INTRODUCTION

Non-specific low back pain (NSLBP) is a prevalent health issue that significantly impacts disability, absences from work, and health service utilization [1]. NSLBP is defined as low back pain (LBP) not caused by a distinct, well-established illness, such as a tumor, infection, osteoporosis, fracture, structural abnormality, inflammatory disease, radicular syndrome, or cauda equina syndrome [2]. Characterized by pain not attributable to identifiable structural or pathological conditions, NSLBP may present acutely but often progresses to a chronic state, leading to prolonged disability and diminished quality of life [3]. The transition from acute to chronic pain is particularly concerning, as chronic LBP is associated with complex biopsychosocial factors, including physical, psychological, and environmental influences [4].

Understanding the factors that predict the progression from acute to chronic NSLBP is critical for effective clinical management and intervention strategies [5]. Recent studies have highlighted a range of potential prognostic indicators, including age, comorbidities, psychological factors (such as anxiety and depression), lifestyle factors, and pain severity [6]. The relationship between sleep disturbances and LBP has been well documented, with studies showing that poor sleep quality and lower sleep efficiency may increase next-day pain intensity; furthermore, depression can weaken patients’ ability to cope with LBP through negatively biased attitudes and perceptions, potentially influenced by common genetic factors [7,8]. Stress modulates the pain system through the hypothalamic-pituitaryadrenal axis, with chronic stress leading to cortisol dysfunction, resulting in inflammatory response irregularities and tissue degeneration that can manifest as chronic pain [9]. Nevertheless, comprehensive tools that accurately stratify patients based on their risk of chronicity are still required to enable targeted therapeutic approaches.

The development of a prognostic score for acute NSLBP is essential for guiding clinicians in their treatment decisions by providing early identification of patients at heightened risk of chronicity. This scoring system could facilitate customized management strategies, allowing healthcare providers to implement timely interventions that address both the physical and psychological aspects of pain [1].

In this study, we aimed to identify significant prognostic factors associated with the transition from acute to chronic NSLBP and develop a robust scoring system to effectively predict outcomes. By focusing on the interplay of various clinical and psychosocial variables, we hope to contribute valuable insights to the existing literature while providing a clinically applicable framework for managing NSLBP. Ultimately, our goal is to improve patient outcomes by equipping healthcare professionals with a comprehensive understanding of the risk factors influencing the progression of LBP.

METHODS

Study Design and Setting

This prospective cohort study was conducted at 3 primary health care units in Surabaya, Indonesia, from April 2024 to August 2024. Participants were recruited during routine visits. Preliminary outreach was made to 151 individuals, with those meeting the inclusion criteria invited to enroll. Of the 151 initially contacted, 35 could not be reached and 4 did not complete the follow-up period, yielding a final sample of 112 participants.

The participants were adults aged 21 years to 55 years presenting with acute NSLBP (with or without leg pain and lasting less than 6 weeks), defined as pain confined to the area between the lower margin of the 12th rib and the gluteal folds. Pain intensity was measured using the Numerical Rating Scale (NRS), with a minimum score of 0 out of 10. Participants were required to be able to read and understand the local language and to provide written informed consent. The exclusion criteria encompassed certain spinal abnormalities (including vertebral fractures, spinal tumors, infections, inflammatory disorders, and cauda equina syndrome), spinal surgery within the prior 12 months, significant psychiatric disorders requiring current treatment, pregnancy, and other comorbid conditions (including fibromyalgia, rheumatoid arthritis, and diabetic neuropathy) that could confound the results [1].

Data Collection

The primary outcome was the transition from acute to chronic LBP, defined as persistent pain lasting more than 3 months from the initial assessment. At the follow-up visits, pain intensity was measured using the NRS, and chronic LBP was considered present if the participant reported a pain intensity of at least 1. Several predictor variables were also analyzed. The questionnaire for the prognostic scoring model for the transition from acute to chronic NSLBP in primary health care units consisted of 4 sections (Supplemental Material 1).

Section 1

This section collected demographic characteristics including age (in years), sex (male or female), education level (categorized as low [elementary education, junior secondary education, or senior secondary education] or high [university education, consisting of the diploma, undergraduate, and postgraduate levels]), work status (not working or actively working), marital status (single, divorced, or married), body mass index (in kg/m 2 ), smoking history (yes or no), previous pain treatment (yes or no), history of regular heavy lifting (yes or no), and relevant comorbidities, such as hypertension and diabetes, as assessed by self-report.

Section 2

The NRS consists of a horizontal line with an 11-point numerical scale ranging from 0 to 10. This tool, which can be administered verbally, categorizes pain as follows: 0 for no pain, 1-3 for mild pain, 4-6 for moderate pain, 7-9 for severe pain, and 10 for the worst pain imaginable [10].

Section 3

The Depression, Anxiety, and Stress Scale-21 (DASS-21), a self-report instrument developed by Lovibond and Lovibond [11], was used to measure the psychological factors of anxiety, depression, and stress. This abbreviated version of the original 42-item scale evaluates specific symptoms. The depression subscale assesses dysphoria, hopelessness, worthlessness, and lack of interest; the anxiety subscale measures somatic symptoms, situational anxiety, and subjective experiences of anxiety; and the stress subscale examines persistent arousal and tension through symptoms such as difficulty relaxing, agitation, anger, and impatience. The Indonesian version of the DASS-21 has been validated for use in Indonesia [12]. Each subscale score is multiplied by 2 before interpretation, with specific scoring ranges for depression (normal: 0-9, mild: 10-13, moderate: 14-20, severe: 23-27, extremely severe: ≥28), anxiety (normal: 0-7, mild: 8-9, moderate: 10-14, severe: 15-19, extremely severe: ≥20), and stress (normal: 0-14, mild: 15-18, moderate: 19-25, severe: 26-33, extremely severe: ≥34).

Section 4

Overall sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), employing the standardized Indonesian version [13]. The PSQI includes 19 questions designed to evaluate 7 components of sleep quality over the past month: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. Each component is scored from 0 (excellent) to 3 (poor), resulting in a total score ranging from 0 to 21. An overall score greater than 5 indicates poor sleep quality.

The questionnaires took approximately 45 minutes to complete. Follow-up assessments were conducted at 3 months to assess the primary outcome of transition to chronic NSLBP, defined as the persistence of pain for at least 12 weeks after the onset of symptoms.

Study Size

The minimum sample size for this study was determined by considering factors that influence the validity and reliability of the results. To investigate the transition of LBP from acute to chronic, we established a minimum sample size using statistical formulas that account for a 95% confidence level and a 5% margin of error. The sample size was calculated using the following formula [14]:

n=(Z1−α22P(1−P)+Z1−βP1(1−P1)+P2(1−P2))2(P1−P2).

Based on this calculation, a minimum sample size of 110 respondents was proposed to ensure that the analyses accurately reflected broader population conditions. This sample size also accounts for potential participant loss during the study process, thereby maintaining data integrity. Establishing a sufficient minimum sample size improves our capacity to detect significant predictors and obtain reliable findings regarding the dynamics of LBP transition.

Statistical Analysis

Data were analyzed using SPSS version 24.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were computed for demographic and clinical characteristics. Bivariate analysis was performed using chi-square tests to assess the association between prognostic variables and the outcome, with odds ratios (ORs) and 95% confidence intervals (CIs) calculated. Variables with p-values of less than 0.25 in the bivariate analysis were included in the subsequent multivariable analysis. The backward stepwise method in logistic regression was used to identify the most significant prognostic factors. The quality of the final model was evaluated using calibration (Hosmer-Lemeshow test) and discrimination (area under the receiver operating characteristic curve [AUC]). Finally, a prognostic scoring card was developed to predict the probability of a poor prognosis, and a cut-off point was determined to classify participants as high-risk or low-risk.

Ethics Statement

Ethical approval for this study was obtained from the Health Research Ethics Committee, Faculty of Nursing, Universitas Airlangga (No. 3174-KEPK). All participants received oral and written information about the study and provided written informed consent before participation.

RESULTS

Participant Characteristics

This study enrolled 112 participants, all of whom completed the follow-up assessments at 3 months. Table 1 summarizes their baseline characteristics. The participants had a mean age of 38.02±9.61 years, and 51.8% were female. Most participants had a low education level: 0.9% had not completed primary education, 8.0% had completed elementary or junior high school, and 45.5% had completed senior high school as their highest level of educational attainment. Of the study sample, 50.9% were employed. Body mass index (BMI) data included classifications of underweight in 8.0% of respondents, overweight in 17.8%, and obesity in 35.7%, collectively termed “abnormal” BMI. The most frequently reported pain scores were 1 (25.0%), 5 (16.1%), 3 (13.4%), 4 (8.9%), and 2 (4.5%). Regarding pain treatment history, 23.2% used medication only, 3.6% underwent physiotherapy, and 2.7% received a combination of medication and physiotherapy.

Analysis of Clinical Factors

Bivariate analysis identified 11 significant risk factors for transition to chronic NSLBP. These were age above 30 years (OR, 0.42; 95% CI, 0.18 to 0.97), education level (OR, 1.25; 95% CI, 0.88 to 1.75), work status (OR, 1.71; 95% CI, 0.68 to 4.27), BMI (OR, 1.37; 95% CI, 1.03 to 1.83), hypertension (OR, 2.09; 95% CI, 0.87 to 5.00), pain score (OR, 1.97; 95% CI, 1.14 to 3.35), pain intensity (OR, 1.50; 95% CI, 1.11 to 2.04), history of heavy lifting (OR, 1.58; 95% CI, 0.91 to 2.74), previous pain treatment (OR, 0.86; 95% CI, 0.66 to 1.10), depression (OR, 2.33; 95% CI, 0.72 to 7.51), and anxiety (OR, 2.00; 95% CI, 0.76 to 5.23). Table 2 presents the overall results of the bivariate analysis.

Multivariable logistic regression was then used to analyze the variables that were significant in the bivariate analysis. This process yielded 3 statistically significant risk factors for transition to chronic NSLBP: age, education level, and pain intensity. Table 3 displays the results of the multivariable analysis.

Scoring System

A series of steps was undertaken to construct the prognostic model. First, a bivariate analysis was conducted to identify potential variables associated with the transition from acute to chronic LBP. Of the variables tested, 3 were found to be significant in multivariate logistic analysis: age, education level, and pain intensity. The model validation process involved calculating a prognostic score based on the B value and standard error from the logistic regression analysis (Supplemental Material 2). Each variable was assigned a weighted score according to its contribution. Validation using receiver operating characteristic curves yielded an AUC of 0.705, indicating good discrimination ability (Figure 1). The optimal cut-off point was set at a score of 2.5, with a sensitivity of 70.8% and specificity of 62.5%. Patients with a score of 3 or higher were categorized as being at high-risk of progressing to chronic pain (Supplemental Material 3). The model represents a simple tool for risk stratification in primary healthcare, enabling early intervention for high-risk patients.

DISCUSSION

The transition from acute to chronic NSLBP is a concern in clinical practice, posing challenges for both patients and healthcare providers. We aimed to develop a prognostic scoring card to predict this transition. Our study identified 3 significant predictors: age, education level, and pain intensity. These factors, which play key roles in the progression to chronic NSLBP, collectively form the basis for a practical tool for clinicians to assess the risk of chronic NSLBP in acute cases.

The importance of age as a predictor aligns with the existing literature on chronic pain conditions [15,16]. Age-related factors contribute to the increased risk of transitioning from acute to chronic NSLBP among older individuals. These factors include higher pain sensitivity, lower pain thresholds, and decreased brain activity regulating endogenous pain [17,18]. Comorbidities also significantly increase the likelihood of persistent LBP in older adults. Physical and psychosocial changes associated with aging, such as reduced fitness and altered life goals, can influence the capacity to manage pain [19]. Neurophysiological changes, including modifications in pain perception, central pain processing, and neuroplasticity, may result in heightened pain sensitivity and prolonged hyperalgesia [20,21]. Additionally, age-related changes in brain regions involved in pain processing may reduce descending pain inhibition [22,23]. Collectively, these factors contribute to the increased likelihood of older adults developing chronic LBP.

While some prior studies have not identified education as a predictor of back pain outcomes, in our research, a lower education level was significantly associated with an increased likelihood of transitioning from acute to chronic LBP. Several factors may explain this link. First, individuals with lower educational attainment often demonstrate reduced health literacy, particularly in the areas of prevention and health promotion, which may hinder effective acute pain management [24]. Second, jobs with higher physical demands, which are more common among those with less education, may increase the risk of chronic pain [25]. Lastly, individuals with higher education levels generally acquire new skills more readily and have better access to social and medical services, promoting healthier lifestyles that may mitigate the development of chronic pain [26]. Notably, education may also serve as a marker for other unmeasured prognostic characteristics, underscoring the complex relationship between educational attainment and chronic pain development.

Using the NRS as a measure of pain intensity, our research identified greater intensity as a significant predictor of the transition from acute to chronic NSLBP. Intense pain often leads to increased fear-avoidance behaviors, where individuals limit their movements and activities to prevent further pain, inadvertently contributing to muscle deconditioning and prolonging recovery [27]. Higher pain levels may trigger more pronounced central sensitization, a process in which the central nervous system becomes hypersensitive to pain signals, perpetuating the experience of pain even after the initial tissue damage has healed [28]. Severe pain can also lead to abnormal biomechanics and movement patterns, potentially resulting in compensatory behaviors that exacerbate LBP and lead to additional musculoskeletal problems [29].

The prognostic scoring card developed based on these factors has several clinical implications (Table 4). First, it represents a quick and easy-to-use tool for risk stratification in primary care settings, allowing high-risk patients to receive faster access to more intensive interventions or specialist referrals. Second, it supports informed shared decision-making between clinicians and patients regarding treatment options and the potential need for closer follow-up. However, this study has limitations that warrant consideration. The specific demographic and geographic characteristics of our sample may limit the generalizability of these findings. Therefore, external validation in diverse populations is necessary to confirm the broader applicability of this model. Future research should focus on validating the prognostic scoring card in different clinical settings and exploring additional factors that may improve its predictive accuracy. Furthermore, interventional studies targeting the identified risk factors could provide valuable insights into preventing the transition to chronic NSLBP.

Supplemental Materials

Notes

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

This research is funded by the Institut Teknologi Sepuluh Nopember under the scientific research scheme No. 1178/PKS/ITS/2024.

Acknowledgements

None.

Author Contributions

Conceptualization: Kuswanto D, Radiansyah RS, Rinaldi AS. Data curation: Eljatin DS, Haykal MN, Karimah R. Formal analysis: Indriani RD, Syulthoni ZB, Furaidah E. Funding acquisition: Kuswanto D, Radiansyah RS. Methodology: Radiansyah RS, Eljatin DS, Haykal MN, Wirayuda AAB. Project administration: Kuswanto D, Rinaldi AS. Visualization: Putri HN, Adi JF. Writing – original draft: Kuswanto D, Radiansyah RS, Eljatin DS, Haykal MN, Karimah R, Indriani RD, Rinaldi AS. Writing – review & editing: Syulthoni ZB, Furaidah E, Putri HN, Adi JF, Wirayuda AAB.

Figure. 1.

ROC analysis of the prognostic score of transition from acute to chronic NSLBP. AUC: 0.705, best cut-off point: 2.5, sensitivity: 70.8%, and specificity: 62.5%. ROC, receiver operating characteristic; NSLBP, non-specific low back pain; AUC, area under the receiver operating characteristic curve.

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Figure & Data

References

Citations

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