# Ddr Globem Analysis

> Analyze a specific participant's longitudinal passive-sensing and psychological data in the GLOBEM digital depression research dataset. Use this skill whenever the task involves: analyzing a user's mental health or behavioral data from wearables/smartphones, generating QA pairs about behavioral/psychological changes over time, working with EMA, depression scores, activity, sleep, communication, location, or phone-usage data, or any user-profile analysis in the DDR/GLOBEM context.

- Skill: `zjunlp/ddr-globem-analysis-7` (Agent Skill)
- Install (CLI): `npx skillmds@latest add zjunlp/ddr-globem-analysis-7`
- Raw SKILL.md: https://api.skillmd.com/api/skills/zjunlp/ddr-globem-analysis-7/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: zjunlp (https://skillmd.com/u/zjunlp)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/zjunlp/ddr-globem-analysis-7

---


# DDR GLOBEM Participant Analysis

## Task Overview

Analyze all available data for a specified participant (`pid`, e.g., `INS-W_011`) and submit QA pairs covering their behavioral and psychological changes across the observation period. Aim for **28–37 high-quality, distinct QA pairs**. Each QA pair covers ONE specific dimension — never bundle multiple modalities or metrics into a single pair.

**Critical workflow rule**: Always compute all numbers BEFORE calling `submit_qa_pair()`. Never submit a QA pair with a placeholder, partial, or incomplete answer — doing so produces an unfixable record. Compute first, construct the full answer string, then submit.

**Submit incrementally**: Submit each QA pair immediately after computing that modality's data. Do not batch all submissions at the end.

## Dataset Structure

All CSV files share columns `pid` and `date`. Filter every file by the target `pid`.

**Sensor files** (92 days per participant, many NaN rows are normal):
- `activity_allday_raw.csv` — daily step count, active/sedentary bout counts and durations
- `sleep_allday_raw.csv` — sleep duration (minutes), efficiency, bedtime/wake-time
- `communication_allday_raw.csv` — incoming/outgoing/missed call counts, durations, distinct contacts
- `location_allday_raw.csv` — distance traveled, radius of gyration, home time, significant places, circadian routine, location entropy, location transitions, top-location times, flight characteristics, average speed
- `phone_usage_allday_raw.csv` — unlock episode count, total/avg/std duration; location-context columns
- `connectivity_allday_raw.csv` — Bluetooth scan count, unique devices

**Assessment files** (one row per observation per participant):
- `ema.csv` — `negative_affect_EMA` score, timestamped
- `dep_weekly.csv` — weekly `feel_anxious`, `feel_depressed`, `dep_weekly_subscale`, `anx_weekly_subscale`, `BDI2` (endterm only), `dep`
- `pre.csv` / `post.csv` — baseline vs. end-of-study psychological scales (columns use `_PRE` / `_POST` suffixes)
- `dep_endterm.csv` — final depression label and BDI2 score

**Schema tip**: Use `get_field_description(data_file="<filename>")` for the six sensor CSV files.

**Known column pitfalls**:
- `platform.csv` uses column `platform`, not `os`
- Home time: use `barnett_hometime` (not `barnett_homelabel`)
- Sleep duration: use `summary_rapids_sumdurationasleepmain` for daily sleep duration; bedtime/waketime use `summary_rapids_firstbedtimemain` / `summary_rapids_lastwaketimemain`
- `summary_rapids_*` columns repeat period-wide values — use `intraday_rapids_*` for daily activity variation
- Phone usage location columns: suffixes `_locmap_home`, `_locmap_living`, `_locmap_study`, `_locmap_greens`, `_locmap_exercise`
- Proactivity ratio with near-zero incoming: use `max(incoming, 0.1)` to avoid artifacts; a ratio >10 is likely a near-zero denominator — report raw counts instead
- Pre/post columns use exact names like `CESD_9items_PRE`, `UCLA_10items_PRE`, `2waySSS_receiving_emotional_PRE`, `BRS_PRE`, `CHIPS_PRE`, `STAIS_PRE`, `MAAS_7items_PRE`, `2waySSS_social_fit_PRE`

## Starter Code — Define Once, Reuse Throughout

```python
import pandas as pd
import numpy as np

pid = "INS-W_XXX"  # replace with actual pid

def early_late_thirds(df, pid, value_col):
    sub = df[df['pid'] == pid].copy()
    sub = sub.sort_values('date').dropna(subset=[value_col])
    n = len(sub)
    if n < 2:
        return None, None, None, None, None
    mid = n // 2
    early = sub.iloc[:mid][value_col].mean()
    late = sub.iloc[mid:][value_col].mean()
    third = n // 3
    if third > 0:
        t1 = sub.iloc[:third][value_col].mean()
        t2 = sub.iloc[third:2*third][value_col].mean()
        t3 = sub.iloc[2*third:][value_col].mean()
        if t1 < t2 > t3: pattern = "inverted-U"
        elif t1 > t2 < t3: pattern = "U-shaped"
        elif t1 < t2 < t3: pattern = "progressive increase"
        elif t1 > t2 > t3: pattern = "progressive decline"
        else: pattern = "mixed/stable"
    else:
        t1 = t2 = t3 = pattern = None
    pct = (late - early) / early * 100 if early and early != 0 else 0
    return early, late, pct, (t1, t2, t3), pattern

def mins_to_hhmm(mins):
    if pd.isna(mins): return "N/A"
    h = int(mins // 60) % 24
    m = int(mins % 60)
    return f"{h:02d}:{m:02d}"
```

## Analysis Workflow

### 1. Orient to the participant

Check data availability across all files first:
```python
files = ["ema.csv", "dep_weekly.csv", "activity_allday_raw.csv",
         "sleep_allday_raw.csv", "communication_allday_raw.csv",
         "location_allday_raw.csv", "phone_usage_allday_raw.csv",
         "connectivity_allday_raw.csv"]
for f in files:
    df = pd.read_csv(f)
    sub = df[df['pid'] == pid]
    n_valid = sub.select_dtypes('number').notna().any(axis=1).sum()
    print(f"{f}: {len(sub)} rows, {n_valid} with any numeric data")
```

Sparse coverage is normal for some modalities (communication can be <30% valid). Always extract what data exists rather than skipping modalities. If valid days <5, note sparsity in the QA answer but still report available trends.

### 2. Temporal segmentation — apply to ALL modalities including derived metrics

Always `dropna()` on the target column before splitting. The `early_late_thirds()` function handles this automatically.

**Early/late split** (primary comparison unit): first vs second half of valid observations.

**Thirds segmentation — mandatory for every quantitative column**, including derived ones:
- Ratios: active/sedentary ratio, proactivity ratio, scan efficiency (scans-per-device)
- Timing: first-use timing, avg flight duration, avg speed
- Counts: active bouts, sedentary bouts, network diversity, significant places
- Scores: sleep efficiency, circadian routine

Compute T1/T2/T3 and name the pattern for every such metric. Thirds reveal non-linear patterns (U-shaped, inverted-U, progressive, stable) that early/late splits hide.

**Thirds NOT required for** (early/late only is sufficient):
- EMA spike analysis (per-date comparisons, not aggregated thirds)
- Sleep timing in HH:MM (shift direction is the key insight)
- Communication and phone first-use timing windows (window shift, not thirds)
- Cross-modal correlations (no temporal split)
- Weekday vs. weekend pairs (no temporal split)

**Weekday vs weekend** — compute for every modality with ≥10 weekday and ≥5 weekend valid days. Check at minimum: steps, home time, distance, phone unlocks, sleep duration, Bluetooth scan count, active duration. When difference >15%, create a **dedicated QA pair for that specific modality** (e.g., "How did the user's step count differ between weekdays and weekends?"). Do NOT bundle all modalities into one catch-all QA pair.

### 3. Compute sub-dimension insights per modality

**Sleep timing phase shift**: Convert bedtime/wake-time from minutes-from-midnight to HH:MM using `mins_to_hhmm()`. Report shift direction and magnitude.

**Active/sedentary ratio**: Compute `active_duration / sedentary_duration` for early vs late, plus T1/T2/T3 and named pattern. Also report avg sedentary bout duration with T1/T2/T3.

**Communication count vs duration dissociation**: If outgoing call count increases but mean duration decreases (or vice versa), name this explicitly. Compute `mean_duration_per_call = sumduration / count`. Apply same check to incoming calls.

**Proactivity ratio**: `outgoing_count / max(incoming_count, 0.1)` early vs late + T1/T2/T3 + named pattern. Report raw counts if ratio >10.

**Communication timing window**: From `rapids_outgoing_timefirstcall` and `rapids_outgoing_timelastcall` (minutes from midnight → HH:MM), report whether the calling window shifted or expanded (window = last - first). This is early/late only, no thirds needed.

**Phone session variability**: Compare `rapids_stddurationunlock` and `rapids_maxdurationunlock` early vs late. Increasing variability indicates more extreme phone sessions.

**Phone first-use timing**: Compute `rapids_firstuseafter00unlock` early/late plus T1/T2/T3 with named pattern. Earlier values = more disrupted sleep/wake.

**Phone by location context**: Compare home vs study vs other contexts early vs late using `_locmap_home`, `_locmap_study` columns.

**Top-location time distribution**: Extract `doryab_timeattop1location`, `doryab_timeattop2location`, `doryab_timeattop3location` early vs late. Report % change and concentration shift.

**Movement characteristics**: Extract `barnett_avgflightdur` (seconds) early vs late plus T1/T2/T3. Also compute `barnett_stdflightdur` for variability. Shorter avg flight = shift to local movement.

**Average speed**: `doryab_avgspeed` (km/hr) early vs late plus T1/T2/T3 and named pattern. Report as transport mode shift when applicable.

**EMA spike analysis**: Identify top 3 highest negative-affect days. For each spike date, look up distance, home time, sleep duration, and phone unlock count and compare each against the participant's personal mean for that metric. Label the behavioral pattern (e.g., "isolation", "disrupted sleep", "travel-related stress"). All comparison values must be computed before submission.

**Bluetooth scan efficiency**: `scans / unique_devices` early vs late plus T1/T2/T3 with named pattern. Rising ratio = fewer devices each scanned more repeatedly.

**Cross-modal correlations**: Merge EMA with each behavioral modality on date (inner join), then dropna on both columns together, compute Pearson r. Require ≥10 shared valid days. For every |r| > 0.3 found, create a **dedicated QA pair**: "What is the relationship between the user's [EMA metric] and [behavioral metric]?" If two closely related columns yield similar correlations (e.g., phone unlock count and phone total duration both ~r=-0.5), report only the one with higher |r| — avoid near-duplicate cross-modal pairs.

```python
# Correct cross-modal correlation pattern
merged = ema_sub[['date','negative_affect_EMA']].merge(
    sensor_sub[['date', col]], on='date', how='inner')
merged = merged.dropna()
if len(merged) >= 10:
    from scipy import stats
    r, p = stats.pearsonr(merged['negative_affect_EMA'], merged[col])
    if abs(r) > 0.3:
        print(f"r={r:.2f}, n={len(merged)}")
```

**Self-report vs behavioral discrepancy**: When survey scores contradict behavioral signals (e.g., perceived support falls but outgoing calls rise, or weekly depression remains low while mobility declines substantially), name this explicitly as a separate QA pair.

### 4. Formulate and submit QA pairs

**Mandatory answer format** — every answer must include ALL of the following that apply:
1. **Early and late values** with units (e.g., "9.3 (early) to 10.4 (late)")
2. **% change and direction** (e.g., "+12.0%")
3. **T1/T2/T3 values** when applicable (all quantitative modality QAs)
4. **Named pattern** when thirds are computed (inverted-U, U-shaped, progressive increase, progressive decline, mixed/stable)
5. **Brief interpretation** — what the numbers suggest behaviorally or clinically

If you realize you are missing a needed number while writing the answer, compute it before calling `submit_qa_pair()`.

**One dimension per QA pair** — never combine two modalities or two metrics in one question:
- ✗ "How did the user's home time AND location entropy change?" → split
- ✗ "How did the user's proactivity ratio AND network diversity change?" → split
- ✗ "Were there weekday vs weekend differences?" (all modalities at once) → one dedicated pair per modality with >15% difference

**Prioritize checklist coverage** — aim to generate QA pairs for every checklist item before adding novel sub-metrics. If approaching 37 pairs, skip niche additions rather than skip a checklist item.

**Avoid metric duplication** — when barnett and doryab cover the same behavioral concept (e.g., radius of gyration, home time, distance), use the barnett or checklist-specified column. Do not generate separate QA pairs for doryab-specific equivalents (e.g., `doryab_totaltime`, `doryab_radiusofgyration`, `barnett_maxdiam`) when a checklist column already covers that concept.

Valid QA pair types:
- **Early/late change**: "How did X change between early and late periods?"
- **Trajectory-only**: "What was the trajectory pattern of X across the three study periods?" (when the thirds pattern is more informative than early/late)
- **Sub-dimension**: "Was there a count/duration dissociation in X?"
- **Cross-modal**: "What is the relationship between the user's X and Y?"
- **Meta-pattern**: "Is there a discrepancy between self-reported X and behavioral Y?"

**Good QA pair examples**:

Early/late change with dissociation:
> Q: "How did outgoing call frequency and duration change, and was there a count/duration dissociation?"
> A: "Outgoing calls increased from 1.24 to 3.67/day (+196%), but mean duration per call fell sharply from 305s to 58s (-81%) — a count/duration dissociation: more frequent but much shorter conversations in the late period."

Trajectory with thirds (mandatory named pattern):
> Q: "What was the trajectory pattern of the user's location entropy across the three study periods?"
> A: "Location entropy showed a U-shaped pattern: 0.449 (T1) → 0.485 (T2) → 0.238 nats (T3). Early mean 0.467, late mean 0.362 (-22.5%). The sharp 51% drop in the final third indicates substantially reduced spatial diversity toward study end."

EMA spike analysis:
> Q: "Did the user's peak negative affect episodes coincide with specific behavioral events?"
> A: "The highest EMA day (June 10: 9.0) coincided with minimal travel (1.12 km vs 39.59 km participant avg) and elevated home time (1424 min vs 1029 min avg), suggesting social isolation. The second peak (April 22: 8.0) showed above-average sleep duration (406 min vs 425 min avg) and reduced phone use (20 unlocks vs 54 avg). The third peak (April 29: 8.0) also showed high home time (1301 min vs 1029 avg). Isolation appears most strongly linked to peak negative affect."

Weekday vs weekend (separate pair per modality):
> Q: "How did the user's step count differ between weekdays and weekends?"
> A: "Step count showed a substantial weekday/weekend difference: weekday average 9,899 steps/day vs weekend 5,803 steps/day (-41.4%), indicating significantly lower physical activity on weekends."

Cross-modal:
> Q: "What is the relationship between the user's daily step count and time spent at home?"
> A: "Steps and home time were strongly negatively correlated (r=-0.56, p<0.001, n=60 days). Higher step counts coincided with less home time, indicating more active days involved greater time away from home."

Submit: `submit_qa_pair(q="...", a="...")`

### 5. QA coverage checklist

Aim to cover all of these (skip only if data is entirely NaN):

**Psychological — assessment**
- [ ] EMA negative affect: early/late change + named thirds pattern
- [ ] EMA spike analysis: top 3 specific high-affect dates with multi-dimensional behavioral comparison (distance, home time, sleep, phone use) vs participant average
- [ ] Weekly depression: `feel_depressed` trajectory (early/late + named thirds) + final BDI2 score
- [ ] Weekly anxiety: `feel_anxious` trajectory (early/late + named thirds) — separate pair from depression
- [ ] Weekly depression subscale: `dep_weekly_subscale` early/late + named thirds — separate pair
- [ ] Weekly anxiety subscale: `anx_weekly_subscale` early/late + named thirds — separate pair
- [ ] Pre/post: psychological state (depression CESD, anxiety STAIS, stress PSS, loneliness UCLA — all in one pair)
- [ ] Pre/post: social support (all 4 dimensions + social fit `2waySSS_social_fit` — all in one pair)
- [ ] Pre/post: emotion regulation / coping / resilience / mindfulness (ERQ reappraisal, ERQ suppression, BRS, CHIPS, MAAS — all in one pair)

**Activity**
- [ ] Steps: early/late + named thirds pattern
- [ ] Active/sedentary duration ratio (early/late + T1/T2/T3 + named pattern) + avg sedentary bout duration (early/late + T1/T2/T3 + named pattern) — combine in one pair
- [ ] Active bout count: early/late + named thirds — separate pair
- [ ] Sedentary bout count: early/late + named thirds — separate pair

**Sleep**
- [ ] Sleep duration: early/late + named thirds pattern
- [ ] Sleep efficiency: early/late + named thirds — separate from duration
- [ ] Sleep timing phase shift: bedtime and wake-time in HH:MM, shift direction and magnitude

**Communication**
- [ ] Outgoing call count + mean duration: early/late, note dissociation if count/duration diverge
- [ ] Incoming call count + mean duration per call: early/late, note dissociation
- [ ] Missed call trend: early/late + named thirds
- [ ] Proactivity ratio: early/late + T1/T2/T3 + named pattern (use raw counts if ratio >10)
- [ ] Network diversity (distinct contacts): early/late + T1/T2/T3 + named pattern
- [ ] Communication timing window: first/last outgoing call in HH:MM, window duration shift

**Location**
- [ ] Mobility: distance traveled early/late + named thirds
- [ ] Radius of gyration: early/late + named thirds
- [ ] Home time: early/late + named thirds
- [ ] Circadian routine: early/late + named thirds
- [ ] Location entropy: early/late + named thirds
- [ ] Location transitions: early/late + named thirds — separate pair from entropy
- [ ] Top-location time distribution (top-1, top-2, top-3) shift
- [ ] Movement characteristics: avg flight duration (early/late + T1/T2/T3) + std flight duration
- [ ] Average speed: early/late + T1/T2/T3 + named pattern
- [ ] Significant places visited: early/late + named thirds

**Phone usage**
- [ ] Unlock count + total duration: early/late + named thirds, note dissociation if count/duration diverge
- [ ] Session length shift (avg duration) + variability (std, max): early/late
- [ ] First-use timing: early/late + T1/T2/T3 + named pattern
- [ ] Phone by location context (home vs study): early vs late

**Connectivity**
- [ ] Scan count + unique devices: early/late + named thirds (combine in one pair)
- [ ] Bluetooth scan efficiency (scans-per-device ratio): early/late + T1/T2/T3 + named pattern

**Cross-modal & meta**
- [ ] Cross-modal correlation: one dedicated QA pair for EACH distinct |r| > 0.3 found (≥10 shared days); skip near-duplicate correlations between closely related columns
- [ ] Weekday vs weekend: one dedicated QA pair per modality with >15% difference (check at minimum: steps, home time, distance, phone unlocks, sleep duration, Bluetooth, active duration)
- [ ] Self-report vs behavioral discrepancy (if survey direction contradicts behavioral signal)

## Common Pitfalls

- **Submit only complete answers** — never call `submit_qa_pair()` with "let me compute" or a partial placeholder. Compute all values, form the full answer, then submit.
- **Thirds for derived metrics are mandatory** — ratios (active/sedentary, proactivity, scan efficiency), timing (first-use, avg flight duration, avg speed), and counts (active bouts, network diversity, significant places) all require T1/T2/T3 and named pattern, not just early/late.
- **Named pattern is always required** when thirds are computed — state inverted-U, U-shaped, progressive increase, progressive decline, or mixed/stable explicitly.
- **Never split raw arrays without dropna** — always `dropna()` on the target column before early/late or thirds.
- **`summary_rapids_*` are period-wide summaries** — use `intraday_rapids_*` for daily activity variation; `summary_rapids_sumdurationasleepmain` is the exception (valid daily sleep duration).
- **`get_field_description` won't work for `ema.csv` or `dep_weekly.csv`** — infer from column names.
- **BDI2 appears in `dep_weekly.csv` only in the final row** — it's the endterm score.
- **Pre/post column names use suffixes** `_PRE` / `_POST`: `CESD_9items_PRE`, `UCLA_10items_PRE`, `2waySSS_receiving_emotional_PRE`, `BRS_PRE`, `CHIPS_PRE`, `STAIS_PRE`, `MAAS_7items_PRE`, `2waySSS_social_fit_PRE`.
- **Pre/post scale directions vary**: higher UCLA = more loneliness (bad), higher ERQ_reappraisal = better (good), higher PSS = more stress (bad), higher BRS = better resilience (good), lower CHIPS = lower impulsivity (good).
- **`barnett_homelabel` does not exist** — use `barnett_hometime`.
- **Distance outliers**: filter values >10× median before computing means.
- **Communication data is often sparse** — note sparsity in QA answer but still extract available patterns.
- **Cross-modal correlation**: merge on date first (inner join), then dropna together — never dropna each series independently before pearsonr.
- **No doryab duplicates** — do not generate separate QA pairs for `doryab_totaltime`, `doryab_radiusofgyration`, or `barnett_maxdiam` when barnett or intraday equivalents are already in the checklist. Checklist columns take priority.
- **EMA spike analysis must include participant averages** — for each spike date, compare distance/home time/sleep/phone against the participant's personal mean (not just describe as "low" or "high").
- **platform.csv uses `platform` column**, not `os`.
- **Pre/post QA pairs must be split into exactly three pairs**: psychological state, social support, and emotion regulation/coping.