Introduction

Frozen shoulder (FS) is a common shoulder disorder characterized by shoulder pain, stiffness, and limited range of motion [1]. Frozen shoulder is a chronic and specific inflammatory condition characterized by pain and limited mobility, which may lead to atrophy of the shoulder muscles due to disuse [2, 3]. It is normally classified into inflammatory and structural origin [4]. Throughout the condition period, there is persistent pain and a gradual decrease in the passive and active range of motion in the shoulder [5]. In the general population, the prevalence rate is as high as 8.2% in males and 10.1% in females, with a peak incidence at 55 years of age [6]. FS can severely impact the patient’s daily life and workability, and it has more adverse effects for patients who use their shoulders more often. For example, among 59 employees who used a computer for 3 to 6 h daily, 71.1% of them reported there is musculoskeletal discomfort in their neck, shoulder, and back [7]. It has been shown that shoulder stiffness can cause higher absence rates at work and a significant economic burden to the healthcare system [8]. Also, on average, 50% of patients will suffer consistent shoulder pain or stiffness even seven years after the onset of the disease [9].

The labor force population in China has a relatively high percentage of people aged 40 to 59, with more than 30% of people aged 40–49 and more than 20% of people aged 50–59 [10]. Therefore, frozen shoulder causes significant functional disability and pain in a population group consisting of patients who are often middle-aged and working [11]. Although numerous studies have investigated the epidemiology and treatment of frozen shoulder, however detailed studies on its risk factors are still limited. Therefore, the objective of the current study is to investigate in detail the risk factors associated with FS using a retrospective study design to analyze the relationship between gender and diabetes with frozen shoulder further. Chen et al. conducted a logistic multivariate regression analysis among post-treatment breast cancer survivors and found that physical disabilities were prevalent, with 20.3% of patients experiencing limited shoulder joint mobility \* MERGEFORMAT [12]. The study by Cao et al. identified potential risk factors for FS in middle-aged adults within one year following venous infusion therapy. Additionally, the type and frequency of venous infusions may also play a role in the occurrence of FS [13]. Zhang et al. employed logistic regression for a multivariate analysis of the clinical data from 592 patients who had arthroscopic rotator cuff repair. The multivariate analysis did not indicate that a history of smoking was a risk factor for the development of frozen shoulder [14].

Frozen shoulder, a prevalent musculoskeletal condition, profoundly affects patients' quality of life. Investigating its risk factors through rigorous case–control studies is crucial for develo** a nuanced understanding of the condition, which in turn is vital for crafting effective prevention and treatment approaches. Understanding the pathogenesis of frozen shoulder, particularly within specific geographic regions, helps to pinpoint vulnerable populations. Variables such as age, gender, regional variability, seasonal patterns of incidence, and the duration of the disease can provide critical insights into the disease's dynamics, laying the groundwork for informed prevention and management strategies. Early identification and intervention are paramount in reducing the incidence of frozen shoulder. For those at higher risk, proactive measures like timely education and physical therapy can significantly mitigate the likelihood of contraction, alleviating patient distress and enhancing overall well-being. From a public health perspective, decreasing the incidence of frozen shoulder not only boosts patients' quality of life but also eases the load on healthcare systems. Findings from case–control studies offer policymakers a solid scientific foundation for devising more impactful public health strategies and ensuring a more efficient distribution of healthcare resources. In essence, exploring the risk factors associated with frozen shoulder and conducting in-depth case–control analyses are instrumental in enhancing our knowledge of the condition, formulating robust preventive strategies, refining treatment protocols, guiding the development of public health policies, and propelling forward research in this field.

To confirm the possible risk factors related to the development of FS, we have examined 1205 case data obtained in the **njiang region of China from the year 2018 to 2023 with the consideration of various factors such as gender, occupation, place of residence, marital status, age, seasons of disease onset, duration of illness, etiology, medical history, sleep quality, smoking and alcohol consumption, constipation, family history, muscle strength, etc. This comprehensive analysis will help us to determine those factors related to the development of FS, and enable us to improve our understanding and diagnosis of FS, and provide us with more effective prevention and treatment methods for patients.

Method

Inclusion criteria

  1. (1)

    Pain in the affected shoulder joint, with significant limitations in movement (flexion < 90°) and consistent degrees of active and passive movement limitations;

  2. (2)

    No significant improvement in shoulder joint mobility after conservative treatment.

  3. (3)

    Patients with frozen shoulder supported by imaging and laboratory data such as X-ray, CT, and MRI.

Exclusion criteria

  1. (1)

    Exclusion of patients with systemic lupus erythematosus or other specific medical conditions that could potentially interfere with the assessment or management of shoulder pain;

  2. (2)

    Exclusion of patients with incomplete medical records, precluding a precise case definition;

General information

According to the above inclusion and exclusion criteria, 1205 cases of patients who visited the hospital from January 2018 to June 2023 were collected and included in this study. Among them, 573 patients diagnosed with frozen shoulder were selected as the observation group, and 632 age-gender matched patients were randomly chosen as the blank control group. In the control group, there were 163 males and 469 females, while in the observation group, there were 189 males and 384 females. The average age of the control group was 58 years old, and the average age of the observation group was 59 years old.

Evaluation indicators

Gender, occupation, place of origin, marital status, age, season of onset, duration of illness, cause of disease, surgical history, hypertension, diabetes, respiratory system diseases, knee joint disease, hyperlipidemia, cardiovascular and cerebrovascular diseases, cervical spondylosis, lumbar disc herniation, rheumatoid arthritis, hyperuricemia, sleep status, smoking and drinking alcohol, and whether constipation occurs.

Statistical methods

SPSS 26.0 software was used in all statistical analyses. For measurement data that adhered to the normal distribution, the mean ± standard deviation \(\overline x\pm s\) was used to represent the data, and the t-test was employed for comparison. For data that did not follow the normal distribution, the median and interquartile range were used to represent the data, and the rank sum test was applied. Count data were expressed as composition ratio or rate (%) and compared using the χ2 test. Graded data were also expressed as composition ratio or rate (%) and compared using the rank sum test. Influencing factors were analyzed using a multifactor Logistic regression model, and a P-value of less than 0.05 was considered statistically significant.

Results

Single-factor logistic regression analysis

As shown in Table 1, single-factor logistic regression analysis revealed that the observation group has significantly more females, more patients with diabetes, knee joint disease, constipation, and poor sleep quality than the control group (P < 0.05). It also shows that there were no significant differences between the two groups in terms of occupation, place of origin, marital status, age, duration of illness, season of disease onset, etiology, surgical history, hypertension, respiratory diseases, hyperlipidemia, cardiovascular diseases, cervical spondylosis, lumbar disc herniation, rheumatoid arthritis, hyperuricemia, smoking, and alcohol consumption history (P > 0.05).

Table 1 Results of single-factor logistic regression analysis
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Multifactor logistic regression analysis results

In the univariate analysis, several factors were found to be statistically different from the healthy group at a significance level of P < 0.05. These factors included female gender, diabetes, knee joint disease, constipation, and poor sleep quality. In the final multivariate model, four variables were retained: gender, history of diabetes, sleep quality, and constipation. Among these, diabetes (odds ratio [OR] = 2.349, 95% confidence interval [CI]: 1.790–3.082), gender (OR = 1.765, 95% CI: 1.375–2.265), poor sleep quality (OR = 1.716, 95% CI: 1.349–2.183), and constipation (OR = 2.150, 95% CI: 1.608–2.876) were identified as risk factors for the development of frozen shoulder(Table 2).

Table 2 Results of multifactor logistic regression analysis
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Multivariate logistic regression analysis revealed that diabetes and gender are significant factors affecting the risk of FS. The ROC curves of the predictive model and each factor was compared as shown in Fig. 1. The area under the ROC curve (AUC) of the predictive model was 0.682, and the 95% confidence interval (CI) was 0.652–0.712. After a thorough multivariate logistic regression analysis, we identified diabetes and gender as two key factors influencing the risk of frozen shoulder. Specifically, the risk for diabetic patients was significantly higher than that for non-diabetic patients, and in terms of gender differences, female patients were more likely to develop frozen shoulder than male patients. This finding has important implications for clinical practice, potentially aiding doctors in diagnosing and predicting the occurrence of frozen shoulder more effectively.

Fig. 1
figure 1

ROC curve of the predictive model and the various factors

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Furthermore, we developed a predictive model to estimate the likelihood of an individual develo** frozen shoulder. The model's AUC of 0.682 suggests that it has moderate predictive power. However, the 95% CI of 0.652–0.712 indicates a degree of uncertainty in the estimate of predictive performance. Consequently, future studies will explore additional potential predictive factors to refine the model and enhance its predictive accuracy. Currently, the model's capability in predicting frozen shoulder is acceptable but has room for improvement. The AUC value of 0.682 indicates a moderate accuracy in distinguishing between patients with and without frozen shoulder, yet it is not ideal. The clinical value of the model will depend on its performance and its ability to assist doctors in making more precise diagnostic and treatment decisions. Overall, this study offers a new perspective for the early identification and treatment of frozen shoulder and has the potential to facilitate the development of more effective clinical management strategies.

Discussion

Frozen shoulder

Frozen shoulder, also known as adhesive capsulitis of the shoulder joint, is a common shoulder problem [15]. It is characterized by limited shoulder joint mobility, pain, and stiffness [16]. Frozen shoulder primarily involves inflammation and fibrosis of the soft tissues around the shoulder joint, leading to a restricted range of motion (ROM) and pain [17]. The pathogenesis of frozen shoulder is divided into three stages: the freezing phase, the stiff phase, and the recovery phase [18]. The disease process generally consists of three stages: an initial painful freezing phase, which lasts about 2 to 9 months, during which the patient experiences severe and generalized shoulder pain, particularly exacerbated at night; followed by the freezing phase, which lasts 4 to 12 months, where the pain begins to subside, but the ROM of the shoulder joint is gradually decreased; and finally, the thawing phase, where the ROM of the shoulder joint gradually increases, a process that may take from 5 months to 2 years [19]. The exact cause of frozen shoulder is not yet fully understood, but several risk factors are assumed to be associated with its occurrence. In addition to the factors mentioned in the discussion section regarding gender and diabetes, age, shoulder injuries, prolonged poor posture, and certain systemic diseases can all increase the risk of frozen shoulder.

Diagnosis of periarthritis of the shoulder

Through literature review [14] we found that the diagnostic terms for cases of unexplained shoulder joint stiffness are as follows: frozen shoulder (31%), stiff shoulder (22%), periarthritis (16%), the Japanese term “Gojukata,” which refers to shoulder joint issues in people aged 50 and older (16%), idiopathic frozen shoulder (6%), primary frozen shoulder (4%), adhesive periarthritis (3%), and others (2%). Conduct radiological studies to rule out other secondary causes of shoulder pain [20]. The diagnosis of frozen shoulder was established through an evaluation of the patient’s medical history and a physical assessment [21].

This may be due to the differences in the understanding and classification of shoulder diseases between Western and Chinese medicine. In Western countries, frozen shoulder and adhesive capsulitis are more common shoulder disease diagnoses [22]. In China, periarthritis is a broader concept that includes frozen shoulder, adhesive capsulitis, and other shoulder diseases. This may be because Chinese medical research and clinical practice emphasize a holistic view of the disease and dialectical treatment, thus categorizing some similar shoulder diseases using the common term periarthritis during diagnosis and treatment. The diagnosis of frozen shoulder in this study is based primarily on clinical findings, including limitation of ROM and pain in the shoulder. Key diagnostic steps include a detailed patient history, particularly the onset time of pain and decreased ROM, and a thorough physical examination to look for tenderness and muscle wasting in the shoulder area. Imaging has played a supporting role in this diagnosis process to rule out other causes and evaluate shoulder structures. These diagnostic processes ensure accurate identification and effective treatment of frozen shoulder [23].

MRI, CT, and DR imaging results of patients' shoulder joints are shown in Figs. 2, 3, and 4. In Fig. 2, MRI imaging showed left shoulder joint bone hyperplasia, joint space narrowing, and uneven signal of the humeral head, suggesting the possibility of osteoarthritis. In particular, the mixed high signal on T2WI and the high signal on the fat suppression sequence may be related to degenerative changes and inflammatory responses in the articular cartilage. The increased signal of the supraspinatus tendon, the high signal on T2WI, and the thickening of the shoulder joint capsule further support the hypothesis of muscle and soft tissue injury or inflammation.

Fig. 2
figure 2

MRI image, Coronal T2WI. Male, 69 years old

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Fig. 3
figure 3

Plain CT scan of the humerus. Male, 80 years old

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Fig. 4
figure 4

X-ray of the left humerus. Male, 50 years old

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CT scan results (Fig. 3) showed right humeral head bone hyperplasia and sclerosis, with rough edges accompanied by osteophyte formation, consistent with osteoarthritis's typical characteristics. The cystic changes below the joint surface may be due to the destruction of the articular cartilage and local osteoporosis. Notably, although there was bone hyperplasia, there was no significant narrowing of the joint space, which may be related to the early stage of the disease.

DR imaging (Fig. 4) further confirmed the bone hyperplasia of the left humeral greater tuberosity, increased bone density, and slight swelling of the surrounding soft tissue, which may be related to local inflammatory reactions or muscle injuries.

Comprehensive imaging results suggest that the shoulder joint lesions in this study may be related to senile osteoarthritis. The progression of the disease can lead to limited joint movement and pain, affecting the patient's quality of life. Therefore, early diagnosis and treatment are crucial for alleviating symptoms and delaying disease progression. Future studies should explore more effective treatment strategies for such lesions and how to monitor disease changes through non-invasive methods.

Due to the lack of large-scale data, the study could not thoroughly compare and analyze the similarities and differences in the diagnostic criteria and methods for frozen shoulder between Western countries and the ** the condition [32]. It was found that glutamate injected into the masseter muscle produced more intense pain in women than in men [44]. These gender differences may be related to physiological and psychological factors, such as gonadal hormones and central hyperexcitability [57], and emphasizes the importance of gender in the development of frozen shoulder. There are several possible mechanisms for why gender is related to the frozen shoulder, as discussed above.

Frozen shoulder and emotional connection

In addition to the limitation of shoulder function, the pain of frozen shoulder may also negatively affect the emotional state of patients. The main characteristics of frozen shoulder are a restricted range of motion in the shoulder joint and chronic pain around the deltoid insertion, which severely affects the patient's limb function, work capacity, and quality of life [58]. Pain is closely related to emotions and can cause physical discomfort and suffering, which in turn affects an individual's psychological state [59]. The pain of frozen shoulder may lead to anxiety and depression in patients. Long-term pain and limited shoulder joint function bring inconvenience to the patient's daily life, making them feel powerless and frustrated. This persistent negative emotion may further exacerbate the perception of pain, creating a vicious cycle [60]. The pain of frozen shoulder is not only a problem in itself but may also lead to other diseases, such as negative emotional states, depression, anxiety, insomnia, and poor sleep quality. These may interfere with patients' motivation to participate in rehabilitation and their overall health [61]. The shoulder pain and decreased mobility caused by frozen shoulder may reduce overall physical activity in daily life [62]. Side effects of some medications for frozen shoulder may also be related to constipation. For example, nonsteroidal anti-inflammatory drugs (NSAIDs) are a commonly used medication for frozen shoulder treatment, but they are thought to cause problems such as gastrointestinal distress and constipation [46]. Pain from a frozen shoulder can also hurt social and interpersonal relationships. Patients may find it difficult to perform normal physical activities such as shaking hands, hugging, or lifting heavy objects due to pain, which can cause them to avoid social activities, develop feelings of loneliness and inferiority, and even isolate themselves from friends and family, further exacerbating emotional problems. Also, the patients can not sleep well due to pain [63]. Because the pain can cause emotional problems, emotional issues may also increase the perception of pain, and thus, when treating frozen shoulder, attention should also be given to the patient's mental health in addition to the pain itself [

Availability of data and materials

The data used to support the findings of this study are included within the article.

Abbreviations

FS:

Frozen shoulder

NSAID:

Non-steroidal anti-inflammatory drugs

PPT:

Pressure pain

ROM:

Restricted range of motion

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Acknowledgements

Not applicable.

Funding

The study was supported by the National Natural Science Foundation of China (No.: 82060417).

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Author notes

  1. **arepa Abudula and Palida Maimaiti co-first author.

Authors and Affiliations

  1. Nursing School of **njiang Medical University, Urumqi, 830054, China

    **arepa Abudula, Palida Maimaiti, Jiaojiao Shu, Asiguli Tuerxun, Halimire Abudujilili & Ruiqi Yang

  2. Hospital of **njiang Traditional Uyghur Medicine, Urumqi, China

    Ailiyaer Yasheng

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  1. **arepa Abudula
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  2. Palida Maimaiti
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  4. Jiaojiao Shu
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  6. Halimire Abudujilili
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Contributions

Concept, literature search, and data collection:XA, PM, AY, JJS, AT, RQY, HA. Statistics, data analysis, and interpretation: XA, PM, JJS. Drafting article: XA, PM, AY, JJS. Critical revision of article: XA, PM. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Palida Maimaiti.

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Ethics approval and consent to participate

The study design was approved by the Ethics Committee of **njiang Medical University before data collection and analysis(Approval Number: 20200326–03). All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from the participants.

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Not applicable.

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The authors declare no competing interests.

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Abudula, X., Maimaiti, P., Yasheng, A. et al. Factors associated with frozen shoulder in adults: a retrospective study. BMC Musculoskelet Disord 25, 493 (2024). https://doi.org/10.1186/s12891-024-07614-8

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