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World J Psychiatry. Oct 19, 2025; 15(10): 108009
Published online Oct 19, 2025. doi: 10.5498/wjp.v15.i10.108009
Neck pain and emotional state in cervical spondylosis: A dual trajectory model analysis
Jia-Qi Yang, Department of Basic Medicine, Chengde Medical University, Chengde 067000, Hebei Province, China
Jie Wu, Jian-En Guo, Zhi-Xin Yang, Jin-Ying Liu, Yu-Man Wang, Yin-Juan Zhang, Department of Traditional Chinese Medicine, Chengde Medical University, Chengde 067000, Hebei Province, China
ORCID number: Jia-Qi Yang (0009-0008-7067-1543); Yin-Juan Zhang (0009-0006-2429-6223).
Author contributions: Zhang YJ designed the study; Yang JQ analyzed the data and wrote the manuscript; Wu J and Guo JE provided access to patient records and resources; Yang ZX reviewed and revised the manuscript; Liu JY and Wang YM collected the data. All authors have read and approve the final manuscript.
Supported by 2022 Chinese Medicine Scientific Research Project of Hebei Administration of Traditional Chinese Medicine, No. 2022157; and 2025 Annual Scientific Research Project of Higher Education Institutions in Hebei Province, No. QN2025654.
Institutional review board statement: The Ethics Committee of Chengde Medical College approved this study (approval number: 2021018).
Informed consent statement: All study participants, or their legal guardians, provided written consent prior to study enrollment.
Conflict-of-interest statement: There are no any conflicts of interest.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: There are no data sharing statement.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yin-Juan Zhang, Lecturer, Department of Traditional Chinese Medicine, Chengde Medical University, Anyuan Road, Chengde 067000, Hebei Province, China. zhang_1986@cdmc.edu.cn
Received: May 14, 2025
Revised: June 17, 2025
Accepted: July 30, 2025
Published online: October 19, 2025
Processing time: 135 Days and 0.2 Hours

Abstract
BACKGROUND

Neck pain, a primary symptom of cervical spondylosis, affects patients’ physical and mental health, reducing their quality of life. Pain and emotional state interact; however, their longitudinal interrelationship remains unclear. In this study, we applied a dual-trajectory model to assess how neck pain and emotional state evolve together over time and how clinical interventions, particularly acupuncture, influence these trajectories.

AIM

To investigate the longitudinal relationship between neck pain and emotional state in patients with cervical spondylosis.

METHODS

This prospective cohort study included 472 patients with cervical spondylosis from eight Chinese hospitals. Participants received acupuncture or medication and were followed up at baseline, and at 1, 2, 4, 6, and 8 weeks. Neck pain and emotional distress were assessed using the Northwick Park Neck Pain Questionnaire (NPQ) and the affective subscale of the Short-Form McGill Pain Questionnaire (SF-MPQ), respectively. Group-based trajectory models and dual trajectory analysis were used to identify and correlate pain-emotion trajectories. Multivariate logistic regression identified predictors of group membership.

RESULTS

Three trajectory groups were identified for NPQ and SF-MPQ scores (low, medium, and high). Higher NPQ trajectory was associated with older age (OR = 1.058, P < 0.001) and was significantly reduced by acupuncture (OR = 0.382, P < 0.001). Similarly, acupuncture lowered the odds of high SF-MPQ trajectory membership (OR = 0.336, P < 0.001), while age increased it (OR = 1.037, P < 0.001). Dual-trajectory analysis revealed bidirectional associations: 69.1% of patients with low NPQ had low SF-MPQ scores, and 42.6% of patients with high SF-MPQ also had high NPQ scores. Gender was a predictor for medium SF-MPQ trajectory (OR = 1.629, P = 0.094). Occupation and education levels differed significantly across the trajectory groups (P < 0.05).

CONCLUSION

Over time, neck pain and emotional distress are closely associated in patients with cervical spondylosis. Acupuncture alleviates both outcomes significantly, while age is a risk factor. Integrated approaches to pain and emotional management are encouraged.

Key Words: Cervical spondylosis; Neck pain; Emotional state; Group-based trajectory model; Dual trajectory analysis

Core Tip: This study employed group-based dual trajectory modeling to analyze longitudinal correlations between neck pain (Neck Pain Questionnaire scores) and emotional state (Short-Form McGill Pain Questionnaire affective scores) in patients with cervical spondylosis. Key findings indicate that acupuncture significantly reduces high pain and emotional distress trajectories, whereas aging increases the risk. Dual trajectory analysis revealed bidirectional associations: 69.1% of patients with low pain had emotional distress, whereas 42.6% of patients with high distress had pain. Integrated pain-emotion management strategies are recommended.



INTRODUCTION

Neck pain is a primary clinical manifestation of cervical spondylosis. Severe pain has a great impact[1] on patients’ quality of life and physical and mental health, and causes a heavy disease burden. The economic burden of neck pain is significant, encompassing treatment costs, productivity losses, and job-related issues. In 2016, low back and neck pain topped United States healthcare spending among 154 conditions, with an estimated $134.5 billion. Globally in 2017, neck pain had age-standardized prevalence and incidence rates of 3551.1 and 806.6 per 100000, respectively[2]. Accurate and comprehensive pain assessment tools are important in clinical practice to evaluate the nature and degree of neck pain and guide the formulation of personalized treatment plans. The most commonly used pain assessment tools are the Neck Pain Questionnaire (NPQ) and the Short-Form McGill Pain Questionnaire (SF-MPQ), and both are valuable in assessing the intensity, form, and impact of pain in patients with neck pain. NPQ is primarily used to assess the dysfunction caused by neck pain, and is simple to use and suitable for routine clinical practice[3]. SF-MPQ can better assess the texture of pain through a multidimensional description, particularly in identifying the emotional dimension of pain. The detailed information provided by SF-MPQ is helpful for accurate diagnosis and treatment[4]. Recently, dual trajectory analysis, a statistical method for analyzing bivariate trajectories in longitudinal data, has attracted extensive attention from clinical researchers. The use of dual trajectory analysis can significantly improve the understanding[5] of patient characteristics over time. Therefore, we aimed to investigate the longitudinal relationship between neck pain and emotional state in patients with cervical spondylosis. This study adopted a real-world research design to collect longitudinal data of the NPQ neck pain scores and the SF-MPQ emotional item scores of patients with cervical spondylosis in a real clinical environment and applied a group-based model dual trajectory analysis (GBMDTA). The GBMDTA method combines NPQ and SF-MPQ affective item scores to describe the correlation between neck pain and emotional state to improve the accuracy and reliability of pain assessment, guide the formulation of clinically personalized treatment plans, and provide a theoretical basis for reducing the burden of cervical spondylosis and neck pain.

MATERIALS AND METHODS
Data sources

In this prospective cohort study, we investigated patients with confirmed cervical spondylosis who voluntarily accepted acupuncture treatment from December 2021 to September 2022 in eight hospitals: Chengde Hospital of Traditional Chinese Medicine; Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine; Handan Hospital of Traditional Chinese Medicine; Baoding First Central Hospital; Qinhuangdao First Hospital; Hengshui People's Hospital; Qian 'an Hospital of Traditional Chinese Medicine; and Xingtai People's Hospital. Patients’ baseline demographic characteristics (including age, gender, occupation, and education level), the type of treatment received, and neck pain scores were collected. The patients were followed up at the beginning of treatment and 1, 2, 4, 6, and 8 weeks. The inclusion criteria were: (1) Patients diagnosed with cervical spondylosis according to the diagnostic criteria in the Expert Consensus on the Classification, Diagnosis, and Non-surgical Treatment of Cervical Spondylosis (2018)[6]; (2) Neck pain as the chief complaint; (3) Aged 14-75 years, conscious and able to communicate normally; and (4) Oral or written informed consent from patients or their legal guardians. We excluded: (1) Patients not suitable to participate due to serious systemic diseases; (2) Those unable to complete multiple surveys due to personal factors or other reasons; and (3) Those unwilling to participate in the study. The exposure status was determined by whether or not acupuncture treatment was received, with the acupuncture treatment group designated as the exposed group and those who did not receive acupuncture treatment as the non-exposed group. Acupuncture treatment protocol: Physicians selected acupoints such as Fengchi, Wangu, Tianzhu, Baihui, Sishencong, cervical Jiaji points, and Ashi points for acupuncture treatment with reference to the Standards for Acupoint Names and Locations (GB/T 12346-2021). The number of acupuncture treatments was 5 times or more. Seventeen individuals with data missing due to loss to follow-up were excluded, we included 472 participants in the final data analysis. To minimize bias in the survey process, measures such as investigator training, a combination of online and offline surveys, and dedicated personnel responsible for data verification were adopted. The Ethics Committee of Chengde Medical College approved this study (approval number: 2021018). Table 1 for variables assignment.

Table 1 Variable assignment.
Variables
Assignment
Gender1 = male, 2 = female
Level of education1 = elementary school or below, 2 = junior high school, 3 = high school, 4 = college degree, 5 = bachelor's degree or above
Occupation1 = farmer, 2 = worker, 3 = student, 4 = company clerk, 5 = others
Treatment options1 = acupuncture treatment, 0 = medication
Observation indicators

(1) NPQ score: The evaluation items included the degree of neck pain, interference of neck pain with sleep, degree of arm numbness at night, daily duration of symptoms, and effects of neck pain on carrying objects, reading, work or housework, driving, and social activities. The total score ranged from 0 to 100, with higher scores indicating more severe neck pain and a greater impact on the comprehensive quality of life; and (2) Emotional items of the SF-MPQ: The evaluation items included tiring-exhausting, fearful, sickening, and punishing. The total score ranged from 0 to 12, with higher scores indicating more severe negative emotions and a worse emotional state.

Statistical analysis

Statistical analysis was performed using SPSS 25.0 and Stata 17 software. Quantitative data are presented as mean and standard deviation, and the differences between groups were compared by analysis of variance. Qualitative data are presented as constituent ratios, and the differences between groups were compared using the χ2 test. Multivariate logistic regression was used for multivariate analysis. The traj package of Stata 17 was used to fit the group-based model and the two-trajectory model. First, the group-based trajectory model of the two indicators was constructed separately, and the parameters of the two group-based trajectory models[7-9] were obtained and inputted in the two-trajectory model as the initial values for fitting. And Missing values were handled using the maximum likelihood estimation method in the model fitting process. P < 0.05 was considered statistically significant.

RESULTS
Fitting the basic trajectory model of the single index group

The basic trajectory models of NPQ and SF-MPQ emotion item scores were constructed. The results of the screening indexes of the NPQ scoring model showed that the cubic curve model of the three groups had the best fitting effect. Entropy was highest when Bayesian Information Criterion (BIC) (n = 2832), BIC (n = 472), Akaike Information Criterion (AIC) and log-likelihood (LL) were small, and all parameter estimates were significant (P < 0.05). Table 2 presents the results of the NPQ scoring model screening indicators.

Table 2 Screening indicators of Neck Pain Questionnaire score trajectory model.
Model
BIC (n = 2832)
BIC (n = 472)
AIC
LL
Entropy
Linear
    1 set-12401.78-12399.09-12392.86-12389.86-
    2 sets-11669.82-11664.44-11651.97-11645.970.89
    3 sets-11355.83-11347.77-11329.06-11320.060.90
    4 sets-11226.21-11215.46-11190.52-11178.520.89
Conic curve
    1 set-12366.22-12362.63-12354.32-12350.32-
    2 sets-11590.01-11582.84-11566.21-11558.210.90
    3 sets-11240.17-11229.42-11204.48-11192.480.92
    4 sets-11088.25-11073.92-11040.66-11024.660.90
Cubic curve
    1 set-12303.67-12299.19-12299.19-12283.8-
    2 sets-11435.43-11426.47-11405.69-11395.690.91
    3 sets-10999.25-10985.81-10954.63-10939.630.93
    4 sets-10790.52-10772.6-10731.03-10711.030.92

The fitting results of the cubic curve of the NPQ score trajectory of the three groups showed that the change in NPQ score trajectory over time was divided into three groups, namely, low-, middle-, and high-level groups. The initial level of NPQ (intercept) in the low-, middle-, and high-level groups was 22.170, 45.211, and 69.448, respectively. The middle-level group accounted for the highest proportion (approximately 44.5%). Table 3 presents the trajectory parameter estimation results of the cubic curve NPQ score in the three groups, and Figure 1 shows the trajectory morphology. There were significant differences in age, treatment plan, occupation and educational level among the three groups (P < 0.05) (Table 4).

Figure 1
Figure 1 Neck Pain Questionnaire score trajectory of patients with cervical spondylosis. The distribution of the characteristics of each trajectory group of neck pain questionnaire scores showed differences in age, treatment plan, occupation, and education level distribution among the low-, medium-, and high-level groups (P < 0.05). NPQ: Neck Pain Questionnaire.
Table 3 Results of the Neck Pain Questionnaire score trajectory parameter estimation.
Groups
Parameters
Estimated value
Standard error
T score
P value
LowIntercept22.170 0.752 29.462 < 0.001
Linear-5.624 0.666 -8.447 < 0.001
Quadratic curve1.104 0.140 7.861 < 0.001
Cubic curve-0.052 0.008 -6.798 < 0.001
MediumIntercept45.211 0.681 66.393 < 0.001
Linear-10.425 0.578 -18.046 < 0.001
Quadratic curve2.138 0.122 17.582 < 0.001
Cubic curve-0.106 0.007 -15.839 < 0.001
HighIntercept69.448 0.919 75.597 < 0.001
Linear-14.400 0.816 -17.654 < 0.001
Quadratic curve2.986 0.172 17.392 < 0.001
Cubic curve-0.148 0.009 -15.676 < 0.001
Group members (%)
Low 33.2502.309 14.397 < 0.001
Medium 44.5272.436 18.276 < 0.001
High 22.2232.002 11.102 < 0.001
Table 4 Distribution of characteristics in each trajectory group of Neck Pain Questionnaire score.
Variables
Value
Group (%)
F/χ2
P value
Low
Medium
High
Age34.23 ± 15.5341.02 ± 13.9143.68 ± 14.8815.592< 0.001
Treatment options073 (46.20)109 (52.66)69 (64.49)8.6050.014
185 (53.80)98 (47.34)38 (35.51)
Gender278 (49.37)108 (52.17)51 (47.66)0.6420.725
180 (50.63)99 (47.83)56 (52.34)
Career114 (8.86)47 (22.71)35 (32.71)55.274< 0.001
214 (8.86)27 (13.04)15 (14.02)
363 (39.87)31 (14.98)13 (12.15)
425 (15.82)28 (13.53)13 (12.15)
542 (26.58)74 (35.75)31 (28.97)
Level of education17 (4.43)14 (6.76)7 (6.54)23.3610.003
230 (18.99)56 (27.05)31 (28.97)
321 (13.29)44 (21.26)27 (25.23)
48 (5.06)11 (5.31)9 (8.41)
592 (58.23)82 (39.61)33 (30.84)

Multivariate logistic regression revealed that the factors influencing trajectory grouping of the NPQ scores were treatment regimen and age (P < 0.05). Acupuncture treatment regimen (assigned to 1) reduced the probability of individuals entering the high-level group (OR = 0.382). The probability of individuals entering the middle- and high-level groups increased with increasing age (OR = 1.034 and 1.058, respectively) (Table 5).

Table 5 Factors influencing Neck Pain Questionnaire score trajectory grouping.
Medium-level group
Regression coefficients
SE
z score
Wald χ2
P value
OR
OR with 95%CI
Treatment options-0.4140.220-1.8793.5300.0600.6610.429-1.018
Age0.0330.0113.0409.2420.0021.0341.012-1.057
Career-0.0280.084-0.3290.1080.7420.9730.824-1.148
Level of education-0.0190.121-0.1560.0240.8760.9810.774-1.244
Intercept-0.6130.746-0.8210.6740.4120.5420.125-2.340
High-level group
Treatment options-0.9610.273-3.52512.427< 0.0010.3820.224-0.653
Age0.0560.0144.01516.12< 0.0011.0581.029-1.087
Career-0.1870.101-1.8613.4630.0630.8290.681-1.010
Degree of education0.1500.1590.9430.8880.3461.1610.851-1.586
Intercept-2.0840.977-2.1324.5450.0330.1240.018-0.845

The results of the screening indicators of the SF-MPQ affective item scoring model showed that the cubic curve model of the three groups had the best fitting effect, and entropy was highest when BIC (n = 2832), BIC (n = 472), AIC, and LL were small. All parameter estimates were significant (P < 0.05). Table 6 presents the screening index results of the SF-MPQ affective item scoring model, and Figure 2 shows the trajectory morphology.

Figure 2
Figure 2 Trajectory of the Short-Form McGill Pain Questionnaire affective item score in patients with cervical spondylosis. The distribution results of the characteristics of each trajectory group of the Short-Form McGill Pain Questionnaire affective item score showed differences in age, treatment plan, and occupation distribution among the low-, medium-, and high-level groups (P < 0.05).
Table 6 Evaluation indicators for fitting the trajectory model of the Short-Form McGill Pain Questionnaire affective item score.
Model
BIC (n = 2832)
BIC (n = 472)
AIC
LL
Entropy
Linear
    1 set-6029.91-6027.22-6020.98-6017.98-
    2 sets-5384.25 -5378.87 -5366.40-5360.40 0.87
    3 sets-5000.66-4992.60-4973.90-4964.900.92
    4 sets-4858.84-4848.09-4823.14-4811.140.88
Conic curve
    1 set-5986.74-5983.16-5974.85-5970.85-
    2 sets-5293.68-5286.51-5269.89-5261.890.88
    3 sets-4859.95-4849.20-4824.26-4812.260.93
    4 sets-4630.62-4616.29-4583.03-4567.030.99
Cubic curve
    1 set-5955.26-5950.78-5940.38-5935.38-
    2 sets-5225.70-5216.74-5195.96-5185.960.89
    3 sets-4750.45-4737.01-4705.83-4690.830.93
    4 sets-4521.59-4503.67-4462.10-4442.100.98

The fitting results of the cubic curve of SF-MPQ affective item score trajectory showed that the change in the trajectory over time was divided into three groups, namely low-, medium-, and high-level groups. The McGill onset level (intercept) was 1.238, 4.609, and 6.764 in the low-, medium-, and high-level groups. The middle-level group accounted for the highest proportion (approximately 52.2%). Table 7 shows the parameter estimation results of the cubic curve of SF-MPQ emotion item score trajectory in the three groups. There were significant differences in age, treatment plan and occupation among the three groups (P < 0.05) (Table 8).

Table 7 Parameter estimation results of Short-Form McGill Pain Questionnaire affective item score trajectory.
Groups
Parameters
Estimated value
SE
T value
P value
LowIntercept1.238 0.11211.017 < 0.001
Linear-1.1930.104 -11.437 < 0.001
Quadratic curve0.209 0.022 9.603 < 0.001
Cubic curve-0.010 0.001 -8.132 < 0.001
MediumIntercept4.069 0.089 45.869 < 0.001
Linear-1.212 0.069 -17.593 < 0.001
Quadratic curve0.210 0.014 14.549 < 0.001
Cubic curve-0.010 0.001 -12.287 < 0.001
HighIntercept6.764 0.137 49.355 < 0.001
Linear-0.846 0.121 -6.962 < 0.001
Quadratic curve0.151 0.026 5.916 < 0.001
Cubic curve-0.0070.001 -5.102 < 0.001
Group members (%)
Low 31.4282.536 12.408 < 0.001
Medium 52.1612.628 19.838 < 0.001
High 16.4111.779 9.224 < 0.001
Table 8 Distribution of characteristics in each trajectory group of the Short-Form McGill Pain Questionnaire affective item score.
Variables
Value
Group (%)
F/χ2
P value
Low
Medium
High
Age35.53 ± 15.8140.88 ± 14.9841.56 ± 13.116.8840.001
Treatment options063 (43.45)137 (54.80)51 (66.23)11.0480.004
182 (56.55)113 (45.20)26 (33.77)
Gender282 (56.55)120 (48.00)35 (45.45)3.5180.172
163 (43.45)130 (52.00)42 (54.55)
Career116 (11.03)60 (24.00)20 (25.97)38.186< 0.001
215 (10.34)34 (13.60)7 (9.09)
355 (37.93)43 (17.20)9 (11.69)
424 (16.55)32 (12.80)10 (12.99)
535 (24.14)81 (32.40)31 (40.26)
Level of education15 (3.45)20 (8.00)3 (3.90)10.7470.216
233 (22.76)63 (25.20)21 (27.27)
325 (17.24)53 (21.20)14 (18.18)
46 (4.14)15 (6.00)7 (9.09)
576 (52.41)99 (39.60)32 (41.56)

Multiple logistic regression with the low-level group as the reference showed that the factors influencing the grouping of the SF-MPQ emotional item score trajectory were treatment plan, gender, and age (P < 0.05). A treatment plan comprising acupuncture and moxibustion (assigned as 1) reduced the probability of individuals entering the middle- and high-level groups (OR = 0.570 and 0.336, respectively). In addition, the probability of individuals entering the middle- and high-level groups increased with age (OR = 1.029 and 1.037, respectively). Females had a higher probability of entering the middle-level group (OR = 1.629) than males (Table 9).

Table 9 Factors influencing the grouping of the Short-Form McGill Pain Questionnaire affective item score trajectories.
Medium level group
Regression coefficients
SE
z score
Wald χ2
P value
OR
OR with 95%CI
Treatment options-0.5620.218-2.5846.6750.0100.5700.372-0.873
Gender0.3680.2191.6762.8080.0941.4440.940-2.220
Age0.0280.0083.70013.691< 0.0011.0291.013-1.044
Career0.0030.0760.0380.0010.9701.0030.863-1.165
Intercept-0.4270.454-0.9410.8850.3470.6520.268-1.588
High level group
Treatment options-1.0910.304-3.59112.8990.0000.3360.185-0.609
Gender0.5320.3001.7753.1500.0761.7030.946-3.066
Age0.0360.0103.53812.516< 0.0011.0371.016-1.057
Career0.1060.1011.0481.0970.2951.1120.912-1.355
Intercept-2.1390.628-3.40611.6020.0010.1180.034-0.403
Two-trajectory analysis of the basic model

Based on the probability of the SF-MPQ trajectory groups under the condition of NPQ trajectory groups, in Table 10, the probability adds up to 1 in each row. Individuals in the low NPQ group had the highest probability of having a low score for the SF-MPQ emotional item (69.1%), with only a 9.1% probability of being in the high-score group. Individuals in the medium-level NPQ group had the highest probability of being in the medium-level of the SF-MPQ emotional item score (69.8%), and the probability of being in the high-level group increased to 14.3%. Individuals in the high NPQ group had the highest probability of being in the middle level of the SF-MPQ emotional item score (62.6%), and the probability of being in the high-level of the SF-MPQ emotional item score increased to 32.0%. The specific probabilities are listed in Table 10.

Table 10 Probability (%) of Short-Form McGill Pain Questionnaire trajectory group based on Neck Pain Questionnaire trajectory group condition.
NPQ score grouping
Short-form McGill affective item score grouping
Low
Medium
High
Low69.1 21.8 9.1
Medium 15.9 69.8 14.3
High5.4 62.6 32.0

Based on the probability of the NPQ trajectory groups under the SF-MPQ trajectory group condition, the probability of each column of Table 11 adds up to 1. Individuals in the low-level group of the SF-MPQ emotion item score had the highest probability of being in the low-level group of NPQ (73.6%) score, with only a 3.8% probability of being in the high-level group. Individuals in the medium-level group of the SF-MPQ emotional item score had the highest probability of being in the medium-level group of NPQ (59.8%), and the probability of being in the high-level group of NPQ increased to 26.2%. Individuals in the high-level group for the SF-MPQ emotional item score had the highest probability of a high NPQ level (42.6%). The specific probabilities are shown in Table 11.

Table 11 Probability of Neck Pain Questionnaire trajectory group based on Short-Form McGill Pain Questionnaire trajectory group condition (%).
NPQ score grouping
SF-MPQ affective item score grouping
Low
Medium
High
Low73.614.018.6
Medium 22.759.838.8
High3.826.242.6

Based on the joint probability of the NPQ and SF-MPQ trajectory groups, the probability of the nine cells adds up to one (Table 12). The probability that an individual was in the SF-MPQ and NPQ low-, medium-, and high-level groups was 23.1, 31.2, and 7.0, respectively. The specific probabilities are shown in Table 12.

Table 12 Joint probability of Neck Pain Questionnaire and Short-Form McGill Pain Questionnaire trajectory groups (%).
NPQ score group
SF-MPQ affective item score grouping
Low
Medium
High
Low23.17.33.0
Medium 7.131.26.4
High1.213.77.0
DISCUSSION

In this study, a group-based model was employed to conduct a longitudinal two-trajectory analysis of the NPQ and SF-MPQ emotional item scores in patients with neck pain, and explore the changing trends of pain degree and emotional state across different patient groups. The NPQ and SF-MPQ affective item scores of patients with neck pain were significantly heterogeneous, and the pain level and emotional state of different subgroups differed significantly. The change in the trajectories of NPQ and SF-MPQ affective item scores could be divided into three main paths, indicating that neck pain has different developmental patterns in patients with cervical spondylosis. Patients in the first group had higher pain levels and poorer emotional states. The pain level of the second group was moderate compared with the emotional state. The third group had lower pain levels and better emotional states. This is consistent with previous studies[10] in which the course of chronic pain had large individual differences and may have been influenced by multiple factors, mainly cognitive-emotional, social environment, and lifestyle factors. Our findings revealed that acupuncture significantly reduced the probability of patients entering the high NPQ and SF-MPQ emotional item score groups, suggesting that acupuncture relieves pain and improves the emotional state of patients with pain. Consistent with the existing literature[11-13], acupuncture, a traditional Chinese medicine therapy, effectively relieves neuropathic pain and muscle spasm. Acupuncture stimulates specific meridians and acupoints, regulates nerve conduction, and enhances blood circulation, thereby relieving pain and exerting positive effects. The anterior cingulate cortex (ACC) plays a role in pain and pain-related anxiety, and the regulatory mechanism of acupuncture on pain-induced negative emotions may be related to the ACC. Therefore, acupuncture can be used as an adjunctive therapy in clinical treatment, especially for patients with a poor response to drug therapy or severe side effects. Thus, providing a feasible alternative. Age is also one of the influencing factors. Our results showed that the probability of patients in the high NPQ and SF-MPQ emotional item score groups increased significantly with age. This age-related trend suggests that clinicians should consider patients’ age when creating personalized treatment plans.

Based on the probability of the SF-MPQ trajectory group under the condition of the NPQ trajectory group, our findings showed the negative emotion was more severe in patients with high pain levels. Many studies have proven the relationship between pain and emotional state[14]. Chronic pain is highly correlated with depression. Depression can induce pain, and pain can also induce depression. This bidirectional relationship increases patients’ burden, with a significant impact on their quality of life. Therefore, understanding the mechanisms by which pain affects mood is important in clinical management. There is a close and complex bidirectional relationship between pain and emotional state. Pain is a physiological phenomenon but can also profoundly affect the psychological and emotional states of individuals. Pain is closely related to multiple neurobiological mechanisms. It is primarily perceived by the human body through the pain afferent pathway, which involves multiple cortical and subcortical structures. The central nervous system processes and regulates pain signals by receiving them from the peripheral nervous system. Studies have shown that individuals with higher pain scores exhibit reduced gray matter in specific brain regions, including the posterior cingulate cortex, precuneus, and posterior parietal cortex. These regions play a role in internal thinking and attentional control, and alterations in their function may directly affect emotion regulation. In addition, emotional regulation is closely related to the perception of pain, and areas such as the ACC, amygdala, and periaqueductal gray matter play key roles in this process[15].

Based on the probability of the NPQ trajectory group under the SF-MPQ trajectory group condition, our findings showed that pain levels were higher when patients had more severe negative emotions. The effects of affective states on pain perception and related mechanisms have been studied extensively. First, positive affective states such as happiness and pleasantness improve pain tolerance. This effect may be related to the increased secretion of neurotransmitters, such as dopamine and endorphins, which induce the brain's reward circuitry to produce an analgesic effect. Studies have shown that activities that trigger pleasurable emotions, such as music or social interactions, can increase the brain's ability to tolerate pain through the ascending pain inhibitory system, particularly the raphe nucleus chemical release activity[16,17]. Second, negative emotional states such as depression and anxiety exacerbate pain and are directly linked to the neural transmission of pain[15]. A negative emotional state is often accompanied by an increase in stress hormones, such as cortisol, which enhances the transmission of pain signals, thereby increasing the intensity of perceived pain. In addition, overactivation of the amygdala and dysregulation of the periaqueductal gray matter may lead to an increased sensitivity to pain. Research has found that negative emotions may trigger a psychological phenomenon called pain catastrophizing, in which patients tend to exaggerate pain negatively, further aggravating pain perception[18].

According to the relationship between pain and emotional state, effective pain management can relieve physical discomfort and significantly improve the psychological state of patients, especially for those with chronic pain. Studies have shown that pain reduction is often associated with decreased anxiety and depressive symptoms, revealing the positive impact of pain management on mental health[19]. In addition, psychotherapy plays an important role in pain relief. Psychotherapy can effectively reduce pain perception in patients with chronic pain, by changing the patient's cognitive and emotional response to pain. Cognitive behavioral therapy (CBT), one of the most commonly recommended methods, helps patients identify and change negative thought patterns and enhance their ability to cope with pain. Through a deep understanding of patients' thought patterns, CBT encourages them to establish a more positive mindset, thereby reducing the negative impact of pain[20]. In addition to CBT, mindfulness therapy can reduce chronic pain. Mindfulness, through the technique of focusing on the present moment, helps reduce the intensity of pain and promote the recovery of function[21]. Therefore, in clinical practice focused on of reducing the intensity of pain and improving the emotional state of patients, a comprehensive treatment plan, including physical therapy and psychotherapy, should be formulated to maximize the pain relief of patients and improve their quality of life.

Despite the strong clinical value of this study, it has some limitations. First, the sample (472 patients) only comes from some hospitals in China, its promotion may be affected by regional and cultural factors. In future research, we will consider including patients from different regions to enhance the generalizability of the research conclusions. Second, this study did not investigate the potential factors that may affect pain trajectory, such as lifestyle, social support, and subtypes of cervical spondylosis. These variables may have a significant impact on the pain trajectory of patients and require further investigation. Third, this study relied mainly on patients’ self-rated questionnaires, which may have caused some subjective bias. In the future, objective physiological indicators, such as imaging examinations or biomarkers, can be combined to improve the reliability of this study. In addition, we will use more psychological scales to conduct a more comprehensive assessment of psychological states. We will also extend the observation period (e.g., to over one year) to evaluate the long - term evolution of pain and emotions.

CONCLUSION

This study identified the dual-trajectory pattern of the NPQ and SF-MPQ affective item scores in patients with neck pain using GBMDTA, which revealed a correlation between neck pain and the affective state in this patient population. Our findings help to identify high-risk patients more accurately and provide a scientific basis for developing individualized pain management strategies. Future research should further explore the key factors affecting the pain trajectory to optimize the comprehensive management of chronic pain.

ACKNOWLEDGEMENTS

The authors thank the clinical staff at the hospitals involved in this research for their assistance with patient recruitment and data collection. We also acknowledge the contributions of the research assistants who supported the follow-up procedures and data entry. Their efforts were essential to the completion of this study.

Footnotes

Provenance and peer review: Unsolicited article; Externally peer reviewed.

Peer-review model: Single blind

Corresponding Author's Membership in Professional Societies: China Medicine Education Association, No. ZYYJY-0434.

Specialty type: Psychiatry

Country of origin: China

Peer-review report’s classification

Scientific Quality: Grade B, Grade C

Novelty: Grade B, Grade C

Creativity or Innovation: Grade B, Grade B

Scientific Significance: Grade C, Grade C

P-Reviewer: Kauer-Sant'Anna M, PhD, Associate Professor, Brazil; Keks NA, Associate Professor, Australia S-Editor: Qu XL L-Editor: A P-Editor: Zhang XD

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