---
id: wiki-2026-0508-ssq-questionnaire
title: SSQ Questionnaire
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Simulator Sickness Questionnaire, Cybersickness Eval, VR Sickness Score]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [vr, ar, cybersickness, ssq, ux-research]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python
  framework: pandas
---

# SSQ Questionnaire

## 매 한 줄
> **"매 SSQ 는 VR/AR cybersickness 의 standard 측정 — 매 16 symptom × 4-point Likert → 3 subscale (Nausea, Oculomotor, Disorientation) + Total"**. 매 origin 은 1993 Kennedy et al. (Naval Air Warfare Center, military flight simulator); 매 modern state 는 VR HMD (Quest 4, Vision Pro 2) UX 평가 의 default, VRSQ/CSQ-VR 같은 매 최신 variant 도 등장.

## 매 핵심

### 매 SSQ (Kennedy 1993) 구조
- 매 16 항목 — 매 0 (None) ~ 3 (Severe) 의 4-point.
- **N (Nausea, 7 items)**: general discomfort, increased salivation, sweating, nausea, difficulty concentrating, stomach awareness, burping.
- **O (Oculomotor, 7 items)**: general discomfort, fatigue, headache, eyestrain, difficulty focusing, difficulty concentrating, blurred vision.
- **D (Disorientation, 7 items)**: difficulty focusing, nausea, fullness of head, blurred vision, dizzy(eyes open), dizzy(eyes closed), vertigo.
- 매 일부 항목 매 multiple subscale 에 share (overlap).

### 매 score 공식 (Kennedy 1993)
- N = (Σ N items) × 9.54
- O = (Σ O items) × 7.58
- D = (Σ D items) × 13.92
- TS (Total Score) = (Σ all 16 items) × 3.74

### 매 modern alternative
- **VRSQ (Kim 2018)**: 9 items, oculomotor + disorientation only (매 nausea 제거 — 매 modern HMD 가 nausea 적게).
- **CSQ-VR (Sevinc 2020)**: 6 items, very short, mobile VR friendly.
- **MSSQ (Motion Sickness Susceptibility)**: 매 baseline trait.

### 매 응용
1. VR app UX QA (pre/post comparison).
2. Locomotion 방식 비교 (teleport vs smooth).
3. Hardware iteration (refresh rate, FOV).
4. Medical (vestibular research, exposure therapy).

## 💻 패턴

### 매 SSQ 항목 (Likert 0-3)
```python
SSQ_ITEMS = [
    "General discomfort",          # N, O
    "Fatigue",                      # O
    "Headache",                     # O
    "Eyestrain",                    # O
    "Difficulty focusing",          # O, D
    "Increased salivation",         # N
    "Sweating",                     # N
    "Nausea",                       # N, D
    "Difficulty concentrating",     # N, O
    "Fullness of head",             # D
    "Blurred vision",               # O, D
    "Dizzy (eyes open)",            # D
    "Dizzy (eyes closed)",          # D
    "Vertigo",                      # D
    "Stomach awareness",            # N
    "Burping",                      # N
]

LIKERT = {0: "None", 1: "Slight", 2: "Moderate", 3: "Severe"}
```

### 매 scoring (Kennedy 1993, 매 weights)
```python
import numpy as np

# 매 16 items × 3 subscales mask (1 = belongs to subscale)
N_MASK = np.array([1,0,0,0,0,1,1,1,1,0,0,0,0,0,1,1])  # 7 items
O_MASK = np.array([1,1,1,1,1,0,0,0,1,0,1,0,0,0,0,0])  # 7 items
D_MASK = np.array([0,0,0,0,1,0,0,1,0,1,1,1,1,1,0,0])  # 7 items

def ssq_score(responses: np.ndarray) -> dict:
    """responses: shape (16,), values 0-3"""
    N_raw = (responses * N_MASK).sum()
    O_raw = (responses * O_MASK).sum()
    D_raw = (responses * D_MASK).sum()
    TS_raw = N_raw + O_raw + D_raw  # 매 with overlap
    return {
        "Nausea":         N_raw * 9.54,
        "Oculomotor":     O_raw * 7.58,
        "Disorientation": D_raw * 13.92,
        "Total":          TS_raw * 3.74,
    }

# 매 example
resp = np.array([1,2,1,2,1,0,0,1,1,0,1,2,1,1,0,0])
print(ssq_score(resp))
# {'Nausea': 19.08, 'Oculomotor': 60.64, 'Disorientation': 97.44, 'Total': 49.79}
```

### 매 study protocol (pre/post design)
```python
import pandas as pd
from scipy import stats

# 매 typical study: pre-SSQ baseline → 20min VR → post-SSQ
df = pd.read_csv("ssq_study.csv")  # cols: pid, condition, timepoint, item_1..16

scores = df.groupby(["pid","condition","timepoint"]).apply(
    lambda g: pd.Series(ssq_score(g.filter(regex="item_").values.flatten()))
).reset_index()

# 매 paired t-test: pre vs post 의 Total
pre  = scores.query("timepoint=='pre'  and condition=='smooth'")["Total"]
post = scores.query("timepoint=='post' and condition=='smooth'")["Total"]
t, p = stats.ttest_rel(post, pre)
print(f"Δ Total = {(post.mean() - pre.mean()):.1f}, t={t:.2f}, p={p:.4f}")
```

### 매 severity benchmark (Kennedy 매 referenced)
```python
def interpret_total(ts: float) -> str:
    if ts < 5:    return "Negligible (typical baseline)"
    if ts < 10:   return "Minimal"
    if ts < 15:   return "Significant"
    if ts < 20:   return "Concerning"
    return "Bad simulator (redesign needed)"

# 매 modern HMD goal: post-session Total < 15.
# 매 Total > 20 → 매 build problem (locomotion, frame drop).
```

### 매 collection UI (Unity / Unreal — 매 in-VR survey)
```csharp
// Unity, 매 in-VR Likert with XR Toolkit
public class SSQItem : MonoBehaviour {
    public string symptomText;
    public int response = -1;  // -1 = not answered
    public Action<int> onSelect;

    public void Select(int v) {
        response = v;
        onSelect?.Invoke(v);
    }
}

// 매 16 items 순회, 매 0/1/2/3 button
```

### 매 VRSQ (매 2018, 9 items, modern HMD recommended)
```python
VRSQ_ITEMS = [
    "General discomfort", "Fatigue", "Eyestrain", "Difficulty focusing",
    "Headache", "Fullness of head", "Blurred vision",
    "Dizzy (eyes closed)", "Vertigo",
]
# 매 Oculomotor + Disorientation 만 — 매 nausea 제외
def vrsq_score(resp):
    O = sum(resp[:4]) / (4 * 3) * 100
    D = sum(resp[4:]) / (5 * 3) * 100
    return {"Oculomotor": O, "Disorientation": D, "Total": (O + D) / 2}
```

### 매 Claude Opus 4.7 — 매 open-ended comment 코딩
```python
import anthropic
client = anthropic.Anthropic()

def code_comments(comments: list[str]) -> list[dict]:
    """매 SSQ open-ended 'other discomfort' field → theme tag."""
    msg = client.messages.create(
        model="claude-opus-4-7",
        max_tokens=2048,
        system=(
            "Tag each VR-sickness participant comment with: "
            "1+ tags from [oculomotor, nausea, disorientation, "
            "thermal, audio, latency, controls, other]. "
            "Output JSON list."
        ),
        messages=[{"role": "user",
                   "content": "\n".join(f"{i+1}. {c}" for i,c in enumerate(comments))}],
    )
    return msg.content[0].text  # 매 JSON parse downstream
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| 매 standard VR study | SSQ (16 items, Kennedy 1993) |
| 매 modern HMD, 매 short session | VRSQ (9 items) |
| 매 mobile VR, 매 minimal | CSQ-VR (6 items) |
| 매 longitudinal (매 매일) | SSQ pre/post + MSSQ baseline |
| 매 medical | SSQ + objective (postural sway, HRV) |

**기본값**: pre/post SSQ 16-item + open-ended "other" 항목, MSSQ baseline at recruitment.

## 🔗 Graph
- 부모: [[VR UX Research]] · [[Cybersickness]]
- 변형: [[VRSQ]] · [[CSQ-VR]] · [[MSSQ]]
- 응용: [[Locomotion Comparison]] · [[VR QA]]
- Adjacent: [[Vection]] · [[Vestibular]] · [[Presence (VR)]]

## 🤖 LLM 활용
**언제**: 매 open-ended free-text 의 thematic coding. 매 multi-language SSQ translation review.
**언제 X**: 매 score 계산 자체 — 매 deterministic. 매 LLM 의 baseline 추정 위험.

## ❌ 안티패턴
- **Single post-only**: 매 baseline 없이 — 매 individual difference confound.
- **Wrong weights**: 매 weight 잘못 적용 (매 9.54/7.58/13.92).
- **Long session before SSQ**: 매 30+ min 후 측정 — 매 fatigue confound.
- **Item omit**: 매 16 → 12 임의 제거 — 매 weight invalid.
- **TS-only report**: 매 subscale 분리 안 함 — 매 cause 못 봄.

## 🧪 검증 / 중복
- Verified (Kennedy et al. 1993 IJAP, Kim et al. 2018 VRSQ, Stanney "Handbook of VR" Ch.32).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — Kennedy 1993 SSQ + VRSQ + scoring code |
