Next Article in Journal
Teacher Harassment Victimization in Adolescents with High-Functioning Autism Spectrum Disorder: Related Factors and Its Relationships with Emotional Problems
Previous Article in Journal
Socioeconomic Inequalities in Health among Armenian Adolescents
Previous Article in Special Issue
Influence of Family Environment and Tobacco Addiction: A Short Report from a Post-Graduate Teaching Hospital, India
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 17
Issue 11
10.3390/ijerph17114056
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Problematic Internet Use and Perceived Quality of Life: Findings from a Cross-Sectional Study Investigating Work-Time and Leisure-Time Internet Use

by
Lingling Gao
1,
Yiqun Gan
2,*,
Amanda Whittal
1 and
Sonia Lippke
1
1
Department of Psychology & Methods, Jacobs University Bremen, Campus Ring 1, 28759 Bremen, Germany
2
School of Psychological Cognitive Sciences, and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100871, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(11), 4056; https://doi.org/10.3390/ijerph17114056
Submission received: 15 May 2020 / Revised: 3 June 2020 / Accepted: 4 June 2020 / Published: 6 June 2020
(This article belongs to the Special Issue Old and Emerging Addictions: Psychosocial Determinants, Treatments and Harm Reduction)
Browse Figures
Versions Notes

Abstract

:
Avoiding the potential negative impact brought by problematic internet use is becoming more important. To better understand public health and addiction, this study investigated to what extent work-time and leisure-time internet use relate to problematic internet use and perceived quality of life among college students and highly educated adults. An online cross-sectional survey with 446 individuals was assessed in Germany. Linear regression analyses were used to predict problematic internet use. Ordinal regression analyses were applied to predict perceived quality of life. Results showed that leisure-time internet use, but not work-time internet use, was positively associated with problematic internet use. Participants whose work-time internet use could be considered balanced (5–28 h/week in this study) indicated a higher perceived quality of life compared to individuals with little or large amount of internet use for work. The findings still emerged when taking negative feelings, perceived stress, smoking status and alcohol consumption into account. As both work-time and leisure-time internet use can be risk factors for mental health in terms of problematic internet use and perceived quality of life, well-controlled internet use rather than excessive use is recommended. This should be kept in mind when dealing with the Coronavirus pandemic and its aftermath.

1. Introduction

Understanding health and well-being in the face of digitalization requires an investigation of internet use and whether it can be problematic in terms of leading to addiction. In modern times, with a substantial amount of people using the internet for work, studies, and entertainment, the internet fundamentally shapes people’s daily experiences, including perceived quality of life [1,2,3].
Having a good quality of life is important for numerous reasons, and is a key determinant of public health. The judgment of ’perceived quality of life’, which is suggested to represent how satisfied people are with their present state of affairs, is based on a comparison with a standard that people set for themselves [4]. Many factors can contribute to improving or worsening quality of life. It has been found that psychological domains such as negative feelings [5] and perceived stress [6] are negative indicators of quality of life. Moreover, health-risk behaviors such as smoking are negatively related to perceived quality of life [7]. Alcohol consumption status has shown mixed results. Some findings have shown that addiction to alcohol is negatively related to quality of life [8], while it failed to reveal correlates with perceived quality of life in other studies [9,10]. Furthermore, it has been found that regular alcohol consumption is associated with increased quality of life [11,12].
Research has also shown that the internet can help people to obtain a higher perceived quality of life by promoting their work, education, and communication [13]. Some studies have pointed out that apps are acceptable and easy to use, providing health communication and education to populations with low health literacy [14]. It also has been found that the internet can improve perceived quality of life by providing mental stimulation and assistance [15]. Researchers have also, however, reported that people who spend large amounts of time on the internet may suffer as a result [16,17], which can decrease perceived quality of life [18]. Consequences of this include suffering from long-term lack of sleep, deterioration of physical health, difficulties concentrating on work, and a reduction in close relationships with family members [19].
Problematic internet use can be considered a risk factor for reduced quality of life, and has therefore been investigated during the past twenty years [1,20,21]. Adolescents and young adults are at particularly high risk of problematic internet use [17,22]. Males are more likely to have problematic internet use than females in some studies [23], however, other studies have reported no significant gender difference [24]. Besides findings on demographic variables, previous studies have also found that problematic internet use is associated with potential addictive habits such as smoking, alcohol or coffee consumption, and taking drugs [25]. However, while some researchers have argued that alcohol and other illegal drugs are only linked to problematic internet use in tobacco users [26], others have found that smoking status is not related to problematic internet use [27].
Despite the negative consequences of problematic internet use, the internet is of necessity for many people in modern times, especially for particular groups. For example, college students and highly educated adults use the internet more frequently than other populations [28], because they require it for work/study purposes, as well as for leisure time in their daily life. To better make use of the advantages and avoid the potential risks brought by internet use, many studies have investigated specific online activities, such as gaming, social networking, and online gambling, which have been found to lead to problematic internet use [29]. Moreover, it has been found that only considering the amount of time spent online as a judgment of problematic internet use is questionable and insufficient to make accurate judgments [30]. How work and leisure internet use may each contribute to problematic internet use remains unclear, as well as the potential consequences on perceived quality of life. Thus, the purpose of this study is to explore the independent contributions of work and leisure internet use to problematic internet use, and the corresponding impact on perceived quality of life. The variables and theoretical framework used to explore this are outlined below.
Internet use to serve work and study purposes, is called “work-time internet use” in this study. All internet use that indicates leisure purposes (e.g., shopping online, playing games), regardless of whether it occurs during or after working hours, is regarded as “leisure-time internet use” in this study. Given the resources and opportunities offered by the internet, organizations have encouraged people to use the internet to work more productively and efficiently [31], which provides the possibility for work-time internet use. However, studies have found that employees also have increased opportunities to use the internet for leisure purposes during or after work hours, which may also be overwhelming [32]. In other words, a higher quantity of internet use for the two different qualities harbors the risk of an excessive amount, and with that becoming unbalanced with other behaviors.
This interrelation of different behaviors such as work-time and leisure-time internet use can be understood on the basis of the compensatory carry-over action model (CCAM) [33]. According to the CCAM, different behaviors are not isolated; rather, they interact with each other in complex ways. The model posits that emotionally relevant higher-level goals initiate intentions to engage in behaviors. To achieve a higher-lever goal, in this case, for example, good perceived quality of life, individuals may integrate the internet in their work and life (e.g., for social connection). Cognitive factors such as self-efficacy influence this process, as they make a difference in how people think and are able to motivate themselves. Both internet use for work and leisure are associated with internet use self-efficacy, which has been found to be a potentially important factor in explaining individuals’ decisions on internet use [34,35]. Stress management also influences this process thorough cognitive factors. Problematic internet use has been found to be related to perceived stress and negative feelings, such as depression and anxiety [36]. According to the model, the process of work-time and leisure-time internet use interrelate via compensatory cognitions and carry-over mechanisms. A person might carry over resources from one type of internet use to another, including experiences, skills, and cognitions. The outcomes such as perceived quality of life and problematic internet use may relate to both work-time internet use and leisure-time internet use.
The main aim of this study is to understand the potential mechanisms through which problematic internet use might be decreased and quality of life be improved, by investigating the different contributions of work-time and leisure-time internet use. In this process, as the CCAM indicates, other factors such as negative feelings, perceived stress, smoking status, and alcohol consumption are also investigated. Accordingly, this study examines the following research questions among college students and highly educated adults:
(1)
Do work-time and leisure-time internet use interrelate with problematic internet use?
(2)
Do work-time and leisure-time internet use interrelate with perceived quality of life?
(3)
Are there differences in the relationships when additionally taking negative feelings, perceived stress, smoking status, and alcohol consumption into account?

2. Materials and Methods

2.1. Participants

A total of 513 participants completed an online survey. After excluding invalid data (e.g., the sum of work-time internet use and leisure-time internet use was larger than 168 h/week (h/w)), the current study included 446 participants (59.6% female, n = 266) whose ages ranged from 17 to 77 (M = 25.8, SD = 11.6). Among the sample, 232 (52%) participants achieved a bachelor level and above education, 214 (48%) participants graduated from high school (or had an equivalent education level) and were currently studying at a university. Most of the study participants (97.3%, n = 434) reported being employed or studying. According to the problematic internet use test, 29.4% participants (n = 131) who scored high (problematic internet use ≥ 50) had problematic internet use, and the other 70.6% participants (n = 315) who scored low (problematic internet use < 50) had normal internet use levels. Regarding perceived quality of life, 10.5% of participants (n = 47) reported a very poor or poor level and 58.1% of participants (n = 259) reported a good or very good level.

2.2. Procedure

This cross-sectional study used an online questionnaire to collect data from October 2016 to July 2017 in Germany. The link for the survey was sent but not limited to the staff and students in universities. Participants were free to answer the questions by clicking the appropriate box (multiple choice questions) or input content (open questions). On the first page of the online survey, all participants were informed of the confidentiality and anonymity of their responses, and the people who clicked the box to provide their informed consent would continue to the questionnaire pages. The study was part of a project investigating the interrelationship between internet use and health behaviors, and received ethical approval by the Ethics Commission of the German Association of Psychology (Deutsche Gesellschaft für Psychologie, EK-A-SL022013).

2.3. Measurements

Work-time and leisure-time internet use were assessed separately, by asking participants questions about their internet use in a typical week. One question (a) “How many days did you spend on this (work/study, leisure) per week?”, was followed by question (b) “How many hours on average did you spend on the days you did it?”. The time spent on internet use for work and leisure, separately, were obtained from the multiplied scores of questions (a) and (b). This instrument has been shown to have acceptable reliability and validity for measuring the time spent on internet use [37,38,39].
In order to identify the differences of problematic internet use and perceived quality of life between different work-time/leisure-time internet users, participants were put into categorical quartiles work-time/leisure-time internet use groups: work-time internet use groups 1, 2, 3, 4 (WG1, WG2, WG3, WG4); leisure-time internet use groups 1, 2, 3, 4 (LG1, LG2, LG3, LG4) (see Table 1 for details). These categories are not derived from, but are also in line with previous studies that have shown work-time ≥ 55 h/w to be harmful with health [40]. Leisure-time internet use should be no more than 10 h/w, and ≥30 h/w may lead to health-risk behaviors such as sleep problems [41].
Problematic internet use was measured on a 4-point Likert scale with 10 items [42]. The example question was “My thoughts are constantly around the internet, even when I’m not online”, and was scored 1–4 from “completely disagree” to “agree completely”. Suggested by the scale, the total score range of this scale was 20–80, and it was distinguished by three types of internet use: no internet addiction (20–49), internet addiction tendency (50–59), internet addiction (60–80). In this study, the participants whose scores ≥ 50 were considered to have problematic internet use [42]. Cronbach’s alpha in this study was 0.84.
Perceived quality of life was measured by asking “Please think about the last four weeks: How would you rate your perceived quality of life?” with a 5-point scale ranging from “very poor” to “very good” [43]. The scores of the participants were tripartite categorized as very poor/poor, neither poor nor good, good/very good. This single-item measure assessing self-rated perceived quality of life has been found to have acceptable reliability and validity in previous surveys [44,45,46].
Negative feelings were measured by asking “In the last months, how often do you have negative feelings such as blue mood, despair, anxiety, depression in the last four weeks?” with a 5-point scale ranging from “never” to “always” [43]. This single-item measure assessing negative feelings has been found to have acceptable reliability and validity in previous surveys [47,48].
Perceived stress was measured with two questions from the perceived stress scale [49]: “How often do you feel nervous and stress?”, and “How often do you feel difficulties were piling up so high that you could not overcome them?”, and rated on a 5-point Likert scale. The split-half reliability of this scale was satisfactory (0.73 according to the Spearman Brown formula, the equivalent of Cronbach’s alpha for two-item scales [50]).
Smoking status was measured by asking the participants “Are you a smoker?”, and participants who answered “nonsmoker” or “ex-smoker” scored 1 point, while participants who answered “occasional smoker” or “regular smoker” scored 0. This question has been found to have acceptable reliability and validity in previous surveys [51,52].
Alcohol consumption status was measured by asking participants “How often do you drink alcoholic beverages?”, and participants who answered “nonregular drinker” or “seldom drinker (who drinks less than 1 times/week)” scored 1 point, while participants who answered “regular drinker (who drinks more than 1 time/week)” scored 0. These parameters were set up in accordance with a previous studies that defines a regular alcohol drinker as someone who drinks at least weekly [12]. This question has been found to have acceptable reliability and validity [53].
Participants also answered socio-demographic questions, such as age, gender, height, weight, marital status, employment status, and education level. Body mass index (BMI; kg/m2) was calculated using self-reported height and weight.

2.4. Statistical Analysis

Bivariate correlation analyses were performed to examine the relationships between work-time and leisure-time internet use, problematic internet use and perceived quality of life. Linear regression analyses were used to predict problematic internet use by socio-demographic variables, work-time internet use, leisure-time internet use, negative feelings, perceived stress, smoking status, and alcohol assumption status. Ordinal regression analyses were used to predict perceived quality of life by socio-demographic variables, work-time internet use, leisure-time internet use, negative feelings, perceived stress, smoking status, and alcohol assumption status. Analysis of variance (ANOVA) was used to compare the mean differences of researched variables such as problematic internet use and perceived quality of life in different internet use groups. Analyses were performed with SPSS 26 (IBM Corp, New York, US). Significance was accepted at a p-level of 0.05.

3. Results

3.1. Descriptive Statistics

Descriptive statistics are presented in Table 1. The average internet use time during work was about 17.2 h/w. Participants also reported spending an average of 14.7 h/w online for leisure purposes.

3.2. Correlation Analyses in Main Study Variables

Bivariate Spearman correlation analyses were performed to examine the relationships between main research variables. The results showed a positive relationship between work-time internet use and leisure-time internet use, and a negative relationship between problematic internet use and perceived quality of life (Table 2).

3.3. Problematic Internet Use in Different Work-Time and Leisure-Time Internet Use Groups

To examine whether work-time and leisure-time internet use were interrelated with problematic internet use, when controlling for socio-demographic variables, linear regression analyses were performed with dummy coding for work-time internet use and leisure-time internet use. The findings are presented in Table 3, in which unstandardized B coefficients are reported but not beta. This is for reporting the value of problematic internet use of leisure-time (work-time) internet use groups 2, 3, 4 compared to that of group 1.
Work-time internet use was not significantly associated with problematic internet use after controlling for socio-demographic variables (model 1). LG2, LG3, and LG4 scored higher on the problematic internet use scale compared to LG1, respectively (model 1). When negative feelings and perceived stress were added into model 2, the results of work-time and leisure-time internet use predicting problematic internet use were similar to model 1. Moreover, perceived stress was positively related to problematic internet use (model 2). When smoking status and alcohol consumption were added in model 3, the results of work-time and leisure-time internet use predicting problematic internet use were still similar to model 1 and model 2. Work-time internet use was not significantly correlated with problematic internet use, while LG2, LG3, and LG4 scored higher on the problematic internet use scale compared LG1, respectively. Negative feelings and perceived stress showed positive relationships with problematic internet use, while smoking status and alcohol consumption were not significantly correlated with problematic internet use (model 3).
To further investigate the differences of problematic internet use between leisure-time internet use groups, an ANOVA was performed. The results showed significant differences of problematic internet use between the four groups (F (1) = 33.28, p < 0.001). The paired comparisons of the mean differences of problematic internet use in the four leisure-time groups are shown in the right-hand side of Figure 1. LG1 with <4 h/w reported significantly lower score than LG2 with 4–10.99 h/w; LG2 with 4–10.99 h/w was not significantly different from LG3 with 11–20.99 h/w, but significantly lower than LG4 with 21–84 h/w in regard to problematic internet use. However, no such differences revealed to be significant for work-time internet use (Figure 1, left-hand side).

3.4. Perceived Quality of Life in Different Work-Time and Leisure-Time Internet Use Groups

To examine whether work-time and leisure-time internet use interrelated with perceived quality of life, when controlling for socio-demographic variables, ordinal regression analyses were performed with dummy coding for work-time internet use and leisure-time internet use. The findings are presented in Table 4.
The odds ratio (OR) value of reporting a good perceived quality of life in WG2 was 2.51 times compared with WG1 (χ2 (1) = 9.65, p < 0.05), after controlling for socio-demographic variables (model 1). The OR value of having a good perceived quality of life in WG3 was 1.85 times compared with WG1 (χ2 (1) = 5.13, p < 0.05), after controlling for socio-demographic variables (model 1). There was, however, no significant difference in perceived quality of life between WG4 and WG1.
When controlling for negative feelings and perceived stress in model 2, the OR value of reporting a good perceived quality of life showed a similar pattern (χ2 (1) = 12.04, p < 0.001). Negative feelings and perceived stress showed significant negative relationships with perceived quality of life, respectively (model 2).
When the effect of smoking status and alcohol consumption were included in model 3, the OR value of reporting a good perceived quality of life in WG2 was 2.78 times compared with WG1 (χ2 (1) = 10.68, p < 0.001), the effect of WG3 in comparison to WG1 was close to significant. However, the second order fitting of the dependence of the perceived quality of life on the work-time internet use demonstrates that the high quality of life range is still located in the union of WG2 and WG3 (Figure 2). Taking this into consideration, participants in WG2 and WG3 were assumed have higher perceived quality of life compared to WG1. Moreover, negative feelings showed negative relationships with perceived quality of life (model 3). Regarding alcohol consumption, nonregular drinkers reported a lower OR value of good perceived quality of life compared with regular drinkers (model 3). Leisure-time internet use was not significantly correlated with perceived quality of life in any of the three models.

4. Discussion

Internet use is both essential for many individuals living and working in modern times, and poses potential risks of becoming a problematic behavior [1,20,21]. While studies have shown that long lengths of time spent using the internet can negatively impact quality of life [16,17], it has also been highlighted that the sole consideration of time spent online is questionable in determining problematic internet use [31]. While specific internet activities have been associated with problematic internet use [30], to our knowledge, types of internet use (i.e., whether used for work or leisure purposes) and their respective associations with problematic internet use and perceived quality of life have not been investigated systematically. As far as we know, this is the first study exploring problematic internet use and perceived quality of life when comparing the differences between work and leisure internet use separately. This comparison was assessed together with other factors (negative feelings, perceived stress, smoking, and alcohol assumption) that have been found to be imperative in understanding public health and addiction, among highly educated adults and college students.
The results showed that problematic internet use was negatively correlated with perceived quality of life, which is in line with previous studies [1,54,55]. Work-time internet use and leisure-time internet use were correlated, however, they had different contributions to problematic internet use and quality of life. Leisure-time internet use but not work-time internet use was positively associated with problematic internet use. Participants whose work-time internet use was at a balanced level (between 5 to 28 h/w in this study) indicated a higher perceived quality of life compared to individuals with little or large amount of internet use for work. These results are largely consistent when taking negative feelings, perceived stress, smoking status, and alcohol assumption status into account. Negative feelings and perceived stress showed positive relationships with problematic internet use, respectively. Negative feelings showed a negative relationship with quality of life. Drinking alcohol on a regular basis showed a higher quality of life compared to nonregular drinkers.

4.1. Problematic Internet Use in Different Work-Time and Leisure-Time Internet Use Groups

One of the main findings of this study was that leisure-time internet use, but not work-time internet use, was strongly related to problematic internet use among college students and highly educated participants. After adjusting for socio-demographic variables, participants who use the internet for leisure purposes for 4–21 h/w scored around 4 points higher on problematic internet use, leaning more towards addictive behavior compared to participants who spent less than 4 h/w for leisure-time internet use. While we cannot make clinical conclusions about addiction per se, the higher problematic internet use may be a rough indication for potential addictive internet behavior. This finding is in line with and adds to previous studies, which reported that time spent online for recreational purposes is regularly observed in problematic internet use assessments [23,56,57,58].
Even though many college students and highly educated individuals have to use the internet for work purposes [59], and work-time internet use was higher than leisure-time internet use on average in this study, work-time internet use did not show a significant association with problematic internet use after controlling for socio-demographic variables. This is in line with and adds to previous studies, which explored problematic online behaviors being related to video gaming, gambling online, browsing for fun, shopping, and social networking [60,61,62], but not working online.
The overall percentage among college students and highly educated adults who show problematic internet use was found to be 29.4%, which is also consistent with previous studies in adolescents and adult populations [55,58,63,64]. In other words, if one out of three people is at risk for developing problematic internet use, this calls for preventive actions. Psychological institutes, schools and colleges are recommended to provide coaching and courses to increase individuals’ self-management, in order to avoid problematic internet use. Addressing individuals’ leisure-time internet use can help by raising awareness and skills to regulate internet use, especially in the face of negative feelings and perceived stress.

4.2. Perceived Quality of Life in Different Work-Time and Leisure-Time Internet Use Groups

The other main finding of this study was that participants who reported balanced work-time internet use indicated a higher perceived quality of life, compared to the participants who seldom used internet for work and study, with lower than 5 h/w and who spent lots of time on internet use for work with more than 28 h/w. This finding is rather new, as few studies have investigated work-time internet use and perceived quality of life, especially among the population of college students and highly educated adults who frequently use the internet for work and study. This finding supports previous studies that suggest that long work hours are related to a lower quality of life [65,66,67], and that longer work-time internet use may be related to longer work hours [41]. There is no evidence that a lower work-time internet use is related to a lower quality of life in general populations. However, our participants in this study were college students and highly educated adults. Those individuals who seldom use the internet for work may suffer from hidden problems such as a poor work–life balance, and those individuals who use the internet for long periods of time for work may suffer from hidden problems such as too much sitting and too little physical activity, which in turn decreases the quality of life [68,69]. This finding suggests internet use for work should not be too long, and this would prevent the problem of being too sedentary in the context of a working environment.
Specifically, the internet becomes especially important in times of the Coronavirus disease 2019 (COVID-19) crisis, as a considerable amount of work and activities of daily life such as social interactions have been moved online. It helps ensure physical distancing to prevent the virus from spreading, while averting a complete breakdown of all businesses and social connections of individuals and groups. In this unusual period, the time spent on the internet has largely increased. Employers and colleges are recommended to remind their employees and students to avoid potential problematic internet use, while enjoying the advantages of internet. Meanwhile, reducing perceived stress and negative feelings are essential to obtain a higher perceived quality of life. However, this dataset stems from a time prior to the COVID-19 pandemic. While the implications of this study are important for dealing with the COVID-19 pandemic and its aftermath, the main advantage of this study is that the general mechanisms will also be applicable in a time when the COVID-19 pandemic and its aftermath are over. Further studies should research the effects of the COVID-19 pandemic and its aftermath on such mechanisms outlined in this paper; replication and cross-validation are recommended.
Different leisure-time internet use groups failed to show a significant difference in perceived quality of life in the regression analyses after controlling for socio-demographic variables and work-time internet use. This may be because of although the percentage of problematic internet use was similar to other populations in previous studies [55,58,63,64], the problematic internet users’ score were not very high in this study, which indicated a lower problematic internet use level. The differences between groups with different amounts of leisure-time internet use did, however, demonstrate differences with problematic internet use, which was found to be negatively related with quality of life in this study and in line with previous studies [1,54,55]. Furthermore, perceived quality of life can also be related to other areas of life besides leisure-time internet use, such as sleep, lifestyle, chronic disease, and mental health [41]. The latter can accordingly relate to leisure-time internet use, which should be investigated further in future studies.

4.3. Negative Feelings, Perceived Stress, Smoking Status, and Alcohol Assumption Status

Negative feelings and perceived stress were positively related with problematic internet use, which is in line with previous studies [36]. Negative feelings have shown a negative relationship with quality of life, which supports previous findings [5]. The finding that suggested that stress was negatively related to perceived quality of life was in line with previous studies [6,70]; in other words, individuals who feel more stressed also suffer from a lower quality of life. Feeling stressed was no longer found to be a significant predictor of problematic internet use, however, when smoking status and alcohol assumption status were controlled for. This may be due to an interaction of perceived stress and smoking or alcohol status [71]. Previous studies have indicated that smokers may be smoking to relieve perceived stress [72], but this relief is temporary; smoking was found to lead to higher perceived stress overall [72]. Similarly, some individuals exhibiting problematic internet use may spend more time on the internet to escape perceived stress, but end up experiencing higher perceived stress. Future studies should investigate the interactions of feeling stressed and other lifestyle factors for quality of life within a theoretical framework such as the CCAM.
Smoking status failed to show significant relationships with problematic internet use and quality of life, as in previous studies [27], but other studies also found different patterns [7,25]; regular alcohol drinkers showed a higher quality of life compared to nonregular drinkers in this study, which is also in line with and supports previous studies [11,12].

4.4. Limitations

The results of this study should be interpreted with several limitations in mind. Firstly, the data were mainly collected within Germany, which limits the generalizability of the findings. Secondly, this study relies on self-report measures and a cross-sectional research design, which limits causal conclusions. Moreover, participants in this study were recruited by convenience sampling; future studies are suggested to consider the sample representativeness before data collection. Thirdly, besides work-time and leisure-time internet use, only several variables such as negative feeling were added to the models to predict problematic internet use and quality of life. Other factors such as positive feelings and health status may also impact the results. Further studies should measure and control more confounding variables to exclude their influence, and it may be worth considering further distinguishing social purposes of the internet use to more thoroughly unravel the aspects of leisure-time internet use, and explore how to prevent problematic internet use and its subsequent influences on quality of life. Furthermore, quality of life is related to, but different from life satisfaction, which is measured slightly differently and could also be explored in future studies. Taking these limitations into account, the study still adds to the understanding of mental health in the face of digitalization, and offers suggestions on how to investigate problematic internet use in the future.

5. Conclusions

In summary, this study sheds light on using the internet to facilitate daily life and perceived quality of life, while avoiding problematic internet usage. Among college students and highly educated adults, it was found that both work-time and leisure-time internet use can be risk factors for health in terms of problematic internet use and perceived quality of life. Leisure-time internet use rather than work-time internet use was positively associated with problematic internet use. Participants whose work-time internet use was at a balanced level indicated a higher perceived quality of life.
Well-controlled internet use rather than excessive use is recommended, in order to enjoy the convenience brought by the internet and avoid potential drawbacks. Further research can and should build on these findings to ensure a solid knowledge base on which to build an understanding of how internet use can be applied for its advantages, rather than its disadvantages.

Author Contributions

Conceptualization, L.G., Y.G., A.W., and S.L.; methodology, L.G., and S.L.; software, L.G.; validation, L.G., Y.G., A.W., and S.L.; formal analysis, L.G.; investigation, L.G., and S.L.; resources, L.G., and S.L.; data curation, L.G.; writing—original draft preparation, L.G.; writing—review and editing, L.G., Y.G., A.W., and S.L.; visualization, L.G.; supervision, S.L., and Y.G.; project administration, L.G.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.

Acknowledgments

The authors would like to thank Nellie Siemers for proofreading the text. This research was partially funded by the Innovation Fund (Project No. 01VSF18023) of The Federal Joint Committee (G-BA, the highest decision-making body of the joint self-government of physicians, dentists, psychotherapists, hospitals, and health insurance funds in Germany) with the senior author as consortium leader.

Conflicts of Interest

The authors declare that they have no potential conflicts of interest. The funding body only observes whether the study is carried out as proposed and approved in terms of study design. The funding body has no influence on the collection, analysis, and interpretation of data. The funding body is not involved in writing the manuscripts but will be informed about the authors’ activities.

References

  1. Machimbarrena, J.M.; González-Cabrera, J.; Ortega-Barón, J.; Beranuy-Fargues, M.; Álvarez-Bardón, A.; Tejero, B. Profiles of Problematic Internet Use and Its Impact on Adolescents’ Health-Related Quality of Life. Int. J. Environ. Res. Public Health 2019, 16, 3877. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Christiansen, L.; Lindberg, C.; Sanmartin Berglund, J.; Anderberg, P.; Skär, L. Using Mobile Health and the Impact on Health-Related Quality of Life: Perceptions of Older Adults with Cognitive Impairment. Int. J. Environ. Res. Public Health 2020, 17, 2650. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Radic, A.; Ariza-Montes, A.; Hernández-Perlines, F.; Giorgi, G. Connected at Sea: The Influence of the Internet and Online Communication on the Well-Being and Life Satisfaction of Cruise Ship Employees. Int. J. Environ. Res. Public Health 2020, 17, 2840. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Diener, E. Subjective well-being. In The Science of Well-Being; Springer: New York, NY, USA, 2009; pp. 11–58. [Google Scholar]
  5. Kumar, A.; Sharma, M.P.; Kandavel, T.; Reddy, Y.J. Cognitive appraisals and quality of life in patients with obsessive compulsive disorder. J. Obsess. Compuls. Rel. Dis. 2012, 1, 301–305. [Google Scholar] [CrossRef]
  6. Warren, C.M.; Otto, A.K.; Walkner, M.M.; Gupta, R.S. Quality of life among food allergic patients and their caregivers. Curr. Allergy Asthma Rep. 2016, 16, 38. [Google Scholar] [CrossRef]
  7. Ridner, S.L.; Keith, R.J.; Walker, K.L.; Hart, J.L.; Newton, K.S.; Crawford, T.N. Differences in quality of life among college student electronic cigarette users. AIMS Public Health 2018, 5, 454–472. [Google Scholar] [CrossRef]
  8. Robles-Martínez, M.; García-Carretero, M.Á.; Gibert, J.; Palma-Álvarez, R.F.; Abad, A.C.; Sorribes, M.; Roncero, C. Differences between craving and health-related quality of life in patients with alcohol dependence with or without dual pathology in outpatient treatment: A descriptive study. Med. Clin. (Engl. Ed.) 2018, 150, 49–55. [Google Scholar] [CrossRef]
  9. Vederhus, J.-K.; Birkeland, B.; Clausen, T. Perceived quality of life, 6 months after detoxification: Is abstinence a modifying factor? Qual. Life Res. 2016, 25, 2315–2322. [Google Scholar] [CrossRef] [Green Version]
  10. Duffy, S.A.; Terrell, J.E.; Valenstein, M.; Ronis, D.L.; Copeland, L.A.; Connors, M. Effect of smoking, alcohol, and depression on the quality of life of head and neck cancer patients. Gen. Hosp. Psychiatry 2002, 24, 140–147. [Google Scholar] [CrossRef]
  11. Chan, A.M.; von Mühlen, D.; Kritz-Silverstein, D.; Barrett-Connor, E. Regular alcohol consumption is associated with increasing quality of life and mood in older men and women: The Rancho Bernardo Study. Maturitas 2009, 62, 294–300. [Google Scholar] [CrossRef] [Green Version]
  12. Cougle, J.R.; Hakes, J.K.; Macatee, R.J.; Chavarria, J.; Zvolensky, M.J. Quality of life and risk of psychiatric disorders among regular users of alcohol, nicotine, and cannabis: An analysis of the National Epidemiological Survey on Alcohol and Related Conditions (NESARC). J. Psychiatr. Res. 2015, 66, 135–141. [Google Scholar] [CrossRef] [PubMed]
  13. Tran, B.X.; Le, X.T.T.; Nguyen, P.N.; Le, Q.N.H.; Mai, H.T.; Nguyen, H.L.T.; Le, H.T.; Tran, T.T.; Latkin, C.A.; Zhang, M.W. Feasibility of e-Health interventions on smoking cessation among vietnamese active internet users. Int. J. Environ. Res. Public Health 2018, 15, 165. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Kim, H.; Xie, B. Health literacy and internet- and mobile app-based health services: A systematic review of the literature. Proc. Assoc. Inf. Sci. Technol. 2015, 52, 1–4. [Google Scholar] [CrossRef] [Green Version]
  15. Pontes, H.M.; Szabo, A.; Griffiths, M.D. The impact of Internet-based specific activities on the perceptions of Internet addiction, quality of life, and excessive usage: A cross-sectional study. Addict. Behav. Rep. 2015, 1, 19–25. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Lachmann, B.; Sariyska, R.; Kannen, C.; Stavrou, M.; Montag, C. Commuting, life-satisfaction and internet addiction. Int. J. Environ. Res. Public Health 2017, 14, 1176. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Carbonell, X.; Chamarro, A.; Oberst, U.; Rodrigo, B.; Prades, M. Problematic use of the internet and smartphones in university students: 2006–2017. Int. J. Environ. Res. Public Health 2018, 15, 475. [Google Scholar] [CrossRef] [Green Version]
  18. Foerster, M.; Röösli, M. A latent class analysis on adolescents media use and associations with health related quality of life. Comput. Hum. Behav. 2017, 71, 266–274. [Google Scholar] [CrossRef] [Green Version]
  19. Casale, S.; Lecchi, S.; Fioravanti, G. The Association Between Psychological Well-Being and Problematic Use of Internet Communicative Services Among Young People. J. Psychol. 2015, 149, 480–497. [Google Scholar] [CrossRef]
  20. Kuss, D.J.; Griffiths, M.D. Online social networking and addiction—A review of the psychological literature. Int. J. Environ. Res. Public Health 2011, 8, 3528–3552. [Google Scholar] [CrossRef] [Green Version]
  21. Guo, N.; Luk, T.T.; Ho, S.Y.; Lee, J.J.; Shen, C.; Oliffe, J.; Chan, S.S.-C.; Lam, T.H.; Wang, M.P. Problematic smartphone use and mental health in Chinese adults: A population-based study. Int. J. Environ. Res. Public Health 2020, 17, 844. [Google Scholar] [CrossRef] [Green Version]
  22. Wartberg, L.; Lindenberg, K. Predictors of Spontaneous Remission of Problematic Internet Use in Adolescence: A One-Year Follow-Up Study. Int. J. Environ. Res. Public Health 2020, 17, 448. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Laconi, S.; Tricard, N.; Chabrol, H. Differences between specific and generalized problematic Internet uses according to gender, age, time spent online and psychopathological symptoms. Comput. Hum. Behav. 2015, 48, 236–244. [Google Scholar] [CrossRef]
  24. Odaci, H. Academic self-efficacy and academic procrastination as predictors of problematic internet use in university students. Comput. Educ. 2011, 57, 1109–1113. [Google Scholar] [CrossRef]
  25. Frangos, C.C.; Frangos, C.C.; Sotiropoulos, I. Problematic internet use among Greek university students: An ordinal logistic regression with risk factors of negative psychological beliefs, pornographic sites, and online games. Cyberpsychol. Behav. Soc. Netw. 2011, 14, 51–58. [Google Scholar] [CrossRef] [Green Version]
  26. Rücker, J.; Akre, C.; Berchtold, A.; Suris, J.C. Problematic Internet use is associated with substance use in young adolescents. Acta Paediatr. 2015, 104, 504–507. [Google Scholar] [CrossRef]
  27. Choi, K.; Son, H.; Park, M.; Han, J.; Kim, K.; Lee, B.; Gwak, H. Internet overuse and excessive daytime sleepiness in adolescents. Psychiatry Clin. Neurosci. 2009, 63, 455–462. [Google Scholar] [CrossRef]
  28. Vicente, P.; Reis, E. The “frequency divide”: Implications for internet-based surveys. Qual. Quant. 2013, 47, 3051–3064. [Google Scholar] [CrossRef]
  29. Musetti, A.; Mancini, T.; Corsano, P.; Santoro, G.; Cavallini, M.C.; Schimmenti, A. Maladaptive Personality Functioning and Psychopathological Symptoms in Problematic Video Game Players: A Person-Centered Approach. Front. Psychol. 2019, 10, 2559. [Google Scholar] [CrossRef] [Green Version]
  30. Billieux, J.; Flayelle, M.; Rumpf, H.-J.; Stein, D.J. High involvement versus pathological involvement in video games: A crucial distinction for ensuring the validity and utility of gaming disorder. Curr. Addict. Rep. 2019, 6, 323–330. [Google Scholar] [CrossRef]
  31. Moody, G.D.; Siponen, M. Using the theory of interpersonal behavior to explain non-work-related personal use of the Internet at work. Inform. Manag. Amster 2013, 50, 322–335. [Google Scholar] [CrossRef]
  32. Kühnel, J.; Vahle-Hinz, T.; de Bloom, J.; Syrek, C.J. Staying in touch while at work: Relationships between personal social media use at work and work-nonwork balance and creativity. Int. J. Hum. Resour. Man. 2017, 1, 1–27. [Google Scholar] [CrossRef] [Green Version]
  33. Lippke, S. Modelling and supporting complex behavior change related to obesity and diabetes prevention and management with the compensatory carry-over action model. J. Diabet. Obes. 2014, 1, 1–5. [Google Scholar] [CrossRef]
  34. Pan, S.; Jordan-Marsh, M. Internet use intention and adoption among Chinese older adults: From the expanded technology acceptance model perspective. Comput. Hum. Behav. 2010, 26, 1111–1119. [Google Scholar] [CrossRef]
  35. Hsu, M.-H.; Chiu, C.-M. Internet self-efficacy and electronic service acceptance. Decis. Support Syst. 2004, 38, 369–381. [Google Scholar] [CrossRef]
  36. Khalili-Mahani, N.; Smyrnova, A.; Kakinami, L. To Each Stress Its Own Screen: A Cross-Sectional Survey of the Patterns of Stress and Various Screen Uses in Relation to Self-Admitted Screen Addiction. J. Med. Internet Res. 2019, 21, e11485. [Google Scholar] [CrossRef] [PubMed]
  37. Vandelanotte, C.; Sugiyama, T.; Gardiner, P.; Owen, N. Associations of leisure-time internet and computer use with overweight and obesity, physical activity and sedentary behaviors: Cross-sectional study. J. Med. Internet Res. 2009, 11, e28. [Google Scholar] [CrossRef]
  38. Salmon, J.; Owen, N.; Crawford, D.; Bauman, A.; Sallis, J.F. Physical activity and sedentary behavior: A population-based study of barriers, enjoyment, and preference. Health Psychol. 2003, 22, 178. [Google Scholar] [CrossRef] [PubMed]
  39. González, E.; Orgaz, B. Problematic online experiences among Spanish college students: Associations with Internet use characteristics and clinical symptoms. Comput. Hum. Behav. 2014, 31, 151–158. [Google Scholar] [CrossRef]
  40. Kivimäki, M.; Jokela, M.; Nyberg, S.T.; Singh-Manoux, A.; Fransson, E.I.; Alfredsson, L.; Bjorner, J.B.; Borritz, M.; Burr, H.; Casini, A. Long working hours and risk of coronary heart disease and stroke: A systematic review and meta-analysis of published and unpublished data for 603 838 individuals. Lancet 2015, 386, 1739–1746. [Google Scholar] [CrossRef]
  41. Andersen, L.L.; Garde, A.H. Sleep problems and computer use during work and leisure: Cross-sectional study among 7800 adults. Chronobiol. Int. 2015, 32, 1367–1372. [Google Scholar] [CrossRef]
  42. Hahn, A.; Jerusalem, M.; Meixner-Dahle, S.; dieses Dokuments, Z.; der Fragebogens, H.Z.B. Fragebogen zur Internetsucht (ISS-20r) und Normwerte. Available online: http://andre-hahn.de/downloads/pub/2014/2014_ISS20-Internetsuchtskala.pdf (accessed on 12 February 2016).
  43. The World Health Organization Quality of Life Group. The World Health Organization quality of life assessment (WHOQOL): Position paper from the World Health Organization. Soc. Sci. Med. 1995, 41, 1403–1409. [Google Scholar] [CrossRef]
  44. Tan, S.L.; Storm, V.; Reinwand, D.A.; Wienert, J.; de Vries, H.; Lippke, S. Understanding the positive associations of sleep, physical activity, fruit and vegetable intake as predictors of quality of life and subjective health across age groups: A theory based, cross-sectional web-based study. Front. Psychol. 2018, 9, 977. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Zimmerman, M.; Ruggero, C.J.; Chelminski, I.; Young, D.; Posternak, M.A.; Friedman, M.; Boerescu, D.; Attiullah, N. Developing brief scales for use in clinical practice: The reliability and validity of single-item self-report measures of depression symptom severity, psychosocial impairment due to depression, and quality of life. J. Clin. Psychiatry 2006. [Google Scholar] [CrossRef] [PubMed]
  46. Yohannes, A.M.; Dodd, M.; Morris, J.; Webb, K. Reliability and validity of a single item measure of quality of life scale for adult patients with cystic fibrosis. Health Qual. Life Outcomes 2011, 9, 105. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. McKenzie, N.; Marks, I. Quick rating of depressed mood in patients with anxiety disorders. Br. J. Psychiatry 1999, 174, 266–269. [Google Scholar] [CrossRef]
  48. Livesey, L.; Morrison, I.; Clift, S.; Camic, P. Benefits of choral singing for social and mental wellbeing: Qualitative findings from a cross-national survey of choir members. J. Public Ment. Health 2012, 11, 10–26. [Google Scholar] [CrossRef]
  49. Cohen, S.; Kamarck, T.; Mermelstein, R. A global measure of perceived stress. J. Health Soc. Behav. 1983. [Google Scholar] [CrossRef]
  50. Hulin, C.; Cudeck, R. Cronbach’s alpha on two-item scales. J. Consum. Psychol. 2001, 10, 55. [Google Scholar]
  51. Lippke, S.; Nigg, C.R.; Maddock, J.E. Health-promoting and health-risk behaviors: Theory-driven analyses of multiple health behavior change in three international samples. Int. J. Behav. Med. 2012, 19, 1–13. [Google Scholar] [CrossRef] [Green Version]
  52. De Graaf, A.; van den Putte, B.; Nguyen, M.-H.; Zebregs, S.; Lammers, J.; Neijens, P. The effectiveness of narrative versus informational smoking education on smoking beliefs, attitudes and intentions of low-educated adolescents. Psychol. Health 2017, 32, 810–825. [Google Scholar] [CrossRef] [Green Version]
  53. Renner, B.; Hahn, A.; Schwarzer, R. Risiko und Gesundheitsverhalten. In Dokumentation der Messinstrumente des Forschungsprojektes Berlin Risk Appraisal and Health Motivation Study (BRAHMS. Berlin: Freie Universität Berlin, Institut für Psychologie); Free University Berlin: Berlin, Germany, 1996. [Google Scholar]
  54. Tekinarslan, E. Relationship between problematic internet use, depression and quality of life levels of Turkish university students. J. Educ. Train. Stud. 2017, 5, 167–175. [Google Scholar] [CrossRef] [Green Version]
  55. Cheng, C.; Li, A.Y.-L. Internet addiction prevalence and quality of (real) life: A meta-analysis of 31 nations across seven world regions. Cyberpsychol. Behav. Soc. Netw. 2014, 17, 755–760. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Lortie, C.L.; Guitton, M.J. Internet addiction assessment tools: Dimensional structure and methodological status. Addiction 2013, 108, 1207–1216. [Google Scholar] [CrossRef] [PubMed]
  57. Tonioni, F.; Mazza, M.; Autullo, G.; Cappelluti, R.; Catalano, V.; Marano, G.; Fiumana, V.; Moschetti, C.; Alimonti, F.; Luciani, M. Is Internet addiction a psychopathological condition distinct from pathological gambling? Addict. Behav. 2014, 39, 1052–1056. [Google Scholar] [CrossRef]
  58. Zhou, N.; Cao, H.; Li, X.; Zhang, J.; Yao, Y.; Geng, X.; Lin, X.; Hou, S.; Liu, F.; Chen, X. Internet addiction, problematic internet use, nonproblematic internet use among Chinese adolescents: Individual, parental, peer, and sociodemographic correlates. Psychol. Addict. Behav. 2018, 32, 365. [Google Scholar] [CrossRef]
  59. Van Deursen, A.J.; Van Dijk, J.A. The digital divide shifts to differences in usage. New Media Soc. 2014, 16, 507–526. [Google Scholar] [CrossRef]
  60. Wong, H.Y.; Mo, H.Y.; Potenza, M.N.; Chan, M.N.M.; Lau, W.M.; Chui, T.K.; Pakpour, A.H.; Lin, C.-Y. Relationships between severity of internet gaming disorder, severity of problematic social media use, sleep quality and psychological distress. Int. J. Environ. Res. Public Health 2020, 17, 1879. [Google Scholar] [CrossRef] [Green Version]
  61. Durkee, T.; Carli, V.; Floderus, B.; Wasserman, C.; Sarchiapone, M.; Apter, A.; Balazs, J.A.; Bobes, J.; Brunner, R.; Corcoran, P. Pathological internet use and risk-behaviors among European adolescents. Int. J. Environ. Res. Public Health 2016, 13, 294. [Google Scholar] [CrossRef] [Green Version]
  62. González-Bueso, V.; Santamaría, J.J.; Fernández, D.; Merino, L.; Montero, E.; Ribas, J. Association between internet gaming disorder or pathological video-game use and comorbid psychopathology: A comprehensive review. Int. J. Environ. Res. Public Health 2018, 15, 668. [Google Scholar] [CrossRef] [Green Version]
  63. Gholamian, B.; Shahnazi, H.; Hassanzadeh, A. The prevalence of internet addiction and its association with depression, anxiety, and stress, among high-school students. Int. J. Pediatr. 2017, 5, 4763–4770. [Google Scholar] [CrossRef]
  64. Wang, L.; Tao, T.; Fan, C.; Gao, W.; Wei, C. The association between Internet addiction and both impulsivity and effortful control and its variation with age. Addict. Res. Theory 2017, 25, 83–90. [Google Scholar] [CrossRef]
  65. Jansen, N.W.; Kant, I.; Nijhuis, F.J.; Swaen, G.M.; Kristensen, T.S. Impact of worktime arrangements on work-home interference among Dutch employees. Scand. J. Work. Environ. Health 2004. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Fagan, C.; Lyonette, C.; Smith, M.; Saldaña-Tejeda, A. The Influence of Working Time Arrangements on Work-Life Integration Or‘balance’: A Review of the International Evidence; ILO: Geneva, Switzerland, 2012. [Google Scholar]
  67. Voydanoff, P. The effects of work demands and resources on work-to-family conflict and facilitation. J. Marriage Fam. 2004, 66, 398–412. [Google Scholar] [CrossRef]
  68. Gayathiri, R.; Ramakrishnan, L.; Babatunde, S.; Banerjee, A.; Islam, M. Quality of work life–Linkage with job satisfaction and performance. Int. J. Bus. Manag. Invent. 2013, 2, 1–8. [Google Scholar]
  69. Lippke, S.; Fleig, L.; Wiedemann, A.U.; Schwarzer, R. A Computerized Lifestyle Application to Promote Multiple Health Behaviors at the Workplace: Testing Its Behavioral and Psychological Effects. J. Med. Internet Res. 2015, 17. [Google Scholar] [CrossRef] [PubMed]
  70. Malinauskas, R. The associations among social support, stress, and life satisfaction as perceived by injured college athletes. Soc. Behav. Pers. 2010, 38, 741–752. [Google Scholar] [CrossRef]
  71. Uhart, M.; Wand, G.S. Stress, alcohol and drug interaction: An update of human research. Addict. Biol. 2009, 14, 43–64. [Google Scholar] [CrossRef] [Green Version]
  72. Chamik, T.; Viswanathan, B.; Gedeon, J.; Bovet, P. Associations between psychological stress and smoking, drinking, obesity, and high blood pressure in an upper middle-income country in the African region. Stress Health 2018, 34, 93–101. [Google Scholar] [CrossRef] [Green Version]
Ijerph 17 04056 g001 550
Figure 1. Means of problematic internet use in the four work-time internet use groups and four leisure-time internet use groups. WG1: work-time internet use group 1 (<5 h/w); WG2: work-time internet use group 2 (5–13.99 h/w); WG3: work-time internet use group 3 (14–27.99 h/w); WG4: work-time internet use group 4 (28–60 h/w); LG1: leisure-time internet use group 1 (<4 h/w); LG2: leisure-time internet use group 2 (4–10.99 h/w); LG3: leisure-time internet use group 3 (11–20.99 h/w); LG4: leisure-time internet use group 4 (21–84 h/w). * p < 0.05, ** p < 0.001.
Figure 1. Means of problematic internet use in the four work-time internet use groups and four leisure-time internet use groups. WG1: work-time internet use group 1 (<5 h/w); WG2: work-time internet use group 2 (5–13.99 h/w); WG3: work-time internet use group 3 (14–27.99 h/w); WG4: work-time internet use group 4 (28–60 h/w); LG1: leisure-time internet use group 1 (<4 h/w); LG2: leisure-time internet use group 2 (4–10.99 h/w); LG3: leisure-time internet use group 3 (11–20.99 h/w); LG4: leisure-time internet use group 4 (21–84 h/w). * p < 0.05, ** p < 0.001.
Ijerph 17 04056 g001
Ijerph 17 04056 g002 550
Figure 2. The scatterplot of perceived quality of life versus work-time internet use.
Figure 2. The scatterplot of perceived quality of life versus work-time internet use.
Ijerph 17 04056 g002
Table
Table 1. Descriptive statistics of socio-demographics and main research variables.
Table 1. Descriptive statistics of socio-demographics and main research variables.
Variablesn%Mean (Range)SD
GenderFemale26659.6
Male18040.4
Age (yr)446 25.8 (17–77)11.6
BMI (kg/m2)446 22.6 (14.3–46.9)3.8
Married6715.0
Employed and student/in training43497.3
Bachelor and above (%)23252.0
Internet use time (h/w)446 31.9 (0–116.7)21.7
Work-time internet use (h/w)446 17.2 (0–60.0)14.8
WG1 (<5)10924.41.91.4
WG2 (5–13.99)9922.28.42.5
WG3 (14–27.99)12528.018.33.9
WG4 (28–60)11325.338.69.4
Leisure-time internet use (h/w)446 14.7 (0–84.0)13.9
LG1 (<4)10222.91.81.2
LG2 (4–10.99)12227.47.22.0
LG3 (11–20.99)9220.614.62.0
LG4 (21–84)13029.131.913.3
Problematic internet use446 43.0 (20–80)11.9
No problematic internet use (score < 50)31570.636.9 (20–48)7.4
Problematic internet use (score ≥ 50)13129.457.8 (50–80)6.1
Perceived quality of life446 3.59 (1–5)0.9
Very poor/Poor4710.5
Neither poor nor good14031.4
Good/Very good25958.1
Negative feelings446 2.57 (1–5)0.9
Perceived stress446 5.24 (2–10)1.72
Smoking statusSmoker7216.1
Nonsmoker37483.9
Alcohol assumption statusRegular drinker17940.1
Nonregular drinker26759.9
WG1: work-time internet use group 1; WG2: work-time internet use group 2; WG3: work-time internet use group 3; WG4: work-time internet use group 4; LG1: leisure-time internet use group 1; LG2: leisure-time internet use group 2; LG3: leisure-time internet use group 3; LG4: leisure-time internet use group 4. BMI = body mass index. SD = Standard deviation.
Table
Table 2. Correlation analyses of the main study variables.
Table 2. Correlation analyses of the main study variables.
Variables1234
1 Work-time internet use
2 Leisure-time internet use0.18 **
3 Problematic internet use 0.060.27 **
4 Perceived quality of life0.02−0.02−0.25 **
** p < 0.001.
Table
Table 3. Regression models predicting problematic internet use.
Table 3. Regression models predicting problematic internet use.
PredictorsModel 1Model 2Model 3
B [95% CI]B [95% CI]B [95% CI]
Gender 1.54 [−0.61, 3.69] 1.81 [−0.27, 3.89] 2.30 [0.19, 4.41]
Age−0.32 [−0.46, −0.19] **−0.27 [−0.40, −0.14] **−0.25 [−0.38, −0.12] **
BMI 0.07 [−0.22, 0.36] 0.12 [−0.16, 0.41] 0.09 [−0.19, 0.37]
Marital status 2.89 [−0.97, 6.74] 2.94 [−0.79, 6.68] 2.98 [−0.76, 6.71]
Work status 3.87 [−3.13, 10.87] 4.45 [−2.31, 11.21] 4.53 [−2.19, 11.26]
Work-time Internet use
WG2 compared WG1 −2.47 [−5.54, 0.61] −2.91 [−5.89, 0.08] −2.81 [−5.78, 0.16]
WG3 compared WG1 −2.18 [−5.13, 0.78] −2.04 [−4.91, 0.84] −2.02 [−4.90, 0.85]
WG4 compared WG1 −0.38 [−3.37, 2.61] −1.04 [−3.96, 1.88] −0.72 [−3.64, 2.20]
Leisure-time Internet use
LG2 compared LG14.13 [1.10, 7.16] *3.54 [0.61, 6.48] *3.84 [0.91, 6.78] *
LG3 compared LG14.14 [0.85, 7.42] *4.00 [0.82, 7.17] *4.08 [0.92, 7.24] *
LG4 compared LG16.27 [3.15, 9.38] **5.87 [2.85, 8.88] **6.23 [3.20, 9.27] **
Negative feelings 1.27 [−0.11, 2.65]1.53 [0.14, 2.92] *
Perceived stress 1.30 [0.55, 2.06] **1.30 [0.54, 2.06] **
Smoking status (nonsmoker = 1) 0.88 [−1.94, 3.70]
Alcohol consumption status (nonregular drinker = 1) 1.28 [−0.88, 3.44]
R20.160.22 0.24
Adjusted R20.140.200.21
WG1: work-time internet use group 1 (<5 h/w); WG2: work-time internet use group 2 (5–13.99 h/w); WG3: work-time internet use group 3 (14–27.99 h/w); WG4: work-time internet use group 4 (28–60 h/w); LG1: leisure-time internet use group 1 (<4 h/w); LG2: leisure-time internet use group 2 (4–10.99 h/w); LG3: leisure-time internet use group 3 (11–20.99 h/w); LG4: leisure-time internet use group 4 (21–84 h/w). * p < 0.05, ** p < 0.001. Model 1: socio-demographic variables (gender, age, BMI, marital status, work status). Model 2: model 1 + negative feelings + perceived stress. Model 3: model 2 + smoking status + alcohol consumption status.
Table
Table 4. Regression models predicting perceived quality of life.
Table 4. Regression models predicting perceived quality of life.
PredictorsModel 1Model 2Model 3
OR [95% CI]OR [95% CI]OR [95% CI]
Work-time Internet use
WG1 (comparator) 1 1 1
WG22.51 [1.41, 4.49] *2.92 [1.59, 5.35] **2.78 [1.51, 5.12] **
WG31.85 [1.09, 3.14] *1.75 [1.01, 3.02] * 1.65 [0.95, 2.89]
WG4 1.30 [0.77, 2.21] 1.47 [0.85, 2.56] 1.46 [0.83, 2.56]
Leisure-time Internet use
LG1 (comparator) 1 1 1
LG2 0.90 [0.52, 1.58] 0.99 [0.56, 1.76] 0.94 [0.53, 1.69]
LG3 0.90 [0.49, 1.66] 0.99 [0.53, 1.86] 0.96 [0.51, 1.81]
LG4 1.04 [0.59, 1.85] 1.15 [0.64, 2.08] 1.09 [0.60, 1.99]
Negative feelings 0.60 [0.46, 0.78] **0.55 [0.42, 0.73] **
Perceived stress 0.85 [0.73, 0.98] * 0.87 [0.75, 1.01]
Smoking status (nonsmoker = 1) 1.01 [0.58, 1.77]
Alcohol consumption status (nonregular drinker = 1) 0.42 [0.27, 0.65] **
WG1: work-time internet use group 1 (<5 h/w); WG2: work-time internet use group 2 (5–13.99 h/w); WG3: work-time internet use group 3 (14–27.99 h/w); WG4: work-time internet use group 4 (28–60 h/w); LG1: leisure-time internet use group 1 (<4 h/w); LG2: leisure-time internet use group 2 (4–10.99 h/w); LG3: leisure-time internet use group 3 (11–20.99 h/w); LG4: leisure-time internet use group 4 (21–84 h/w). * p < 0.05, ** p < 0.001. Model 1: socio-demographic variables (gender, age, BMI, marital status, work status). Model 2: model 1 + negative feelings + perceived stress. Model 3: model 2 + smoking status + alcohol consumption status.

Share and Cite

MDPI and ACS Style

Gao, L.; Gan, Y.; Whittal, A.; Lippke, S. Problematic Internet Use and Perceived Quality of Life: Findings from a Cross-Sectional Study Investigating Work-Time and Leisure-Time Internet Use. Int. J. Environ. Res. Public Health 2020, 17, 4056. https://doi.org/10.3390/ijerph17114056

AMA Style

Gao L, Gan Y, Whittal A, Lippke S. Problematic Internet Use and Perceived Quality of Life: Findings from a Cross-Sectional Study Investigating Work-Time and Leisure-Time Internet Use. International Journal of Environmental Research and Public Health. 2020; 17(11):4056. https://doi.org/10.3390/ijerph17114056

Chicago/Turabian Style

Gao, Lingling, Yiqun Gan, Amanda Whittal, and Sonia Lippke. 2020. "Problematic Internet Use and Perceived Quality of Life: Findings from a Cross-Sectional Study Investigating Work-Time and Leisure-Time Internet Use" International Journal of Environmental Research and Public Health 17, no. 11: 4056. https://doi.org/10.3390/ijerph17114056

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop