Journal of Gambling Studies (2024) 40:307–332

https://doi.org/10.1007/s10899-023-10200-7

ORIGINAL PAPER

Development of Gambling Behaviour and Its Relationship

with Perceived Social Support: A Longitudinal Study
of Young Adult Male Gamblers

Andreas M. Bickl1 · Ludwig Kraus1,2,3 · Johanna K. Loy1 · Peter Kriwy4 ·

Pawel Sleczka5 · Larissa Schwarzkopf1,6

Accepted: 12 March 2023 / Published online: 14 April 2023

© The Author(s) 2023

Abstract
Young adult men who gamble frequently face an elevated risk of developing gambling-
related problems. So far, little is known about how changing levels of perceived social sup-
port interact with the course of gambling behaviour and gambling-related problems in this
population. Using data from a prospective single-arm cohort study (Munich Leisure Time
Study), we applied hierarchical linear models to investigate the longitudinal association
of changes in perceived emotional and social support (hereafter PESS; operationalized as
ENRICHD Social Support Instrument score) with gambling intensity, gambling frequency,
and fulfilled criteria for gambling disorder. Pooling data from three time points (baseline,
12-month and 24-month follow-ups) to assess two 1-year intervals, these models disentan-
gle the associations of (a) “level of PESS” (cross-sectional, between participants) and (b)
“changes in individual PESS” (longitudinally, within-participants). Among the 169 study
participants, higher levels of PESS were associated with fewer gambling-related problems
(− 0.12 criteria met; p = 0.014). Furthermore, increasing individual PESS was associated
with lower gambling frequency (− 0.25 gambling days; p = 0.060) and intensity (− 0.11
gambling hours; p = 0.006), and fewer gambling-related problems (− 0.19 problems;
p < 0.001). The results suggest a mitigating influence of PESS on gambling behaviour and
gambling-related problems. Increasing individual PESS appears more decisive for this
pathway than high initial levels of PESS. Treatment and prevention strategies that activate
and reinforce beneficial social resources in people with gambling-related problems are rec-
ommended and promising.

Keywords Gambling disorder · Social support · Longitudinal study · Young adults

* Ludwig Kraus
kraus@ift.de
Extended author information available on the last page of the article

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308 Journal of Gambling Studies (2024) 40:307–332

Background

The increasing availability of gambling services via everyday technologies has made
gambling convenient and accessible to many segments of the population (Sulkunen et al.,
2018). This development is of particular importance for young adult men, who have been
repeatedly identified as a risk group for the development of gambling-related problems
(Banz, 2019; Buth et al., 2022; Shead et al., 2010; Welte et al., 2008). Although most stud-
ies do not explicitly distinguish between adolescence and young adulthood, a recent meta-
analysis of studies of college students revealed that approximately 10% of students met the
criteria for problem gambling and another 6% met the criteria for pathological gambling.
Furthermore, male gender was substantiated as a significant predictor of higher rates of
pathological gambling (Nowak, 2018).
Compared to during middle and late adulthood, people in the early stages of life are
often less resilient, have less impulse and self-control, and behave in a more risk-seeking
manner, i.e., they tend to develop increased risk-taking behaviour, which can be accom-
panied by feelings of omnipotence (Meyer & Bachmann, 2017). Especially in the context
of gambling, these tendencies are reinforced by greater needs for sensation seeking (Riley
et al., 2021), illusions of control over the outcome of games (Moore & Ohtsuka, 1999),
poor understanding of statistical probability (Delfabbro et al., 2006), and group-oriented
behaviour (Zhai et al., 2017). Given this background, the development of effective pre-
ventive and treatment strategies that protect young people from gambling-related harms is
paramount.
Predictive factors, i.e., risk and protective factors, reflect traits or exposures that sup-
posedly influence the likelihood of problem gambling. So far, a wide range of risk factors
associated with problem gambling has been identified in multiple original studies and sys-
tematic reviews (Beynon et al., 2020). Focussing on problem gambling in young people,
these risk factors are—amongst others—male gender, lower socio-economic status, early
gambling onset, an extroverted or impulsive personality, maladaptive coping styles, stress,
substance use, ADHD symptoms, anxiety, depression, peer influence, poor academic per-
formance, parental substance abuse or problem gambling, inconsistent parental discipline,
and family problems (Dowling et al., 2017; Riley et al., 2021). However, previous research
emphasized factors that enhance the risk of problem gambling but paid little attention to
protective factors that reduce this risk. These factors are often attributable to interpersonal
and community level dimensions (Dowling et al., 2017).
Indeed, a stronger emphasis on social factors as important determinants of problem
gambling could be a promising approach to youth-based interventions (Donati et al., 2013).
Parental (Vachon et al., 2004; Walters, 2021) and peer gambling behaviour (Langhinrich-
sen-Rohling et al., 2004; Zhai et al., 2017) have been established as risk factors for the
development of problem gambling. On the other hand, social support, defined as the provi-
sion of psychological and material resources by a social network, has been identified as
a protective counteracting factor (Cohen, 2004). Social support can bolster individuals’
capacities to find alternatives to harmful behaviour, provide moral guidance, and facili-
tate problem recognition. Social support has been proven to act as a buffer during periods
of stress and negative life events in smokers (Hershberger et al., 2016), problem drinkers
(Pauley & Hesse, 2009) and people with internet addiction (Esen & Gündoğdu, 2010).
These support mechanisms may also work among people with gambling problems,
as some evidence indicates that adolescents who gamble perceive their familial and peer
social support as poor (Hardoon et al., 2004) and their social support is lower compared to

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Journal of Gambling Studies (2024) 40:307–332 309

their peers who do not gamble (Weinstock & Petry, 2008). In addition, low baseline social
support level was associated with increased severity of gambling, family, and psychiatric
problems, and poorer post-treatment outcomes, in a sample of help-seeking pathological
gamblers (Petry & Weiss, 2009). Furthermore, a study of Finnish eighth- and ninth grad-
ers revealed that social support from parents and school staff helped reduce gambling fre-
quency (Räsänen et al., 2016).
However, most of the evidence relies on cross-sectional data (Dowling et al., 2017; Har-
doon et al., 2004; Räsänen et al., 2016; Weinstock & Petry, 2008), refers exclusively to
adolescent or other (i.e. older) adult populations (Bilt et al., 2004; Hardoon et al., 2004),
or disregards all gambling-related behaviours and other dimensions except problem sever-
ity (Edgerton et al., 2015; Weinstock & Petry, 2008). Studies on longitudinal associations
between social support and changes in both gambling behaviour and gambling-related
problems are largely lacking, especially for the epidemiologically relevant group of young
adult men.
To close some of these knowledge gaps, this paper analyses a cohort of young adult
male gamblers with elevated risk of developing gambling-related problems. Based on a
study period of two years, it (1) depicts categorical changes in perceived emotional and
social support (PESS), gambling-related problems, and gambling behaviour over time; (2)
indicates the annual change in their respective outcome measures (ENRICHD Social Sup-
port Instrument (ESSI) score; number of gambling disorder (GD) diagnostic criteria ful-
filled; gambling frequency and intensity); and (3) investigates how changes in PESS inter-
act with changes in gambling behaviour and gambling-related problems.

Methods

Study Design and Procedure

Data stem from assessments performed at baseline (T0), 12-month follow-up (T1), and
24-month follow-up (T2) of the Munich Leisure-time Study (MLS), an online-based longi-
tudinal cohort study of young adult male gamblers conducted between 2014 and 2016. The
MLS analysed individuals’ information on gambling behaviour, gambling-related prob-
lems, attitudes towards gambling, gambling motives, coping strategies, personality traits,
and substance use. In addition, PESS, perceived availability of social resources, and the
participants’ social environment were assessed. Further details of the study’s design, meas-
ures, and methods have been published elsewhere (Sleczka et al., 2016, 2018).

Participants

The MLS included males aged 18–25 who were recruited using two methods: (1) random
sampling of 25,000 men in that age group drawn from the Munich citizens’ registry and
(2) sending open invitations via targeted ads for study participation to Facebook users
with gambling listed as an interest on their profiles in order to gather a convenience sam-
ple. First, interested persons were screened for gambling behaviour and gambling-related
problems. Persons were invited to participate in the longitudinal study (at T0, T1, and T2)
if they met at least one of the following requirements: they (a) fulfilled at least one cri-
terion for gambling disorder (GD) (Stinchfield, 2003); (b) scored positively on the Lie-
Bet questionnaire (Johnson et al., 1997); or (c) reported gambling once a week (i.e. they

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answered “yes” to the open question “Do you participate in gambling activities at least
once a week?”);
Of the 2588 individuals from the Munich citizens’ registry who completed the screen-
ing, 328 were considered eligible. Of those, 115 responded at T0. Five participants were
excluded retrospectively because of missing values or inconsistent responses, leaving 110
participants from the citizens’ registry. Another 105 participants were recruited via Face-
book. Of those, 12 were excluded due to suspicion of multiple survey responses (based
on IP addresses). Of the remaining 93 participants, 70 were considered eligible. After
removing seven participants due to repeated non-varying answers, 63 participants from
Facebook remained.
Of the entire sample (i.e., participants from Facebook and citizen’s registry) (n = 173),
four participants did not respond to items in the ESSI at T0, T1, or T2 and therefore
were excluded. Thus, 169 study participants were included in the analyses. Of those,
118 (69.8%) responded only at T1; 124 (73.4%) responded only at T2; and 112 (66.3%)
responded at T0, T1, and T2.

Materials

Gambling‑Related Problems and Gambling Behaviour

Gambling-related problems during the 12 months prior to T0, T1, and T2 were assessed
based on the DSM-IV-oriented “Stinchfield criteria”, a validated tool to operational-
ize severity of GD (Stinchfield, 2003). The tool (hereafter: GD criteria questionnaire)
was translated into German. To adapt it to fit the DSM-5, items about the eighth crite-
rion referring to illegal activities were removed. Of the remaining 17 yes–no items, two
items address nine criteria for GD and one item addresses a single criterion (“withdrawal”)
(Table B1). Each criterion was considered fulfilled if at least one of the questions regarding
that criterion was answered with “yes”. The sum of the number of criteria met was calcu-
lated at each time point (T0, T1, T2), yielding a score ranging from 0 to 9 (hereafter: GD
score). The GD score’s internal reliability (Cronbach’s alpha) was α = 0.83 at T0, α = 0.91
at T1, and α = 0.85 at T2. Gambling behaviour during the 12 months prior to each time
point consisted of two components: (1) the average number of gambling days per month
(hereafter: gambling frequency) and (2) the average hours spent gambling per gambling
day (hereafter: gambling intensity).

Level of Perceived Emotional and Social Support (PESS)

PESS was assessed using a standardized, validated German Version of the ENRICHD
Social Support Instrument (ESSI) (Cordes et al., 2009; Kendel et al., 2011). The ESSI
measures PESS based on five items (Table B2). Each item is rated on a 5-point Likert scale
with higher scores indicating a higher level of PESS. The level of PESS was obtained by
calculating the unweighted sum of all items (range: 5 to 25). To differentiate between indi-
viduals with low and high PESS, a score of ≤ 3 on 2 or more of the items and cut-off value
of ≤ 18 for the total score was applied (Kendel et al., 2011). The ESSI was completed at T0,
T1, and T2); change in PESS was calculated as the difference between the ESSI score at a
given time point and the ESSI score at the previous time point. The internal consistency of
the ESSI (Cronbach’s alpha) was α = 0.92 at T0, α = 0.92 at T1, and α = 0.91 at T2.

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 Table 1  Participant demographics, PESS, and gambling characteristics, with comparison of participants with and without ESSI score ≤ 18 at baseline
Variables Baseline ESSI score > 18 ESSI score ≤ 18 Comparison Associated
(n = 169)† (n = 129)† (n = 38)† test statistics probability (p
value)

Migration background
n, (%) n = 169 n = 129 n = 38 2.13a 0.144
Yes 46 (27.2%) 32 (24.8%) 14 (36.8%)
No 121 (72.8%) 97 (75.2%) 24 (63.2%)
Partnership status (Having a partner)
n, (%) n = 164 n = 125 n = 37 15.31a 0.000
Yes 63 (38.4%) 58 (46.4%) 4 (10.8%)
No 101 (61.6%) 67 (53.6%) 33 (89.2%)
Journal of Gambling Studies (2024) 40:307–332

Tobacco use within last 12 months

n, (%) n = 168 n = 129 n = 38 − 0.67a 0.502
Less than twice a week 114 (67.9%) 89 (69.0%) 24 (63.2%)
Twice per week or more 54 (32.1%) 40 (31.0%) 14 (36.8%)
Alcohol use within last 12 months
n, (%) n = 168 n = 127 n = 36 1.27a 0.205
Less than twice a week 104 (63.4%) 77 (60.6%) 26 (72.2%)
Twice per week or more 60 (36.6%) 50 (39.4%) 10 (27.8%)
ESSI score1 n = 167 n = 129 n = 38 18.86b 0.000
M, (SD) 21.2 (4.2) 23.1 (2.0) 14.8 (3.3)
SCL-90-R
Depression Scale (DEP)² n = 168 n = 129 n = 38 − 5.00b 0.000
M, (SD) 0.4 (0.6) 0.3 (0.5) 0.8 (0.9)
Anxiety Scale (ANX)³ n = 168 n = 129 n = 38 − 3.44b 0.000
M, (SD) 0.3 (0.5) 0.2 (0.4) 0.5 (0.7)
Gambling characteristics
GD ­score4 n = 169 n = 129 n = 38 − 1.21b 0.229
311

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 Table 1  (continued)
312

Variables Baseline ESSI score > 18 ESSI score ≤ 18 Comparison Associated

(n = 169)† (n = 129)† (n = 38)† test statistics probability (p

13
value)

M, (SD) 1.4 (2.0) 1.3 (1.9) 1.7 (2.5)

Gambling frequency (mean number of gambling days/month) n = 160 n = 122 n = 37 1.64b 0.104
M, (SD) 7.4 (7.8) 8.0 (8.2) 5.6 (6.2)
Gambling intensity (mean hours of gambling/gambling day) n = 168 n = 129 n = 38 − 1.66b 0.100
M, (SD) 2.2 (2.4) 2.1 (2.2) 2.8 (3.2)

PESS perceived emotional and social support, ESSI ENRICHD Social Support Instrument, GD gambling disorder
†
For some analyses, n differed due to missings and is reported separately
a
Pearson chi-square test for ordinal, nominal variables
b
Student’s t-test for interval variables
1
ESSI score (measure of PESS): unweighted sum of all ESSI items
2
SCL-90-R Depression Scale (DEP): unweighted mean of all items on depression
3
SCL-90-R Anxiety Scale (ANX): unweighted mean of all items on anxiety
4
GD score (measure of gambling-related problems): sum of no. of fulfilled DSM-5 criteria for GD based on participant endorsement of criteria (via yes–no question)
*p < 0.1, **p < 0.05, ***p < 0.01
Journal of Gambling Studies (2024) 40:307–332
 Table 2  Mixed model outputs: changes in PESS, gambling-related problems, gambling frequency, and gambling intensity in entire sample
T0 T1 T2 First year ­change1 Second year c­ hange1
(T0–T1) (T1–T2)
Adjusted SE (90% CI) Adjusted SE (90% CI) Adjusted SE (90% CI)
Mean Mean Mean
Δ between p value Δ between p value
T0–T1 T1–T2

ESSI ­score2 21.08 0.31 (20.57–21.59) 20.52 0.35 (19.95–21.10) 20.88 0.36 (20.28–21.48) − 0.56 0.1487 0.36 0.3531
Gambling characteristics
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GD ­score3 1.69 0.31 (1.18–2.20) 1.66 0.31 (1.16–2.16) 2.22 0.40 (1.56–2.89) − 0.03 0.8945 0.56 0.0509*
Gambling 7.97 0.91 (6.47–9.47) 7.32 0.93 (5.79–8.86) 6.07 0.79 (4.78–7.36) − 0.65 0.4055 − 1.25 0.0943*
­frequency4
Gambling 2.20 0.16 (1.94–2.46) 1.91 0.18 (1.62–2.20) 1.92 0.17 (1.64–2.20) − 0.29 0.1640 0.01 0.9615
­intensity5

PESS perceived emotional and social support, ESSI ENRICHD Social Support Instrument, GD gambling disorder
Data are presented as adjusted means, standard error (SE), 90% confidence interval (CI)
1
p values based on Wald test statistics
2
ESSI score (measure of PESS): unweighted sum of all ESSI items
3
GD score (measure of gambling-related problems): sum of no. of fulfilled DSM-5 criteria for GD based on participant endorsement of criteria (via yes–no question)
4
Mean no. of gambling days/month
5
Mean hours spent gambling/gambling day
*p < 0.1, **p < 0.05, ***p < 0.01
313

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Covariates Considered

As having a partner is known to positively affect the uptake of treatment for gambling-
related problems (Ingle et al., 2008), partnership status (yes/no) at T0 was included as a
covariate in our statistical models. Migration background represents a relevant risk fac-
tor for the development of maladaptive and problematic gambling behaviour (Donati et al.,
2013; Kastirke et al., 2015). Hence, having a migration background (yes/no) (defined as
having migrated to Germany oneself or being born in Germany as a (grand)son of people
who had immigrated to Germany) was also included in the models.
Initially we also intended to include the onset of gambling (defined as the age when
gambling started), because it plays a crucial role in the development of subsequent gam-
bling-related problems (Jiménez-Murcia et al., 2016). As that covariate seriously hampered
model convergence without showing any significant association with the gambling charac-
teristics of interest, we refrained from including it.
Furthermore, psychological problems (i.e., depression and anxiety) and concomitant
alcohol- or tobacco use are known to be intertwined with gambling behaviour (Jauregui
et al., 2016; McGrath & Barrett, 2009). Therefore, we addressed regular alcohol and
tobacco use via self-reported consumption patterns in the last 12 months (0 = less than
twice a week/1 = twice per week or more) and psychological problems in form of anxiety
(10 items) and depression (13 items) via the corresponding subscales (mean value on a
5-point Likert scale) of the Symptom Checklist-90-revised (Derogatis & Unger, 2010) in a
sensitivity analysis.

Statistical Analysis

The entire sample’s characteristics were analysed using summary statistics (means and
standard deviations (SD), or frequencies and percentages) at T0. The socio-demographic
and gambling characteristics of participants with low and high PESS (ESSI score > 18 vs.
ESSI score ≤ 18) were compared using χ2 and t-tests.
To evaluate directional changes in gambling behaviour and the extent of gambling-
related problems, the differences between values for GD score, gambling frequency, and
gambling intensity observed at consecutive time points (Δ T1–T0; Δ T2–T1) were calcu-
lated. Subsequently, we calculated the proportions of individuals who remained stable (dif-
ference = 0), improved (difference < 0), or deteriorated (difference > 0).
Mixed-effects regression models were used to estimate covariate-adjusted, continuous
within-participant changes in GD score, gambling frequency, and gambling intensity. As
these outcome measures are count data with right skewed distributions, we used negative
binomial models. Since the negative binomial model for changes in the ESSI score did not
converge, a Poisson regression model was implemented instead.
To analyse the impact of PESS on gambling behaviour and gambling-related prob-
lems, we applied hierarchical linear models (HLM), which are suited to analysing unbal-
anced longitudinal datasets and allow the simultaneous inclusion of time-invariant and
time-variant covariates. In other words, these models enable disentangling the effects of
participant-specific changes in a distinct variable (longitudinal, within-participant change
in ESSI score) from effects of that variable on the sample level (cross-sectional, between-
participant differences in ESSI score at T0, T1, and T2) (Hedeker, 2004). To do so, our
model considered observations to be nested in individuals and operationalized the original

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Journal of Gambling Studies (2024) 40:307–332 315

independent variable (ESSI score) as mean over time (between-participant differences) and
the deviation from the mean over time (within-participant change).
( ) ( )
Outcomeji = 𝛽0 + 𝛽Time (Time)ji + 𝛽BS ESSI i + 𝛽WS ESSI ji − ESSI i + 𝛽ji Xji + 𝜐0i + 𝜀ji
(1)
The
( HLM ) is illustrated in Eq. 1 and contains
( the following
) terms: a between-participant
𝛽BS ESSI i and a within-participant 𝛽WS ESSI ji − ESSI i component; a term accounting
for the influence of the individual on his own repeated observations (𝜐0i ), an intercept (𝛽0);
linear change over time (𝛽Time (Time)ji ); the covariates mentioned
( ) above (𝛽ji Xji ); and an
error term (𝜀ji ). The between-participant component 𝛽BS ESSI i is defined as the person-
specific mean value of the ESSI score over all time points and allows determining the influ-
ence of higher levels of PESS itself (via(cross-sectional,)between-participant comparisons).
The within-participant component 𝛽WS ESSI ji − ESSI i reflects how an individual partici-
pant’s PESS at a given time point differs from that individual’s mean PESS over all time
points. Thus, the influence of increasing PESS (longitudinal, within-participant change)
can be modelled.
To examine the robustness of our results and to test whether participants with low PESS
differed from those with high PESS at T0, we repeated our analysis, excluding the 38 par-
ticipants with low levels of PESS at T0 (sensitivity analysis 1; SA1). Within the second
sensitivity analysis we additionally accounted for some aspects of psychological burden by
including information on concomitant anxiety or depression and regular consumption of
alcohol or tobacco, respectively (sensitivity analysis 2; SA2). All statistical analyses were
conducted using Stata/SE 15.1 (Stata Corp LP; College Station, TX, USA). An alpha level
of 0.1 was used to account for the small sample size.

Ethics

This study received ethical approval from the ethics committee of the German Association
of Psychology (reference number: LK092014).

Results

Sample Description

At T0, 27.2% (n = 46) of the 169 participants had a migration background and 38.4%
(n = 63) had a partner (Table 1). Within the past 12 months, 32.1% of the 54 participants
(n = 54) reported to have used tobacco at least twice per week, 36.6% of the participants
(n = 60) admitted consuming alcohol at least twice per week. The participants’ mean ESSI
score was 21.2 (SD = 4.2) and their mean GD score was 1.4 (SD = 2.0). On the SCL-90-R
depression scale, they scored 0.4 (SD = 0.6), and on the SCL-90-R anxiety scale, their
score was 0.3 (SD = 0.5). They gambled on average, 7.4 (SD = 7.8) days per month with an
average of 2.2 (SD = 2.4) hours per gambling day. At T0, 23 respondents (13.6%) fulfilled
the diagnostic criteria for GD.

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Respondents with low levels of PESS less frequently had a partner, achieved higher
scores on depression and anxiety, gambled less frequently but with higher intensity than
participants with high levels of PESS (Table 1).

Changes in Perceived Social Support and Gambling‑Related Outcomes During

the Observation Period

At T1, PESS had increased in 40.2% (n = 47) of the respondents and decreased in 44.4%
(n = 52). In parallel, the GD score had improved in 30.8% (n = 52) but deteriorated in
26.0% (n = 44), gambling frequency was reduced in 46.6% (n = 49) and increased in 39.1%
(n = 41), and gambling intensity was lower in 34.9% (n = 37) yet higher in 27.4% (n = 29)
(Table 4 in"Appendix").
Compared to T1, at T2, PESS had increased in 41.2% (n = 46) and decreased in 38.4%
(n = 43) of the respondents. In parallel, the GD score had improved in 30.6% (n = 38) but
deteriorated in 32.3% (n = 40), gambling frequency was reduced in 42.7% (n = 44) but
increased in 31.1% (n = 32), and gambling intensity was lower in 25.5% (n = 25) but higher
in 31.6% (n = 31) (Table 4 in "Appendix").
In the entire sample, no significant changes were detected in ESSI score or gambling
characteristics (i.e. gambling-related problems, gambling frequency, gambling intensity)
between T0 and T1, whilst between T1 and T2 we observed a statistically significant
increase in GD score (0.56; p < 0.051) and a statistically significant reduction in gambling
frequency (− 1.25; p < 0.094) (Table 2).

Associations of PESS with Development of Gambling‑Related Outcomes

Higher levels of PESS (cross-sectional, between-participants comparison) were associated

with a significantly improved GD score after twelve months (− 0.12 criteria; p = 0.014)
(Table 3). Within-participant PESS was associated with the following statistically signifi-
cant changes after twelve months: improved GD score (− 0.19 criteria; p < 0.001), reduced
gambling frequency (− 0.25 days; p = 0.060), and decreased gambling intensity (− 0.11 h;
p = 0.006) (Table 3).

Sensitivity Analysis

Within the SA1 we observed lower mean GD scores and higher mean gambling frequency
at T0, T1, and T2, as well as a slightly lower mean gambling intensity at baseline, com-
pared with the main analysis. In addition, the mean PESS was slightly higher at each time
point (Table 6 in "Appendix"). Unlike in the main analysis, there was no statically sig-
nificant increase of the GD score between T0 and T1 (0.35 criteria; p = 0.189) but there
was a statistically significant decrease of PESS (− 1.69 units; p < 0.001). Furthermore, the
reduction in gambling frequency between T1 and T2 was more pronounced (− 1.95 days;
p < 0.045).
Within the HLM models, the between-subject (cross-sectional) comparisons showed
associations between PESS level and GD score were more pronounced (− 0.21 criteria;
p < 0.001) (Table 7 in "Appendix"). Furthermore, there was a statistically significant neg-
ative association between PESS level and gambling frequency (− 0.46 days; p = 0.017).
The within-participant (i.e., longitudinal change) estimators for gambling frequency and

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Table 3  Estimates for associations of cross-sectional PESS level and longitudinal PESS with gambling-
related problems, gambling frequency, and gambling intensity
GD ­score1 Gambling ­frequency2 Gambling ­intensity3
Estimate [90% CI] Estimate [90% CI] Estimate [90% CI]

ESSI ­score4: between- − 0.12** − 0.17 − 0.05

participants [− 0.21 to − 0.02] [− 0.11 to 0.46] [− 0.12 to 0.02]
ESSI ­score4: within- − 0.19*** − 0.25* − 0.11**
participants [− 0.26 to − 0.12] [− 0.51 to 0.01] [− 0.19 to − 0.03]

ESSI ENRICHD Social Support Instrument, PESS perceived emotional and social support, GD gambling
disorder
Hierarchical linear models (HLM) adjusted for migration background and partnership status
Interpretation: Negative estimates indicate improvement in the respective gambling characteristic. ESSI
score between-participants: cross-sectional differences in PESS level between participants. ESSI score
within-participants: longitudinal change in PESS within individual participants over time
1
GD score (measure of gambling-related problems): sum of no. of fulfilled DSM-5 criteria for GD based on
participant endorsement of criteria (via yes–no question)
2
Mean no. of gambling days/month
3
Mean hours spent gambling/gambling day
4
ESSI score (measure of PESS): unweighted sum of all ESSI items
*p < 0.10; **p < 0.05; ***p < 0.01

gambling intensity effects became weaker and lost statistical significance in the sensitivity
analysis (Table 7 in "Appendix").
Within SA2 only regular tobacco consumption was consistently associated with worse
GD score as well as worse gambling frequency and intensity. Anxiety and regular alcohol
consumption only affected the GD score detrimentally (Table 9 in "Appendix").
Within the HLM models, the between-subject (cross-sectional) comparisons showed
associations between PESS level and GD score lost statistical significance and the within
participant (i.e., longitudinal change) estimators were less pronounced (− 0.06 criteria;
p = 0.062; Table 9 in "Appendix"). Vice versa to the main analyses the association between
PESS level and gambling frequency was significant for the between estimator (0.28 days;
p = 0.074) but not for the within estimator. Regarding gambling intensity, the associations
for the within estimator were slightly less pronounced (− 0.10 h; p = 0.038).

Discussion

Pooling data from two consecutive 12-months intervals, we delineated associations of

(changing) PESS with gambling-related problems, gambling frequency, and gambling
intensity. The analyses revealed that an increasing within-participant PESS over time is
associated with beneficial changes in all three gambling characteristics, whilst higher cross-
sectional levels of PESS were only associated with reduced gambling-related problems.
Using the established cut-off of an ESSI score ≤ 18 as an indicator for low PESS
(Berkman et al., 2000), the mean PESS was inconspicuous in our entire sample at T0
(mean = 21.2). Only 38 of 167 study participants (22.8%) were categorized as having low
PESS at T0, which is consistent with an acknowledged ceiling effect of the ESSI score
(Kendel et al., 2011). It is worth noting that these participants reported significantly

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318 Journal of Gambling Studies (2024) 40:307–332

higher gambling intensity at baseline (mean = 2.8 h per day versus mean = 2.1 h per day
among those without low PESS; p = 0.100), which supports existing evidence on associa-
tions between gambling-related problems and low levels of social support (Hardoon et al.,
2004; Petry & Weiss, 2009). In contrast, participants with a low PESS reported lower
gambling frequency than those with high PESS at T0 (mean = 5.6 vs. mean = 8.0, respec-
tively; p = 0.104). This counterintuitive finding might be partially explained by Raymen
and Smith’s (2020) argument that gambling is already culturally embedded and normalized
as a legitimate and integral feature of the wider masculine weekend leisure experience, and
defines itself as a social arena driven by the allure of the gambling win. As it substantiates
that hypothesis, it is notable that various (online) gambling services apparently promote
peer approval and social pressure to gamble through structural features in the game, further
enhancing social aspects (King et al., 2010). Accordingly, study participants may perceive
some gambling days as a consciously chosen leisure activity with peers.
Although most individual participants experienced changes in gambling characteristics
and PESS during the study period, these developments were not consistently accompanied
by changing sample-level means for GD score and gambling behaviour, as improvements
and deteriorations compensated for each other. This conflicts with the findings of Petry
and Weiss, who reported increases in the average level of social support over 12 months
among treatment-seeking individuals with GD who reported an increasing average level of
social support among treatment-seeking individuals with GD during the treatment period
(Petry & Weiss, 2009) and might be explained by the fact that our sample reflects an at-risk
population that presumably has different characteristics than a sample of treatment-seeking
individuals diagnosed with GD.
The insight that the individual perspective might be decisive when interpreting longitu-
dinal courses led to our key finding that both the cross-sectional level of PESS and individ-
ual changes in PESS over time may improve gambling characteristics. Indeed, our analysis
showed that within-participant improvements in PESS were significantly associated with
reductions in gambling intensity, gambling frequency, and extent of gambling-related prob-
lems, independent of high levels of PESS. Interestingly, the sensitivity analysis of only
participants with high PESS at T0 indicated, on average, more favourable gambling char-
acteristics and both reduced relevance of within-participant PESS changes and increased
relevance of PESS level (i.e., cross-sectionally). Therefore, we argue the converse can be
assumed: individuals with initially low perceived social support may benefit more from
improving their individual perceived social supports than people who already have an
established supportive social environment.
Previous cross-sectional studies have already demonstrated an association of greater
social support with decreased gambling-related problems (Hardoon et al., 2004; van der
Maas, 2016; Weinstock & Petry, 2008). Our study expands on that evidence by empha-
sizing that changing individual-specific PESS is an even more decisive factor in improv-
ing gambling characteristics, particularly for people with lower PESS at baseline. Despite
being statistically significant, the effect sizes in our study were small. This was anticipated,
considering previous evidence on the impact of social support (Peirce et al., 2000) and
the comparatively high initial average PESS levels, and the rather mild forms of gambling
behaviour of our study participants. Yet evidence of a directional association of social
support with gambling behaviour remains inconclusive, as an initially high level of PESS
might fail to positively influence problem gambling or its trajectory (Edgerton et al., 2015)

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Journal of Gambling Studies (2024) 40:307–332 319

or, in cases with poor-quality support, it might even promote gambling (Kalischuk et al.,
2006).
Indeed, social support must be understood as an interactive process. It particularly needs
to be kept in mind that our approach cannot differentiate whether (a) improving PESS was
followed by improving gambling characteristics or (b) improving gambling characteris-
tics preceded rising levels of PESS. Future (quasi-)experimental study designs could help
to disentangle this time sequence and thus shed further light on the reciprocal interaction
between changing social support and changes in gambling behaviour and extent of gam-
bling-related problems. In addition, further research identifying qualitative characteristics
of perceived positive social support, especially within at-risk populations, is recommended
so that preventive services can meet individuals’ needs. Support characteristics can depend
on situational elements of life context, individual factors such as personality characteristics
or expectations, and interpersonal structures of the relationship between support recipient
and support provider (Kienle et al., 2006).

Limitations and Strengths

In addition to the “sequence-caveat”, the following limitations ought to be considered when

interpreting this study’s results. First, our study population reflects a city/state-specific con-
venience sample of young adult males who were regular gamblers. Hence, generalizing
our results to other population groups may not be appropriate. This particularly applies to
female gamblers. As women are known to usually mobilize, perceive, and experience more
social support than men (Matud et al., 2003), we suppose a different role of (changing)
PESS is likely among women. Second, owing to the small sample size, and issues with
model convergence, subgroup analyses and comprehensive covariate adjustments were lim-
ited. Therefore, results have to interpreted very carefully as we might have missed relevant
co-predictors that might mitigate or strengthen the observed associations of PESS with
gambling outcomes. We included partnership status (having a partner or not) and migration
background because an intimate partner is often considered the main source of emotional
support, especially among men (Barbee et al., 1993) and because migration backgrounds
can strongly affect the thematization of emotions and feelings, e.g. in context of certain role
expectations (Durik et al., 2006). A potential moderating role of other factors (e.g., gam-
bling peers, duration of gambling career) was therefore disregarded. We however addressed
morbidity aspects within SA 2. As PESS level respectively change remained significant
predictors within this model, the results indicate that social support is a robust predictor
for several gambling-related outcomes even so effect sizes might have been mis-estimated.
Third, the effect sizes found are low, which was expected to some extent, considering that
our analyses rely on an at-risk sample and not a clinical sample of people with GD, where
different effect sizes would be conceivable. Furthermore, in a meta-analysis of longitudi-
nal studies, it has already been demonstrated, that associations between the development
of problem gambling and (social-) risk factors usually only achieve medium to low effect
sizes (Dowling et al., 2017). Fourth, the HLMs are used with the assumption that increas-
ing/decreasing social support affects individuals with both high and low initial PESS levels
in the same way, even though it appears fair to assume that individuals with low initial
PESS levels might benefit from increases more substantially than individuals with higher
initial levels of PESS. This was also supported by our sensitivity analysis. This hypothesis

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should be tested via stratified subgroup analyses or quantile-regression in larger samples

(Koenker & Hallock, 2001).
Nevertheless, our focus on young adult males represents a vital contribution to the
development of targeted prevention strategies. Here, the preventive potential of social sup-
port within a known risk group provides a suitable starting point. Another strength of our
study is that social support and gambling behaviour were both operationalized using stand-
ardized and validated instruments, which enables a substantial comparison of our study
findings to the existing body of evidence. In addition, unlike previous research, our analy-
ses not only examined gambling-related problems but also gambling frequency and gam-
bling intensity, which may be more sensitive to short term changes. Thus, we were able to
draw a more comprehensive picture of the associations between gambling behaviour and
perceived social support. Finally, the advanced analytical approach differentiated between
longitudinal within-participant changes and cross-sectional level effects (between-partici-
pant differences), which supports a sound understanding of how different aspects of PESS
(both change over time and level at distinct time points) interact with the changes in gam-
bling characteristics.

Conclusion and Further Research

The results suggest that strengthening social-emotional resources—regardless of the initial

cross-sectional level of PESS—may be a promising strategy for mitigating or even pre-
venting gambling-related problems. Since social support is most effective when it matches
situational and environmental needs, it is important to create spaces in which young men
are allowed to discuss stress and emotional problems that may underlie their gambling
behaviour so that an atmosphere of understanding can be established. For young people in
particular, online help forums, which have already been evaluated as helpful among people
with substance use disorders (Liu et al., 2020) and gambling-related problems (Wood &
Wood, 2009), could serve as a first setting where one may perceive social support, even
before treatment. Males, who consider mobilization and acceptance of emotional social
support generally more challenging than females (Matud et al., 2003), can particularly ben-
efit from the degree of perceived anonymity in online forums. At the same time, social
awareness campaigns directed at close contacts of gamblers could raise awareness of the
issue of problem gambling and related negative consequences. Given the knowledge of
a substantial, enduring association of male gender and young age with gambling-related
problems in Germany, development of subpopulation-targeted preventive and early inter-
vention strategies that systematically incorporate the social support networks of young men
is paramount.

Appendix

See Tables 4, 5, 6, 7, 8, 9, 10, 11.

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Journal of Gambling Studies (2024) 40:307–332 321

Table 4  Changes in PESS, T0–T1 T1–T2

gambling-related problems,
gambling frequency, and Total ­sample† Total sample
gambling intensity from T0 to T1
and T1 to T2 ESSI scorea
n 117† 112†
Deterioration 52 (44.4) 43 (38.4)
No change 18 (15.4) 23 (20.5)
Improvement 47 (40.2) 46 (41.2)
GD scoreb
n 169† 124†
Deterioration 44 (26.0) 40 (32.3)
No change 73 (43.2) 46 (37.1)
Improvement 52 (30.8) 38 (30.6)
Gambling frequencyc
n 105† 103†
Deterioration 41 (39.1) 32 (31.1)
No change 15 (14.3) 27 (26.2)
Improvement 49 (46.6) 44 (42.7)
Gambling intensityd
N 106† 98†
Deterioration 29 (27.4) 31 (31.6)
No change 40 (37.7) 42 (42.9)
Improvement 37 (34.9) 25 (25.5)

PESS perceived social and emotional support, ESSI ENRICHD Social

Support Instrument, GD gambling disorder
†
For some analyses, n differed due to missings and is reported sepa-
rately
a
ESSI score (measure of PESS): unweighted sum of all ESSI items
b
GD score (measure of gambling-related problems): sum of no. of ful-
filled DSM-5 criteria for GD based on participant endorsement of cri-
teria (via yes–no question)
c
Mean no. of gambling days/month
d
Mean hours spent gambling/gambling day

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Table 5  Unadjusted means Variables Total sample ESSI ≤ 18 (n = 38)†

for ESSI score and gambling
outcome measures at n M SD n M SD
baseline (T0) and follow-
ups (T1 = 12 months, ESSI score1
T2 = 24 months) for total sample T0 167 21.19 4.20 38 14.82 3.30
and participants with ESSI
score ≤ 18 at T0 T1 126 20.52 4.23 28 17.96 5.68
T2 111 20.72 4.16 31 18.58 4.42
GD score2
T0 167 1.38 2.06 38 1.74 2.53
T1 118 2.37 2.86 38 1.00 2.28
T2 124 2.10 2.54 31 2.26 2.63
Gambling frequency3
T0 159 7.40 7.81 37 5.57 6.24
T1 111 6.41 7.24 28 3.82 3.48
T2 119 5.62 7.20 31 5.39 7.22
Gambling intensity4
T0 167 2.25 2.43 38 2.82 3.16
T1 107 1.89 1.65 28 1.96 1.67
T2 118 1.95 1.83 30 1.97 1.61

ESSI ENRICHD Social Support Instrument, M unadjusted mean, SD

standard deviation
†
For some analyses, n differed due to missings and is reported sepa-
rately
1
ESSI score (measure of PESS): unweighted sum of all ESSI items
2
GD score (measure of gambling-related problems): sum of no. of ful-
filled DSM-5 criteria for GD based on participant endorsement of cri-
teria (via yes–no question)
3
Mean no. of gambling days per month
4
Mean hours spent gambling/gambling day

13
 Table 6  Mixed model outputs from sensitivity analysis 1 (excluding participants with ESSI score ≤ 18 at T0 (n = 38)): changes in PESS, gambling-related problems, gambling
frequency, and gambling intensity
T0 T1 T2 First year ­change1 (T0– Second year c­ hange1
T1) (T1–T2)
Adjusted SE (90% CI) Adjusted SE (90% CI) Adjusted SE (90% CI) Δ between p value Δ between p value
Mean Mean Mean T0–T1 T1–T2

ESSI ­score2 23.01 0.17 (22.72– 21.32 0.34 (20.76– 21.62 0.42 (20.93– − 1.69 0.0000*** 0.30 0.4767
23.29) 21.88) 22.31)
Journal of Gambling Studies (2024) 40:307–332

Gambling characteristics
GD ­score3 1.54 0.31 (1.02–2.05) 1.89 0.37 (1.29–2.50) 1.96 0.40 (1.30–2.62) 0.35 0.1887 0.07 0.8247
Gambling 8.76 1.11 (6.93– 8.13 1.17 (6.20– 6.18 0.92 (4.67–7.70) − 0.63 0.5221 − 1.95 0.0449**
­frequency4 10.59) 10.06)
Gambling 2.05 0.16 (1.78–2.32) 1.91 0.20 (1.58–2.25) 1.92 0.19 (1.60–2.23) − 0.14 0.5722 0.01 0.9775
­intensity5

PESS perceived emotional and social support, ESSI ENRICHD Social Support Instrument, GD gambling disorder
Data are presented as adjusted means, standard error (SE), 90% confidence interval (CI)
1
p values based on Wald test statistics
2
ESSI score (measure of PESS): unweighted sum of all ESSI items
3
GD score (measure of gambling-related problems): sum of no. of fulfilled DSM-5 criteria for GD based on participant endorsement of criteria (via yes–no question)
4
Mean no. of gambling days/month
5
Mean hours spent gambling/gambling day
*p < 0.10; **p < 0.05; ***p < 0.01
323

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Table 7  Estimates for associations of cross-sectional PESS level and longitudinal PESS with gambling-
related problems, gambling frequency, and gambling intensity from sensitivity analysis 1 (excluding partici-
pants with ESSI score ≤ 18 at T0 (n = 38))

Outcome GD ­score1 Gambling ­frequency2 Gambling ­intensity3

Estimator [90% CI] Estimator [90% CI] Estimator [90% CI]

ESSI ­score4: between-subjects − 0.21*** − 0.46** − 0.03

[− 0.30 to–0.12] [− 0.83 to–0.08] [− 0.14 to 0.07]
ESSI ­score4: within-subjects − 0.25*** 0.00 − 0.04
[− 0.40 to − 0.10] [− 0.50 to 0.50] [− 0.15 to 0.07]

ESSI ENRICHD Social Support Instrument, GD gambling disorder, PESS perceived emotional and social
support
Hierarchical linear models (HLM) adjusted for migration background and partnership status; model for
gambling frequency did not converge
Interpretation: Negative estimates indicate improvement in the respective outcome parameter. ESSI
between-participants: cross-sectional difference in PESS between subjects. ESSI within-participants: longi-
tudinal change in PESS in individuals over time
1
GD score (measure of gambling-related problems): sum of no. of fulfilled DSM-5 criteria for GD based on
participant endorsement of criteria (via yes–no question)
2
Mean no. gambling days/month
3
Mean hours spent gambling/gambling day
4
ESSI score (measure of PESS): unweighted sum of all ESSI items
*p < 0.10; **p < 0.05; ***p < 0.01

13
 Table 8  Mixed-effects negative-binomial and Poisson regression estimates for associations of time, partnership status, and migration background with PESS, gambling-related
problems, gambling frequency, and gambling intensity in entire sample and in participants with ESSI score ≤ 18 at baseline (n = 38)
Covariates ESSI ­score1 (Poisson) GD ­score2 (negative binomial) Gambling ­frequency3 (negative binomial) Gambling ­intensity4 (nega-
tive binomial)

Entire sample (IRR) Reduced sample Entire sample Reduced sample Entire sample (IRR) Reduced sample Entire sam- Reduced sam-
(IRR) (IRR) (IRR) (IRR) ple (IRR) ple (IRR)

Time
T0 REF REF REF REF REF REF REF REF
T1 0.97 0.93*** 0.98 1.23 0.92 0.93 0.87 0.94
(0.02) (0.02) (0.14) (0.19) (0.09) (0.11) (0.09) (0.11)
T2 0.99 0.94*** 1.31* 1.28 0.76*** 0.71*** 0.87 0.94
(0.02) (0.02) (0.19) (0.21) (0.08) (0.09) (0.09) (0.11)
Journal of Gambling Studies (2024) 40:307–332

Partnership status (having a partner)

No REF REF REF REF REF REF REF REF
Yes 1.13*** 1.07*** 0.80 0.81 0.90 0.84 1.00 1.11
(0.03) (0.03) (0.16) (0.19) (0.11) (0.12) (0.12) (0.14)
Migration background
No REF REF REF REF REF REF REF REF
Yes 0.95 1.01 0.69* 0.61* 0.96 0.88 0.84 0.68**
(0.03) (0.03) (0.15) (0.16) (0.13) (0.14) (0.11) (0.10)
Observations 395 297 448 337 380 282 381 283
Number of groups 164 125 164 125 161 123 164 125

ESSI ENRICHD Social Support Instrument, GD gambling disorder

Standard errors in parentheses; estimators reported as incidence rate ratios (IRR)
1
ESSI score (measure of PESS): unweighted sum of all ESSI items
2
GD score (measure of gambling-related problems): sum of no. of fulfilled DSM-5 criteria for GD based on participant endorsement of criteria (via yes–no question)
3
Mean no. of gambling days per month
4
Mean hours spent gambling/gambling day
325

13
*p < 0.10; **p < 0.05; ***p < 0.01
 Table 9  Estimates for associations of covariables, morbidity cross-sectional PESS level and longitudinal PESS with gambling-related problems, gambling frequency, and
326

gambling intensity from sensitivity analysis 2

Covariates GD ­score2 Gambling ­frequency3 Gambling ­intensity4

13
Main analysis Morbidity adjusted Main analysis Morbidity adjusted Main analysis Morbidity adjusted

ESSI1
ESSI score: between-subjects − 0.12** − 0.05 0.17 0.28* − 0.05 − 0.03
(0.05) (0.05) (0.15) (0.15) (0.04) (0.04)
ESSI score: within-subjects − 0.19*** − 0.06* − 0.25* 0.00 − 0.11*** − 0.10**
(0.03) (0.03) (0.13) (0.15) (0.04) (0.05)
Time
T0 REF REF REF REF REF REF
T1 0.86*** 0.44** − 0.78 − 1.70** − 0.42* − 0.51*
(0.21) (0.18) (0.80) (0.81) (0.24) (0.26)
T2 0.57*** 0.40** − 1.67** − 1.79** − 0.36 − 0.35
(0.20) (0.17) (0.77) (0.74) (0.23) (0.24)
Partnership status (having a partner)
No REF REF REF REF REF REF
Yes − 0.17 − 0.08 − 1.33 − 1.16 0.25 0.34
(0.35) (0.32) (1.05) (1.02) (0.26) (0.27)
Migration background
No REF REF REF REF REF REF
Yes − 0.90** − 0.69** − 0.52 − 0.51 − 0.38 − 0.25
(0.36) (0.33) (1.08) (1.06) (0.27) (0.28)
Tobacco Use within last 12 months
Less than twice a week REF REF REF
Twice per week or more 0.96*** 1.21 0.36
(0.26) (0.96) (0.27)
Alcohol Use within last 12 months
Journal of Gambling Studies (2024) 40:307–332

Less than twice a week REF REF REF

 Table 9  (continued)
Covariates GD ­score2 Gambling ­frequency3 Gambling ­intensity4
Main analysis Morbidity adjusted Main analysis Morbidity adjusted Main analysis Morbidity adjusted

Twice per week or more 0.53** 0.39 0.20

(0.21) (0.84) (0.24)
SCL-90-R
Depression Scale (DEP)5 0.32 0.33 0.38
(0.20) (0.83) (0.25)
Anxiety Scale (ANX)6 0.66** 1.62 − 0.05
(0.28) (1.10) (0.33)
Observations 395 365 379 354 380 355
Journal of Gambling Studies (2024) 40:307–332

Number of groups 164 162 161 160 164 162

ESSI ENRICHD Social Support Instrument, GD gambling disorder

Standard errors in parentheses
Interpretation: Negative estimates indicate improvement in the respective outcome parameter. ESSI between-participants: cross-sectional difference in PESS between subjects.
ESSI within-participants: longitudinal change in PESS in individuals over time
1
ESSI score (measure of PESS): unweighted sum of all ESSI items
2
GD score (measure of gambling-related problems): sum of no. of fulfilled DSM-5 criteria for GD based on participant endorsement of criteria (via yes–no question)
3
Mean no. of gambling days per month
4
Mean hours spent gambling/gambling day
5
SCL-90-R Depression Scale (DEP): unweighted mean of all items on depression
6
SCL-90-R Anxiety Scale (ANX): unweighted mean of all items on anxiety
*p < 0.10; **p < 0.05; ***p < 0.01
327

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328 Journal of Gambling Studies (2024) 40:307–332

Table 10  Original English language items in questionnaire on gambling-related problems and correspond-
ing gambling disorder (GD) criteria (GD criteria questionnaire)
Gambling disorder (GD) Criteria Original 17 items measuring DSM-5 criteria for GD
(in English)

Needs to gamble with increasing amounts of money Have you had periods when you needed to gamble
in order to achieve the desired excitement more often in order to obtain the same excitement?
Have you needed to gamble with larger amounts of
money or with larger bets in order to obtain the
same feeling of excitement?
Is restless or irritable when attempting to cut down Did you feel quite restless or irritable after you tried
or stop gambling to cut down or stop gambling?
Has made repeated unsuccessful efforts to control, Have you tried to cut down or control your gambling
cut back, or stop gambling several times in the past and found it difficult?
Have you tried to stop gambling several times in the
past and been unsuccessful?
Is often preoccupied with gambling (e.g., having Have there been periods in the past year when you
persistent thoughts of reliving past gambling spent a lot of time thinking about past gambling
experiences, handicapping or planning the next experiences or thinking about future gambling
venture, thinking of ways to get money with ventures?
which to gamble) Have you frequently thought about ways of getting
money with which to gamble?
Often gambles when feeling distressed (e.g., help- Do you feel that you gamble as a way to escape
less, guilty, anxious, depressed) personal problems?
Does gambling seem to relieve uncomfortable emo-
tions, such as anxiety or depression?
After losing money gambling, often returns another When you lose money on a given day, do you often
day to get even (“chasing” one’s losses) return soon another day to win back your losses?
When you had a large gambling debt, did you gamble
more often in the hopes of winning back your-
money?
Lies to conceal the extent of involvement with Have you often lied to family members, friends,
gambling co-workers or teachers about the extent of your
gambling or of your gambling debt?
Have you often hidden or tried to hide your gambling
from others (e.g. family members)?
Has jeopardized or lost a significant relationship, Have you had periods when your gambling caused
job, or educational or career opportunity because problems in your relationships with family, friends,
of gambling co-workers or teachers?
Have you missed work, school or important social or
family activities because of gambling?
Relies on others to provide money to relieve desper- Have you asked people to lend you money because of
ate financial situations caused by gambling your financial problems due to gambling?
Have you had others pay your gambling debts for you
(i.e. bail you out) when you felt desperate about
your financial situation?

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Journal of Gambling Studies (2024) 40:307–332 329

Table 11  Original English language items in standardized, validated German version of the ENRICHD
Social Support Instrument (ESSI)
Original 5 items in the ESSI (in English)

Is there someone available to you who you can count on to listen when you need to talk?
Is there someone available to give you good advice about a problem?
Is there someone available to you who shows you love and affection?
Can you count on anyone to provide you with emotional support, such as talking over problems or helping
you make difficult decisions?
Do you have as much contact as you would like with someone you feel close to, someone you can trust and
confide in?

Acknowledgements The authors thank the young male residents of the city of Munich for their participa-
tion in the study. We especially acknowledge the study participants’ willingness to take part in the ‘Münch-
ner Freizeit Studie’ from enrolment to end of follow-up. We furthermore express our heartfelt thanks to our
colleagues Joana Daniel and Pia Wullinger in drafting this paper. Furthermore, we thank our anonymous
reviewers, whose constructive suggestions and remarks substantially improved precision and quality of this
paper.

Author Contributions PS and LK designed the study, including choosing participant characteristics to be
assessed and measures to be applied. AB performed the statistical analyses under the supervision of LS. The
first draft of the manuscript was written by AB. All authors contributed substantially to earlier drafts of the
manuscript and approved its final version.

Funding This study was conducted by the Bavarian Coordination Centre for Gambling Issues (Bayerische
Landesstelle Gluecksspielsucht (LSG)). The LSG is funded by the Bavarian State Ministry of Public Health
and Care Services. The State of Bavaria provides gambling services (lotteries, sports betting, and casino
games) within the State gambling monopoly via the State Lottery Administration and provided the Bavarian
Coordination Centre for Gambling Issues an unrestricted Grant as funding. LK was also supported by the
Swedish programme Grant ‘‘Responding to and Reducing Gambling Problems—Studies in Help-seeking,
Measurement, Comorbidity and Policy Impacts (REGAPS)’’ financed by the Swedish Research Council for
Health, Working Life and Welfare (Forte), Grant No. 2016-07091.

Data Availability The data from this study are available on request from the first author, AB.

Declarations
Conflict of interest The authors declare that they have no conflict of interest.

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References
Banz, M. (2019). Glücksspielverhalten und Glücksspielsucht in Deutschland. Ergebnisse des Surveys 2019
und Trends (BZgA-Forschungsbericht).

13
330 Journal of Gambling Studies (2024) 40:307–332

Barbee, A. P., Cunningham, M. R., Winstead, B. A., Derlega, V. J., Gulley, M. R., Yankeelov, P. A., &
Druen, P. B. (1993). Effects of gender role expectations on the social support process. Journal of
Social Issues, 49(3), 175–190. https://​doi.​org/​10.​1111/j.​1540-​4560.​1993.​tb011​75.x
Berkman, L., Carney, R., Blumenthal, J., Czakowski, S., Hosking, J., Jaffe, A., Babyak, M., Carels, R., Coleman,
E., & Curtis, S. (2000). Enhancing recovery in coronary heart disease patients (ENRICHD): Study design
and methods. American Heart Journal, 139(1), 1–9. https://​doi.​org/​10.​1016/​s0002-​8703(00)​90301-6
Beynon, C., Pearce-Smith, N., & Clark, R. (2020). Risk factors for gambling and problem gambling: A pro-
tocol for a rapid umbrella review of systematic reviews and meta-analyses. Systematic Reviews, 9, 198.
https://​doi.​org/​10.​1186/​s13643-​020-​01455-x
Bilt, J. V., Dodge, H. H., Pandav, R., Shaffer, H. J., & Ganguli, M. (2004). Gambling participation and
social support among older adults: A longitudinal community study. Journal of Gambling Studies,
20(4), 373–389. https://​doi.​org/​10.​1007/​s10899-​004-​4580-0
Buth, S., Meyer, G., & Kalke, J. (2022). Glücksspielteilnahme und glücksspielbezogene Probleme in der
Bevölkerung—Ergebnisse des Glücksspiel-Survey 2021.
Cohen, S. (2004). Social Relationships and Health. American Psychologist, 59(8), 676. https://​doi.​org/​
10.​1037/​0003-​066X.​59.8.​676
Cordes, A., Herrmann-Lingen, C., Büchner, B., & Hessel, A. (2009). Repräsentative Normierung des
ENRICHD-Social-Support-Instrument (ESSI)-Deutsche Version. Klinische Diagnostik Und Evalu-
ation, 2(1), 16–32.
Delfabbro, P., Lahn, J., & Grabosky, P. (2006). It’s not what you know, but how you use it: Statisti-
cal knowledge and adolescent problem gambling. Journal of Gambling Studies, 22(2), 179–193.
https://​doi.​org/​10.​1007/​s10899-​006-​9009-5
Derogatis, L. R., & Unger, R. (2010). Symptom checklist-90-revised. The Corsini Encyclopedia of Psy-
chology. https://​doi.​org/​10.​1002/​97804​70479​216.​corps​y0970
Donati, M. A., Chiesi, F., & Primi, C. (2013). A model to explain at-risk/problem gambling among
male and female adolescents: Gender similarities and differences. Journal of Adolescence, 36(1),
129–137. https://​doi.​org/​10.​1016/j.​adole​scence.​2012.​10.​001
Dowling, N., Merkouris, S. S., Greenwood, C., Oldenhof, E., Toumbourou, J., & Youssef, G. (2017).
Early risk and protective factors for problem gambling: A systematic review and meta-analysis of
longitudinal studies. Clinical Psychology Review, 51, 109–124. https://​doi.​org/​10.​1016/j.​cpr.​2016.​
10.​008
Durik, A. M., Hyde, J. S., Marks, A. C., Roy, A. L., Anaya, D., & Schultz, G. (2006). Ethnicity and gen-
der stereotypes of emotion. Sex Roles, 54(7), 429–445. https://​doi.​org/​10.​1007/​s11199-​006-​9020-4
Edgerton, J. D., Melnyk, T. S., & Roberts, L. W. (2015). Problem gambling and the youth-to-adulthood
transition: Assessing problem gambling severity trajectories in a sample of young adults. Journal
of Gambling Studies, 31(4), 1463–1485. https://​doi.​org/​10.​1007/​s10899-​014-​9501-2
Esen, B. K., & Gündoğdu, M. (2010). The relationship between internet addiction, peer pressure and
perceived social support among adolescents. The International Journal of Educational Research-
ers, 2(1), 29–36.
Hardoon, K. K., Gupta, R., & Derevensky, J. L. (2004). Psychosocial variables associated with adoles-
cent gambling. Psychology of Addictive Behaviors, 18(2), 170. https://​doi.​org/​10.​1037/​0893-​164X.​
18.2.​170
Hedeker, D. (2004). An introduction to growth modeling. The Sage handbook of quantitative methodol-
ogy for the social sciences, 215–234.
Hershberger, A., Zapolski, T., & Aalsma, M. C. (2016). Social support as a buffer between discrimi-
nation and cigarette use in juvenile offenders. Addictive Behaviors, 59, 7–11. https://​doi.​org/​10.​
1016/j.​addbeh.​2016.​03.​003
Ingle, P. J., Marotta, J., McMillan, G., & Wisdom, J. P. (2008). Significant others and gambling
treatment outcomes. Journal of Gambling Studies, 24(3), 381–392. https://​doi.​org/​10.​1007/​
s10899-​008-​9092-x
Jauregui, P., Estévez, A., & Urbiola, I. (2016). Pathological gambling and associated drug and alcohol
abuse, emotion regulation, and anxious-depressive symptomatology. Journal of Behavioral Addic-
tions, 5(2), 251–260. https://​doi.​org/​10.​1556/​2006.5.​2016.​038
Jiménez-Murcia, S., Granero, R., Tárrega, S., Angulo, A., Fernández-Aranda, F., Arcelus, J., Fagundo,
A. B., Aymamí, N., Moragas, L., & Sauvaget, A. (2016). Mediational role of age of onset in gam-
bling disorder, a path modeling analysis. Journal of Gambling Studies, 32(1), 327–340. https://​doi.​
org/​10.​1007/​s10899-​015-​9537-y
Johnson, E. E., Hamer, R., Nora, R. M., Tan, B., Eisenstein, N., & Engelhart, C. (1997). The Lie/Bet
Questionnaire for screening pathological gamblers. Psychological Reports, 80(1), 83–88. https://​
doi.​org/​10.​2466/​pr0.​1997.​80.1.​83

13
Journal of Gambling Studies (2024) 40:307–332 331

Kalischuk, R. G., Nowatzki, N., Cardwell, K., Klein, K., & Solowoniuk, J. (2006). Problem gambling
and its impact on families: A literature review. International Gambling Studies, 6(1), 31–60. https://​
doi.​org/​10.​1080/​14459​79060​06441​76
Kastirke, N., Rumpf, H. J., John, U., Bischof, A., & Meyer, C. (2015). Demographic risk factors and
gambling preference may not explain the high prevalence of gambling problems among the popula-
tion with migration background: Results from a German Nationwide Survey. Journal of Gambling
Studies, 31(3), 741–757. https://​doi.​org/​10.​1007/​s10899-​014-​9459-0
Kendel, F., Spaderna, H., Sieverding, M., Dunkel, A., Lehmkuhl, E., Hetzer, R., & Regitz-Zagrosek,
V. (2011). Eine deutsche adaptation des ENRICHD social support inventory (ESSI). Diagnostica.
https://​doi.​org/​10.​1026/​0012-​1924/​a0000​30
Kienle, R., Knoll, N., & Renneberg, B. (2006). Soziale Ressourcen und Gesundheit: soziale Unterstüt-
zung und dyadisches Bewältigen. In Gesundheitspsychologie (pp. 107–122). Springer. https://​doi.​
org/​10.​1007/​978-3-​540-​47632-0_7
King, D., Delfabbro, P., & Griffiths, M. (2010). The convergence of gambling and digital media: Impli-
cations for gambling in young people. Journal of Gambling Studies, 26(2), 175–187. https://​doi.​
org/​10.​1007/​s10899-​009-​9153-9
Koenker, R., & Hallock, K. F. (2001). Quantile regression. Journal of Economic Perspectives, 15(4),
143–156. https://​doi.​org/​10.​1257/​jep.​15.4.​143
Langhinrichsen-Rohling, J., Rohde, P., Seeley, J. R., & Rohling, M. L. (2004). Individual, family, and
peer correlates of adolescent gambling. Journal of Gambling Studies, 20(1), 23–46. https://​doi.​org/​
10.​1023/B:​JOGS.​00000​16702.​69068.​53
Liu, Y., Kornfield, R., Shaw, B. R., Shah, D. V., McTavish, F., & Gustafson, D. H. (2020). Giving and
receiving social support in online substance use disorder forums: How self-efficacy moderates
effects on relapse. Patient Education and Counseling, 103(6), 1125–1133. https://​doi.​org/​10.​1016/j.​
pec.​2019.​12.​015
Matud, M. A. P., Ibañez, I., Bethencourt, J. M., Marrero, R., & Carballeira, M. (2003). Structural gender
differences in perceived social support. Personality and Individual Differences, 35(8), 1919–1929.
https://​doi.​org/​10.​1016/​S0191-​8869(03)​00041-2
McGrath, D. S., & Barrett, S. P. (2009). The comorbidity of tobacco smoking and gambling: A review
of the literature. Drug and Alcohol Review, 28(6), 676–681. https://​doi.​org/​10.​1111/j.​1465-​3362.​
2009.​00097.x
Meyer, G., & Bachmann, M. (2017). Spielsucht. Ursachen, Therapie und Prävention von glücksspielbe-
zogenem Suchtverhalten (4. Ed. ed.). Springer. https://​doi.​org/​10.​1007/​978-3-​662-​54839-4
Moore, S. M., & Ohtsuka, K. (1999). Beliefs about control over gambling among young people, and
their relation to problem gambling. Psychology of Addictive Behaviors, 13(4), 339. https://​doi.​org/​
10.​1037/​0893-​164X.​13.4.​339
Nowak, D. E. (2018). A meta-analytical synthesis and examination of pathological and problem gam-
bling rates and associated moderators among college students, 1987–2016. Journal of Gambling
Studies, 34(2), 465–498. https://​doi.​org/​10.​1007/​s10899-​017-​9726-y
Pauley, P. M., & Hesse, C. (2009). The effects of social support, depression, and stress on drinking
behaviors in a college student sample. Communication Studies, 60(5), 493–508. https://​doi.​org/​10.​
1080/​10510​97090​32603​35
Peirce, R. S., Frone, M. R., Russell, M., Cooper, M. L., & Mudar, P. (2000). A longitudinal model of
social contact, social support, depression, and alcohol use. Health Psychology, 19(1), 28. https://​
doi.​org/​10.​1037//​0278-​6133.​19.1.​28
Petry, N. M., & Weiss, L. (2009). Social support is associated with gambling treatment outcomes in
pathological gamblers. American Journal on Addictions, 18(5), 402–408. https://​doi.​org/​10.​3109/​
10550​49090​30778​61
Räsänen, T., Lintonen, T., Tolvanen, A., & Konu, A. (2016). The role of social support in the associa-
tion between gambling, poor health and health risk-taking. Scandinavian Journal of Public Health,
44(6), 593–598. https://​doi.​org/​10.​1177/​14034​94816​654380
Raymen, T., & Smith, O. (2020). Lifestyle gambling, indebtedness and anxiety: A deviant leisure per-
spective. Journal of Consumer Culture, 20(4), 381–399. https://​doi.​org/​10.​1177/​14695​40517​
736559
Riley, B. J., Oster, C., Rahamathulla, M., & Lawn, S. (2021). Attitudes, risk factors, and behaviours of
gambling among adolescents and young people: A literature review and gap analysis. International
Journal of Environmental Research and Public Health, 18(3), 984. https://​doi.​org/​10.​3390/​ijerp​
h1803​0984
Shead, N. W., Derevensky, J. L., & Gupta, R. (2010). Risk and protective factors associated with youth
problem gambling. International Journal of Adolescent Medicine and Health, 22(1), 39.

13
332 Journal of Gambling Studies (2024) 40:307–332

Sleczka, P., Braun, B., Grüne, B., Bühringer, G., & Kraus, L. (2016). Proactive coping and gambling
disorder among young men. Journal of Behavioral Addictions, 5(4), 639–648. https://​doi.​org/​10.​
1556/​2006.5.​2016.​080
Sleczka, P., Braun, B., Grüne, B., Bühringer, G., & Kraus, L. (2018). Family functioning and gambling
problems in young adulthood: The role of the concordance of values. Addiction Research & The-
ory, 26(6), 447–456. https://​doi.​org/​10.​1080/​16066​359.​2017.​13935​31
Stinchfield, R. (2003). Reliability, validity, and classification accuracy of a measure of DSM-IV diagnos-
tic criteria for pathological gambling. American Journal of Psychiatry, 160(1), 180–182. https://​
doi.​org/​10.​1176/​appi.​ajp.​160.1.​180
Sulkunen, P., Babor, T. F., Ornberg, J. C., Egerer, M., Hellman, M., Livingstone, C., Marionneau, V.,
Nikkinen, J., Orford, J., & Room, R. (2018). Setting limits: Gambling, science and public policy.
Oxford University Press.
Vachon, J., Vitaro, F., Wanner, B., & Tremblay, R. E. (2004). Adolescent gambling: Relationships with
parent gambling and parenting practices. Psychology of Addictive Behaviors, 18(4), 398. https://​
doi.​org/​10.​1037/​0893-​164X.​18.4.​398
van der Maas, M. (2016). The social side of the pathways model: Examining the mediation of social
support on the relationship between psychopathology and problem gambling. Journal of Gambling
Issues, 32, 11–27. https://​doi.​org/​10.​4309/​jgi.​2016.​32.2
Walters, G. D. (2021). Parental gambling as a moderator of the child delinquency-gambling relation-
ship: Does having a role model in the home make a difference? Journal of Gambling Studies, 37(1),
27–41. https://​doi.​org/​10.​1007/​s10899-​020-​09962-1
Weinstock, J., & Petry, N. M. (2008). Pathological gambling college students’ perceived social support.
Journal of College Student Development, 49(6), 625. https://​doi.​org/​10.​1353/​csd.0.​0047
Welte, J. W., Barnes, G. M., Tidwell, M.-C.O., & Hoffman, J. H. (2008). The prevalence of problem gam-
bling among US adolescents and young adults: Results from a national survey. Journal of Gambling
Studies, 24(2), 119–133. https://​doi.​org/​10.​1007/​s10899-​007-​9086-0
Wood, R. T., & Wood, S. A. (2009). An evaluation of two United Kingdom online support forums designed
to help people with gambling issues. Journal of Gambling Issues, 23, 5–30. https://​doi.​org/​10.​4309/​jgi.​
2009.​23.1
Zhai, Z. W., Yip, S. W., Steinberg, M. A., Wampler, J., Hoff, R. A., Krishnan-Sarin, S., & Potenza, M.
N. (2017). Relationships between perceived family gambling and peer gambling and adolescent prob-
lem gambling and binge-drinking. Journal of Gambling Studies, 33(4), 1169–1185. https://​doi.​org/​10.​
1007/​s10899-​017-​9670-x

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institutional affiliations.

Authors and Affiliations

Andreas M. Bickl1 · Ludwig Kraus1,2,3 · Johanna K. Loy1 · Peter Kriwy4 ·

Pawel Sleczka5 · Larissa Schwarzkopf1,6
Andreas M. Bickl
bickl@ift.de
1
IFT Institut Für Therapieforschung, Munich, Germany
2
Institute of Psychology, ELTE, Eötvös Loránd University, Budapest, Hungary
3
Department of Public Health Sciences, Centre for Social Research on Alcohol and Drugs,
Stockholm University, Stockholm, Sweden
4
Institute of Sociology, Chemnitz University of Technology, Chemnitz, Germany
5
German University of Health and Sport, Ismaning, Germany
6
Department of Psychiatry and Psychotherapy, Klinikum der Universität München, Munich,
Germany

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