2nd/10_Wiki/Topic_Programming/Architecture/Experience-Sampling-Method.md

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

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
wiki-2026-0508-experience-sampling-method	Experience Sampling Method	10_Wiki/Topics	verified	self	ESM EMA Ecological Momentary Assessment Diary Studies	none	A	0.9	applied	research-methodology psychology ux-research mobile		2026-05-10	pending	language framework typescript react-native

Experience Sampling Method

매 한 줄

"매 retrospection bias 의 in-the-moment self-report 의 replace". Experience Sampling Method (ESM, Csikszentmihalyi & Larson 1987) 매 participants 의 day 의 multiple times 의 random/scheduled prompt, 매 current activity/affect/context 의 record. Mobile-era 매 EMA (Ecological Momentary Assessment) 의 generalize — 매 mental health, UX, productivity research 의 gold standard.

매 핵심

매 Why ESM

• Retrospection bias: 매 "지난주 어땠나" 매 peak-end bias, mood-congruent recall 의 distort.
• Ecological validity: 매 in-context 매 lab 의 not-replicate.
• Within-subject variance: 매 person × situation 의 interaction 의 capture.

매 Sampling schedules

• Signal-contingent: 매 random beep 매 day 매 6-8 prompts.
• Interval-contingent: 매 fixed times (9am/12pm/3pm/6pm).
• Event-contingent: 매 specific event (meal, exercise) 의 trigger.
• Hybrid: 매 baseline random + event triggers.

매 응용

1. Mood / affect tracking (depression, bipolar).
2. Pain studies (chronic pain).
3. UX product research — feature use in-context.
4. Flow state research (Csikszentmihalyi original).
5. LLM agent behavior tracking — analog 매 process.

💻 패턴

Mobile prompt scheduler (React Native)

import * as Notifications from 'expo-notifications';

interface ESMConfig {
  startHour: number; endHour: number;
  promptsPerDay: number;
  minIntervalMinutes: number;
}

async function schedulePrompts(cfg: ESMConfig, days = 7) {
  const slots = generateRandomSlots(cfg, days);
  for (const slot of slots) {
    await Notifications.scheduleNotificationAsync({
      content: {
        title: 'Quick check-in (30s)',
        body: 'How are you feeling right now?',
        data: { promptId: slot.id, scheduledFor: slot.time.toISOString() },
      },
      trigger: { date: slot.time },
    });
  }
}

function generateRandomSlots(cfg: ESMConfig, days: number) {
  const slots = [];
  for (let d = 0; d < days; d++) {
    const dayStart = new Date();
    dayStart.setDate(dayStart.getDate() + d);
    const windowMs = (cfg.endHour - cfg.startHour) * 3600_000;
    const minGap = cfg.minIntervalMinutes * 60_000;
    const times: number[] = [];
    while (times.length < cfg.promptsPerDay) {
      const candidate = Math.random() * windowMs;
      if (times.every(t => Math.abs(t - candidate) >= minGap)) {
        times.push(candidate);
      }
    }
    times.sort((a, b) => a - b).forEach((offset, i) => {
      const t = new Date(dayStart);
      t.setHours(cfg.startHour, 0, 0, 0);
      t.setTime(t.getTime() + offset);
      slots.push({ id: `${d}-${i}`, time: t });
    });
  }
  return slots;
}

Brief response form (PANAS-short, 30s budget)

interface ESMResponse {
  promptId: string;
  respondedAt: Date;
  latencyMs: number;
  affect: {
    valence: number;    // -3..+3
    arousal: number;    // -3..+3
  };
  activity: string;     // dropdown: work | social | rest | exercise | other
  social: 'alone' | 'with_others';
  freeText?: string;
}

Compliance tracking

function complianceMetrics(responses: ESMResponse[], scheduled: number) {
  const completed = responses.length;
  const onTime = responses.filter(r => r.latencyMs < 15 * 60_000).length;
  const meanLatency = responses.reduce((s, r) => s + r.latencyMs, 0) / completed;
  return {
    completionRate: completed / scheduled,    // target > 0.7
    onTimeRate: onTime / scheduled,            // target > 0.5
    meanLatencyMin: meanLatency / 60_000,
  };
}

Multilevel analysis (within vs between)

import statsmodels.formula.api as smf

# 매 each row 매 prompt response, 매 participant_id 매 grouping
model = smf.mixedlm(
    'valence ~ activity + social + time_of_day',
    data=df,
    groups=df['participant_id'],
    re_formula='~time_of_day',
).fit()
print(model.summary())
# 매 within-person variance (situation) 매 between-person (trait) 의 separate

Sliding-window mood detection

function detectMoodEpisode(responses: ESMResponse[], windowDays = 7, threshold = -1.5) {
  const sorted = [...responses].sort((a, b) =>
    a.respondedAt.getTime() - b.respondedAt.getTime());
  const episodes = [];
  for (let i = 0; i < sorted.length; i++) {
    const start = sorted[i].respondedAt;
    const end = new Date(start.getTime() + windowDays * 86400_000);
    const window = sorted.filter(r =>
      r.respondedAt >= start && r.respondedAt <= end);
    if (window.length < 5) continue;
    const meanV = window.reduce((s, r) => s + r.affect.valence, 0) / window.length;
    if (meanV < threshold) episodes.push({ start, end, meanV, n: window.length });
  }
  return mergeOverlapping(episodes);
}

Privacy: 매 on-device aggregation

// 매 raw responses 매 device 의 stay, 매 weekly summary 만 의 server 의 send
async function uploadWeeklySummary(responses: ESMResponse[]) {
  const summary = {
    week: getCurrentWeek(),
    n: responses.length,
    valenceMean: mean(responses.map(r => r.affect.valence)),
    valenceStd: std(responses.map(r => r.affect.valence)),
    activityHistogram: histogram(responses.map(r => r.activity)),
  };
  await api.post('/esm/summary', summary);
}

매 결정 기준

상황	Approach
Trait measurement (depression baseline)	매 ESM unnecessary — 매 single questionnaire 매 fine
Within-day variation 의 question	매 ESM signal-contingent
Specific event 매 rare	매 event-contingent
Compliance fragile	매 prompt count 의 reduce, 매 incentive
Privacy-sensitive (clinical)	매 on-device aggregation 또는 federated

기본값: 매 6-8 prompts/day, 매 14 days, 매 30s response — 매 compliance > 70% 의 target.

🔗 Graph

• 부모: Research-Methodology
• 변형: Ecological-Momentary-Assessment
• 응용: Flow_State

🤖 LLM 활용

언제: 매 in-the-moment subjective state 의 measure. Within-person variance 의 study. Retrospective bias 의 likely. 언제 X: 매 stable trait. 매 single-shot decision study. 매 intrusive sampling 매 acceptable 의 X.

❌ 안티패턴

• Too many prompts: 매 12+/day 매 fatigue → compliance crash.
• Long forms: 매 5min response 매 ecological 의 break.
• Ignoring missing-not-at-random: 매 prompts during depressive episode 매 skipped — 매 selection bias.
• Cross-sectional analysis 의 hierarchical data: 매 multilevel model 의 use, 매 OLS 의 std error 의 underestimate.

🧪 검증 / 중복

• Verified (Csikszentmihalyi & Larson 1987 JNMD; Shiffman et al. 2008 Ann Rev Clin Psych).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — ESM scheduler + analysis + privacy patterns