Globem User Analysis

GLOBEM Individual User Analysis

Dataset Overview

The GLOBEM dataset tracks college students over a 92-day period (~April–July) using passive smartphone sensing. Each participant has:

Sensor CSVs (daily rows, columns: Unnamed: 0, pid, date, + features):

Mental-health / survey files (read directly via execute_code; get_field_description will fail on these):

Exact Pre/Post Survey Column Names

pre.csv (suffix _PRE): UCLA_10items_PRE, SocialFit_PRE, 2waySSS_receiving_emotional_PRE, 2waySSS_giving_emotional_PRE, 2waySSS_giving_instrumental_PRE, 2waySSS_receiving_instrumental_PRE, ERQ_reappraisal_PRE, ERQ_suppression_PRE, BRS_PRE, CHIPS_PRE, PSS_10items_PRE, STAIS_PRE, MAAS_7items_PRE, CESD_9items_PRE, CESD_10items_PRE, BFI10_extroversion_PRE, BFI10_agreeableness_PRE, BFI10_conscientiousness_PRE, BFI10_neuroticism_PRE, BFI10_openness_PRE

post.csv (suffix _POST; BFI10 not in POST): UCLA_10items_POST, SocialFit_POST, 2waySSS_receiving_emotional_POST, 2waySSS_giving_emotional_POST, 2waySSS_giving_instrumental_POST, 2waySSS_receiving_instrumental_POST, ERQ_reappraisal_POST, ERQ_suppression_POST, BRS_POST, CHIPS_POST, PSS_10items_POST, STAIS_POST, MAAS_7items_POST, CESD_9items_POST, CESD_10items_POST

Scale meanings (higher = more unless noted):

• STAIS — state anxiety; PSS_10items — perceived stress; CESD_9/10items — depression symptoms (higher = worse)
• UCLA_10items — loneliness; BRS — resilience (higher = good); ERQ_reappraisal — adaptive coping (higher = good)
• ERQ_suppression — emotional suppression; CHIPS — health stressors; MAAS_7items — mindfulness (higher = good)
• SocialFit — social fit; 2waySSS_* — social support exchange; BFI10_* — Big Five personality

Analysis Pipeline

Phase 1 — Orientation (1–2 calls)

# 1. list_files to confirm available files
# 2. get_field_description on 2-3 sensor files to learn extra column names
# Do NOT call get_field_description for dep_weekly, ema, pre, post, dep_endterm, platform

Phase 2 — Per-modality stats (one call per modality)

Filter all sensor DFs by pid == '<user_id>'. For each modality compute:

• Mean ± std, min/max over valid (non-NaN) rows
• Count of valid days (report as n/92)
• Weekday vs. weekend difference (df['date'].dt.dayofweek → 0–4 weekday, 5–6 weekend)
• Three-period temporal trend: split into thirds T1/T2/T3 and report each mean, plus early (T1) vs. late (T3) comparison and % change

user_df['date'] = pd.to_datetime(user_df['date'])
d_min, d_max = user_df['date'].min(), user_df['date'].max()
span = (d_max - d_min) / 3
t1 = user_df[user_df['date'] < d_min + span]
t2 = user_df[(user_df['date'] >= d_min + span) & (user_df['date'] < d_min + 2*span)]
t3 = user_df[user_df['date'] >= d_min + 2*span]
# Report T1_mean, T2_mean, T3_mean and classify trajectory:
# progressive increase/decline: monotonic T1→T2→T3
# inverted-U: T2 > T1 and T2 > T3; U-shaped: T2 < T1 and T2 < T3
# mixed/stable: otherwise

Activity — intraday_rapids_sumsteps, intraday_rapids_countepisodesedentarybout, intraday_rapids_countepisodeactivebout. Also compute active/sedentary ratio and avg sedentary bout duration.

Sleep — summary_rapids_avgdurationasleepmain (minutes), summary_rapids_avgefficiencymain (already 0–100, never multiply by 100), summary_rapids_avgdurationtofallasleepmain, summary_rapids_firstbedtimemain, summary_rapids_lastwaketimemain. Convert bedtime/wake minutes-since-midnight to HH:MM.

Communication — rapids_outgoing_count, rapids_incoming_count, rapids_missed_count, rapids_outgoing_meanduration, rapids_incoming_meanduration, rapids_outgoing_distinctcontacts. Compute:

• Outgoing/incoming ratio (>1 = proactive)
• Count/duration dissociation: note when count and duration trend in opposite directions
• Call timing window: rapids_outgoing_timefirstcall and rapids_outgoing_timelastcall (minutes from midnight → convert to HH:MM); report first-half vs. second-half shift

Location — barnett_disttravelled, barnett_rog, barnett_hometime (minutes/day at home), barnett_circdnrtn (0–1 circadian consistency), barnett_siglocsvisited, barnett_siglocentropy (entropy in nats), barnett_avgflightdur ± barnett_stdflightdur (seconds), doryab_avgspeed (km/hr), doryab_numberlocationtransitions, doryab_timeattop1location / doryab_timeattop2location / doryab_timeattop3location (minutes). Filter GPS outliers: drop values > median × 10 for barnett_disttravelled and barnett_rog before averaging.

Phone Usage — rapids_countepisodeunlock, rapids_sumdurationunlock (minutes), rapids_avgdurationunlock or compute as sum/count, rapids_stddurationunlock (session variability), rapids_firstuseafter00unlock (minutes from midnight to first unlock → convert to HH:MM), rapids_sumdurationunlock_locmap_home, rapids_countepisodeunlock_locmap_home, rapids_countepisodeunlock_locmap_study. Report home-use fraction and count/duration dissociation if present.

Connectivity — rapids_countscans, rapids_uniquedevices. Also compute scan efficiency = countscans / uniquedevices (higher = fewer distinct devices, more repeat scanning).

Phase 3 — Mental health profile (1–2 calls)

dep_weekly = pd.read_csv('dep_weekly.csv')
dep_endterm = pd.read_csv('dep_endterm.csv')
ema = pd.read_csv('ema.csv')
pre = pd.read_csv('pre.csv')
post = pd.read_csv('post.csv')
platform = pd.read_csv('platform.csv')

uid = '<user_id>'
user_dep = dep_weekly[dep_weekly['pid'] == uid].copy()
user_dep['date'] = pd.to_datetime(user_dep['date'])
user_endterm = dep_endterm[dep_endterm['pid'] == uid]
user_ema = ema[ema['pid'] == uid].copy()
user_ema['date'] = pd.to_datetime(user_ema['date'])
user_pre = pre[pre['pid'] == uid]
user_post = post[post['pid'] == uid]
user_platform = platform[platform['pid'] == uid]

Extract and report:

• Platform (iOS/Android)
• Depression: weekly flag rate (n/total weeks), end-term BDI2 + dep status; also extract feel_depressed, feel_anxious, dep_weekly_subscale, anx_weekly_subscale with T1/T2/T3 temporal trends
• EMA: mean, std, min/max, T1/T2/T3 trend with trajectory pattern classification
• Pre→Post survey changes for ALL key scales (report Pre value, Post value, % change, ↑↓ arrows, and interpret direction: improved or worsened)
• Personality (BFI10 pre only)

Phase 4 — Cross-modal correlation & synthesis (1–2 calls)

This phase drives the most analytically rich insights. Complete all sub-analyses:

1. EMA ↔ Behavioral correlations (Pearson r with p-value and n): Merge EMA with each sensor modality on pid + date (inner join), then correlate negative_affect_EMA with:

• intraday_rapids_sumsteps (activity)
• summary_rapids_avgdurationasleepmain (sleep duration)
• barnett_hometime (home time)
• rapids_sumdurationunlock (screen time)
• intraday_rapids_countepisodesedentarybout (sedentary behavior)
• barnett_siglocentropy (location entropy)

2. Peak EMA event analysis: Find top 3 highest EMA days and for each, compare behavioral metrics to their respective modality averages with % deviation:

user_ema_sorted = user_ema.sort_values('negative_affect_EMA', ascending=False).head(3)
for _, row in user_ema_sorted.iterrows():
    peak_date = row['date']
    # Compare barnett_disttravelled, barnett_hometime, rapids_sumdurationunlock, etc. on that day vs. mean
    # Report: "Date X (EMA=Y): distance=Z vs avg=W (+/-N%)"

3. High vs. low EMA day comparisons: Split days by EMA median → report behavioral metric means for each group:

ema_median = user_ema['negative_affect_EMA'].median()
high_ema_dates = user_ema[user_ema['negative_affect_EMA'] > ema_median]['date']
low_ema_dates = user_ema[user_ema['negative_affect_EMA'] <= ema_median]['date']
# Compare steps, screen time, home time, etc. on high vs. low EMA days

4. Depression-flagged week behavior comparison: Aggregate daily sensor data to weekly means (group by week). Join on dep_weekly dates (±7 days), then compare depressed vs. non-depressed week means for steps, sleep duration, phone unlocks, home time.

5. Temporal trends across modalities: Summarize T1 → T3 changes for all key metrics in a consolidated table (steps, sleep, phone unlocks, distance, EMA). Classify each as progressive increase/decline, inverted-U, U-shaped, or mixed/stable.

Phase 5 — Data quality check (integrate into output)

For each modality, report valid days as n/92. Flag modalities with <20% coverage as "critically sparse — interpret with caution."

Synthesis Template

## Comprehensive Analysis of User <pid>

### Study Context
- Platform, study period (date range), data completeness per modality (n/92 days)

### Physical Activity
- Steps (mean ± std, min/max, valid days), sedentary/active balance and ratio, weekday vs. weekend
- Temporal trend: T1/T2/T3 step means and trajectory pattern

### Sleep
- Duration (hours), efficiency (%), timing (bedtime HH:MM, wake HH:MM), variability
- Temporal trend: T1/T2/T3 sleep duration and trajectory pattern

### Communication
- Call frequency (outgoing/incoming/missed), proactivity ratio, social diversity (distinct contacts)
- Call timing window: first-call HH:MM and last-call HH:MM, early vs. late shift
- Temporal trend: T1/T2/T3 outgoing count; note any count/duration dissociation

### Location & Mobility
- Daily distance, home time (hours/day, %), circadian routine score, radius of gyration
- Location entropy (nats), transitions/day, significant places visited
- Top-3 location time distribution (minutes)
- Avg flight duration ± std (seconds), avg speed (km/hr)
- Temporal trend: T1/T2/T3 distance and home time

### Phone Usage
- Unlock count, screen time (hours), avg session duration ± std (minutes), first-use HH:MM
- Home vs. study unlock count and duration split
- Temporal trend: T1/T2/T3 unlock count and duration; count/duration dissociation if present

### Social Proximity (Connectivity)
- BT scan rate, unique devices per day, scan efficiency (scans/device)
- Temporal trend: T1/T2/T3

### Mental Health
- Depression trajectory: weekly flag rate (n/total weeks), feel_depressed/feel_anxious means, T1/T2/T3 trends
- End-term BDI2 + dep status
- EMA negative affect: mean ± std, T1/T2/T3 trend and trajectory pattern
- Pre→Post survey changes (all key scales with ↑↓ changes and interpretation)

### Cross-Modal Patterns
- EMA correlations with behavioral signals (list r, p, n for each)
- Peak EMA days: top 3 dates with behavioral context (deviations from mean)
- Behavioral differences on high vs. low negative affect days
- Behavioral differences in depressed vs. non-depressed weeks
- Consolidated temporal shift table (T1→T3 for all key metrics with trajectory pattern)

### User Profile
- 3–5 sentence synthesis connecting behavioral patterns, temporal trends, and mental health

Common Pitfalls

1. Home time column: Use barnett_hometime (minutes/day). barnett_homelabel and doryab_homelabel are cluster labels, not durations.

2. Sleep efficiency: summary_rapids_avgefficiencymain is already a percentage (e.g., 93.5). Never multiply by 100.

3. Minute encoding: Bedtime/wake/call times are minutes-since-midnight. Convert: f"{int(m//60):02d}:{int(m%60):02d}". Values ≥ 1440 span next day (e.g., 1500 → 01:00 next day).

4. Survey columns must use exact names with _PRE/_POST suffix. Use df.columns.tolist() on first access; get_field_description does NOT work on survey files.

5. Three-period analysis: Split by thirds of the study period (not calendar months). Always report T1, T2, T3 means individually and classify the pattern.

6. Sparse data: Always check df[col].notna().sum() before computing stats. Some users have <14/92 days for some modalities — correlations require >5 overlapping days.

7. Location GPS outliers: barnett_disttravelled and barnett_rog can have extreme GPS errors. Use values[values < values.median() * 10] before averaging.

8. Weekly vs. daily merge: dep_weekly is weekly; sensor data is daily. Aggregate daily data into 7-day windows aligned with each dep_weekly date row.

9. EMA correlation requires inner merge on date: Report n for each correlation; p-values are unreliable when n < 10.

10. 2waySSS, ERQ, BRS are frequently asked about: Always extract emotion regulation (ERQ_reappraisal/suppression), social support (2waySSS all four dimensions), resilience (BRS), and mindfulness (MAAS) from pre/post surveys.

11. Scan efficiency = rapids_countscans / rapids_uniquedevices. Rising efficiency with declining unique devices suggests narrowing social environment.

12. Trajectory pattern classification: Label each metric's temporal trend as: progressive increase, progressive decline, inverted-U (peak in T2), U-shaped (trough in T2), or mixed/stable (no clear pattern).