https://orcid.org/0000-0002-6323-1354
Abstract
Background
Problematic use of internet (PUI) may have negative impacts on psychological distress and quality of life (QoL). This situation might be more profound in people with attention-deficit/hyperactivity disorder (ADHD) due to poorer behavioral control and regulatory capacity. However, there is little evidence regarding mediated effects in the associations between PUI, psychological distress, and QoL in people with ADHD.
Aims
To investigate mediating effects of psychological distress in the associations of problematic smartphone use (PSPU), problematic use of social media (PUSM), and problematic gaming (PG) with QoL in individuals with ADHD.
Methods and Procedures
PUI behaviors of participants with ADHD (n = 99) were assessed using the Smartphone Application-Based Addiction Scale, Bergen Social Media Addiction Scale, and Internet Gaming Disorder-Short Form. Psychological distress was assessed using the Depression, Anxiety, Stress Scale and QoL using the Kid-KINDL.
Outcomes and Results
Psychological distress mediated the associations between PUI and different domains of QoL, except for self-esteem QoL. There were also positively direct effects between PG and physical QoL, PUSM and friends’ QoL, and PSPU and physical QoL.
Conclusions and Implications
PUI may associate with poor QoL in people with ADHD via psychological distress. Programs on reducing PUI for people with ADHD are needed.
Introduction
Problematic use of the internet (PUI) involves excessive, poorly controlled use of the internet that results in distress and impaired functionality.Citation1 Due to the high percentage of people using smartphones worldwide, many individuals with PUI may experience problematic smartphone use (PSPU).Citation2 Problematic gaming (PG) and problematic use of social media use (PUSM) are two types of specific forms of PUI that may also involve smartphone use.Citation3,Citation4 However, given different natures of these activities, their relationships with psychological distress and other aspects of well-being may vary and be of interest to healthcare providers.Citation5 Therefore, in-depth investigation of the relevant associations as well as potential mediating factors is needed to improve understanding, especially in populations vulnerable to PUI, such as those with psychiatric disorders.Citation6
In previous studies, people with psychiatric disorders, such as eating disorders, attention-deficit/hyperactivity disorder (ADHD), and addictive disorders, have demonstrated higher frequencies of PUI.Citation7,Citation8 Although underlying mechanisms are incompletely understood, the co-occurrence may relate to poor self-control or disease-related symptoms, such as impulsivity and difficulties in emotional regulation, in patients with psychiatric disorders.Citation9 Individuals with psychiatric disorders may be more vulnerable to developing PUI, and PUI may worsen symptoms of existing disorders. For example, PG may associate bidirectionally with psychological and disease-related symptoms in adolescents with ADHD.Citation10 ADHD is a common psychiatric problem in children and adolescents. According to the DSM-5,Citation3 ADHD is a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity. These symptoms suggest that individuals with ADHD may have compromised inhibitory control that leads to poor self-regulation of internet use.Citation11 PUSM has been associated with ADHD and impulsivity,Citation12 and the severity of PG has been associated with symptoms of ADHD.Citation13
Considering that individuals with psychiatric disorders often experience psychological distress,Citation14 understanding whether PUI may have negative influences on psychological conditions in individuals with poorer adaptability and resilience, such as those with ADHD, is important.Citation15 Available data suggest that PUI may generate psychological distress, anger, and suicidal behaviors in college students.Citation16–18 Wong et al found that severities of PG and PUSM associated with higher level of psychological distress.Citation19 During the COVID-19 pandemic, time spent on the internet and smartphones in school-aged children increased substantially during quarantines and distance learning. Concurrently, children showed increased psychological distress associated with PUI and PUSM.Citation20,Citation21 Interestingly, impulsivity and hyperactivity/inattention mediated relationships between PG and psychiatric distress.Citation22,Citation23 Given that impulsivity, hyperactivity, and inattention are features of ADHD, we speculated that people with ADHD may experience more significant psychological distress resulting from online gaming activities and possibly other types of PUI.Citation7,Citation24
PUI may influence the quality of life (QoL), especially in vulnerable populations like individuals with ADHD. QoL reflects the well-being and happiness of people in the context of daily living.Citation25 Thus, enhancing QoL is important for human beings. PUI may negatively affect QoL in people without psychiatric disorders. For example, PUI and PG were found to be negatively correlated with QoL in adolescents and other youth.Citation26,Citation27 However, whether similar relationships exist in people with ADHD remains unknown. Moreover, significant associations were found between inattention-/disorganization-induced distress and QoL in people with ADHD.Citation28 Therefore, it is possible that psychological distress may show mediating effects between PUI and QoL in people with ADHD. By investigating and addressing existing knowledge gaps, gathered information may help develop more efficacious programs to help people with ADHD improve their QoL.
There is a lack of evidence regarding associations between PUI and QoL in the ADHD literature and the mediating role of psychological distress. Moreover, no studies have assessed how different types of PUI are associated with QoL in different domains. Therefore, the aims of this study were to investigate in children, adolescents, and young adults with ADHD, possible 1) direct effects between PUI and QoL for individuals with ADHD, by taking different types of PUI and domains of QoL into account and 2) mediating effects of psychological distress in associations between PUI and QoL.
Materials and Methods
Participants and Data Collection Procedure
Ninety-nine children, adolescents, and young adults with ADHD who were diagnosed by a psychiatrist from the Jianan Psychiatric Center in Tainan, Taiwan were recruited. The clinical diagnosis of ADHD was also confirmed with the SNAP-IV MTA (Swanson, Nolan, and Pelham, Version IV MTA)Citation29 from their legal guardians or school teachers for the assessment of current ADHD symptoms. The psychiatric comorbidities of participants were further determined by diagnostic interview (e.g., autism spectrum disorder and major psychotic disorder) conducted by two co-authors (KYL or KCC) and psychological assessment (e.g., autism spectrum disorder and intellectual disability) conducted by qualified psychologists. The inclusion criteria were (i) an outpatient from the child and adolescent psychiatric outpatient clinics in the Jianan Psychiatric Center; (ii) aged between 7 and 20 years old; and, (iii) having at minimum a primary school educational level. The exclusion criteria were (i) any of the following diagnoses: autism spectrum disorder, major psychotic disorder, intellectual disability, or epilepsy; or (ii) communication difficulties verifying by an experienced research assistant. All questionnaire assessments were conducted after obtaining the written consent/assent forms from both participants and their guardians. The assessments were conducted face-to-face to ensure the quality of answers when completing questionnaires. The study was approved by the Institutional Review Board of Jianan Psychiatric Center (20–026). Moreover, all participants and their legal guardians were informed as to the purpose of the study prior to giving their consent. For those participants under the age of 18 years, their legal guardians provided written consent; for those participants over the age of 18, both them and their legal guardians provided written consent.
Measures
Demographics
Demographic data included the following variables: age, sex, age of first contact with games, age of first contact with social media, age of first contact with smartphones, physical disease, and who were the primary caregivers.
Problematic Gaming (PG)
Participants’ PG was assessed using the Internet Gaming Disorder-Short Form (IGDS9-SF). The IGDS9-SF contains nine items corresponding to the nine inclusionary criteria for internet gaming disorder.Citation3 All IGDS9-SF items are rated on a five-point Likert scale (1 = never; 5 = very often), with higher scores reflecting more severe PG. All nine item scores are summed to assess levels of PG. The psychometric properties of the IGDS9-SF have been found to be satisfactory (e.g., construct validity supported by its one-factor structure) across different language versions,Citation30 including Chinese.Citation31,Citation32 The internal consistency of the IGDS9-SF in the present sample with ADHD was acceptable (α = 0.77).
Problematic Use of Social Media (PUSM)
Severity of PUSM was assessed using the Bergen Social Media Addiction Scale (BSMAS). The BSMAS contains nine items designed according to six components of an addiction model.Citation33,Citation34 All BSMAS items are rated on a five-point Likert scale (1 = very rarely; 5 = very often), with higher scores reflecting more severe PUSM. All six item scores are summed. The psychometric properties of the BSMAS have been found to be satisfactory (e.g., construct validity supported by its one-factor structure) across different language versions,Citation35–40 including Chinese.Citation6,Citation31,Citation32 The internal consistency of the BSMAS in the present sample with ADHD was acceptable (α = 0.89).
Problematic Smartphone Use (PSPU)
The participants’ level of PSPU was assessed using Smartphone Application-Based Addiction Scale (SABAS). The SABAS contains nine items designed according to the six components in a model of addiction.Citation33,Citation34 All SABAS items are rated on a five-point Likert scale (1 = strongly disagree; 5 = strongly agree) with higher scores reflecting more severe PSPU. All six item scores are summed. Construct validity has been supported by its one-factor structure across different language versionsCitation41–43 including Chinese.Citation6,Citation31,Citation32 The internal consistency of the SABAS in the present sample with ADHD was acceptable (α = 0.88).
Psychological Distress
Psychological distress was assessed using the Depression, Anxiety, Stress Scale (DASS-21). The DASS-21 contains 21 items designed according to three types of psychological distress, including depression, anxiety, and stress.Citation44 All DASS-21 items are rated on Likert scales (0 = did not apply to me at all; 5 = applied to me very much or most of the time), with higher scores reflecting greater psychological distress. All 21 item scores are summed up and then multiplied by 2. The psychometric properties of the DASS-21 have been found to be satisfactory (e.g., construct validity supported by its three-factor structure) across different language versions,Citation45 including Chinese.Citation46,Citation47 The internal consistency of the DASS-21 in the present sample with ADHD was acceptable (α = 0.88).
Quality of Life (QoL)
QoL was assessed using the Kid-KINDL. The Kid-KINDL contains 24 items designed according to six domains of QoL, including physical, emotional, self-esteem, family, friends, and school QoL.Citation48,Citation49 All Kid-KINDL items are rated on a five-point Likert scale (1 = never; 5 = always). The six domains of QoL and an overall QoL are converted from the five-point Likert scale to a 0–100 scale, of which a higher score indicates higher levels of QoL. During the conversion, scores of negatively worded items were recoded conversely to make higher score reflect better QoL. The psychometric properties of the Kid-KINDL have been found to be satisfactory (e.g., construct validity supported by its six-factor structure) across different language versionsCitation50–52 including Chinese.Citation53–56 The internal consistency of the Kid-KINDL in the present sample with ADHD was acceptable (α=0.84).
Statistical Analysis
Participants’ characteristics were first analyzed using descriptive statistics, including mean and frequency. Then, Hayes’ Process macro was used to construct the mediation models. A total of 21 mediation models were constructed to examine and explore mediating roles of psychological distress in associations between each PUI (PG, PUSM, and PSPU were each treated as an independent variable in each model) and different aspects of QoL (overall QoL in the main model and physical, emotional, self-esteem, family, friends, and school QoL in exploratory models were each treated as a dependent variable in each model). All mediation models controlled for age, sex, and physical disease. A bootstrapping method was used to examine the significance level of psychological distress in associations between PUI and QoL. In each mediation model, 5000 bootstrapping resamples were used. When the 95% lower limit confidence interval (LLCI) and upper limit confidence interval (ULCI) did not cross 0, a mediating effect of psychological distress was supported.Citation57
Moreover, given that QoL contains multidimensions and the present study did not have a large sample size (N = 99), the present study treated overall QoL as the primary model and different domains of QoL as exploratory models. Moreover, power analyses with 1000 Monte Carlo simulations were computed for the interaction effects in the analyzed mediation models. The power analyses were conducted using the following online calculator: https://schoemanna.shinyapps.io/mc_power_med/
Results
Most of the 99 participants were boys (n = 84; 84.8%) and had no physical disease (n = 93; 93.9%). The mean age of the participants was 10.82 (SD = 3.08) years old. On average, the participants first played video games at 7.14 (SD = 3.18) years old, first contacted social media at 4.83 (SD = 5.34) years old, and first used smartphones at 6.54 (SD = 2.90) years old. Almost all participants’ primary caregivers were their parents (n = 93; 93.9%). Mean measure scores of PG, PUSM, PSPU, psychological distress, and QoL of participants are reported in .
Table 1 Participants’ Characteristics (N = 99)
While direct effects between PG and QoL were only significant for physical QoL (β=0.19; p = 0.04) (), mediating effects of psychological distress in the associations between PG and QoL were supported for overall QoL (standardized coefficient [β]=−0.20; LLCI, ULCI=−0.66, −0.18), physical QoL (β=−0.17; LLCI, ULCI=−0.77, −0.20), emotional QoL (β=−0.21; LLCI, ULCI=−1.19, −0.24), family QoL (β=−0.38; LLCI, ULCI=−0.70, −0.07), friends QoL (β=−0.15; LLCI, ULCI=−0.92, −0.18), and school QoL (β=−0.11; LLCI, ULCI=−0.55, −0.11), but not self-esteem QoL (β=−0.23; LLCI, ULCI=−0.51, 0.08). presents the power for all mediation effects.
Table 2 Mediating Effects of Psychological Distress in the Associations Between Problematic Gaming (PG) and Quality of Life (QoL)
While direct effects between PUSM and QoL were only significant for friends QoL (β=0.27; p = 0.01) (), mediating effects of psychological distress in the associations between PUSM and QoL were supported for overall QoL (β=−0.20; LLCI, ULCI=−1.20, −0.22), physical QoL (β=−0.14; LLCI, ULCI=−1.21, −0.18), emotional QoL (β=−0.21; LLCI, ULCI=−1.87, −0.35), family QoL (β=−0.15; LLCI, ULCI=−1.32, −0.013), friends QoL (β=−0.19; LLCI, ULCI=−1.91, −0.36), and school QoL (β=−0.11; LLCI, ULCI=−1.00, −0.12), but not self-esteem QoL (β=−0.03; LLCI, ULCI=−0.82, 0.32). presents the power for all mediation effects.
Table 3 Mediating Effects of Psychological Distress in the Associations Between Problematic Use of Social Media (PUSM) and Quality of Life (QoL)
While direct effects between PSPU and QoL were only significant for physical QoL (β=0.31; p = 0.002) and family QoL (β=−0.22; p = 0.03) (), mediating effects of psychological distress in the associations between PSPU and QoL were supported for overall QoL (β=−0.23; LLCI, ULCI=−0.59, −0.19), physical QoL (β=−0.20; LLCI, ULCI=−0.72, −0.23), emotional QoL (β=−0.24; LLCI, ULCI=−1.04, −0.26), family QoL (β=−0.15; LLCI, ULCI=−0.58, −0.08), friends QoL (β=−0.16; LLCI, ULCI=−0.77, −0.20), and school QoL (β=−0.12; LLCI, ULCI=−0.47, −0.11), but not self-esteem QoL (β=−0.06; LLCI, ULCI=−0.43, 0.09). presents the power for all mediation effects.
Table 4 Mediating Effects of Psychological Distress in the Associations Between Problematic Smartphone Use (PSPU) and Quality of Life (QoL)
Discussion
The present study aimed to investigate direct relationships between PUI and QoL and mediating effects of psychological distress in the associations between PUI and QoL for youth with ADHD. To acquire comprehensive information, different types of PUI and various domains of QoL were investigated. Our results indicated that psychological distress has mediating effects between most types of PUI (i.e., PG, PUSM, and PSPU) and QoL (i.e., overall, physical, emotional, family, friends, and school QoL), with more severe PUI linked to poorer QoL and operating via psychological distress in all cases in which significant relationships were observed. However, there were positively direct relationships between PG and physical QoL, PUSM and friends QoL, and PSPU and physical QoL, and a negative relationship between PSPU and family QoL. Implications are discussed below.
Previous studies have suggested that PUI may generate or worsen psychological distress in both children and adults,Citation19,Citation20,Citation58 and this situation might be even more concerning for people with psychiatric disorders such as ADHD. In addition, features of psychological distress have been associated with poor QoL.Citation59 Therefore, the present study extended the current knowledge that psychological distress mediates associations between PUI and QoL in people with ADHD, a vulnerable population to PUI and psychosocial problems. The present work serves as an initial study to support the hypotheses that PUI relates to different domains of QoL, except self-esteem QoL, and these relationships are mediated through psychological distress.
Among all QoL domains, only self-esteem QoL was not associated with any mediation models linking types of PUI to QoL via psychological distress. A possible explanation involves cultural considerations. Participants were Taiwanese and raised in a society with extensive Asian values, which tend to value humble behaviors over confident expressions. Indeed, Chinese parents are arguably more psychological controlling than some Western (e.g., US) parents, which may result in lower self-esteem among Chinese youth.Citation60 Therefore, self-esteem QoL in the present study might be consistently lower or be underestimated, and thus fail to reflect relationships with PUI and psychological distress. Obtaining cross-cultural data is warranted to investigate this speculation and other possibilities.
Unexpectedly, there were positive direct associations between specific types of PUI and several QoL domains. First, both PG and PSPU were positively correlated with physical QoL with small to moderate effects. Because many people currently seek health information online, it is possible that some physical QoL concerns may be eased by self-exploration of possible causes and management approaches through internet searches. However, more details regarding the purposes and perceptions of using smartphones during daily life are warranted to confirm this speculation. Regarding the positive correlation between physical QoL and PG, the types of gaming activities may warrant consideration. For example, physically interactive or fitness video games, such as Wii or personal trainer apps, may promote physical activity and health,Citation61 which may potentially elevate physical QoL. Although this finding does not support the original hypothesis, a similar finding has been reported previously in that PUSM and PG were positively associated with physical activity in Taiwanese students.Citation62
In addition to the positive correlations between PSPU/PG and physical QoL, a positive correlation between PUSM and friends QoL was found, although with small effect. Social media is a way for people to make social connections, especially when face-to-face interactions are not feasible. Although PUSM has been associated with negative outcomes (e.g., psychological distress, disorder symptomatology, and loneliness),Citation63,Citation64 the use of social media may at certain levels support people with ADHD to reduce potential impairments in social QoL,Citation65,Citation66 and such use may extend into adulthood into forums like online communities that are perceived as beneficial.Citation67,Citation68 As reported previously, children with ADHD may have trouble appropriately processing social cues or learning social skills through observation due to the symptoms of impulsivity and inattention.Citation69 As a result, the situation of peer-rejection may significantly harm friends QoL in people with ADHD.Citation70 Thus, some people with ADHD may find it easier or less stressful to interact with others through social media instead of in-person activities.Citation71 However, this speculation may apply most to those without psychological distress related to the use of social media. This relationship may also differ based on the duration of exposure to PUSM.Citation72 Most importantly, how to determine the appropriate amount of time, purposes, and behaviors when using social media should be taken into consideration in future studies to enhance friends QoL as well as social performance in children with ADHD through more precise coaching programs.
There was a negative association between PSPU and family QoL. It is possible that prolonged smartphone use time and addictive behaviors may negatively impact the quantity and quality of family time,Citation73 thus resulting in decreased family QoL as found in the present study. Meanwhile, parents/caregivers may experience difficulties in managing smartphone use in their children, especially in youth with more severe ADHD or oppositional behaviors.Citation74,Citation75 This may generate conflicts in the family and further decrease family QoL from the children’s perspective. However, cause-and-effect relationships could not be identified in the present study. As the previous study indicated, poorer family environment, such as family unpredictability and parent–child relationships,Citation76 may also generate PSPU in children and adolescents. Therefore, investigating longitudinalrelationships between PSPU and family QoL in children with ADHD is important.
There are several limitations of this study. First, PUI and QoL were assessed through self-reported questionnaires, which are vulnerable to biases despite the psychometric properties of these questionnaires having been established. Second, the present study did not investigate how the severities and types of symptoms in ADHD may influence relationships between PUI and QoL. Third, this study did not assess parental/legal guardians’ perceptions regarding children and adolescents’ PUI and QoL, which could also provide reliable and valuable information for the purposes of assessment and the development of coping strategies.Citation77–79 Thus, engaging parents and legal guardians may be considered in future studies to generate a more comprehensive understanding. Fourth, this study did not include subjects with typical development as the comparison group, and thus it is inconclusive whether the current results are exclusive to individuals with ADHD. Lastly, causal relationships between psychological distress, PUI, and QoL cannot be inferred in this cross-sectional study. For example, we cannot exclude the possibility that PUI is a consequence of psychological distress or poor QoL.
Conclusion
In conclusion, poorer QoL is associated with different types of PUI via psychological distress in children with ADHD. Associations between PUI and QoL in this population indicate a need to acquire more detailed information, especially longitudinal data, regarding PUI behaviors in this population.
Abbreviations
ADHD, attention-deficit/hyperactivity disorder; BSMAS, Bergen Social Media Addiction Scale; DASS-21, Depression, Anxiety, Stress Scale; IGDS9-SF, Internet Gaming Disorder-Short Form; LLCI, lower limit confidence interval; PG, problematic gaming; PSPU, problematic smartphone use; PUI, problematic use of internet; PUSM, problematic use of social media; QoL, quality of life; SABAS, Smartphone Application-Based Addiction Scale; ULCI, upper limit confidence interval.
Data Sharing Statement
Data will be made available on reasonable request. Datasets that support the findings of this study are not readily available. It will be made available by the corresponding author upon reasonable request for academic use.
Ethics Approval and Informed Consent
The study was approved by the Institutional Review Board of Jianan Psychiatric Center (20-026). All questionnaire assessments were conducted after obtaining the written consent/assent forms from both participants and their guardians. Specifically, all participants and their legal guardians were informed as to the purpose of the study prior to giving their consent. For those participants under the age of 18 years, their legal guardians provided written consent; for those participants over the age of 18, both they and their legal guardians provided written consent. Also, the study was conducted in accordance with the guidelines outlined in the Declaration of Helsinki including obtaining written informed consent/assent forms mentioned above, protection of privacy and confidentiality of personal information.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Disclosure
Dr Marc Potenza has a patent application “Glutamate and Impulse Control” with Yale and Novartis; advisory board of Opiant, outside the submitted work. The authors declare no other competing interests in this work.
Acknowledgments
We thank all participants, and their parents, who were willing to participate in this study.
Additional information
Funding
References
- Shapira NA, Lessig MC, Goldsmith TD, et al. Problematic internet use: proposed classification and diagnostic criteria. Depression Anxiety. 2003;17(4):207–216. doi:10.1002/da.10094
- Taufik RJ, Tiatri S, Allida VB. Problematic smartphone use and problematic internet use: the two faces of technological addiction. Presented At: Advances in Health Sciences Research; 2021.
- American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5®). American Psychiatric Association Publishing; 2013.
- Seo J, Lee CS, Lee YJ, Bhang SY, Lee D. The type of daily life stressors associated with social media use in adolescents with problematic internet/smartphone use. Psychiatry Invest. 2021;18(3):241–248. doi:10.30773/pi.2020.0060
- Király O, Griffiths MD, Urbán R, et al. Problematic internet use and problematic online gaming are not the same: findings from a large nationally representative adolescent sample. Cyberpsychol Behav Soc Netw. 2014;17(12):749–754. doi:10.1089/cyber.2014.0475
- Leung H, Pakpour AH, Strong C, et al. Measurement invariance across young adults from Hong Kong and Taiwan among three internet-related addiction scales: Bergen Social Media Addiction Scale (BSMAS), Smartphone Application-Based Addiction Scale (SABAS), and Internet Gaming Disorder Scale-Short Form (IGDS-SF9) (Study Part A). Addict Behav 2020;101:105969. doi:10.1016/j.addbeh.2019.04.027
- Restrepo A, Scheininger T, Clucas J, et al. Problematic internet use in children and adolescents: associations with psychiatric disorders and impairment. BMC Psychiatry. 2020;20(1):252. doi:10.1186/s12888-020-02640-x
- Tayhan Kartal F, Yabancı Ayhan N. Relationship between eating disorders and internet and smartphone addiction in college students. Eat Weight Disord. 2021;26(6):1853–1862. doi:10.1007/s40519-020-01027-x
- Ko CH, Yen JY, Yen CF, Chen CS, Chen CC. The association between internet addiction and psychiatric disorder: a review of the literature. Eur Psychiatry. 2012;27(1):1–8. doi:10.1016/j.eurpsy.2010.04.011
- Vadlin S, Åslund C, Hellström C, Nilsson KW. Associations between problematic gaming and psychiatric symptoms among adolescents in two samples. Addict Behav. 2016;61:8–15. doi:10.1016/j.addbeh.2016.05.001
- Yoo HJ, Cho SC, Ha J, et al. Attention deficit hyperactivity symptoms and internet addiction. Psych Clin Neurosci. 2004;58(5):487–494. doi:10.1111/j.1440-1819.2004.01290.x
- Grant JE, Lust K, Chamberlain SR. Problematic smartphone use associated with greater alcohol consumption, mental health issues, poorer academic performance, and impulsivity. J Behav Addict. 2019;8(2):335–342. doi:10.1556/2006.8.2019.32
- Concerto C, Rodolico A, Avanzato C, et al. Autistic traits and attention-deficit hyperactivity disorder symptoms predict the severity of internet gaming disorder in an Italian adult population. Brain Sci. 2021;11(6):774. doi:10.3390/brainsci11060774
- Chang KC, Chang YH, Yen CF, et al. A longitudinal study of the effects of problematic smartphone use on social functioning among people with schizophrenia: mediating roles for sleep quality and self-stigma. J Behav Addict. 2022;11(2):567–576. doi:10.1556/2006.2022.00012
- Lee KY, Chen CY, Chen JK, et al. Exploring mediational roles for self-stigma in associations between types of problematic use of internet and psychological distress in youth with ADHD. Res Dev Disabil. 2023;133:104410. doi:10.1016/j.ridd.2022.104410
- Herruzo C, Sánchez-Guarnido AJ, Pino MJ, Lucena V, Raya AF, Herruzo FJ. Suicidal behavior and problematic internet use in college students. Psicothema. 2023;35(1):77–86. doi:10.7334/psicothema2022.153
- Tung SEH, Gan WY, Chen JS, et al. Internet-related instruments (Bergen social media addiction scale, smartphone application-based addiction scale, internet gaming disorder scale-short form, and nomophobia questionnaire) and their associations with distress among Malaysian University Students. Healthcare. 2022;10(8). doi:10.3390/healthcare10081448
- Vally Z. Anger and worry are related to problematic smartphone use: a cross-sectional examination of novel psychopathological constructs in a college-aged sample in the United Arab Emirates. Heliyon. 2022;8(10):e10917. doi:10.1016/j.heliyon.2022.e10917
- Wong HY, Mo HY, Potenza MN, et al. 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(6):1879. doi:10.3390/ijerph17061879
- Chen CY, Chen IH, Hou WL, et al. The relationship between children’s problematic internet-related behaviors and psychological distress during the onset of the COVID-19 pandemic: a longitudinal study. J Addict Med. 2022;16(2):e73–e80. doi:10.1097/adm.0000000000000845
- Fung XCC, Siu AMH, Potenza MN, et al. Problematic use of internet-related activities and perceived weight stigma in school children: a longitudinal study across different epidemic periods of COVID-19 in China. Front Psychiatry. 2021;12:675839. doi:10.3389/fpsyt.2021.675839
- Su W, Király O, Demetrovics Z, Potenza MN. Gender moderates the partial mediation of impulsivity in the relationship between psychiatric distress and problematic online gaming: online survey. JMIR Ment Health. 2019;6(3):e10784. doi:10.2196/10784
- Wartberg L, Kriston L, Zieglmeier M, Lincoln T, Kammerl R. A longitudinal study on psychosocial causes and consequences of Internet gaming disorder in adolescence. Psychol Med. 2019;49(2):287–294. doi:10.1017/s003329171800082x
- De Rossi P, D’Aiello B, Pretelli I, Menghini D, Di Vara S, Vicari S. Age-related clinical characteristics of children and adolescents with ADHD. Front Psychiatry. 2023;14:1069934. doi:10.3389/fpsyt.2023.1069934
- Solly JE, Grant JE, Chamberlain SR. Pharmacological interventions for Problematic Usage of the Internet (PUI): a narrative review of current progress and future directions. Curr Opin Behav Sci. 2022;46:101158. doi:10.1016/j.cobeha.2022.101158
- Machimbarrena JM, Beranuy M, Vergara-Moragues E, Fernández-González L, Calvete E, González-Cabrera J. Problematic Internet use and Internet gaming disorder: overlap and relationship with health-related quality of life in adolescents. [Uso problemático de Internet y trastorno de juego por Internet: solapamiento y relación con la calidad de vida relacionada con la salud en adolescentes]. uso problemático de Internet; trastorno de juego por Internet; calidad de vida relacionada con la salud; adolescentes; consecuencias negativas. Adicciones. 2022;35(2):12. Spanish. doi:10.20882/adicciones.1494
- Chamberlain SR, Ioannidis K, Grant JE. The impact of comorbid impulsive/compulsive disorders in problematic internet use. J Behav Addict. 2018;7(2):269–275. doi:10.1556/2006.7.2018.30
- Nicastro R, Desseilles M, Prada P, Weibel S, Perroud N, Gex-Fabry M. Subjective distress associated with adult ADHD: evaluation of a new self-report. ADHD Attention Deficit Hyperactivity Disord. 2018;10(1):77–86. doi:10.1007/s12402-017-0234-9
- Correia Filho AG, Bodanese R, Silva TL, Alvares JP, Aman M, Rohde LA. Comparison of risperidone and methylphenidate for reducing ADHD symptoms in children and adolescents with moderate mental retardation. J Am Acad Child Adolesc Psychiatry. 2005;44(8):748–755. doi:10.1097/01.chi.0000166986.30592.67
- Poon LYJ, Tsang HWH, Chan TYJ, et al. Psychometric properties of the Internet Gaming Disorder Scale-Short-Form (IGDS9-SF): systematic review. J Med Internet Res. 2021;23(10):e26821. doi:10.2196/26821
- Chen IH, Ahorsu DK, Pakpour AH, Griffiths MD, Lin CY, Chen CY. Psychometric properties of three simplified Chinese online-related addictive behavior instruments among Mainland Chinese primary school students. Front Psychiatry. 2020;11:875. doi:10.3389/fpsyt.2020.00875
- Yam CW, Pakpour AH, Griffiths MD, et al. Psychometric testing of three Chinese online-related addictive behavior instruments among Hong Kong University students. Psychiatr Q. 2019;90(1):117–128. doi:10.1007/s11126-018-9610-7
- Griffiths MD. Internet addiction – time to be taken seriously? Addict Res. 2000;8(5):413–418. doi:10.3109/16066350009005587
- Griffiths MD. A ‘components’ model of addiction within a biopsychosocial framework. . J Subst Use. 2005;10(4):191–197. doi:10.1080/14659890500114359
- Bányai F, Zsila Á, Király O, et al. Problematic social media use: results from a large-scale nationally representative adolescent sample. PLoS One. 2017;12(1):e0169839. doi:10.1371/journal.pone.0169839
- Dadiotis A, Bacopoulou F, Kokka I, et al. Validation of the Greek version of the Bergen social media addiction scale in undergraduate students. EMBnet J. 2021;26(1):e975. doi:10.14806/ej.26.1.975
- Lin CY, Broström A, Nilsen P, Griffiths MD, Pakpour AH. Psychometric validation of the Persian Bergen social media addiction scale using classic test theory and Rasch models. J Behav Addict. 2017;6(4):620–629. doi:10.1556/2006.6.2017.071
- Monacis L, de Palo V, Griffiths MD, Sinatra M. Social networking addiction, attachment style, and validation of the Italian version of the Bergen social media addiction scale. J Behav Addict. 2017;6(2):178–186. doi:10.1556/2006.6.2017.023
- Pontes HM, Andreassen CS, Griffiths MD. Portuguese validation of the Bergen Facebook addiction scale: an empirical study. Int J Ment Health Addict. 2016;14(6):1062–1073. doi:10.1007/s11469-016-9694-y
- Schou Andreassen C, Billieux J, Griffiths MD, et al. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: a large-scale cross-sectional study. Psychol Addict Behav. 2016;30(2):252–262. doi:10.1037/adb0000160
- Csibi S, Demetrovics Z, Szabó A. [Development and psychometric validation of the Brief Smartphone Addiction Scale (BSAS) with schoolchidren] A Rövid Okostelefon Addikció Kérdőív (ROTAK) kidolgozása és pszichometriai validálása iskoláskorú gyermekekkel. Psychiatr Hung. 2016;31(1):71–77. Hungarian.
- Lin CY, Imani V, Broström A, et al. Smartphone application-based addiction among Iranian adolescents: a psychometric study. Int J Ment Health Addict. 2018;17(4):765–780. doi:10.1007/s11469-018-0026-2
- Soraci P, Ferrari A, Antonino U, Griffiths MD. Psychometric properties of the Italian version of the Smartphone Application-Based Addiction Scale (SABAS). Int J Ment Health Addict. 2021;19(4):1261–2373. doi:10.1007/s11469-020-00222-2
- Lovibond SH, Lovibond PF. Manual for the Depression Anxiety & Stress Scales. 2 ed. Psychology Foundation.; 1995.
- Lee JL, Lee EH, Moon SH. Systematic review of the measurement properties of the depression anxiety stress scales-21 by applying updated COSMIN methodology. Qual Life Res. 2019;28(9):2325–2339. doi:10.1007/s11136-019-02177-x
- Jiang LC, Yan YJ, Jin ZS, et al. The depression anxiety stress scale-21 in Chinese hospital workers: reliability, latent structure, and measurement invariance across genders. Front Psychol. 2020;11:247. doi:10.3389/fpsyg.2020.00247
- Wang K, Shi HS, Geng FL, et al. Cross-cultural validation of the depression anxiety stress scale-21 in China. Psychol Assess. 2016;28(5):e88–e100. doi:10.1037/pas0000207
- Bullinger M, Brütt AL, Erhart M, Ravens-Sieberer U. Psychometric properties of the KINDL-R questionnaire: results of the BELLA study. Eur Child Adolesc Psychiatry. 2008;Suppl 17(S1):125–132. doi:10.1007/s00787-008-1014-z
- Ravens-Sieberer U, Bullinger M. Assessing health-related quality of life in chronically ill children with the German KINDL: first psychometric and content analytical results. Qual Life Res. 1998;7(5):399–407. doi:10.1023/a:1008853819715
- Erhart M, Ellert U, Kurth BM, Ravens-Sieberer U. Measuring adolescents’ HRQoL via self reports and parent proxy reports: an evaluation of the psychometric properties of both versions of the KINDL-R instrument. Health Qual Life Outcomes. 2009;7:77. doi:10.1186/1477-7525-7-77
- Essaddam L, Ben Mansour A, Ben Amor A, Ravens-Sieberer U, Klein TM, Ben Becher S. Validation of the Arabic and Tunisian Arabic version of the KINDL questionnaires for children with diabetes type 1. Libyan J Med. 2019;14(1):1537457. doi:10.1080/19932820.2018.1537457
- Hanć T, Ravens-Sieberer U. The adaptation process and preliminary psychometric evaluation of the Polish version of Kiddo-KINDL questionnaire. Anthropol Rev. 2019;82(3):287–295. doi:10.2478/anre-2019-0021
- Lee CT, Lin CY, Tsai MC, Strong C, Lin YC. Psychometric evaluation and wording effects on the Chinese version of the parent-proxy Kid-KINDL. Health Qual Life Outcomes. 2016;14(1):123. doi:10.1186/s12955-016-0526-3
- Lin CY, Luh WM, Cheng CP, Yang AL, Ma HI. Evaluating the wording effect and psychometric properties of the Kid-KINDL: using the multitrait-multimethod approach. Eur J Psychol Assess. 2014;30(2):100–109. doi:10.1027/1015-5759/a000175
- Lin CY, Strong C, Tsai MC, Lee CT. Raters interpret positively and negatively worded items similarly in a quality of life instrument for children. Inquiry. 2017;54:46958017696724. doi:10.1177/0046958017696724
- Pakpour AH, Chen CY, Lin CY, Strong C, Tsai MC, Lin YC. The relationship between children’s overweight and quality of life: a comparison of Sizing Me Up, PedsQL and Kid-KINDL. Int J Clin Health Psychol. 2019;19(1):49–56. doi:10.1016/j.ijchp.2018.06.002
- Lin CY, Tsai MC. Effects of family context on adolescents’ psychological problems: moderated by pubertal timing, and mediated by self-esteem and interpersonal relationships. Appl Res Qual Life. 2016;11:907–923. doi:10.1007/s11482-015-9410-2
- Chen IH, Pakpour AH, Leung H, et al. Comparing generalized and specific problematic smartphone/internet use: longitudinal relationships between smartphone application-based addiction and social media addiction and psychological distress. J Behav Addict. 2020;9(2):410–419. doi:10.1556/2006.2020.00023
- Agarwal R, Goldenberg M, Perry R, IsHak WW. The quality of life of adults with attention deficit hyperactivity disorder: a systematic review. Innov Clin Neurosci. 2012;9(5–6):10–21.
- Chen H-Y, Ng J, Pomerantz EM. Why is self-esteem higher among American than Chinese early adolescents? The role of psychologically controlling parenting. J Youth Adolesc. 2021;50(9):1856–1869. doi:10.1007/s10964-021-01474-4
- He F, Qi Y, Zhou Y, et al. Meta-analysis of the efficacy of digital therapies in children with attention-deficit hyperactivity disorder. Front Psychiatry. 2023;14:1054831. doi:10.3389/fpsyt.2023.1054831
- Huang PC, Chen JS, Potenza MN, et al. Temporal associations between physical activity and three types of problematic use of the internet: a six-month longitudinal study. J Behav Addict. 2022;11(4):1055–1067. doi:10.1556/2006.2022.00084
- Dibb B, Foster M. Loneliness and Facebook use: the role of social comparison and rumination. Heliyon. 2021;7(1):e05999. doi:10.1016/j.heliyon.2021.e05999
- Shuai L, He S, Zheng H, et al. Influences of digital media use on children and adolescents with ADHD during COVID-19 pandemic. Global Health. 2021;17(1):48. doi:10.1186/s12992-021-00699-z
- Lee YC, Yang HJ, Chen VC, et al. Meta-analysis of quality of life in children and adolescents with ADHD: by both parent proxy-report and child self-report using PedsQL™. Res Dev Disabil. 2016;51–52:160–172. doi:10.1016/j.ridd.2015.11.009
- Ros R, Graziano PA. Social functioning in children with or at risk for attention deficit/hyperactivity disorder: a meta-analytic review. J Clin Child Adolesc Psychol. 2018;47(2):213–235. doi:10.1080/15374416.2016.1266644
- Ginapp CM, Macdonald-Gagnon G, Angarita GA, Bold KW, Potenza MN. The lived experiences of adults with attention-deficit/hyperactivity disorder: a rapid review of qualitative evidence. Front Psychiatry. 2022;13:949321. doi:10.3389/fpsyt.2022.949321
- Ginapp CM, Greenberg NR, Macdonald-Gagnon G, Angarita GA, Bold KW, Potenza MN. The experiences of adults with ADHD in interpersonal relationships and online communities: a qualitative study. SSM Qual Res Health. 2023;3:100223. doi:10.1016/j.ssmqr.2023.100223
- Hoza B. Peer functioning in children with ADHD. J Pediatric Psychol. 2007;32(6):655–663. doi:10.1093/jpepsy/jsm024
- Mikami AY. The importance of friendship for youth with attention-deficit/hyperactivity disorder. Clin Child Fam Psychol Rev. 2010;13(2):181–198. doi:10.1007/s10567-010-0067-y
- Dawson AE, Wymbs BT, Evans SW, DuPaul GJ. Exploring how adolescents with ADHD use and interact with technology. J Adolesc. 2019;71(1):119–137. doi:10.1016/j.adolescence.2019.01.004
- Thorell LB, Burén J, Ström Wiman J, Sandberg D, Nutley SB. Longitudinal associations between digital media use and ADHD symptoms in children and adolescents: a systematic literature review. Eur Child Adolesc Psychiatry. 2022. doi:10.1007/s00787-022-02130-3
- Cheng YC, Yang TA, Lee JC. The relationship between smartphone addiction, parent–child relationship, loneliness and self-efficacy among senior high school students in Taiwan. Sustainability. 2021;13(16):9475. doi:10.3390/su13169475
- Chou W-J, Hsiao RC, Yen C-F. Parental efficacy in managing smartphone use of adolescents with attention-deficit/hyperactivity disorder: parental and adolescent related factors. Int J Environ Res Public Health. 2022;19(15):9505. doi:10.3390/ijerph19159505
- Lee JL, Hsiao RC, Tsai CS, Yen CF. Caregivers’ difficulty in managing smartphone use of children with attention-deficit/hyperactivity disorder during the COVID-19 pandemic: relationships with caregiver and children factors. Int J Environ Res Public Health. 2022;19(9):1.
- Lai X, Huang S, Nie C, et al. Trajectory of problematic smartphone use among adolescents aged 10–18 years: the roles of childhood family environment and concurrent parent-child relationships. J Behav Addict. 2022;11(2):577–587. doi:10.1556/2006.2022.00047
- Cheng C-P, Luh W-M, Yang A-L, C-t S, Lin C-Y. Agreement of children and parents scores on Chinese version of pediatric quality of life inventory version 4.0: further psychometric development. Appl Res Qual Life. 2016;11(3):891–906. doi:10.1007/s11482-015-9405-z
- Lin Y-C, Strong C, Tsai M-C, Lin C-Y, Fung XCC. Validating sizing them up: a parent-proxy weight-related quality-of-life measure, with community-based children. Int J Clin Health Psychol. 2018;18(1):81–89. doi:10.1016/j.ijchp.2017.10.001
- Paschke K, Austermann MI, Thomasius R. Assessing ICD-11 gaming disorder in adolescent gamers by parental ratings: development and validation of the Gaming Disorder Scale for Parents (GADIS-P). J Behav Addict. 2021;10(1):159–168. doi:10.1556/2006.2020.00105