Investigating the Relationship Between Smartphone Addiction and Suffering from Degenerative Diseases of the Cervical Spine

Investigating the Relationship Between Smartphone Addiction and Suffering from Degenerative Diseases of the Cervical Spine | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigating the Relationship Between Smartphone Addiction and Suffering from Degenerative Diseases of the Cervical Spine Misagh Shafizad, Maryam Rahmani Nia, Pedram Pirmoradian, Seyed Mohammad Sakhaei, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8800006/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Background: Smartphone use has increased globally and has been associated with neck pain, yet evidence linking smartphone addiction to objective cervical spine degeneration on magnetic resonance imaging (MRI) remains inconsistent. Methods: In this cross-sectional study, 163 adults aged 18–55 years with chronic neck pain underwent cervical spine MRI. Smartphone addiction was assessed using the Smartphone Addiction Scale (SAS), and neck disability was evaluated with the Neck Disability Index (NDI). Cervical disc degeneration and canal stenosis were graded using established MRI-based classification systems. Associations between SAS scores and imaging findings were analyzed. Results: The mean SAS score was 83.0 ± 27.4, reflecting moderate addiction severity. SAS scores were not significantly associated with NDI, body mass index, cervical disc degeneration, or cervical canal stenosis at any level. A single isolated association at the C3–C4 level was observed based on a very small number of cases and was interpreted cautiously. Smartphone addiction severity was higher among single participants and those with postgraduate education, with no differences by gender or residence. Conclusion: Smartphone addiction severity was not associated with MRI-detected cervical disc degeneration or clinically significant cervical canal stenosis. These findings suggest that cervical spine degeneration is unlikely to be driven by smartphone addiction alone and support the multifactorial nature of cervical spine disorders. smartphone addiction cervical spine degeneration neck disability MRI musculoskeletal disorders Introduction Smartphone ownership has expanded rapidly worldwide, with young adults among the heaviest users ( 1 – 6 ). While these devices provide significant social and academic benefits, prolonged use has been linked to stress, sleep disturbance, anxiety, reduced physical activity, and impaired academic performance ( 1 , 3 ). Musculoskeletal symptoms are also common. Neck pain is reported in 17% − 68% of users, with lifetime prevalence exceeding 50% ( 3 , 7 ). The most frequently affected regions are the neck, shoulders, and upper back, and smartphone addiction appears to further increase the risk of musculoskeletal disorders ( 3 ). One major contributing factor is posture. Smartphone use typically involves sustained forward head flexion of 30°- 45° ( 4 , 7 – 10 ). Even modest flexion angles markedly increase cervical loading, with the effective head weight rising from ~ 12 pounds in neutral position to as much as 60 pounds at 60° ( 10 ). Such non-neutral posture increases neck muscle activity, reduces endurance, and alters proprioception ( 8 , 10 – 13 ). Young adults with mild neck pain show greater cervical flexion during smartphone tasks than asymptomatic peers, suggesting that pain may alter motor control ( 8 ). Eventually, these biomechanical stresses may contribute to pain, muscular imbalance, and abnormalities in spinal alignment ( 4 , 8 – 10 , 12 ). Cervical spondylosis is a chronic degenerative disorder involving disc dehydration, osteophyte formation, and secondary changes in ligaments, nerves, and the spinal cord ( 14 ). It is now the third most common cause of chronic pain in the United States and the fourth worldwide, with a growing impact on younger populations ( 14 ). Among students, neck discomfort is highly prevalent, and chronic symptoms are associated with central nervous system changes, disability, and negative emotional states ( 14 ). Disease progression can lead to radiculopathy or cervical spondylotic myelopathy, presenting with symptoms that range from pain and stiffness to motor and sensory deficits ( 15 ). Magnetic resonance imaging (MRI) is considered the gold standard for the detection of these structural changes ( 16 ). Recent evidence suggests that modern behavioral factors may accelerate these processes. Prolonged smartphone and media use, often more than five hours daily, is strongly associated with musculoskeletal complaints, especially among female students, and correlates with both addiction behaviors and reduced academic performance ( 17 ). These findings highlight how lifestyle and technology use may exacerbate underlying degenerative changes in the cervical spine ( 14 – 17 ). Emerging data also indicate potential links between smartphone overuse and early myelopathy. Students using smartphones for nine or more hours per day were nearly twice as likely to demonstrate positive myelopathic signs, including Hoffmann’s and Trömner’s tests ( 2 ). Prolonged flexion narrows the cervical canal, stretches the spinal cord, and may provoke ligamentous edema ( 2 , 10 ). MRI studies show that up to one-third of patients with neck pain already exhibit degenerative or myelopathic changes, underscoring the importance of imaging in early detection ( 2 ). However, the current literature has important limitations. Much of the available evidence is derived from cross-sectional, experimental, or small case–control studies, and investigations focusing on imaging-based outcomes remain relatively scarce, thereby limiting causal inference ( 5 ). Additionally, findings are heterogeneous: several studies of young adults report no clear connection between texting posture ("text neck") and neck pain, highlighting the need for objective endpoints like MRI to address these inconsistencies ( 18 ). Therefore, this study was developed to assess MRI-detected changes in the cervical spine of young adults with high smartphone exposure. Through the integration of imaging and clinical features, we aimed to establish whether prolonged use and addiction-like engagement with smartphones are associated with early degenerative changes. This focus on structural markers offers evidence that could guide preventive measures, ergonomic recommendations, and public health initiatives for younger populations ( 2 , 3 , 5 – 7 , 10 ). Methods 2.1 Study design This analytical cross-sectional study was conducted between December 2023 and April 2025. Data collection employed standardized demographic questionnaires, the Neck Disability Index (NDI), and the Smartphone Addiction Scale (SAS). 2.2 Ethical approval Ethical approval was obtained from the Research Ethics Committee of Sari University of Medical Sciences (IR.MAZUMS.IMAMHOSPITAL.REC.1402.087). Written informed consent was secured from all individual participants prior to their inclusion. 2.3 Participants Eligible participants were adults aged 18–55 years who presented to Imam Khomeini Hospital with chronic neck pain (duration ≥3 months), with or without associated neurological signs, during the study period. All participants were regular smartphone users. Exclusion criteria comprised cervical neoplasm, active infection of the cervical region, congenital cervical spine deformity, history of traumatic injury to the neck or shoulder girdle, previous cervical spine surgery, current major psychiatric disorder, use of sedative medication within 48 hours of assessment, or unwillingness to participate. 2.4 Data collection Data were sourced from self-administered questionnaires and clinical records. Collected variables included demographic characteristics, occupation, socioeconomic and educational status, level of physical activity, neck pain characteristics, the Neck Disability Index (NDI) score (19), and smartphone use behavior as quantified by the validated Persian version of the Smartphone Addiction Scale (SAS) (20). All participants underwent cervical spine magnetic resonance imaging (MRI) for the evaluation of intervertebral disc degeneration. The validated Persian versions of the NDI and SAS were used in this study(19,20). 2.5 Smartphone addiction assessment (SAS) Smartphone addiction was evaluated using the 33-item Smartphone Addiction Scale (SAS). Each item is rated on a 6-point Likert scale from 1 (“strongly disagree”) to 6 (“strongly agree”). The total score ranges from 33 to 198. 2.6 Neck Disability Index (NDI) Neck-related disability was assessed using the 10-item Persian version of the Neck Disability Index (NDI) (21). Items are scored from 0 to 5, yielding a total score between 0 and 50. Disability severity was categorized as follows: 0–10 (mild), 11–20 (moderate), 21–30 (severe), 31–40 (complete disability), and 41–50 (very severe disability). For interpretive clarity, scores were also converted to percentage values, with categories defined as 0–20% (mild), 21–40% (moderate), 41–60% (severe), 61–80% (complete), and 81–100% (very severe disability). 2.7 Assessment of Cervical Disc Degeneration Cervical intervertebral disc degeneration was evaluated on mid-sagittal T2-weighted MRI sequences using the Modified Pfirrmann Grading System. This system classifies discs into eight grades based on nucleus pulposus signal intensity, distinction of the annular fibers, and disc height. A Grade 1 disc, for instance, demonstrates uniform hyperintensity with signal intensity equivalent to cerebrospinal fluid, a distinct posterior junction between inner and outer annular fibers, and preserved disc height. Grade 2 is characterized by hyperintense signal between that of presacral fat and cerebrospinal fluid, with or without a hypointense intranuclear cleft, while maintaining a distinct annular junction and normal disc height. In Grade 3, the disc appears hyperintense but with signal intensity lower than that of presacral fat; the annular junction remains distinct, and disc height is preserved. Grade 4 demonstrates a mildly hyperintense signal, slightly greater than the outer annular fibers, accompanied by an indistinct annular junction but without loss of disc height. Grade 5 is defined by a hypointense signal equal to that of the outer annular fibers, with an indistinct annular junction and normal disc height. Grades 6 through 8 indicate progressive disc height loss, with a hypointense signal and an indistinct annular junction. According to the Modified Pfirrmann system, Grade 6 degeneration is characterized by a disc height reduction of less than 30%, Grade 7 by a 30–60% reduction, and Grade 8 by a loss exceeding 60%, indicative of end-stage degeneration (22). For the purpose of statistical analysis, the original eight-grade classification was condensed into three ordinal categories: mild degeneration (Grades 1–2), moderate degeneration (Grades 3–5), and severe degeneration (Grades 6–8). Differences in smartphone addiction scores across these groups were assessed using the Kruskal–Wallis test. All cervical discs were graded independently by two fellowship-trained radiologists blinded to the patients’ clinical and demographic information. In cases of discordance, a consensus reading was reached through adjudication by a third senior radiologist. 2.8 Assessment of Cervical Spinal Canal Stenosis Cervical spinal canal stenosis was graded according to the Kang classification system. This MRI-based system stratifies severity by the degree of spinal cord compression as follows: no stenosis; mild stenosis (>50% obliteration of the subarachnoid space without spinal cord deformity); moderate stenosis (central canal narrowing with cord deformity but without intramedullary T2 signal change); and severe stenosis (cord deformity with associated intramedullary T2 hyperintensity at the compressive level) (23). For inferential analyses, the Kang classification was dichotomized by grouping Grades 0 and 1 as absent to mild canal stenosis, and Grades 2 and 3 as moderate to severe stenosis. This categorization was applied to facilitate statistical comparison of smartphone addiction scores across clinically meaningful stenosis severity groups. The Kang classification is a simple and reproducible tool that facilitates the evaluation of canal narrowing, informs treatment planning, and helps predict clinical outcomes. Nevertheless, imaging findings were interpreted in the context of each patient’s clinical presentation and were not used in isolation for therapeutic decision-making. 2.9 Sample size stimulation Based on the study by Zhuang et al. (24) which compared individuals with and without excessive smartphone use, the minimum required sample size was calculated using the standard formula for two independent groups, assuming a type I error rate of 0.05 and a statistical power of 90%. This yielded a minimum of 32 participants. However, given that the Smartphone Addiction Scale (SAS) comprises 33 items, we applied the rule of thumb recommended by Rencher’s multivariate analysis text (25). which suggests recruiting at least five participants per questionnaire item to ensure stable and reliable parameter estimation. Accordingly, the target sample size was increased to 160 participants, and this number of questionnaires was administered and completed by the patients. Data analysis For the reporting of descriptive statistics, quantitative variables were presented as mean (standard deviation) and qualitative variables as frequency (percent). The Kolmogorov-Smirnov test was used to assess the normal distribution of quantitative variables. For inter-group comparisons of normally distributed quantitative variables, means were compared using the independent samples t-test; the non-parametric Mann-Whitney U test was applied for non-normally distributed data. Associations between two quantitative variables were assessed using Pearson’s correlation coefficient for normally distributed data and Spearman’s rank correlation coefficient otherwise. To assess the independent relationship between smartphone addiction and degenerative cervical spine findings while adjusting for potential confounders, multiple linear regression analysis was performed. First, univariate regression models were fitted. All variables with a P-value < 0.25 in the univariate analysis were subsequently entered into the multivariable model. All analyses were conducted using SPSS software (version 20.0), with statistical significance defined as a two-tailed P-value < 0.05. Results Participant Characteristics The study included 163 participants (77.3% male; 22.7% female). Most were married (80.4%), with the remainder single (19.6%). Nearly half (49.1%) had mid-level education (Level 2); 30.7% had the lowest level, 19.6% a higher level (Level 3), and one participant (0.6%) held the highest level (Level 4). The majority lived in urban areas (70.6%) versus rural (29.4%). Anthropometric and clinical measures indicated a moderately overweight sample. Mean height was 166.0 cm ( SD = 7.8) and mean weight 75.3 kg ( SD = 11.3), corresponding to a mean BMI of 27.3 ( SD = 4.0). The average Neck Disability Index (NDI) score was 19.73 ( SD = 7.86) out of 50, reflecting moderate neck-related disability. The mean SAS score was 83.0 ( SD = 27.4), indicating substantial variability in the outcome measure. Cervical Disc Degeneration (Pfirrmann Grades) All evaluated cervical discs (C2–C3 through C7–T1) showed degeneration. Pfirrmann Grade 4 (moderate degeneration) was the most common grade at each level, accounting for roughly 35–42% of discs per level. Very few discs were nearly normal (Grades 1–2; <10% at any level), and most discs exhibited some degeneration. Severe grades (6–8) were present only in a minority of discs. The mid-cervical levels tended to show more severe degeneration: for example, at C5–C6 33.7% of discs were Grades 6–8 (including 8.0% Grade 8), whereas at the cervicothoracic junction (C7–T1) no Grade 8 discs were observed. In summary, moderate degeneration predominated across all levels, while very mild or very severe changes were comparatively rare. Cervical Spine Stenosis (Kang Grades) Most cervical segments demonstrated no evidence of spinal canal stenosis (Kang Grade 0), particularly in the upper and lower cervical spine. For example, 93.9% of segments at C2–C3 and 96.3% at C7–T1 were classified as Grade 0. The prevalence and severity of canal stenosis increased in the mid-cervical region. At the C5–C6 level, only 52.1% of segments were Grade 0, while 23.9% showed Grade 1 stenosis and 23.9% exhibited more advanced stenosis, including 18.4% with Grade 2 and 5.5% with Grade 3 involvement. A similar, though less pronounced, pattern was observed at C4–C5, where 65.0% of segments were Grade 0, 22.7% Grade 1, and 12.2% showed moderate to severe stenosis (10.4% Grade 2 and 1.8% Grade 3). In contrast, lower cervical levels predominantly retained a normal canal diameter, with 79.1% of C6–C7 and 96.3% of C7–T1 segments classified as Grade 0. Overall, cervical canal stenosis was most frequently observed at mid-cervical levels, particularly at C5–C6, while the upper and lower segments were largely spared. Associations with SAS Scores Correlations with Clinical Measures: Pearson correlations showed that SAS scores were not significantly related to neck disability or anthropometrics. SAS did not correlate with NDI ( r (161) = .10, p = .192), height ( r = .11, p = .154), weight ( r = .09, p = .283), or BMI ( r = .01, p = .856). All correlations were small and non-significant, indicating no linear relationship between SAS and these measures. SAS by Disc Degeneration (Pfirrmann): We compared SAS across Pfirrmann grades (1–3) at each cervical level using Kruskal–Wallis tests. No significant differences were found at any level (all p > .05). For example, at C5–C6 the largest contrast had H (2) = 3.80, p = .149, which was not significant. Thus, SAS scores did not vary by degree of disc degeneration at any single level. SAS by Segmental Stenosis (Kang): Mann–Whitney tests compared SAS between Kang grade 1 (no and mild stenosis) versus grade 2 (moderate to severe stenosis) at each level. No significant differences were observed at most levels (all p > .25). The only exception was C3–C4, where the small grade-2 group (n = 5) had lower SAS scores than grade-1 segments ( U = 182.5, p = .041). This isolated finding (with a very small n) should be viewed with caution. Apart from this, SAS did not differ by segmental stenosis at any level. SAS by Demographic Factors: Group comparisons were conducted using independent-samples t -tests or ANOVA. SAS did not differ by gender: males (n = 126) had mean M = 83.37 ( SD = 26.72) and females (n = 37) M = 81.78 ( SD = 29.82), t (161) = 0.31, p = .758. Residence also had no significant effect: urban residents (n = 115) M = 84.86 ( SD = 27.70) versus rural (n = 48) M = 78.56 ( SD = 26.29), t (161) = 1.34, p = .181. In contrast, marital status was significantly associated with SAS. Single participants (n = 32) had higher SAS scores ( M = 94.75, SD = 29.55) than married participants (n = 131; M = 80.14, SD = 26.13), t (161) = 2.76, p = .006, indicating that singles tended to report greater alignment scores than married individuals. SAS by Educational Level: A one-way ANOVA showed a significant effect of education on SAS scores, F (2,159) = 6.55, p = .002. Mean SAS increased with education: diploma or lower ( M = 75.54, SD = 23.96, n = 50), bachelor’s degree ( M = 81.74, SD = 27.02, n = 80), and postgraduate ( M = 97.00, SD = 28.73, n = 32). Post hoc comparisons (LSD) indicated that the postgraduate group scored significantly higher than both the bachelor’s (p = .007) and diploma (p < .001) groups; the bachelor’s and diploma groups did not differ significantly (p = .196). These results suggest a positive association between higher education level and SAS. Multivariate Analysis (GLM) A generalized linear model (normal distribution, identity link) tested marital status as a predictor of SAS. The model (with marital status as a covariate) fit significantly better than a null model (likelihood-ratio χ²(1) = 7.55, p = .006). The Wald chi-square for marital status was χ²(1) = 7.73, p = .005, confirming a significant effect. The unstandardized parameter estimate for being single (vs. married) was B = 14.61 ( SE = 5.26), 95% CI [4.31, 24.91], p = .005. The intercept (mean for married participants) was B = 80.14 ( SE = 2.33), 95% CI [75.57, 84.70]. These results indicate that, on average, single individuals scored about 14.6 points higher on the SAS than married individuals. Thus, even in the multivariate model, marital status significantly predicted SAS scores, with single status associated with higher SAS. Discussion Smartphone addiction in the present study was assessed using the original Smartphone Addiction Scale (SAS) on its native 33–198 scale. The observed mean SAS score in our cohort was comparable to values reported in previous studies of patients with chronic neck pain, including the study by Zhuang et al. (24), suggesting a similar level of smartphone addiction severity across clinical populations. Our overall NDI scores were higher than those reported by Kim et al., who studied young adults with mild neck pain(26). This discrepancy is likely attributable to differences in the study population: our participants were patients with chronic neck pain presenting to a hospital setting, whereas Kim et al. recruited university students with only mild, self-reported neck pain from the community. The higher disability burden in our cohort is therefore consistent with the expectation that clinically referred patients exhibit more severe functional limitations than community samples with subclinical or mild symptoms. In the present study, smartphone addiction severity varied according to selected demographic characteristics. Marital status emerged as a significant and independent correlate of SAS scores, with single participants reporting higher levels of smartphone addiction than married individuals; this association remained robust in multivariate analysis. This pattern may reflect differences in daily social structure, leisure activities, and reliance on digital communication, such that unmarried individuals may use smartphones more frequently for social interaction and entertainment, consistent with previous studies reporting higher SAS scores among unmarried compared with married individuals (27). Educational attainment was also positively associated with SAS scores, with postgraduate participants exhibiting the highest levels of smartphone addiction severity compared with those holding bachelor’s or diploma-level education. This association may be driven by greater academic and professional demands among highly educated individuals, leading to more intensive and functionally driven smartphone use, which may elevate SAS scores without necessarily reflecting maladaptive behavior. Consistent with some prior investigations, no significant differences in smartphone addiction were observed between males and females, suggesting that gender may not be a robust predictor of problematic smartphone use in all populations (28). Additionally, no significant differences in smartphone addiction severity were observed according to place of residence, suggesting that smartphone addiction behaviors in this clinical population are broadly distributed across demographic groups and are not confined to specific gender roles or urban-rural contexts. In the present study, no significant association was identified between smartphone addiction severity (SAS) and cervical intervertebral disc degeneration as assessed by the modified Pfirrmann grading system. Smartphone addiction scores were comparable across different degrees of disc degeneration at all cervical levels, indicating that degeneration severity was not related to the extent of smartphone use. Moderate levels of smartphone use were most common across the cohort, whereas high levels of use were relatively uncommon. These findings indicate that, in a population of patients with chronic neck pain, smartphone addiction severity (SAS) alone is unlikely to be a major determinant of cervical disc degeneration. This interpretation is consistent with the prevailing view that cervical degenerative changes result from a complex interplay of factors, including age-related processes, genetic susceptibility, occupational loading, biomechanical stress, and prior cervical injury. Although previous studies have suggested that prolonged smartphone use may contribute to early degenerative changes of the cervical spine (24,29), we did not observe a significant association in the present study. This may, in part, be attributable to the limited proportion of heavy smartphone users in our sample or to the inherent constraints of a cross-sectional design in establishing causal relationships (24,29). We did not observe a significant association in the present study. This may, in part, be attributable to the limited proportion of heavy smartphone users in our sample or to the inherent constraints of a cross-sectional design in establishing causal relationships. An isolated and unexpected finding was observed at the C3–C4 level, where lower Smartphone Addiction Scale (SAS) scores were associated with greater cervical canal stenosis severity. This association was limited to a single cervical segment and was based on a small number of cases with higher-grade stenosis; therefore, it should be interpreted with considerable caution. No consistent or significant associations between smartphone addiction scores and canal stenosis were identified at other cervical levels. This pattern contrasts with prior reports suggesting that excessive smartphone use preferentially affects lower cervical segments through sustained flexion and biomechanical stress(2). Given the cross-sectional design of the present study, a causal relationship cannot be inferred. A plausible explanation for this isolated inverse association is reverse causality, whereby individuals with pre-existing cervical stenosis and related symptoms, such as neck pain or neurological complaints, may intentionally limit smartphone use to avoid symptom exacerbation during prolonged neck flexion. In this context, reduced smartphone use is therefore more likely a consequence rather than a cause of cervical canal stenosis. Although our findings did not demonstrate a significant association between smartphone addiction severity and disc degeneration, mechanistically, prolonged smartphone use entails sustained neck flexion, resulting in muscular fatigue and biomechanical overload of cervical structures (24,29). Experimental evidence supports the relationship between increased cervical flexion angles during smartphone use and elevated mechanical load, with disc degeneration detectable even in individuals in their twenties (29). Cevik et al. also documented alterations in cervical sagittal balance attributable to smartphone use, including Modic changes (29). Electromyographic studies report increased activity in cervical erector spinae muscles and reduced upper trapezius activation under sustained flexion, particularly in users with neck pain, reflecting altered muscular control (8,10,30). While neck flexion is prevalent during smartphone use and posited as a causative factor in cervical spine disorders among addicted users (7,8), some conflicting evidence exists. Suzuki et al. found only a slight inverse correlation between cervical vertical angle and upper trapezius muscle stiffness, with no significant differences between symptomatic and asymptomatic subjects (11). Simultaneously, several studies challenge a direct causal link between neck posture and pain based on self-assessment and clinical evaluations, noting that forces causing spinal instability vastly exceed those attributable to poor posture alone (18,31–33). Clinically, subtle modifications in neck flexion angle may hold limited significance; however, correcting posture angles could reduce pain and dysfunction in smartphone users with neck symptoms (8). Modified cervical exercises have demonstrated potential benefits in improving posture and alleviating adverse effects related to smartphone use (6). Therefore, ergonomic interventions deserve further investigation in this context. This study has several notable strengths. First, the relatively large sample size of 163 participants enhances statistical power and improves the generalizability of the findings compared with many previous studies that included smaller cohorts. Second, the use of validated and culturally adapted instruments, namely the Persian versions of the Smartphone Addiction Scale (SAS) and the Neck Disability Index (NDI), supports the reliability and relevance of the collected clinical and behavioral data. Third, the objective assessment of cervical spine degeneration and spinal canal stenosis using magnetic resonance imaging (MRI) represents an important methodological strength, allowing direct and accurate visualization of anatomical changes. In addition, MRI evaluations were performed independently by two radiologists who were blinded to clinical information, with adjudication to resolve discrepancies, thereby reducing observer bias and strengthening diagnostic validity. The integration of clinical questionnaires, self-reported smartphone behavior, and detailed radiological grading provides a comprehensive perspective on the relationship between smartphone addiction and cervical spine pathology. Despite these strengths, several limitations should be considered when interpreting the results. The cross-sectional design precludes the establishment of causal or temporal relationships between smartphone addiction and the development or progression of cervical spine degeneration. Moreover, reliance on self-reported smartphone use introduces the potential for recall and reporting bias, which may affect the accuracy of exposure assessment. Direct measurements of smartphone-related posture, such as cervical flexion angles or ergonomic assessments, were not included and could have helped clarify the biomechanical mechanisms underlying the observed associations. In addition, several potential confounders, including workplace ergonomics, physical activity levels, and psychosocial stressors, were not systematically measured or adjusted for and may have influenced neck disability and degenerative changes. The single-center design, conducted at a medical facility in Iran, may also limit the generalizability of the findings to other populations. Finally, variability in smartphone device characteristics, such as size and weight, which may independently influence neck posture and mechanical loading, was not accounted for and could have confounded the relationship between smartphone use and cervical spine health. Overall, these strengths and limitations provide a balanced framework for interpreting the findings and highlight the need for cautious interpretation, as well as further longitudinal and mechanistic studies to better elucidate causal pathways and inform preventive strategies. Conclusion The present findings suggest nuanced associations between smartphone addiction and cervical spine–related outcomes. While smartphone addiction severity was not significantly linked to MRI-detected disc degeneration or canal stenosis, demographic and behavioral correlates of smartphone addiction were evident. Objective MRI findings demonstrated disc degeneration and canal narrowing, especially at mid-cervical levels. Overall, these results emphasize the multifactorial nature of cervical spine disorders and the need for longitudinal research to better define causal pathways and guide preventive strategies in the digital era. Declarations Acknowledgements This article is derived from the academic thesis of MRN, conducted at Mazandaran University of Medical Sciences. The authors would like to thank all individuals who contributed to this study. Funding The authors received no funding for this study. Authors Contribution PP and PS made substantial contributions to the conception and design of the study, data acquisition, data analysis, interpretation of the findings, and drafting of the manuscript. KS contributed to data analysis, statistical interpretation, and critical review of the results. MS provided senior scientific supervision, contributed to study design, and critically revised the manuscript for important intellectual content. MR, SMS and PS were involved in participant recruitment, data collection, data entry, and preliminary data processing. HNA, SE and AA contributed to data management, software-based analyses, and assistance with manuscript preparation. All authors participated in manuscript revision, approved the final version for publication, and agree to be accountable for all aspects of the work. Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The data supporting the findings of this study are available from the corresponding author upon reasonable request. 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Mersal FA, Mohamed Abu Negm LM, Fawzy MS, Rajennal AT, Alanazi RS, Alanazi LO. Effect of Mobile Phone Use on Musculoskeletal Complaints: Insights From Nursing Students at Northern Border University, Arar, Saudi Arabia. Cureus [Internet]. 2024 Mar 29 [cited 2025 Aug 23]; Available from: https://www.cureus.com/articles/239367-effect-of-mobile-phone-use-on-musculoskeletal-complaints-insights-from-nursing-students-at-northern-border-university-arar-saudi-arabia Damasceno GM, Ferreira AS, Nogueira LAC, Reis FJJ, Andrade ICS, Meziat-Filho N. Text neck and neck pain in 18–21-year-old young adults. Eur Spine J. 2018 Jun;27(6):1249–54. Mousavi SJ, Parnianpour M, Montazeri A, Mehdian H, Karimi A, Abedi M, et al. Translation and Validation Study of the Iranian Versions of the Neck Disability Index and the Neck Pain and Disability Scale: Spine. 2007 Dec;32(26):E825–31. Shaahmadi Z, Jouybari TA, Lotfi B, Aghaei A, Gheshlagh RG. The validity and reliability of Persian version of smartphone addiction questionnaire in Iran. Subst Abuse Treat Prev Policy. 2021 Dec;16(1):69. صراف ف, واریانی عص, ورمزیار س. ارتباط بین اطلاعات دموگرافیک و وزن کیف با شاخص ناتوانی گردن، زوایا و وضعیت سر و گردن در بین دانشجویان. Griffith JF, Wang YXJ, Antonio GE, Choi KC, Yu A, Ahuja AT, et al. Modified Pfirrmann Grading System for Lumbar Intervertebral Disc Degeneration. Spine [Internet]. 2007;32(24). Available from: https://journals.lww.com/spinejournal/fulltext/2007/11150/modified_pfirrmann_grading_system_for_lumbar.28.aspx Waheed H, Khan MS, Muneeb A, Jahanzeb S, Ahmad MN. Radiologic Assessment of Cervical Canal Stenosis Using Kang MRI Grading System: Do Clinical Symptoms Correlate with Imaging Findings? Cureus [Internet]. 2019 Jul 3 [cited 2025 Sep 20]; Available from: https://www.cureus.com/articles/20318-radiologic-assessment-of-cervical-canal-stenosis-using-kang-mri-grading-system-do-clinical-symptoms-correlate-with-imaging-findings Zhuang L, Wang L, Xu D, Wang Z, Liang R. Association between excessive smartphone use and cervical disc degeneration in young patients suffering from chronic neck pain. J Orthop Sci. 2021 Jan;26(1):110–5. Rencher AC, Christensen WF. Methods of Multivariate Analysis [Internet]. 1st ed. Wiley; 2012 [cited 2025 Aug 23]. (Wiley Series in Probability and Statistics). Available from: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118391686 Kim MS. Influence of neck pain on cervical movement in the sagittal plane during smartphone use. J Phys Ther Sci. 2015;27(1):15–7. Burcu Erdogdu, Serpil Demirag. Smartphone Addiction: Generational Differences. 2025 Jul 20 [cited 2026 Feb 4]; Available from: https://zenodo.org/doi/10.5281/zenodo.16197938 Chen B, Liu F, Ding S, Ying X, Wang L, Wen Y. Gender differences in factors associated with smartphone addiction: a cross-sectional study among medical college students. BMC Psychiatry. 2017 Oct 10;17(1):341. Cevik S, Kaplan A, Katar S. Correlation of Cervical Spinal Degeneration with Rise in Smartphone Usage Time in Young Adults. Niger J Clin Pract. 2020;23(12):1748. Lee S, Choi YH, Kim J. Effects of the cervical flexion angle during smartphone use on muscle fatigue and pain in the cervical erector spinae and upper trapezius in normal adults in their 20s. J Phys Ther Sci. 2017;29(5):921–3. Grob D, Frauenfelder H, Mannion AF. The association between cervical spine curvature and neck pain. Eur Spine J. 2007 May 1;16(5):669–78. Kumagai G, Ono A, Numasawa T, Wada K, Inoue R, Iwasaki H, et al. Association between roentgenographic findings of the cervical spine and neck symptoms in a Japanese community population. J Orthop Sci. 2014 May 1;19(3):390–7. Richards KV, Beales DJ, Smith AJ, O’Sullivan PB, Straker LM. Neck Posture Clusters and Their Association With Biopsychosocial Factors and Neck Pain in Australian Adolescents. Phys Ther. 2016 Oct 1;96(10):1576–87. Tables Table 1. Demographic Characteristics of Participants (N=163). All values are given as counts with percentages in parentheses. Characteristic Category N (%) Gender Male 126 (77.3%) Female 37 (22.7%) Marital status Single 32 (19.6%) Married 131 (80.4%) Education level Level 1 (lowest) 50 (30.7%) Level 2 80 (49.1%) Level 3 32 (19.6%) Level 4 (highest) 1 (0.6%) Residence Urban 115 (70.6%) Rural 48 (29.4%) Table 2. Anthropometric and Clinical Measures (Mean ± SD and Range). Summary of participants’ height, weight, body mass index (BMI), Neck Disability Index (NDI), and SAS scores. Variable N Mean ± SD Range (Min–Max) Height (cm) 163 165.99 ± 7.83 137 – 188 Weight (kg) 163 75.29 ± 11.29 48 – 103 Body Mass Index (kg/m²) 161 27.25 ± 4.00 19.10 – 39.40 Neck Disability Index (NDI) (0–50) 163 19.73 ± 7.86 5 – 40 SAS (score, units**) 163 83.01 ± 27.37 33 – 157 Note: BMI was available for n=161 (two missing values). SAS = instrument score with possible range not specified (higher scores indicate greater severity; observed range 33–157). Table 3. Cervical Disc Degeneration Grades (Pfirrmann) at Each Spinal Level. Values are count of discs at each grade with percentage of the level total in parentheses. Pfirrmann grades range from 1 (healthy disc) to 8 (most severe degeneration). Spinal Level Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 Grade 6 Grade 7 Grade 8 C2–C3 1 (0.6%) 13 (8.0%) 25 (15.3%) 68 (41.7%) 35 (21.5%) 7 (4.3%) 13 (8.0%) 1 (0.6%) C3–C4 1 (0.6%) 10 (6.1%) 24 (14.7%) 67 (41.1%) 39 (23.9%) 12 (7.4%) 9 (5.5%) 1 (0.6%) C4–C5 0 (0.0%) 10 (6.1%) 28 (17.2%) 63 (38.7%) 31 (19.0%) 15 (9.2%) 12 (7.4%) 4 (2.5%) C5–C6 1 (0.6%) 3 (1.8%) 25 (15.3%) 53 (32.5%) 26 (16.0%) 25 (15.3%) 17 (10.4%) 13 (8.0%) C6–C7 3 (1.8%) 21 (12.9%) 25 (15.3%) 58 (35.6%) 26 (16.0%) 12 (7.4%) 10 (6.1%) 8 (4.9%) C7–T1 2 (1.2%) 28 (17.2%) 41 (25.2%) 60 (36.8%) 14 (8.6%) 7 (4.3%) 11 (6.7%) 0 (0.0%) Table 4. Segmental Cervical Stenosis Grades (Kang criteria) at Each Level. Values are count of segments with percentage in parentheses. Kang grades range from 0 (normal) to 3 (severe Stenosis). Spinal Level Grade 0 (normal) Grade 1 (minor stenosis) Grade 2 (moderate Stenosis) Grade 3 (severe Stenosis) C2–C3 153 (93.9%) 8 (4.9%) 0 (0.0%) 1 (0.6%) C3–C4 137 (84.0%) 21 (12.9%) 4 (2.5%) 1 (0.6%) C4–C5 106 (65.0%) 37 (22.7%) 17 (10.4%) 3 (1.8%) C5–C6 85 (52.1%) 39 (23.9%) 30 (18.4%) 9 (5.5%) C6–C7 129 (79.1%) 19 (11.7%) 11 (6.7%) 4 (2.5%) C7–T1 157 (96.3%) 2 (1.2%) 4 (2.5%) 0 (0.0%) One C2–C3 segment (0.6%) had a Kang grade recorded as “7,” which is outside the standard 0–3 range; this outlier is not included in the table calculations. Table 5. Pearson Correlations between SAS and Selected Variables Variables N r p SAS and NDI 163 0.10 .192 SAS and Height (cm) 163 0.11 .154 SAS and Weight (kg) 163 0.09 .283 SAS and BMI 161 0.01 .856 Table 6. SAS by Pfirrmann Disc Degeneration Grade at Each Cervical Level (Kruskal–Wallis Tests) Cervical Level Grade 1 (n, Mean Rank) Grade 2 (n, Mean Rank) Grade 3 (n, Mean Rank) χ²(2) p C2–C3 14, 84.11 128, 82.80 21, 75.71 0.44 .804 C3–C4 11, 90.32 130, 82.32 22, 75.95 0.71 .702 C4–C5 10, 90.40 122, 82.01 31, 79.26 0.42 .810 C5–C6 4, 72.38 104, 87.43 55, 72.44 3.80 .149 C6–C7 24, 92.96 109, 80.72 30, 77.88 1.60 .449 C7–T1 30, 90.07 115, 81.87 18, 69.36 2.17 .338 Table 7. SAS by Kang Grade at Each Cervical Level (Mann–Whitney U Tests) Cervical Level Kang Grade 1(n, Mean Rank) Kang Grade 2 (n, Mean Rank) U p C2–C3 161, 81.3 1, 110.0 52.0 .542 C3–C4 158, 83.3 5, 39.5 182.5 .041 C4–C5 143, 83.5 20, 71.2 1213.0 .272 C5–C6 124, 84.0 39, 75.6 2167.0 .329 C6–C7 148, 83.3 15, 69.1 916.0 .265 C7–T1 159, 82.6 4, 58.0 222.0 .303 Table 8. Independent t -Tests for SAS by Gender, Marital Status, and Residence Variable Group 1 (n) – Mean (SD) Group 2 (n) – Mean (SD) t (df) p Gender Male (126) – 83.37 (26.72) Female (37) – 81.78 (29.82) 0.31 (161) .758 Marital Status Single (32) – 94.75 (29.55) Married (131) – 80.14 (26.13) 2.76 (161) .006 Residence Urban (115) – 84.86 (27.70) Rural (48) – 78.56 (26.29) 1.34 (161) .181 Table 9. SAS Scores by Educational Level, One-way ANOVA showed a significant group effect (F(2,159)=6.55, p=.002). Education Level n M (SD) Diploma or lower 50 75.54 (23.96) Bachelor’s degree 80 81.74 (27.02) Postgraduate degree 32 97.00 (28.73) Table 10. Summary of GLM (normal identity link) examining marital status as a predictor of SAS scores (N = 163). Predictor B SE 95% CI for B Wald χ²(1) p Intercept (Married) 80.137 2.329 [75.572, 84.702] 1183.866 < .001 Marital Status: Single vs Married 14.613 5.257 [4.310, 24.915] 7.728 .005 Note. The omnibus likelihood ratio test for the model versus an intercept-only model was χ²(1) = 7.550, p = .006. The intercept represents the mean SAS score for married participants (reference category). B = unstandardized regression coefficient; SE = standard error; CI = confidence interval. Additional Declarations No competing interests reported. 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Sobhanian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYBACewglAcQ8bBAGewOQLoCIYQOGDRhaeA4wMBwwwK3F4ACcCdYC0pVAQMvtswcffNxjIS/f3nvswc8dFvLmko+PSX8wsJGTbGB++OgGFi3n8pINZzyTMNxw5ly6Ye8ZCcOds9PSJA4YpBlLM7AZG+dg0XKGx0ya54AE4waJHDMJ3jYg4zaQccDgcOI8oFOl8Wixnz8jx0zyb5uE/YabZ4jTkthwI8dMGmhL4oYbPBAts3FoMezhA/rlgEQy0C9p0rJtIEZassUZoF8km7H7xZ6H9+CDDwfqbOcDQ0zybVud7Ybjhw/eqKiwkZM43vzwMRYtwOjAJggGzLgkcGsZBaNgFIyCUQABAPp5ZKajojalAAAAAElFTkSuQmCC","orcid":"","institution":"Mazandaran University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Pooria","middleName":"","lastName":"Sobhanian","suffix":""}],"badges":[],"createdAt":"2026-02-05 18:09:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8800006/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8800006/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102536524,"identity":"c665a66e-d645-40b7-bbf3-9419a9a26c98","added_by":"auto","created_at":"2026-02-12 17:33:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1547445,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8800006/v1/4193d738-d34c-4f98-8ae7-7bfe909af190.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating the Relationship Between Smartphone Addiction and Suffering from Degenerative Diseases of the Cervical Spine","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSmartphone ownership has expanded rapidly worldwide, with young adults among the heaviest users (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). While these devices provide significant social and academic benefits, prolonged use has been linked to stress, sleep disturbance, anxiety, reduced physical activity, and impaired academic performance (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Musculoskeletal symptoms are also common. Neck pain is reported in 17% \u0026minus;\u0026thinsp;68% of users, with lifetime prevalence exceeding 50% (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The most frequently affected regions are the neck, shoulders, and upper back, and smartphone addiction appears to further increase the risk of musculoskeletal disorders (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne major contributing factor is posture. Smartphone use typically involves sustained forward head flexion of 30\u0026deg;- 45\u0026deg; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Even modest flexion angles markedly increase cervical loading, with the effective head weight rising from ~\u0026thinsp;12 pounds in neutral position to as much as 60 pounds at 60\u0026deg; (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Such non-neutral posture increases neck muscle activity, reduces endurance, and alters proprioception (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Young adults with mild neck pain show greater cervical flexion during smartphone tasks than asymptomatic peers, suggesting that pain may alter motor control (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Eventually, these biomechanical stresses may contribute to pain, muscular imbalance, and abnormalities in spinal alignment (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCervical spondylosis is a chronic degenerative disorder involving disc dehydration, osteophyte formation, and secondary changes in ligaments, nerves, and the spinal cord (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). It is now the third most common cause of chronic pain in the United States and the fourth worldwide, with a growing impact on younger populations (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Among students, neck discomfort is highly prevalent, and chronic symptoms are associated with central nervous system changes, disability, and negative emotional states (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDisease progression can lead to radiculopathy or cervical spondylotic myelopathy, presenting with symptoms that range from pain and stiffness to motor and sensory deficits (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Magnetic resonance imaging (MRI) is considered the gold standard for the detection of these structural changes (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent evidence suggests that modern behavioral factors may accelerate these processes. Prolonged smartphone and media use, often more than five hours daily, is strongly associated with musculoskeletal complaints, especially among female students, and correlates with both addiction behaviors and reduced academic performance (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). These findings highlight how lifestyle and technology use may exacerbate underlying degenerative changes in the cervical spine (\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmerging data also indicate potential links between smartphone overuse and early myelopathy. Students using smartphones for nine or more hours per day were nearly twice as likely to demonstrate positive myelopathic signs, including Hoffmann\u0026rsquo;s and Tr\u0026ouml;mner\u0026rsquo;s tests (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Prolonged flexion narrows the cervical canal, stretches the spinal cord, and may provoke ligamentous edema (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). MRI studies show that up to one-third of patients with neck pain already exhibit degenerative or myelopathic changes, underscoring the importance of imaging in early detection (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). However, the current literature has important limitations. Much of the available evidence is derived from cross-sectional, experimental, or small case\u0026ndash;control studies, and investigations focusing on imaging-based outcomes remain relatively scarce, thereby limiting causal inference (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Additionally, findings are heterogeneous: several studies of young adults report no clear connection between texting posture (\"text neck\") and neck pain, highlighting the need for objective endpoints like MRI to address these inconsistencies (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, this study was developed to assess MRI-detected changes in the cervical spine of young adults with high smartphone exposure. Through the integration of imaging and clinical features, we aimed to establish whether prolonged use and addiction-like engagement with smartphones are associated with early degenerative changes. This focus on structural markers offers evidence that could guide preventive measures, ergonomic recommendations, and public health initiatives for younger populations (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis analytical cross-sectional study was conducted between December 2023 and April 2025. Data collection employed standardized demographic questionnaires, the Neck Disability Index (NDI), and the Smartphone Addiction Scale (SAS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Ethical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Research Ethics Committee of Sari University of Medical Sciences \u003cem\u003e(IR.MAZUMS.IMAMHOSPITAL.REC.1402.087).\u003c/em\u003e Written informed consent was secured from all individual participants prior to their inclusion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEligible participants were adults aged 18\u0026ndash;55 years who presented to Imam Khomeini Hospital with chronic neck pain (duration \u0026ge;3 months), with or without associated neurological signs, during the study period. All participants were regular smartphone users. Exclusion criteria comprised cervical neoplasm, active infection of the cervical region, congenital cervical spine deformity, history of traumatic injury to the neck or shoulder girdle, previous cervical spine surgery, current major psychiatric disorder, use of sedative medication within 48 hours of assessment, or unwillingness to participate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Data collection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were sourced from self-administered questionnaires and clinical records. Collected variables included demographic characteristics, occupation, socioeconomic and educational status, level of physical activity, neck pain characteristics, the Neck Disability Index (NDI) score (19), and smartphone use behavior as quantified by the validated Persian version of the Smartphone Addiction Scale (SAS) (20). All participants underwent cervical spine magnetic resonance imaging (MRI) for the evaluation of intervertebral disc degeneration. The validated Persian versions of the NDI and SAS were used in this study(19,20).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Smartphone addiction assessment (SAS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSmartphone addiction was evaluated using the 33-item Smartphone Addiction Scale (SAS). Each item is rated on a 6-point Likert scale from 1 (\u0026ldquo;strongly disagree\u0026rdquo;) to 6 (\u0026ldquo;strongly agree\u0026rdquo;). The total score ranges from 33 to 198.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Neck Disability Index (NDI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeck-related disability was assessed using the 10-item Persian version of the Neck Disability Index (NDI) (21). \u0026nbsp;Items are scored from 0 to 5, yielding a total score between 0 and 50. Disability severity was categorized as follows: 0\u0026ndash;10 (mild), 11\u0026ndash;20 (moderate), 21\u0026ndash;30 (severe), 31\u0026ndash;40 (complete disability), and 41\u0026ndash;50 (very severe disability). For interpretive clarity, scores were also converted to percentage values, with categories defined as 0\u0026ndash;20% (mild), 21\u0026ndash;40% (moderate), 41\u0026ndash;60% (severe), 61\u0026ndash;80% (complete), and 81\u0026ndash;100% (very severe disability).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Assessment of Cervical Disc Degeneration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCervical intervertebral disc degeneration was evaluated on mid-sagittal T2-weighted MRI sequences using the Modified Pfirrmann Grading System. This system classifies discs into eight grades based on nucleus pulposus signal intensity, distinction of the annular fibers, and disc height. A Grade 1 disc, for instance, demonstrates uniform hyperintensity with signal intensity equivalent to cerebrospinal fluid, a distinct posterior junction between inner and outer annular fibers, and preserved disc height. Grade 2 is characterized by hyperintense signal between that of presacral fat and cerebrospinal fluid, with or without a hypointense intranuclear cleft, while maintaining a distinct annular junction and normal disc height. In Grade 3, the disc appears hyperintense but with signal intensity lower than that of presacral fat; the annular junction remains distinct, and disc height is preserved. Grade 4 demonstrates a mildly hyperintense signal, slightly greater than the outer annular fibers, accompanied by an indistinct annular junction but without loss of disc height. Grade 5 is defined by a hypointense signal equal to that of the outer annular fibers, with an indistinct annular junction and normal disc height. Grades 6 through 8 indicate progressive disc height loss, with a hypointense signal and an indistinct annular junction. According to the Modified Pfirrmann system, Grade 6 degeneration is characterized by a disc height reduction of less than 30%, Grade 7 by a 30\u0026ndash;60% reduction, and Grade 8 by a loss exceeding 60%, indicative of end-stage degeneration\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e(22).\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eFor the purpose of statistical analysis, the original eight-grade classification was condensed into three ordinal categories: mild degeneration (Grades 1\u0026ndash;2), moderate degeneration (Grades 3\u0026ndash;5), and severe degeneration (Grades 6\u0026ndash;8). Differences in smartphone addiction scores across these groups were assessed using the Kruskal\u0026ndash;Wallis test.\u003c/p\u003e\n\u003cp\u003eAll cervical discs were graded independently by two fellowship-trained radiologists blinded to the patients\u0026rsquo; clinical and demographic information. In cases of discordance, a consensus reading was reached through adjudication by a third senior radiologist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Assessment of Cervical Spinal Canal Stenosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCervical spinal canal stenosis was graded according to the Kang classification system. This MRI-based system stratifies severity by the degree of spinal cord compression as follows: no stenosis; mild stenosis (\u0026gt;50% obliteration of the subarachnoid space without spinal cord deformity); moderate stenosis (central canal narrowing with cord deformity but without intramedullary T2 signal change); and severe stenosis (cord deformity with associated intramedullary T2 hyperintensity at the compressive level) (23).\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eFor inferential analyses, the Kang classification was dichotomized by grouping Grades 0 and 1 as absent to mild canal stenosis, and Grades 2 and 3 as moderate to severe stenosis. This categorization was applied to facilitate statistical comparison of smartphone addiction scores across clinically meaningful stenosis severity groups. The Kang classification is a simple and reproducible tool that facilitates the evaluation of canal narrowing, informs treatment planning, and helps predict clinical outcomes. Nevertheless, imaging findings were interpreted in the context of each patient\u0026rsquo;s clinical presentation and were not used in isolation for therapeutic decision-making.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9 Sample size stimulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the study by Zhuang et al. (24) which compared individuals with and without excessive smartphone use, the minimum required sample size was calculated using the standard formula for two independent groups, assuming a type I error rate of 0.05 and a statistical power of 90%. This yielded a minimum of 32 participants. However, given that the Smartphone Addiction Scale (SAS) comprises 33 items, we applied the rule of thumb recommended by Rencher\u0026rsquo;s multivariate analysis text (25). which suggests recruiting at least five participants per questionnaire item to ensure stable and reliable parameter estimation. Accordingly, the target sample size was increased to 160 participants, and this number of questionnaires was administered and completed by the patients.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1770917407.png\" width=\"437\" height=\"103\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the reporting of descriptive statistics, quantitative variables were presented as mean (standard deviation) and qualitative variables as frequency (percent). The Kolmogorov-Smirnov test was used to assess the normal distribution of quantitative variables. For inter-group comparisons of normally distributed quantitative variables, means were compared using the independent samples t-test; the non-parametric Mann-Whitney U test was applied for non-normally distributed data. Associations between two quantitative variables were assessed using Pearson\u0026rsquo;s correlation coefficient for normally distributed data and Spearman\u0026rsquo;s rank correlation coefficient otherwise.\u003c/p\u003e\n\u003cp\u003eTo assess the independent relationship between smartphone addiction and degenerative cervical spine findings while adjusting for potential confounders, multiple linear regression analysis was performed. First, univariate regression models were fitted. All variables with a P-value \u0026lt; 0.25 in the univariate analysis were subsequently entered into the multivariable model. All analyses were conducted using SPSS software (version 20.0), with statistical significance defined as a two-tailed P-value \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipant Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included 163 participants (77.3% male; 22.7% female). Most were married (80.4%), with the remainder single (19.6%). Nearly half (49.1%) had mid-level education (Level 2); 30.7% had the lowest level, 19.6% a higher level (Level 3), and one participant (0.6%) held the highest level (Level 4). The majority lived in urban areas (70.6%) versus rural (29.4%).\u003c/p\u003e\n\u003cp\u003eAnthropometric and clinical measures indicated a moderately overweight sample. Mean height was 166.0 cm (\u003cem\u003eSD\u003c/em\u003e = 7.8) and mean weight 75.3 kg (\u003cem\u003eSD\u003c/em\u003e = 11.3), corresponding to a mean BMI of 27.3 (\u003cem\u003eSD\u003c/em\u003e = 4.0). The average Neck Disability Index (NDI) score was 19.73 (\u003cem\u003eSD\u003c/em\u003e = 7.86) out of 50, reflecting moderate neck-related disability. The mean SAS score was 83.0 (\u003cem\u003eSD\u003c/em\u003e = 27.4), indicating substantial variability in the outcome measure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCervical Disc Degeneration (Pfirrmann Grades)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll evaluated cervical discs (C2\u0026ndash;C3 through C7\u0026ndash;T1) showed degeneration. Pfirrmann Grade 4 (moderate degeneration) was the most common grade at each level, accounting for roughly 35\u0026ndash;42% of discs per level. Very few discs were nearly normal (Grades 1\u0026ndash;2; \u0026lt;10% at any level), and most discs exhibited some degeneration. Severe grades (6\u0026ndash;8) were present only in a minority of discs. The mid-cervical levels tended to show more severe degeneration: for example, at C5\u0026ndash;C6 33.7% of discs were Grades 6\u0026ndash;8 (including 8.0% Grade 8), whereas at the cervicothoracic junction (C7\u0026ndash;T1) no Grade 8 discs were observed. In summary, moderate degeneration predominated across all levels, while very mild or very severe changes were comparatively rare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCervical Spine Stenosis (Kang Grades)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMost cervical segments demonstrated no evidence of spinal canal stenosis (Kang Grade 0), particularly in the upper and lower cervical spine. For example, 93.9% of segments at C2\u0026ndash;C3 and 96.3% at C7\u0026ndash;T1 were classified as Grade 0. The prevalence and severity of canal stenosis increased in the mid-cervical region. At the C5\u0026ndash;C6 level, only 52.1% of segments were Grade 0, while 23.9% showed Grade 1 stenosis and 23.9% exhibited more advanced stenosis, including 18.4% with Grade 2 and 5.5% with Grade 3 involvement. A similar, though less pronounced, pattern was observed at C4\u0026ndash;C5, where 65.0% of segments were Grade 0, 22.7% Grade 1, and 12.2% showed moderate to severe stenosis (10.4% Grade 2 and 1.8% Grade 3). In contrast, lower cervical levels predominantly retained a normal canal diameter, with 79.1% of C6\u0026ndash;C7 and 96.3% of C7\u0026ndash;T1 segments classified as Grade 0. Overall, cervical canal stenosis was most frequently observed at mid-cervical levels, particularly at C5\u0026ndash;C6, while the upper and lower segments were largely spared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociations with SAS Scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelations with Clinical Measures:\u003c/strong\u003e Pearson correlations showed that SAS scores were not significantly related to neck disability or anthropometrics. SAS did not correlate with NDI (\u003cem\u003er\u003c/em\u003e(161) = .10, \u003cem\u003ep\u003c/em\u003e = .192), height (\u003cem\u003er\u003c/em\u003e = .11, \u003cem\u003ep\u003c/em\u003e = .154), weight (\u003cem\u003er\u003c/em\u003e = .09, \u003cem\u003ep\u003c/em\u003e = .283), or BMI (\u003cem\u003er\u003c/em\u003e = .01, \u003cem\u003ep\u003c/em\u003e = .856). All correlations were small and non-significant, indicating no linear relationship between SAS and these measures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSAS by Disc Degeneration (Pfirrmann):\u003c/strong\u003e We compared SAS across Pfirrmann grades (1\u0026ndash;3) at each cervical level using Kruskal\u0026ndash;Wallis tests. No significant differences were found at any level (all \u003cem\u003ep\u003c/em\u003e \u0026gt; .05). For example, at C5\u0026ndash;C6 the largest contrast had \u003cem\u003eH\u003c/em\u003e(2) = 3.80, \u003cem\u003ep\u003c/em\u003e = .149, which was not significant. Thus, SAS scores did not vary by degree of disc degeneration at any single level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSAS by Segmental Stenosis (Kang):\u003c/strong\u003e Mann\u0026ndash;Whitney tests compared SAS between Kang grade 1 (no and mild stenosis) versus grade 2 (moderate to severe stenosis) at each level. No significant differences were observed at most levels (all \u003cem\u003ep\u003c/em\u003e \u0026gt; .25). The only exception was C3\u0026ndash;C4, where the small grade-2 group (n = 5) had lower SAS scores than grade-1 segments (\u003cem\u003eU\u003c/em\u003e = 182.5, \u003cem\u003ep\u003c/em\u003e = .041). This isolated finding (with a very small n) should be viewed with caution. Apart from this, SAS did not differ by segmental stenosis at any level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSAS by Demographic Factors:\u003c/strong\u003e Group comparisons were conducted using independent-samples \u003cem\u003et\u003c/em\u003e-tests or ANOVA. SAS did not differ by gender: males (n = 126) had mean \u003cem\u003eM\u003c/em\u003e = 83.37 (\u003cem\u003eSD\u003c/em\u003e = 26.72) and females (n = 37) \u003cem\u003eM\u003c/em\u003e = 81.78 (\u003cem\u003eSD\u003c/em\u003e = 29.82), \u003cem\u003et\u003c/em\u003e(161) = 0.31, \u003cem\u003ep\u003c/em\u003e = .758. Residence also had no significant effect: urban residents (n = 115) \u003cem\u003eM\u003c/em\u003e = 84.86 (\u003cem\u003eSD\u003c/em\u003e = 27.70) versus rural (n = 48) \u003cem\u003eM\u003c/em\u003e = 78.56 (\u003cem\u003eSD\u003c/em\u003e = 26.29), \u003cem\u003et\u003c/em\u003e(161) = 1.34, \u003cem\u003ep\u003c/em\u003e = .181. In contrast, marital status was significantly associated with SAS. Single participants (n = 32) had higher SAS scores (\u003cem\u003eM\u003c/em\u003e = 94.75, \u003cem\u003eSD\u003c/em\u003e = 29.55) than married participants (n = 131; \u003cem\u003eM\u003c/em\u003e = 80.14, \u003cem\u003eSD\u003c/em\u003e = 26.13), \u003cem\u003et\u003c/em\u003e(161) = 2.76, \u003cem\u003ep\u003c/em\u003e = .006, indicating that singles tended to report greater alignment scores than married individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSAS by Educational Level:\u003c/strong\u003e A one-way ANOVA showed a significant effect of education on SAS scores, \u003cem\u003eF\u003c/em\u003e(2,159) = 6.55, \u003cem\u003ep\u003c/em\u003e = .002. Mean SAS increased with education: diploma or lower (\u003cem\u003eM\u003c/em\u003e = 75.54, \u003cem\u003eSD\u003c/em\u003e = 23.96, \u003cem\u003en\u003c/em\u003e = 50), bachelor\u0026rsquo;s degree (\u003cem\u003eM\u003c/em\u003e = 81.74, \u003cem\u003eSD\u003c/em\u003e = 27.02, \u003cem\u003en\u003c/em\u003e = 80), and postgraduate (\u003cem\u003eM\u003c/em\u003e = 97.00, \u003cem\u003eSD\u003c/em\u003e = 28.73, \u003cem\u003en\u003c/em\u003e = 32). Post hoc comparisons (LSD) indicated that the postgraduate group scored significantly higher than both the bachelor\u0026rsquo;s (p = .007) and diploma (p \u0026lt; .001) groups; the bachelor\u0026rsquo;s and diploma groups did not differ significantly (p = .196). These results suggest a positive association between higher education level and SAS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate Analysis (GLM)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA generalized linear model (normal distribution, identity link) tested marital status as a predictor of SAS. The model (with marital status as a covariate) fit significantly better than a null model (likelihood-ratio \u0026chi;\u0026sup2;(1) = 7.55, \u003cem\u003ep\u003c/em\u003e = .006). The Wald chi-square for marital status was \u0026chi;\u0026sup2;(1) = 7.73, \u003cem\u003ep\u003c/em\u003e = .005, confirming a significant effect. The unstandardized parameter estimate for being single (vs. married) was \u003cem\u003eB\u003c/em\u003e = 14.61 (\u003cem\u003eSE\u003c/em\u003e = 5.26), 95% CI [4.31, 24.91], \u003cem\u003ep\u003c/em\u003e = .005. The intercept (mean for married participants) was \u003cem\u003eB\u003c/em\u003e = 80.14 (\u003cem\u003eSE\u003c/em\u003e = 2.33), 95% CI [75.57, 84.70]. These results indicate that, on average, single individuals scored about 14.6 points higher on the SAS than married individuals. Thus, even in the multivariate model, marital status significantly predicted SAS scores, with single status associated with higher SAS.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSmartphone addiction in the present study was assessed using the original Smartphone Addiction Scale (SAS) on its native 33\u0026ndash;198 scale. The observed mean SAS score in our cohort was comparable to values reported in previous studies of patients with chronic neck pain, including the study by Zhuang et al.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e(24),\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003esuggesting a similar level of smartphone addiction severity across clinical populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur overall NDI scores were higher than those reported by Kim et al., who studied young adults with mild neck pain(26). This discrepancy is likely attributable to differences in the study population: our participants were patients with chronic neck pain presenting to a hospital setting, whereas Kim et al. recruited university students with only mild, self-reported neck pain from the community. The higher disability burden in our cohort is therefore consistent with the expectation that clinically referred patients exhibit more severe functional limitations than community samples with subclinical or mild symptoms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the present study, smartphone addiction severity varied according to selected demographic characteristics. Marital status emerged as a significant and independent correlate of SAS scores, with single participants reporting higher levels of smartphone addiction than married individuals; this association remained robust in multivariate analysis. This pattern may reflect differences in daily social structure, leisure activities, and reliance on digital communication, such that unmarried individuals may use smartphones more frequently for social interaction and entertainment, consistent with previous studies reporting higher SAS scores among unmarried compared with married individuals (27). Educational attainment was also positively associated with SAS scores, with postgraduate participants exhibiting the highest levels of smartphone addiction severity compared with those holding bachelor\u0026rsquo;s or diploma-level education. This association may be driven by greater academic and professional demands among highly educated individuals, leading to more intensive and functionally driven smartphone use, which may elevate SAS scores without necessarily reflecting maladaptive behavior. Consistent with some prior investigations, no significant differences in smartphone addiction were observed between males and females, suggesting that gender may not be a robust predictor of problematic smartphone use in all populations (28). Additionally, no significant differences in smartphone addiction severity were observed according to place of residence, suggesting that smartphone addiction behaviors in this clinical population are broadly distributed across demographic groups and are not confined to specific gender roles or urban-rural contexts.\u003c/p\u003e\n\u003cp\u003eIn the present study, no significant association was identified between smartphone addiction severity (SAS) and cervical intervertebral disc degeneration as assessed by the modified Pfirrmann grading system. Smartphone addiction scores were comparable across different degrees of disc degeneration at all cervical levels, indicating that degeneration severity was not related to the extent of smartphone use.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModerate levels of smartphone use were most common across the cohort, whereas high levels of use were relatively uncommon. These findings indicate that, in a population of patients with chronic neck pain, smartphone addiction severity (SAS) alone is unlikely to be a major determinant of cervical disc degeneration. This interpretation is consistent with the prevailing view that cervical degenerative changes result from a complex interplay of factors, including age-related processes, genetic susceptibility, occupational loading, biomechanical stress, and prior cervical injury. Although previous studies have suggested that prolonged smartphone use may contribute to early degenerative changes of the cervical spine (24,29), we did not observe a significant association in the present study. This may, in part, be attributable to the limited proportion of heavy smartphone users in our sample or to the inherent constraints of a cross-sectional design in establishing causal relationships (24,29). We did not observe a significant association in the present study. This may, in part, be attributable to the limited proportion of heavy smartphone users in our sample or to the inherent constraints of a cross-sectional design in establishing causal relationships.\u003c/p\u003e\n\u003cp\u003eAn isolated and unexpected finding was observed at the C3\u0026ndash;C4 level, where lower Smartphone Addiction Scale (SAS) scores were associated with greater cervical canal stenosis severity. This association was limited to a single cervical segment and was based on a small number of cases with higher-grade stenosis; therefore, it should be interpreted with considerable caution. No consistent or significant associations between smartphone addiction scores and canal stenosis were identified at other cervical levels. This pattern contrasts with prior reports suggesting that excessive smartphone use preferentially affects lower cervical segments through sustained flexion and biomechanical stress(2). Given the cross-sectional design of the present study, a causal relationship cannot be inferred. A plausible explanation for this isolated inverse association is reverse causality, whereby individuals with pre-existing cervical stenosis and related symptoms, such as neck pain or neurological complaints, may intentionally limit smartphone use to avoid symptom exacerbation during prolonged neck flexion. In this context, reduced smartphone use is therefore more likely a consequence rather than a cause of cervical canal stenosis.\u003c/p\u003e\n\u003cp\u003eAlthough our findings did not demonstrate a significant association between smartphone addiction severity and disc degeneration, mechanistically, prolonged smartphone use entails sustained neck flexion, resulting in muscular fatigue and biomechanical overload of cervical structures (24,29). Experimental evidence supports the relationship between increased cervical flexion angles during smartphone use and elevated mechanical load, with disc degeneration detectable even in individuals in their twenties (29). Cevik et al. also documented alterations in cervical sagittal balance attributable to smartphone use, including Modic changes (29). Electromyographic studies report increased activity in cervical erector spinae muscles and reduced upper trapezius activation under sustained flexion, particularly in users with neck pain, reflecting altered muscular control (8,10,30).\u003c/p\u003e\n\u003cp\u003eWhile neck flexion is prevalent during smartphone use and posited as a causative factor in cervical spine disorders among addicted users (7,8), some conflicting evidence exists. Suzuki et al. found only a slight inverse correlation between cervical vertical angle and upper trapezius muscle stiffness, with no significant differences between symptomatic and asymptomatic subjects (11). Simultaneously, several studies challenge a direct causal link between neck posture and pain based on self-assessment and clinical evaluations, noting that forces causing spinal instability vastly exceed those attributable to poor posture alone (18,31\u0026ndash;33).\u003c/p\u003e\n\u003cp\u003eClinically, subtle modifications in neck flexion angle may hold limited significance; however, correcting posture angles could reduce pain and dysfunction in smartphone users with neck symptoms (8).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModified cervical exercises have demonstrated potential benefits in improving posture and alleviating adverse effects related to smartphone use (6). Therefore, ergonomic interventions deserve further investigation in this context.\u003c/p\u003e\n\u003cp\u003eThis study has several notable strengths. First, the relatively large sample size of 163 participants enhances statistical power and improves the generalizability of the findings compared with many previous studies that included smaller cohorts. Second, the use of validated and culturally adapted instruments, namely the Persian versions of the Smartphone Addiction Scale (SAS) and the Neck Disability Index (NDI), supports the reliability and relevance of the collected clinical and behavioral data. Third, the objective assessment of cervical spine degeneration and spinal canal stenosis using magnetic resonance imaging (MRI) represents an important methodological strength, allowing direct and accurate visualization of anatomical changes. In addition, MRI evaluations were performed independently by two radiologists who were blinded to clinical information, with adjudication to resolve discrepancies, thereby reducing observer bias and strengthening diagnostic validity. The integration of clinical questionnaires, self-reported smartphone behavior, and detailed radiological grading provides a comprehensive perspective on the relationship between smartphone addiction and cervical spine pathology.\u003c/p\u003e\n\u003cp\u003eDespite these strengths, several limitations should be considered when interpreting the results. The cross-sectional design precludes the establishment of causal or temporal relationships between smartphone addiction and the development or progression of cervical spine degeneration. Moreover, reliance on self-reported smartphone use introduces the potential for recall and reporting bias, which may affect the accuracy of exposure assessment. Direct measurements of smartphone-related posture, such as cervical flexion angles or ergonomic assessments, were not included and could have helped clarify the biomechanical mechanisms underlying the observed associations. In addition, several potential confounders, including workplace ergonomics, physical activity levels, and psychosocial stressors, were not systematically measured or adjusted for and may have influenced neck disability and degenerative changes. The single-center design, conducted at a medical facility in Iran, may also limit the generalizability of the findings to other populations. Finally, variability in smartphone device characteristics, such as size and weight, which may independently influence neck posture and mechanical loading, was not accounted for and could have confounded the relationship between smartphone use and cervical spine health.\u003c/p\u003e\n\u003cp\u003eOverall, these strengths and limitations provide a balanced framework for interpreting the findings and highlight the need for cautious interpretation, as well as further longitudinal and mechanistic studies to better elucidate causal pathways and inform preventive strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present findings suggest nuanced associations between smartphone addiction and cervical spine\u0026ndash;related outcomes. While smartphone addiction severity was not significantly linked to MRI-detected disc degeneration or canal stenosis, demographic and behavioral correlates of smartphone addiction were evident. Objective MRI findings demonstrated disc degeneration and canal narrowing, especially at mid-cervical levels. Overall, these results emphasize the multifactorial nature of cervical spine disorders and the need for longitudinal research to better define causal pathways and guide preventive strategies in the digital era.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article is derived from the academic thesis of MRN,\u003csup\u003e\u0026nbsp;\u003c/sup\u003econducted at Mazandaran University of Medical Sciences. The authors would like to thank all individuals who contributed to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors received no funding for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePP and PS made substantial contributions to the conception and design of the study, data acquisition, data analysis, interpretation of the findings, and drafting of the manuscript. KS contributed to data analysis, statistical interpretation, and critical review of the results. MS provided senior scientific supervision, contributed to study design, and critically revised the manuscript for important intellectual content. MR, SMS and PS were involved in participant recruitment, data collection, data entry, and preliminary data processing. HNA, SE and AA contributed to data management, software-based analyses, and assistance with manuscript preparation. All authors participated in manuscript revision, approved the final version for publication, and agree to be accountable for all aspects of the work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003cbr\u003e\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Generative AI and AI-assisted Technologies\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors used generative artificial intelligence-assisted tools solely for language editing and grammatical refinement. Following the use of these tools, the authors reviewed, edited, and verified the content and take full responsibility for the accuracy, originality, and integrity of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThom\u0026eacute;e S, H\u0026auml;renstam A, Hagberg M. Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults - a prospective cohort study. BMC Public Health. 2011 Dec;11(1):66. \u003c/li\u003e\n\u003cli\u003ePuntumetakul R, Chatprem T, Saiklang P, Phadungkit S, Kamruecha W, Sae-Jung S. Prevalence and Associated Factors of Clinical Myelopathy Signs in Smartphone-Using University Students with Neck Pain. Int J Environ Res Public Health. 2022 Apr 17;19(8):4890. \u003c/li\u003e\n\u003cli\u003eHanphitakphong P, Keeratisiroj O, Thawinchai N. Smartphone addiction and its association with upper body musculoskeletal symptoms among university students classified by age and gender. J Phys Ther Sci. 2021;33(5):394\u0026ndash;400. \u003c/li\u003e\n\u003cli\u003eBr\u0026uuml;hl M, Hmida J, Tomschi F, Cucchi D, Wirtz DC, Strauss AC, et al. Smartphone Use\u0026mdash;Influence on Posture and Gait during Standing and Walking. Healthcare. 2023 Sep 14;11(18):2543. \u003c/li\u003e\n\u003cli\u003eToh SH, Coenen P, Howie EK, Straker LM. The associations of mobile touch screen device use with musculoskeletal symptoms and exposures: A systematic review. Baur H, editor. PLOS ONE. 2017 Aug 7;12(8):e0181220. \u003c/li\u003e\n\u003cli\u003eKong YS, Kim YM, Shim J myung. The effect of modified cervical exercise on smartphone users with forward head posture. J Phys Ther Sci. 2017;29(2):328\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003eAlshahrani A, Samy Abdrabo M, Aly SM, Alshahrani MS, Alqhtani RS, Asiri F, et al. Effect of Smartphone Usage on Neck Muscle Endurance, Hand Grip and Pinch Strength among Healthy College Students: A Cross-Sectional Study. 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The association between mobile phone usage duration, neck muscle endurance, and neck pain among university students. Sci Rep. 2024 Aug 29;14(1):20116. \u003c/li\u003e\n\u003cli\u003eAbu Halimah J, Mojiri M, Hakami S, Mobarki O, Alanazi S, Alharbi A, et al. Musculoskeletal Health Risks Associated With Smartphone Use: A Retrospective Study from Riyadh, Saudi Arabia. Cureus [Internet]. 2024 Jun 29 [cited 2025 Aug 19]; Available from: https://www.cureus.com/articles/270498-musculoskeletal-health-risks-associated-with-smartphone-use-a-retrospective-study-from-riyadh-saudi-arabia\u003c/li\u003e\n\u003cli\u003eZhang W, Chen Z. Functional Brain Changes in Younger Population of Cervical Spondylosis Patients with Chronic Neck Pain. J Pain Res. 2024 Dec;Volume 17:4433\u0026ndash;45. \u003c/li\u003e\n\u003cli\u003eCai Z, Wang C, Tian F, He W, Zhou Y. 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Smartphone Addiction: Generational Differences. 2025 Jul 20 [cited 2026 Feb 4]; Available from: https://zenodo.org/doi/10.5281/zenodo.16197938\u003c/li\u003e\n\u003cli\u003eChen B, Liu F, Ding S, Ying X, Wang L, Wen Y. Gender differences in factors associated with smartphone addiction: a cross-sectional study among medical college students. BMC Psychiatry. 2017 Oct 10;17(1):341. \u003c/li\u003e\n\u003cli\u003eCevik S, Kaplan A, Katar S. Correlation of Cervical Spinal Degeneration with Rise in Smartphone Usage Time in Young Adults. Niger J Clin Pract. 2020;23(12):1748. \u003c/li\u003e\n\u003cli\u003eLee S, Choi YH, Kim J. Effects of the cervical flexion angle during smartphone use on muscle fatigue and pain in the cervical erector spinae and upper trapezius in normal adults in their 20s. J Phys Ther Sci. 2017;29(5):921\u0026ndash;3. \u003c/li\u003e\n\u003cli\u003eGrob D, Frauenfelder H, Mannion AF. The association between cervical spine curvature and neck pain. Eur Spine J. 2007 May 1;16(5):669\u0026ndash;78. \u003c/li\u003e\n\u003cli\u003eKumagai G, Ono A, Numasawa T, Wada K, Inoue R, Iwasaki H, et al. Association between roentgenographic findings of the cervical spine and neck symptoms in a Japanese community population. J Orthop Sci. 2014 May 1;19(3):390\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eRichards KV, Beales DJ, Smith AJ, O\u0026rsquo;Sullivan PB, Straker LM. Neck Posture Clusters and Their Association With Biopsychosocial Factors and Neck Pain in Australian Adolescents. Phys Ther. 2016 Oct 1;96(10):1576\u0026ndash;87. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Demographic Characteristics of Participants (N=163).\u003c/strong\u003e All values are given as counts with percentages in parentheses.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e126 (77.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 (19.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e131 (80.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLevel 1 (lowest)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50 (30.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80 (49.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 (19.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLevel 4 (highest)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e115 (70.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48 (29.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Anthropometric and Clinical Measures (Mean \u0026plusmn; SD and Range).\u003c/strong\u003e Summary of participants\u0026rsquo; height, weight, body mass index (BMI), Neck Disability Index (NDI), and SAS scores.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange (Min\u0026ndash;Max)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e165.99 \u0026plusmn; 7.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e137 \u0026ndash; 188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75.29 \u0026plusmn; 11.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48 \u0026ndash; 103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBody Mass Index (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.25 \u0026plusmn; 4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.10 \u0026ndash; 39.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNeck Disability Index (NDI) (0\u0026ndash;50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.73 \u0026plusmn; 7.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 \u0026ndash; 40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSAS (score, units**)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e83.01 \u0026plusmn; 27.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33 \u0026ndash; 157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e BMI was available for n=161 (two missing values). \u003cstrong\u003eSAS\u003c/strong\u003e = instrument score with possible range not specified (higher scores indicate greater severity; observed range 33\u0026ndash;157).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Cervical Disc Degeneration Grades (Pfirrmann) at Each Spinal Level.\u003c/strong\u003e Values are count of discs at each grade with percentage of the level total in parentheses. Pfirrmann grades range from 1 (healthy disc) to 8 (most severe degeneration).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpinal Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade 6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade 7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade 8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC2\u0026ndash;C3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (15.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68 (41.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35 (21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC3\u0026ndash;C4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67 (41.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39 (23.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC4\u0026ndash;C5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63 (38.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC5\u0026ndash;C6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (15.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53 (32.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (15.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC6\u0026ndash;C7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (15.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58 (35.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC7\u0026ndash;T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41 (25.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60 (36.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Segmental Cervical Stenosis Grades (Kang criteria) at Each Level.\u003c/strong\u003e Values are count of segments with percentage in parentheses. Kang grades range from 0 (normal) to 3 (severe Stenosis).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpinal Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade 0 (normal)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade 1 (minor stenosis)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade 2 (moderate Stenosis)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade 3 (severe Stenosis)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC2\u0026ndash;C3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e153 (93.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC3\u0026ndash;C4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e137 (84.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC4\u0026ndash;C5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e106 (65.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC5\u0026ndash;C6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85 (52.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39 (23.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC6\u0026ndash;C7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e129 (79.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC7\u0026ndash;T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e157 (96.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eOne C2\u0026ndash;C3 segment (0.6%) had a Kang grade recorded as \u0026ldquo;7,\u0026rdquo; which is outside the standard 0\u0026ndash;3 range; this outlier is not included in the table calculations.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Pearson Correlations between SAS and Selected Variables\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003er\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSAS and NDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSAS and Height (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSAS and Weight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSAS and BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. SAS by Pfirrmann Disc Degeneration Grade at Each Cervical Level (Kruskal\u0026ndash;Wallis Tests)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCervical Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade\u0026nbsp;1 (n, Mean Rank)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade\u0026nbsp;2 (n, Mean Rank)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade\u0026nbsp;3 (n, Mean Rank)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026chi;\u0026sup2;(2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC2\u0026ndash;C3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14, 84.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e128, 82.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21, 75.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.804\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC3\u0026ndash;C4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11, 90.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e130, 82.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22, 75.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.702\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC4\u0026ndash;C5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10, 90.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e122, 82.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31, 79.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.810\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC5\u0026ndash;C6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4, 72.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e104, 87.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55, 72.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC6\u0026ndash;C7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24, 92.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e109, 80.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30, 77.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.449\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC7\u0026ndash;T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30, 90.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e115, 81.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18, 69.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7. SAS by Kang Grade at Each Cervical Level (Mann\u0026ndash;Whitney U Tests)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCervical Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKang Grade 1(n, Mean Rank)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKang Grade 2 (n, Mean Rank)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eU\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC2\u0026ndash;C3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e161, 81.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1, 110.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC3\u0026ndash;C4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e158, 83.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5, 39.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e182.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC4\u0026ndash;C5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e143, 83.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20, 71.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1213.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC5\u0026ndash;C6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e124, 84.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39, 75.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2167.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.329\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC6\u0026ndash;C7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e148, 83.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15, 69.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e916.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.265\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC7\u0026ndash;T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e159, 82.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4, 58.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e222.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8. Independent \u003cem\u003et\u003c/em\u003e-Tests for SAS by Gender, Marital Status, and Residence\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup 1 (n) \u0026ndash; Mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup 2 (n) \u0026ndash; Mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(df)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale (126) \u0026ndash; 83.37 (26.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale (37) \u0026ndash; 81.78 (29.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.31 (161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSingle (32) \u0026ndash; 94.75 (29.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMarried (131) \u0026ndash; 80.14 (26.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.76 (161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrban (115) \u0026ndash; 84.86 (27.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural (48) \u0026ndash; 78.56 (26.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.34 (161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 9. SAS Scores by Educational Level, One-way ANOVA showed a significant group effect (F(2,159)=6.55, p=.002).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003en\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eM\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiploma or lower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75.54 (23.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81.74 (27.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePostgraduate degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e97.00 (28.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 10. Summary of GLM (normal identity link) examining marital status as a predictor of SAS scores (N = 163).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI for B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWald \u0026chi;\u0026sup2;(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u003c/strong\u003e (Married)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[75.572, 84.702]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1183.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status:\u003c/strong\u003e Single vs Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[4.310, 24.915]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e The omnibus likelihood ratio test for the model versus an intercept-only model was \u0026chi;\u0026sup2;(1) = 7.550, \u003cem\u003ep\u003c/em\u003e = .006. The intercept represents the mean SAS score for married participants (reference category). B = unstandardized regression coefficient; SE = standard error; CI = confidence interval.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-spine-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esjo","sideBox":"Learn more about [European Spine Journal](http://link.springer.com/journal/586)","snPcode":"586","submissionUrl":"https://submission.springernature.com/new-submission/586/3","title":"European Spine Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"smartphone addiction, cervical spine degeneration, neck disability, MRI, musculoskeletal disorders","lastPublishedDoi":"10.21203/rs.3.rs-8800006/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8800006/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eSmartphone use has increased globally and has been associated with neck pain, yet evidence linking smartphone addiction to objective cervical spine degeneration on magnetic resonance imaging (MRI) remains inconsistent.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eIn this cross-sectional study, 163 adults aged 18\u0026ndash;55 years with chronic neck pain underwent cervical spine MRI. Smartphone addiction was assessed using the Smartphone Addiction Scale (SAS), and neck disability was evaluated with the Neck Disability Index (NDI). Cervical disc degeneration and canal stenosis were graded using established MRI-based classification systems. Associations between SAS scores and imaging findings were analyzed.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe mean SAS score was 83.0\u0026thinsp;\u0026plusmn;\u0026thinsp;27.4, reflecting moderate addiction severity. SAS scores were not significantly associated with NDI, body mass index, cervical disc degeneration, or cervical canal stenosis at any level. A single isolated association at the C3\u0026ndash;C4 level was observed based on a very small number of cases and was interpreted cautiously. Smartphone addiction severity was higher among single participants and those with postgraduate education, with no differences by gender or residence.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eSmartphone addiction severity was not associated with MRI-detected cervical disc degeneration or clinically significant cervical canal stenosis. These findings suggest that cervical spine degeneration is unlikely to be driven by smartphone addiction alone and support the multifactorial nature of cervical spine disorders.\u003c/p\u003e","manuscriptTitle":"Investigating the Relationship Between Smartphone Addiction and Suffering from Degenerative Diseases of the Cervical Spine","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 17:33:29","doi":"10.21203/rs.3.rs-8800006/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-09T21:11:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-08T23:13:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-06T21:32:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-15T15:38:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145071164757666997224779611457253727784","date":"2026-02-11T21:04:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162161282631995791747364070404234565069","date":"2026-02-09T23:50:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166923373435749985093028820310665017364","date":"2026-02-09T21:11:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-09T20:43:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-09T02:20:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-09T02:19:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Spine Journal","date":"2026-02-05T17:54:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-spine-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esjo","sideBox":"Learn more about [European Spine Journal](http://link.springer.com/journal/586)","snPcode":"586","submissionUrl":"https://submission.springernature.com/new-submission/586/3","title":"European Spine Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f1f44ca1-7d78-4cfb-bc32-7d58f000e1a2","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T21:23:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 17:33:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8800006","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8800006","identity":"rs-8800006","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}