Antigravity Agent
212 lines
7.4 KiB
Markdown
212 lines
7.4 KiB
Markdown
---
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id: wiki-2026-0508-experience-sampling-method
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title: Experience Sampling Method
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [ESM, EMA, Ecological Momentary Assessment, Diary Studies]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [research-methodology, psychology, ux-research, mobile]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: typescript
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framework: react-native
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---
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# Experience Sampling Method
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## 매 한 줄
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> **"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.
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## 매 핵심
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### 매 Why ESM
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- **Retrospection bias**: 매 "지난주 어땠나" 매 peak-end bias, mood-congruent recall 의 distort.
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- **Ecological validity**: 매 in-context 매 lab 의 not-replicate.
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- **Within-subject variance**: 매 person × situation 의 interaction 의 capture.
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### 매 Sampling schedules
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- **Signal-contingent**: 매 random beep 매 day 매 6-8 prompts.
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- **Interval-contingent**: 매 fixed times (9am/12pm/3pm/6pm).
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- **Event-contingent**: 매 specific event (meal, exercise) 의 trigger.
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- **Hybrid**: 매 baseline random + event triggers.
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### 매 응용
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1. Mood / affect tracking (depression, bipolar).
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2. Pain studies (chronic pain).
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3. UX product research — feature use in-context.
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4. Flow state research (Csikszentmihalyi original).
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5. LLM agent behavior tracking — analog 매 process.
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## 💻 패턴
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### Mobile prompt scheduler (React Native)
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```typescript
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import * as Notifications from 'expo-notifications';
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interface ESMConfig {
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startHour: number; endHour: number;
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promptsPerDay: number;
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minIntervalMinutes: number;
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}
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async function schedulePrompts(cfg: ESMConfig, days = 7) {
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const slots = generateRandomSlots(cfg, days);
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for (const slot of slots) {
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await Notifications.scheduleNotificationAsync({
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content: {
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title: 'Quick check-in (30s)',
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body: 'How are you feeling right now?',
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data: { promptId: slot.id, scheduledFor: slot.time.toISOString() },
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},
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trigger: { date: slot.time },
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});
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}
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}
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function generateRandomSlots(cfg: ESMConfig, days: number) {
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const slots = [];
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for (let d = 0; d < days; d++) {
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const dayStart = new Date();
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dayStart.setDate(dayStart.getDate() + d);
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const windowMs = (cfg.endHour - cfg.startHour) * 3600_000;
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const minGap = cfg.minIntervalMinutes * 60_000;
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const times: number[] = [];
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while (times.length < cfg.promptsPerDay) {
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const candidate = Math.random() * windowMs;
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if (times.every(t => Math.abs(t - candidate) >= minGap)) {
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times.push(candidate);
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}
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}
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times.sort((a, b) => a - b).forEach((offset, i) => {
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const t = new Date(dayStart);
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t.setHours(cfg.startHour, 0, 0, 0);
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t.setTime(t.getTime() + offset);
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slots.push({ id: `${d}-${i}`, time: t });
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});
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}
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return slots;
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}
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```
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### Brief response form (PANAS-short, 30s budget)
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```typescript
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interface ESMResponse {
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promptId: string;
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respondedAt: Date;
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latencyMs: number;
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affect: {
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valence: number; // -3..+3
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arousal: number; // -3..+3
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};
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activity: string; // dropdown: work | social | rest | exercise | other
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social: 'alone' | 'with_others';
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freeText?: string;
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}
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```
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### Compliance tracking
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```typescript
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function complianceMetrics(responses: ESMResponse[], scheduled: number) {
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const completed = responses.length;
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const onTime = responses.filter(r => r.latencyMs < 15 * 60_000).length;
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const meanLatency = responses.reduce((s, r) => s + r.latencyMs, 0) / completed;
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return {
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completionRate: completed / scheduled, // target > 0.7
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onTimeRate: onTime / scheduled, // target > 0.5
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meanLatencyMin: meanLatency / 60_000,
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};
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}
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```
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### Multilevel analysis (within vs between)
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```python
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import statsmodels.formula.api as smf
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# 매 each row 매 prompt response, 매 participant_id 매 grouping
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model = smf.mixedlm(
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'valence ~ activity + social + time_of_day',
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data=df,
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groups=df['participant_id'],
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re_formula='~time_of_day',
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).fit()
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print(model.summary())
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# 매 within-person variance (situation) 매 between-person (trait) 의 separate
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```
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### Sliding-window mood detection
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```typescript
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function detectMoodEpisode(responses: ESMResponse[], windowDays = 7, threshold = -1.5) {
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const sorted = [...responses].sort((a, b) =>
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a.respondedAt.getTime() - b.respondedAt.getTime());
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const episodes = [];
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for (let i = 0; i < sorted.length; i++) {
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const start = sorted[i].respondedAt;
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const end = new Date(start.getTime() + windowDays * 86400_000);
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const window = sorted.filter(r =>
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r.respondedAt >= start && r.respondedAt <= end);
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if (window.length < 5) continue;
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const meanV = window.reduce((s, r) => s + r.affect.valence, 0) / window.length;
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if (meanV < threshold) episodes.push({ start, end, meanV, n: window.length });
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}
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return mergeOverlapping(episodes);
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}
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```
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### Privacy: 매 on-device aggregation
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```typescript
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// 매 raw responses 매 device 의 stay, 매 weekly summary 만 의 server 의 send
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async function uploadWeeklySummary(responses: ESMResponse[]) {
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const summary = {
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week: getCurrentWeek(),
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n: responses.length,
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valenceMean: mean(responses.map(r => r.affect.valence)),
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valenceStd: std(responses.map(r => r.affect.valence)),
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activityHistogram: histogram(responses.map(r => r.activity)),
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};
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await api.post('/esm/summary', summary);
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}
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```
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## 매 결정 기준
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| 상황 | Approach |
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| Trait measurement (depression baseline) | 매 ESM unnecessary — 매 single questionnaire 매 fine |
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| Within-day variation 의 question | 매 ESM signal-contingent |
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| Specific event 매 rare | 매 event-contingent |
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| Compliance fragile | 매 prompt count 의 reduce, 매 incentive |
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| Privacy-sensitive (clinical) | 매 on-device aggregation 또는 federated |
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**기본값**: 매 6-8 prompts/day, 매 14 days, 매 30s response — 매 compliance > 70% 의 target.
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## 🔗 Graph
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- 부모: [[Research-Methodology]] · [[Psychometrics]]
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- 변형: [[Ecological-Momentary-Assessment]] · [[Diary-Study]] · [[Day-Reconstruction]]
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- 응용: [[Mood-Tracking-Apps]] · [[UX-Research]] · [[Flow-State]]
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- Adjacent: [[Csikszentmihalyi]] · [[Multilevel-Modeling]] · [[Mobile-Sensing]]
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## 🤖 LLM 활용
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**언제**: 매 in-the-moment subjective state 의 measure. Within-person variance 의 study. Retrospective bias 의 likely.
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**언제 X**: 매 stable trait. 매 single-shot decision study. 매 intrusive sampling 매 acceptable 의 X.
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## ❌ 안티패턴
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- **Too many prompts**: 매 12+/day 매 fatigue → compliance crash.
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- **Long forms**: 매 5min response 매 ecological 의 break.
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- **Ignoring missing-not-at-random**: 매 prompts during depressive episode 매 skipped — 매 selection bias.
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- **Cross-sectional analysis 의 hierarchical data**: 매 multilevel model 의 use, 매 OLS 의 std error 의 underestimate.
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## 🧪 검증 / 중복
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- Verified (Csikszentmihalyi & Larson 1987 JNMD; Shiffman et al. 2008 Ann Rev Clin Psych).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — ESM scheduler + analysis + privacy patterns |
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