ddr-globem-analysis

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.

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 35–45 high-quality, distinct QA pairs. Each QA pair covers ONE specific dimension — never bundle multiple modalities or metrics into a single pair.

Four non-negotiable rules:

1. Compute-then-submit: Run code to compute every number before writing any QA answer. Never estimate, recall, or compute inline while writing. If a value is missing from your computed output, run another code block to get it before submitting.
2. No duplicates: Track submitted question topics internally. Before calling submit_qa_pair(), confirm no prior pair already covered the same metric.
3. Exactly 3 EMA spikes: The spike analysis covers the top-3 highest-EMA dates only. Never report a 4th spike.
4. Complete answers only: An answer is ready to submit only when it contains ALL computed values with units. Any answer containing "need to compute", "let me check", unfinished sentences, or unfilled placeholders must NOT be submitted — run the missing code first, then write and submit the complete answer.

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; 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 >10: when the ratio exceeds 10, the denominator is near-zero and the ratio is not informative. Do NOT report the ratio value. Instead state: "raw counts: outgoing X/day (early) → Y/day (late); incoming A/day (early) → B/day (late)". Apply max(incoming, 0.1) only when computing the ratio to avoid division errors, but switch to raw counts in the answer.
• Pre/post columns: 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

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

 ():
     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")

Handling 0% valid modalities: When a sensor file has 0 valid rows (all NaN), do NOT silently skip its checklist items. For each checklist item in that modality, submit a dedicated QA pair noting unavailability:

Q: "How did the user's sleep duration change across the study period?"
A: "No valid sleep data available (0/92 days). The sleep tracker recorded no measurements for this participant."

This preserves checklist coverage. Sparse coverage (<30% valid) is normal; sparse is not the same as missing.

2. Batch-compute all metrics per modality BEFORE submitting

For each modality, run ONE comprehensive code block that computes all needed metrics (early/late, T1/T2/T3, weekday/weekend, derived ratios, timing shifts, dissociation values). Print all results. THEN write and submit QA pairs for that modality.

Example batch-computation pattern for activity:

act = pd.read_csv("activity_allday_raw.csv")
sub = act[act['pid'] == pid].copy()
sub['date'] = pd.to_datetime(sub['date'])

e_steps, l_steps, pct_steps, t_steps, pat_steps = early_late_thirds(sub, pid, 'intraday_rapids_sumsteps')
e_ratio = (sub['intraday_rapids_sumdurationactivebout'] /
           sub['intraday_rapids_sumdurationsedentarybout'].replace(0, np.nan)).dropna()

sub_valid = sub.dropna(subset=['intraday_rapids_sumsteps'])
sub_valid['dow'] = sub_valid['date'].dt.dayofweek
wday_steps = sub_valid[sub_valid['dow'] < 5]['intraday_rapids_sumsteps'].mean()
wend_steps = sub_valid[sub_valid['dow'] >= 5]['intraday_rapids_sumsteps'].mean()

print(f"Steps early={e_steps:.1f}, late={l_steps:.1f}, pct={pct_steps:.1f}%")
print(f"Steps T1={t_steps[0]:.1f}, T2={t_steps[1]:.1f}, T3={t_steps[2]:.1f} — {pat_steps}")
print(f"Steps weekday={wday_steps:.1f}, weekend={wend_steps:.1f}")

Only after all values are printed and verified, write and submit QA pairs.

3. 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.

Thirds segmentation — mandatory for every quantitative column, including ratios, timing, counts, and scores. Compute T1/T2/T3 and name the pattern for every such metric. Thirds reveal non-linear patterns that early/late splits hide.

Thirds NOT required for:

• 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
• 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. Do NOT bundle all modalities into one catch-all QA pair.

4. 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 in minutes.

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. When ratio >10 in either period, do NOT report the ratio at all — report raw outgoing and incoming counts instead. Example: "raw counts: outgoing 3.8/day (early) → 5.1/day (late); incoming 0.3/day (early) → 0.9/day (late)."

Communication timing window: From rapids_outgoing_timefirstcall and rapids_outgoing_timelastcall (minutes from midnight → HH:MM): (1) first call time shift, (2) last call time shift, (3) window duration = last - first, (4) whether window expanded or contracted. 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.

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.

Average speed: doryab_avgspeed (km/hr) early vs late plus T1/T2/T3 and named pattern.

EMA spike analysis — first check if EMA varies at all:

ema_sub = pd.read_csv("ema.csv"); ema_sub = ema_sub[ema_sub['pid'] == pid]
max_ema = ema_sub['negative_affect_EMA'].max()

• If max_ema == 0 or all EMA values are identical: skip the spike analysis pair. In the EMA trajectory pair's answer, add: "All EMA assessments scored 0; spike analysis is not applicable."
• Otherwise, compute participant means for ALL 4 behavioral metrics FIRST, then identify exactly the top-3 spike dates using .nlargest(3, 'negative_affect_EMA'). For each spike date, include actual_value vs participant_avg, deviation% for distance, home time, sleep, and phone unlocks. Conclude with the common behavioral pattern across all 3 spikes.

Bluetooth scan efficiency: scans / unique_devices early vs late plus T1/T2/T3 with named pattern.

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 AND |r| > 0.3. For every qualifying correlation, create a dedicated QA pair. Systematically check EMA vs. at minimum: home time, distance traveled, phone unlock count, sleep duration, Bluetooth scan count, and outgoing call count. If two closely related columns yield similar correlations, report only the one with higher |r|.

# Correct cross-modal correlation pattern
merged = ema_sub[['date','negative_affect_EMA']].merge(
    sensor_sub[['date', col]], on='date', how='inner')
merged = merged.dropna()  # dropna on merged frame, not each series separately
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"{col}: r={r:.2f}, p={p:.4f}, n={len(merged)}")

Self-report vs behavioral discrepancy: When survey scores contradict behavioral signals (e.g., perceived support falls but outgoing calls rise), name this explicitly as a separate QA pair.

5. 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

Preferred compact answer format: "X.XX (early) to X.XX (late), +Y.Y%. T1=a, T2=b, T3=c — pattern_name. [Interpretation sentence.]" Keep answers information-dense.

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
• ✗ "Were there weekday vs weekend differences?" (all modalities at once) → one dedicated pair per modality with >15% difference

Deduplication check before each submission: Before calling submit_qa_pair(), verify that no previously submitted pair already addressed the same metric.

Prioritize checklist coverage — aim to generate QA pairs for every checklist item before adding novel sub-metrics. If approaching 45 pairs, skip niche additions rather than skip a checklist item.

Avoid metric duplication — when barnett and doryab cover the same behavioral concept, use the barnett or checklist-specified column.

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?"
• 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%), with a U-shaped pattern: T1=1.57, T2=1.43, T3=4.13. Mean duration per call fell sharply from 305s to 58s (-81%) — 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 (include absolute value, participant avg, % deviation for ALL metrics; top-3 only):

Q: "Did the user's peak negative affect episodes coincide with specific behavioral events?" A: "Highest EMA day (June 10: 9.0) showed minimal travel (1.12 km vs 39.59 km avg, -97%), elevated home time (1424 min vs 1029 avg, +38%), below-average sleep (406 min vs 425 avg, -5%), reduced phone use (20 vs 54 avg, -63%). Second peak (April 22: 8.0)... Third peak (April 29: 8.0)... Social isolation appears most strongly linked to peak negative affect."

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

6. QA coverage checklist

Aim to cover all of these (for 0-valid-day modalities, still submit a QA noting data unavailability):

Psychological — assessment

• EMA negative affect: early/late change + named thirds pattern (note if all-zero and spike analysis skipped)
• EMA spike analysis: top-3 specific high-affect dates with multi-dimensional behavioral comparison (distance, home time, sleep, phone use) vs participant average — include actual_value vs participant_avg, deviation% for each metric; conclude with common pattern; exactly 3 spikes, no more (skip if EMA is all-zero)
• 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 — report as continuous mean value, not as binary endorsement count or percentage; separate pair
• Weekly anxiety subscale: anx_weekly_subscale early/late + named thirds — report as continuous mean value; 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 in minutes

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 (switch to raw counts if ratio >10 in either period)
• Network diversity (distinct contacts): early/late + T1/T2/T3 + named pattern
• Communication timing window: first/last outgoing call in HH:MM + window duration (last − first); report shift direction and whether window expanded or contracted

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, dropna merged frame). Systematically test EMA vs. home time, distance, phone unlocks, sleep duration, Bluetooth scans, and outgoing call count at minimum.
• 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 — if your answer draft contains any placeholder, unfinished sentence, or "let me check"-style text, do NOT call submit_qa_pair(). Run the missing computation first, then write and submit a fully resolved answer.
• 0% valid modalities still need QA pairs — when a sensor has 0 valid days, submit a QA for each checklist item noting "No valid data available (0/92 days)." Never silently skip a checklist section because data is entirely missing.
• No duplicate questions — track submitted topics. If you realize a pair was already submitted, skip and move on.
• Exactly 3 EMA spikes — never report a 4th spike. Use .nlargest(3, 'negative_affect_EMA') strictly. If all EMA values are 0, skip this pair entirely and note it in the trajectory answer.
• Proactivity ratio >10 means report raw counts — do not include the ratio value at all when it exceeds 10. State outgoing and incoming counts directly for early and late periods.
• dep_weekly_subscale and anx_weekly_subscale are reported as continuous means — always report their mean value (e.g., "0.12") with T1/T2/T3. Never report as endorsement counts (e.g., "1/7 = 14.3%").
• 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.
• 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.
• EMA spike analysis requires participant means computed in code — compute participant's personal mean for distance, home time, sleep, and phone unlocks BEFORE describing each spike date.
• 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.
• : 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).