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WELL Living Laboratory in China (WELL-China)
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Sample details

The Stanford WELL for Life Study (WELL) is a unique longitudinal study on wellbeing in the U.S. and globally which collects comprehensive and multidimensional data from participants from the San Francisco Bay Area, China, Taiwan (Tapei and Changua), Singapore, and Thailand. WELL-China is the largest cohort in the study with over 10,000 participants aged between 18 and 80 years, recruited from three districts in the City of Hangzhou, China, between 2016 and 2019. Two levels of follow-up will be carried out starting in late autumn 2020: the biennial active follow-up of the entire cohort and the biannual follow-up of a nested-omics cohort of 250–500 participants.

Study design
Cohort, Biobank

Number of participants at first data collection

10,268 (participants)

Age at first data collection

≥ 18 years (participants)

Participant year of birth

Varied (participants)

Participant sex
All

Representative sample at baseline?
No

Sample features

Multi-site
Nested cohort
Dataset details

Country

China

Year of first data collection
2016

Primary Institutions

Stanford University

Zhejiang University (浙江大学, ZJU)

Links

med.stanford.edu/wellforlife/initiatives1/WellChinaPage.html

med.stanford.edu/wellforlife/wellchinadataspecimens.html

Profile paper DOI

doi.org/10.1093/ije/dyaa283

Funders

Amway

Cyrus Tang Foundation

Stanford Department of Medicine

Zhejiang University Education Foundation

Ongoing?
Yes

Data types collected

mentalHealthData
qualitativeData
Quantitative data collection
  • Activity log (e.g. food, sleep, exercise)
  • Interview – face-to-face
  • Physical or biological assessment (e.g. blood, saliva, gait, grip strength, anthropometry)
Qualitative data collection
  • Interviews or focus groups
Neuroimaging data collection
  • None
Linked or secondary data
  • None
Features

Engagement

  • Patients, service users, lived experience involvement
  • Community engagement
  • Keywords

    Biological samples/biospecimens
    Diet and nutrition
    Eye health
    Health and wellbeing
    Metabolomics
    Microbiome
    Physical health
    Resilience
    Sleep
    Social networks and relationships
    Stress
    Sub-studies
    Traditional medicine

    Consortia and dataset groups

    WELL for Life
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