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Internet use disorder (IUD) is a new type of behavioral addiction in the digital age. At the same time, internet applications and eHealth can also provide useful support in medical treatment.
The purpose of this study is to examine if an internet-based eHealth service can reach individuals with IUD. In particular, it should be investigated whether both male and female individuals with more severe IUDs can be reached.
Data were retrieved from the OMPRIS (online-based motivational intervention to reduce problematic internet use and promote treatment motivation in internet gaming disorder and internet use disorder) project (DRKS00019925), an internet-based motivational intervention to reduce problematic internet use and promote treatment motivation in internet gaming disorder and IUD. During the recruitment process (August 2020-March 2022), a total of 3007 individuals filled out the standardized scale for the assessment of internet and computer game addiction (AICA-S). The assessment was accessible via the project homepage. There was no preselection of participants at this stage of the study; however, the offer was addressed to people with hazardous internet use and IUDs. The web-based assessment was free and could be found via search engines, but attention was also drawn to the service via newspaper articles, radio reports, and podcasts.
Out of 3007 who participated in the web-based self-assessment, 1033 (34.4%) are female, 1740 (57.9%) are male, 67 (2.2%) are diverse individuals, and 167 (5.5%) did not disclose their gender. The IUD symptom severity score showed a wide range between the AICA-S extreme values of 0 and 27 points. On average, the total sample (mean 8.19,
Using a large sample, the study showed that both mildly and severely IUD-affected individuals can be reached via the internet. An internet-based eHealth offer can thus be a good way to reach patients with IUD where they are addicted—on the internet. In addition, eHealth services increase the likelihood of reaching female patients, who hardly ever come to specialized outpatient clinics and hospitals. Since social problems, especially unemployment, have a strong association with disease severity, the integration of social counseling into treatment seems advisable in terms of a multidisciplinary approach.
German Clinical Trials Register (DRKS) DRKS00019925; https://drks.de/search/de/trial/DRKS00019925
Internet use disorder (IUD) is a collective term defined as an uncontrolled and excessive use of different internet applications in terms of a behavioral addiction. Excessive internet use parallels other addictions. Several neural pathways in human additive behaviors, including IUD, are discussed, although the mechanisms are not yet clear [
The effectiveness of eHealth interventions for patients with behavioral addictions is poorly studied. A systematic review from 2016 found a total of 16 studies testing internet-related interventions in substance addiction (11 studies in smoking, drinking, and opioid abuse) and behavioral addiction (5 studies in pathological gambling). Although only 5 of the 16 studies mentioned effect sizes (
An RCT (Short-Term Treatment of Internet- and Computer Game Addiction [STICA]) on analogue psychotherapy in clinical patients with IUDs published in 2019 provided a good insight into the clinical history and internet usage patterns of IUD [
A review published in 2022 by leading experts in the research field still identifies major knowledge gaps that need to be closed. In particular, research is needed to understand the course and development of IUDs in different age groups and genders. In addition, the need for reliable methods for the early detection of people at risk, as well as preventive and therapeutic interventions, is stated [
This leads to the following research questions: (1) is it possible to reach individuals experiencing IUD symptoms via an internet-based health care service? (2) If this is possible, is the symptom exposure in a range that can be described as pathologic or clinically relevant? (3) Is there a gender difference in terms of accessibility? In particular, is it possible to reach female individuals with IUD symptoms as well? (4) If so, do female individuals differ from male individuals in terms of IUD severity, IUD prevalence, type of problematic internet application, and sociodemographic data?
Data was retrieved from the OMPRIS
The trial was registered in the German Clinical Trials Register (DRKS00019925) and was approved by the ethics committee for the Faculty of Medicine, Ruhr University Bochum, (approval 19-6779). All participants had to provide informed consent upon registration for the study.
The AICA-S scale [
The average number of hours spent on the internet on weekdays and weekends was asked. In addition, subjective evaluations regarding problematic internet consumption was assessed. Furthermore, the type of internet use that is subjectively experienced as most problematic was asked. Individuals were also asked how they became aware of the web-based self-assessment. Finally, the following sociodemographic data were collected: age, gender, and current occupational situation.
Analyses were conducted with
The survey sample size (N=3007) varied over the period of 80 weeks between 5 (week 13) and 317 participations per week (week 26), with a mean of 37.59 (SD 42.91) participants per week. Demographics are shown in
Demographic data of the total sample (N=3007).
Variables | Values | |
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Female | 1033 (34.4) |
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Male | 1740 (57.9) |
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Divers | 67 (2.2) |
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Missing dataa | 167 (5.6) |
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<18 | 704 (23.4) |
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18-24 | 744 (24.7) |
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25-34 | 643 (21.4) |
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35-44 | 359 (11.9) |
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45-54 | 289 (9.6) |
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55-64 | 163 (5.4) |
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65-74 | 49 (1.6) |
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>75 | 11 (0.4) |
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Missing dataa | 45 (1.5) |
Age (years), mean (SD) | 29.17 (14.55) | |
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Full-time employed | 779 (25.9) |
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Working part-time | 274 (9.1) |
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Self-employment | 142 (4.7) |
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Unemployed | 285 (9.5) |
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In vocational training | 252 (8.4) |
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Studying at the university | 621 (20.7) |
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Others (eg, pupil, retired, unable to work) | 631 (21.0) |
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Missing dataa | 23 (0.8) |
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Family and friends | 433 (14.4) |
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Internet search | 864 (28.7) |
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Medical institution | 219 (7.3) |
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Municipal institution | 44 (1.5) |
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School or university | 521 (17.3) |
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Press (radio, newspaper, etc) | 521 (17.3) |
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Others | 361 (12.0) |
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Missing dataa | 5 (0.2) |
aTechnical difficulty.
The results show that all 3007 individuals (1740 males, 1033 females, 67 divers individuals, and 167 participants who did not disclose their gender) could be reached during the 80-week web-based assessment, with 34.4% (n=1033) of the participants being women. About one-fourth of the participants were younger than 18 years (n=704, 23.4%), approximately another quarter ranged between 18 and 24 years old (n=744, 24.7%), and about one-fifth were aged between 25 and 34 years (n=643, 21.4%). The remaining participants were older than 35 years (n=871, 29.0%), with the higher age categories less frequently represented. Overall, 35.0% (n=1053) of participants were currently employed (full-time: n=779, 25.9%, part-time: n=274, 9.1%, students: n=621, and unemployed: n=285, 9.5%). A large proportion (n=863, 28.7%) of participants found the survey via internet search. However, referrals through family and friends (n=433, 14.4%), school or university (n=561, 18.7%), and press reports (n=521, 17.3%) were also relevant ways of contact.
The general behavior regarding the exact type of internet use and web-based games, as well as the time spent on the internet, were asked. Furthermore, participants were asked whether they subjectively believed that they used the internet in a problematic way. Finally, symptoms of IUD were assessed.
IUD symptom severity showed a wide range between the AICA-S extreme values of 0 and 27 points. Referring to the AICA-S cutoff, the total sample scored in the range of hazardous IUD behavior (mean 8.19,
Furthermore, differences in the AICA-S scores were investigated between the different types of problematic IUD. The level of IUD symptoms (as measured by the AICA-S total score) differed significantly for the different types of problematic IUD (Welch
On comparing the 3 AICA-S groups, inconspicuous use (mean 3.96, SD 1.52), hazardous use (mean 9.52, SD 1.87), and pathological use (mean 17.42, SD 3.38) by age, there were significant differences with higher age in the inconspicuous group (mean 30.8 years vs 27.6 years vs 27.8 years, respectively; Welch
Internet use and symptoms of IUD sorted by gendera.
Variables | Female participants (n=1033) | Male participants (n=1740) | Effect size | ||||
Age (years), mean (SD) | 30.97 (14.56) | 28.31 (14.30) | <.001 | ||||
Time (in hours) spent on the internet on a weekday (Mon-Fri), mean (SD) | 4.92 (3.41) | 5.91 (3.93) | <.001 | ||||
Time (in hours) spent on the internet on a weekend or holiday, mean (SD) | 5.24 (3.26) | 6.70 (3.92) | <.001 | ||||
Average calculated hours spent on the internet per week, mean (SD) | 35.07 (21.64) | 42.96 (25.23) | <.001 | ||||
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.21 | Cramér |
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Every day | 1005 (97.3) | 1689 (97.1) |
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2-3 times per week | 19 (1.8) | 41 (2.4) |
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1 time per week | 7 (0.7) | 3 (0.2) |
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1 time per month | 1 (0.1) | 3 (0.2) |
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Less than 1 time per month | 1 (0.1) | 3 (0.2) |
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<.001 | Cramér |
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<1 | 155 (15.0) | 136 (7.8) |
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1-2 | 279 (27.0) | 362 (20.8) |
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3-4 | 267 (25.8) | 473 (27.2) |
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5-6 | 184 (17.8) | 347 (19.9) |
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>6 | 148 (14.3) | 422 (24.3) |
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AICA-Sb total sample, mean (SD) | 7.45 (5.20) | 8.62 (5.57) | <.001 | ||||
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<.001 | Cramér |
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Unproblematic use | 579 (56.1) | 815 (46.8) |
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Hazardous use | 303 (29.3) | 562 (32.3) |
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Pathological use | 151 (14.6) | 363 (20.9) |
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Web-based games (eg, role-playing games and ego shooters) | 169 (16.4) | 838 (48.2) | .048 | |||
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Web-based shopping (eg, eBay and Amazon) | 187 (18.1) | 184 (10.6) | .22 | φ=0.059 | ||
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Chats or forums | 267 (25.9) | 329 (18.9) | .34 | φ=0.038 | ||
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Writing emails | 76 (7.4) | 99 (5.7) | .40 | φ=0.057 | ||
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Web-based sex (eg, pornographic material) | 16 (1.6) | 393 (22.6) | <.001 | φ=0.297 | ||
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Web-based gambling (eg, poker, casinos, betting) | 15 (1.5) | 52 (3.0) | .40 | φ=0.087 | ||
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Web-based communities (eg, Facebook and Instagram) | 437 (42.3) | 435 (25.0) | .10 | φ=0.055 | ||
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Information searching (eg, Wikipedia) | 190 (18.4) | 294 (16.0) | .25 | φ=0.049 | ||
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Web-based streaming services (eg, Netflix, Amazon Prime, YouTube) | 577 (55.9) | 963 (55.3) | >.99 | φ=0.000 | ||
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Others | 67 (6.5) | 118 (6.8) | .02 | φ=0.152 | ||
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<.001 | Cramér |
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Web-based games (eg, role-playing games and ego shooters) | 124 (12.0) | 515 (29.6) |
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Web-based shopping (eg, eBay and Amazon) | 73 (7.1) | 49 (2.8) |
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Chats or forums | 66 (6.4) | 42 (2.4) |
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Writing emails | 12 (1.2) | 14 (0.8) |
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Web-based sex (eg, pornographic material) | 29 (2.8) | 276 (15.9) |
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Web-based gambling (eg, poker, casinos, and betting) | 89 (8.6) | 174 (10.0) |
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Web-based communities (eg, Facebook and Instagram) | 279 (27.0) | 167 (9.6) |
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Information searching (eg, Wikipedia) | 68 (6.6) | 66 (3.8) |
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Web-based streaming services (eg, Netflix and YouTube) | 293 (28.4) | 437 (25.1) |
aMissing values for gender: n=167. We excluded participants with the “divers” gender (n=67).
bAICA-S: Assessment of Internet and Computer Game Addiction Scale.
Frequency of AICA-S scores among women and men. AICA-S: Assessment of Internet and Computer Game Addiction Scale.
Assessment of Internet and Computer Game Addiction Scale (AICA-S) scores differentiated by type of internet use disordera.
Variable | Participants, n | AICA-S score, mean (SD) |
Web-based streaming | 795 | 9.01 (5.57) |
Web-based gaming | 678 | 9.23 (5.82) |
Social networks or communities | 493 | 7.76 (4.97) |
Web-based pornography or sex | 333 | 8.22 (5.43) |
Web-based gambling | 283 | 5.77 (3.68) |
Information search | 145 | 7.48 (5.32) |
Web-based shopping | 128 | 5.98 (4.89) |
Web-based chats | 121 | 8.40 (6.37) |
Emails | 27 | 6.44 (5.08) |
aMissing values:
Male participants used the internet for 5.91 (
The most frequently mentioned types of excessive internet use among female users were (multiple answers possible) web-based streaming (577/1033, 55.3%), social networking (437/1033, 42.3%), and chats or forums (267/1033, 25.9%). In contrast, the most frequently mentioned types of excessive internet use for men were web-based streaming (963/1740, 55.3%), gaming (838/1740, 48.2%), and web-based pornography (393/1740, 22.6%). The largest significant gender difference with moderate effect size was found for web-based pornography (
Descriptive statistics and correlation for internet use disorder symptoms.
Variable | Participants, n | AICA-Sa score, mean (SD) | Correlations, |
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AICA-S score | Time of use per week (h) |
AICA-S score | 3007 | 8.20 (5.47) | —b | — |
Time of use per week (h) | 3007 | 39.95 (24.33) | 0.523c | — |
Age | 2962 | 29.17 (14.55) | –0.090c | –0.164c |
aAICA-S: Assessment of Internet and Computer Game Addiction Scale.
bNot determined.
cCorrelation is significant when
The association between gender and IUD symptoms (AICA-S) was significant (η=0.098, η2=0.0096,
Stepwise multiple regression analysis was performed within the total sample to test if sociodemographic factors including age, occupational status (divided into 3 categories: employed or self-employed, unemployed, and being in vocational training or studying) and gender (male or female) were significantly associated with IUD symptom severity. The following characteristics were chosen as references for the dummy variables: female gender and occupational status (employed or self-employed).
A significant regression equation was found (
Due to the significant influence of unemployment and vocational status, two 2-way ANOVAs were conducted to explore the main and interaction effects of unemployment (yes or no), being in vocational training or studying (yes or no), and gender (female or male) on levels of IUD symptom severity (AICA-S score). First, there was a significant main effect for unemployment (
Two-way ANOVA: main and interaction effects of gender and unemployment (left) or being in vocational training or studying (right). AICA-S: Assessment of Internet and Computer Game Addiction Scale.
First, the results showed that there was continuous participation in the web-based assessment during 80 weeks, with a very wide range in IUD symptom burden (between 0 and 27 points) reported (research question 1). The high number of 3007 participants showed that the web-based offer was generally accepted. The mean symptom burden of the total group was 8.19 (SD 5.47), indicating
With regard to research question 3, it could be shown that both women and men used the service of a web-based self-assessment for IUD symptoms. The proportion of women participating (1033/3007, 34.4%) was much higher than expected and exceeded the proportion of women who visited IUD specialist outpatient clinics in Germany [
Regression analysis showed that the male gender was significantly associated with the AICA-S score. The effect of gender, however, was less significant than the effect of unemployment. In terms of symptom severity, other sociodemographic variables appear to have had an even greater effect than the question of gender. Furthermore, there were no interaction effects of gender and sociodemographic factors on symptom severity. As a result, it mattered whether someone was a man or woman, or unemployed or working. However, the interaction effect was not significant. The results confirm previous findings describing unemployment as a risk factor in IUD and gaming disorders [
A significant difference was found in the subjective problematic use of pornography. Significantly more men (393/1740, 22.6%) reported this; pornography was less often relevant for women (16/1033, 1.6%). This is in line with previous research literature and experience from therapeutic practice [
In summary, and with regard to research question 4, there are some differences with small effect sizes between men and women with regard to IUD symptom severity (mean differences of 1.17 AICA-S points). At most, there are individual gender differences in the type of problematic internet use. A secondary finding was that being unemployed as well as undergoing vocational training or studying at a university were significantly associated with IUD. It can be assumed that the free time allocation promotes a higher usage time. Perhaps being at home (possibly alone because others are busy) or not working in a specific workspace or outside the home plays an important role. It is possible that being at work is a protective factor, as has been reported in a previous study [
The study was conducted on a large population using a questionnaire. The cutoff of the questionnaire was used to classify the severity. This type of diagnosis does not replace a clinical diagnosis by an expert and is therefore naturally prone to false positives or false negatives. However, numerous population-based studies have been conducted using this methodology. In selecting the questionnaire, an established instrument that is more clinically oriented and makes a conservative assessment was chosen. Moreover, it was used as the main outcome in a previous high-quality RCT [
Using a very large sample, the study shows that both people with mild and those with severe IUD can be reached via the internet. Thus, a free eHealth offer can be a good way to reach patients with IUD where they are addicted—on the internet. In addition, eHealth services increase the likelihood of reaching female patients, who, by analogy, hardly ever come to specialized outpatient clinics and hospitals. Nevertheless, men appeared to be affected by IUD symptoms somewhat more frequently and severely than women, although the differences tended to have small effect sizes. Since social problems, especially unemployment, have a strong effect on disease severity, the integration of social counseling into treatment seems advisable in terms of a multidisciplinary approach.
Assessment of Internet and Computer Game Addiction Scale
internet use disorder
online-based motivational intervention to reduce problematic internet use and promote treatment motivation in internet gaming disorder and internet use disorder
randomized controlled trial
short-term treatment of internet- and computer game addiction
We thank the whole OMPRIS research group, notably Raffaela Böswald, Lorraine Cornelsen, Michael Dreier, Linny Geisler, Sofie Groen, Ina Krahn, Dennis Lowin, Alicia Menze, Anja Niemann, Silke Neusser, Christian Suelmann, Julia Weretecki and the German Fachverband Medienabhängigkeit e.V. We acknowledge support by the DFG Open Access Publication Funds of the Ruhr-Universität Bochum. This publication was produced within a project funded by the German Innovation Fund of Germany’s Federal Joint Committee under grant number 01VSF18043, awarded to JDH. The recipient is Ruhr University Bochum, Universitätsstraße 150, 44801 Bochum, Germany. Furthermore, we acknowledge support from the Open Access Publication Funds of the Ruhr-Universität Bochum.
JDH did the main conceptualization of the manuscript, conducted the statistical analysis, interpreted the data, and wrote the first draft of the manuscript. LB and MP supported the conceptualization, designed and supervised the data collection, and performed a critical review, commentary, and revision. All other authors supported the interpretation of data and performed a critical review, commentary, and revision. All authors have read and approved the manuscript.
The study was part of an RCT study titled “Online-based motivational intervention to reduce problematic internet use and promote treatment motivation in internet gaming disorder and internet use disorder” (OMPRIS) that is funded by the German Innovation Fund of Germany’s Federal Joint Committee. JDH obtained the funding. The authors declare that they do not receive any financial support from the industry, in particular the computer games industry.