In response to our study, the commentary by Infanti et al. (2024) raised critical points regarding (i) the conceptualization and utility of the user-avatar bond in addressing gaming disorder (GD) risk, and (ii) the optimization of supervised machine learning techniques applied to assess GD risk. To advance the scientific dialogue and progress in these areas, the present paper aims to: (i) enhance the clarity and understanding of the concepts of the avatar, the user-avatar bond, and the digital phenotype concerning gaming disorder (GD) within the broader field of behavioral addictions, and (ii) comparatively assess how the user-avatar bond (UAB) may predict GD risk, by both removing data augmentation before the data split and by implementing alternative data imbalance treatment approaches in programming.
- MeSH
- avatar MeSH
- lidé MeSH
- netholismus * MeSH
- řízené strojové učení MeSH
- strojové učení * MeSH
- uživatelské rozhraní počítače MeSH
- videohry MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
BACKGROUND AND AIMS: The question of when and how to treat truly asymptomatic patients with severe aortic stenosis (AS) and normal left ventricular (LV) systolic function is still subject to debate and ongoing research. Here, the results of extended follow-up of the AVATAR trial are reported (NCT02436655, ClinicalTrials.gov). METHODS: The AVATAR trial randomly assigned patients with severe, asymptomatic AS and LV ejection fraction ≥ 50% to undergo either early surgical aortic valve replacement (AVR) or conservative treatment with watchful waiting strategy. All patients had negative exercise stress testing. The primary hypothesis was that early AVR will reduce a primary composite endpoint comprising all-cause death, acute myocardial infarction, stroke, or unplanned hospitalization for heart failure (HF), as compared with conservative treatment strategy. RESULTS: A total of 157 low-risk patients (mean age 67 years, 57% men, mean Society of Thoracic Surgeons score 1.7%) were randomly allocated to either the early AVR group (n = 78) or the conservative treatment group (n = 79). In an intention-to-treat analysis, after a median follow-up of 63 months, the primary composite endpoint outcome event occurred in 18/78 patients (23.1%) in the early surgery group and in 37/79 patients (46.8%) in the conservative treatment group [hazard ratio (HR) early surgery vs. conservative treatment 0.42; 95% confidence interval (CI) 0.24-0.73, P = .002]. The Kaplan-Meier estimates for individual endpoints of all-cause death and HF hospitalization were significantly lower in the early surgery compared with the conservative group (HR 0.44; 95% CI 0.23-0.85, P = .012, for all-cause death and HR 0.21; 95% CI 0.06-0.73, P = .007, for HF hospitalizations). CONCLUSIONS: The extended follow-up of the AVATAR trial demonstrates better clinical outcomes with early surgical AVR in truly asymptomatic patients with severe AS and normal LV ejection fraction compared with patients treated with conservative management on watchful waiting.
- MeSH
- aortální chlopeň chirurgie MeSH
- aortální stenóza * chirurgie mortalita terapie MeSH
- asymptomatické nemoci terapie MeSH
- avatar MeSH
- cévní mozková příhoda MeSH
- chirurgická náhrada chlopně * metody MeSH
- hospitalizace statistika a číselné údaje MeSH
- konzervativní terapie * metody MeSH
- lidé středního věku MeSH
- lidé MeSH
- následné studie MeSH
- pozorné vyčkávání MeSH
- senioři MeSH
- tepový objem fyziologie MeSH
- výsledek terapie MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- multicentrická studie MeSH
- randomizované kontrolované studie MeSH
- srovnávací studie MeSH
Research has shown a link between depression risk and how gamers form relationships with their in-game figure of representation, called avatar. This is reinforced by literature supporting that a gamer's connection to their avatar may provide broader insight into their mental health. Therefore, it has been argued that if properly examined, the bond between a person and their avatar may reveal information about their current or potential struggles with depression offline. To examine whether the connection with an individuals' avatars may reveal their risk for depression, longitudinal data from 565 adults/adolescents (Mage = 29.3 years, SD = 10.6) were evaluated twice (six months apart). Participants completed the User-Avatar-Bond [UAB] scale and Depression Anxiety Stress Scale to measure avatar bond and depression risk. A series of tuned and untuned artificial intelligence [AI] classifiers analyzed their responses concurrently and prospectively. This allowed the examination of whether user-avatar bond can provide cross-sectional and predictive information about depression risk. Findings revealed that AI models can learn to accurately and automatically identify depression risk cases, based on gamers' reported UAB, age, and length of gaming involvement, both at present and six months later. In particular, random forests outperformed all other AIs, while avatar immersion was shown to be the strongest training predictor. Study outcomes demonstrate that UAB can be translated into accurate, concurrent, and future, depression risk predictions via trained AI classifiers. Assessment, prevention, and practice implications are discussed in the light of these results.
