by Ryan Crosbie
A 2016 study out of the Pew Research Center reported that 72% of adults in the United States own a smartphone (Pew Research Center, 2016). Furthermore, the report details the exponential growth in smartphone ownership and usage globally. Since the release of the first touch-screen smartphone 10 years ago, the technology has permeated and transformed countless aspects of daily life, including body posture.
Use of a smartphone tends to result in neck flexion and prolonged periods of arm extension (Gustafsson, 2011). Observational and experimental studies have detailed changes in posture and muscle activity when operating a smartphone (Liang, 2016; Gustafsson, 2010). Surface electromyography techniques have been utilized to measure the increased activation of neck extensor muscles while text messaging on a smartphone (Xie, 2016). Higher activation in these muscle regions is positively correlated with neck and shoulder pain (Xie, 2016; Lee, 2015). Inherent to their design, touchscreens mandate that one’s gaze and hands be at a single point in space. A smartphone user must either flex the head forward or continuously extend the arm up. The posture held while operating a smartphone is a reasonable mechanism to explain neck and shoulder pain.
A survey of over 3,000 Chinese high school students assessed the impact digital product use, including smartphone use, and physical activity had on neck pain (Shan, 2013). Self-reported mobile phone use of greater than two hours a day was associated with a nearly 50% increase in the risk of neck pain. Meanwhile, higher rates of physical activity were associated with a reduced rate of neck pain. The duration of smartphone use and level of physical activity have been shown to be important factors when considering the relationship between smartphones and neck pain.
A study conducted by Gustafsson et al. examined the association between text messaging on a smartphone and pain of the neck in a population of young adults in Sweden (Gustafsson, 2015). After adjusting for confounders, the authors discovered significant associations between texting and neck pain (OR = 2.0 for males, OR = 1.5 for females). Furthermore, a dose-response was observed when respondents were stratified by the number of messages sent per day. Results suggested that individuals that text-message more are at increased risk of neck pain. A detailed understanding of smartphone uses associated with pain, especially text messaging, is fundamental to the development of preventative strategies.
The aim of this study is to investigate whether smartphone use is associated with neck and/or shoulder pain in a population of adults. This study secondarily investigates the relationship of neck pain to characteristics of smartphone use such as duration and means of utilization (i.e. common uses). Additionally, the impact of age, gender, ethnicity, employment status and activity level on the relationship between smartphone use and pain is examined.
An epidemiological, self-report questionnaire was administered to a convenience sample consisting of 27 graduate students at the Fairbank’s School of Public Health, 66 colleagues affiliated with the Indiana University Neurology Department, and 61 personal friends and family members. A brief personalized greeting and explanation of the research were distributed along with the hyperlink to the online questionnaire. Additionally, respondents willing to assist with data collection were encouraged to circulate the questionnaire within their social circles. For this reason, the true rate of response cannot be calculated.
The Smartphone Usage and Neck Pain Questionnaire (Appendix 1) developed for this study consisted of 29 items grouped into three parts: 1) History of Neck Pain, 2) Smartphone Usage, and 3) Demographics. The sections were completed in that order for each administration. Section 1 primarily focused on the frequency of neck and/or shoulder pain and the average intensity of the pain (if present). Additionally, section 1 inquired about the utilization of three common therapies within the last 6 months (chiropractic adjustment, massage and painkillers), history of cervical spine surgery and the presence of arthritis in the neck or spine. In section 2, respondents indicated whether or not they owned a smartphone. Those that indicated ownership went on to provide the type of phone, estimate the duration of their daily use, list the top three uses, and state if use was required for their occupation. In section 3, respondents provided their age, gender, ethnicity, employment status, workplace activity level, and the average amount of time spent exercising in a week. The questionnaire was built using REDcap and respondents accessed the survey anonymously through an online portal. At the conclusion of the data collection phase, the data was exported to an Excel spreadsheet using the export function within REDcap.
SAS statistical package version 9.4 was used for all of the statistical analyses.
Percentages were calculated for demographic variables. Age was captured as a continuous variable and stratified into four groups for subsequent analyses. Cases were defined as those respondents that affirmed any frequency of neck pain (once, monthly, weekly, or daily) within the last six months. Controls were defined as those that reported no occurrences of neck pain. Odds ratios were calculated and chi-square tests were used to assess the association between average daily duration of use and neck pain. Associations of duration of use and neck pain were also assessed after stratifying by gender and age group. The outcome of neck and/or shoulder pain was assessed using a logistic regression model. The removal limit was set at 0.20. As a result, duration of use and workplace activity were built into the model as the two explanatory variables. Age, gender and time spent exercising were included in the model as suspected confounding variables based on a review of the literature. The level of significance is set to 0.05 and 95% confidence intervals are provided.
A total of 110 individuals responded to the survey. Six respondents failed to complete the questionnaire in its entirety. Three incomplete questionnaires contained no data, two indicated the presence of neck and/or shoulder pain before discontinuing the questionnaire, and one indicated no pain before discontinuing. These incomplete records were removed from the dataset prior to analysis. As a result, data from 104 completed questionnaires were utilized in this study.
The respondents’ ages ranged from 19 to 68 and the mean age was 33.9 years old (SD = 13.2). A majority of respondents were in the age group of 19-30 years old (61%). The majority of respondents were female (79%), Caucasian (85%) and employed full-time (64%). Ninety-four percent of the respondents owned and operated a smartphone within the last six months. The overall prevalence of neck and/or shoulder pain, reported at any frequency, was 79%. Demographic and lifestyle statistics for cases and controls are provided in Table 1.
Reporting any frequency of neck pain within the last six months is significantly associated with owning a smartphone (OR = 8.3 [1.42, 48.81]. Among the exposed, smartphone use is responsible for 87.9% of neck pain. Smartphone use is responsible for 70.3% of neck pain in the total population.
Stratifying by gender, pain reported by female respondents is significantly associated with smartphone ownership (OR = 8.1 [1.35, 48.66]). All of the male respondents reported owning a smartphone and, therefore, the association between neck pain and smartphone ownership could not be assessed in this stratum. There was no association between gender and neck pain (OR = 0.7368 [0.22, 2.44]).
The association between neck pain and smartphone ownership was assessed within each age group. All respondents within the 19-30, >30-40 and >40-50 age groups owned smartphones. Of the 19 respondents from the >50 age group, 6 reported owning a smartphone and pain, 7 reported owning a smartphone and no pain, and 2 reported pain without owning a smartphone. The resulting odds ratio was calculated to be 1.71 [0.23, 12.89].
The average daily duration of smartphone use was categorized into five group: 1) 0 – 30 minutes; 2) >30 minutes – 1 hour; 3) >1 – 2 hours; 4) >2 – 4 hours; 5) >4 hours. Measures of association were calculated for each group as compared to a reverent population of respondents that did not operate a smartphone within the last six months. Odds ratios and 95% confidence intervals are provided in Table 2. The lowest level of usage (0 – 30 minutes) corresponded with a two-fold increase in the risk for neck pain that tended toward significance. The remaining groups all displayed robust associations with neck pain. The >1 – 2 hour group displaying the greatest risk association (OR = 38.00 [13.15, 109.79]).
Age, gender, daily duration of smartphone use, average time exercising per week and workplace activity level were built into the final logistic regression model for neck pain. Results of the regression model are summarized in Table 3. Age was inversely associated with a reduced risk for neck pain. Interestingly, reporting a moderate level of workplace activity was significantly associated with an increased risk for neck pain as compared to a sedentary level, yet there was no association with a very active level.
The utilization of back pain therapies was assessed using chi-square tests. Painkiller use was not significantly associated with using a smartphone for greater than two hours daily (OR = 1.45 [0.58 3.68]). Similarly, there was no association between smartphone use and having at least one chiropractic adjustment within the last six months (0.46 [0.12, 1.77]). Lastly, 20 participants reported having a massage within the last six months to treat back pain. However, there was no association between smartphone use and massage (2.14 [0.57, 7.98]).
Texting as the primary use was negatively associated with neck pain (0.14, [0.04, 0.46]). Of the 42 respondents that reported that they primarily used their smartphone for texting, 27 reported pain. Of those that do not report texting as their primary use, 52 experienced pain and 4 did not. This represents a seven-time reduction in the risk for pain. However, when stratified by age group, only those in the 19-30 age group experienced a reduction in risk (11.6250, [.28, 105.05]).
Consistent with similar studies, a positive association between smartphone use and neck pain was found in this study. However, with a crude odds ratio of 8.3, the relative risk is much greater in this study as compared to previous publications. To more fully evaluate these results, the data were stratified to assess for confounding.
The association was reassessed after stratifying by gender. Unfortunately, all of the males reported owning a smartphone. The result was that there was no comparison group within the male stratum and an odds ratio could not be calculated. Furthermore, a Mantel-Hanzel summary odds ratio could not be calculated and confounding could not fully be assessed. However, gender does not appear to be a confounding factor given that a strong association exists within the female stratum, the majority of males reported neck pain and no association was found between gender and neck pain.
Next, the association was reassessed after stratifying by age group. Similar to gender, stratifying resulted in the absence of data within certain cells of the matrix. In fact, only respondents from the >50 age group indicated that they did not own a smartphone. As a result, the relative risk could not be calculated. Smartphone use within the >50 age group was associated with a 71% increase in neck pain. These results are more consistent with the odds ratios reported related publications.
A univariate logistic regression was used to assess the relationship between the amount of time spent on a smartphone and neck pain. Again, the results supported a robust association between smartphone use and neck pain. Despite significant associations, the risk ratios do not necessarily follow the suspected trend of the frequency of pain increasing with duration. The risk of neck pain increases along with increasing usage up until the >1 – 2-hour tier and then begin to decrease with more usage.
A multivariate logistic regression model was built to predict neck pain as a function of duration and potential confounders. Within this study, duration of smartphone use did not appear to predict the outcome of pain. Curiously, a moderately active level of activity at work was associated with an increased rate of pain. This may be an anomaly; however, it is possible that activity level correlates to other smartphone use behaviors that cause pain.
As one would expect, age is a major source of confounding. The robust relationship between smartphone use and pain was greatly reduced when adjusted for pain. Similarly, the association between text messaging as a primary use and its association with pain is better explained by the fact that more younger adults primarily text-message.
A major weakness of this study is related to the fact that age is a confounding variable. Given that age is a confounder and those that do not own phones are all over the age of 50, it is problematic that those that had not used a phone were used as the referent population to assess relationships. To improve this in future studies, appropriate samples sizes should be recruited to allow for age fully adjusted.
This study was limited by sample size. The low number of respondents resulted in low power. Additionally, a small sample size limited the analysis because stratification could not be done in a meaningful way. Furthermore, the sample is likely not representative of the population of interest as this was conducted as a convenience survey.
The prevalence of neck pain in this study population is high. Neck pain is associated with the use of a smartphone. The frequency and intensity of neck pain are strongly associated with smartphone use. Smartphone use was not associated with the use of painkillers, massage, or chiropractic adjustments to treat back pain. When age was controlled, text-messaging as a primary use was not associated with neck pain. Higher duration of use were strongly correlated with and increased risk of neck pain.
Appendix 1: Smartphone Usage and Neck Pain Questionnaire
Gustafsson, E., Johnson, P.W., Hagberg, Ml. (2010). Thumb postures and physical loads during mobile phone use – a comparison of young adults with and without musculoskeltal symptoms. J. Electromyogr. Kinesiol 20, 127 – 135. https://www-ncbi-nlm-nih-gov.proxy.medlib.uits.iu.edu/pubmed/?term=thumb+posture+and+physical+loads+during+mobile+phone+use
Gustafsson, E., Johnson, PW., Lindegard, A., Hagberg, M. (2011). Technique, muscle activity and kinematic differences in young adults texting on mobile phones. Egronomics. 2011 May; 54(5): 477-487. https://www-ncbi-nlm-nih-gov.proxy.medlib.uits.iu.edu/pubmed/21547792
Gustafsson, E., Thomée, S., Grimby-Ekman, A., & Hagberg, M. (2017). Texting on mobile phones and musculoskeletal disorders in young adults: A five-year cohort study. Applied Ergonomics, 58, 208. Retrieved from http://ulib.iupui.edu/cgi-bin/proxy.pl?url=http://search-proquest-com.proxy.ulib.uits.iu.edu/docview/1824292084?accountid=7398
Korpinen, L., Pääkkönen, R., & Gobba, F. (2013). Self-reported neck symptoms and use of personal computers, laptops and cell phones among finns aged 18-65. Ergonomics, 56(7), 1134. Retrieved from http://ulib.iupui.edu/cgi-bin/proxy.pl?url=http://search-proquest-com.proxy.ulib.uits.iu.edu/docview/1411286477?accountid=7398
Lee, S., Kang, H., & Shin, G. (2015). Head flexion angle while using a smartphone. Ergonomics, 58(2), 220. Retrieved from http://ulib.iupui.edu/cgi-bin/proxy.pl?url=http://search-proquest-com.proxy.ulib.uits.iu.edu/docview/1648345150?accountid=7398
Liang, H., & Hwang, Y. (2016). Mobile phone use behaviors and postures on public transportation systems. PLoS One, 11(2) doi:http://dx.doi.org.proxy.ulib.uits.iu.edu/10.1371/journal.pone.0148419
Poushter, Jacob. (2016). Smartphone ownership ad internet usage continues to clime in emerging economies. Pew Research Center: Global trends and attitudes. http://www.pewglobal.org/2016/02/22/smartphone-ownership-and-internet-usage-continues-to-climb-in-emerging-economies/
Seong-Yeol, K., Sung-Ja, K. (2015). Effect of duration of smartphone use on muscle fatigue and pain caused by forward head posture in adults. J. Phys. Ther. Sci. 28: 1669-1672. https://www-ncbi-nlm-nih-gov.proxy.medlib.uits.iu.edu/pubmed/?term=Effect+of+duration+of+smartphone+use+on+muscle+fatigue+and+pain
Shan, Z., Deng, G., Li, J., Li, Y., Zhang, Y., & Zhao, Q. (2013). Correlational analysis of neck/shoulder pain and low back pain with the use of digital products, physical activity and psychological status among adolescents in shanghai. PLoS One, 8(10) doi:http://dx.doi.org.proxy.ulib.uits.iu.edu/10.1371/journal.pone.0078109
Xie, Y., Szeto, G. P. Y., Dai, J., & Madeleine, P. (2016). A comparison of muscle activity in using touchscreen smartphone among young people with and without chronic neck-shoulder pain. Ergonomics, 59(1), 61-72. doi:http://dx.doi.org.proxy.ulib.uits.iu.edu/10.1080/00140139.2015.1056237
Xie, Y., Szeto, G., Dai. J. (2016). Prevalence and risk factors associated with musculoskeletal complaints among users of mobile handheld devices: A systematic review. Appl Ergon. 2017 Mar; 59 (pt A): 132-142. https://www-ncbi-nlm-nih-gov.proxy.medlib.uits.iu.edu/pubmed?term=prevalence%20and%20risk%20factor%20associated%20with%20musculoskeletal%20complaints%20among%20users%20of%20mobile%20handheld%20devices&cmd=correctspelling