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Related Experiment Video

Updated: Jul 29, 2025

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Characterizing Fall Circumstances in Community-Dwelling Older Adults: A Mixed Methods Approach.

Yurun Cai1,2, Suzanne G Leveille2, Olga Andreeva3

  • 1Department of Community and Health Systems, University of Pittsburgh School of Nursing, Pittsburgh, Pennsylvania, USA.

The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences
|May 21, 2023
PubMed
Summary
This summary is machine-generated.

Older adults often fall while walking, with slips and trips being common causes. Understanding these fall circumstances, including location and activity, is key for developing effective prevention strategies.

Keywords:
AgingFallsMachine learningMobilityNatural language processing

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Area of Science:

  • Gerontology
  • Public Health
  • Data Science

Background:

  • Understanding fall circumstances is crucial for identifying causes and developing targeted prevention programs for older adults.
  • This study focuses on describing fall circumstances using both quantitative and qualitative data analysis methods.

Purpose of the Study:

  • To describe the circumstances surrounding falls in community-dwelling older adults.
  • To utilize conventional statistical and machine learning approaches for fall data analysis.

Main Methods:

  • The MOBILIZE Boston Study enrolled 765 older adults (≥70 years) over a 4-year period.
  • Fall data, including location, activity, and self-reported causes, were collected via monthly calendars and interviews.
  • Natural language processing (NLP) was employed to analyze open-ended interview responses.

Main Results:

  • 64% of participants experienced at least one fall, totaling 1,829 falls.
  • Walking was the most common activity during falls (50.0%), followed by standing (9.6%).
  • Slips/trips (51.6%) and inappropriate footwear (24.3%) were leading self-reported causes; NLP provided detailed insights into activities and obstacles.

Conclusions:

  • Self-reported fall circumstances reveal intrinsic and extrinsic factors contributing to falls in older adults.
  • Further research is needed to validate these findings and refine narrative data analysis techniques for fall prevention.