Abstract
Background
While problematic smartphone use screening tools are widely used and increasingly evaluated in student populations, their applicability and clinical stability across diverse groups—particularly those with comorbid substance use—remain unclear. Moreover, the overlap between problematic smartphone use and internet gaming disorder has not been well established in clinical contexts. This study explores the behavioral and psychological characteristics associated with problematic smartphone use, focusing on its relationship with online gaming behaviors among ketamine users referred for court-ordered addiction treatment.
Methods
The study involved 233 participants diagnosed with ketamine use disorder. Participants reported their daily smartphone use and primary usage purposes. Ketamine dependence was assessed using the Chinese version of the Severity of Dependence Scale. Emotional distress was evaluated using the Brief Symptom Rating Scale and Generalized Anxiety Disorder 7-Item Scale. Attention-deficit/hyperactivity disorder (ADHD) symptoms were assessed using the Adult ADHD Self-Report Scale (ASRS-V1.1). Problematic smartphone use risk was evaluated using the Short Form of the Problematic smartphone use Inventory (SPAI-SF). Logistic regression was used to analyze factors related to problematic smartphone use risk.
Results
The problematic smartphone use risk group reported significantly longer daily smartphone usage (odds ratio [OR]: 1.64; 95% confidence interval [CI]: 1.29–2.08), higher ASRS-V1.1 scores (OR: 1.14; 95% CI: 1.05–1.23), and a greater likelihood of using smartphones for online gaming (OR: 2.26; 95% CI: 1.19–4.29).
Conclusions
Excessive smartphone use in ketamine users is closely linked to online gaming, and ADHD symptoms may increase the risk of problematic smartphone use in this population.
Keywords: Problematic smartphone use, Internet gaming, Attention-deficit/hyperactivity disorder, Ketamine, Behavioral addiction
Background
The Diagnostic and Statistical Manual of Mental Disorders focuses on addictive disorders related to substances, such as alcohol, heroin, and cocaine [1]. Increasing evidence highlights similarities between the neurobiological causes, psychological mechanisms, and clinical presentations of substance and non-substance addictions [2, 3]. Individuals may concurrently exhibit substance and non-substance addiction issues, which can influence and impact assessment and treatment outcomes [4]. Non-substance addictions that are currently under discussion include gambling [5], compulsive sexual behaviors [6], internet gaming [7], and problematic smartphone use, which affects approximately 20% of young adults [8]. These daily addictive processes may reflect a pursuit of subjective comfort, escapism from high stress and low self-esteem [7], or maladaptive coping strategies influenced by other mental health issues, such as depression and anxiety [9].
Debates have emerged regarding the establishment of a diagnosis for internet gaming disorder. Diagnosing internet gaming addiction requires rigorous and standardized epidemiological research, as gaming often carries a negative stigma. The time and financial costs associated with gaming can be easily scrutinized, increasing the possibility of false positives and over-diagnosis [9]. Neurophysiological studies have found that individuals with internet gaming addiction exhibit impairments in areas such as the bilateral middle and inferior temporal gyri and prefrontal cortex compared with regular internet gamers. These deficits affect auditory and visual processing, emotional regulation, and cognition [10, 11]. Neural feedback issues are common among individuals with substance and gambling addictions [12]. Given the similarities in physiological impairments and psychological distress between problematic gaming behaviors and other addictive disorders, establishing a diagnosis for internet gaming addiction is necessary [13].
While excessive smartphone use exhibits features similar to other behavioral addictions—most notably Internet gaming disorder (IGD), which has been more clearly defined in recent diagnostic frameworks—the extent to which problematic smartphone use overlaps with or diverges from IGD is still under discussion. To date, there is no universally accepted definition of problematic smartphone use [14]. Studies on excessive smartphone use have found similarities with substance and gambling addiction, such as tolerance, dependence, withdrawal, loss of control, and functional impairment [15, 16]. Studies on general adult populations have linked excessive smartphone use to symptoms of depression [17, 18], anxiety [19, 20], and attention-deficit/hyperactivity disorder (ADHD) [18]. Several self-report scales have been developed to screen for problematic smartphone use, including the Mobile Phone Problem Use Scale (MPPUS), the 20-item Nomophobia Questionnaire (NMP-Q), and the Problematic Smartphone Use Inventory (PSUI). These tools aim to capture various dimensions of excessive smartphone engagement, such as emotional dependence, withdrawal symptoms, and functional impairment [21–23]. Although some studies have applied these tools to clinical populations—such as individuals with schizophrenia—their overall psychometric validity across different psychiatric diagnoses remains inconclusive. Therefore, the clinical reliability and generalizability of findings derived from these smartphone-use assessment scales are still subject to further investigation [14, 24]. Understanding the purpose of smartphone use is crucial when investigating excessive smartphone use because users are likely not addicted to the smartphone itself but to specific activities performed on the device [16]. Some studies on high school and university students have highlighted a significant association between online gaming via smartphones and excessive smartphone use [17, 25].
Clinical cases of substance addiction may simultaneously exhibit multiple addictive behaviors [26]. If an individual’s addictive behaviors are not properly identified, treatment may easily fail [27]. Ketamine is the most commonly used illicit substance in Taiwan [28]. Users of ketamine who enter the criminal justice system are often granted deferred prosecution by the prosecutor, who then refers them to psychiatric outpatient clinics for addiction treatment. In recent years, we have observed a large number of individuals from this population appearing in deferred prosecution outpatient clinics. While waiting for their appointments, they are almost always using smartphones. Although this is a common social phenomenon, we suspect that some of them may have already reached the level of problematic smartphone use. If we can effectively identify the addictive behavior patterns among these substance users, we may be able to develop more effective intervention programs. This study assessed a population of ketamine users referred by prosecutors for addiction treatment to examine the correlation of problematic smartphone use among ketamine users with addictive behaviors, emotional symptoms, ADHD symptoms, and the purposes of smartphone use.
Methods
Participants and procedures
This retrospective cross-sectional study was conducted at a psychiatric hospital in Taiwan. The inclusion criteria for our study consisted of ketamine users who were granted deferred prosecution by a prosecutor and referred to outpatient addiction treatment. These individuals were approached by research psychologists in a randomized manner to assess their willingness to participate in the study. The exclusion criteria included the following: underage substance users; individuals who had used substances such as methamphetamine or heroin—which are associated with more severe legal penalties—but were nonetheless referred to outpatient addiction treatment by prosecutors or the court; and individuals with intellectual disabilities, as they were unlikely to be able to independently complete all self-report questionnaires.
In this study, an addiction treatment specialist and clinical psychologist assessed the substance use profiles of court-referred individuals. Participation did not provide additional rewards or compensation, nor did it influence the content or duration of their deferred prosecution. Ketamine use disorder was diagnosed using the DSM-5 [1]. The participants were assessed using self-reported questionnaires and clinician-rated scales. The data collection period ranged from March 2019 to December 2019, and 233 individuals diagnosed with ketamine use disorder were included.
Measures
Sociodemographic data included gender, age, marital status, university education, and types of substances previously used. Participants were asked to retrospectively report their average daily smartphone usage, which was categorized into three intervals: 0–5, 5–9, and over 9 h per day. This classification was based on prior findings, where researchers analyzing smartphone app usage data identified 4.62 h as the threshold for excessive smartphone use [29]. They specified the purposes for their most frequent smartphone use, including watching videos, playing online mobile games, online shopping, searching for information, listening to music, messaging, browsing social media, and using dating apps.
The Chinese version of the Severity of Dependence Scale (SDS) was used to assess the level of substance dependence. The reliability and validity of the Chinese version of the SDS for ketamine users have been previously verified [30]. The scale consists of five questions. Each question is scored from 0 to 3 points. A total score of ≥ 4 indicates significant dependence on ketamine, thereby potentially requiring dose reduction or addiction treatment [31].
The Brief Symptom Rating Scale (BSRS-5) is a screening tool used to assess emotional distress. It includes five symptoms-related questions. Each item is scored from 0 to 4, with a total score of 6–9 indicating mild distress, 10–14 indicating moderate distress, and ≥ 15 indicating severe distress. If the score for suicidal ideation is ≥ 1, the severity is automatically categorized as severe [32]. The reliability and validity of the Chinese version of the BSRS-5 have been confirmed through previous research [33].
The Generalized Anxiety Disorder 7-Item Scale (GAD-7) is a self-reported measure used to assess the severity of anxiety symptoms over the past 2 weeks. The scale contains the following seven items. Each item is scored on a 4-point scale: 0 (not at all), 1 (several days), 2 (more than half the days), or 3 (nearly every day). The total score was categorized into mild, moderate, or severe based on the cutoff points of 5, 10, and 15, respectively. This tool has demonstrated good internal consistency and test–retest reliability [34], while the Chinese version has shown good validity and reliability [35].
The Adult ADHD Self-Report Scale (ASRS-V1.1) is used to screen for attention-deficit and hyperactivity/impulsivity. Part A includes items 1–4, which assess attention deficits, and items 5 and 6, which assess hyperactivity/impulsivity. Each item is scored using a 5-point scale: 0 (never), 1 (rarely), 2 (sometimes), 3 (often), or 4 (very often). A total score of 4 or higher in Part A is used as the cutoff point. The ASRS-V1.1 has shown good internal consistency and test–retest reliability [36], while the Chinese version has also demonstrated good validity and reliability [37].
The Short Form of the Smartphone Addiction Inventory (SPAI-SF) is a self-report scale used to screen for problematic smartphone use risk [29]. It consists of 10 items that cover four dimensions: compulsive behavior, functional impairment, withdrawal, and tolerance. Each item is scored on a 4-point scale: 1 (strongly disagree), 2 (disagree), 3 (agree), or 4 (strongly agree). A total score of ≥ 25 indicates a potential risk of problematic smartphone use. The scale demonstrates good internal consistency (Cronbach’s α = 0.84), and the total score is significantly correlated with the original SPAI scale (r = 0.94, p < 0.01) [21, 38].
Statistical analysis
Statistical analyses were performed using SPSS software version 26.0 (SPSS Inc., Chicago, IL, USA). A participant would be categorized as problematic smartphone use risky group if the total SPAI-SF score was ≥ 25. Bivariate analyses were performed using the independent t-test and chi-square test. Logistic regression was used to analyze factors associated with problematic smartphone use risk, including the number of substances used in the past, daily smartphone usage, BSRS-5 score, GAD-7 score, ASRS-V1.1 score, and purpose of smartphone use. These factors were inputted into the statistical model using the enter method. The Hosmer–Lemeshow goodness-of-fit test was used to examine the adequacy of the multivariate model. All reported p-values were two-tailed and considered significant if p < 0.05.
Results
A total of 233 ketamine users participated in this study. Their sociodemographic data are presented in Table 1. The average age was 28.45 ± 6.35 years. Of the participants, 80.3% were male, 88.0% did not receive a university education, 70% were unmarried, and 6.9% were unemployed. 73.4% had a history of alcohol use, 22.3% had tried 3,4-methylenedioxymethamphetamine (MDMA), 30% had used nitrous oxide, and 30% used novel psychoactive substances, most of which were mixtures such as drug-laced coffee bags. The average number of substances used was 2.76 ± 1.66. Regarding smartphone usage, 62.7% reported using their phones for 0–5 h per day, while 12.0% reported using their phones for > 9 h per day.
Table 1.
Sociodemographic data of ketamine users (n = 233)
| Mean ± SD | |
|---|---|
| Age | 28.45 ± 6.35 |
| Total number of previous substance use | 2.76 ± 1.66 |
| N (%) | |
| Male | 187 (80.3) |
| No college education | 205 (88.0) |
| Unemployed | 16 (6.9) |
| Unmarried | 163 (70) |
| Previous substance use | |
| Alcohol | 171 (73.4) |
| Ketamine | 233 (100) |
| Cannabis | 15 (6.4) |
| Amphetamine | 17 (7.3) |
| Heroin | 4 (1.7) |
| 3,4-Methylenedioxymethamphetamine | 52 (22.3) |
| Nitrous oxide | 70 (30.0) |
| Novel psychoactive substances | 70 (30.0) |
| Time spent using smartphones | |
| 0–5 h | 146 (62.7) |
| 5–9 h | 59 (25.3) |
| >9 h | 28 (12.0) |
Note SD: standard deviation
Table 2 presents the clinical assessment data of the participants. The mean SDS score for ketamine use was 2.88 ± 2.27; the mean BSRS-5 score was 2.09 ± 3.59; the mean GAD-7 score was 1.99 ± 3.86; the mean ASRS-V1.1 score was 9.43 ± 4.43; and the mean SPAI-SF score was 22.87 ± 6.65. Overall, participants exhibited mild levels of emotional distress and anxiety.
Table 2.
Clinical data of ketamine users (n = 233)
| Mean ± SD | |
|---|---|
| SDS | 2.88 ± 2.27 |
| BSRS-5 | 2.09 ± 3.59 |
| GAD7 | 1.99 ± 3.86 |
| ASRS-V1.1 | 9.43 ± 4.43 |
| Inattention | 6.42 ± 3.10 |
| Hyperactivity/impulsivity | 3.01 ± 1.83 |
| SPAI-SF | 22.87 ± 6.65 |
Note SD: standard deviation; SDS: Severity of Dependence Scale; BSRS-5: Brief Symptom Rating Scale; GAD7: General Anxiety Disorder-7; ASRS-V1.1: Adult ADHD Self-Report Scale; SPAI-SF: Smartphone Addiction Inventory–Short Form screening cutoff point
Table 3 presents the differences in sociodemographic data and clinical assessment scores between the problematic smartphone use risk and non-risk groups. Compared with the non-risk group, the problematic smartphone use risk group had significantly longer daily smartphone usage (χ2 = 22.467, p < 0.001) and higher scores on the BSRS-5 (p = 0.02), GAD-7 (p = 0.01), and ASRS-V1.1 (p < 0.001). No significant differences were found between the groups in terms of age, gender, education level, unemployment rate, unmarried status, number of substances used, or ketamine dependence as measured by the SDS.
Table 3.
Comparison of sociodemographic and clinical data between smartphone addiction high risk group and non-high risk group (n = 233)
| Risky group (n = 93) | Non-risky group (n = 140) | |||
|---|---|---|---|---|
| N (%) | χ 2 | p | ||
| Male | 73 (78.5) | 114 (81.4) | 0.296 | 0.586 |
| No college study | 86 (92.5) | 119 (85.0) | 4.075 | 0.288 |
| Unemployed | 5 (5.4) | 11 (7.9) | 8.117 | 0.617 |
| Unmarried | 60 (64.5) | 103 (73.6) | 6.411 | 0.179 |
| Time spent using smartphones | ||||
| 0–5 h | 42 (45.2) | 104 (74.3) | 20.811 | < 0.001 |
| 5–9 h | 33 (35.5) | 26 (18.6) | ||
| >9 h | 18 (19.4) | 10 (7.1) | ||
| Mean ± SD | t | p | ||
| Age | 28.41 ± 6.24 | 28.48 ± 6.45 | −0.082 | 0.935 |
| Total number of previous substance use | 2.99 ± 1.75 | 2.61 ± 1.59 | 1.695 | 0.092 |
| SDS | 3.15 ± 2.35 | 2.70 ± 2.20 | 1.488 | 0.138 |
| BSRS-5 | 2.80 ± 4.12 | 1.61 ± 3.12 | 2.353 | 0.020 |
| GAD7 | 2.86 ± 4.80 | 1.41 ± 2.96 | 2.609 | 0.010 |
| ASRS-V1.1 | 10.75 ± 4.05 | 8.56 ± 4.47 | 3.888 | < 0.001 |
| Inattention | 7.17 ± 2.69 | 5.92 ± 3.25 | 3.191 | 0.002 |
| Hyperactivity/impulsivity | 3.58 ± 1.85 | 2.64 ± 1.73 | 3.976 | < 0.001 |
Note SPAI-SF: Smartphone Addiction Inventory–Short Form screening cutoff point; SD: standard deviation; SDS: Severity of Dependence Scale; BSRS5: Brief Symptom Rating Scale; GAD7: General Anxiety Disorder-7; ASRS-V1.1: Adult ADHD Self-Report Scale
Table 4 presents the differences in the purposes of smartphone use between the addiction risk and non-risk groups. Compared with the non-risk group, the problematic smartphone use risk group had a significantly higher proportion of individuals who used their smartphones for playing online games (χ2 = 12.682, p < 0.001) and a significantly lower proportion of individuals who used their smartphones for online research (χ2 = 6.263, p < 0.013).
Table 4.
Comparison of purpose of smartphone use between smartphone addiction high risk group and non-high risk group (n = 233)
| Risky group (n = 93) | Non-risky group (n = 140) | χ2 | p value | |
|---|---|---|---|---|
| n (%) | n (%) | |||
| Purpose of smartphone use | ||||
| Watching videos | 58 (62.4) | 70 (50.0) | 3.451 | 0.080 |
| Playing online games | 51 (54.8) | 44 (31.4) | 12.682 | < 0.001 |
| Role-playing games | 24 (25.8) | 34 (24.3) | 0.069 | 0.877 |
| Adventure games | 24 (25.8) | 27 (19.3) | 1.390 | 0.260 |
| Shooter games | 26 (28.0) | 35 (25.0) | 0.253 | 0.650 |
| Puzzle games | 28 (30.1) | 51 (36.4) | 0.996 | 0.327 |
| Sports games | 17 (18.3) | 27 (19.3) | 0.037 | 1.000 |
| Online gambling | 22 (23.7) | 19 (13.6) | 3.919 | 0.054 |
| Shopping online | 19 (20.4) | 29 (20.7) | 0.003 | 1.000 |
| Internet search | 19 (20.4) | 50 (35.7) | 6.263 | 0.013 |
| Listening to music | 37 (39.8) | 49 (35.0) | 0.549 | 0.490 |
| Texting | 26 (28.0) | 40 (28.6) | 0.010 | 1.000 |
| Using social media | 56 (60.2) | 68 (48.6) | 3.043 | 0.084 |
| Using dating applications | 8 (8.6) | 10 (7.1) | 0.167 | 0.803 |
Table 5 presents the relationships between the number of substances previously used; length of smartphone usage; BSRS-5, GAD-7, and ASRS-V1.1 scores; smartphone usage purposes; and problematic smartphone use. After adjustment, longer smartphone usage (odds ratio [OR]: 1.64; 95% confidence interval [CI]: 1.29–2.08; p < 0.001), higher ASRS-V1.1 scores (OR: 1.14; 95% CI: 1.05–1.23; p = 0.001), and smartphone use for online gaming (OR: 2.26; 95% CI: 1.19–4.29; p = 0.012) were significantly associated with problematic smartphone use. Conversely, individuals who used their smartphones for online research were less likely at risk for problematic smartphone use (OR: 0.33; 95% CI: 0.15–0.75; p = 0.007). This model was appropriately fitted (Hosmer–Lemeshow statistics = 12.562, p = 0.128).
Table 5.
Associative factors of smartphone addiction risky group
| OR | 95% CI | p value | |
|---|---|---|---|
| Total number of previous substance use | 1.050 | [0.853–1.292] | 0.645 |
| Time spent using smartphones | 1.640 | [1.293–2.081] | < 0.001 |
| BSRS-5 | 1.017 | [0.882–1.173] | 0.813 |
| GAD 7 | 1.101 | [0.964–1.257] | 0.155 |
| ASRS-V1.1 | 1.139 | [1.053–1.232] | 0.001 |
| Purpose of smartphone use | |||
| Watching videos | 1.284 | [0.682–2.419] | 0.438 |
| Playing online games | 2.263 | [1.194–4.288] | 0.012 |
| Shopping online | 0.671 | [0.292–1.543] | 0.348 |
| Searching on the Net | 0.333 | [0.149–0.745] | 0.007 |
| Listening to the music | 1.168 | [0.611–2.230] | 0.639 |
| Texting | 1.840 | [0.856–3.955] | 0.118 |
| Using social media | 1.223 | [0.641–2.336] | 0.541 |
| Using dating applications | 0.904 | [0.265–3.077] | 0.872 |
Note OR: odds ratio; CI: confidence interval; BSRS5: Brief Symptom Rating Scale; GAD7: General Anxiety Disorder-7; ASRS-V1.1: Adult ADHD Self-Report Scale
Hosmere–Lemeshow statistics = 12.562, p = 0.128
Discussion
This study examined the factors associated with problematic smartphone use among individuals with ketamine use disorder. Our study found that longer smartphone usage, ADHD symptoms, and using smartphones for online gaming were associated with an increased risk of problematic smartphone use. In contrast, individuals who used smartphones for online research were less likely to be at risk. Although our study did not aim to validate smartphone addiction screening tools, our findings highlight important concerns regarding their clinical application. Given the overlapping features between problematic smartphone use and internet gaming disorder, current screening tools may lack specificity in differentiating these conditions. Further research is needed to assess their diagnostic utility across various clinical subgroups.
This study found that the problematic smartphone use risk group experienced mild emotional distress, which differs from the results of a previous systematic literature review [39]. This discrepancy may be due to differences in study samples and psychological health issues. Most studies on problematic smartphone use focus on adolescents and university students, whose excessive smartphone use may be a coping mechanism for depression, anxiety, or social problems [26, 40]. For these individuals, smartphone use may provide enjoyment and reduce suffering, offering a form of escapism from life’s difficulties, although issues such as low self-esteem and social isolation remain unresolved [14, 26]. Excessive smartphone use in student populations may indicate underlying emotional issues that warrant further exploration.
In contrast, the present study sample consisted of adults referred for addiction treatment by prosecutors because of substance use problems. This population was predominantly male, without a university education, with a history of using more than two substances, and with mean ASRS-V1.1 scores exceeding the cutoff point. A more pressing issue for this population might be multiple addictions [27, 41], which could co-occur with ADHD. Over half of individuals with comorbid ADHD and substance use problems do not receive regular treatment [42].
Furthermore, individuals with ADHD and substance use problems may exhibit biological vulnerabilities associated with ADHD during developmental stages, including deficits in emotional regulation, executive functioning, sustained attention, and reward systems. These impairments may lead to learning difficulties, behavioral problems, and social issues, which, combined with interactions with environmental and peer influences, can contribute to the development of addictive behaviors [43]. Therefore, excessive smartphone use may be part of a broader context of addictive behavior among such individuals.
The participants in this study were individuals with deferred prosecution with ketamine use as their main addictive behavior, which may have influenced the results of the emotional distress assessments. Psychiatric cases from the judicial system often have diagnostic prevalence rates that differ significantly from those in the general population [44, 45]. The prevalence of substance use problems and personality disorders in this population may be much higher than that in the general population, with a greater proportion of individuals diagnosed with addictive and personality disorders than mood disorders and other diagnoses [45]. However, the presentation of symptoms at the time of assessment may be influenced by the level of deprivation the patients experience in their environment [44].
The participants of the present study were referred to treatment for substance-related criminal issues but had deferred prosecution. Over 90% were still able to maintain employment, indicating that this group of predominantly young men, with an average age of < 30 years, may not be representative of individuals with severe addiction, anxiety [46], or depression [47]. The participants’ self-reported SDS scores also indicated a relatively low level of ketamine dependence. Regarding this issue, we speculate that it may be related to the characteristics of our study population. The participants were individuals with ketamine use disorder who had been referred by the court to receive outpatient addiction treatment. Given their involvement in a judicially mandated treatment process, there may have been a considerable degree of underreporting in their self-reported data concerning health status, addictive behaviors, smartphone use problems, and emotional issues. At the same time, we need to consider whether ketamine use may influence emotional problems in this population, especially since existing clinical studies have shown that ketamine can have a certain degree of benefit for depressive and anxiety symptoms [48].
In this study, over half of the problematic smartphone use risk group used their phones for > 5 h per day, which is higher than that in the non-risk group. However, > 20% of the non-risk group also used their phones for over 5 h per day. Thus, defining excessive smartphone use based solely on daily usage time is controversial, if not inaccurate [14]. Longer usage times may indicate other life activities being conducted through smartphones rather than problematic smartphone use [49]. Such discrepancies highlight the need to refine the definitions, terminology, and cutoff points used in excessive smartphone-use assessments, which may require further studies involving different population groups to ensure the consistency of screening tools [14]. At the same time, we also need to consider that issues such as self-stigma faced by individuals with substance use disorders, crises in self-identity, difficulties in daily life arrangements during the recovery process, and problems with insomnia may all be related to the worsening of problematic smartphone use among individuals with substance use disorders [50–52].
More than half of the problematic smartphone use risk group primarily used their smartphones for playing online mobile games, whereas using phones for information searches was less frequent. This indicates that the purpose of smartphone use influences the degree of problematic smartphone use risk. Studies on adolescents and young adults revealed that playing online games on smartphones is significantly associated with problematic smartphone use, with some participants also exhibiting internet gaming addiction [17, 25, 53].
Limitations
This study has several limitations. First, due to its cross-sectional study design, causal relationships could not be established. Second, the study participants were individuals referred by prosecutors for ketamine addiction treatment under deferred prosecution and thus were not representative of other community or incarcerated populations. Third, structured diagnostic tools for psychiatric comorbidities were not used. Instead, the participants’ levels of emotional distress and anxiety were assessed via self-report questionnaires under the supervision of clinical psychologists. Finally, the study did not employ specific screening or diagnostic tools for internet gaming addiction, so it could not confirm whether participants who primarily used their smartphones for online gaming also met the criteria for internet gaming addiction. Moreover, the prevalence of gaming disorder is higher in males than in females [54], and our sample consists almost entirely of male participants. Therefore, when interpreting our findings, it is important to consider that males may be more commonly represented both in court-referred cases and among individuals with gaming disorder.
Conclusions
The results of this study, conducted in a population of individuals with ketamine use disorder, indicate that those at risk of problematic smartphone use spent more time on their phones, exhibited more ADHD symptoms, and were more likely to use smartphones for online gaming compared to the non-risk group. Given the unique clinical characteristics of this population, these findings offer important insights into the behavioral patterns and comorbidities associated with smartphone use in substance-using individuals. The relationship between smartphone use and internet gaming addiction remains unclear, and clinicians should interpret screening results with caution, particularly when working with court-referred populations. This study highlights the need for more nuanced approaches to assessing and addressing behavioral addictions in substance use treatment settings.
Acknowledgements
Not applicable.
Author contributions
Chia-Hsiang Chan contributed to the study conceptualization, design, supervision, and interpretation of data. Yang-lin Lin contributed to the integrity of the data and help confirm the accuracy of data analysis. Chia-heng Lin contributed to statistical analysis, reviewing, writing and editing the final draft. All authors reviewed the manuscript.
Funding
The author(s) reported that there is no funding associated with the work featured in this article.
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
The study procedures were carried out in accordance with the Declaration of Helsinki. Informed consent was obtained from all patients for being included in the study. The Institutional Review Board of the Taoyuan Psychiatric Center approved the study. All subjects were informed about the study and provided written informed consent (approval number B20190902). The authors had no access to information that could identify individual participants during or after data collection.
Consent for publication
The authors confirm that all participants involved in this study have provided written informed consent for the publication of anonymized data and findings. No personally identifiable information is included in the publication.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.