This study employed a cross-sectional survey design to examine how smartphone addiction and nomophobia predict neck–related functional disability among undergraduate university students. The design allowed assessment of associations between behavioural dependence, posture-related exposures, and musculoskeletal outcomes at a single point in time, which is appropriate for digital health research in student populations. Reporting of the study follows the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for cross-sectional studies.

The study was conducted at the University of Nigeria, Enugu Campus (UNEC), located in Enugu, southeastern Nigeria. UNEC is one of the two main campuses of the University of Nigeria and comprises six faculties. The campus provides a suitable context for investigating digital health concerns because undergraduate students commonly use smartphones for academic activities (such as online lectures and access to digital learning resources), social interaction, and entertainment.

The target population comprised of all full-time undergraduate students at UNEC, aged 18 years and above, enrolled in any programme at the time of data collection. Students from all six faculties were eligible provided they reported regular smartphone use for academic or personal purposes. This population was chosen because they represent a digitally active group of young adults with high exposure to prolonged smartphone use and associated posture-related risks. Both male and female students across different academic years were included to capture variation in smartphone use habits and neck posture behaviours.

A non-probability convenience sampling technique was used to recruit students who were accessible and willing to participate during the data collection period. Although this sampling method may limit generalisability of the findings, it is pragmatic and frequently used in exploratory digital health research within university settings.

The required sample size was estimated using the Taro Yamane (1967) formula for finite populations [21]:

where (N) is the population size and (e) is the margin of error. Based on official statistics from the Student Affairs Division, UNEC (2023), the total undergraduate population was 11,606 students. Assuming a 5% margin of error and a 95% confidence level, the minimum sample size was approximately 387 participants. To account for an anticipated 10% non-response rate, the target sample size was increased to 426. In total, 399 students completed the survey, yielding a response rate of 93.7%.

To provide a clear overview of participant progression through the study, the following numbers were recorded at each stage of recruitment: a total of 11,606 undergraduate students constituted the potentially eligible population; 426 students were approached during the data collection period; all 426 individuals met the inclusion criteria and were invited to participate; all invited students consented and were included in the study; 399 students successfully completed the questionnaire; and all 399 completed responses were included in the final analysis.

Inclusion criteria were: full-time undergraduate students aged ≥ 18 years, regular smartphone use for at least one hour per day (for academic or personal purposes), and willingness to provide informed consent and complete the questionnaire. Students were excluded if they were postgraduate students; reported diagnosed cervical spine pathology, significant cervical trauma, or congenital cervical disorders; had systemic neurological or musculoskeletal conditions known to cause neck pain (e.g. cervical disc disease, spinal cord injury); were currently receiving treatment for neck pain or disability; or reported severe psychological or neurological conditions that could interfere with reliable questionnaire completion. All eligibility and exclusion criteria were based on self-report.

Data were collected using a structured, self-administered questionnaire consisting of four sections: (1) socio-demographic and smartphone-use characteristics, (2) smartphone addiction, (3) nomophobia, and (4) neck-related functional disability.

Before the main study, the full questionnaire was pilot-tested with 30 undergraduate students who were not included in the final sample. The pilot assessed clarity of items, layout, and completion time, and evaluated internal consistency of the scales in the local context. Based on feedback, minor wording and formatting adjustments were made to improve comprehension and relevance; no items were removed.

Data were collected over a four-month period during the academic term. Trained research assistants visited lecture halls, the university library, and student hostels to recruit participants. The aims and procedures of the study were explained, and eligible students were invited to participate. Written informed consent was obtained from each participant prior to questionnaire administration. Students completed the paper-based questionnaire on-site in a single session. Immediately after completion, research assistants checked each questionnaire for missing or unclear responses and clarified any issues with the participant to minimise data loss and enhance reliability. Completed questionnaires were assigned unique identification numbers, anonymised, and stored securely in locked cabinets and encrypted electronic files.

All statistical analyses were conducted using IBM SPSS Statistics version 25.0. Descriptive statistics were first generated to summarise the sample characteristics and key study variables. Continuous measures including smartphone addiction (SAS-SV), nomophobia (NMP-Q), neck disability (NDI), and average daily hours of smartphone use were examined using means, standard deviations, medians, and ranges. Categorical variables such as sex, academic level, and break frequency during smartphone use were summarised as counts and percentages.

Before conducting inferential analyses, the distributional properties of continuous variables were assessed to ensure that the assumptions for parametric procedures were met. Normality was evaluated using the Shapiro-wilk test, supported by inspection of skewness and kurtosis indices, histogram plots, and Q–Q plots. The NFD variable demonstrated significant positive skew (Fig. 1); therefore, a logarithmic transformation was applied to generate log NFD (Fig. 2), which exhibited substantially improved symmetry and was subsequently used as the dependent variable in all regression models. Smartphone addiction and nomophobia scores showed only mild deviations from normality and were retained in their original forms.

Associations among smartphone addiction, nomophobia, NFD, behavioural smartphone-use indicators, posture angle, and demographic characteristics were examined using Pearson correlation coefficients, with statistical significance set at p < 0.05 (two-tailed). These analyses addressed Objective 2 of the study.

To evaluate the multivariable associations between smartphone addiction, nomophobia, and NFD (Objective 3), multiple linear regression models were fitted using logNDI as the outcome variable. An initial full model included smartphone addiction, nomophobia, daily smartphone use, break frequency, neck flexion angle, sex, age, and academic year. To identify the most parsimonious and best-fitting predictors, stepwise procedures were employed, including forward selection, backward elimination, and bidirectional stepwise selection. Competing models were compared using the coefficient of determination (R²), adjusted R², Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC).

Model diagnostics were performed to verify adherence to regression assumptions. These included assessments of residual normality using Q–Q plots and the Shapiro-wilk test, evaluation of homoscedasticity via residuals-versus-fitted plots, examination of multicollinearity using variance inflation factors, and checks for influential observations. A significance level of α = 0.05 was applied throughout.

A total of 399 undergraduate students participated in the study. The mean age was 24.0 ± 1.37 years (range: 19–29), and 53.8% were males. Participants were drawn from all faculties and academic levels (years 1–5) of the University of Nigeria, Enugu Campus. The mean academic level was 3.68 ± 1.31, indicating broad representation across programme stages. Demographic characteristics are presented in Table 1.

Data from 399 participants were included in the analysis. Undergraduate students at UNEC demonstrated high smartphone exposure, moderate to high smartphone addiction, elevated nomophobia, and mostly mild but increasingly significant NFD (Tables 2–5). Forward-flexed neck posture was common and worsened in higher academic years. Faculty and class-level analyses confirmed consistent escalation in digital dependence and musculoskeletal symptoms, particularly in years 4 and 5. The mean NFD score was 8.76 (SD 6.38; range 1–34), indicating generally low to moderate NFD in the sample. The mean smartphone addiction score was 35.60 (SD 9.99; range 10–58), and the mean nomophobia score was 95.58 (SD 31.57; range 17–139) (Table 2).

Assessment of distributional properties showed that the Neck Disability total score was substantially right-skewed, as indicated by the Shapiro-wilk test (W = 0.856, p < 0.001), skewness (1.10), and kurtosis (1.09). To improve normality and stabilise variance, the NFD score was log-transformed to produce log NFD. The transformed variable demonstrated markedly improved distributional characteristics, with skewness near zero (0.01), reduced kurtosis (–1.23), and Q-Q plots that aligned more closely with the theoretical normal distribution. Accordingly, log neck disability score was used as the dependent variable in all subsequent regression analyses.

Smartphone addiction and nomophobia scores demonstrated only mild departures from normality (skewness < 0.5; kurtosis < 1), despite statistically significant shapiro–wilk results due to the large sample size (Smartphone addiction: W = 0.986, p = 0.001; Nomophobia: W = 0.942, p < 0.001). Given their acceptable symmetry and theoretical interpretability, both variables were retained in their original scales for correlation and regression modelling. A summary of the normality tests for all continuous variables is presented in Table 8.

The correlation analysis showed several statistically significant associations between digital dependence, posture-related behaviour, and NFD (Table 9). Smartphone addiction demonstrated a strong positive correlation with nomophobia (r = 0.556, p < 0.001), indicating substantial overlap between problematic smartphone use and anxiety related to being without the device. Smartphone addiction was also moderately correlated with daily smartphone use (r = 0.322, p < 0.001) and neck flexion angle (r = 0.326, p < 0.001), suggesting that higher addiction scores were associated with greater exposure time and more forward-flexed neck posture. Its association with NFD was smaller but statistically significant (r = 0.249, p < 0.001).

Nomophobia showed significant positive correlations with daily smartphone use (r = 0.251, p < 0.001) and neck flexion angle (r = 0.239, p < 0.001), but its correlation with NFD was small and not statistically significant (r = 0.087, p = 0.084) (Table 9). Neck-related functional Disability displayed its strongest correlation with neck flexion angle (r = 0.155, p = 0.002), supporting the role of posture as an ergonomic contributor to neck-related symptoms, while its association with hours of use was weak (r = 0.066, p = 0.186).

Daily smartphone use correlated significantly with both smartphone addiction and nomophobia, but only weakly with NFD. Age and academic year showed small but significant associations with neck angle and with each other, but minimal relationships with smartphone addiction, nomophobia, or neck disability. Overall, these findings suggest that smartphone addiction and forward-flexed posture are more consistently associated with neck-related symptoms than either nomophobia or duration of use alone.

Values are Pearson’s r. with p < 0.05*; ** p < 0.01; *** p < 0.001.*

To examine the extent to which smartphone addiction and nomophobia were associated with neck-related disability, a series of multiple linear regression models was fitted with log-transformed NFD scores as the dependent variable. The initial full model included smartphone addiction, nomophobia, hours of smartphone use per day, break frequency, neck flexion angle, age, academic year, and sex.

Across all analyses:

These results highlight the concurrent influence of behavioural dependence and posture-related load on neck-related disability in university students.

This study examined patterns of smartphone use, posture-related behaviours, and neck disability among undergraduate students, and explored the associations between smartphone addiction, nomophobia, and text-neck–related symptoms. Three key findings emerged. First, university students demonstrated high daily smartphone use, prevalent forward-flexed neck postures, and mild but notable levels of neck disability. Second, smartphone addiction showed strong associations with nomophobia, smartphone use intensity, and neck flexion angle, and a moderate association with NFD. Third, regression modelling identified smartphone addiction as the strongest independent correlate of NFD, whereas nomophobia did not remain significant once addiction was accounted for. Neck flexion angle also demonstrated a small but meaningful association with disability. Together, these findings highlight the combined behavioural and ergonomic risk profile associated with smartphone use in this population.

These findings reinforce global concerns that university students represent a high-risk group for digital-behavioural dependence and cervical musculoskeletal strain, as their academic, social, and recreational routines increasingly converge around smartphone-based tasks [1, 2]. The concurrence of heavy device use and posture-related stress observed in this study therefore reflects not only individual behaviour but also broader shifts in digital learning environments and youth communication patterns.

The high daily smartphone use documented in this study is consistent with global trends showing increasing reliance on mobile technologies among university students [37, 38]. The predominance of forward-flexed neck postures (30°–60°) similarly aligns with findings from earlier ergonomic investigations, which report that smartphone users commonly maintain sustained flexed positions that elevate mechanical loading on the cervical spine [39, 40]. Flexion angles exceeding 30° markedly increase compressive forces on the cervical vertebrae and surrounding soft tissues, predisposing individuals to discomfort and fatigue [41]. This aligns with previous research indicating that high volumes of smartphone use in young adults are associated with musculoskeletal symptoms, particularly neck pain and disability [9, 10]. Several recent studies further suggest that young adults often underestimate the degree of neck flexion they adopt during smartphone use, which may partially explain the high prevalence of text-neck symptoms even among individuals who perceive their posture as acceptable [22].

Environmental factors such as poorly designed lecture halls, inadequate seating, and prolonged screen-based academic tasks may further promote forward-flexed postures, contributing to cumulative cervical strain [6]. Consequently, the posture patterns observed in this study closely reflect established biomechanical risk pathways.

The moderate-to-high levels of smartphone addiction and nomophobia observed in this sample mirror evidence from studies conducted in Asia, the Middle East, and Africa [25, 42, 43]. The strong relationship between smartphone addiction and nomophobia is well established, with both constructs reflecting closely overlapping behavioural tendencies, including compulsive checking, fear of disconnection, and difficulty disengaging from online activities. This alignment is reinforced by studies documenting their frequent co-occurrence and highlighting how these shared behaviours contribute to the interconnected nature of the two constructs [44, 45].

Research also indicates that heightened nomophobia may be driven not only by behavioural factors but also by psychological mechanisms such as anxiety sensitivity, low tolerance for uncertainty, and reliance on digital communication for emotional regulation [46, 47]. These mechanisms may intensify the reinforcing cycle between smartphone addiction and nomophobia, particularly in academic environments where constant online connectivity is perceived as essential. Taken together, these overlapping psychosocial features help explain the consistent co-occurrence of addiction and nomophobia observed in this study.

In this study, NFD was generally mild among the participants but showed an increase with higher smartphone addiction scores and advancing academic levels. This trend is consistent with previous research indicating that musculoskeletal symptoms are more prevalent among individuals who engage in heavy or problematic smartphone use [48, 49]. Similar positive associations between smartphone addiction and neck pain or disability have been reported in studies conducted in Saudi Arabia, India, and South Africa [50, 51]. Within our sample, the neck flexion angle emerged as the strongest ergonomic factor associated with neck disability, aligning with existing literature that highlights cervical posture as a mediator in the relationship between smartphone exposure and musculoskeletal strain [13, 52]. Furthermore, regression modeling in our study identified smartphone addiction as the most significant independent predictor of neck disability, whereas nomophobia did not maintain significance once addiction was considered. The neck flexion angle also showed a small yet meaningful association with disability, underscoring the importance of ergonomic factors in understanding neck-related symptoms. The pattern seen here also reflects emerging evidence that even mild elevations in neck disability among young adults may predict later musculoskeletal complications if high-risk usage behaviors persist over several years [36]. Moreover, recent meta-analytic work suggests that cumulative exposure rather than single-session duration is a more reliable predictor of neck disability, further supporting the observed association between addiction-related behaviors and musculoskeletal burden [53].

The findings align with emerging African evidence documenting a high prevalence of text-neck symptoms and problematic smartphone use among university students [18, 19, 20, 51]. The progressive increase in addiction, nomophobia, and neck disability across academic levels parallels observations in Ethiopia, South Korea, Italy, and India, where upper-level students report more entrenched digital behaviours and higher musculoskeletal symptom burden [54–57]. These international parallels suggest that the behavioural and ergonomic patterns observed in this Nigerian cohort are part of a broader global phenomenon affecting contemporary student populations. Similar trajectories have been noted in university cohorts in China, Malaysia, and Turkey, where students in later academic years demonstrate heavier reliance on smartphones for academic content delivery, online assessments, and professional networking [15, 58, 59, 60]. This indicates that digital dependence is likely reinforced not only by personal habits but also by structural academic requirements, which may explain the consistent escalation across year groups observed in this study.

Smartphone addiction may influence neck-related symptoms through behavioural pathways that increase both the duration and intensity of device engagement [49]. Addicted users tend to engage in more frequent and prolonged sessions, often characterised by compulsive scrolling, repeated checking, and reduced attentional control over posture [61–63]. These behaviours restrict opportunities for micro-breaks; brief interruptions that typically allow for postural adjustment and cervical muscle recovery [49]. Over time, such patterns can lead to prolonged exposure to ergonomically unfavorable positions, increasing the risk of discomfort or disability [41, 23, 13].

Additionally, behavioural studies indicate that individuals with high smartphone addiction often show diminished interoceptive awareness, reflecting a reduced ability to detect bodily discomfort or fatigue [64]. This may delay postural correction and accelerate musculoskeletal strain [65]. Such diminished posture awareness further strengthens the behavioural pathway linking addiction to text-neck–related symptoms.

Ergonomic factors play a central role in the development of text-neck symptoms. Forward-flexed neck postures significantly increase mechanical load on the cervical spine. Biomechanical modelling indicates that at 30° of neck flexion, the effective weight of the head increases to approximately 18 kg, and at 60° it can approach 27 kg [41]. This substantial rise in load places continuous tension on cervical musculature, ligamentous structures, and intervertebral discs. Prolonged maintenance of such positions has been associated with muscle fatigue, impaired proprioception, and accumulated strain, all of which may contribute to neck discomfort and functional limitation [13, 23, 48].

Recent electromyographic studies also indicate that forward-flexed posture increases upper trapezius and levator scapulae activation even during passive smartphone viewing, confirming that submaximal muscle loading persists throughout device interaction [48, 66]. Over time, this sustained muscular activation may contribute not only to pain but also to altered cervical alignment and reduced neuromuscular control [67, 68].

The results of this study support an integrated behavioural–ergonomic framework for understanding text-neck symptoms. Smartphone addiction appears to heighten ergonomic risk by increasing total exposure time and reducing posture awareness, thereby amplifying the duration spent in forward-flexed positions [61]. Neck flexion angle, in turn, represents the biomechanical pathway through which behavioural dependence manifests as physical strain [13, 41]. Although nomophobia demonstrated strong correlations with addiction, it did not independently predict NFD once addiction was accounted for, suggesting that its influence may operate indirectly through behavioural reinforcement rather than direct musculoskeletal mechanisms.

Together, these pathways highlight the interplay between digital behaviours and physical posture, offering a more comprehensive understanding of how problematic smartphone use may contribute to the development of neck-related symptoms in university students. This integrated perspective aligns with emerging digital-health models that conceptualize musculoskeletal disorders related to technology use as multifactorial syndromes involving behavioural, ergonomic, psychological, and environmental determinants [8, 38, 49]. By situating addiction and posture within a shared risk model, the present findings contribute to a growing body of work emphasizing the need for interdisciplinary prevention strategies.

The high prevalence of smartphone addiction and nomophobia identified in this study suggests that routine screening within university health services may be warranted. Validated instruments, such as the Smartphone Addiction Scale and the Nomophobia Questionnaire, could help identify students at elevated risk for behavioural dependence and associated musculoskeletal symptoms. Early identification may enable timely referral for counselling, behavioural interventions, or ergonomic guidance.

Educational programmes targeting safe and healthy patterns of smartphone use represent an important preventive strategy. University-wide campaigns could highlight the risks associated with prolonged device use, compulsive checking behaviour, and sustained forward-flexed posture. Digital-wellness interventions, such as workshops or awareness modules, have been shown to reduce problematic smartphone use and enhance ergonomic behaviours among students [69, 70]. Embedding such content into orientation programmes or general studies curricula may improve reach and effectiveness.

Ergonomic education is essential, given the strong association between neck flexion angle and NFD observed in this study. Training students to maintain neck-neutral postures, elevate devices to eye level, and incorporate regular postural breaks may reduce cervical loading [5, 13, 66]. Integrating ergonomic modules into student health courses, physiotherapy outreach, or campus wellness initiatives could promote safer device-use habits. Practical demonstrations and infographic-based learning may further facilitate adoption.

Digital hygiene practices, such as screen-time limits, app-usage tracking, scheduled “phone-free” study intervals, and mindfulness-based approaches, may mitigate compulsive smartphone use and encourage healthier engagement patterns [69, 70]. Universities could support these efforts by providing access to digital-wellness apps, structured study-skills coaching, or brief cognitive-behavioural strategies aimed at reducing problematic use.

Within Nigerian universities, the musculoskeletal burden associated with smartphone use may be amplified by contextual factors, including high digital demands across academic programmes and limited ergonomic infrastructure in libraries, lecture halls, and dormitories. Poorly designed seating, inadequate lighting, and overcrowded study spaces can exacerbate forward-flexed posture and discomfort. Institutional responses should therefore include ergonomic improvements to study environments, incorporation of digital-wellness policies into student support systems, and collaboration between university health units, physiotherapy departments, and ICT units to deliver integrated interventions. Such measures could help mitigate the behavioural and ergonomic risks identified in this study and support healthier digital engagement among students.

This study has several methodological strengths. First, it draws on a large and diverse sample of undergraduate students across all academic levels and faculties, enhancing internal representativeness within the institution. Second, the study employed validated and widely used instruments to assess smartphone addiction, nomophobia, and neck disability, strengthening measurement reliability. Third, the analytical strategy was rigorous: normality was assessed and appropriately corrected, and a combination of correlation analysis and multivariable regression including systematic model comparison was used to evaluate the relationships among behavioural and ergonomic variables. Together, these features enhance the robustness of the findings.

Several limitations should be acknowledged when interpreting the results. Because the study used a cross-sectional design, temporal ordering cannot be established and causal inference is not possible. All primary variables, including smartphone use, posture, and musculoskeletal symptoms, were self-reported, introducing the potential for recall bias and social desirability bias. Convenience sampling limits generalisability beyond the study population. Additionally, neck posture was assessed via self-reported visual estimation rather than objective biomechanical measurement, which may have introduced measurement imprecision. Despite these limitations, the patterns observed are consistent with prior literature and provide important insights into behavioural and ergonomic risk factors among university students.

Future studies should adopt longitudinal designs to track smartphone-use behaviour, posture habits, and musculoskeletal outcomes over time, enabling stronger inferences about temporal associations. Intervention-based or experimental studies are needed to evaluate the effectiveness of ergonomic training, posture-correction strategies, digital hygiene programmes, and smartphone-addiction reduction interventions. Further research should also explore psychosocial moderators such as stress, coping strategies, academic workload, and sleep quality that may influence the relationship between digital dependence and musculoskeletal outcomes. Incorporating biomechanical tools (e.g. inclinometers or motion-tracking) could improve precision in posture assessment.