There are many reasons why young adults have the heaviest and most hazardous alcohol use. In part, it’s because they overestimate how much their peers drink. This study analyzed data from a trial of a smartphone app to see if it worked by correcting such misperceptions about peer drinking and increasing awareness of health risks.
l
Young adults (e.g., ages 18-25) – many of whom are college students – engage in the highest levels and most hazardous forms of alcohol use of any age group. According to social norms theory, students calibrate their drink to levels considered typical among their peers (i.e., “peer norms”). Research consistently shows that students often overestimate how much their peers drink, and these inflated perceptions can lead to heavier personal alcohol use.
Brief interventions – whether delivered in person or digitally – that provide personalized normative feedback with accurate information about peer drinking levels have been shown to reduce alcohol consumption. Another approach, personalized health risk feedback, is grounded in health behavior theories such as the health belief model and aims to reduce risky drinking by increasing individuals’ awareness of potential health consequences. While many effective brief interventions include both approaches, there has been limited research on how these multicomponent interventions work to reduce drinking. Specifically, few studies have examined whether changes in perceived peer drinking norms or perceived health risks actually help explain reductions in alcohol use.
This study analyzed data from a randomized controlled trial of a smartphone app intervention designed to reduce alcohol use among college students by providing personalized normative feedback and health risk information. The objective was to determine whether the app’s effectiveness could be explained by changes in students’ perceptions of how much their peers drink and the degree to which they believed their own drinking was risky. The researchers also examined whether these indirect effects on recalibrated drinking norms were strongest among students who overestimated peer drinking to a greater degree – as this group, theoretically, would benefit the most from this type of intervention.
This study was a secondary analysis of a randomized controlled trial conducted in 2021 at 4 universities in Switzerland. The trial tested the effects of a smartphone app for reducing alcohol use in college students. Participants were 1,770 students (mean age = 22 years) who screened positive for hazardous alcohol use (defined as scoring >4 for men and >3 for women on the Alcohol Use Disorders Identification Test-Consumption [AUDIT-C ]) and were randomly assigned to receive either access to the app (n = 884) or no intervention (control group: n = 886). Very high numbers of participants (95%) completed the 3-month and 6-month follow-ups. The smartphone app intervention included 6 components (see graphic below).
At baseline, students reported drinking an average of 8.6 standard drinks per week and having 3.5 heavy drinking days in the prior month. On average, participants believed their peers consumed alcohol slightly more (8.7 drinks per week, 3.7 heavy drinking days), and 78% overestimated their peers’ alcohol use. Participants also perceived relatively low personal health risks associated with their drinking (mean risk rating = 2.35 out of 10). The primary outcome was the average number of standard drinks consumed per week at 6 months, calculated from drinking frequency and quantity over the past 30 days. The secondary outcome was the number of heavy drinking days (defined as > 5 drinks for men and >4 drinks for women in a day) during the last month. The study examined two mediators, or variables thought to explain how the intervention worked : 1) perceived drinking norms – assessed by asking participants to estimate how much a typical peer of the same age and sex drank weekly and how often they engaged in heavy drinking, and 2) perceived health risks – measured by asking participants to rate the potential health risks associated with their drinking on a scale ranging from 1 (no risk) to 10 (high risk). To identify students who overestimated peer drinking, participants were asked what percentage of people their age and sex they believed drank more than they did. This estimate was compared to actual Swiss population data. If a participant’s guess was more than 5 percentage points higher than the real number, they were classified as having overestimated others’ drinking.
The researchers tested whether changes in alcohol consumption at 6 months were explained by earlier changes in perceived peer drinking norms and health risks at 3 months. They also conducted moderated mediation analysis to explore whether these mechanisms of change differed between students who initially overestimated peer drinking and those who did not. As shown in the graphic below, the intervention was anticipated to improve drinking outcomes by changing perceived norms and risks. They also anticipated it would work better for participants that overestimated their peers’ drinking to a greater degree because they had more opportunity to benefit from an intervention focused on changing these peer drinking norms.
The intervention produced drinking reductions at least in part by correcting perceptions of peer drinking norms
The intervention significantly reduced students’ weekly alcohol consumption over 6 months compared to the control. A key factor contributing to this reduction was the app’s impact on students’ beliefs about peer drinking. Specifically, the intervention led to statistically significant changes in perceived drinking norms at 3 months, which were then associated with lower weekly drinking levels at 6 months. These changes in perceived drinking norms accounted for approximately 14% of the intervention’s total effect on reducing weekly drinking (see graph below). The intervention also led to greater reductions in the number of heavy drinking days at 6 months relative to the control group. While the app’s direct effect on heavy drinking days was statistically significant, changes in perceived drinking norms did not explain this reduction.
Perceived health risks did not mediate drinking outcomes
Although the app included feedback about the health risks of drinking, the intervention did not significantly increase students’ perceptions of health risks associated with their drinking, and perceived health risks did not account for reductions in either weekly drinking volume or number of heavy drinking days.
The reduction in drinking through corrected norms was only significant for those who had misperceptions about peer drinking at baseline
The intervention’s effect on changing perceived norms – and subsequently reducing alcohol use – was significant only among students who initially overestimated how much their peers drink. For these students (78% of the entire sample), changes in perceived drinking norms at 3 months were associated with greater reductions in both weekly drinking and heavy drinking days at 6 months. In this subgroup, shifts in perceived drinking norms explained 17% for the intervention’s effect on weekly drinking volume and 19% of its effect on heavy drinking days. In contrast, students who began the study with more accurate perceptions of peer drinking did not experience the same benefits from the normative feedback.
Results from this study reinforce the value of addressing misperceptions about peer drinking as a strategy to reduce alcohol use among college students. Findings add to growing evidence supporting the effectiveness of digital interventions that use personalized normative feedback, particularly for students who overestimate how much their peers drink. In contrast, perceived health risks did not explain changes in drinking behavior, suggesting that this approach may be less relevant for young adults who exhibit some heavy drinking. Notably, the two proposed mediators – perceived norms and perceived risks – accounted for modest portion of the intervention’s overall effect on drinking, highlighting the need for further research to clarify the mechanisms through which brief alcohol interventions influence alcohol-related behavior change.
The finding that the intervention led to changes in students’ perceptions of peer drinking norms at 3 months – and that these changes in perceptions were associated with reduced weekly drinking volume at 6 months – is consistent with prior research on personalized normative feedback interventions and their use in digital interventions. This suggests that perceived drinking norms may be one of the mechanisms through which brief alcohol interventions help college students reduce their alcohol use. However, these changes in perceived peer drinking norms explained only 14% of the intervention’s overall effect, indicating that much of how the intervention reduces drinking remains unclear. This highlights the need to identify which components of multicomponent interventions are most effective, as personalized normative feedback alone may not be sufficient to produce lasting reductions in alcohol use.
Notably, the intervention’s effects that were explained in part by changes in their perceived peer drinking norms depended on students’ perceptions of peer drinking at the start of the study. Those who overestimated their peers’ alcohol use experienced greater benefits from the intervention. This finding aligns with the theoretical underpinnings of personalized normative feedback, which state that overestimating peer drinking contributes to heavier personal alcohol use, and that correcting these misperceptions can lead to reductions in drinking. In contrast, students who already had accurate perceptions of peer drinking perhaps somewhat obviously may not benefit as much from normative feedback alone and may require additional strategies to support drinking reductions.
The finding that health risk feedback is less effective among young adults is not unexpected, as this age group tends to be less focused on long-term health consequences. While incorporating health risk information into interventions is appropriate, this content alone may not be sufficient to produce meaningful changes in alcohol-related behavior among young adults who show some patterns of recent heavy drinking. Interventions centered primarily on health risks may be better suited for older populations, who are typically more responsive to messages about health risks.
Digital alcohol interventions that correct misperceptions about peer drinking can help reduce alcohol use among college students, particularly for those who believe their peers drink more than they actually do. In contrast, health risk feedback may be less effective for this age group, as young adults, on average, may be less concerned about long-term consequences. Smartphone-based tools that deliver personalized normative feedback offer a scalable and promising approach for reducing alcohol-related harm on college campuses. However, additional intervention components may be needed to enhance and sustain their effects. Further research is needed to determine which features of these digital interventions are most effective in driving meaningful changes in alcohol-related behavior.
Studer, J., Cunningham, J. A., Schmutz, E., Gaume, J., Adam, A., Daeppen, J. B., & Bertholet, N. (2025). Smartphone-based intervention targeting norms and risk perception among university students with unhealthy alcohol use: Secondary mediation analysis of a randomized controlled trial. Journal of Medical Internet Research, 27. doi: 10.2196/55541.
l
Young adults (e.g., ages 18-25) – many of whom are college students – engage in the highest levels and most hazardous forms of alcohol use of any age group. According to social norms theory, students calibrate their drink to levels considered typical among their peers (i.e., “peer norms”). Research consistently shows that students often overestimate how much their peers drink, and these inflated perceptions can lead to heavier personal alcohol use.
Brief interventions – whether delivered in person or digitally – that provide personalized normative feedback with accurate information about peer drinking levels have been shown to reduce alcohol consumption. Another approach, personalized health risk feedback, is grounded in health behavior theories such as the health belief model and aims to reduce risky drinking by increasing individuals’ awareness of potential health consequences. While many effective brief interventions include both approaches, there has been limited research on how these multicomponent interventions work to reduce drinking. Specifically, few studies have examined whether changes in perceived peer drinking norms or perceived health risks actually help explain reductions in alcohol use.
This study analyzed data from a randomized controlled trial of a smartphone app intervention designed to reduce alcohol use among college students by providing personalized normative feedback and health risk information. The objective was to determine whether the app’s effectiveness could be explained by changes in students’ perceptions of how much their peers drink and the degree to which they believed their own drinking was risky. The researchers also examined whether these indirect effects on recalibrated drinking norms were strongest among students who overestimated peer drinking to a greater degree – as this group, theoretically, would benefit the most from this type of intervention.
This study was a secondary analysis of a randomized controlled trial conducted in 2021 at 4 universities in Switzerland. The trial tested the effects of a smartphone app for reducing alcohol use in college students. Participants were 1,770 students (mean age = 22 years) who screened positive for hazardous alcohol use (defined as scoring >4 for men and >3 for women on the Alcohol Use Disorders Identification Test-Consumption [AUDIT-C ]) and were randomly assigned to receive either access to the app (n = 884) or no intervention (control group: n = 886). Very high numbers of participants (95%) completed the 3-month and 6-month follow-ups. The smartphone app intervention included 6 components (see graphic below).
At baseline, students reported drinking an average of 8.6 standard drinks per week and having 3.5 heavy drinking days in the prior month. On average, participants believed their peers consumed alcohol slightly more (8.7 drinks per week, 3.7 heavy drinking days), and 78% overestimated their peers’ alcohol use. Participants also perceived relatively low personal health risks associated with their drinking (mean risk rating = 2.35 out of 10). The primary outcome was the average number of standard drinks consumed per week at 6 months, calculated from drinking frequency and quantity over the past 30 days. The secondary outcome was the number of heavy drinking days (defined as > 5 drinks for men and >4 drinks for women in a day) during the last month. The study examined two mediators, or variables thought to explain how the intervention worked : 1) perceived drinking norms – assessed by asking participants to estimate how much a typical peer of the same age and sex drank weekly and how often they engaged in heavy drinking, and 2) perceived health risks – measured by asking participants to rate the potential health risks associated with their drinking on a scale ranging from 1 (no risk) to 10 (high risk). To identify students who overestimated peer drinking, participants were asked what percentage of people their age and sex they believed drank more than they did. This estimate was compared to actual Swiss population data. If a participant’s guess was more than 5 percentage points higher than the real number, they were classified as having overestimated others’ drinking.
The researchers tested whether changes in alcohol consumption at 6 months were explained by earlier changes in perceived peer drinking norms and health risks at 3 months. They also conducted moderated mediation analysis to explore whether these mechanisms of change differed between students who initially overestimated peer drinking and those who did not. As shown in the graphic below, the intervention was anticipated to improve drinking outcomes by changing perceived norms and risks. They also anticipated it would work better for participants that overestimated their peers’ drinking to a greater degree because they had more opportunity to benefit from an intervention focused on changing these peer drinking norms.
The intervention produced drinking reductions at least in part by correcting perceptions of peer drinking norms
The intervention significantly reduced students’ weekly alcohol consumption over 6 months compared to the control. A key factor contributing to this reduction was the app’s impact on students’ beliefs about peer drinking. Specifically, the intervention led to statistically significant changes in perceived drinking norms at 3 months, which were then associated with lower weekly drinking levels at 6 months. These changes in perceived drinking norms accounted for approximately 14% of the intervention’s total effect on reducing weekly drinking (see graph below). The intervention also led to greater reductions in the number of heavy drinking days at 6 months relative to the control group. While the app’s direct effect on heavy drinking days was statistically significant, changes in perceived drinking norms did not explain this reduction.
Perceived health risks did not mediate drinking outcomes
Although the app included feedback about the health risks of drinking, the intervention did not significantly increase students’ perceptions of health risks associated with their drinking, and perceived health risks did not account for reductions in either weekly drinking volume or number of heavy drinking days.
The reduction in drinking through corrected norms was only significant for those who had misperceptions about peer drinking at baseline
The intervention’s effect on changing perceived norms – and subsequently reducing alcohol use – was significant only among students who initially overestimated how much their peers drink. For these students (78% of the entire sample), changes in perceived drinking norms at 3 months were associated with greater reductions in both weekly drinking and heavy drinking days at 6 months. In this subgroup, shifts in perceived drinking norms explained 17% for the intervention’s effect on weekly drinking volume and 19% of its effect on heavy drinking days. In contrast, students who began the study with more accurate perceptions of peer drinking did not experience the same benefits from the normative feedback.
Results from this study reinforce the value of addressing misperceptions about peer drinking as a strategy to reduce alcohol use among college students. Findings add to growing evidence supporting the effectiveness of digital interventions that use personalized normative feedback, particularly for students who overestimate how much their peers drink. In contrast, perceived health risks did not explain changes in drinking behavior, suggesting that this approach may be less relevant for young adults who exhibit some heavy drinking. Notably, the two proposed mediators – perceived norms and perceived risks – accounted for modest portion of the intervention’s overall effect on drinking, highlighting the need for further research to clarify the mechanisms through which brief alcohol interventions influence alcohol-related behavior change.
The finding that the intervention led to changes in students’ perceptions of peer drinking norms at 3 months – and that these changes in perceptions were associated with reduced weekly drinking volume at 6 months – is consistent with prior research on personalized normative feedback interventions and their use in digital interventions. This suggests that perceived drinking norms may be one of the mechanisms through which brief alcohol interventions help college students reduce their alcohol use. However, these changes in perceived peer drinking norms explained only 14% of the intervention’s overall effect, indicating that much of how the intervention reduces drinking remains unclear. This highlights the need to identify which components of multicomponent interventions are most effective, as personalized normative feedback alone may not be sufficient to produce lasting reductions in alcohol use.
Notably, the intervention’s effects that were explained in part by changes in their perceived peer drinking norms depended on students’ perceptions of peer drinking at the start of the study. Those who overestimated their peers’ alcohol use experienced greater benefits from the intervention. This finding aligns with the theoretical underpinnings of personalized normative feedback, which state that overestimating peer drinking contributes to heavier personal alcohol use, and that correcting these misperceptions can lead to reductions in drinking. In contrast, students who already had accurate perceptions of peer drinking perhaps somewhat obviously may not benefit as much from normative feedback alone and may require additional strategies to support drinking reductions.
The finding that health risk feedback is less effective among young adults is not unexpected, as this age group tends to be less focused on long-term health consequences. While incorporating health risk information into interventions is appropriate, this content alone may not be sufficient to produce meaningful changes in alcohol-related behavior among young adults who show some patterns of recent heavy drinking. Interventions centered primarily on health risks may be better suited for older populations, who are typically more responsive to messages about health risks.
Digital alcohol interventions that correct misperceptions about peer drinking can help reduce alcohol use among college students, particularly for those who believe their peers drink more than they actually do. In contrast, health risk feedback may be less effective for this age group, as young adults, on average, may be less concerned about long-term consequences. Smartphone-based tools that deliver personalized normative feedback offer a scalable and promising approach for reducing alcohol-related harm on college campuses. However, additional intervention components may be needed to enhance and sustain their effects. Further research is needed to determine which features of these digital interventions are most effective in driving meaningful changes in alcohol-related behavior.
Studer, J., Cunningham, J. A., Schmutz, E., Gaume, J., Adam, A., Daeppen, J. B., & Bertholet, N. (2025). Smartphone-based intervention targeting norms and risk perception among university students with unhealthy alcohol use: Secondary mediation analysis of a randomized controlled trial. Journal of Medical Internet Research, 27. doi: 10.2196/55541.
l
Young adults (e.g., ages 18-25) – many of whom are college students – engage in the highest levels and most hazardous forms of alcohol use of any age group. According to social norms theory, students calibrate their drink to levels considered typical among their peers (i.e., “peer norms”). Research consistently shows that students often overestimate how much their peers drink, and these inflated perceptions can lead to heavier personal alcohol use.
Brief interventions – whether delivered in person or digitally – that provide personalized normative feedback with accurate information about peer drinking levels have been shown to reduce alcohol consumption. Another approach, personalized health risk feedback, is grounded in health behavior theories such as the health belief model and aims to reduce risky drinking by increasing individuals’ awareness of potential health consequences. While many effective brief interventions include both approaches, there has been limited research on how these multicomponent interventions work to reduce drinking. Specifically, few studies have examined whether changes in perceived peer drinking norms or perceived health risks actually help explain reductions in alcohol use.
This study analyzed data from a randomized controlled trial of a smartphone app intervention designed to reduce alcohol use among college students by providing personalized normative feedback and health risk information. The objective was to determine whether the app’s effectiveness could be explained by changes in students’ perceptions of how much their peers drink and the degree to which they believed their own drinking was risky. The researchers also examined whether these indirect effects on recalibrated drinking norms were strongest among students who overestimated peer drinking to a greater degree – as this group, theoretically, would benefit the most from this type of intervention.
This study was a secondary analysis of a randomized controlled trial conducted in 2021 at 4 universities in Switzerland. The trial tested the effects of a smartphone app for reducing alcohol use in college students. Participants were 1,770 students (mean age = 22 years) who screened positive for hazardous alcohol use (defined as scoring >4 for men and >3 for women on the Alcohol Use Disorders Identification Test-Consumption [AUDIT-C ]) and were randomly assigned to receive either access to the app (n = 884) or no intervention (control group: n = 886). Very high numbers of participants (95%) completed the 3-month and 6-month follow-ups. The smartphone app intervention included 6 components (see graphic below).
At baseline, students reported drinking an average of 8.6 standard drinks per week and having 3.5 heavy drinking days in the prior month. On average, participants believed their peers consumed alcohol slightly more (8.7 drinks per week, 3.7 heavy drinking days), and 78% overestimated their peers’ alcohol use. Participants also perceived relatively low personal health risks associated with their drinking (mean risk rating = 2.35 out of 10). The primary outcome was the average number of standard drinks consumed per week at 6 months, calculated from drinking frequency and quantity over the past 30 days. The secondary outcome was the number of heavy drinking days (defined as > 5 drinks for men and >4 drinks for women in a day) during the last month. The study examined two mediators, or variables thought to explain how the intervention worked : 1) perceived drinking norms – assessed by asking participants to estimate how much a typical peer of the same age and sex drank weekly and how often they engaged in heavy drinking, and 2) perceived health risks – measured by asking participants to rate the potential health risks associated with their drinking on a scale ranging from 1 (no risk) to 10 (high risk). To identify students who overestimated peer drinking, participants were asked what percentage of people their age and sex they believed drank more than they did. This estimate was compared to actual Swiss population data. If a participant’s guess was more than 5 percentage points higher than the real number, they were classified as having overestimated others’ drinking.
The researchers tested whether changes in alcohol consumption at 6 months were explained by earlier changes in perceived peer drinking norms and health risks at 3 months. They also conducted moderated mediation analysis to explore whether these mechanisms of change differed between students who initially overestimated peer drinking and those who did not. As shown in the graphic below, the intervention was anticipated to improve drinking outcomes by changing perceived norms and risks. They also anticipated it would work better for participants that overestimated their peers’ drinking to a greater degree because they had more opportunity to benefit from an intervention focused on changing these peer drinking norms.
The intervention produced drinking reductions at least in part by correcting perceptions of peer drinking norms
The intervention significantly reduced students’ weekly alcohol consumption over 6 months compared to the control. A key factor contributing to this reduction was the app’s impact on students’ beliefs about peer drinking. Specifically, the intervention led to statistically significant changes in perceived drinking norms at 3 months, which were then associated with lower weekly drinking levels at 6 months. These changes in perceived drinking norms accounted for approximately 14% of the intervention’s total effect on reducing weekly drinking (see graph below). The intervention also led to greater reductions in the number of heavy drinking days at 6 months relative to the control group. While the app’s direct effect on heavy drinking days was statistically significant, changes in perceived drinking norms did not explain this reduction.
Perceived health risks did not mediate drinking outcomes
Although the app included feedback about the health risks of drinking, the intervention did not significantly increase students’ perceptions of health risks associated with their drinking, and perceived health risks did not account for reductions in either weekly drinking volume or number of heavy drinking days.
The reduction in drinking through corrected norms was only significant for those who had misperceptions about peer drinking at baseline
The intervention’s effect on changing perceived norms – and subsequently reducing alcohol use – was significant only among students who initially overestimated how much their peers drink. For these students (78% of the entire sample), changes in perceived drinking norms at 3 months were associated with greater reductions in both weekly drinking and heavy drinking days at 6 months. In this subgroup, shifts in perceived drinking norms explained 17% for the intervention’s effect on weekly drinking volume and 19% of its effect on heavy drinking days. In contrast, students who began the study with more accurate perceptions of peer drinking did not experience the same benefits from the normative feedback.
Results from this study reinforce the value of addressing misperceptions about peer drinking as a strategy to reduce alcohol use among college students. Findings add to growing evidence supporting the effectiveness of digital interventions that use personalized normative feedback, particularly for students who overestimate how much their peers drink. In contrast, perceived health risks did not explain changes in drinking behavior, suggesting that this approach may be less relevant for young adults who exhibit some heavy drinking. Notably, the two proposed mediators – perceived norms and perceived risks – accounted for modest portion of the intervention’s overall effect on drinking, highlighting the need for further research to clarify the mechanisms through which brief alcohol interventions influence alcohol-related behavior change.
The finding that the intervention led to changes in students’ perceptions of peer drinking norms at 3 months – and that these changes in perceptions were associated with reduced weekly drinking volume at 6 months – is consistent with prior research on personalized normative feedback interventions and their use in digital interventions. This suggests that perceived drinking norms may be one of the mechanisms through which brief alcohol interventions help college students reduce their alcohol use. However, these changes in perceived peer drinking norms explained only 14% of the intervention’s overall effect, indicating that much of how the intervention reduces drinking remains unclear. This highlights the need to identify which components of multicomponent interventions are most effective, as personalized normative feedback alone may not be sufficient to produce lasting reductions in alcohol use.
Notably, the intervention’s effects that were explained in part by changes in their perceived peer drinking norms depended on students’ perceptions of peer drinking at the start of the study. Those who overestimated their peers’ alcohol use experienced greater benefits from the intervention. This finding aligns with the theoretical underpinnings of personalized normative feedback, which state that overestimating peer drinking contributes to heavier personal alcohol use, and that correcting these misperceptions can lead to reductions in drinking. In contrast, students who already had accurate perceptions of peer drinking perhaps somewhat obviously may not benefit as much from normative feedback alone and may require additional strategies to support drinking reductions.
The finding that health risk feedback is less effective among young adults is not unexpected, as this age group tends to be less focused on long-term health consequences. While incorporating health risk information into interventions is appropriate, this content alone may not be sufficient to produce meaningful changes in alcohol-related behavior among young adults who show some patterns of recent heavy drinking. Interventions centered primarily on health risks may be better suited for older populations, who are typically more responsive to messages about health risks.
Digital alcohol interventions that correct misperceptions about peer drinking can help reduce alcohol use among college students, particularly for those who believe their peers drink more than they actually do. In contrast, health risk feedback may be less effective for this age group, as young adults, on average, may be less concerned about long-term consequences. Smartphone-based tools that deliver personalized normative feedback offer a scalable and promising approach for reducing alcohol-related harm on college campuses. However, additional intervention components may be needed to enhance and sustain their effects. Further research is needed to determine which features of these digital interventions are most effective in driving meaningful changes in alcohol-related behavior.
Studer, J., Cunningham, J. A., Schmutz, E., Gaume, J., Adam, A., Daeppen, J. B., & Bertholet, N. (2025). Smartphone-based intervention targeting norms and risk perception among university students with unhealthy alcohol use: Secondary mediation analysis of a randomized controlled trial. Journal of Medical Internet Research, 27. doi: 10.2196/55541.
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