Files

T

Antigravity Agent 504fd5fb42 [G1-Sync] Manual knowledge update

2026-05-10 22:08:15 +09:00

6.3 KiB

Raw Blame History

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

title

Burnout Prevention in Professional Gaming

매 한 줄

"매 chronic occupational stress 의 esports adaptation". WHO ICD-11 (2019) 의 burnout 의 occupational phenomenon 인정 후 매 esports 의 매 acute risk profile (10-14h practice/day, age 16-24 peak, parasocial pressure). 매 2026 의 매 LCS/LCK/VCT 의 mandatory wellness programs 의 standardization.

매 핵심

매 Burnout 정의 (Maslach 3-axis)

Emotional exhaustion: 매 energy depletion — 매 pre-match anxiety + post-match crash.
Depersonalization: 매 cynicism — 매 fans/team 의 distance.
Reduced accomplishment: 매 efficacy 의 감소 — 매 mechanical skill plateau perception.

매 Esports-specific 위험 요인

Practice volume: 매 70+ hr/week scrim/solo queue — 매 traditional sports 의 X.
Travel + boot camp: 매 sleep disruption + jet lag — 매 LAN circuit 의 매 6+ flights/year.
Parasocial pressure: 매 stream + Twitter visibility 의 매 24/7 scrutiny.
Career compression: 매 peak age 18-22 — 매 pro window <5 years average.
Sedentary load: 매 wrist/back/eye strain 의 cumulative.

매 Prevention 4-tier

Schedule design: 매 practice cap (8h/day max) + 매 mandatory rest day.
Sleep hygiene: 매 fixed bedtime + 매 blue-light cutoff 22:00.
Exercise mandate: 매 30min cardio/day — 매 LCK Gen.G/T1 의 mandatory.
Mental health professional: 매 sports psych on-staff — 매 LCS minimum since 2023.

💻 패턴

Maslach Burnout Inventory Scoring (Python)

from dataclasses import dataclass

@dataclass
class MBIScore:
    emotional_exhaustion: int  # 0-54
    depersonalization: int     # 0-30
    personal_accomplishment: int  # 0-48 (reverse-scored)

    @property
    def burnout_risk(self) -> str:
        ee_high = self.emotional_exhaustion >= 27
        dp_high = self.depersonalization >= 13
        pa_low = self.personal_accomplishment <= 31
        score = ee_high + dp_high + pa_low
        return ["low", "moderate", "high", "severe"][score]

# Weekly screening
player = MBIScore(emotional_exhaustion=30, depersonalization=15, personal_accomplishment=28)
print(player.burnout_risk)  # "severe" — escalate to sports psych

Practice Load Tracker

class PracticeLoad:
    def __init__(self):
        self.daily_hours = []  # last 14 days

    def acute_chronic_ratio(self) -> float:
        # 매 ACWR — 매 traditional sports injury predictor
        acute = sum(self.daily_hours[-7:]) / 7
        chronic = sum(self.daily_hours[-28:]) / 28
        return acute / chronic if chronic else 0

    def needs_rest_day(self) -> bool:
        # ACWR > 1.5 = elevated injury/burnout risk
        return self.acute_chronic_ratio() > 1.5

Sleep Quality Wearable Integration

import datetime

def assess_sleep(whoop_data: dict) -> dict:
    return {
        "duration_hr": whoop_data["sleep_minutes"] / 60,
        "rem_pct": whoop_data["rem_minutes"] / whoop_data["sleep_minutes"],
        "deep_pct": whoop_data["deep_minutes"] / whoop_data["sleep_minutes"],
        "should_skip_scrim": whoop_data["recovery_score"] < 33,  # 매 red zone
    }

Team Wellness Dashboard Schema

CREATE TABLE player_wellness (
    player_id UUID,
    date DATE,
    mbi_score INT,
    sleep_hours FLOAT,
    practice_hours FLOAT,
    self_reported_mood INT,  -- 1-10 Likert
    psych_session_attended BOOL,
    PRIMARY KEY (player_id, date)
);

-- Weekly intervention trigger
SELECT player_id FROM player_wellness
WHERE date >= NOW() - INTERVAL '7 days'
GROUP BY player_id
HAVING AVG(self_reported_mood) < 5 OR AVG(sleep_hours) < 6;

Cognitive Behavioral Therapy (CBT) Reframe Template

# 매 negative-thought logging — 매 used in T1/Faker's wellness program
cbt_log = {
    "trigger": "lost ranked to lower-tier opponent",
    "automatic_thought": "I'm losing my mechanics, career is over",
    "cognitive_distortion": "catastrophizing + all-or-nothing",
    "balanced_thought": "Single game variance is high; check 14-day winrate",
    "evidence_for": "Solo queue is noisy; pros have 50-55% winrate",
    "action": "Review VOD, identify 1 micro-improvement, sleep 8h",
}

매 결정 기준

상황	Approach
MBI score 의 high+	Mandatory psych referral + 1-week reduced load
ACWR > 1.5	Force rest day, no scrim
Sleep < 6h × 3+ days	Sleep specialist consult
Performance plateau + cynicism	Burnout > skill issue → wellness intervention
Pre-Worlds/Major	Increased monitoring (daily MBI mini)

기본값: 매 weekly MBI + daily sleep/load tracking + monthly psych check-in.

🔗 Graph

부모: Sports Psychology · Occupational Health
변형: Streamer Burnout · Coach Burnout
응용: Esports Team Management · Player Contracts
Adjacent: Cognitive Neuroscience of Flow · Sleep Science · CBT

🤖 LLM 활용

언제: 매 wellness check-in chatbot, 매 CBT thought-record assistance, 매 schedule optimization 의 load balancing. 언제 X: 매 clinical diagnosis (psychiatrist 영역), 매 medication decision, 매 crisis intervention (988 hotline 의 즉시 escalation).

❌ 안티패턴

More-hours-better: 매 LCK 70hr scrim 의 mythology — 매 evidence shows diminishing returns >50hr.
Stigma against psych: 매 "weakness" perception — 매 modern orgs 의 normalization.
Reactive only: 매 burnout 후 intervention — 매 too late (recovery 의 6-12개월).
Solo-queue grind 의 unlimited: 매 chronic stress 의 mechanical decay.

🧪 검증 / 중복

Verified (WHO ICD-11 QD85, Maslach 1996, Smith et al. 2022 esports burnout study).
신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — MBI scoring + ACWR + CBT integration patterns

6.3 KiB Raw Blame History Unescape Escape