Student mental health, development, and pain
Pain Biobank is a cohort-based research platform focused primarily on student mental health, with pain, sleep, somatic symptoms, educational stress, social resources, and multimodal deep phenotyping measured as key developmental contexts.
Research cohorts
The cohorts are presented as related resources, but each dataset has its own sampling frame, timing, and scientific use case.
A university-entry cohort with 32,296 baseline students and 10,113 matched follow-up participants. This cohort supports research on family background, home province, university transition, depression, anxiety, sleep, pain, and somatic symptoms.
A two-wave university student survey conducted during the COVID era. The survey includes 10,286 students in Wave 1 and 22,570 students in Wave 2, with 2,875 students observed in both waves.
Child and adolescent student datasets are presented separately because developmental stage, family context, school environment, and educational pressure differ from university cohorts.
Freshman cohort growth
The freshman transition cohort is designed to grow each year. The next September wave is expected to include new entrants and multiple follow-up waves from previously enrolled students.
Expected new participants entering the cohort in September.
Students from prior entry waves expected to complete their first follow-up.
Students expected to contribute repeated longitudinal measurements.
Early data snapshots
These snapshots are intentionally descriptive and are meant to orient collaborators before formal adjusted analyses.
Blue = depression, orange = anxiety. Cohorts differ in sampling frame and timing.
Sample composition
These tables summarize the first-pass sample composition used to orient collaborators before detailed modeling.
| Classification | Group | N |
|---|---|---|
| Source area | County/banner | 5,058 |
| Source area | Urban district | 3,129 |
| Source area | County-level city | 1,887 |
| Family SES | Q1 lowest | 2,861 |
| Family SES | Q2 | 2,352 |
| Family SES | Q3 | 2,373 |
| Family SES | Q4 highest | 2,527 |
| Household income | Income code 1-5 | 10,113 |
| Classification | Group | N |
|---|---|---|
| Source area | Urban district | 9,780 |
| Source area | County/banner | 7,364 |
| Source area | County-level city | 4,335 |
| Household income | Income code 1 | 17,228 |
| Household income | Income code 2 | 5,342 |
| Campus residence | Residence code 1 | 22,261 |
| Medical status | Non-medical students | 21,822 |
| Medical status | Medical students | 748 |
| Dataset | Subset | N |
|---|---|---|
| Freshman cohort | Baseline | 32,296 |
| Freshman cohort | Matched first follow-up | 10,113 |
| Expected September wave | New enrollment | 10,000+ |
| Expected September wave | First follow-up | 10,000+ |
| Expected September wave | Second follow-up | 10,000+ |
| COVID-era survey | Wave 1 | 10,286 |
| COVID-era survey | Wave 2 | 22,570 |
| COVID-era survey | Observed in both waves | 2,875 |
Income and residence categories are currently displayed as codes until the codebook is finalized for public presentation.
Data explorer
These visualizations and tables summarize sample composition and longitudinal changes. They are exploratory and descriptive, intended to help collaborators understand the data structure before formal modeling.
Freshman follow-up
COVID-era Wave 2
| Group | N | Depression baseline | Depression follow-up | Anxiety baseline | Anxiety follow-up |
|---|---|---|---|---|---|
| Q1 lowest SES | 2,861 | 7.8% | 6.7% | 5.0% | 4.2% |
| Q2 | 2,352 | 7.7% | 6.0% | 4.4% | 3.3% |
| Q3 | 2,373 | 7.0% | 6.0% | 4.2% | 3.6% |
| Q4 highest SES | 2,527 | 7.4% | 6.0% | 5.1% | 4.9% |
| County/banner | 5,058 | 7.8% | 6.4% | 4.6% | 3.8% |
| Urban district | 3,129 | 7.2% | 5.9% | 5.0% | 4.2% |
| County-level city | 1,887 | 6.9% | 6.3% | 4.5% | 4.6% |
| Academic field | N | Depression baseline | Depression follow-up | Anxiety baseline | Anxiety follow-up |
|---|---|---|---|---|---|
| Management / business (code 10) | 3,338 | 7.3% | 5.5% | 4.1% | 3.3% |
| Medicine / health sciences (code 12) | 3,094 | 7.7% | 6.3% | 5.2% | 4.4% |
| Other / unspecified (code 14) | 1,182 | 9.4% | 7.3% | 5.8% | 4.6% |
| Engineering (code 8) | 747 | 5.9% | 5.4% | 4.0% | 3.3% |
| Education (code 4) | 636 | 6.8% | 5.5% | 4.7% | 4.2% |
| Science (code 7) | 379 | 5.3% | 3.7% | 4.5% | 3.2% |
| Group | N | Depression wave 1 | Depression wave 2 | Anxiety wave 1 | Anxiety wave 2 |
|---|---|---|---|---|---|
| Wuhan Textile University | 1,819 | 34.8% | 25.9% | 25.3% | 19.0% |
| Hankou University | 346 | 40.8% | 37.9% | 33.5% | 29.2% |
| Wuhan City Polytechnic | 265 | 41.1% | 33.6% | 28.7% | 27.2% |
| Education (code 4) | 686 | 33.8% | 24.5% | 24.3% | 17.3% |
| Engineering (code 8) | 556 | 35.8% | 27.0% | 25.9% | 20.1% |
| Law / social sciences (code 3) | 362 | 39.5% | 31.2% | 33.4% | 22.7% |
| Group | N | Depression | Anxiety |
|---|---|---|---|
| Hankou University | 6,319 | 31.9% | 23.2% |
| Wuhan Textile University | 2,639 | 33.9% | 27.3% |
| Wuchang Shouyi University | 2,477 | 41.5% | 31.6% |
| Hubei home province | 14,051 | 35.9% | 27.3% |
| Henan home province | 984 | 37.2% | 27.4% |
| Shandong home province | 790 | 27.2% | 23.5% |
| Other / unspecified (code 14) | 4,614 | 36.5% | 27.9% |
| Management / business (code 10) | 4,603 | 35.7% | 26.7% |
| Engineering (code 8) | 2,784 | 32.2% | 25.0% |
Academic field labels are mapped from questionnaire major codes and should be finalized against the study codebook before publication.
Coverage and diversity
Available variables include home province, city and county information, source-area type, and campus residence context.
Available variables include family SES indicators, parental education, household income codes, and related background measures.
Available variables include university, academic field category, medical student status, academic satisfaction, and school relationships.
Measures collected
PHQ-9 depression symptoms, GAD-7 anxiety symptoms, psychological stress, structured clinical interviews in subsamples, and related help-seeking indicators.
Pain presence, pain severity, pain interference, somatic symptoms, and functional impact.
Sleep disturbance, insomnia-related measures, physical activity, smoking, alcohol use, and other lifestyle factors.
Family SES, home region, school environment, social support, resilience, and academic satisfaction.
Deep phenotyping
Beyond survey follow-up, the platform includes or is developing deep phenotyping modules in selected subsamples to support mechanistic studies of mental health, sleep, pain, and somatic distress.
SCID and structured clinical characterization for psychiatric phenotyping.
fMRI and related brain measures for neural correlates of symptoms and resilience.
One-week actigraphy / physical activity data for sleep, rhythm, and daily functioning.
PSG and EEG modules for sleep architecture and neurophysiological profiles.
Microbiota data to explore gut-brain, inflammation, pain, and mental-health pathways.
Repeated measures of mental health, sleep, pain, SES, school context, support, and resilience.
Research themes
How depression, anxiety, sleep, stress, and social context change across educational transitions.
How pain, somatic symptoms, depression, anxiety, and sleep problems cluster and change over time.
How university entry, academic context, major satisfaction, and school belonging shape mental health risk.
How family SES, home province, residential context, and social support interact with student well-being.
How clinical interviews, fMRI, activity monitoring, PSG, EEG, and microbiota can deepen cohort findings.
Collaboration
We welcome collaborations on student mental health, pain and somatic symptoms, sleep, developmental context, educational stress, social support, resilience, and multimodal deep phenotyping. Detailed data access is handled through project-specific agreements.