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Abstract
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BACKGROUND: Understanding fall circumstances can help researchers better identify causes of falls and develop effective and tailored fall prevention programs. This study aims to describe fall circumstances among older adults from quantitative data using conventional statistical approaches and qualitative analyses using a machine learning approach.
METHODS: The MOBILIZE Boston Study enrolled 765 community-dwelling adults aged 70 years and older in Boston, Massachusetts. Occurrence and circumstances of falls (i.e., locations, activities, and self-reported causes of falls) were recorded using monthly fall calendar postcards and fall follow-up interviews with open- and close-ended questions during a 4-year period. Descriptive analyses were used to summarize circumstances of falls. Natural language processing was used to analyze narrative responses from open-ended questions.
RESULTS: During the 4-year follow up, 490 participants (64%) had at least one fall. Among 1,829 falls, 965 falls occurred indoors and 804 falls occurred outdoors. Commonly reported activities when the fall occurred were walking (915, 50.0%), standing (175, 9.6%), and going down stairs (125, 6.8%). The most commonly reported causes of falls were slip or trip (943, 51.6%) and inappropriate footwear (444, 24.3%). Using qualitative data, we extracted more detailed information on locations and activities, and additional information on obstacles related to falls and commonly reported scenarios such as "lost my balance and fell".
CONCLUSIONS: Self-reported fall circumstances provide important information on both intrinsic and extrinsic factors contributing to falls. Future studies are warranted to replicate our findings and optimize approaches to analyzing narrative data on fall circumstances in older adults.
Language: en |