Park Features Linked to Depression in Women: A Cross-Sectional Analysis of 329,363 Adults - Takeaways - MDSpire

Park Features Linked to Depression in Women: A Cross-Sectional Analysis of 329,363 Adults

  • By

  • Jonathan R. Olsen

  • Natalie Nicholls

  • Fiona M. Caryl

  • Thomas Astell-Burt

  • Jill P. Pell

  • Donald M. Lyall

  • Frederick K. Ho

  • Xiaoqi Feng

  • Richard Mitchell

  • January 21, 2026

  • 0 min

Share

  • 1

    The study analyzed data from 329,363 adults to explore the relationship between park features and depression in women.

  • 2

    Greenspace exposure is linked to reduced depressive symptoms, but specific park characteristics influencing this relationship remain unclear.

  • 3

    Different types of parks, including recreational, sport, and nature spaces, provide varying mental health benefits through distinct mechanisms.

  • 4

    Proximity to parks with specific amenities significantly correlates with improved mental health outcomes compared to parks without such features.

  • 5

    The research emphasizes the need for comprehensive studies on park characteristics to inform evidence-based planning and mental health interventions.

Original Source(s)

Park Features Linked to Depression in Women: A Cross-Sectional Analysis of 329,363 Adults

  • by Jonathan R. Olsen, Natalie Nicholls, Fiona M. Caryl, Thomas Astell-Burt, Jill P. Pell, Donald M. Lyall, Frederick K. Ho, Xiaoqi Feng, Richard Mitchell

  • January 21, 2026

Related Content

Could Digital Twins Forecast Forensic Risk?

Researchers outline a proposed three-level framework spanning patient monitoring, ward operations, and forensic system planning.

Implication of machine learning models versus traditional models for the prediction of suicidal thoughts or ideation in west of Iran; data mining approaches on a population-based cross-sectional study

Development of a machine learning-based depression risk prediction model for middle-aged and elderly Chinese heart disease patients: Evidence from CHARLS data