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They were considered as addicted to smartphones. The cut-off value of SAS-SV was determined based on the consultation results with the clinical psychologists. Figure 1 showed the ROC curve by gender. In boys, the AUC value was 0. As for the girls, the AUC value was 0.

Based on the cut-off values, this scale was considered as an appropriate tool for evaluating smartphone addiction. Kim et al. In other words, adolescents tend to proactively accept new media and substitute the previous one. Based on comparisons with adults, teenagers are more vulnerable to smartphone addiction. As a result, preventive measures should be taken into consideration and the adolescents, who were predicted to develop smartphone addiction, should be identified.

In addition, the importance of cut-off values, which were suggested through the development and validation of smartphone addiction scale, had been highlighted. The general use of smartphones showed an SAS-SV score of 26, which was the highest in the usage of messenger or SNS applications compared with other forms of use. The detailed research showed that the messenger or SNS included Facebook, Twitter, and popular applications in South Korea such as Kakao Story and Kakao game, which were both connected to the messenger.

The use of these applications was considered to reflect the social characteristics of SNS as people were able to play games and interact with their friends and acquaintances. As for the self-assessment of smartphone addiction, the group who assessed themselves as addicted to smartphones showed 34 points in the SAS-SV score, while the group who assessed themselves as not addicted to smartphone was This result tells us that the high SAS-SV score reflects the self-awareness about the seriousness of smartphone addiction.

However, the KS-scale showed moderate correlation. This result showed that the propensity of smartphone addiction and internet addiction was not completely identical, but rather they had moderate correlation [15].

As for the use of smartphones, the previous studies on mobile phones showed different usage patterns or propensities of addiction by gender. Billieux et al. In addition, Takao et al. Other studies on smartphone addiction, which were conducted in South Korea, also showed a difference on the degree of addiction by gender. It suggested that the female participants were more aware of their addiction based on the higher self-reporting scores [7].

At the development stage of the SAS, Kwon et al. As for self-reporting, the female participants have the tendency to be aware and expressed their problems more openly than the male participants. According to the gender differences in adolescent symptomatology in the previous study, male students have the tendency to externalize their addiction symptoms while female participants relatively internalized them; therefore, the self-awareness showed difference by gender [18].

In this study, the SAS-SV score was 24 in boys and 28 in girls, which showed significant difference by gender. As a result, we suggested a different cut-off value by gender group through the ROC analysis reflecting the different characteristics of gender. By using this figure with a negative predictive value, which is relatively higher, the predicted addiction subjects can be efficiently identified in order to prevent the addiction beforehand.

In the smartphone addiction group in this study, These results were considered because the Smartphone Addiction Scale SAS is not designed to pathologically diagnose smartphone addiction but more to identify the level of the smartphone addiction risk and to distinguish the high-risk group. Smartphones are popular media that are easier to access than other media. As such, using this smartphone addiction scale as a screening tool can help prevent smartphone addiction in communities or schools.

This study was conducted in order to develop and validate the short version of the smartphone addiction scale and suggest a cut-off value for diagnosis to efficiently evaluate the smartphone addiction in the community and research areas. A total of participants were included for analysis and 10 final questions were selected based on the validity determined by the experts.

Both the validity and efficiency were evaluated in the 10 questions through internal consistency reliability, concurrent validity, and ROC analysis. The 33 questions that were previously used for the SAS were inefficient for the adolescent group due to its large number of questions and its use was limited to diagnose addiction as cut-off values were not suggested.

With regards to the SAPS, which consisted of 15 questions, its cut-off value was determined through a statistical method and not through consultation with experts in its selection process with the participants. Moreover, the value was not suggested for both genders regardless of the gender differences.

As such, for this scale, questions were formed based on smartphone use, which are not found in the conventional Internet addiction scales. This scale is a short version that contains only 10 questions for easy smartphone addiction screening of adolescents who are considered vulnerable to addiction.

This scale also provides a cut-off value to evaluate the level of addiction, to evaluate the treatment effect and to provide evidence of interventions different from those in the conventional scales. This scale has a high value as a screening tool because gender differences can be reflected in the results by providing a cut-off value for both genders, and the screening process that includes the evaluation by clinical psychologists is not merely the simple percentage calculation method but reflects the characteristics of the participants.

As a result, this sample group was hard to be generalized and further studies with various sample groups should be conducted in order to evaluate the validity of this scale. Another limitation of this study concerns the use of its findings in clinical practice for diagnosis, as it was not performed under clinical settings. Therefore, further studies with well-controlled clinical settings and various participants are suggested.

However, this short-version scale was considered to be an effective means to predict the smartphone addiction based on the experts' diagnoses despite those aforementioned limitations. In addition, the SAS-SV can be used to identify a potential high-risk group for smartphone addiction, both in the community and educational fields. Further investigation of their characteristics in the future, development of program, and arrangement of plans should be taken into consideration for the prevention of smartphone addiction.

Performed the experiments: MK HC. Analyzed the data: MK HC. Wrote the paper: MK. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field.

Abstract Objective This study was designed to investigate the revised and short version of the smartphone addiction scale and the proof of its validity in adolescents. Method A set of questionnaires were provided to a total of selected participants from April to May of Results The 10 final questions were selected using content validity.

Funding: These authors have no support or funding to report. Introduction Nowadays, addiction not only refers to drug or substance abuse, but it also refers to gambling, internet, games, or even smartphones. Materials and Methods Participants The participants of this study were students in their 2 nd year of junior high school from two schools in the Kangwon province of South Korea. Measurement SAS. The six factors were daily-life disturbance, positive anticipation, withdrawal, cyberspace-oriented relationship, overuse, and tolerance.

During its development stages, the internal-consistency test result Cronbach's alpha was 0. In this study, the internal-consistency test result Cronbach's alpha of SAS was 0. Dan White Dec 3, Ecler, a Spanish pro audio company with a long history of making DJ mixers, has been out of the DJ products game ….

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This can become a costly endeavor if you are both producing and DJing, as you likely have …. Load More Posts. Drop your email address here, we'll send you news, tutorials, and special offers once a week. DJ Gear. Software Tutorials. DJTT Store. The largest difference was for persons with functional disability, followed by severe pain, moderate pain, arthritis pain, and joint pain.

The regression results for the indirect cost analysis are reported in Tables C , C , and C As with the health care cost models, we interpreted the coefficients on the pain measures by exponentiating them. The first step models were logistic regressions, so the exponentiated coefficients on the indicator variables were ORs. The second step models were log-linear using the generalized linear model. Thus, the exponentiated coefficients were percent changes in the dependent variables.

For example, in Table C , Model 1, the coefficients on moderate pain were 0. We interpreted these coefficients as follows. Compared with a person with no pain, someone with moderate pain had 64 percent greater odds of having at least one missed day of work during the year, and having moderate pain increased the number of days missed by 63 percent.

Tables C and C display the impact of pain conditions on the likelihood of working, the number of hours worked, and hourly wages. The pain conditions had a significant negative impact on the likelihood of working. The impact on hours worked and wages was negative but modest and in several cases insignificant.

This means that the negative impact of pain conditions on hours worked and wages occurred largely through the decision to work or not. Persons with pain were less likely to work than persons without pain. The calculated incremental costs are reported in Tables C to C The average incremental number of days of work missed was greatest for severe pain, with estimates ranging from 5.

Arthritis caused the fewest days of work missed—0. Almost 70 million working adults reported having one of the pain conditions. More persons reported joint pain, but severe pain was more costly. Including functional disability in these models did not affect the estimates for the other pain conditions.

Pain also was associated with fewer annual hours worked. For Model 1, severe pain was associated with the largest reduction, hours. However, when we included functional disability in the model, the impact of severe pain fell to 30 hours, while the reduction associated with having a functional disability was hours. Office-based services and hospital stays accounted for 36 percent and 33 percent of the total costs, respectively.

This indicates that most of the health care costs were attributable not to a direct diagnosis of pain but to the impact of pain on the treatment of other conditions. These cost-of-condition estimates differ from our cost-of-pain estimate. NIH combined personal health care costs reported in the MEPS and the costs of premature death due to these conditions; however, the NIH estimates do not include lost productivity.

We do not consider the costs of premature death due to pain because pain is not considered a direct cause of death as are heart disease, cancer, and stoke. Unlike these diagnosed conditions, pain affects a much larger number of people, by a factor of about four compared with heart disease and diabetes and a factor of nine compared with cancer.

Thus, the per person cost of pain is lower than that of the other conditions, but the total cost of pain is higher. Our estimate of the cost of chronic pain is conservative for several reasons. First, we did not account for the cost of pain for institutionalized and noncivilian populations. In particular, the incremental health care costs for nursing home residents, military personnel, and prison inmates with pain were not included and may be substantial.

Second, we did not include the costs of pain for persons under age Third, we did not include the cost of pain to caregivers. For example, we did not consider time a spouse or adult child might lose from work to care for a loved one with chronic pain.

Fourth, we considered the indirect costs of pain only for working-age adults. We did not estimate these costs for working persons over the age of 65 or under the age of While there are persons in these age categories who are retired or continuing their education, there also are persons in both age categories who are working or willing to work.

We did not capture the value of their lost productivity. Fifth, we also did not include the value of time lost for other, non-work-related activities. Sixth, we did not include other indirect costs—lost tax revenue, costs for replacement workers, legal fees, and transportation costs for patients to reach providers.

Finally, in our cost estimates we did not attempt to measure the psychological or emotional toll of chronic pain. Our analysis has a few limitations.

First, it is a cross-sectional analysis, so we cannot infer causality. Second, our measures of pain are limited. We cannot estimate the impact of pain associated with musculoskeletal conditions or cancer. Third, our functional disability may include persons who do not have chronic pain. Finally, we used two-part models to control for unobserved differences between persons with pain and persons without pain.

However, we recognize that the two-part approach may not fully capture the unobserved differences between the two groups and if so, our estimates of costs associated with pain will be too large. In general, given the magnitude of the economic costs of pain, society should consider investing in research, education, and care designed to reduce the impact of pain. Eliminating pain may be impossible, but helping people live better with pain may be achievable.

Turn recording back on. National Center for Biotechnology Information , U. Search term. Author Information Authors Darrell J. Objective We estimated 1 the annual economic costs of pain in the United States and 2 the annual costs of treating patients with a primary diagnosis of pain.

Methodology The annual economic costs of pain can be divided into two components: 1 the incremental costs of medical care due to pain, and 2 the indirect costs of pain due to lower economic productivity associated with lost wages, disability days, and fewer hours worked. Key Independent Variables We defined persons with pain as those who reported that they experienced pain that limited their ability to work, that they were diagnosed with joint pain or arthritis, or that they had a disability that limited their ability to work.

Dependent Variables We used total expenditures as the dependent variable to predict the incremental costs of care for individuals with selected pain conditions compared with those without these conditions. Health Care Expenditure Models We estimated a standard two-part expenditure model to address issues of sample selection and heterogeneity and computed the economic burden for patients with the different types of pain conditions noted above compared with those without any pain Manning, ; Mullahy, ; Manning and Mullahy, ; Buntin and Zaslavsky, ; Deb et al.

Indirect Cost Models As with the health care expenditure models, we used two-part models to estimate the indirect costs of pain. Indirect Costs Table C-7 shows the dependent and independent variables for the analysis of incremental indirect costs. Economic costs of diabetes in the US in Diabetes Care. Aday LA, Andersen R. A framework for the study of access to medical care. Health Services Research.

Becker GS. A theory of marriage: Part I. The Journal of Political Economy. A theory of marriage: Part II. Too much ado about two-part models and transformation? Journal of Health Economics. Bureau of the Census. Statistical abstracts of the United States.

Washington, DC: Bureau of the Census; Microeconometrics methods and applications. New York: Cambridge University Press; Ettner SL. The impact of parent care on female labor supply decisions. Greene WH. Econometric analysis. Heckman J. Sample selection bias as a specification error. Burden of migraine in the United States: Disability and economic costs. Archives of Internal Medicine. Killingsworth MR. Labor supply. Lubeck PA. Review of the direct costs of rheumatoid arthritis. Comprehensive review of epidemiology, scope and impact of spinal pain.