This study employed a cross-sectional design, collecting data online using Google Forms. The target population comprised young adults aged 18 to 25 who reported playing freemium games at least once a week over the past 12 months. Convenience and snowball sampling were employed to recruit participants over six months through personal connections, two social media platforms (i.e., Facebook and Instagram), and two gaming discussion forums (i.e., LIHKG-Game Zone and Games Animation Forum). Individuals who could not read Chinese were excluded. To prevent missing data, all questions in the online survey were made mandatory, ensuring a complete dataset for analysis.

Before conducting the survey, participants were presented with a clear description of the study’s objectives. Informed consent was obtained online. To indicate their voluntary agreement, participants were required to select a checkbox labeled “Agree to participate in this study.” This action was a mandatory prerequisite for beginning the survey. Ethical approval for this study was granted by the ethics committee of the first author’s affiliated institution.

G*Power version 3.1.9.7 was used to calculate the sample size. Due to the limited prior evidence, conventional study power and effect size, as described by Cohen (1992) (33), were used to calculate the sample size. The minimum sample size required to achieve a study power of 0.8, with an alpha of 0.05 and a medium effect size for association (f²=0.15), was 131 for 13 independent variables (30). The 13 variables included four socio-demographic variables (i.e., age, gender, education level, and monthly income), four gaming-related variables (i.e. daily gaming time, number of gacha games played, frequency of gacha purchases, and monthly spending on gacha purchases), and five QoL-related variables (i.e. overall QoL and the four QoL domains). A total of 281 participants were recruited, which was more than sufficient for hypothesis testing.

SPSS version 29 was used to conduct all statistical analyses. There was no missing data in the dataset. Descriptive statistics were calculated to summarize all socio-demographic and other study variables among the problem gambling risk levels. Means and standard deviations were reported for continuous variables, while frequencies and percentages were computed for categorical and ordinal variables. To assess group differences, the Chi-squared test was used for gender, the Kruskal-Wallis test was applied to ordinal variables (i.e., education level, monthly income, and the four gaming behavior variables), and One-way ANOVA was utilized for overall quality of life and its four domain scores. Post hoc analysis with Bonferroni correction for multiple tests was performed to examine specific group differences for continuous variables. To verify the study’s hypothesis, Pearson Correlation Analysis or Spearman Rank Correlation Analysis was used, depending on the data’s normality, to explore correlations between education level, monthly income, gaming behaviors, quality of life and total problem gambling scores. Stepwise regression analysis was conducted with the total problem gambling score as the dependent variable to identify significant predictors. A two-sided p-value below 0.05 was considered statistically significant.

A total of 281 participants were collected with no missing data. Of the participants, 43% (n=121) were male and 56.9% were female. The majority of the participants aged 21–25 years (57.7%, n=162) and held a bachelor’s degree or higher (78.6%, n=221). Regarding problem gambling risk, 63.3% (n=178) were classified as low-risk, 25.6% (n=72) as moderate-risk, and 11% (n=31) as high-risk.

As detailed in Table 1 , significant differences were observed among the three problem gambling groups for age (χ²=12.657, p=0.002), gender (χ²=8.214, p=0.016), and education level (H=6.223, p=0.045). The high-risk group was composed of a greater proportion of older male participants as compared to the low- and moderate-risk groups. Among the gacha gaming behaviors, only monthly gacha expenses differed significantly across the groups (H=7.688, p=0.021). It was also noted that 38.7% of the high-risk group had a monthly income of HKD 10,000 or more. 87.5–89.3% of those in the low- and moderate-risk groups reported incomes of HKD10,000 or below.

Significant differences were also found for overall quality of life, physical, psychological and environment QoL (p<0.01 for all). Post hoc analysis showed that the high-risk group scored significantly lower than both the low- and moderate-risk groups on overall QoL (p<0.001), physical QoL (p<0.001), psychological QoL (p<0.05), and environment QoL (p<0.05).

Correlation analysis revealed a significant positive association between monthly expenses on gacha purchases and total PGSI scores (p<0.05). Conversely, all quality of life domains except for the social domain were significantly and negatively correlated with total PGSI scores (p<0.01). Table 2 presents the results.

The regression analysis ( Table 3 ) indicated that daily time spent on gacha gaming and social QoL were positively associated with the total PGSI scores (p<0.05). In contrast, overall QoL, education level, and physical QoL were negatively related to the PGSI scores (p<0.05). The final regression model explained 14.2% of the variance in total PGSI scores (Adjusted R²=0.113, F=8.113, p<0.001). The effect sizes (f²) for the significant variables were small, ranging from 0.014 for daily gacha gaming time to 0.04 for social QoL. These findings suggest that gacha gamers with lower education levels, more daily gaming time, and lower overall and physical QoL, combined with higher social QoL, tend to exhibit greater problem gambling tendencies.

The current study reveals that overall QoL and its physical domain were negatively associated with problem gambling risk among young adult gacha gamers in Hong Kong. It suggests that gamers with better general and physical wellbeing are less likely to have problem gambling. Conversely, social QoL was positively associated with problem gambling risk, which may indicate that specific peer influences or social dynamics within gaming communities could increase vulnerability. No significant associations were found for psychological and environmental QoL. The mean scores for these QoL domains in the present high-risk problem gambling sample were 11.94-11.35, which were notably lower than those reported for the general adult population in Hong Kong (i.e. 13-14) (37, 38). Their insignificant associations with problem gambling risk suggest that they may be less relevant to gacha gaming’s social and reward-driven dynamics as compared to physical and social QoL.

Additionally, while daily time spent on gacha purchases was a significant predictor, its smallest effect size among all significant predictors in the regression model suggests that it may be a less potent indicator of problem gambling risk as compared to other QoL variables. These findings fill a gap in the existing body of knowledge about the role of quality of life in problem gambling within the context of gacha gaming in Hong Kong. The present findings highlight the importance of paying special attention to gamers with low physical and overall wellbeing, and high social wellbeing to facilitate early identification of at-risk cases in primary healthcare settings.

Our results indicate a positive association between social QoL, as measured by the WHOQOL-BREF, and problem gambling risk in gacha gamers, suggesting that greater social wellbeing may paradoxically encourage compulsive gacha spending and promote gambling behaviors. This finding is consistent with those in the broader gambling literature, such as Oyelade et al. (2023) (39). The mean social QoL scores in the present sample (13.93-14.13 on the 4–20 scale) were comparable to normative data from the Hong Kong general adult population pre- and post-COVID-19 pandemic. Wong et al. (2018) (37) and Census and Population Department (2011) (38) reported a mean social QoL score of 63.96 and 61.44 on a 0–100 scale, respectively, which are equivalent to 13–15 on the 4–20 scale in the general adult population. Hung et al. (2022) (40) reported a mean social QoL of 14.0 on a 4–20 scale among young adults during the COVID-19 pandemic. The consistent findings across different periods may reflect the increasing role of screen-based social interactions, which likely complement traditional forms of social engagement in maintaining perceived wellbeing.

The social QoL reported by gacha gamers, despite their problem gambling risk, can be explained by three mechanisms that challenge traditional views of social wellbeing. First, gacha gaming communities foster a strong group identity where gamers adopt community norms and view themselves as dedicated gamers (41). The acquisition of rare in-game items signals status and commitment, driving increased gaming and spending (42). Second, the emotionally resonant narratives in gacha games can create strong attachments to characters, prompting gacha spending that is driven more by emotional connection than rational decision-making (43). Third, online gaming communities can inadvertently normalize problem gambling behaviors. Gamers sharing outcomes in forums receive praise for successes or sympathy for losses, reinforcing a sense of belonging while embedding in-game spending as routine (42). Features like ‘pity systems’ further integrate in-game spending into the social fabric of the game. These mechanisms suggest that high social QoL scores in this context may reflect engagement with gambling-supportive virtual networks rather than genuine psychological resilience.

The issue of using general QoL instruments to measure gamblers’ wellbeing has been raised by researchers in the gambling field (44). While the WHOQOL-BREF is a well-validated tool for assessing broad domains of QoL, it has notable limitations when applied to behavioral addictions such as Gaming Disorder and problem gambling. As a general measure designed for diverse populations, it lacks sensitivity to the domain-specific harms of these addictions, such as financial pressures, preoccupation with gaming mechanics, and the potentially maladaptive nature of social interactions in virtual environments. Qualitative evidence confirms that gamers often experience anxiety about being “left behind” if they fail to participate in spending cycles, a nuance not captured by standard QoL items (41). In gacha gaming, the general QoL instrument may overestimate social QoL by capturing perceived ‘support’ from online communities that simultaneously normalize and encourage exploitative spending, misinterpreting engagement in such networks as a sign of wellbeing.

This highlights the need to revisit the traditional definition of social QoL in the digital age. While virtual communities can provide a genuine sense of belonging and shared achievement, it is crucial to critically evaluate whether they also embed gambling mechanics that foster dependency and isolation rather than psychological wellbeing. Future research would benefit from developing or adapting gaming-specific QoL instruments capable of distinguishing between resilience-building social connections and engagement in networks that may perpetuate harm.

The mean physical QoL scores in the present sample were 14.88, 14.76, and 13.13 (on the 4–20 scale) for the low-, moderate-, and high-risk problem gambling groups, respectively. These scores are notably lower than pre-COVID-19 normative data in Hong Kong’s general adult population, with reported mean scores equivalent to 15–16 on a 4–20 scale (37, 38). Furthermore, the mean physical QoL score of the high-risk gambling group (13.13) was also lower than the mean score (13.71) reported among young adults during the COVID-19 pandemic (40). This suggests that gacha gamers, particularly those at high risk for problem gambling, may experience poorer physical wellbeing as compared to the general population.

The negative association between overall and physical QoL and problem gambling risk likely stems from prolonged engagement in gaming activities, which aligns with broader research on other behavioral addictions. A systematic review conducted by Noroozi et al. (2021) identified significant negative correlations between internet addictive behaviors and physical health outcomes, including sleep disturbances and reduced wellbeing (25). Similarly, a meta-analysis by (45) found that gamers with Gaming Disorder often experienced physical health issues such as chronic pain, sleep problems, and decreased physical activity, all of which diminish daily physical wellbeing. In the context of gacha gaming, gamers with poorer physical QoL may turn to gaming as a coping mechanism for negative real-life experiences, such as physical discomfort or fatigue. The immersive nature of gacha games, due to their demanding reward systems, may exacerbate these physical health issues by reducing time for physical activity and rest, further lowering physical QoL and increasing susceptibility to excessive in-game spending.

Daily gaming time was found to have a modest yet significant positive association with problem gambling risk in the present study. This finding is consistent with broader research on behavioral addiction, which indicates that prolonged engagement in reward-driven activities can impair cognitive functions related to decision-making. The diminished self-control and irrational spending decisions observed in frequent gacha gamers may stem from impaired inhibitory control caused by prolonged gaming (46). The core mechanic of gacha games, i.e., repeated “pulling” for randomized rewards, likely reinforces this effect by habituating gamers to a cycle of reward-seeking, gradually eroding their resistance to impulsive spending.

While this study identified an association between certain QoL domains and gacha-related gambling risk, the observed effect sizes were small. Although methodological differences limit direct comparisons with other studies, these modest effect sizes are consistent with findings in the broader literature on behavioral addictions. For instance, a systematic review by Bonfils et al. (2019) noted that QoL variables often yield smaller effect sizes in gambling research, a limitation they attributed to the use of general, non-specific QoL instruments (44).

Furthermore, research consistently shows that protective factors tend to have smaller effect sizes than risk factors. A meta-analysis of Gaming Disorder by Ropovik et al. (2023) found that protective factors, such as life satisfaction, had small effects, whereas risk factors, including anxiety and depression, had moderate effect sizes (47). Similarly, another meta-analysis conducted by Dowling et al. (2017) reported that protective factors for problem gambling, such as parental supervision, had weaker predictive power than risk factors such as poor academic performance and tobacco use (48).

This cross-sectional study has five significant limitations. First, it employed convenience and snowball sampling, which may introduce selection bias and limit generalizability beyond the predominantly well-educated (78.6% with bachelor’s degrees) Chinese young adults in Hong Kong. Second, the imbalanced sample sizes across the three problem gambling groups, with only 11% (n=31) in the high-risk group, may reduce statistical power and increase the risk of Type II errors, potentially weakening the ability to detect true associations. This imbalance may also explain the small effect sizes of significant independent variables and the low explanatory power of the model. However, protective factors often show smaller effects than risk factors, as explained in Section 6.5. Third, the use of the WHOQOL-BREF, a general quality of life instrument, may not fully capture the unique social dynamics of gacha gaming communities, such as virtual relationships that normalize in-game spending, potentially leading to an overestimation of social QoL. Fourth, the cross-sectional design precludes causal conclusions. Fifth, conducting the study solely in Hong Kong may limit the cultural and contextual applicability of findings to other Asian regions. To address these limitations, future research should adopt longitudinal designs to establish causality, use probability or quota sampling for broader representativeness, include samples from other Asian regions for generalizability, and develop or validate gaming-specific QoL instruments to better capture the social and behavioral nuances of gacha gaming.