Pathological gambling, as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), refers to persistent and recurrent maladaptive

gambling behavior that significantly disrupts an individual's personal, family, and vocational life (American Psychiatric Association, 2013). With the rise of digital and online gambling platforms, gambling behaviors have become increasingly prevalent, leading to significant psychiatric and socioeconomic consequences. Digital gambling encompasses both offline and online activities, whereas online gambling specifically requires internet access for activities such as online casinos, sports betting, and virtual poker (2). The accessibility, anonymity, and immersive nature of online gambling have contributed to its rapid growth, making it a global public health concern (3).

The increasing prevalence of online gambling is particularly notable in younger demographics, including in India, where digital gambling activities have been expanding at an unprecedented rate (4). Studies have highlighted the wide-ranging prevalence of pathological gambling, with rates varying between 0.2% and 5.3% worldwide (5,6). The expansion of digital gambling platforms through websites, mobile applications, and social media has diversified gambling opportunities, integrating activities such as esports betting, social casino games, and real-money gaming (7). Notably, research has demonstrated that social casino games, which mimic gambling experiences without real-money transactions, often serve as a gateway to real-money gambling, especially among younger players (8). The rise of esports betting has also introduced new regulatory concerns, as younger individuals with limited awareness of gambling risks increasingly participate in such activities (9).

One of the most concerning aspects of online gambling is its strong association with psychiatric comorbidities. Pathological gambling has been linked to major depressive episodes, with nearly half of affected individuals experiencing severe depression (10). Anxiety disorders are also prevalent, with generalized anxiety disorder affecting over one-third of pathological gamblers (11). Furthermore, gambling addiction frequently coexists with substance use disorders, with studies reporting a comorbidity rate of 73.2% (12). Other psychiatric conditions, such as antisocial personality disorder, have been observed in up to 60% of pathological gamblers (13). These high comorbidity rates highlight the complex interplay between gambling addiction and psychiatric disorders, complicating both diagnosis and treatment outcomes (8).

Beyond psychiatric comorbidities, digital and online gambling has substantial socioeconomic consequences. Problematic gambling behaviors often lead to financial distress, employment instability, and deteriorating social relationships (14). Individuals affected by gambling addiction frequently experience job loss, debt accumulation, and family conflicts, exacerbating their psychological distress. The constant availability of online gambling platforms, coupled with their dynamic betting options and ease of access, further exacerbates these issues, making it increasingly difficult for affected individuals to regulate their gambling behaviors (15).

Despite growing awareness of the risks associated with online gambling, research gaps remain in understanding the specific psychiatric and socioeconomic impacts among individuals diagnosed with pathological gambling. While previous studies have explored general gambling behaviors and their consequences, there is a need for a more focused examination of digital and online gambling patterns, particularly in emerging markets such as India. Furthermore, while research has established associations between gambling addiction and psychiatric disorders, there is limited understanding of how different types of digital gambling activities influence psychiatric outcomes and socioeconomic well-being.

This study aims to address these research gaps by investigating the psychiatric comorbidities and socioeconomic impacts of digital and online gambling among individuals diagnosed with pathological gambling. Specifically, the study will focus on identifying associated psychiatric disorders, assessing financial and social consequences, and analyzing behavioral patterns related to online gambling. Key behavioral aspects such as time spent on gambling, money spent per month, types of devices used, and specific gambling activities will be examined. By providing a comprehensive analysis, this study aims to inform targeted interventions and regulatory measures to mitigate the harms associated with digital and online gambling.

This study employs a cross-sectional design to determine the psychiatric comorbidities and socioeconomic impacts of digital and online gambling among individuals diagnosed with pathological gambling. The study population consisted of 300 individuals diagnosed with pathological gambling, meeting the following

Data collection was conducted through structured questionnaires and clinical interviews to obtain comprehensive insights into participants' psychiatric symptoms, gambling behaviors, and socioeconomic consequences. The study also explored behavioral patterns such as time spent on gambling, money spent per month, types of devices used, and specific gambling activities.

Inclusion criteria:

1. Diagnosed Pathological Gambling Patients – Participants were formally diagnosed with pathological gambling based on standardized diagnostic criteria outlined in the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, 5th Edition) or ICD-10 (International Classification of Diseases, 10th Revision).
2. Engagement in Digital and Online Gambling – Participants must have engaged in digital or online gambling activities (e.g., online casinos, sports betting, virtual poker, esports betting) at least once in the past six months.
3. Adults (18 years and above) – Participants must be aged 18 years and above to ensure legal eligibility for informed consent and autonomy in decision-making.
4. Willingness to Participate – Participants voluntarily consented to participate after being informed about the study’s objectives and procedures.

Exclusion Criteria

Participants were excluded from the study if they met any of the following criteria:

1. Age Below 18 Years – Individuals under 18 years were excluded due to ethical considerations regarding informed consent.
2. Non-Consenting Individuals – Participants unwilling to provide informed consent were not included in the study.
3. Severe Cognitive Impairment or Psychiatric Disorders – Individuals with severe psychiatric disorders, such as schizophrenia or severe cognitive impairment, which could affect their ability to provide accurate responses, were excluded.
4. Current Treatment for Other Addictions – Participants undergoing treatment for other substance use disorders or behavioral addictions were excluded to prevent confounding effects.

Data Collection

Data were collected using a structured questionnaire and clinical interview. The questionnaire covered demographic information, gambling behaviors, psychiatric symptoms, and socioeconomic impacts. The following validated tools and custom measures were used to assess gambling-related psychiatric and socioeconomic factors:

1. Brief Psychiatric Rating Scale (BPRS): A clinician-rated instrument designed to measure the severity of psychiatric symptoms, including psychosis, depression, and anxiety. The BPRS consists of 18 items, each rated on a 7-point scale, providing an overall assessment of psychiatric distress.
2. South Oaks Gambling Screen (SOGS): A 20-item questionnaire widely used for screening pathological gambling behaviors. It evaluates gambling frequency, the types of gambling activities engaged in, and the personal and social consequences of gambling.
3. Financial Impact Questionnaire: A custom-developed tool specifically designed for this study to assess the financial impact of gambling, including debt levels, financial stress, and the effect of gambling on personal and family finances.
4. Work Performance Scale (WPS): A scale assessing the impact of gambling on work performance and productivity, including absenteeism, decline in work quality, and overall job performance.
5. Behavioral Gambling Patterns Questionnaire: A custom-developed tool to explore gambling behaviors, including time spent on different gambling activities, money spent per month, types of devices used for gambling (smartphones, tablets, computers), and the types of digital and online gambling activities engaged in (e.g., sports betting, online casinos, virtual poker).

Statistical Analysis

All collected data were analyzed using SPSS software version 22. Descriptive statistics, including means, standard deviations, frequencies, and percentages, were used to summarize demographic and gambling-related variables. Inferential statistical tests, such as chi-square tests, t-tests, and logistic regression analysis, were conducted to examine associations between gambling behaviors, psychiatric comorbidities, and socioeconomic impacts. Correlation analyses were performed to explore relationships between gambling patterns and financial, occupational, and social consequences. A p-value of <0.05 was considered statistically significant

Table 1: Demographic and Gambling Characteristics of the Study Population

The study includes a sample with a mean age of 35.6 years (SD = 8.3). Among the participants, 210 are male and 90 are female. Education levels vary, with 120 having completed high school, 140 holding a graduate degree, and 40 possessing a postgraduate degree. Marital status is distributed as 130 single, 140 married, and 30 divorced individuals. Employment status shows 180 employed, 80 unemployed, and 40 students. The average monthly income is INR 45,000 (SD = 15,000). The mean duration of gambling is 5.2 years, with a standard deviation of 2.1 years.

      Figure 1: Mean BPRS Scores for Selected Psychiatric Symptoms

                    Table 2: Proportion of Gambling-Related Behaviors Endorsed

The table 2 presents the proportion of participants endorsing various gambling-related behaviors. Chasing losses was reported by 49.3%, while borrowing money to gamble was endorsed by 47.7%. Lying about gambling was reported by 52.0%, and family conflicts due to gambling had the highest proportion at 52.7%. Feelings of guilt after gambling were experienced by 50.0% of participants.

             Table 3: Behavioral Gambling Patterns among Pathological Gamblers

This table 3 presents the mean and standard deviation values for key behavioral gambling patterns among individuals diagnosed with pathological gambling. On average, participants spent 20.68 hours per week on gambling activities, with a standard deviation of 3.11 hours. The monthly gambling expenditure averaged ₹53,940, showing considerable variability (SD = ₹15,392). Regarding device usage, gambling through smartphones was the most common (7.61 hours per week, SD = 1.34), followed by computers (6.10 hours per week, SD = 4.02) and tablets (2.60 hours per week, SD = 2.73). These findings highlight the substantial time and financial commitments associated with digital and online gambling in this population.

                              Table 4: Financial Consequences of Gambling

The table 4 shows the financial struggles experienced by individuals due to gambling. High debt levels were reported by 65%, making it the most common issue. Difficulty paying bills affected 58%, while frequent borrowing of money was reported by 60%. Loss of savings due to gambling impacted 62%, and family financial distress was experienced by 55%.

                  Table 5: Workplace Impact of Gambling

The table 5 shows the negative effects of gambling on occupational performance and workplace relationships. Increased absenteeism was reported by 55% of individuals, while 60% experienced a decline in work quality. Reduced productivity was noted by 57%, and 45% faced job loss due to gambling. Additionally, 50% reported conflicts with colleagues or supervisors.

                     Table 6: Correlation between Financial Impact and Work Performance Impact

The correlation matrix indicates the relationship between financial difficulties and work performance issues due to gambling. The correlation coefficient between financial impact and work performance impact is -0.055, suggesting a very weak negative correlation. This implies that financial struggles due to gambling are not strongly associated with workplace performance issues in this dataset. While both areas are significantly affected by gambling, their relationship appears minimal, meaning financial distress does not necessarily predict work-related problems or vice versa.

               Table 7: Correlation Analysis of Gambling-Related Factors

The correlation analysis reveals significant relationships between psychiatric symptoms, gambling severity, financial impact, and work performance. The BPRS (Brief Psychiatric Rating Scale) and SOGS (South Oaks Gambling Screen) scores show a moderate positive correlation (r = 0.42, p = 0.001), indicating that higher psychiatric symptom severity is associated with greater gambling severity. The SOGS and financial impact demonstrate a stronger positive correlation (r = 0.55, p = 0.0005), suggesting that increased gambling severity leads to greater financial difficulties. Conversely, the financial impact and work performance have a moderate negative correlation (r = -0.38, p = 0.005), implying that greater financial distress is linked to declining workplace performance. All correlations are statistically significant, underscoring the interconnected impact of gambling on mental health, finances, and work life.

                             Figure 2: Multiple Regression of Observed vs. Predicted Financial Impact

The figure 2 compares the observed and predicted financial impact scores using a multiple regression model. The observed values (yellow circles, solid line) represent actual financial impact scores, while the predicted values (red squares, dashed line) indicate model-generated estimates. The close alignment between the two suggests that the regression model effectively captures variations in financial impact, though some deviations are present. This visualization highlights the model's predictive accuracy and potential areas for improvement in forecasting financial distress due to gambling.

The present study provides significant insights into the psychiatric comorbidities and socioeconomic consequences of digital and online gambling among individuals diagnosed with pathological gambling. The findings indicate substantial financial distress, occupational impairment, and psychiatric symptom severity among participants, reinforcing the complex interplay between gambling behavior, mental health, and socioeconomic stability.

The mean BPRS scores suggest that psychiatric symptoms, such as anxiety, depression, hostility,

hallucinations, and unusual thought content, are prevalent among individuals engaged in

pathological gambling. The moderate correlation (r = 0.42, p = 0.001) between BPRS and SOGS scores indicates that psychiatric distress is closely associated with gambling severity, aligning with previous research that has demonstrated higher levels of psychiatric symptoms among individuals with gambling disorder (16).

The study highlights significant financial distress, with 65% of participants reporting high debt levels and 62% experiencing a loss of savings due to gambling. These findings are consistent with previous studies (17), which suggest that financial instability is one of the most profound consequences of pathological gambling. Furthermore, the strong correlation between SOGS and financial impact (r = 0.55, p = 0.0005) underscores the detrimental economic effects of compulsive gambling behavior.

The present study shows that participants spent an average of 20.68 hours per week on gambling, with ₹53,940 in monthly expenditures, indicating significant time and financial commitments. Smartphone gambling was most common (7.61 hours/week), followed by computers (6.10 hours/week) and tablets (2.60 hours/week). This high engagement in digital gambling may contribute to increased financial distress and psychiatric comorbidities, highlighting the need for targeted interventions. These observations are in accordance with earlier study (18).

In terms of occupational impact, 60% of participants reported a decline in work quality, while 55% experienced increased absenteeism. The negative correlation between financial impact and work performance (r = -0.38, p = 0.005) suggests that individuals experiencing financial distress due to gambling are more likely to suffer from reduced workplace productivity and conflicts with supervisors or colleagues. This aligns with findings from earlier research (19), which indicated that gambling-related financial stress often leads to job instability and loss.

The observed vs. predicted financial impact results from multiple regression analysis demonstrate that the predictive model effectively captures variations in financial distress among participants. The model's ability to closely approximate actual financial impact scores suggests its potential utility in identifying at-risk individuals and developing targeted interventions.

Comparing these findings with earlier studies, our results align with those of Lorains et al. (20), who found that psychiatric comorbidities, financial strain, and work-related impairments were key consequences of gambling disorder. However, our study expands on these findings by specifically focusing on digital and online gambling, which has unique risk factors, including ease of access, anonymity, and continuous betting opportunities (15).

This study highlights the significant psychiatric and socioeconomic consequences associated with pathological gambling, particularly in the digital and online gambling domain. The findings emphasize that psychiatric symptoms, financial distress, and workplace impairments are prevalent among individuals with gambling disorder. The moderate correlations between gambling severity, psychiatric distress, and financial instability suggest a need for integrated interventions addressing both mental health and financial counseling.

Given the growing prevalence of digital gambling, policymakers should consider targeted regulations to mitigate gambling-related harm, such as self-exclusion programs, financial spending limits, and public awareness campaigns. Future research should explore longitudinal patterns of gambling behavior and evaluate the effectiveness of intervention strategies in reducing gambling-related psychiatric and socioeconomic consequences.

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