutubuki EN, Beljon Y, Maas ET, Huygen FJPM, Ostelo RWJG, van Tulder MW, et al. The longitudinal relationships between pain severity and disability versus health-related quality of life and costs among chronic low back pain patients. Qual Life Res. 2020;29(1):275–87. doi: 10.1007/s11136-019-02302-w [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Semeru GM, Halim MS. Acceptance versus catastrophizing in predicting quality of life in patients with chronic low back pain. Korean J Pain. 2019;32(1):22–9. doi: 10.3344/kjp.2019.32.1.22 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Vlaeyen JWS, Maher CG, Wiech K, Van Zundert J, Meloto CB, Diatchenko L, et al. Low back pain. Nat Rev Dis Primers. 2018;4(1):52. doi: 10.1038/s41572-018-0052-1 [DOI] [PubMed] [Google Scholar]
- 10.Costa L da CM, Maher CG, McAuley JH, Hancock MJ, Herbert RD, Refshauge KM, et al. Prognosis for patients with chronic low back pain: inception cohort study. BMJ. 2009;339:b3829. doi: 10.1136/bmj.b3829 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Andersson GB. Epidemiological features of chronic low-back pain. Lancet. 1999;354(9178):581–5. doi: 10.1016/S0140-6736(99)01312-4 [DOI] [PubMed] [Google Scholar]
- 12.Brown A, Angus D, Chen S, Tang Z, Milne S, Pfaff J. Costs and outcomes of chiropractic treatment for low back pain. National Library of Canada, Canadian Coordinating Office for Health Technology Assessment. 2005. [Google Scholar]
- 13.Wilson IB, Cleary PD. Linking clinical variables with health-related quality of life. A conceptual model of patient outcomes. JAMA. 1995;273(1):59–65. doi: 10.1001/jama.1995.03520250075037 [DOI] [PubMed] [Google Scholar]
- 14.Hayden JA, van Tulder MW, Tomlinson G. Systematic review: strategies for using exercise therapy to improve outcomes in chronic low back pain. Ann Intern Med. 2005;142(9):776–85. doi: 10.7326/0003-4819-142-9-200505030-00014 [DOI] [PubMed] [Google Scholar]
- 15.Hildebrandt J, Pfingsten M, Saur P, Jansen J. Prediction of success from a multidisciplinary treatment program for chronic low back pain. Spine (Phila Pa 1976). 1997;22(9):990–1001. doi: 10.1097/00007632-199705010-00011 [DOI] [PubMed] [Google Scholar]
- 16.Moradi B, Hagmann S, Zahlten-Hinguranage A, Caldeira F, Putz C, Rosshirt N, et al. Efficacy of multidisciplinary treatment for patients with chronic low back pain: a prospective clinical study in 395 patients. J Clin Rheumatol. 2012;18(2):76–82. doi: 10.1097/RHU.0b013e318247b96a [DOI] [PubMed] [Google Scholar]
- 17.Guzmán J, Esmail R, Karjalainen K, Malmivaara A, Irvin E, Bombardier C. Multidisciplinary rehabilitation for chronic low back pain: systematic review. BMJ. 2001;322(7301):1511–6. doi: 10.1136/bmj.322.7301.1511 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Bayliss EA, Ellis JL, Steiner JF. Barriers to self-management and quality-of-life outcomes in seniors with multimorbidities. Ann Fam Med. 2007;5(5):395–402. doi: 10.1370/afm.722 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Buchanan M. The impact of clinical factors and sociodemographic variables on health-related quality of life in venous leg ulceration. Queen’s University. 2010. https://qspace.library.queensu.ca/server/api/core/bitstreams/887a3000-e625-4e48-a5d2-8f4bb149de9b/content [Google Scholar]
- 20.Bentsen SB, Wahl AK, Strand LI, Hanestad BR. Relationships between demographic, clinical and pain variables and health-related quality of life in patients with chronic low back pain treated with instrumented fusion. Scand J Caring Sci. 2007;21(1):134–43. doi: 10.1111/j.1471-6712.2007.00440.x [DOI] [PubMed] [Google Scholar]
- 21.Klemenc-Ketiš Z. Predictors of health-related quality of life and disability in patients with chronic non-specific low back pain. Slovenian Med J. 2011;80:379–85. [Google Scholar]
- 22.Lamé IE, Peters ML, Vlaeyen JWS, Kleef M v, Patijn J. Quality of life in chronic pain is more associated with beliefs about pain, than with pain intensity. Eur J Pain. 2005;9(1):15–24. doi: 10.1016/j.ejpain.2004.02.006 [DOI] [PubMed] [Google Scholar]
- 23.Horng Y-S, Hwang Y-H, Wu H-C, Liang H-W, Mhe YJ, Twu F-C, et al. Predicting health-related quality of life in patients with low back pain. Spine (Phila Pa 1976). 2005;30(5):551–5. doi: 10.1097/01.brs.0000154623.20778.f0 [DOI] [PubMed] [Google Scholar]
- 24.Licciardone JC, Gatchel RJ, Kearns CM, Minotti DE. Depression, somatization, and somatic dysfunction in patients with nonspecific chronic low back pain: results from the OSTEOPATHIC Trial. J Am Osteopath Assoc. 2012;112(12):783–91. [PubMed] [Google Scholar]
- 25.Guclu DG, Guclu O, Ozaner A, Senormanci O, Konkan R. The relationship between disability, quality of life and fear-avoidance beliefs in patients with chronic low back pain. Turk Neurosurg. 2012;22(6):724–31. doi: 10.5137/1019-5149.JTN.6156-12.1 [DOI] [PubMed] [Google Scholar]
- 26.Antunes RS, de Macedo BG, Amaral T da S, Gomes H de A, Pereira LSM, Rocha FL. Pain, kinesiophobia and quality of life in chronic low back pain and depression. Acta Ortop Bras. 2013;21(1):27–9. doi: 10.1590/S1413-78522013000100005 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Bair MJ, Wu J, Damush TM, Sutherland JM, Kroenke K. Association of depression and anxiety alone and in combination with chronic musculoskeletal pain in primary care patients. Psychosom Med. 2008;70(8):890–7. doi: 10.1097/PSY.0b013e318185c510 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Elliott TE, Renier CM, Palcher JA. Chronic pain, depression, and quality of life: correlations and predictive value of the SF-36. Pain Med. 2003;4(4):331–9. doi: 10.1111/j.1526-4637.2003.03040.x [DOI] [PubMed] [Google Scholar]
- 29.Takeyachi Y, Konno S, Otani K, Yamauchi K, Takahashi I, Suzukamo Y, et al. Correlation of low back pain with functional status, general health perception, social participation, subjective happiness, and patient satisfaction. Spine (Phila Pa 1976). 2003;28(13):1461–6; discussion 1467. doi: 10.1097/01.BRS.0000067091.88283.B6 [DOI] [PubMed] [Google Scholar]
- 30.Atkins R. Self-efficacy and the promotion of health for depressed single mothers. Ment Health Fam Med. 2010;7(3):155–68. [PMC free article] [PubMed] [Google Scholar]
- 31.Börsbo B, Gerdle B, Peolsson M. Impact of the interaction between self-efficacy, symptoms and catastrophising on disability, quality of life and health in with chronic pain patients. Disabil Rehabil. 2010;32(17):1387–96. doi: 10.3109/09638280903419269 [DOI] [PubMed] [Google Scholar]
- 32.Cross MJ, March LM, Lapsley HM, Byrne E, Brooks PM. Patient self-efficacy and health locus of control: relationships with health status and arthritis-related expenditure. Rheumatology (Oxford). 2006;45(1):92–6. doi: 10.1093/rheumatology/kei114 [DOI] [PubMed] [Google Scholar]
- 33.Levin KA. Study design III: Cross-sectional studies. Evid Based Dent. 2006;7(1):24–5. doi: 10.1038/sj.ebd.6400375 [DOI] [PubMed] [Google Scholar]
- 34.World Health Organization. International classification of functioning, disability and health (ICF). 2025. https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health
- 35.Valderas JM, Alonso J. Patient reported outcome measures: a model-based classification system for research and clinical practice. Qual Life Res. 2008;17(9):1125–35. doi: 10.1007/s11136-008-9396-4 [DOI] [PubMed] [Google Scholar]
- 36.Ahmed S, Visca R, Gogovor A, Eilayyan O, Finlayson R, Valois MF, et al. Implementation of an integrated primary care prevention and management program for chronic low back pain (LBP): Patient-reported outcomes and predictors of pain interference after six months. BMC health services research. 2024;24(1):611. doi: 10.1186/s12913-024-11031-x PubMed ; PubMed Central PMCID: PMCPMC11083802. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Rand Health Care. 12-Item Short Form Survey (SF-12) [cited 2022 February]. Available from: https://www.rand.org/health-care/surveys_tools/mos/12-item-short-form.html. [Google Scholar]
- 38.DeSalvo KB, Bloser N, Reynolds K, He J, Muntner P. Mortality prediction with a single general self-rated health question. A meta-analysis. J Gen Intern Med. 2006;21(3):267–75. doi: 10.1111/j.1525-1497.2005.00291.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Bowling A. Just one question: If one question works, why ask several?. J Epidemiol Community Health. 2005;59(5):342–5. doi: 10.1136/jech.2004.021204 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Cleeland CS. The brief pain inventory, user guide. Houston, Texas: Charles S. Cleeland. 2009. [Google Scholar]
- 41.Keller S, Bann CM, Dodd SL, Schein J, Mendoza TR, Cleeland CS. Validity of the brief pain inventory for use in documenting the outcomes of patients with noncancer pain. Clin J Pain. 2004;20(5):309–18. doi: 10.1097/00002508-200409000-00005 [DOI] [PubMed] [Google Scholar]
- 42.Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13. doi: 10.1046/j.1525-1497.2001.016009606.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Herrmann C. International experiences with the Hospital Anxiety and Depression Scale--a review of validation data and clinical results. J Psychosom Res. 1997;42(1):17–41. doi: 10.1016/s0022-3999(96)00216-4 [DOI] [PubMed] [Google Scholar]
- 44.Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr Scand. 1983;67(6):361–70. doi: 10.1111/j.1600-0447.1983.tb09716.x [DOI] [PubMed] [Google Scholar]
- 45.Woolrich RA, Kennedy P, Tasiemski T. A preliminary psychometric evaluation of the Hospital Anxiety and Depression Scale (HADS) in 963 people living with a spinal cord injury. Psychol Health Med. 2006;11(1):80–90. doi: 10.1080/13548500500294211 [DOI] [PubMed] [Google Scholar]
- 46.Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977;84(2):191–215. doi: 10.1037//0033-295x.84.2.191 [DOI] [PubMed] [Google Scholar]
- 47.Lorig K, Chastain RL, Ung E, Shoor S, Holman HR. Development and evaluation of a scale to measure perceived self-efficacy in people with arthritis. Arthritis Rheum. 1989;32(1):37–44. doi: 10.1002/anr.1780320107 [DOI] [PubMed] [Google Scholar]
- 48.Fairbank JC, Pynsent PB. The Oswestry Disability Index. Spine (Phila Pa 1976). 2000;25(22):2940–52; discussion 2952. doi: 10.1097/00007632-200011150-00017 [DOI] [PubMed] [Google Scholar]
- 49.SAS Institute Inc. SAS® 9.4 Software 2013. Available from: http://support.sas.com/software/94/index.html. [Google Scholar]
- 50.Muthén L, Muthén B. Mplus user’s guide. 8th ed. Muthén & Muthén. 2017. [Google Scholar]
- 51.Kline RB. Introduction to path analysis. 2 ed. New York: Guilford Press. 2005. [Google Scholar]
- 52.Ahn J. Beyond single equation regression analysis: path analysis and multi-stage regression analysis. Am J Pharm Educ. 2002;66(Spring):37–42. [Google Scholar]
- 53.Wang J, Wang X. Introduction to structural equation modeling. Structural equation modeling: applications using Mplus. 2nd ed. Hoboken, NJ: John Wiley & Sons Ltd. 2020. p. 1–32. [Google Scholar]
- 54.Kline R. Principles and practice of structural equation modeling. 5th ed. ed. Little T, editor. New York: The Guilford Press. 2016. [Google Scholar]
- 55.Kline RB. Introduction. 3 ed. New York: Guilford Press. 2011. [Google Scholar]
- 56.Fine PG. Long-term consequences of chronic pain: mounting evidence for pain as a neurological disease and parallels with other chronic disease states. Pain Med. 2011;12(7):996–1004. doi: 10.1111/j.1526-4637.2011.01187.x [DOI] [PubMed] [Google Scholar]
- 57.Patient-Reported Outcomes Measurement Information System. Domain Frameworks; PROMIS Adult Self-Reported Health: PROMIS.org; 2024. [cited 2022 January 14]. Available from: http://www.nihpromis.org/measures/domainframework1. [Google Scholar]
- 58.Bandura A. Self-efficacy. In: Ramachaudran V, editor. Encyclopedia of human behavior. New York: Academic Press. 1994. p. 71–81. [Google Scholar]
- 59.Bandura A. Social foundations of thought andaction: A socialcognitive theory: Prentice-Hall; 1986. [Google Scholar]
- 60.Bandura A, Cioffi D, Taylor CB, Brouillard ME. Perceived self-efficacy in coping with cognitive stressors and opioid activation. J Pers Soc Psychol. 1988;55(3):479–88. doi: 10.1037//0022-3514.55.3.479 [DOI] [PubMed] [Google Scholar]
- 61.Bandura A, Taylor CB, Williams SL, Mefford IN, Barchas JD. Catecholamine secretion as a function of perceived coping self-efficacy. J Consult Clin Psychol. 1985;53(3):406–14. doi: 10.1037//0022-006x.53.3.406 [DOI] [PubMed] [Google Scholar]
- 62.Basbaum AI, Fields HL. Endogenous pain control mechanisms: review and hypothesis. Ann Neurol. 1978;4(5):451–62. doi: 10.1002/ana.410040511 [DOI] [PubMed] [Google Scholar]
- 63.Gatchel RJ, Peng YB, Peters ML, Fuchs PN, Turk DC. The biopsychosocial approach to chronic pain: scientific advances and future directions. Psychol Bull. 2007;133(4):581–624. doi: 10.1037/0033-2909.133.4.581 [DOI] [PubMed] [Google Scholar]
- 64.Linton SJ, Shaw WS. Impact of psychological factors in the experience of pain. Phys Ther. 2011;91(5):700–11. doi: 10.2522/ptj.20100330 [DOI] [PubMed] [Google Scholar]
- 65.Greenhalgh J. The applications of PROs in clinical practice: what are they, do they work, and why?. Qual Life Res. 2009;18(1):115–23. doi: 10.1007/s11136-008-9430-6 [DOI] [PubMed] [Google Scholar]
- 66.Ahmed S, Zidarov D, Eilayyan O, Visca R. Prospective application of implementation science theories and frameworks to inform use of PROMs in routine clinical care within an integrated pain network. Qual Life Res. 2021;30(11):3035–47. doi: 10.1007/s11136-020-02600-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 67.Eilayyan O, Visca R, Zidarov D, Ware P, Bussières A, Ahmed S. Developing theory-informed knowledge translation strategies to facilitate the use of patient-reported outcome measures in interdisciplinary low back pain clinical practices in Quebec: mixed methods study. BMC Health Serv Res. 2020;20(1):789. doi: 10.1186/s12913-020-05616-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 68.Qaseem A, Wilt TJ, McLean RM, Forciea MA, Clinical Guidelines Committee of the American College of Physicians, Denberg TD, et al. Noninvasive Treatments for Acute, Subacute, and Chronic Low Back Pain: A Clinical Practice Guideline From the American College of Physicians. Ann Intern Med. 2017;166(7):514–30. doi: 10.7326/M16-2367 [DOI] [PubMed] [Google Scholar]
- 69.Kamper SJ, Apeldoorn AT, Chiarotto A, Smeets RJEM, Ostelo RWJG, Guzman J, et al. Multidisciplinary biopsychosocial rehabilitation for chronic low back pain: Cochrane systematic review and meta-analysis. BMJ. 2015;350:h444. doi: 10.1136/bmj.h444 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 70.Monticone M, Ambrosini E, Rocca B, Magni S, Brivio F, Ferrante S. A multidisciplinary rehabilitation programme improves disability, kinesiophobia and walking ability in subjects with chronic low back pain: results of a randomised controlled pilot study. Eur Spine J. 2014;23(10):2105–13. doi: 10.1007/s00586-014-3478-5 [DOI] [PubMed] [Google Scholar]
- 71.Hoefsmit N, Houkes I, Nijhuis FJN. Intervention characteristics that facilitate return to work after sickness absence: a systematic literature review. J Occup Rehabil. 2012;22(4):462–77. doi: 10.1007/s10926-012-9359-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 72.Kongsted A, Ris I, Kjaer P, Hartvigsen J. Self-management at the core of back pain care: 10 key points for clinicians. Braz J Phys Ther. 2021;25(4):396–406. doi: 10.1016/j.bjpt.2021.05.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 73.Yang J, Lo WLA, Zheng F, Cheng X, Yu Q, Wang C. Evaluation of Cognitive Behavioral Therapy on Improving Pain, Fear Avoidance, and Self-Efficacy in Patients with Chronic Low Back Pain: A Systematic Review and Meta-Analysis. Pain Res Manag. 2022;2022:4276175. doi: 10.1155/2022/4276175 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 74.Storheim K, Brox JI, Holm I, Bø K. Predictors of return to work in patients sick listed for sub-acute low back pain: a 12-month follow-up study. J Rehabil Med. 2005;37(6):365–71. doi: 10.1080/16501970510040344 [DOI] [PubMed] [Google Scholar]
- 75.Richard S, Dionne CE, Nouwen A. Self-efficacy and health locus of control: relationship to occupational disability among workers with back pain. J Occup Rehabil. 2011;21(3):421–30. doi: 10.1007/s10926-011-9285-5 [DOI] [PubMed] [Google Scholar]
- 76.Scholz C, Schmigalle P, Plessen CY, Liegl G, Vajkoczy P, Prasser F, et al. The effect of self-management techniques on relevant outcomes in chronic low back pain: A systematic review and meta-analysis. Eur J Pain. 2024;28(4):532–50. doi: 10.1002/ejp.2221 [DOI] [PubMed] [Google Scholar]
- 77.Du S, Hu L, Dong J, Xu G, Chen X, Jin S, et al. Self-management program for chronic low back pain: A systematic review and meta-analysis. Patient Educ Couns. 2017;100(1):37–49. doi: 10.1016/j.pec.2016.07.029 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
It summarizes the theoretical and statistical association between the studied variables and the proposed hypotheses.
(DOCX)
It shows the effects of the factors of the outcomes included in the SEM model.
(DOCX)
(DOCX)
Data Availability Statement
The reason for the restriction is patient confidentiality, as enforced by the McGill University Health Centre. Only the research team and ethics boards related to the study have access to participants’ identifiable information. We are providing a non-author contact for data requests. Dr. Benjamin Smith. McGill University Health Centre benjamin.m.smith@mcgill.ca (514) 934-1934 ext. 32235.