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Phys. Ther. Korea 2024; 31(3): 250-261
Published online December 20, 2024
https://doi.org/10.12674/ptk.2024.31.3.250
© Korean Research Society of Physical Therapy
A Regression Analysis Study on the Presence of Pain in Specific Body Regions Based on Smartphone Usage Posture, Smartphone Addiction, Smartphone Usage Patterns
YiXin Wang1 
, PT, BPT, Ye-Jin Kim1 , PT, BPT, Kyeong-Ah Moon1 , PT, BPT, Hye-Seon Jeon1,2 , PT, PhD
1Department of Physical Therapy, The Graduate School, Yonsei University, 2Department of Physical Therapy, College of Health Sciences, Yonsei University, Wonju, Korea
Correspondence to: Hye-Seon Jeon
E-mail: hyeseonj@yonsei.ac.kr
https://orcid.org/0000-0003-3986-2030
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Keywords: Cross-sectional studies, Logistic models, Musculoskeletal pain, Smartphone
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Original Article
Phys. Ther. Korea 2024; 31(3): 250-261
Published online December 20, 2024 https://doi.org/10.12674/ptk.2024.31.3.250
Copyright © Korean Research Society of Physical Therapy.
A Regression Analysis Study on the Presence of Pain in Specific Body Regions Based on Smartphone Usage Posture, Smartphone Addiction, Smartphone Usage Patterns
YiXin Wang1 , PT, BPT, Ye-Jin Kim1 , PT, BPT, Kyeong-Ah Moon1 , PT, BPT, Hye-Seon Jeon1,2 , PT, PhD
1Department of Physical Therapy, The Graduate School, Yonsei University, 2Department of Physical Therapy, College of Health Sciences, Yonsei University, Wonju, Korea
Correspondence to:Hye-Seon Jeon
E-mail: hyeseonj@yonsei.ac.kr
https://orcid.org/0000-0003-3986-2030
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
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.
Keywords: Cross-sectional studies, Logistic models, Musculoskeletal pain, Smartphone
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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 (%)..
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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..