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  • 1 Carthage College, USA
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Background and aims

Smartphones are ubiquitous. As smartphones increased in popularity, researchers realized that people were becoming dependent on their smartphones. The purpose here was to provide a better understanding of the factors related to problematic smartphone use (PSPU).

Methods

The participants were 100 undergraduates (25 males, 75 females) whose ages ranged from 18 to 23 (mean age = 20 years). The participants completed questionnaires to assess gender, ethnicity, year in college, father’s education level, mother’s education level, family income, age, family history of alcoholism, and PSPU. The Family Tree Questionnaire assessed family history of alcoholism. The Mobile Phone Problem Use Scale (MPPUS) and the Adapted Cell Phone Addiction Test (ACPAT) were used to determine the degree of PSPU. Whereas the MPPUS measures tolerance, escape from other problems, withdrawal, craving, and negative life consequences, the ACPAT measures preoccupation (salience), excessive use, neglecting work, anticipation, lack of control, and neglecting social life.

Results

Family history of alcoholism and father’s education level together explained 26% of the variance in the MPPUS scores and 25% of the variance in the ACPAT scores. The inclusion of mother’s education level, ethnicity, family income, age, year in college, and gender did not significantly increase the proportion of variance explained for either MPPUS or ACPAT scores.

Discussion and conclusions

Family history of alcoholism and father’s education level are good predictors of PSPU. As 74%–75% of the variance in PSPU scale scores was not explained, future studies should aim to explain this variance.

Abstract

Background and aims

Smartphones are ubiquitous. As smartphones increased in popularity, researchers realized that people were becoming dependent on their smartphones. The purpose here was to provide a better understanding of the factors related to problematic smartphone use (PSPU).

Methods

The participants were 100 undergraduates (25 males, 75 females) whose ages ranged from 18 to 23 (mean age = 20 years). The participants completed questionnaires to assess gender, ethnicity, year in college, father’s education level, mother’s education level, family income, age, family history of alcoholism, and PSPU. The Family Tree Questionnaire assessed family history of alcoholism. The Mobile Phone Problem Use Scale (MPPUS) and the Adapted Cell Phone Addiction Test (ACPAT) were used to determine the degree of PSPU. Whereas the MPPUS measures tolerance, escape from other problems, withdrawal, craving, and negative life consequences, the ACPAT measures preoccupation (salience), excessive use, neglecting work, anticipation, lack of control, and neglecting social life.

Results

Family history of alcoholism and father’s education level together explained 26% of the variance in the MPPUS scores and 25% of the variance in the ACPAT scores. The inclusion of mother’s education level, ethnicity, family income, age, year in college, and gender did not significantly increase the proportion of variance explained for either MPPUS or ACPAT scores.

Discussion and conclusions

Family history of alcoholism and father’s education level are good predictors of PSPU. As 74%–75% of the variance in PSPU scale scores was not explained, future studies should aim to explain this variance.

Introduction

Mobile phones, including smartphones, are ubiquitous. In support, the mobile phone penetration rate (i.e., the number of mobile phone subscriptions per 100 individuals) has increased dramatically from 2001 to 2015. According to the International Telecommunication Union, from 2005 to 2015, there was a 72% increase (from 68% in 2005 to 118% in 2015), a 55% increase (from 82% in 2005 to 125% in 2015), and a 306% increase (from 23% in 2005 to 93% in 2015) in the penetration rate in the United States, developed countries (including the United States), and developing countries, respectively (International Telecommunication Union, 2016).

Mobile phone use, including smartphone use, has positive and negative outcomes. One positive outcome is an increased connection with family and friends through interactions with others on social networks, watching and sharing videos and pictures, playing video games, exchanging e-mails, and/or utilizing a host of readily available applications. Other positive outcomes include increased productivity while waiting, an increased ability to organize one’s daily life, an enhanced ability to accomplish day-to-day tasks, and convenient access to entertainment (e.g., videos and music). In fact, 65% reported that smartphones made it a lot easier to stay in touch with the people they care about, 69% reported that smartphones made it easier to plan and schedule their daily routine, and 67% reported that smartphones made it easier to be productive while doing things like sitting in traffic or waiting in line (Pew Research Center, 2015). In support of the idea that smartphones make it easier for people to accomplish day-to-day tasks, 68% of smartphone users reported that they had used their phone in the past year to look up information about a health condition, 57% had used their phone to do online banking, 44% had used their phone to look up real estate listings or other information about a place to live, and 18% to submit a job application (Pew Research Center, 2015). Watching videos and listening to music are, in particular, popular with younger smartphone users. About 75% and 64% of respondents ages 18–29 reported watching a video and listening to music, respectively, at least once in the past 2 weeks (Pew Research Center, 2015). Negative outcomes associated with smartphone use include the use of smartphones while driving, which has a detrimental effect on driving performance (Alm & Nilsson, 1995; Consiglio, Driscoll, Witte, & Berg, 2003; Hancock, Lesch, & Simmons, 2003), and increases the number of car accidents (Laberge-Nadeau et al., 2003; Redelmeier & Tibshirani, 1997; Violanti, 1998; Violanti & Marshall, 1996), the accumulation of large financial debts (Funston & McNeill, 2015), and increased cyberbullying (Charlton, Panting, & Hannan, 2002). According to Kamibeppu and Sugiura (2005), smartphone use is associated with behavioral problems, such as staying up late at night exchanging text messages and emotional dependence (e.g., the user thinks he/she cannot live without their smartphone). In addition, smartphone users are more likely than non-users to experience somatic symptoms, insomnia, social dysfunction, anxiety, and depression (Jenaro, Flores, Gómez-Vela, González-Gil, & Cabello, 2007).

It is generally accepted that individuals who have a positive family history of alcohol dependence (i.e., have close biological relatives with alcohol dependence) are themselves at an increased risk of alcohol dependence (Cotton, 1979; Goodwin, Schulsinger, Hermansen, Guze, & Winokur, 1973; Stabenau & Hesselbrock, 1983). In addition, rates of alcohol dependence increase with male sex, younger age, lower education, unmarried status, lower income, and other variables indicative of social disadvantage (Crum, Helzer, & Anthony, 1993; Swendsen et al., 2009). Interestingly, the relationship between parental education and substance abuse was found to differ substantially by race and ethnicity (Bachman, O’Malley, Johnston, Schulenberg, & Wallace, 2011). In light of the fact that problematic smartphone use (PSPU) shares many of the characteristics of drug and alcohol dependence (e.g., Chóliz, 2010), we hypothesized that family history of alcohol dependence would be related to two valid and reliable measures of PSPU; namely, the Mobile Phone Problem Use Scale (MPPUS; Bianchi & Phillips, 2005) and the Adapted Cell Phone Addiction Test (ACPAT; Smetaniuk, 2014). In light of the aforementioned studies that reported a relationship between sociodemographic variables and alcohol dependence, we examined the relationships between age, gender, ethnicity, year in college, the education level of the participants’ father and mother, and family income and MPPUS and ACPAT scores.

Methods

Participants

The participants were 100 undergraduates (25 males, 75 females) from Carthage College who were currently taking a course in the Department of Psychological Science. The age of the participants ranged from 18 to 23 with a mean age of 20 years. Participants completed a questionnaire that asked about the participants’ age, gender, ethnicity, year in college, the education level of their father and mother, their family income, family history of alcohol dependence, whether they owned a smartphone, and PSPU.

Materials

The questionnaires were distributed through hard copy surveys and an online survey completed through Google Docs. Participants were provided with the Family Tree Questionnaire (FTQ), a valid and reliable measure of family history of alcohol use (Mann, Sobell, Sobell, & Pavan, 1985; Stoltenberg, Mudd, Blow, & Hill, 1998). The FTQ is a brief, easily administered questionnaire that was used to gather self-reports of participants’ family history of first-degree (siblings, parents) and second-degree (grandparents, uncles, aunts) relatives’ history of alcohol-related problems. Participants classified their relatives into one of several possible drinker groups ranging from total lifelong abstainers to definite problem drinkers. Family members who were adopted, half-siblings, and step-relatives were excluded. Each family member was scored on a 6-point Likert scale: 1 = never drank (a person who never consumed alcoholic beverages); 2 = social drinker (a person who drinks moderately and is not known to have a drinking problem); 3 = possible problem drinker (a person who you believe or were told might have [or had] a drinking problem, but whom you are certain actually had a drinking problem); 4 = definite problem drinker (persons who are known to have received treatment for a drinking problem), 5 = no relative (only applicable for brothers and sisters), and 6 = don’t know/don’t remember (Mann et al., 1985). The participants who reported having at least one first- or second-degree relative that was a definite problem drinker were considered as having a positive family history of alcohol dependence (Mann et al., 1985). Family drinking density was calculated as the number of definite problem drinkers divided by the total number of relatives (Di Sclafani, Finn, & Fein, 2007, 2008).

Participants completed the MPPUS (Bianchi & Phillips, 2005) to measure the degree of smartphone problem use. The MPPUS is the most widely used and cited PSPU scale and is considered by some as the gold standard of PSPU scales. The MPPUS is a 27-item scale, in which each item is measured on a 10-point Likert scale (1 = not true at all and 10 = extremely true). The total MPPUS score was used to determine the severity of the PSPU. This questionnaire measured the issues of tolerance (i.e., needing more to produce the same initial effect), escape from other problems, withdrawal (e.g., irritability, nervousness, and restlessness), craving, and negative life consequences in the areas of social, familial, work, and financial difficulties. The participants were placed into one of three categories that determined the degree of concern regarding their smartphone use. The range of MPPUS scores that defined each of the degree of concern categories used here were described in published reports (Bianchi & Phillips, 2005; Smetaniuk, 2014). One would have a low-to-moderate, moderate-to-high, and high-to-severe degree of concern for those who scored between 27 and 76, 77 and 126, and greater than 126, respectively (Bianchi & Phillips, 2005; Smetaniuk, 2014).

Participants also completed the ACPAT, another measure of PSPU (Smetaniuk, 2014). The ACPAT is a 20-item scale, in which each item is measured on a 5-point Likert scale (1 = never and 5 = always). Like the MPPUS, the ACPAT produces a total score. The participant’s total score determined the degree of concern regarding their smartphone use. The range of ACPAT scores that defined each of the degree of concern categories used here were described in a published report (Smetaniuk, 2014). One would have a low-to-moderate, moderate-to-high, and high-to-severe degree of concern for those who scored between 20 and 49, 50 and 79, and 80 and 100, respectively. The ACPAT measures preoccupation (salience), excessive use, neglecting work, anticipation, lack of control, and neglecting social life (Wyando & McMurran, 2004).

Statistical analysis

Data were subjected to hierarchical multiple regression analysis to determine the relationship between the study variables. Prior to performing a hierarchical multiple regression, a preliminary data analysis was conducted to determine if the assumptions of the statistical test had been met. α level was set to .05.

Ethics

The study procedures were carried out in accordance with the Declaration of Helsinki. The Institutional Review Board of Carthage College approved the study. All participants were informed about the study and provided informed consent.

Results

Sample characteristics

Of the 100 participants, there were 25 males (25%) and 75 females (75%). The age of the participants ranged from 18 to 23 (M = 20.09, SE = 0.13). Ninety-nine of the 100 participants owned a smartphone. The sociodemographic characteristics of the sample are given in Table 1.

Table 1.

The sociodemographic characteristics of the sample (N = 100). The age data given as M ± SE

Number
GenderMale25
Female75
EthnicityNative American0
Asian3
Black1
White87
Latino6
Multiracial2
Other1
Year in collegeFreshman31
Sophomore18
Junior28
Senior23
Father’s education levelMiddle school4
High school19
Some college13
2 years of college12
4 years of college31
Graduate school21
Mother’s education levelMiddle school2
High school17
Some college18
2 years of college19
4 years of college27
Graduate school17
Family income<$20,000/year8
$21,000–$40,000/year13
$41,000–$60,000/year14
$61,000–$80,000/year17
$81,000–$100,000/year22
>$100,000/year26
Smartphone ownershipNo1
Yes99
Age20.09 ± 0.03

FTQ results

The participants who reported having at least one first- or second-degree relative that was a definite problem drinker were considered as having a positive family history of alcohol dependence (Mann et al., 1985). Twenty-nine of the 100 participants had a positive family history of alcohol dependence. Of those who had a positive family history of alcohol dependence, 17, 1, 2, and 9 reported having 1, 2, 3, and 4 first- or second-degree relatives that were definite problem drinkers, respectively. The drinking density (i.e., the number of definite problem drinkers divided by the number of first- and second-degree relatives) was low (M = 0.0640, SE = 0.0119). Note that participants were asked to recall the drinking behavior of their relatives to classify them (e.g., social drinker and possible problem drinker). Since long-term memory is fallible, discrepancies may exist between the data reported herein and the actual drinking status of the participants’ first- and second-degree relatives.

MPPUS results

Consistent with others (e.g., Bianchi & Phillips, 2005), a Cronbach’s α of .92 was obtained, indicating a high degree of internal consistency. The MPPUS data were slightly positively skewed (skewness = 0.37) and nearly normally distributed (M = 103.1, SE = 3.8). It was determined that 22% of the participants scored in the low-to-moderate degree of concern range (scores between 27 and 76), 42% scored in the moderate-to-high degree of concern range (scores between 77 and 126), and 36% scored in the high-to-severe degree of concern range (scores greater than 126).

A hierarchical multiple regression analysis was conducted to examine the relationship between MPPUS score and the variables drinking density (i.e., the number of definite problem drinkers divided by the number of first- and second-degree relatives), year in college, father’s education level, mother’s education level, family income, ethnicity, age, and gender. These results are given in Table 2. Preliminary data analysis using histograms and scatterplots revealed no threats to the assumption of linearity or to the underlying distributional assumptions of residuals of MPPUS score. To evaluate the idea that participants’ MPPUS score is due, in part, to the degree of positive family history of alcohol dependence (indexed by drinking density), step 1 of a hierarchical multiple regression procedure predicted MPPUS score from drinking density. The R2 in step 1 was statistically significant (R2 = .117, p = .0005). In step 2, the contribution of father’s education level to the prediction of MPPUS score was assessed. The R2 change in step 2 was statistically significant (R2 change = .140, p = .013). Drinking density and father’s education level together explained 25.7% of the variance in MPPUS score. The inclusion of mother’s education level, ethnicity, family income, age, year in college, and gender did not significantly increase the proportion of variance in MPPUS score explained (R2 change: mother’s education level, .054; ethnicity, .040; family income, .047; age, .009; year in college, .027; gender, .002; ps > .05). If we increase drinking density by 1 standard deviation there will be a .353 standard deviation increase in MPPUS score (β = .353, p < .05). With a 1 standard deviation increase in fathers with a middle school education, there will be a .189 standard deviation increase in MPPUS score (β = .189, p < .05). Surprisingly, with a 1 standard deviation increase in fathers with a graduate school education, there will be a 0.253 standard deviation increase in MPPUS score (β = .253, p < .05).

Table 2.

The results of the hierarchical multiple regression analysis with drinking density, father’s education level, mother’s education level, ethnicity, family income, age, year in college, and gender as independent variables and MPPUS scores as the dependent variable

MPPUS scores
StepIndependent variable/predictorBSE BβΔR2ΔF
1Drinking density109.79630.651.342***.11712.831***
2Father’s education level.1402.867*
 Middle school32.66815.628.189*
 High school−15.15510.382−.158
 Some college6.79711.062.063
 2 years of college1.00411.358.009
 4 years of college0.9519.551.012
 Graduate school23.34910.214.253*
3Mother’s education level.0541.335
 Middle school−65.82227.084−.245*
 High school−2.27811.722−.023
 Some college3.62410.804.036
 2 years of college−0.22210.854−.002
 Graduate school−1.62210.725−.016
4Ethnicity.0400.421
 Asian33.11425.565.123
 Black7.36636.285.020
 Latino16.71418.625.106
 Multiracial−33.35026.305−.124
 Other39.30536.182.104
5Family income.0470.975
 <$20,000−5.07432.781−.034
 $21,000–$40,000−22.87432.783−.190
 $41,000–$60,000−24.24831.359−.224
 $61,000–$80,000−10.85532.187−.108
 $81,000–$100,000−27.01431.122−.297
 >$100,000−7.79730.972−.091
6Age−3.2973.036−.114.0091.179
7Year in college.0271.146
 Sophomore15.90812.459.162
 Junior26.25015.218.313
 Senior33.79819.313 .378
8Gender4.3279.561.050.0020.205

Note. B: unstandardized regression coefficient; SE: standard error; β: standardized regression coefficient; ΔR2: change in R-squared; ΔF: change in F.

*p < .05. ***p < .001.

ACPAT results

Consistent with others (e.g., Smetaniuk, 2014), a Cronbach’s α of .92 was obtained, indicating a high degree of internal consistency. The ACPAT data were slightly positively skewed (skewness = 0.47) and nearly normally distributed (M = 39.4, SE = 1.4). It was determined that 76% of the participants scored in the low-to-moderate degree of concern range (scores between 20 and 49), 24% scored in the moderate-to-high degree of concern range (scores between 50 and 79), and 0% scored in the high-to-severe degree of concern range (scores greater than 79).

To evaluate the idea that participants’ ACPAT score is due, in part, to the degree of positive family history of alcohol dependence (indexed by drinking density), step 1 of a hierarchical multiple regression procedure predicted ACPAT score from drinking density. The results of this hierarchical multiple regression analysis are given in Table 3. The R2 in step 1 was statistically significant (R2 = .086, p = .003). In step 2, the contribution of father’s education level to the prediction of ACPAT score was assessed. The R2 change in step 2 was statistically significant (R2 change = .166, p = .005). Drinking density and father’s education level together explained 25.2% of the variance in ACPAT score. The inclusion of mother’s education level, ethnicity, family income, age, year in college, and gender did not significantly increase the proportion of variance in ACPAT score explained (R2 change: mother’s education level, .025; ethnicity, .080; family income, .091; age, .002; year in college, .021; gender, .001; ps > .05). Finally, it should be noted that there was a strong positive correlation between MPPUS score and ACPAT score (r = .848, p < .001).

Table 3.

The results of the hierarchical multiple regression analysis with drinking density, father’s education level, mother’s education level, ethnicity, family income, age, year in college, and gender as independent variables and ACPAT scores as the dependent variable

ACPAT scores
StepIndependent variable/predictorBSE BβΔR2ΔF
1Drinking density33.48311.1000.293**.0869.099**
2Father’s education level.1663.372**
 Middle school−1.2105.582−.020
 High school−5.9913.708−.175
 Some college4.2853.951.111
 2 years of college4.9724.057.125
 4 years of college college1.8363.411.063
 Graduate school10.7693.648.327**
3Mother’s education level.0250.594
 Middle school−10.01410.088−.105
 High school2.1224.612.060
 Some college3.8054.454.107
 2 years of college−.0724.373−.002
 4 years of college2.7403.910−.091
4Ethnicity.0802.009
 Asian23.128 9.058.242*
 Black.11012.857.001
 Latino−.0706.559−.001
 Multiracial−7.203 9.320−.075
 Other22.11612.820.165
5Family income.0912.072
 <$20,000−8.15711.169−.156
 $21,000–$40,000−7.43011.170−.174
 $41,000–$60,000−4.44910.685−.115
 $61,000–$80,000−7.32310.967−.205
 $81,000–$100,000−15.45610.604−.478
 >$100,000−3.61410.553−.118
6Age−0.5251.041−.051.0020.254
7Year in college.0210.944
 Sophomore4.8184.289.138
 Junior8.6945.239.291
 Senior8.6106.648.270
8Gender1.0313.294.033.0010.098

Note. B: unstandardized regression coefficient; SE: standard error; β: standardized regression coefficient; ΔR2: change in R-squared; ΔF: change in F.

*p < .05. **p < .01.

Discussion

This is the first demonstration that the degree of positive family history of alcohol dependence (indexed by drinking density) accounted for a significant amount of variance in the scores from two valid and reliable indices of PSPU; namely, the MPPUS and the ACPAT. This finding is consistent with the prevailing view that compulsive disorders (e.g., PSPU, alcohol dependence, overeating, and pathological gambling) are due to an interaction between heritable and environmental factors. It is well known that family history of alcohol dependency confers a significant risk to children of alcohol-dependent parents to develop alcohol dependency and other substance abuse disorders (e.g., Lieb et al., 2002). Notably, this risk has a genetic basis (e.g., Merikangas, 1990). The heritability of pathological gambling is estimated to be from 50% to 60% (Lobo & Kennedy, 2009) and there have been consistent reports of a higher frequency of pathological gambling among individuals who perceived problematic gambling behavior in their parents (Gambino, Shaffer, Renner, & Gourtnage, 1993). Environmental factors associated with alcohol dependency and other substance abuse disorders include family, developmental, perceived social support, and broader environmental influences (e.g., Marsh & Dale, 2005). According to Ohannessian and Hesselbrock (1993), a high perceived level of perceived social support “buffered” adult children of alcoholics from the negative effects of having a positive family history of alcoholism on drinking beliefs and behaviors. In light of this report, we hypothesize that a high level of perceived social support will “buffer” those with positive family history of alcohol dependence from developing PSMU. This hypothesis will be tested in future studies.

The finding of a relationship between a positive family history of alcoholism and PSPU raises the interesting possibility that compulsive disorders are due, in part, to a similar dysregulation of brain reward pathways that lead to a hyporesponsivity to rewarding stimuli and aberrant behavior. According to the Reward Deficiency Syndrome (RDS) (Blum, Cull, Braverman, & Comings, 1996) hypothesis, rewarding stimuli activate the mesocorticolimbic dopamine pathway and stimulate the release of dopamine from its terminal regions, the nucleus accumbens, amygdala, and prefrontal cortex (e.g., Koob, 1992). The increased dopamine release in these terminal regions decreases negative feelings and increases positive feelings. Notably, a deficiency in dopamine D2 receptors, which is important for coding reward, may predispose individuals to a higher risk of developing multiple addictive and compulsive behaviors. Thus, it is possible that those who suffer from a compulsive disorder carry a variant of the dopamine D2 receptor gene (i.e., the so-called reward gene) (Blum, Noble, Sheridan, Montgomery, & Ritchie, 1990). Interestingly, the predictive value for future RDS behaviors in participants carrying the DRD2 Tag A1 allele was 74% (Wilson, 2010).

Father’s education level accounted for 14.0% and 16.6% of the variance in the MPPUS and ACPAT scores, respectively. As previously mentioned, alcohol dependence rates increase with variables indicative of social disadvantage, such as low education level (Crum et al., 1993; Swendsen et al., 2009). Compared to adults with higher education levels, adults with less education drink in more unrestrained way. That is, they drink larger quantities per drinking episode and are more likely to be problem drinkers (Casswell, Pledger, & Hooper, 2003). Interestingly, the relationship between education level and heavy adolescent drinking is mediated by parental monitoring (i.e., the degree of parental awareness of their child’s whereabouts) and parental rules (i.e., the degree of restrictive rule setting behavior). Specifically, higher frequencies of heavy drinking by adolescents with lower education levels were due, in part, to less restrictive parental rules about alcohol and less parental monitoring (Vermeulen-Smit, Ter Bogt, Verdurmen, Van Dorsselaer, & Vollebergh, 2012). Thus, we hypothesize that the relationship between father’s education level and PSPU is mediated by the smartphone behavior of the parent (which is modeled for the child), parental monitoring, and parental rules. This hypothesis will be tested in future studies.

It should be noted that there was disagreement between the MPPUS and the ACPAT with regard to the percentage of participants in each of the degree of concern categories. About 22%, 42%, and 36% of the participants’ scores on the MPPUS were in the concern range of low-to-moderate, moderate-to-high, and high-to-severe, respectively. In contrast, 76%, 24%, and 0% of the participants’ ACPAT scores were in the concern range of low-to-moderate, moderate-to-high, and high-to-severe, respectively. The most plausible explanation for the disagreement between the scales is they measure non-overlapping aspects of PSPU. Specifically, the MPPUS measures tolerance, escape from other problems, withdrawal, craving, and negative life consequences in the areas of social, familial, work, and financial difficulties, whereas the ACPAT measures preoccupation (salience), excessive use, neglecting work, anticipation, lack of control, and neglecting social life.

The disagreement between the MPPUS and the ACPAT raises larger issues. Namely, that there are perhaps too many indices of PSPU and the range in estimated levels of PSPU is too wide. Greater than 23 different instruments have been developed to measure PSPU. On the whole, the estimated levels of PSPU range from 0% to 38% (Pedrero Pérez, Rodríguez Monje, & Ruiz Sánchez De León, 2012). Some of the variables that explain this wide range are differences in the conceptual basis used to define PSPU, the population studied, and the statistical criteria used to define the PSPU categories.

Positive family history of alcohol dependence and father’s education level together explained 25.7% and 25.2% of the variance in MPPUS scores and ACPAT scores, respectively. The inclusion of mother’s education level, ethnicity, family income, age, year in college, and gender did not significantly increase the proportion of variance explained for either MPPUS or ACPAT scores. Note that present study has limited generalizability due to the fact that a convenience sample was used. Given that 74%–75% of the variance in PSPU scores was not explained and a convenience sample was used, future studies will attempt to explain the remaining variance in PSPU scores by measuring additional psychological constructs in a large, representative sample. PSPU is associated with a variety of psychological constructs, such as anxiety, neuroticism, extroversion, and stress reactivity (Augner & Hacker, 2012; Bianchi & Phillips, 2005; Igarashi, Motoyoshi, Takai, & Yoshida, 2008; Lu et al., 2011; Phillips, Butt, & Blaszczynski, 2006). Thus, in future studies, we will develop a model that accounts for a larger percentage of PSPU scores by adding measures of anxiety, personality, and stress reactivity to our existing model.

Authors’ contribution

AB created the questionnaire, performed data collection, assisted in the study design, and helped write the manuscript. DJR was responsible for the study concept and design, statistical analysis, writing the manuscript, and supervised the study. Both authors had full access to all study data, both what is reported and what is unreported. Both authors had complete freedom to direct its analysis and its reporting. The authors accept responsibility for the integrity of the data; the data analysis is accurate.

Conflict of interest

The authors declare no conflict of interest.

Acknowledgement

The authors thank Linda C. Sobell for giving permission to use the Family Tree Questionnaire.

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  • Charlton, T., Panting, C., & Hannan, A. (2002). Mobile telephone ownership and usage among 10- and 11-year olds. Participation and exclusion. Emotional and Behavioural Difficulties, 7(3), 152163. doi:10.1080/13632750200507013

    • Search Google Scholar
    • Export Citation
  • Chóliz, M. (2010). Mobile phone addiction: A point of issue. Addiction, 105(2), 373374. doi:10.1111/j.1360-0443.2009.02854.x

  • Consiglio, W., Driscoll, P., Witte, M., & Berg, W. P. (2003). Effects of cellular telephone conversations and other potential interference on reaction time in a braking response. Accident Analysis and Prevention, 35, 495500. doi:10.1016/S0001-4575(02)00027-1

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cotton, N. S. (1979). The familial incidence of alcoholism: A review. Journal of Studies on Alcohol, 40(1), 89116. doi:10.15288.jsa.1979.40.89

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crum, R. M., Helzer, J. E., & Anthony, J. C. (1993). Level of education and alcohol abuse and dependence in adulthood: a further inquiry. American Journal of Public Health, 83(6), 830837.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Di Sclafani, V., Finn, P., & Fein, G. (2007). Psychiatric comorbidity in long-term abstinent alcoholic individuals. Alcoholism: Clinical and Experimental Research, 31(5), 795803. doi:10.1111/j.1530-0277.2007.00361.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Di Sclafani, V., Finn, P., & Fein, G. (2008). Treatment-naïve active alcoholics have greater psychiatric comorbidity than normal controls but less than treated abstinent alcoholics. Drug and Alcohol Dependence, 98(1–2), 115122. doi:10.1016/j.drugalcdep.2008.04.019

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Funston, A., & McNeill, K. (2015). Mobile matters: Young people and mobile phones. Melbourne, Australia: Communication Law Centre.

  • Gambino, B. F. R., Shaffer, H., Renner, J., & Gourtnage, P. (1993). Perceived family history of problem gambling and scores on the SOGS. Journal of Gambling Studies, 9(2), 169184. doi:10.1007/BF01014866

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goodwin, D. W., Schulsinger, F., Hermansen, L., Guze, S. B., & Winokur, G. (1973). Alcohol problems in adoptees raised apart from alcoholic biological parents. Archives of General Psychiatry, 28(2), 238243. doi:10.1001/archpsyc.1973.01750320068011

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hancock, P. A., Lesch, M., & Simmons, L. (2003). The distraction effects of phone use during a critical driving maneuver. Accident Analysis and Prevention, 35, 501514. doi:10.1016/S0001/-4575(02)00028-3

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Igarashi, T., Motoyoshi, T., Takai, J., & Yoshida, T. (2008). No mobile, no life: Self-perception and text-message dependency among Japanese high school students. Computers in Human Behavior, 24(5), 23112324. doi:10.1016/j.chb.2007.12.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • International Telecommunication Union. (2016, October 24). ICT facts and figures 2016. Retrieved from http://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2016.pdf

    • Search Google Scholar
    • Export Citation
  • Jenaro, C., Flores, N., Gómez-Vela, M., González-Gil, F., & Cabello, C. (2007). Problematic internet and cell-phone use: Psychological, behavioral, and health correlates. Addiction Research and Theory, 15(3), 309320. doi:10.1080/16066350701350247

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kamibeppu, K., & Sugiura, H. (2005). Impact of the mobile phone on junior high school students’ friendships in the Tokyo metropolitan area. CyberPsychology & Behavior, 8(2), 121130. doi:10.1089/cpb.2005.8.121

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koob, G. F. (1992). Dopamine, addiction and reward. Seminars in Neuroscience, 4(2), 139148. doi:10.1016/1044-5765(92)90012-Q

  • Laberge-Nadeau, C., Maag, U., Bellavance, F., Lapierre, S. D., Messier, S., & Saïdi, A. (2003). Wireless telephones and the risk of road crashes. Accident Analysis and Prevention, 35(5), 649660. doi:10.1016/S0001-4575(02)00043-X

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lieb, R., Merikangas, K. R., Hofler, M., Pfister, H., Isensee, B., & Wittchen, H. U. (2002). Parental alcohol use disorders and alcohol use and disorders in offspring: A community study. Psychological Medicine, 32(1), 6378. doi:10.1017/S0033291701004883

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lobo, D. S., & Kennedy, J. L. (2009). Genetic aspects of pathological gambling: A complex disorder with shared genetic vulnerabilities. Addiction, 104(9), 14541465. doi:10.1111/j.1360-0443.2009.02671.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, X., Watanabe, J., Liu, Q., Uji, M., Shono, M., & Kitamura, T. (2011). Internet and mobile phone text-messaging dependency: Factor structure and correlation with dysphoric mood among Japanese adults. Computers in Human Behavior, 27(5), 17021709. doi:10.1016/j.chb.2011.02.009

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mann, R. E., Sobell, L. C., Sobell, M. B., & Pavan, D. (1985). Reliability of a family tree questionnaire for assessing family history of alcohol problems. Drug and Alcohol Dependence, 15(1–2), 6167. doi:10.1016/0376-8716(85)90030-4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marsh, A., & Dale, A. (2005). Risk factors for alcohol and other drug disorders: A review. Australian Psychologist, 40(2), 7380. doi:10.1080/00050060500094662

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merikangas, K. R. (1990). The genetic epidemiology of alcoholism. Psychological Medicine, 20(1), 1122. doi:10.1017/S0033291700013192

  • Ohannessian, C. M., & Hesselbrock, V. M. (1993). The influence of perceived social support on the relationship between family history of alcoholism and drinking behaviors. Addiction, 88(12), 16511658. doi:10.1111/j.1360-0443.1993.tb02040.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pedrero Pérez, E. J., Rodríguez Monje, M. T., & Ruiz Sánchez De León, J. M. (2012). Mobile phone abuse or addiction. A review of the literature. Adicciones, 24(2), 139152. doi:10.20882/adicciones.107

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pew Research Center. (2015, October 24). The smartphone difference. Retrieved from http://www.pewinternet.org/2015/04/01/us-smartphone-use-in-2015/

    • Search Google Scholar
    • Export Citation
  • Phillips, J. G., Butt, S., & Blaszczynski, A. (2006). Personality and self-reported use of mobile phones for games. CyberPsychology & Behavior, 9(6), 753758. doi:10.1089/cpb.2006.9.753

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Redelmeier, D. A., & Tibshirani, R. J. (1997). Association between cellular telephone calls and motor vehicle collisions. New England Journal of Medicine, 336(7), 453458. doi:10.1056/NEJM199702133360701

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smetaniuk, P. (2014). A preliminary investigation into the prevalence and prediction of problematic cell phone use. Journal of Behavioral Addictions, 3(1), 4153. doi:10.1556/JBA.3.2014.004

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stabenau, J. R., & Hesselbrock, V. M. (1983). Family pedigree of alcoholic and control patients. International Journal of the Addictions, 18(4), 351363. doi:10.3109/10826088309039353

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stoltenberg, S. F., Mudd, S. A., Blow, F. C., & Hill, E. M. (1998). Evaluating measures of family history of alcoholism: Density versus dichotomy. Addiction, 93(10), 15111520. doi:10.1046/j.1360-0443.1998.931015117.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Swendsen, J., Conway, K. P., Degenhardt, L., Dierker, L., Glantz, M., Jin, R., Merikangas, K. R., Sampson, N., & Kessler, R. C. (2009). Socio-demographic risk factors for alcohol and drug dependence: The 10-year follow-up of the national comorbidity survey. Addiction, 104(8), 13461355. doi:10.1111/j.1360-0443.2009.02622.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vermeulen-Smit, E., Ter Bogt, T. F. M., Verdurmen, J. E. E., Van Dorsselaer, S. A. F. M., & Vollebergh, W. A. M. (2012). The role of education, parents and peers in adolescent heavy episodic drinking. Drugs: Education, Prevention and Policy, 19(3), 223226. doi:10.3109/09687637.2012.662542

    • Search Google Scholar
    • Export Citation
  • Violanti, J. M. (1998). Cellular phones and fatal traffic collisions. Accident Analysis and Prevention, 30(4), 519524. doi:10.1016/S0001-4575(97)00094-8

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Violanti, J. M., & Marshall, J. R. (1996). Cellular phones and traffic accidents: An epidemiological approach. Accident Analysis and Prevention, 28(2), 265270. doi:10.1016/0001-4575(95)00070-4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, G. T. (2010). Eating disorders, obesity and addiction. European Eating Disorders Review, 18(5), 341351. doi:10.1002/erv.1048

  • Wyando, L., & McMurran, M. (2004). The psychometric properties of the internet addiction test. CyberPsychology & Behavior, 7(4), 443450. doi:10.1089/cpb.2004.7.443

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alm, H., & Nilsson, L. (1995). The effects of a mobile telephone task on driving behavior in a car following situation. Accident Analysis and Prevention, 27(5), 707715. doi:10.1016/0001-4575(95)00026-V

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    • Export Citation
  • Augner, C., & Hacker, G. W. (2012). Associations between problematic mobile phone use and psychological parameters in young adults. International Journal of Public Health, 57(2), 437441. doi:10.1007/s00038-011-0234-z

    • Crossref
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    • Export Citation
  • Bachman, J. G., O’Malley, P. M., Johnston, L. D., Schulenberg, J. E., & Wallace, J. M. (2011). Racial/ethnic differences in the relationship between parental education and substance abuse among U.S. 8th-, 10th-, and 12th-grade students: Findings from the monitoring the future project. Journal of Studies on Alcohol and Drugs, 72(2), 279285. doi:10.15288/jsad.2011.72.279

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bianchi, A., & Phillips, J. G. (2005). Psychological predictors of problem mobile phone use. Cyberpsychology, Behavior, and Social Networking, 8(1), 3951. doi:10.1089/cpb.2005.8.39

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    • Export Citation
  • Blum, K., Cull, J. G., Braverman, E. R., & Comings, D. E. (1996). Reward deficiency syndrome. American Scientist, 84, 132145.

  • Blum, K., Noble, E. P., Sheridan, P. J., Montgomery, A., & Ritchie, T. (1990). Allelic association of human dopamine D2 receptor gene in alcoholism. Journal of the American Medical Association, 263(15), 20552060. doi:10.1001/jama.263.15.2055

    • Crossref
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  • Casswell, S., Pledger, M., & Hooper, R. (2003). Socioeconomic status and drinking patters in young adults. Addiction, 98(5), 601610. doi:10.1046/j.1360-0443.2003.00331.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Charlton, T., Panting, C., & Hannan, A. (2002). Mobile telephone ownership and usage among 10- and 11-year olds. Participation and exclusion. Emotional and Behavioural Difficulties, 7(3), 152163. doi:10.1080/13632750200507013

    • Search Google Scholar
    • Export Citation
  • Chóliz, M. (2010). Mobile phone addiction: A point of issue. Addiction, 105(2), 373374. doi:10.1111/j.1360-0443.2009.02854.x

  • Consiglio, W., Driscoll, P., Witte, M., & Berg, W. P. (2003). Effects of cellular telephone conversations and other potential interference on reaction time in a braking response. Accident Analysis and Prevention, 35, 495500. doi:10.1016/S0001-4575(02)00027-1

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cotton, N. S. (1979). The familial incidence of alcoholism: A review. Journal of Studies on Alcohol, 40(1), 89116. doi:10.15288.jsa.1979.40.89

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crum, R. M., Helzer, J. E., & Anthony, J. C. (1993). Level of education and alcohol abuse and dependence in adulthood: a further inquiry. American Journal of Public Health, 83(6), 830837.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Di Sclafani, V., Finn, P., & Fein, G. (2007). Psychiatric comorbidity in long-term abstinent alcoholic individuals. Alcoholism: Clinical and Experimental Research, 31(5), 795803. doi:10.1111/j.1530-0277.2007.00361.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Di Sclafani, V., Finn, P., & Fein, G. (2008). Treatment-naïve active alcoholics have greater psychiatric comorbidity than normal controls but less than treated abstinent alcoholics. Drug and Alcohol Dependence, 98(1–2), 115122. doi:10.1016/j.drugalcdep.2008.04.019

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Funston, A., & McNeill, K. (2015). Mobile matters: Young people and mobile phones. Melbourne, Australia: Communication Law Centre.

  • Gambino, B. F. R., Shaffer, H., Renner, J., & Gourtnage, P. (1993). Perceived family history of problem gambling and scores on the SOGS. Journal of Gambling Studies, 9(2), 169184. doi:10.1007/BF01014866

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goodwin, D. W., Schulsinger, F., Hermansen, L., Guze, S. B., & Winokur, G. (1973). Alcohol problems in adoptees raised apart from alcoholic biological parents. Archives of General Psychiatry, 28(2), 238243. doi:10.1001/archpsyc.1973.01750320068011

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hancock, P. A., Lesch, M., & Simmons, L. (2003). The distraction effects of phone use during a critical driving maneuver. Accident Analysis and Prevention, 35, 501514. doi:10.1016/S0001/-4575(02)00028-3

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Igarashi, T., Motoyoshi, T., Takai, J., & Yoshida, T. (2008). No mobile, no life: Self-perception and text-message dependency among Japanese high school students. Computers in Human Behavior, 24(5), 23112324. doi:10.1016/j.chb.2007.12.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • International Telecommunication Union. (2016, October 24). ICT facts and figures 2016. Retrieved from http://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2016.pdf

    • Search Google Scholar
    • Export Citation
  • Jenaro, C., Flores, N., Gómez-Vela, M., González-Gil, F., & Cabello, C. (2007). Problematic internet and cell-phone use: Psychological, behavioral, and health correlates. Addiction Research and Theory, 15(3), 309320. doi:10.1080/16066350701350247

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kamibeppu, K., & Sugiura, H. (2005). Impact of the mobile phone on junior high school students’ friendships in the Tokyo metropolitan area. CyberPsychology & Behavior, 8(2), 121130. doi:10.1089/cpb.2005.8.121

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koob, G. F. (1992). Dopamine, addiction and reward. Seminars in Neuroscience, 4(2), 139148. doi:10.1016/1044-5765(92)90012-Q

  • Laberge-Nadeau, C., Maag, U., Bellavance, F., Lapierre, S. D., Messier, S., & Saïdi, A. (2003). Wireless telephones and the risk of road crashes. Accident Analysis and Prevention, 35(5), 649660. doi:10.1016/S0001-4575(02)00043-X

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lieb, R., Merikangas, K. R., Hofler, M., Pfister, H., Isensee, B., & Wittchen, H. U. (2002). Parental alcohol use disorders and alcohol use and disorders in offspring: A community study. Psychological Medicine, 32(1), 6378. doi:10.1017/S0033291701004883

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lobo, D. S., & Kennedy, J. L. (2009). Genetic aspects of pathological gambling: A complex disorder with shared genetic vulnerabilities. Addiction, 104(9), 14541465. doi:10.1111/j.1360-0443.2009.02671.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, X., Watanabe, J., Liu, Q., Uji, M., Shono, M., & Kitamura, T. (2011). Internet and mobile phone text-messaging dependency: Factor structure and correlation with dysphoric mood among Japanese adults. Computers in Human Behavior, 27(5), 17021709. doi:10.1016/j.chb.2011.02.009

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mann, R. E., Sobell, L. C., Sobell, M. B., & Pavan, D. (1985). Reliability of a family tree questionnaire for assessing family history of alcohol problems. Drug and Alcohol Dependence, 15(1–2), 6167. doi:10.1016/0376-8716(85)90030-4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marsh, A., & Dale, A. (2005). Risk factors for alcohol and other drug disorders: A review. Australian Psychologist, 40(2), 7380. doi:10.1080/00050060500094662

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merikangas, K. R. (1990). The genetic epidemiology of alcoholism. Psychological Medicine, 20(1), 1122. doi:10.1017/S0033291700013192

  • Ohannessian, C. M., & Hesselbrock, V. M. (1993). The influence of perceived social support on the relationship between family history of alcoholism and drinking behaviors. Addiction, 88(12), 16511658. doi:10.1111/j.1360-0443.1993.tb02040.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pedrero Pérez, E. J., Rodríguez Monje, M. T., & Ruiz Sánchez De León, J. M. (2012). Mobile phone abuse or addiction. A review of the literature. Adicciones, 24(2), 139152. doi:10.20882/adicciones.107

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pew Research Center. (2015, October 24). The smartphone difference. Retrieved from http://www.pewinternet.org/2015/04/01/us-smartphone-use-in-2015/

    • Search Google Scholar
    • Export Citation
  • Phillips, J. G., Butt, S., & Blaszczynski, A. (2006). Personality and self-reported use of mobile phones for games. CyberPsychology & Behavior, 9(6), 753758. doi:10.1089/cpb.2006.9.753

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Redelmeier, D. A., & Tibshirani, R. J. (1997). Association between cellular telephone calls and motor vehicle collisions. New England Journal of Medicine, 336(7), 453458. doi:10.1056/NEJM199702133360701

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smetaniuk, P. (2014). A preliminary investigation into the prevalence and prediction of problematic cell phone use. Journal of Behavioral Addictions, 3(1), 4153. doi:10.1556/JBA.3.2014.004

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stabenau, J. R., & Hesselbrock, V. M. (1983). Family pedigree of alcoholic and control patients. International Journal of the Addictions, 18(4), 351363. doi:10.3109/10826088309039353

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stoltenberg, S. F., Mudd, S. A., Blow, F. C., & Hill, E. M. (1998). Evaluating measures of family history of alcoholism: Density versus dichotomy. Addiction, 93(10), 15111520. doi:10.1046/j.1360-0443.1998.931015117.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Swendsen, J., Conway, K. P., Degenhardt, L., Dierker, L., Glantz, M., Jin, R., Merikangas, K. R., Sampson, N., & Kessler, R. C. (2009). Socio-demographic risk factors for alcohol and drug dependence: The 10-year follow-up of the national comorbidity survey. Addiction, 104(8), 13461355. doi:10.1111/j.1360-0443.2009.02622.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vermeulen-Smit, E., Ter Bogt, T. F. M., Verdurmen, J. E. E., Van Dorsselaer, S. A. F. M., & Vollebergh, W. A. M. (2012). The role of education, parents and peers in adolescent heavy episodic drinking. Drugs: Education, Prevention and Policy, 19(3), 223226. doi:10.3109/09687637.2012.662542

    • Search Google Scholar
    • Export Citation
  • Violanti, J. M. (1998). Cellular phones and fatal traffic collisions. Accident Analysis and Prevention, 30(4), 519524. doi:10.1016/S0001-4575(97)00094-8

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Violanti, J. M., & Marshall, J. R. (1996). Cellular phones and traffic accidents: An epidemiological approach. Accident Analysis and Prevention, 28(2), 265270. doi:10.1016/0001-4575(95)00070-4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, G. T. (2010). Eating disorders, obesity and addiction. European Eating Disorders Review, 18(5), 341351. doi:10.1002/erv.1048

  • Wyando, L., & McMurran, M. (2004). The psychometric properties of the internet addiction test. CyberPsychology & Behavior, 7(4), 443450. doi:10.1089/cpb.2004.7.443

    • Crossref
    • Search Google Scholar
    • Export Citation
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