| name | ddr-globem-analysis |
| description | 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.
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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 principle: Submit each QA pair immediately after computing the data for that dimension. Do not batch all submissions at the end — incremental submission ensures complete coverage and prevents missed sub-dimensions.
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 durationssleep_allday_raw.csv — sleep duration (minutes), efficiency, bedtime/wake-timecommunication_allday_raw.csv — incoming/outgoing/missed call counts, durations, distinct contactslocation_allday_raw.csv — distance traveled, radius of gyration, home time, significant places, circadian routine, location entropy, location transitions, top-location times, flight characteristics, average speedphone_usage_allday_raw.csv — unlock episode count, total/avg/std duration; location-context columnsconnectivity_allday_raw.csv — Bluetooth scan count, unique devices
Assessment files (one row per observation per participant):
ema.csv — negative_affect_EMA score, timestampeddep_weekly.csv — weekly feel_anxious, feel_depressed, BDI2 (endterm only), dep, dep_weekly_subscale, anx_weekly_subscalepre.csv / post.csv — baseline vs. end-of-study psychological scales (columns use _PRE / _POST suffixes, e.g., CESD_9items_PRE, CESD_9items_POST)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 (not CESD_sum etc.)
Starter Code — Define Once, Reuse Throughout
Copy this helper at the top of your first code block and reuse for all modalities:
import pandas as pd
import numpy as np
pid = "INS-W_XXX"
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
():
pd.isna(mins):
h = (mins // ) %
m = (mins % )
Analysis Workflow
1. Orient to the participant
Check data availability across all files first:
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
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: apply to EVERY modality. Thirds reveal non-linear patterns (U-shaped, inverted-U, progressive, peak-in-middle, stable-then-drop) that early/late splits hide. Always name the pattern explicitly.
Weekday vs weekend — compute for every modality with ≥10 weekday and ≥5 weekend valid days. Report if difference >15%.
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. Also check if avg sedentary bout duration changed.
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. Do same for incoming calls.
Proactivity ratio: outgoing_count / max(incoming_count, 0.1) early vs late. 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.
Phone session variability: Compare rapids_stddurationunlock and rapids_maxdurationunlock early vs late. Increasing variability (std, max) indicates more extreme phone sessions.
Phone first-use timing: Report if rapids_firstuseafter00unlock shifted (earlier = more disrupted sleep/wake).
Phone by location context: Compare home vs study vs other contexts between early and late using _locmap_home, _locmap_study columns.
Top-location time distribution: Extract doryab_timeattop1location, doryab_timeattop2location, doryab_timeattop3location early vs late. A shift indicates changed location usage pattern.
Movement characteristics: Extract barnett_avgflightdur, barnett_stdflightdur (seconds) early vs late. Shorter avg flight = more frequent short trips vs longer point-to-point travel.
Average speed: doryab_avgspeed (km/hr during movement). Changes signal shifts in transport mode.
EMA spike analysis: Identify top 3 highest negative-affect days. For each, report the date and value, then check distance, home time, sleep duration, and phone unlock count vs participant average. State whether isolation, travel, or disrupted sleep coincided.
Bluetooth scan efficiency: scans / unique_devices early vs late. 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. Report |r| > 0.3.
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 direction contradicts behavioral signal (e.g., perceived support falls but outgoing calls rise), name this explicitly.
4. Formulate and submit QA pairs
Submit immediately after computing each modality — don't wait until all analysis is done.
One dimension per QA pair — never combine two modalities or two metrics in one question.
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?" (thirds T1→T2→T3 + named pattern)
- Sub-dimension: "Was there a count/duration dissociation in X?"
- Cross-modal: "What is the relationship between X and Y?"
- Meta-pattern: "Is there a discrepancy between self-reported X and behavioral Y?"
Each answer must include:
- Concrete numbers (mean values, % change, direction)
- Pattern name when using thirds data
- Magnitude description ("increased substantially" ≈ >20%, "modestly" ≈ 5–20%, "remained stable" ≈ <5%)
Submit: submit_qa_pair(q="...", a="...")
Good QA pair examples:
Early/late change with count/duration 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-only:
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). 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 two highest EMA days (May 20: 5.0, May 24: 8.0) coincided with the two largest travel days (641 km and 4,428 km), suggesting travel-related stress. EMA-distance correlation was moderately positive (r=0.31)."
5. QA coverage checklist
Aim to cover all of these (skip only if data is entirely NaN):
Psychological — assessment
Activity
Sleep
Communication
Location
Phone usage
Connectivity
Cross-modal & meta
Common Pitfalls
- 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, not generic names: CESD_9items_PRE, UCLA_10items_PRE, 2waySSS_receiving_emotional_PRE, BRS_PRE, CHIPS_PRE, STAIS_PRE, MAAS_7items_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 (<30% valid days for some participants) — 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.
- Pre/post QA pairs must be split into three separate pairs: psychological state, social support, and emotion regulation/coping.
- Thirds pattern naming is mandatory — always state whether progressive, U-shaped, inverted-U, peak-in-middle, stable-then-drop, etc.
- One QA pair per dimension — do not bundle "activity and Bluetooth" or "mobility and home time" in one question. Each checklist item deserves its own pair.
- platform.csv uses
platform column, not os.
