| 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 28–32 high-quality, distinct QA pairs. Each QA pair covers ONE specific dimension — never bundle multiple modalities or metrics into a single pair.
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 scalesdep_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)
summary_rapids_* columns repeat period-wide values — use intraday_rapids_* for daily 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
Analysis Workflow
1. Orient to the participant
Check data availability across all files first:
import pandas as pd
pid = "INS-W_011"
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")
2. Temporal segmentation — apply to ALL modalities
Always dropna() on the target column before splitting.
Early/late split (primary comparison unit):
def early_late(df, pid, value_col):
sub = df[df['pid'] == pid].copy()
sub['date'] = pd.to_datetime(sub['date'])
sub = sub.sort_values('date').dropna(subset=[value_col])
mid = len(sub) // 2
return sub.iloc[:mid][value_col].mean(), sub.iloc[mid:][value_col].mean()
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. Data to extract per modality
| Modality | Primary columns | Secondary columns (also extract) |
|---|
| EMA | negative_affect_EMA | spike dates + behavioral coincidence |
| Weekly depression | feel_depressed, feel_anxious, dep, BDI2 | dep_weekly_subscale, anx_weekly_subscale |
| Pre/Post — psychological | CESD*, STAI*, PSS*, UCLA* | — |
| Pre/Post — social | 2waySSS_* (all 4), SocialFit* | — |
| Pre/Post — regulation | ERQ_*, BRS*, MAAS*, CHIPS* | — |
| Activity | intraday_rapids_sumsteps | intraday_rapids_sumdurationactivebout, intraday_rapids_sumdurationsedentarybout, bout counts, avg bout durations |
| Sleep | summary_rapids_sumdurationasleepmain | summary_rapids_avgefficiencymain, summary_rapids_firstbedtimemain, summary_rapids_lastwaketimemain |
| Communication | rapids_outgoing_count, rapids_incoming_count, rapids_missed_count | rapids_outgoing_sumduration, rapids_incoming_sumduration, rapids_outgoing_distinctcontacts, rapids_outgoing_timefirstcall, rapids_outgoing_timelastcall, rapids_incoming_meanduration |
| Location | barnett_disttravelled, barnett_rog, barnett_hometime | barnett_circdnrtn, doryab_locationentropy, doryab_numberlocationtransitions, doryab_avgspeed, barnett_avgflightdur, , , , , |
4. Extract sub-dimension insights
Named thirds pattern: Always state the pattern type: "U-shaped" (low-high-low), "inverted-U" (high-low-high), "progressive decline/increase" (monotone), "peak-then-drop", "stable-then-spike", etc.
Sleep timing phase shift: Convert bedtime/wake-time from minutes-from-midnight to HH:MM. Report shift direction and magnitude (e.g., "bedtime delayed by 90 minutes").
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 = rapids_outgoing_sumduration / rapids_outgoing_count. Do the same for incoming calls.
Proactivity ratio: outgoing_count / max(incoming_count, 0.1) early vs late. Report the actual 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.
Location diversity: Extract doryab_locationentropy and doryab_numberlocationtransitions early/late + thirds.
Top-location time distribution: Extract doryab_timeattop1location, doryab_timeattop2location, doryab_timeattop3location early vs late. A shift (e.g., top-1 decreasing, top-2 increasing) indicates changed location usage pattern.
Movement characteristics: Extract barnett_avgflightdur, barnett_stdflightdur (flight = movement episode, in seconds) early vs late. Shorter avg flight duration can indicate more frequent short trips vs longer point-to-point travel.
Average speed: doryab_avgspeed (km/hr during movement periods). Changes signal shifts in transport mode or travel pattern.
Circadian routine: barnett_circdnrtn (0–1) early/late + thirds.
EMA spike analysis: Identify top 3 highest negative-affect days. For each, report the date and value, then check distance, home time, sleep, and phone usage vs participant average. State whether isolation, travel, or disrupted sleep coincided.
sub['week'] = pd.to_datetime(sub['date']).dt.isocalendar().week
weekly = sub.groupby('week')[col].mean()
anomaly_weeks = weekly[weekly > weekly.median() * 2]
Bluetooth scan efficiency: scans / unique_devices early vs late. A rising ratio means fewer, more-repeatedly-detected devices.
Cross-modal correlations: Compute Pearson r for all pairs with ≥10 shared valid days. Report |r| > 0.3.
Self-report vs behavioral discrepancy: When survey direction contradicts behavioral signal (e.g., perceived support falls but outgoing calls rise), name this explicitly.
Behavioral vs psychological dissociation: If behavioral metrics trend opposite to psychological metrics, surface as a dedicated QA pair.
5. Formulate and submit QA pairs
One dimension per QA pair — never combine two modalities or two metrics in one question. Each item in the checklist below should produce its own QA pair.
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 incrementally: submit_qa_pair(q="...", a="...")
Good QA pair examples:
Early/late change:
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 classic 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)."
6. 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 92-row arrays — always
dropna() on the target column before early/late or thirds.
summary_rapids_* are period-wide summaries — use intraday_rapids_* for daily variation.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 scale directions vary: higher UCLA = more loneliness (bad), higher ERQ_reappraisal = better (good), higher PSS = more stress (bad), higher BRS = better resilience (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 this but still extract available patterns.
- 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. Do not just list T1/T2/T3 numbers without naming the pattern.
- 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.
