Background: The increasing prevalence of smartphone use has been associated with musculoskeletal pain; however, the specific roles of demographic factors, smartphone usage time, posture, contents and state of addiction on pain in the upper-body regions remain unclear. Objects: This study investigated the influence of smartphone usage characteristics, including age, occupation, visual condition, duration, content, and posture, as well as smartphone addiction, on musculoskeletal pain in upper-body regions. This study aimed to comprehensively elucidate the factors contributing to the pain associated with smartphone use. Methods: A cross-sectional survey was conducted with 316 participants aged 20–59 years. Data on personal characteristics, smartphone use patterns, state of addiction (measured using the Smartphone Addiction Scale-Short Version), and musculoskeletal discomfort (Cornell Musculoskeletal Discomfort Questionnaire and Cornell Hand Discomfort Questionnaire) were collected. Binary logistic regression analysis identified significant predictors of pain in different body regions. Results: Younger age (20–30 years), being housewives or students, and vision impairment (shortsightedness) significantly increased the likelihood of neck, shoulder, and hand pain. Prolonged smartphone use (7–10 hours daily) and gaming were strongly associated with elevated pain risk, whereas moderate usage (1–4 hours daily) may be protect against lumbar pain. Non-neutral postures, especially side lying, have emerged as critical risk factors, with left-side lying linked to hand pain and right-side lying linked to upper back pain. Smartphone addiction consistently predicted pain across all regions by amplifying physical strain through prolonged engagement and poor posture. Conclusion: This study highlighted the multifactorial nature of smartphone-related musculoskeletal pain, emphasizing the roles of demographic characteristics, usage patterns, and addiction. These findings provide a foundation for developing tailored ergonomic and behavioral interventions to mitigate pain risks, particularly in high-use populations. Future research should validate these findings through longitudinal studies and objective measures.

INTRODUCTION
MATERIALS AND METHODS
    1. Participants
    2. Survey Development and Distribution
    3. Variables
    4. Data Analysis
RESULTS
    1. General Characteristics
    2. Binary Logistic Regression Analysis According toGeneral Characteristics, Smartphone Addiction, andSmartphone Use Patterns
DISCUSSION
CONCLUSIONS
FUNDING
ACKNOWLEDGEMENTS
CONFLICTS OF INTEREST
AUTHOR CONTRIBUTION
ORCID
REFERENCES

• YiXin Wang(Department of Physical Therapy, The Graduate School, Yonsei University)
• Ye-Jin Kim(Department of Physical Therapy, The Graduate School, Yonsei University)
• Kyeong-Ah Moon(Department of Physical Therapy, The Graduate School, Yonsei University)
• Hye-Seon Jeon(Department of Physical Therapy, The Graduate School, Yonsei University, Department of Physical Therapy, College of Health Sciences, Yonsei University, Wonju, Korea) Corresponding author