Proneness to Smartphone Addiction in Relation to Morningness and Eveningness

Abel Thomas, Samantha Hodges

With an influx of cellular phone use, questions have arisen over its impact on smartphone users. The purpose of the current study is to find out how many students have phone addictions, how severe these addictions are, and how the most severe cases relate to morningness and eveningness. One area in which phone addiction may negatively impact an individual’s quality of life is the amount of sleep he or she loses due to smartphone usage. Previous researchers investigated how smartphone addiction was linked to whether a person was a morning or evening person and found that evening oriented individuals were more likely to have a “severe” addiction than morning oriented individuals. In this previous study, a sample group of German students was given two questionnaires, one to determine whether a person was a morning or evening person based on their answers to the Composite Scale of Morningness (CSM) and another to assess phone addiction based on the Smartphone Addiction Proneness Scale (SAPS). This current study, which followed similar procedures with an American sample, found that there was no significant correlation between being evening oriented or morning oriented and smartphone addiction proneness. With the rise of technology and people’s everyday use of the Internet, the results from this study are important to help understand the consequences that an overuse of these electronic devices may entail.

Abstract

Introduction

Currently, over 150 million individuals worldwide use the Internet, with 70 to 80% of adults in the United States having online access (Woods & Scott, 2016). With this rapid growth of the availability of the Internet, the rise of smartphones was inevitable. The term “smartphone” was coined in 1997 when it became a part of people’s vernacular (Randler et al., 2016). With this influx of cellular phone use, questions have arisen over the impact that cell phones have on smartphone users. These include questions on how much of a hold cell phones have on people, why cellular devices have become so popular, and how much do people prioritize their devices over other forms of technology. Excessive use of mobile phones can affect mental and physical health status. In recent studies, correlations have been found between mobile phone dependency and loss of sleep (Toda & Ezoe, 2013; Toda et al., 2006). This study observes how this mobile phone dependency can be related to whether someone is an evening person or a morning person.

One area of discussion on the topic is determining the level of phone addiction people have and the primary cause of this addiction. “Addiction” is defined in Webster’s Dictionary: a pathologic condition that one cannot tolerate without the continuous administration of a substance. Commonly handled by neuropsychiatric departments, addiction is a phenomenon that manifests tolerance, withdrawal symptoms, and dependence, and it is often accompanied by social problems. The term was once limited to drugs or other physical substances, but it is now also applied to the Internet, gaming, mobile-phone usage, and other behaviors (Lee et al., 2016). The components of smartphone addiction are tolerance, withdrawal, compulsive symptoms, and functional impairment (Lin et al., 2014). Smartphone addiction might be a type of non-substance addiction (Serenko & Turel, 2010). However, the term “addiction” may not be appropriate because studies showing behavioral and neurobiological similarities between mobile phone addiction and other types of addictive behaviors are lacking (Billieux et al., 2015). Therefore, these authors suggested the term “problematic usage” and labeled this behavior as “addiction proneness” (Kim et al., 2014). With the rise of smartphones, more teenagers are growing up with technology and its negative effects like phone addiction proneness (Lepp et al., 2014). The primary purpose of the current study is to find out if a sample of high school students have phone addictions, how severe they might be, and how the most severe cases could relate to morningness and eveningness. Morningness is defined as the characteristic of being most active and alert during the morning, and eveningness is the characteristic of being most active and alert during the evening (Randler et al., 2016). In order to conclude that a person may have addiction proneness, researchers must observe the implications of the addiction on the subject. For this study, it would be observing the correlation between sleep loss and the effect cellular phones have on the people using them. This is prudent as sleep is how people reenergize themselves, and, when reduced, has a slew of negative effects that have been well documented within the literature: increased Internet use is

associated with shorter sleep duration (Anna & Westin, 2011), later bedtimes and rise times (Shochat et al., 2010), longer sleep latencies (Shochat et al., 2010), and increased daytime tiredness in adolescents (Billieux et al., 2008).

The biggest factor in an individual’s loss of sleep is their chronotype which is the manifestation of their circadian rhythms (Randler et al., 2016). Slightly different than morningness and eveningness, chronotype refers to the preference for sleepwake timing: morning types experience peak alertness and performance earlier in the day. Evening types experience their most critical points of alertness at later times of the day (Carney et al., 2006; Suen et al., 2008). A series of studies have shown that morningness increases with age (Curcioa et al., 2006). Individual’s circadian preferences can be grouped into three categories: “morning type,” “neither type,” and “evening type,” but they can also be seen as a continuum (Natale & Cicogna, 2002). There are several variables that have an impact on one’s chronotype, including endogenous factors like one’s genetics, biology, age, and gender, as well as exogenous factors such as culture, society, and environment (Adan et al., 2012). A previous study in Germany examined reasons as to why teenagers became so prone to addiction to their cellular devices. In Germany, 25% of the surveyed 12 to 19-year-old adolescents owned a smartphone in 2011, and this number increased to 62% in 2013 (Medienpädagogischer et al., 2013). The researchers investigated how smartphone addiction can be linked to whether a person may be a morning or evening person. The researchers determined this by giving a sample group of 1,200 students aged 8-17 two questionnaires: one to determine whether a person was a morning or evening person based on their answers to the Composite Scale of Morningness (CSM) and another to assess phone addiction when compared to the Smartphone Addiction Proneness Scale (SAPS). This study found that evening-oriented students were more likely to fall in the most severe category of phone addiction proneness than morning-oriented students.

Even though numerous studies have assessed what initiates phone addiction proneness, none have analyzed the relationship between chronotype and phone addiction in American high schoolers. 83% of American high schoolers have a smartphone, in comparison to the 62% of high schoolers in Germany (Lepp et al., 2014). Because the majority of American high school students are current cell phone users, I chose to examine these students to expand on the previous study done on German students. We hypothesize that, compared to morning-oriented American students, evening-oriented American students should score higher on the smartphone addiction proneness tests. This study will allow us to further explore the correlation between smartphone addiction proneness in relation to being an evening or morning oriented person.

Methods

In this anonymous study, there was a cross sectional survey distributed to a sample from Lovejoy High School, a school of 1,500 students in the suburb of Dallas, TX. The 29-question survey contained 15 questions to evaluate the students against the Smartphone Addiction Proneness Scale (SAPS) and 14 questions to rank the students on their optimal time of alertness by asking questions on when they go to bed, when they wake up, how often they use their cell phones, how much time they spend on their cell phones, and how much time they spend on homework on a daily basis. These last 14 questions were asked for both weekday and weekend scenarios. The first 15 questions used a Likert-scale of Strongly Disagree to Strongly Agree which was then converted into a scoring system from 1-5, respectively.

The Institutional Review Board, the school administrators, the teachers, and the parents of the students were informed of the process. School district administration required parental consent for all student-participant studies. Those that took the survey were required to own a smartphone. Upon the conclusion of the study, the participants were categorized into groups with morning tendencies or evening tendencies and whether either group was more prone to smartphone addiction.

This survey was sent to over 1,500 students, ranging from 13-year-old freshmen to 18-year-old seniors, and 194 students responded in the survey process. Nine student responses of the 194 were voided from the data collected due to improper answers and failure to complete each question from the survey. Thus, data was analyzed from 185 students.

The SAPS measured smartphone addiction proneness in young adolescents. The SAPS helped determine which of these students, based on their scores, had a phone addiction proneness severe enough to be scientifically measured (which was a threshold of 60+ hours of phone time in a week). After determining which students had a phone addiction proneness, their scores were then compared to the Composite Scale of Morningness (CSM). The CSM, developed by Smith, Reily, and Midkiff in 1989, helped determine what kind of sleep patterns people have and when they are most alert. The CSM also showed the highest activity rate the individual had during the day. To classify evening, morning, or neither types, the 20th/80th percentiles were taken as cutoffs in this study (20% and lower = evening types; 80% and higher = morning types; 21-79% = neither types). The responses were then analyzed using a Q-Test, which finds outliers in a distribution, and an ANOVA test, which was used to compare the variance between the two groups.

Results

Of the 185 student responses, 67 of these students qualified as phone addicted according to the SAPS. The scale determines an individual’s extremity of phone addiction proneness, but for this case, the individual had to have used their cellular device for more than half of their day on a regular basis, excluding time spent sleeping. In order to identify the qualifying study group, student wake-up and bedtimes were recorded for all days of the week. The survey included questions asking the students if they believed they had phone addictions. More than half of the students with a quantified phone addiction had a negative belief of their addiction. According to Figure 1, of all the responses, 29.73% agreed that there might be a possibility that they could have a problem.

Number of Students 60 40 30 50 10 70 20 0

27 59

44

49

Strongly Disagree Disagree Neutral Scale Options

Agree

6

Strongly Agree

Figure 1. This shows a bar graph based on the answers given from participants indicating their level of agreement to the question. The population n = 185. Percentage-wise out of 185, Strongly Disagree= 14.59%, Disagree= 31.89%, Neutral= 23.78%, Agree= 26.49%, and Strongly Agree = 3.24%.

Strongly Agree

Strongly Disagree Disagree Neutral Agree Scale Options

0

“I have the habit of spending a lot of time on my smartphone”

9

10 22

20 29

30 41

40 50 Number of Students 60 70 84

80 90

Figure 2. This shows a bar graph based on the answers given from participants indicating their level of agreement to the question. The population n = 185. Percentage-wise, Strongly Agree= 4.86%, Agree= 45.41%, Neutral= 22.16%, Disagree= 15.68%, and Strongly Disagree= 11.89%.

Figure 2 demonstrates that most students at the high school level realize they spend too much time with their cell phones. This figure shows that 50.27% of the student responders agree or strongly agree that they are on their cellular devices too long.

These questions asked in Figures 1 and 2 look at whether the individual sees a problem, but other questions in the survey asked if the people around them noticed their abundant phone use. This was asked to allow the participant to consider an objective viewpoint and eliminate personal bias. The question in Figure 3 itself gives a negative connotation towards smartphones, so the expected result was more students would not agree with this. However, contrary to the expected responses, 35.68% of students agreed.

Sleep is an important factor that helps determine the strength of correlation between morning types and evening types with smartphone usage. Sleep was calculated using the midpoint of sleep, which is the halfway mark between going to bed and waking up. The whole sample size of 185 was used in calculations of the SAPS to determine the 67 students that use their phones for more than a quarter of the day. The 67 responses were only used when testing the relationship between phone addiction and morningness and eveningness.

Figure 4 illustrates the range of hours of smartphone use by all the students, not including phone usage for work or school. The graph shows nearly half of the sample of students, 48.4%, fall between the two to three-hour range. When looking at the total results, sleep duration during the week showed to be negatively correlated to phone usage issues.

Strongly Disagree Disagree Neutral Agree Strongly Agree Scale Options

0 10 15

34

34

3020 Number of Students 40 51

51

50 60

Figure 3. This shows a bar graph based on the answers given from participants indicating their level of agreement to the question. The population is n = 185. Percentage-wise, Strongly agree = 8.11%, Agree= 27.57%, Neutral= 18.38%, Disagree= 27.57%, Strongly Disagree= 18.38%.

50 45 40 35 30 25 20 15 10 5 0

33

[0,1] 45 45 “How oen would you say you are on your phone for non-school-related activities?

(1,2] (2,3] 25

16

Hours (3,4] (4,5] 13

(5,6] 3

(6,7] 5

(7,8]

Figure 4. This shows a histogram based on the answers given from participants to the question. The population is n = 185.

Discussion

The study examined the relationship between phone usage in adolescents and the tendency to be a morning or evening person. The hypothesis before the survey and tests were taken was that compared to morning-oriented students, eveningoriented students should score higher on the smartphone addiction and addiction proneness tests as per the results from the German test. All three groups (morning= 0.93, evening= 0.94, neither= 0.97) showed relatively similar results on the Q-test when compared. All show a high relation to smartphone addiction proneness with bigger differences between the groups themselves. The Q test showed that the p-value between the morning and evening group was 0.996, between evening and neither was 0.9499, and between morning and neither was 0.979. The ANCOVA test showed a p-value of 0.947 between the groups. These tests show that there is not a relationship between a person’s chronotype and their susceptibility to smartphone addictions.

The relationship between smartphone addiction proneness and midpoint of sleep was more prevalent when using the midpoint of sleep on weekends than on weekdays. Because the midpoint of sleep on weekdays is restricted by the school schedule, the midpoint of sleep on weekends reflects the internal biological rhythm. Weekend sleep duration was not related to smartphone addiction proneness because recovery sleep played a role. Recovery sleep is when adolescents sleep longer on the

weekends to make up their sleep debt which accumulated during the school week.

High mobile phone usage was found to be related to later bedtimes (Lemola et al., 2014). Roenneberg (2004) suggested the prevalence of light-emitting electronic devices such as computers, tablets, and mobile phones late in the night is shifting people to a later chronotype (Roenneberg, 2014). These studies support the finding that screens and the light they give off (blue light) shift people to eveningness. Using the mobile phone before going to sleep leads to increased sleep problems (Van den Bulck, 2007). This hypothesis provides evidence that eveningness is associated with a higher potential for addictive behaviors.

In line with this study’s results, Barnes and Meldrum (2015) indicated those reporting sleeping fewer hours at night displayed lower levels of self-control. On the weekends, children and adolescents are able to regulate their sleep duration times on their own in comparison to school days when they must adhere to school times (Barnes and Meldrum, 2015). The results are more generalized in the Barnes and Meldrum study, primarily because only one scale was used to measure smartphone addiction (proneness), and chronotype by two measures (CSM scores, midpoint of sleep), as well as sleep duration. During adolescence (13-18 years), there is a higher tendency to eveningness. The contrasts indicated differences between the freshmen and seniors, and indicates that the SAPS can detect the changes in morningness and eveningness during adolescence (Pilcher et al., 1997).

Limitations to the study include a self-report scale given to each student which showed the relationships between smartphone addiction and chronotypes which could be a problem due to students not taking the survey honestly. Another issue is this survey and study only look at cell phones and do not consider any other types of electronics the students might be using. Correlation does not prove causation and multiple other factors could explain phone addiction such as gender, age, mental age, psychological factors, etc. To overcome these effects, future research should add some physiological measures, such as actigraphy for sleep measurements, blood pressure, and pulse rate when using the smartphone to gather more forms of data. The small sample size could lead to a high margin of error along with the even smaller amount of people who met the requirements for having a phone addiction.

Conclusion

The study presented the relationships between smartphone addiction and chronotype in adolescents. Chronotype was the best predictor of smartphone addiction proneness in adolescents and more important than age, sleep duration, and midpoint of sleep. Neither the morning or evening type was shown to be statistically significant compared to the other on being more prone to smartphone addiction. When sleep duration on weekdays was longer, smartphone addiction was lower. These trends should be considered in sleep education programs as smartphone usage can affect sleep habits. The results from this study can help the general public broaden their mindset to a misconception about phone addictions and provide a basis on

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