Physical Therapy Korea

The widespread use of smartphones has been linked to a growing number of health concerns; particularly musculoskeletal pain caused by prolonged use [1-4], Musculoskeletal pain caused by prolonged smartphone use has become increasingly prominent [5,6]. Numerous studies have reported a correlation between musculoskeletal discomfort and excessive and sustained smartphone usage [7]. Prolonged smartphone use significantly increases discomfort in the neck, shoulders, and upper limbs, negatively affecting daily life [8,9].

In addition to smartphone usage, usage patterns and related characteristics may increase the likelihood of smartphone addiction and adversely affect physical health [10,11]. Occupation type, for instance, can significantly influence the manner and frequency of smartphone use, indirectly affecting musculoskeletal health [12]. Visual conditions such as myopia may lead to postural adaptations, including frequent forward head postures or leaning closer to the screen, imposing an additional burden on the neck and shoulders [13,14]. Moreover, smartphone use patterns such as engaging in social media or typing are associated with increased musculoskeletal discomfort in the upper limbs, particularly in the hands [15,16]. Certain smartphone activities, such as typing or prolonged social media engagement, have also been linked to increased discomfort in the hands and upper limbs.

Previous studies have investigated the relationship between smartphone use and musculoskeletal pain. Increased smartphone usage time and higher addiction levels significantly increase the risk of pain in the neck, shoulders, and other parts of the upper limbs [17]. Furthermore, different smartphone usage positions are associated with different types of musculoskeletal discomfort [18-20]. For instance, lying postures significantly affect lower back discomfort [21], excessive forward head posture significantly exacerbates neck pain [22-24], and single-handed smartphone use increases hand pain compared with two-handed use [25,26].

Nevertheless, most extant research has focused on smartphone addiction and other psychological factors [1], leaving the specific effects of smartphone usage characteristics and lifestyle habits on musculoskeletal discomfort insufficiently addressed. Furthermore, although previous studies have frequently analyzed neck and shoulder pain, research that provides independent analyses of other upper limb regions remains limited. Thus, whether the occurrence of pain varies across regions, or which specific factors influence such variations is unclear.

Therefore, this study aimed to explore the impact of smartphone usage characteristics, including occupational type, visual conditions, usage duration, usage content, and posture, along with lifestyle habits on pain occurrence in specific regions (neck, shoulders, and other upper limbs). By conducting detailed region-specific analyses, through this study, we sought to identify the key factors contributing to pain risk for each specific body part, providing evidence for the prevention and targeted intervention of musculoskeletal pain. The research hypotheses for this logistic regression study are that prolonged smartphone use, non-neutral postures such as side-lying positions, being in a state of smartphone addiction, and primarily using smartphones for social networking and gaming will significantly increase the likelihood of experiencing upper extremity pain. Additionally, younger age groups (20–30 years) are expected to have a significantly higher likelihood of upper extremity pain. Ultimately, this study aims to contribute to the development of strategies for healthier smartphone use.

1. Participants

This cross-sectional study included 326 participants aged 20–59 years recruited from China and Korea between August 2022 and January 2023. The participants were smartphone users with over 5 years of owning a smartphone. The participants were stratified into diverse age groups and occupations. Individuals with a history of musculoskeletal trauma, neurological disorders, or chronic rheumatic diseases were excluded. The minimum sample size (n = 268) was determined using the G-power software, ensuring a statistical power of 0.8 and a significance level of 0.05. To ensure the quality of research data, 10 participants with incomplete responses on critical variables were excluded after thorough assessment. Consequently, the final analysis included data from 316 participants. This study was approved by the Yonsei University Mirae campus Institutional Review Board (IRB no. 1041849-202209-SB-155-03).

2. Survey Development and Distribution

A survey instrument titled "Investigation of Musculoskeletal Pain Pertaining to Smartphone Addiction and Smartphone Usage Posture” was developed in Korea and China. This was validated by expert reviews and pilot testing. The final survey included 84 questions across four sections: demographic information, smartphone usage posture, Smartphone Addiction Scale (SAS-SV), and musculoskeletal pain evaluation (Cornell Musculoskeletal Discomfort Questionnaire [CMDQ] and Cornell Hand Discomfort Questionnaire [CHDQ]). The Korean and Chinese surveys were distributed via Google Forms and Wenjuanxing, respectively. Survey links were distributed via e-mail and social media platforms (e.g., WeChat and KakaoTalk) to invite participants to complete the questionnaire.

3. Variables

The study collected sociodemographic, smartphone usage, and posture-related variables to identify predictors of musculoskeletal pain. Sociodemographic variables included age, sex, and occupation. Smartphone usage variables consisted of daily smartphone usage duration, which was categorized into four groups: less than 1 hour, 1–4 hours, 4–7 hours, and 7–10 hours per day. Content type was classified into six categories: phone calls, social media, gaming, texting, video streaming, and other activities.

Posture-related variables focused on body posture, neck posture, and hand posture during smartphone use. Body posture was self-reported and categorized into sitting, standing, lying on the right side, lying on the left side, supine (face-up), and prone (face-down). Neck posture was classified as normal, moderately flexed, or severely flexed based on reference images [27] included in the questionnaire (Figure 1). Hand posture was further categorized into six styles considering screen orientation (horizontal or vertical), finger usage such as thumb or index finger, and support from the little finger (Figure 2).

Figure 1. Smartphone usage neck posture.

Figure 2. Smartphone usage hand posture.

The SAS-SV, a 10-item questionnaire rated on a 6-point Likert scale, was used to assess smartphone addiction. A total score of 31 or higher for males and 33 or higher for females out of 60 indicated addictions [8,28,29].

Presence of musculoskeletal pain and discomfort were evaluated using the CMDQ and the CHDQ [29,30]. The CMDQ assesses pain frequency, severity, and work interference across nine body regions, while the CHDQ focuses on six parts of the hand. The CMDQ assesses pain frequency, severity, and work interference across nine body regions. However, for binary logistic regression analysis, we simplified the CMDQ pain frequency responses as follows: Participants reporting no pain were coded as 0 (absence of pain) and participants reporting any level of pain frequency (regardless of severity or frequency) were coded as 1 (presence of pain). This binary outcome served as the dependent variable in the logistic regression analysis.

4. Data Analysis

Data were analyzed using the IBM SPSS software (version 27.0; IBM Co.). Binary logistic regression analysis was conducted to identify predictors of specific musculoskeletal pain region (neck or shoulder pain). In the logistic regression models, reference categories were established for the categorical independent variables to facilitate meaningful comparisons. For age, individuals aged ≥ 50 years were designated as the reference group, while the unemployed category was used as the baseline for occupational comparisons. Participants with normal vision were set as the reference group for vision-related analyses. For smartphone usage duration, those who reported using their smartphones for less than 1 hour per day served as the reference category. In terms of posture during smartphone use, the neutral sitting position was defined as the baseline, normal neck flexion was used as the reference for neck posture comparisons, and the posture of holding the smartphone with one hand while touching the screen with the other was selected as the standard for hand posture analysis. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated, with p < 0.05 considered significant. To address potential multicollinearity concerns in the logistic regression models for each body region, variance inflation factor (VIF) values were computed for the independent variables using linear regression. The absence of significant multicollinearity issues was confirmed by all VIF values falling below 10. The Hosmer–Lemeshow test was employed to evaluate model fit, with a p-value exceeding 0.05 indicating satisfactory fit. Additionally, Nagelkerke’s R2 was utilized to assess the overall performance of the models.

1. General Characteristics

Final data obtained from 316 participants were analyzed. The majority of participants were aged 20–30 years (44.9%, n = 142), followed by 30–40 years (22.5%, n = 71), 40–50 years (13.9%, n = 44), and ≥ 50 years (18.7%, n = 59). In terms of occupational distribution, office workers comprised the largest group (47.5%, n = 150), followed by students (32.6%, n = 103), housewives (10.1%, n = 32), freelancers (7.0%, n = 22), and unemployed individuals (2.8%, n = 9). Regarding smartphone usage duration, 41.8% (n = 132) reported using their smartphones for 4–7 hours per day, followed by 41.5% (n = 131) who used them for 1–4 hours. Additionally, 16.4% (n = 52) used smartphones for 7–10 hours, whereas only 0.3% (n = 1) reported usage of < 1 hour per day. Most participants (84.2%) were right-handed (Table 1).

Table 1 . General characteristics of participants.

Characteristic	Value
Sex
Male	140 (44.3)
Female	176 (55.7)
Age (y)
20–30	142 (44.9)
30–40	71 (22.5)
40–50	44 (13.9)
≥ 50	59 (18.7)
Occupation
Student	103 (32.6)
Office worker	150 (47.5)
Freelancer	22 (7.0)
Housewife	32 (10.1)
Unemployed	9 (2.8)
Hours of smartphone use (h)
< 1	1 (0.3)
1–4	131 (41.5)
4–7	132 (41.8)
7–10	52 (16.4)

Values are presented as number (%)..

2. Binary Logistic Regression Analysis According to General Characteristics, Smartphone Addiction, and Smartphone Use Patterns

1) Factors affecting neck pain

The binary logistic regression analysis identified several significant predictors of neck pain (Table 2). Participants aged 20–30 years were more likely to report neck pain than reference group (≥ 50 years old) (OR = 1.955, 95% CI = 1.079–3.576, p = 0.027). Among vision-related factors, both shortsightedness (OR = 1.949, 95% CI = 1.077–3.529, p = 0.027) and farsightedness (OR = 2.406, 95% CI = 1.270–4.557, p = 0.007) were significantly associated with an increased risk of neck pain. Smartphone contents type also played a notable role, with gaming significantly increasing the likelihood of neck pain (OR = 2.301, 95% CI = 1.082–4.893, p = 0.030). Furthermore, smartphone addiction was strongly associated with neck pain, with addicted individuals being more than three times more likely to report neck pain than non-addicted users (OR = 3.075, 95% CI = 1.786–5.295, p < 0.001).

Table 2 . Binary logistic regression table for the factors affecting pain in each body region.

Region	Variable	B	OR	95% CI		p-value
				Lower	Upper
Neck	20–30 y vs. ≥ 50 y	0.675	1.955	1.079	3.576	0.027*
	Shortsightedness vs. normal sight	0.668	1.949	1.077	3.529	0.027*
	Farsightedness vs. normal sight	0.878	2.406	1.270	4.557	0.007**
	Games vs. not game	0.833	2.301	1.082	4.893	0.030*
	Addiction (yes) vs. addiction (no)	1.123	3.075	1.786	5.295	< 0.001
Shoulder	Farsightedness vs. normal sight	–1.000	0.368	0.139	0.975	0.044*
	Office worker vs. no employment	1.747	5.472	0.039	0.785	0.023*
	Housewife vs. no employment	1.717	5.578	0.035	0.928	0.040*
	Games vs. not game	0.848	2.337	0.215	0.854	0.016*
	Right-side lying vs. sitting	0.742	2.100	0.248	0.916	0.026*
	Addiction (yes) vs. addiction (no)	0.820	2.271	1.401	3.682	0.001**
Upper back	Housewife vs. no employment	2.479	11.925	1.225	16.059	0.033*
	Phone call vs. not phone call	0.537	1.711	1.016	2.881	0.043*
	Right-side lying vs. sitting	0.779	2.180	1.162	4.090	0.015*
	Addiction (yes) vs. addiction (no)	0.869	2.385	1.449	3.926	0.001**
Lower back	Farsightedness vs. normal sight	–0.804	0.447	0.221	0.904	0.025*
	1–4 h/day vs. < 1 h/day	–0.676	0.509	0.261	0.991	0.047*
	Social media vs. no social media	0.588	1.800	1.122	2.889	0.015*
	Games vs. not game	0.778	2.177	1.168	4.059	0.014*
	Addiction (yes) vs. addiction (no)	0.501	1.650	1.022	2.665	0.041*
Upper arm	7–10 h/day vs. < 1 h/day	0.741	2.098	1.041	4.231	0.038*
	Phone call vs. not phone call	0.730	2.075	1.133	3.799	0.018*
	Left-side lying vs. sitting	0.937	2.552	1.172	5.557	0.018*
	Addiction (yes) vs. addiction (no)	0.869	2.385	1.449	3.926	< 0.001
Elbow	7–10 h/day vs. < 1 h/day	0.809	2.245	1.062	4.747	0.034*
	Phone call vs. not phone call	–0.862	0.422	0.218	0.818	0.011*
	One hand hold vs. one HH with same thumb control vs. one HH with opposite index control	–0.881	0.414	0.197	0.869	0.020*
	Addiction (yes) vs. addiction (no)	1.663	5.273	2.479	11.216	< 0.001
Forearm	Housewife vs. no employment	1.304	3.683	1.596	8.496	0.013*
	Phone call vs. not phone call	–0.789	0.454	0.250	0.827	0.010*
	Other content vs. all categories except ‘others’	–1.181	0.307	0.117	0.808	0.017*
	One HH with same thumb control and pinky support vs. one HH with opposite index control	–0.878	0.416	0.209	0.828	0.013*
	Addiction (yes) vs. addiction (no)	1.194	3.301	1.759	6.194	< 0.001
Wrist	Student vs. no employment	0.983	2.673	1.225	5.833	0.014*
	Office worker vs. no employment	0.937	2.553	1.235	5.277	0.011*
	7–10 h/day vs. < 1 h/day	1.288	3.626	0.102	0.744	0.011*
	Game vs. not game	0.739	2.094	1.007	4.354	0.048*
	Addiction (yes) vs. addiction(no)	0.694	2.003	1.224	3.278	0.006**
Hand	Student vs. no employment	1.049	2.854	1.206	6.751	0.017*
	4–7 h/day vs. < 1 h/day	1.456	4.293	0.058	0.930	0.039*
	7–10 h/day vs. < 1 h/day	1.985	5.324	0.034	0.562	0.006**
	Shortsightedness vs. normal sight	0.880	2.411	1.109	5.238	0.026*
	Left-side lying vs. sitting	–1.146	0.318	0.146	0.691	0.004**
	Addiction (yes) vs. addiction (no)	0.822	2.275	1.258	4.113	0.007**

OR, odds ratio; CI, confidence interval; HH, hand hold. *p < 0.05, **p < 0.01..

2) Factors affecting shoulder pain

Shoulder pain was significantly influenced by multiple factors, including vision status, occupation, smartphone usage, posture, and addiction (Table 2). Farsightedness was associated with a reduced likelihood of shoulder pain compared to normal vision (OR = 0.368, 95% CI = 0.139–0.975, p = 0.044), suggesting a potential protective effect. Office workers exhibited a higher risk of shoulder pain compared to unemployed individuals (reference group) (OR = 5.472, 95% CI = 0.039–0.785, p = 0.023). Similarly, housewives demonstrated a increased likelihood of reporting shoulder discomfort (OR = 5.578, 95% CI = 0.035–0.928, p = 0.040). In terms of contents type, gaming was identified as a significant risk factor, contributing to a higher probability of shoulder pain (OR = 2.337, 95% CI = 0.215–0.854, p = 0.016). Posture during smartphone use also played a significant role, compared to reference posture with individuals adopting a right-side lying position exhibiting a higher risk (OR = 2.100, 95% CI = 0.248–0.916, p = 0.026). Furthermore, smartphone addiction was strongly associated with shoulder pain, with addicted individuals displaying more than double the risk than non-addicted users (OR = 2.271, 95% CI = 1.401–3.682, p = 0.001). These findings underscore the multifaceted impact of lifestyle and smartphone usage patterns on musculoskeletal health.

3) Factors affecting upper back pain

Compared to other occupational groups, housewives (OR = 11.925, 95% CI = 1.225–16.059, p = 0.033) exhibited a substantially higher likelihood of experiencing upper back pain, highlighting the occupational influence on musculoskeletal discomfort (Table 2). Smartphone usage patterns also played a critical role, with phone calls significantly increasing the risk of upper back pain (OR = 1.711, 95% CI = 1.016–2.881, p = 0.043). Furthermore, posture during smartphone use was identified as a key factor because right-side lying was associated with a significantly elevated risk of upper back pain (OR = 2.180, 95% CI = 1.162–4.090, p = 0.015). Finally, smartphone addiction emerged as a strong predictor, with addicted users more than twice as likely to report upper back pain compared to non-addicted individuals (OR = 2.385, 95% CI = 1.449–3.926, p = 0.001). These findings emphasize the multifactorial nature of upper back and suggest that occupation, specific smartphone activities, posture, and addiction collectively contribute to its prevalence.

4) Factors affecting lower back pain

Farsightedness reduced the risk of lower back pain which was shown in Table 2 (OR = 0.447, 95% CI = 0.221–0.904, p = 0.025). Participants using smartphones for 1–4 hours per day had a lower likelihood of reporting lower back pain than those with other duration categories (OR = 0.509, 95% CI = 0.261–0.991, p = 0.047). Conversely, using Instagram and other media applications (OR = 1.800, 95% CI = 1.122–2.889, p = 0.015) and gaming (OR = 2.177, 95% CI = 1.168–4.059, p = 0.014) significantly increased the risk. Smartphone addiction further increased the likelihood of lumbar pain (OR = 1.650, 95% CI = 1.022–2.665, p = 0.041). These findings emphasize the roles of vision, usage duration, content, and addiction in lumbar pain.

5) Factors affecting upper arm pain

The logistic regression analysis revealed that several factors significantly influenced upper arm pain shown in Table 2. Participants who used their smartphones for 7–10 hours daily were more likely to report upper arm pain than those with shorter usage durations (OR = 2.098, 95% CI = 1.041–4.231, p = 0.038). Making phone calls was identified as a significant risk factor for upper arm pain (OR = 2.075, 95% CI = 1.133–3.799, p = 0.018). Posture during smartphone use also played a role; left-side lying significantly increased the risk of upper arm pain (OR = 2.552, 95% CI = 1.172–5.557, p = 0.018). Moreover, smartphone addiction was associated with a markedly elevated risk of upper arm pain (OR = 2.385, 95% CI = 1.449–3.926, p < 0.001).

6) Factors affecting elbow pain

Table 2 demonstrates a significant correlation between elbow pain and smartphone usage patterns, including addiction. Individuals utilizing smartphones for 7–10 hours per day exhibited an increased propensity for elbow pain (OR = 2.245, 95% CI = 1.062–4.747, p = 0.034). Notably, engaging in phone calls was associated with a reduced risk of elbow discomfort (OR = 0.422, 95% CI = 0.218–0.818, p = 0.011). Regarding hand positioning, single-handed smartphone operation involving thumb manipulation and pinky support correlated with a decreased likelihood of elbow pain (OR = 0.414, 95% CI = 0.197–0.869, p = 0.020). Conversely, smartphone addiction emerged as a robust predictor of elbow pain, with addicted users demonstrating a substantially elevated risk (OR = 5.273, 95% CI = 2.479–11.216, p < 0.001).

7) Factors affecting forearm pain

The study’s findings indicate that occupational factors, smartphone usage patterns, hand positioning, and device dependency significantly impact forearm discomfort, as evidenced in Table 2. Domestic caregivers exhibited a higher susceptibility to forearm discomfort compared to other professional groups (OR = 3.683, 95% CI = 1.596–8.496, p = 0.013). Conversely, engaging in voice calls (OR = 0.454, 95% CI = 0.250–0.827, p = 0.010) and miscellaneous usage purposes (OR = 0.307, 95% CI = 0.117–0.808, p = 0.017) were associated with decreased forearm discomfort risk. Notably, single-handed device manipulation utilizing thumb control and fifth digit support correlated strongly with increased forearm discomfort risk (OR = 0.416, 95% CI = 0.209–0.828, p = 0.013). Moreover, smartphone dependency emerged as a significant predictor of forearm discomfort, with dependent users demonstrating a markedly higher probability of experiencing such discomfort (OR = 3.301, 95% CI = 1.759–6.194, p < 0.001).

8) Factors affecting wrist pain

The analysis revealed several significant predictors of wrist pain, including occupation, smartphone usage patterns, and addiction status, as illustrated in Table 2. The data indicated that students (OR = 2.673, 95% CI = 1.225–5.833, p = 0.014) and office workers (OR = 2.553, 95% CI = 1.235–5.277, p = 0.011) exhibited an elevated probability of experiencing wrist pain. Individuals who engaged with their smartphones for 7–10 hours per day demonstrated a higher likelihood of reporting wrist discomfort compared to those with reduced usage durations (OR = 3.626, 95% CI = 0.102–0.744, p = 0.011). Furthermore, participation in gaming activities was found to be associated with an increased risk of wrist pain (OR = 2.094, 95% CI = 1.007–4.354, p = 0.048). It is noteworthy that subjects diagnosed with smartphone addiction faced a substantially heightened risk of wrist pain (OR = 2.003, 95% CI = 1.224–6.194, p = 0.006).

9) Factors affecting hand pain

The analysis presented in Table 2 reveals significant correlations between hand pain and various factors, including general characteristics, smartphone usage patterns, posture, vision status, and addiction. The study found that students were at a substantially higher risk of experiencing hand pain compared to their non-student counterparts (OR = 2.854, 95% CI = 1.206–6.751, p = 0.017). Prolonged smartphones use also emerged as a risk factor, compared to reference group with individuals using devices for 4–7 hours daily showing a increased likelihood of hand pain (OR = 4.293, 95% CI = 0.058–0.930, p = 0.039). This protective effect was even more pronounced among those with 7–10 hours of daily smartphone usage, who demonstrated a significantly increased likelihood of hand pain (OR =5.324, 95% CI = 0.034–0.562, p = 0.006). Additionally, myopia was identified as a significant contributor to hand pain risk (OR = 2.411, 95% CI = 1.109–5.238, p = 0.026). Among postural considerations, compared to the reference group, lying on the left side during smartphone use was strongly linked to a reduced risk of hand pain (OR = 0.318, 95% CI = 0.146–0.691, p = 0.004). Notably, smartphone addiction emerged as a crucial predictor, with addicted individuals more than twice as likely to report hand pain compared to non-addicted users (OR = 2.275, 95% CI = 1.258–4.113, p = 0.007). These results highlight the complex interplay between smartphone behaviors, individual characteristics, and the occurrence of hand pain.

This study mainly aimed to investigate the effects of smartphone usage on the occurrence of pain in various areas. To the best of our knowledge, this is the first study to use a combination of personal information, smartphone usage patterns, and smartphone addiction. This study identified significant predictors of musculoskeletal discomfort in various body parts associated with smartphone use and addiction. Personal factors, mobile phone use patterns, and mobile phone addiction have an impact on various areas of pain.

According to the study results, younger participants (aged 20–30 years) exhibited a higher prevalence of neck and hand pain, likely reflecting their greater dependence on smartphones for recreational purposes than older participants. This age group often engages in prolonged gaming and social media use, which intensify repetitive motion and static gripping postures [30,31]. By contrast, older participants reported less frequent smartphone use, potentially mitigating their risk of musculoskeletal discomfort but not exempting them entirely [32].

Moreover, occupation was a crucial determinant of musculoskeletal pain, with various groups exhibiting an elevated risk in specific anatomical regions. Housewives demonstrated an increased propensity to experience discomfort in the shoulders, upper back, and upper arms. The repetitive nature of household tasks imposes a fundamental musculoskeletal burden on these areas, potentially compounded by frequent smartphone use during daily routines [33,34]. Moreover, students and office workers were particularly susceptible to wrist and hand discomfort. This correlation is likely attributable to extensive smartphone use, especially among student population [15,35]. In line with prior research, activities involving prolonged typing, gaming, and other repetitive wrist and finger movements combined with sustained static gripping postures have been linked to hand discomfort [36,37]. The absence of ergonomic awareness in these groups further increases the risk of musculoskeletal strain. These occupational factors likely contribute to the observed pain patterns, either independently or in combination with smartphone use. Future studies should consider both occupational and smartphone-related behaviors to disentangle their individual and combined effects, enabling more targeted interventions.

Smartphone users with shortsightedness exhibit a significantly higher risk of neck and hand discomfort. Previous studies have indicated that compared to larger screens, smartphones’ smaller displays demand greater visual effort, prompting users to hold devices closer for clearer viewing [38]. This habitual reduction in viewing distance leads to a forward head posture and rounded shoulders, increasing muscle strain in the neck and shoulders [14,39]. In addition, repetitive hand movements and prolonged static postures while gripping the device may exacerbate hand discomfort [36]. These mechanisms align with the current findings, where users with nearsightedness reported more frequent neck and hand pain, highlighting vision as a crucial factor influencing discomfort. The combined effects of visual fatigue and improper posture contributed to neck and hand discomfort.

This study highlights the critical role of smartphone use posture in musculoskeletal pain, particularly the impact of the side-lying posture. Although previous studies have focused on sitting or standing postures, our findings suggest that nontraditional postures, such as side-lying, could significantly contribute to discomfort, particularly during prolonged smartphone use. The results of the present study show that left side lying was strongly associated with hand pain, whereas right-side lying was associated with upper back discomfort. These results are also similar to those of Fu et al. [21], who have reported that side-lying is an uncomfortable position for smartphone use, especially for the abductor pollicis brevis and upper trapezius. When lying on your side, your body turns to the left or right side. When playing with a cell phone, in addition to the weight of the phone, the weight of the arm opposite the direction of the turn also presses down on the arm of the side on which you are lying, potentially increasing the muscle activity on that side of the hand, arm, and upper back [21].

The duration of smartphone use has a complex relationship with musculoskeletal pain. Moderately less frequent use (1–4 hours per day) was associated with a reduced risk of lumbar spine pain. This also corroborates previous studies that illustrated an increased risk of low back pain with prolonged use [17]. This may also reflect the fact that more balanced usage habits and regular rest reduced discomfort. By contrast, longer use (7–10 hours per day) was significantly associated with an increased risk of discomfort in the hands, wrists, elbows, and upper arms, which is consistent with previous findings [40]. Specific content types also play an important role in pain risk. Gaming increases the risk of neck, lumbar spine, and hand pain, likely owing to its static and repetitive nature, which places constant stress on muscles and joints [41]. Among younger users, gaming was identified as a more significant risk factor for musculoskeletal pain compared to social media use. This is likely due to gaming’s static and repetitive nature, which places prolonged strain on muscles and joints. In contrast, social media usage often involves shorter interactions with more frequent postural adjustments, potentially exerting less sustained strain on the musculoskeletal system. The higher engagement time associated with gaming further amplifies its impact on pain prevalence. Future studies should quantify the specific contributions of these activities, such as session duration, intensity, and postural variations, to better inform ergonomic and behavioral interventions tailored to younger users. By contrast, media consumption may protect against lower back pain, possibly because passive viewing activities encourage a more relaxed and demanding posture. Further research is required to explore whether content-specific postural adaptations can be used for ergonomic interventions and effective pain risk reduction.

Smartphone addiction emerged as a pervasive and consistent predictor of musculoskeletal pain across all body regions examined in this study. Users with addiction have significantly higher discomfort levels driven by prolonged engagement, reduced self-regulation, and heightened dependency [42]. This maladaptive pattern of smartphone addiction exacerbates physical loading, with key manifestations including forward head posture [43], repetitive hand motions [36], and prolonged static postures [44], which together contribute to significant musculoskeletal loading.

To mitigate the risks identified, smartphone users can adopt posture corrections, take regular breaks, and use applications with reminders, especially younger users prone to gaming and social media. Older users may benefit from ergonomic accessories and larger devices. Manufacturers could integrate posture monitoring technologies into smartphones and develop ergonomic accessories to reduce musculoskeletal strain. Educational initiatives targeting schools, workplaces, and public spaces can also promote healthier usage habits across all age groups.

This study introduced a multifaceted framework that synthesizes individual attributes (such as age, occupation, and vision), smartphone usage (including posture, duration, and content), and addiction to predict musculoskeletal pain. Departing from prior investigations that typically examined isolated variables, this study underscores the synergistic impact of demographic and behavioral predictors. Moreover, it sheds light on previously underexamined factors, including side-lying postures and particular smartphone activities such as gaming, offering fresh perspectives on their contribution to musculoskeletal discomfort across diverse anatomical regions.

This study had several limitations. First, its cross-sectional design precluded causal inferences, highlighting the need for longitudinal studies to establish temporal relationships between smartphone use and musculoskeletal pain. Second, the reliance on self-reported data introduced potential recall bias, which could be mitigated in future research by incorporating objective measures such as posture-monitoring devices or digital usage logs to enhance data accuracy. Third, despite the widespread use of smartphones among older adults (aged 60 and above) in both China and South Korea, this study does not include data from this demographic, making it difficult to generalize the findings to older populations. Previous research has shown that older adults increasingly use smartphones for communication, entertainment, and health-related purposes, which may expose them to similar or unique risks of musculoskeletal pain. Therefore, future research is needed to explore the relationship between smartphone use and musculoskeletal pain across a wider range of age groups, including older adults. To address the identified risks, future studies should develop and validate targeted interventions focusing on ergonomic and behavioral factors. For example, posture correction programs could promote awareness of neutral postures and ergonomic habits, particularly for younger users who are prone to side-lying or forward-bending positions during prolonged smartphone use. Additionally, smartphone applications designed to monitor usage and provide reminders for regular breaks or screen time limits, particularly those incorporating gamification elements, have the potential to enhance user compliance, especially among younger populations. Furthermore, age-specific educational campaigns in schools and workplaces could raise awareness about the health risks associated with poor posture and excessive smartphone use, while encouraging healthier behaviors. Despite these limitations, this study provides a foundational framework for understanding the interplay of demographic, behavioral, and addiction-related factors in musculoskeletal pain and offers actionable insights for developing effective preventive strategies in the context of smartphone usage.

This study highlights the multifactorial nature of musculoskeletal pain associated with smartphone use and identified key predictors such as age, occupation, posture, usage duration, content type, and smartphone addiction. By integrating personal characteristics and behavioral patterns, it provides novel insights into underexplored factors, such as side-lying postures and content-specific activities, such as gaming. These findings underscore the importance of targeted ergonomic guidelines and behavioral interventions tailored to smartphone users, particularly younger users and those with higher smartphone addiction levels. Future research should focus on longitudinal studies and objective measures to validate these findings and develop effective preventive strategies.

None.

This study was supported by the “Brain Korea 21 FOUR Project,” the Korean Research Foundation for Department of Physical Therapy in the Graduate School of Yonsei University.

No potential conflicts of interest relevant to this article are reported.

Conceptualization: YXW, HSJ. Data curation: YXW, YJK. Formal analysis: YXW, YJK, HSJ, KAM. Funding acquisition: HSJ. Investigation: YXW, YJK, KAM. Methodology: YXW, YJK, HSJ, KAM. Project administration: YXW, HSJ. Resources: YXW, YJK, KAM. Supervision: YXW, YJK, HSJ, KAM. Validation: YXW. Visualization: YXW. Writing - original draft: YXW. Writing - review & editing: YXW, HSJ.